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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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26 pages, 28717 KiB  
Article
Assessing Land-Cover Change Trends, Patterns, and Transitions in Coalfield Counties of Eastern Kentucky, USA
by Suraj K C, Buddhi R. Gyawali, Shawn Lucas, George F. Antonious, Anuj Chiluwal and Demetrio Zourarakis
Land 2024, 13(9), 1541; https://doi.org/10.3390/land13091541 - 23 Sep 2024
Viewed by 999
Abstract
Surface coal mining and reclamation have greatly reshaped eastern Kentucky’s landscape affecting its socioeconomic, environmental and climatic aspects. This study examined the land-cover changes, trends and patterns in Floyd, Knott, Letcher, Magoffin, Martin, Perry, and Pike counties from 2004 to 2019. Using a [...] Read more.
Surface coal mining and reclamation have greatly reshaped eastern Kentucky’s landscape affecting its socioeconomic, environmental and climatic aspects. This study examined the land-cover changes, trends and patterns in Floyd, Knott, Letcher, Magoffin, Martin, Perry, and Pike counties from 2004 to 2019. Using a random forest classifier, land cover was categorized into seven major classes, i.e., water, barren land, developed land, forest, shrubland, herbaceous, and planted/cultivated, majorly based on Landsat images. The Kappa accuracy ranged from 75 to 89%. The results showed a notable increase in forest area from 5052 sq km to 5305 sq km accompanied by a substantial decrease in barren land from 179 sq km to 91 sq km from 2004 to 2019. These findings demonstrated that reclamation activities positively impacted the forest expansion and reduced the barren land of the study area. Key land-cover transitions included barren land to shrubland/herbaceous, forest to shrubland, and shrubland to forest, indicating vegetation growth from 2004 to 2019. An autocorrelation analysis indicated similar land-cover types clustered together, showing effective forest restoration efforts. As surface coal mining and reclamation significantly influenced the landscapes of the coalfield counties in eastern Kentucky, this study provides a holistic perspective for understanding the repercussions of these transformations, including their effects on humans, society, and environmental health. Full article
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<p>Map of study area: (<b>a</b>) contiguous USA showing KY, (<b>b</b>) Map of KY showing study area counties within blue border, (<b>c</b>) DEM of study area, (<b>d</b>) coalfield counties of study area.</p> Full article ">Figure 2
<p>Study workflow.</p> Full article ">Figure 3
<p>Topographic layers: (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) land capability classes of the study area.</p> Full article ">Figure 4
<p>Land-cover maps of the study area for 2004 and 2019.</p> Full article ">Figure 5
<p>Map showing land-cover change in the study area from 2004 to 2019.</p> Full article ">Figure 6
<p>(<b>a</b>) Land-cover change trends in the study area from 2004 to 2019. (<b>b</b>) Land-cover change trends in the study area from 2004 to 2019.</p> Full article ">Figure 7
<p>Graphical representation of percentage change in land-cover classes between the years 2004 and 2019 in the study area.</p> Full article ">Figure 8
<p>Hot spot and cold spot mapping of herbaceous and developed land-cover change using Gi* Statistic between 2004 and 2019 in eastern Kentucky.</p> Full article ">Figure 9
<p>Hot spot and cold spot mapping of forest and barren land-cover change using Gi* Statistic between 2004 and 2019 in eastern Kentucky.</p> Full article ">Figure 10
<p>Hot spot and cold spot mapping of shrubland land-cover change using Gi* Statistic between 2004 and 2019 in eastern Kentucky.</p> Full article ">Figure 11
<p>Validation points and training samples shown in a map of the study area for 2004 and 2019.</p> Full article ">
32 pages, 7046 KiB  
Article
Urban Greening Management Arrangements between Municipalities and Citizens for Effective Climate Adaptation Pathways: Four Case Studies from The Netherlands
by Sara Romero-Muñoz, Teresa Sánchez-Chaparro, Víctor Muñoz Sanz and Nico Tillie
Land 2024, 13(9), 1414; https://doi.org/10.3390/land13091414 - 2 Sep 2024
Viewed by 2780
Abstract
The transition towards nature-based cities has increasingly become a central focus in political–environmental agendas and urban design practices, aiming to enhance climate adaptation, urban biodiversity, spatial equilibrium, and social well-being as part of the ongoing socio-ecological urban transition process. Climate adaptation in cities [...] Read more.
The transition towards nature-based cities has increasingly become a central focus in political–environmental agendas and urban design practices, aiming to enhance climate adaptation, urban biodiversity, spatial equilibrium, and social well-being as part of the ongoing socio-ecological urban transition process. Climate adaptation in cities is a complex problem and one of the main collective challenges for society, but the relationships between city managers and citizens as to urban green care still face many challenges. Parks design guided by technical-expert and globalised criteria; inflexibility from bureaucratic inertia; and citizens’ demands to participate in the urban green transition, sometimes without the necessary knowledge or time, are some of the challenges that require further research. In this study, we examine four long-lasting approaches to green-space management in four cities in the Netherlands, ranging from municipality-driven to community-driven management forms, and encompassing diverse spatial configurations of greenery within the urban fabric. Utilising the theoretical lens of the Social–Ecological Systems Framework, we employ a multiple-case-study approach and ethnographic fieldwork analysis to gain a comprehensive understanding of the norms, collective-choice rules, and social conventions embodied in each urban green management arrangement. The purpose of this research is applied, that is, to provide urban managers and decision-makers with a deeper understanding of drivers to promote effective collaborative management approaches, focusing on specific organisational rules that may contribute to more sustained planning and maintenance pathways for urban green spaces, regardless of changes in political leadership or significant external funding sources. The results of the investigated cases show that long-lasting collaborative management of forests and parks has established a set of collective-choice rules for resource transfer between municipalities and citizens, including non-monetary resources (such as pruning-training courses or guided tours that attract tourists and researchers). Additionally, these arrangements have been favoured by the existence of legal norms that enable co-ownership of the land, and monitoring and sanctioning mechanisms that offer a slightly different interpretation from the evidence identified so far in the scientific literature on collective resource management and organisational studies. Full article
(This article belongs to the Special Issue Urban Green Spaces: An Effective Tool to Regulate Urban Climate and Biodiversity)
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<p>Social–Ecological Systems (SES) Framework. (Source: adapted from Ostrom [<a href="#B38-land-13-01414" class="html-bibr">38</a>]).</p> Full article ">Figure 2
<p>(<b>a</b>) Urban configuration of “contact” model of MaximaPark. (Source: Authors, adapted from OpenStreetMap). (<b>b</b>) Photograph taken during fieldwork. (Source: Authors).</p> Full article ">Figure 3
<p>Organisational form of MaximaPark. (Source: Authors).</p> Full article ">Figure 4
<p>(<b>a</b>) Urban configuration of the “contact” model of DakPark. (Source: Authors, adapted from OpenStreetMap). (<b>b</b>) Photograph of volunteers pruning, taken during fieldwork. (Source: Authors).</p> Full article ">Figure 5
<p>Organisational form of DakPark. (Source: Authors).</p> Full article ">Figure 6
<p>(<b>a</b>) Urban configuration of the “contract–contact” model of EVA-Lanxmeer. (Source: Authors, adapted from OpenStreetMap). (<b>b</b>) Photograph of interior streets. (Source: Collectif Argos Association).</p> Full article ">Figure 7
<p>Organisational form of EVA-Lanxmeer. (Source: Authors).</p> Full article ">Figure 8
<p>(<b>a</b>) Urban configuration of the “contract” model of Groene Mient. (Source: Authors, adapted from OpenStreetMap). (<b>b</b>) Photograph taken during a guided tour organised by the residents themselves. (Source: Authors).</p> Full article ">Figure 9
<p>Organisational form of Groene Mient. (Source: Authors).</p> Full article ">Figure 10
<p>Comparison of organisational forms: (<b>a</b>) MaximaPark; (<b>b</b>) DakPark; (<b>c</b>) EVA-Lanxmeer; and (<b>d</b>) Groene Mient. (Source: Authors).</p> Full article ">Figure 11
<p>Comparison of property-rights and maintenance forms: (<b>a</b>) MaximaPark; (<b>b</b>) DakPark; (<b>c</b>) EVA-Lanxmeer; and (<b>d</b>) Groene Mient. (Source: Authors).</p> Full article ">Figure A1
<p>Photographs showcasing moments from the interviews and field visits: (<b>a</b>) DakPark: Volunteers at work in the herbal garden; (<b>b</b>) Dakpark: Container to store tools for maintenance; (<b>c</b>) EVA-Lanxmeer: Guided visit with a group of university students; (<b>d</b>) EVA-Lanxmeer: interior street; (<b>e</b>) DakPark: Photo taken of the design prior to the final design, as shown by one of the volunteers; (<b>f</b>) MaximaPark: Visit with the city manager and several professors at TU Delft; (<b>g</b>) Groene Mient: Sequence of archival photos of the collaborative garden construction process; and (<b>h</b>) EVA-Lanxmeer: Screenshot of the cell phone translating two posters written in Dutch about the eco-district.</p> Full article ">Figure A1 Cont.
<p>Photographs showcasing moments from the interviews and field visits: (<b>a</b>) DakPark: Volunteers at work in the herbal garden; (<b>b</b>) Dakpark: Container to store tools for maintenance; (<b>c</b>) EVA-Lanxmeer: Guided visit with a group of university students; (<b>d</b>) EVA-Lanxmeer: interior street; (<b>e</b>) DakPark: Photo taken of the design prior to the final design, as shown by one of the volunteers; (<b>f</b>) MaximaPark: Visit with the city manager and several professors at TU Delft; (<b>g</b>) Groene Mient: Sequence of archival photos of the collaborative garden construction process; and (<b>h</b>) EVA-Lanxmeer: Screenshot of the cell phone translating two posters written in Dutch about the eco-district.</p> Full article ">
31 pages, 6393 KiB  
Article
Assessing the Impact of Land Conversion on Carbon Stocks and GHG Emissions
by Ima Ituen and Baoxin Hu
Land 2024, 13(8), 1291; https://doi.org/10.3390/land13081291 - 15 Aug 2024
Cited by 1 | Viewed by 1879
Abstract
With the recent thrust to convert forests in Ontario’s Clay Belt to agricultural land, a vital need arises to assess the attendant effects on carbon and greenhouse gas (GHG) emissions. This paper examines the possible effect of land conversion on soil organic carbon [...] Read more.
With the recent thrust to convert forests in Ontario’s Clay Belt to agricultural land, a vital need arises to assess the attendant effects on carbon and greenhouse gas (GHG) emissions. This paper examines the possible effect of land conversion on soil organic carbon and GHG emissions within a study area in Northern Ontario, Canada, during the next two decades under different land management schemes. The study established a framework to conduct simulations with the DNDC model for agricultural lands and the CBM for forested areas. The methodology involves a unique change detection method for models’ land cover and disturbance inputs. The work highlights the improvement in carbon simulation accuracy from better inputs to carbon models. Furthermore, it addresses modalities to ensure fewer uncertainties are introduced while merging data from multiple geospatial data sources. The simulations demonstrated that the carbon sequestration potential in the forests was almost double the soil organic carbon accumulation in the agricultural lands. Validations done for the estimation of carbon sequestered included comparisons of the carbon model outputs from field survey data from 2018–2021. In most sites, the carbon amounts from the computer models compared to those from the field survey, within limits of error. The average uncertainties in GHG emissions ranged from ~0.5% to 12.8%. Full article
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<p>Study region (outlined).</p> Full article ">Figure 2
<p>Sample growth and yield curves of (<b>a</b>) white spruce, (<b>b</b>) larch, and (<b>c</b>) black spruce trees.</p> Full article ">Figure 3
<p>Historical disturbances from the past two decades in the Clay Belt: (<b>a</b>) weather, infrastructure, and cuts; (<b>b</b>) fire; (<b>c</b>) forest disease damage; and (<b>d</b>) forest insect damage.</p> Full article ">Figure 3 Cont.
<p>Historical disturbances from the past two decades in the Clay Belt: (<b>a</b>) weather, infrastructure, and cuts; (<b>b</b>) fire; (<b>c</b>) forest disease damage; and (<b>d</b>) forest insect damage.</p> Full article ">Figure 4
<p>Examples of variety of sample sites encountered in field survey: (<b>a</b>–<b>c</b>) is forested. In (<b>c</b>), overstory is trembling aspen, with understory of balsam poplar and raspberries; (<b>d</b>–<b>f</b>) are croplands: barley, cereal, and corn; (<b>g</b>,<b>h</b>) are hay fields (with bromegrass and red clover).</p> Full article ">Figure 5
<p>Comparing SOC from field survey to carbon estimates from CBM model.</p> Full article ">Figure 6
<p>Ecosystem carbon stock for forest sites with conversion to land uses (predicting future carbon stocks from past disturbances).</p> Full article ">Figure 7
<p>Ecosystem carbon stock predicted for natural forest.</p> Full article ">Figure 8
<p>Dead organic matter for sites, barring conversion to agricultural land though experiencing disturbances.</p> Full article ">Figure 9
<p>Comparing the baseline forest carbon to some agricultural sites.</p> Full article ">Figure 10
<p>(<b>a</b>) Sources of ecosystem carbon from deciduous forest estimated by site F1. (<b>b</b>) Sources of ecosystem carbon from coniferous forest estimated by site F2. (<b>c</b>) Sources of ecosystem carbon from mixed forest estimated by site F5.</p> Full article ">Figure 10 Cont.
<p>(<b>a</b>) Sources of ecosystem carbon from deciduous forest estimated by site F1. (<b>b</b>) Sources of ecosystem carbon from coniferous forest estimated by site F2. (<b>c</b>) Sources of ecosystem carbon from mixed forest estimated by site F5.</p> Full article ">Figure 11
<p>Simulated soil organic carbon in (<b>a</b>) winter wheat field, (<b>b</b>) spring wheat field, and (<b>c</b>) CO<sub>2</sub> emissions on oat farm.</p> Full article ">Figure 12
<p>Comparing soil organic carbon stocks and GHG (CO<sub>2</sub>) emissions—simulated from historical records and simulated as climate-impacted (CI) conditions—on different sites: (<b>a</b>) of F1 corn farm field; (<b>b</b>) of F5 soybean farm field, (<b>c</b>) of P3 beef cattle on pasture; and (<b>d</b>) of P5 pig farm on pasture.</p> Full article ">Figure 12 Cont.
<p>Comparing soil organic carbon stocks and GHG (CO<sub>2</sub>) emissions—simulated from historical records and simulated as climate-impacted (CI) conditions—on different sites: (<b>a</b>) of F1 corn farm field; (<b>b</b>) of F5 soybean farm field, (<b>c</b>) of P3 beef cattle on pasture; and (<b>d</b>) of P5 pig farm on pasture.</p> Full article ">Figure 13
<p>Comparing carbon estimates using approximate vs. specific input data for forest trees with respect to (<b>a</b>) Total ecosystem carbon, and (<b>b</b>) Soil organic carbon.</p> Full article ">Figure 14
<p>Comparing carbon estimates from accurate input data vs. estimates when disturbance and land-use input data have been modified for data on forested areas.</p> Full article ">
25 pages, 3265 KiB  
Article
Urban Green Infrastructure Connectivity: The Role of Private Semi-Natural Areas
by Raihan Jamil, Jason P. Julian, Jennifer L. R. Jensen and Kimberly M. Meitzen
Land 2024, 13(8), 1213; https://doi.org/10.3390/land13081213 - 6 Aug 2024
Viewed by 2917
Abstract
Green spaces and blue spaces in cities provide a wealth of benefits to the urban social–ecological system. Unfortunately, urban development fragments natural habitats, reducing connectivity and biodiversity. Urban green–blue infrastructure (UGI) networks can mitigate these effects by providing ecological corridors that enhance habitat [...] Read more.
Green spaces and blue spaces in cities provide a wealth of benefits to the urban social–ecological system. Unfortunately, urban development fragments natural habitats, reducing connectivity and biodiversity. Urban green–blue infrastructure (UGI) networks can mitigate these effects by providing ecological corridors that enhance habitat connectivity. This study examined UGI connectivity for two indicator species in a rapidly developing city in the southern United States. We mapped and analyzed UGI at a high resolution (0.6 m) across the entire city, with a focus on semi-natural areas in private land and residential neighborhoods. Integrating graph theory and a gravity model, we assessed structural UGI networks and ranked them based on their ability to support functional connectivity. Most of the potential habitat corridors we mapped in this project traversed private lands, including 58% of the priority habitat for the Golden-cheeked Warbler and 69% of the priority habitat for the Rio Grande Wild Turkey. Riparian zones and other areas with dense tree cover were critical linkages in these habitat corridors. Our findings illustrate the important role that private semi-natural areas play in UGI, habitat connectivity, and essential ecosystem services. Full article
(This article belongs to the Special Issue Managing Urban Green Infrastructure and Ecosystem Services)
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<p>Study area of San Marcos (Texas, USA) and its Extraterritorial Jurisdiction (ETJ). Important placenames mentioned in article are identified for reference, including the two ecoregions: Edwards Plateau (northwest of I-35) and Blackland Prairie (southeast of I-35).</p> Full article ">Figure 2
<p>Final land cover map of San Marcos ETJ applying Random Forest (RF) classification algorithm (Image: NAIP, Resolution: 0.6 m).</p> Full article ">Figure 3
<p>Potential connected habitat networks for Golden-cheeked Warbler (GCW) in San Marcos ETJ, with suitability ranking (red number) located in the middle of the linear corridor. Major greenspace patches were identified using a threshold patch area of 0.10 km<sup>2</sup>.</p> Full article ">Figure 4
<p>Potential connected habitat networks Rio Grande Wild Turkey (RGWT) in San Marcos ETJ, with suitability ranking (red number) located in the middle of the linear corridor. Major greenspace patches were identified using a threshold patch area of 0.10 km<sup>2</sup>.</p> Full article ">
25 pages, 22407 KiB  
Article
Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
by Jose Antonio Sobrino, Sergio Gimeno, Virginia Crisafulli and Álvaro Sobrino-Gómez
Land 2024, 13(7), 1072; https://doi.org/10.3390/land13071072 - 17 Jul 2024
Viewed by 1665
Abstract
Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four [...] Read more.
Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: ‘Urban Areas’, ‘Dense Vegetation’, ‘Sparse Vegetation’, ‘Water Bodies’, and Other’. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications. Full article
(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)
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<p>Satellite view of the Valencian Community. In the upper-left: a detailed view of Spain.</p> Full article ">Figure 2
<p>Flowchart of the methodology applied in this work to obtain land cover maps.</p> Full article ">Figure 3
<p>Map of the validation points collected along the Valencian Community.</p> Full article ">Figure 4
<p>Land cover classification for the validation obtained with the Random Forest algorithm, using Landsat 9 images collected during September and October 2023.</p> Full article ">Figure 5
<p>Confusion matrix of the 2023 land cover map calculated with Random Forest algorithm and in situ data, gathered on the 30 and 31 of October, 2023.</p> Full article ">Figure 6
<p>Land cover classifications of Valencia region obtained with the Random Forest algorithm for the years 1984 (<b>top</b>) and 2022 (<b>bottom</b>) using Landsat 5 and Landsat 9 images, respectively.</p> Full article ">Figure 7
<p>Graph illustrating the percentage distribution of land cover classes in the Valencian Community. The calculations were performed every 5 years using Landsat 5–8 images, covering the period from 1985 to 2020.</p> Full article ">Figure 8
<p>Graph illustrating the overall percentage distribution of land cover classes in the Valencian Community. The calculations were performed every 5 years using Landsat 5–8 images, covering the period from 1985 to 2020.</p> Full article ">Figure 9
<p>Polynomial regression performed in the Built-up Areas class (<b>top left</b>), Dense + Sparse Vegetation (<b>top right</b>), Water Bodies (<b>bottom left</b>), and Other (<b>bottom right</b>), spanning the period from 1984 to 2022.</p> Full article ">Figure 10
<p>Maps illustrating the expansion of Built-up Areas class in Castellón (<b>top</b>), Valencia (<b>centre</b>), and Alicante (<b>bottom</b>) for the period 1985–2020.</p> Full article ">Figure 10 Cont.
<p>Maps illustrating the expansion of Built-up Areas class in Castellón (<b>top</b>), Valencia (<b>centre</b>), and Alicante (<b>bottom</b>) for the period 1985–2020.</p> Full article ">Figure 11
<p>Maps illustrating the expansion of Built-up Areas class in Castellón (<b>top</b>), Valencia (<b>centre</b>), and Alicante (<b>bottom</b>) between 1985 and 2020. The Built-up Areas class shows a rise of 110.37% (an additional 56.59 km<sup>2</sup>) in Castellón, a 70.47% increase (adding 143.30 km<sup>2</sup>) in Valencia, and a remarkable growth of 157.68% (an addition of 100.20 km<sup>2</sup>) in Alicante.</p> Full article ">Figure 11 Cont.
<p>Maps illustrating the expansion of Built-up Areas class in Castellón (<b>top</b>), Valencia (<b>centre</b>), and Alicante (<b>bottom</b>) between 1985 and 2020. The Built-up Areas class shows a rise of 110.37% (an additional 56.59 km<sup>2</sup>) in Castellón, a 70.47% increase (adding 143.30 km<sup>2</sup>) in Valencia, and a remarkable growth of 157.68% (an addition of 100.20 km<sup>2</sup>) in Alicante.</p> Full article ">Figure 12
<p>Global Land Cover provided by Copernicus for the year 2015 (<b>left</b>) and WorldCover v100 2020 provided by ESA (<b>right</b>).</p> Full article ">Figure 13
<p>Confusion matrix comparing the 2023 land cover map calculated with the Random Forest algorithm and the one provided by Copernicus. Classes 80 and 200 correspond to Water Bodies class; class 50 represents Built-up Areas class; classes 20, 90, and 111–126 pertain to Dense Vegetation; classes 30 and 40 represent Sparse Vegetation; and class 60 represents the Other class.</p> Full article ">Figure 14
<p>Confusion matrix of the 2023 land cover map calculated with Random Forest algorithm and the one provided by ESA. Class 80 corresponds to Water Bodies class; class 50 represents Built-up Areas class; classes 10, 20 and 90 pertain to Dense Vegetation; classes 30, 40, and 60 indicate Sparse Vegetation; and None represents the Other class.</p> Full article ">Figure A1
<p>Land cover classification obtained with the Random Forest algorithm, using Landsat 5–9 imagery for the period 1984–2023.</p> Full article ">Figure A1 Cont.
<p>Land cover classification obtained with the Random Forest algorithm, using Landsat 5–9 imagery for the period 1984–2023.</p> Full article ">Figure A1 Cont.
<p>Land cover classification obtained with the Random Forest algorithm, using Landsat 5–9 imagery for the period 1984–2023.</p> Full article ">
16 pages, 23675 KiB  
Article
Monitoring Sustainable Development Goal Indicator 15.3.1 on Land Degradation Using SEPAL: Examples, Challenges and Prospects
by Amit Ghosh, Pierrick Rambaud, Yelena Finegold, Inge Jonckheere, Pablo Martin-Ortega, Rashed Jalal, Adebowale Daniel Adebayo, Ana Alvarez, Martin Borretti, Jose Caela, Tuhin Ghosh, Erik Lindquist and Matieu Henry
Land 2024, 13(7), 1027; https://doi.org/10.3390/land13071027 - 9 Jul 2024
Cited by 1 | Viewed by 2208
Abstract
A third of the world’s ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land [...] Read more.
A third of the world’s ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land degradation-neutral world by 2030. Land restoration through inclusive and productive growth is indispensable to promote sustainable development by fostering climate change-resistant, poverty-alleviating, and environmentally protective economic growth. The SDG Indicator 15.3.1 is used to measure progress towards a land degradation-neutral world. Earth observation datasets are the primary data sources for deriving the three sub-indicators of indicator 15.3.1. It requires selecting, querying, and processing a substantial historical archive of data. To reduce the complexities, make the calculation user-friendly, and adapt it to in-country applications, a module on the FAO’s SEPAL platform has been developed in compliance with the UNCCD Good Practice Guidance (GPG v2) to derive the necessary statistics and maps for monitoring and reporting land degradation. The module uses satellite data from Landsat, Sentinel 2, and MODIS sensors for primary productivity assessment, along with other datasets enabling high-resolution to large-scale assessment of land degradation. The use of an in-country land cover transition matrix along with in-country land cover data enables a more accurate assessment of land cover changes over time. Four different case studies from Bangladesh, Nigeria, Uruguay, and Angola are presented to highlight the prospect and challenges of monitoring land degradation using various datasets, including LCML-based national land cover legend and land cover data. Full article
(This article belongs to the Section Land – Observation and Monitoring)
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<p>A simplified structure of the SEPAL’s work flows for Google-Earth-Engine-based modules (based on the SEPAL’s architecture diagram [<a href="#B24-land-13-01027" class="html-bibr">24</a>]).</p> Full article ">Figure 2
<p>The three sub-indicators of the indicator 15.3.1.</p> Full article ">Figure 3
<p>The scale and direction of productive trend and productivity state based on <span class="html-italic">z</span> score.</p> Full article ">Figure 4
<p>Possible combinations of the three metrics to get the productivity sub-indicators; dotted lines represent the combination initially proposed in GPG v1.</p> Full article ">Figure 5
<p>Interface of the default transition matrix that uses seven UNCCD land cover categories (D [red] = degraded, S [tan] = stable and I [green] = improved).</p> Full article ">Figure 6
<p>The complete set of combinations of three sub-indicators’ statuses and the corresponding statuses of the final indicator.</p> Full article ">Figure 7
<p>Different sections of SEPAL module 15.3.1’s interface.</p> Full article ">Figure 8
<p>Status of baseline and reporting status of the SDG indicator 15.3.1.</p> Full article ">Figure 9
<p>Final status of SDG indicator after combining the baseline and reporting status.</p> Full article ">Figure 10
<p>Extent of land degradation for reporting period using NDVI and EVI in Nigeria.</p> Full article ">Figure 11
<p>National land cover transition matrix for Uruguay as per SEPAL SDG 15.3.1 specification.</p> Full article ">Figure 12
<p>Land cover sub-indicator indicator (15.3.1) for the baseline period. (<b>a</b>) Based on national custom transition matrix and national land cover data; (<b>b</b>) Based on the default transition matrix and ESA CCI land cover data.</p> Full article ">Figure 13
<p>Comparison of land degradation mapping using MODIS and Landsat satellite.</p> Full article ">
25 pages, 10341 KiB  
Article
Typhoon-Induced Forest Damage Mapping in the Philippines Using Landsat and PlanetScope Images
by Benjamin Jonah Perez Magallon and Satoshi Tsuyuki
Land 2024, 13(7), 1031; https://doi.org/10.3390/land13071031 - 9 Jul 2024
Viewed by 1481
Abstract
Forests provide valuable resources for households in the Philippines, particularly in poor and upland communities. This makes forests an integral part of building resilient communities. This relationship became complex during extreme events such as typhoon occurrence as forests can be a contributor to [...] Read more.
Forests provide valuable resources for households in the Philippines, particularly in poor and upland communities. This makes forests an integral part of building resilient communities. This relationship became complex during extreme events such as typhoon occurrence as forests can be a contributor to the intensity and impact of disasters. However, little attention has been paid to forest cover losses due to typhoons during disaster assessments. In this study, forest damage caused by typhoons was measured using harmonic analysis of time series (HANTS) with Landsat-8 Operation Land Imager (OLI) images. The ΔHarmonic Vegetation Index was computed by calculating the difference between HANTS and the actual observed vegetation index value. This was used to identify damaged areas in the forest regions and create a damage map. To validate the reliability of the results, the resulting maps produced using ΔHarmonic VI were compared with the damage mapped from PlanetScope’s high-resolution pre- and post-typhoon images. The method achieved an overall accuracy of 69.20%. The accuracy of the results was comparable to the traditional remote sensing techniques used in forest damage assessment, such as ΔVI and land cover change detection. To further the understanding of the relationship between forest and typhoon occurrence, the presence of time lag in the observations was investigated. Additionally, different contributing factors in forest damage were identified. Most of the forest damage observed was in forest areas with slopes facing the typhoon direction and in vulnerable areas such as near the coast and hill tops. This study will help the government and forest management sectors preserve forests, which will ultimately result in the development of a more resilient community, by making it easier to identify forest areas that are vulnerable to typhoon damage. Full article
(This article belongs to the Special Issue Geospatial Data in Landscape Ecology and Biodiversity Conservation)
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<p>Leyte Region (black line), overlayed with the classified forest regions (green), validation scenes (red triangle), and typhoon Rai ground track (brown line) (add north arrow).</p> Full article ">Figure 2
<p>Workflow of this study.</p> Full article ">Figure 3
<p>Overall accuracy at varying thresholds for each ΔHarmonic: (<b>a</b>) NDVI, (<b>b</b>) NDII, (<b>c</b>) EVI, and (<b>d</b>) GRVI.</p> Full article ">Figure 4
<p>Producer’s accuracy at varying thresholds for ΔHarmonic EVI: (<b>a</b>) no loss, and (<b>b</b>) loss.</p> Full article ">Figure 5
<p>Overall accuracy at varying thresholds for each <span class="html-italic">ΔVI</span>: (<b>a</b>) NDVI, (<b>b</b>) NDII, (<b>c</b>) EVI, and (<b>d</b>) GRVI.</p> Full article ">Figure 6
<p>Producer’s accuracy at varying thresholds for ΔEVI: (<b>a</b>) no loss, and (<b>b</b>) loss.</p> Full article ">Figure 7
<p>Overall accuracies of 1st-, 3rd-, and 6th-degree harmonic and <span class="html-italic">ΔVI</span> for each VIs: (<b>a</b>) NDVI, (<b>b</b>) NDII, (<b>c</b>) EVI, and (<b>d</b>) GRVI; and accuracy of forest cover loss from (<b>e</b>) LCC detection. The numerical value on the top of each bar indicates its threshold value.</p> Full article ">Figure 8
<p>Forest Damage Map of the Leyte Region generated using (<b>a</b>) 1st-degree ΔHarmonic EVI using February 2022, and (<b>b</b>) ΔEVI for November 2021–February 2022 and (<b>c</b>) November 2021–February 2022 LCC.</p> Full article ">Figure 9
<p>Pixel count of forest and forest loss area of the three ΔHarmonic VIs with the highest accuracy: (<b>a</b>) 6th-degree ΔHarmonic NDVI; (<b>b</b>) 1st-degree ΔHarmonic NDII; (<b>c</b>) 1st-degree ΔHarmonic EVI; (<b>d</b>) LCC; (<b>e</b>) filtered 6th-degree ΔHarmonic NDVI; (<b>f</b>) filtered 1st-degree ΔHarmonic NDII; (<b>g</b>) filtered 1st-degree ΔHarmonic EVI.</p> Full article ">Figure 10
<p>(<b>a</b>) Overall accuracy and (<b>b</b>) Producer’s accuracy of no loss and (<b>c</b>) loss for 1st-degree ΔHarmonic EVI, NDVI, and NDII of merged forest loss areas from December 2021 to February 2022.</p> Full article ">Figure 11
<p>Forest Damage Map of the Leyte Region generated by merging the 1st-degree ΔHarmonic EVI December 2021 to February 2021 maps.</p> Full article ">Figure 12
<p>Comparison of (<b>a</b>) Hindang, Leyte (bounded by black box) PlanetScope images (<b>b</b>) before (20 November 2021) and (<b>c</b>) after typhoon Rai (12 January 2022), and (<b>d</b>) merged Forest Damage Map of 1st-degree ΔHarmonic EVI December 2021 to February 2022.</p> Full article ">Figure 13
<p>Forest loss compared to (<b>a</b>) proximity to typhoon, (<b>b</b>) slope (°), (<b>c</b>) aspect (°), and (<b>d</b>) elevation (m).</p> Full article ">Figure A1
<p>PlanetScope’s images of Maasin City, Leyte, (<b>a</b>) before (25 October 2021) and (<b>b</b>) after Typhoon Rai (19 December 2021). (<b>c</b>) Delineated changes between before and after images. (<b>d</b>) Image bounds intersected with forest cover map and mosaicked with delineated damage areas.</p> Full article ">Figure A2
<p>Side-by-side comparison (<b>a</b>) before and (<b>b</b>) after Typhoon Rai hit the selected areas in Leyte Region as seen from PlanetScope with the (<b>c</b>) delineated forest and forest loss through visual interpretation and land classification and (<b>d</b>) forest and forest loss mapped using HANTS method. Dates of registration of the images were from 21 to 26 March 2024.</p> Full article ">Figure A2 Cont.
<p>Side-by-side comparison (<b>a</b>) before and (<b>b</b>) after Typhoon Rai hit the selected areas in Leyte Region as seen from PlanetScope with the (<b>c</b>) delineated forest and forest loss through visual interpretation and land classification and (<b>d</b>) forest and forest loss mapped using HANTS method. Dates of registration of the images were from 21 to 26 March 2024.</p> Full article ">
27 pages, 19132 KiB  
Article
Urban Geomorphology Methods and Applications as a Guideline for Understanding the City Environment
by Alessia Pica, Luca Lämmle, Martina Burnelli, Maurizio Del Monte, Carlo Donadio, Francesco Faccini, Maurizio Lazzari, Andrea Mandarino, Laura Melelli, Archimedes Perez Filho, Filippo Russo, Leonidas Stamatopoulos, Corrado Stanislao and Pierluigi Brandolini
Land 2024, 13(7), 907; https://doi.org/10.3390/land13070907 - 22 Jun 2024
Cited by 6 | Viewed by 1858
Abstract
Cities all over the world have developed on different geological-geomorphological substrates. Different kinds of human activities have operated for millennia as geomorphic agents, generating numerous and various erosion landforms and huge anthropogenic deposits. Considering the increasing demand for land and the expansion of [...] Read more.
Cities all over the world have developed on different geological-geomorphological substrates. Different kinds of human activities have operated for millennia as geomorphic agents, generating numerous and various erosion landforms and huge anthropogenic deposits. Considering the increasing demand for land and the expansion of the built-up areas involving and disturbing any kind of natural system inside and surrounding the actual urban areas, it is not negligible how important the dynamics of the urban environment and its physical evolution are. In this context, this manuscript addresses insights into eight case studies of urban geomorphological analyses of cities in Italy, Greece, and Brazil. The studies are based on surveying and mapping geomorphological processes and landforms in urban areas, supporting both geo-hazard assessment, historical evolution, and paleomorphologies, as well as disseminating knowledge of urban geoheritage and educating about the anthropogenic impact on urban sustainability. We hypothesize that urban geomorphological analysis of several case studies addresses the physical environment of modern cities in a multi-temporal, multidisciplinary, and critical way concerning global changes. Thus, this study aims to illustrate and propose a novel approach to urban geomorphological investigation as a model for the understanding and planning of the physical urban environment on a European and global scale. Full article
(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)
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<p>Location map of the selected case studies. (<b>On the top</b>), a global view of the countries involved; (<b>below left</b>), the 6 Italian cities selected all along the Italian peninsula: Genoa, Perugia, Rome, Pozzuoli, Benevento, and Potenza, and the Greek city—Patras; (<b>below right</b>), the city located in southern Brazil—São João da Barra.</p> Full article ">Figure 2
<p>Schematic drawings of the landform diversity in Rome, focusing on still recognizable natural landforms modified by human activities (<b>a</b>) and anthropogenic landforms of accumulation (<b>b</b>–<b>d</b>), erosion (<b>e</b>), and mixed examples of both (<b>f</b>). In the top right corner, a geomorphological map excerpt and related legend are depicted showing the mapping of the above-mentioned landforms.</p> Full article ">Figure 3
<p>On the left, drainage network modifications and flood prone areas of the morphological amphitheater on which the historic center of Genoa lies. Legend: (1) poorly modified and/or natural riverbed; (2) culverted stream; (3) concrete channel; (4) eaves channel; (5) abandoned channel; (6) exposed buildings; (7) flooding area with returned period &gt; 200 years; and (8) historical flooded area. On the right, the figures show the upper sector of the Lagaccio stream valley affected by relevant man-made morphological modifications: (<b>a</b>) the present-day situation, with the sports facilities on the fills along the stream; (<b>b</b>) the Lagaccio dam lake in the 1960s; and (<b>c</b>) geomorphological section (BH = boreholes).</p> Full article ">Figure 4
<p>(<b>Upper</b>): Geomorphological map of the emerged and submerged coastland of Pozzuoli: 1, La Starza marine terrace rim; 2, retreating shoreline; 3, edge of continental platform: 3a, retreating; 3b, prograding; 4, valley; 5, edge of marine terrace; 6, underwater bar; 7, submerged paleo fan; 8; submerged paleo sea cliff; 9, volcanic rim; 10, underwater cave; 11, sea stack; 12, undersea gas emission; 13 landslide pile; 14, marine terrace; 15, morphostructural depression; 16, gravel; 17, coarse sand; 18, medium sand; 19, fine sand; 20, very fine sand; 21, silt; 22, silt and clay; 23, pyroclastics (Pleistocene-Holocene); 24, submerged tuff (Late Pleistocene-Holocene); 25, reworked pyroclastics, and alluvial and marine deposits (Holocene); 26, submerged archaeological ruins (Roman age). Depth is in meters a.s.l. and the geographic coordinate system is WGS84. (<b>Lower</b>): Geothematic map of the lowering curves of the Phlegraean Fields coastland related to vertical movements between the Greco-Roman period and the present (after [<a href="#B51-land-13-00907" class="html-bibr">51</a>]). Subsidence is in mm/year and the geographic coordinate system is WGS84; DTM Lidar from MATTM—Environmental Remote Sensing Plan (PT-A).</p> Full article ">Figure 5
<p>Patras (urban area), damage recovery on road in coastal zone affected by powerful sea erosion in November 2021.</p> Full article ">Figure 6
<p>Satellite image of 1985 coastline overlapped with the current one, showing the areal gap due to erosion (modified from [<a href="#B69-land-13-00907" class="html-bibr">69</a>]). Below: coastal sector of the municipality of São João da Barra (Rio de Janeiro) consumed by coastal erosion. The yellow dotted line represents the past position of an avenue (N-S view) (Photo: Laboratory of Geomorphology UNICAMP collection, 2023).</p> Full article ">Figure 7
<p>On the left: Geological-stratigraphic structure of the historic center of Potenza (modified from [<a href="#B71-land-13-00907" class="html-bibr">71</a>]). Below left is an old photo from 1857 (with evidence of the earthquake damage), which shows the asymmetric ridge on which Potenza stands and the entire non-urbanized southern side of which the original morphology can be observed. On the right: Geological schematic section and model along the main axis of the Potenza hilltop town (section A-A’, west–east direction), with evidence of the 3 main areas analyzed and the digital model of the top of the clay substrate.</p> Full article ">Figure 8
<p>The slope angle map of Perugia city is on the right of the figure. The map clearly shows the areas with the highest slope angle values (in red), which are all located in the eastern sector of the city or on the eastern facing slope of the fluvial valleys. Additionally, the map indicates areas with a paleomorphological order (black numbered dots) that differs from the current one. All the areas are located close to the downtown area, where the highest and oldest settlement is limited from the ancient Etruscan Wall. On the left side of the image is Grimana square (point 6 in left figure); (<b>a</b>) the initial topographic layout pre-urbanization; (<b>b</b>) the actual topographic layout; (<b>c</b>) volumes of filled material (in red) and eroded areas (in blue).</p> Full article ">Figure 9
<p>(<b>Upper box</b>)—Schematic geomorphological map of the Benevento urban territory. (<b>Lower box</b>)—Simplified maps of the urban expansion of Benevento town from Roman times to the present-day.</p> Full article ">
25 pages, 19458 KiB  
Article
Evaluating Urban Green Space Inequity to Promote Distributional Justice in Portland, Oregon
by Evan Elderbrock, Kory Russel, Yekang Ko, Elizabeth Budd, Lilah Gonen and Chris Enright
Land 2024, 13(6), 720; https://doi.org/10.3390/land13060720 - 21 May 2024
Cited by 2 | Viewed by 2523
Abstract
Access and exposure to urban green space—the combination of parks and vegetative cover in cities—are associated with various health benefits. As urban green space is often unequally distributed throughout cities, understanding how it is allocated across socio-demographic populations can help city planners and [...] Read more.
Access and exposure to urban green space—the combination of parks and vegetative cover in cities—are associated with various health benefits. As urban green space is often unequally distributed throughout cities, understanding how it is allocated across socio-demographic populations can help city planners and policy makers identify and address urban environmental justice and health equity issues. To our knowledge, no studies have yet combined assessments of park quality, park availability, and green cover to inform equitable urban green space planning. To this end, we developed a comprehensive methodology to identify urban green space inequities at the city scale and applied it in Portland, OR, USA. After auditing all public parks in Portland and gathering green cover data from publicly accessible repositories, we used a suite of statistical tests to evaluate distribution of parks and green cover across Census block groups, comprising race, ethnicity, income, and educational attainment characteristics. Right-of-way tree canopy cover was the most significant urban green space inequity identified in bivariate analysis (rs = −0.73). Spatial autoregressive models identified that right-of-way, private, and overall tree canopy cover (Nagelkerke pseudo-R2 = 0.66, 0.77, and 0.67, respectively) significantly decreased with the proportion of minoritized racial population and increased with median income. The results were then used to identify priority locations for specific urban green space investments. This research establishes a process to assess intra-urban green space inequities, as well as identify data-informed and spatially explicit planning priorities to promote health equity and environmental justice. Full article
(This article belongs to the Special Issue Sustainable Urban Greenspace Planning, Design and Management)
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<p>Schematic diagram depicting the methodology used to identify systemic inequities in urban green space access and develop data-informed urban green space equity planning priorities.</p> Full article ">Figure 2
<p>Context map of Portland, Oregon, USA, depicting city limits and Census block group boundaries.</p> Full article ">Figure 3
<p>Representation of the example methodology for identifying priority block groups based on urban green space and socio-demographic characteristics.</p> Full article ">Figure 4
<p>(<b>a</b>) Block group median household income (MHI) grouped in quartiles (<span class="html-italic">n</span> = 471); (<b>b</b>) cluster analysis using Local Moran’s I to identify clusters of high- and low-income block groups.</p> Full article ">Figure 5
<p>(<b>a</b>) Block group percent minoritized racial population (i.e., non-white, or multiracial; <span class="html-italic">n</span> = 497) in quartiles; (<b>b</b>) cluster analysis using Local Moran’s I to identify block group clusters with high and low minoritized racial populations.</p> Full article ">Figure 6
<p>(<b>a</b>) Block group percent total tree canopy cover grouped in quartiles (<span class="html-italic">n</span> = 496); (<b>b</b>) cluster analysis using Local Moran’s I to identify clusters of high and low total tree canopy cover.</p> Full article ">Figure 7
<p>(<b>a</b>) Block group percent right-of-way (ROW) tree canopy cover grouped in quartiles (<span class="html-italic">n</span> = 496); (<b>b</b>) cluster analysis using Local Moran’s I to identify clusters of high and low right-of-way tree canopy cover.</p> Full article ">Figure 8
<p>Priority block groups for increased (<b>a</b>) total tree canopy cover, based on percent of total tree canopy cover, median household income, and percent of minoritized racial population, and (<b>b</b>) total green cover, based on percent of total green cover and of percent Hispanic/Latinx.</p> Full article ">Figure 9
<p>Priority block groups for increased (<b>a</b>) right-of-way tree canopy cover based on percent right-of-way tree canopy cover, median household income, and percent minoritized racial population, and (<b>b</b>) right-of-way green cover based on percent right-of-way green cover, median household income, and percent minoritized racial population.</p> Full article ">Figure 10
<p>Priority block groups for (<b>a</b>) parkland tree canopy expansion based on block group parkland tree canopy cover and percent of Hispanic/Latinx, and (<b>b</b>) park amenity enhancements based on block group Neighborhood Environment Scoring Tool (NEST) amenities domain scores, median household income, and percent of minoritized racial population.</p> Full article ">Figure 11
<p>Priority block groups to improve (<b>a</b>) park usability and (<b>b</b>) natural aesthetics; block group selection based on Neighborhood Environmental Scoring Tool (NEST) usability and natural aesthetics domain scores (<b>a</b> and <b>b</b>, respectively), as well as percent of block group population with no postsecondary education.</p> Full article ">
24 pages, 3035 KiB  
Article
Transformative Impact of Technology in Landscape Architecture on Landscape Research: Trends, Concepts and Roles
by Xiwei Shen, Mary G. Padua and Niall G. Kirkwood
Land 2024, 13(5), 630; https://doi.org/10.3390/land13050630 - 8 May 2024
Cited by 2 | Viewed by 3176
Abstract
The role of technology in landscape architecture (TLA) has significantly evolved since the 19th century, increasingly integrating with digital tools and technologies in the 21st century. Despite its growing importance, there is a notable deficiency in the scholarly literature regarding the progression of [...] Read more.
The role of technology in landscape architecture (TLA) has significantly evolved since the 19th century, increasingly integrating with digital tools and technologies in the 21st century. Despite its growing importance, there is a notable deficiency in the scholarly literature regarding the progression of TLA trends and their interplay with the core domains and research themes within landscape research. The influence of TLA on landscape research remains ambiguous, especially concerning its ability to generate new knowledge and impact design and sustainability practices. Furthermore, there is a critical need to delineate how TLA differs from allied general digital technology tools and to identify specific specializations that are emerging within the TLA field. To explore the above gaps, this study utilized a mixed methods approach involving secondary data from peer-reviewed publications, primary data from the archival research of winning projects, and expert interviews based on the two major research types of “Research through Design (RTD)” and “Research for Design (RFD)” to explore the TLA’s contribution. This research is significant as it: (1) identified the trend of TLA; (2) conceptualized the TLA, and (3) identified its role in relation to the core domains and research themes of landscape research. Full article
(This article belongs to the Special Issue Landscape Design, Evaluation and Management Created by Novel Technologies)
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<p>Nine primarily considered core domains in landscape research.</p> Full article ">Figure 2
<p>Analytical framework.</p> Full article ">Figure 3
<p>Secondary research—finding: yearly annual change of intersected landscape core domains with TLA (2013–2021).</p> Full article ">Figure 4
<p>Secondary research—findings: yearly patterns of research themes (2013–2021).</p> Full article ">Figure 5
<p>Secondary research—findings: annual change for categories of digital tools and technology (2013–2021).</p> Full article ">Figure 6
<p>Archival research—finding: annual change of intersected landscape core domains with TLA for the professional projects (2005–2021).</p> Full article ">Figure 7
<p>Archival research—finding: annual change of landscape core domains intersecting with TLA for the student projects (2005–2021).</p> Full article ">Figure 8
<p>Archival research—findings: yearly patterns of research themes in the professional projects (2005–2021).</p> Full article ">Figure 9
<p>Archival research—findings: yearly patterns of research themes in the student projects (2005–2021).</p> Full article ">Figure 10
<p>Archival research—findings: annual change for categories of digital tools and technology for the professional projects (2005–2021).</p> Full article ">Figure 11
<p>Archival research—findings: annual change for categories of digital tools and technology for the student projects (2005–2021).</p> Full article ">Figure 12
<p>Relationships of categories and specializations of TLA in the 21st century.</p> Full article ">Figure 13
<p>Structure of TLA in the 21st century.</p> Full article ">
15 pages, 2220 KiB  
Review
Consequences of Land Use Changes on Native Forest and Agricultural Areas in Central-Southern Chile during the Last Fifty Years
by Alejandro del Pozo, Giordano Catenacci-Aguilera and Belén Acosta-Gallo
Land 2024, 13(5), 610; https://doi.org/10.3390/land13050610 - 1 May 2024
Cited by 5 | Viewed by 2848
Abstract
Chile’s central-south region has experienced significant land use changes in the past fifty years, affecting native forests, agriculture, and urbanization. This article examines these changes and assesses their impact on native forest cover and agricultural land. Agricultural data for Chile (1980–2020) were obtained [...] Read more.
Chile’s central-south region has experienced significant land use changes in the past fifty years, affecting native forests, agriculture, and urbanization. This article examines these changes and assesses their impact on native forest cover and agricultural land. Agricultural data for Chile (1980–2020) were obtained from public Chilean institutions (INE and ODEPA). Data on land use changes in central and south Chile (1975–2018), analysed from satellite images, were obtained from indexed papers. Urban area expansion in Chile between 1993 and 2020 was examined using publicly available data from MINVIU, Chile. Additionally, photovoltaic park data was sourced from SEA, Chile. Field crop coverage, primarily in central and southern Chile, decreased from 1,080,000 ha in 1980 to 667,000 ha in 2020, with notable decreases observed in cereal and legume crops. Conversely, the coverage of export-oriented orchards and vineyards increased from 194,947 ha to 492,587 ha. Forest plantations expanded significantly, ranging from 18% per decade in northern central Chile to 246% in the Maule and Biobío regions. This was accompanied by a 12.7–27.0% reduction per 10 years in native forest. Urban areas have experienced significant growth of 91% in the last 27 years, concentrated in the Mediterranean climate region. Solar photovoltaic parks have begun to increasingly replace thorn scrub (Espinal) and agricultural land, mirroring transformations seen in other Mediterranean nations like Spain and Portugal. Full article
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<p>Land use changes have occurred in the central-south region of Chile, impacting the Mediterranean sclerophyllous and Nothofagus forests. These forests have been deforested or burned, giving way to a thorn scrub agroecosystem known as Espinal. Additionally, significant areas of native forests and Espinal have been transformed into forest plantations with non-native species, vineyards, or fruit orchards over the past fifty years. The expansion of urban areas has extended to croplands, grasslands, vineyards, and orchards. Furthermore, the emergence of solar parks poses a new threat to agricultural land in the central-south region of Chile. The vegetation map of central Chile was adapted based on the original figure published by Sustainability, MDPI [<a href="#B20-land-13-00610" class="html-bibr">20</a>].</p> Full article ">Figure 2
<p>Distribution of the <span class="html-italic">Espinal</span> agroecosystem (highlighted in yellow) and changes in tree cover, specifically <span class="html-italic">V. caven</span>, resulting from activities such as tree-cutting for charcoal production, land clearance for crops, and overgrazing.</p> Full article ">Figure 3
<p>Change in Chilean population between 1980 and 2020 (<b>A</b>), distribution of the population among administrative regions (Arica and Parinacota is the northernmost region and Magallanes the southernmost) in 2020 (<b>B</b>), changes in field crops (<b>C</b>), orchards (<b>D</b>), and forest plantation (<b>E</b>) areas in Chile between 1980 and 2020. Data were obtained from INE (2023) and ODEPA (2023) [<a href="#B21-land-13-00610" class="html-bibr">21</a>,<a href="#B22-land-13-00610" class="html-bibr">22</a>].</p> Full article ">Figure 4
<p>(<b>A</b>) Expansion of urban areas in Chile and (<b>B</b>) the three main cities located in the Mediterranean region from 1993 to 2020. Source: [<a href="#B23-land-13-00610" class="html-bibr">23</a>].</p> Full article ">Figure 5
<p>(<b>A</b>) Distribution of area of photovoltaic parks in regions of Central Chile; and (<b>B</b>) proportion of the type of vegetation replaced by photovoltaic parks in different regions of Central Chile.</p> Full article ">
16 pages, 3794 KiB  
Article
Exploring Urban Service Location Suitability: Mapping Social Behavior Dynamics with Space Syntax Theory
by Saleh Qanazi, Ihab H. Hijazi, Isam Shahrour and Rani El Meouche
Land 2024, 13(5), 609; https://doi.org/10.3390/land13050609 - 30 Apr 2024
Cited by 8 | Viewed by 3513
Abstract
Assessing urban service locations is a key issue within city planning, integral to promoting the well-being of citizens, and ensuring effective urban development. However, many current approaches emphasize spatial analysis focused solely on physical attributes, neglecting the equally vital social dimensions essential for [...] Read more.
Assessing urban service locations is a key issue within city planning, integral to promoting the well-being of citizens, and ensuring effective urban development. However, many current approaches emphasize spatial analysis focused solely on physical attributes, neglecting the equally vital social dimensions essential for enhancing inhabitants’ comfort and quality of life. When social factors are considered, they tend to operate at smaller scales. This paper addresses this gap by prioritizing integrating social factors alongside spatial analysis at the community level. By employing space syntax theory, this study investigates urban service suitability in Hajjah, a Palestinian urban community, presenting a novel approach in the literature. The research identifies good spots for essential governmental facilities like health clinics and fire stations using axial map analysis. It also suggests reallocation for some schools. Additionally, it shows ways to improve the placement of community amenities, finding ideal park locations but suboptimal mosque placements. Commercial services also exhibit areas for enhancement including gas stations and shops. The insights from this research can offer policymakers and planners insights to create more efficient, equitable, and accessible cities. The research approach incorporates social behavior dynamics into spatial analysis, promoting inclusive urban planning. Full article
(This article belongs to the Section Land Planning and Landscape Architecture)
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<p>Research methodology.</p> Full article ">Figure 2
<p>Study area. Note: Hajjah, a Palestinian urban community located in the eastern part of the governorate of Qalqiliya, is depicted on an ArcGIS-generated map (v10.7) (Data from the Palestinian Ministry of Local Government Geospatial Portal).</p> Full article ">Figure 3
<p>Spatial configuration of urban services in Hajjah depending on the different metrics. The metrics include the following: (<b>a</b>) integration; (<b>b</b>) choice; (<b>c</b>) connectivity. Note. The map exhibits a discernible pattern in the distribution of metrics across the town. It was created using the integration between DEPTHMAP and ArcGIS (v10.7).</p> Full article ">Figure 3 Cont.
<p>Spatial configuration of urban services in Hajjah depending on the different metrics. The metrics include the following: (<b>a</b>) integration; (<b>b</b>) choice; (<b>c</b>) connectivity. Note. The map exhibits a discernible pattern in the distribution of metrics across the town. It was created using the integration between DEPTHMAP and ArcGIS (v10.7).</p> Full article ">Figure 4
<p>Comparison between the average metrics and their corresponding ideal metrics. The metrics include the following: (<b>a</b>) integration; (<b>b</b>) choice; (<b>c</b>) connectivity.</p> Full article ">Figure 5
<p>Comparison between the average values of different urban services and their corresponding ideal values. Note: the graph serves to summarize the evaluation of the effectiveness of each service’s current location.</p> Full article ">
31 pages, 25016 KiB  
Article
Natural Climate Protection through Peatland Rewetting: A Future for the Rathsbruch Peatland in Germany
by Petra Schneider, Tino Fauk, Florin-Constantin Mihai, Harald Junker, Bernd Ettmer and Volker Lüderitz
Land 2024, 13(5), 581; https://doi.org/10.3390/land13050581 - 27 Apr 2024
Cited by 2 | Viewed by 1837
Abstract
Draining peatlands to create agricultural land has been the norm in Europe, but in the context of climate change and the loss of biodiversity, these rich ecosystems may reactivate their functions as greenhouse gas sinks and retreat spaces for animals and plants. Against [...] Read more.
Draining peatlands to create agricultural land has been the norm in Europe, but in the context of climate change and the loss of biodiversity, these rich ecosystems may reactivate their functions as greenhouse gas sinks and retreat spaces for animals and plants. Against this background, the National Moor Rewetting Strategy was put into effect in Germany in 2023, together with the Natural Climate Protection Action Plan. This article examines the methodology of peatland rewetting from scientific, administrative, social, and technical perspectives. The article focuses on an example of moor rewetting in central Germany: the Rathsbruch moor near the municipality of Zerbst, Saxony-Anhalt. To illustrate the importance of rewetting projects for degraded peatlands, five scenarios with different target soil water levels were considered, and the associated greenhouse gas emissions were calculated for a period of five years. For the planning solution, an estimate of the medium-to-long-term development of the habitat types was made based on current use and the dynamics typical of the habitat. The results for the Rathsbruch moor area showed that increasing the water level in steps of 1, 0.8, or 0.5 m has no significant influence on reducing the CO2 emissions situation, while a depth of 0.3 m has a slight influence. When the water was raised to 0.1 m below the surface (Scenario 5), a significant CO2 reduction was observed. The calculated avoided CO2 costs due to environmental damage show that the environmental benefits multiply with every decimeter of water level increase. The rising groundwater levels and extensification favor the establishment of local biotopes. This means that two of the biggest man-made problems (extinction of species and climate change) can be reduced. Therefore, this research is applicable to the development and planning of recultivation work at municipal and regional levels in Germany and beyond within the framework of EU restoration policy. Full article
(This article belongs to the Special Issue Monitoring and Simulation of Wetland Ecological Processes)
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<p>Location of the Rathsbruch Peatland and the current land use and cover (LULC) of the investigated area. For the rewetted area, please refer to Figure 6A. For more details, refer to Figure 14A,B. Background map: OpenStreetMap Foundation (OSM); LULC map: based on DLM 50 [<a href="#B29-land-13-00581" class="html-bibr">29</a>] (rearrangement by Tino Fauk).</p> Full article ">Figure 2
<p>Impressions of the investigation area: (<b>A</b>) rewetting area with blooming <span class="html-italic">Ranunculus repens</span>; (<b>B</b>) example of a drainage (photo credit: Petra Schneider).</p> Full article ">Figure 3
<p>Impressions of the soil investigations: (<b>A</b>) excavation and (<b>B</b>) drilling impression (photo credit: Petra Schneider).</p> Full article ">Figure 4
<p>(<b>A</b>) Elevation level above zero, based on the digital terrain model [<a href="#B29-land-13-00581" class="html-bibr">29</a>]; (<b>B</b>) topsoil species based on soil maps [<a href="#B34-land-13-00581" class="html-bibr">34</a>] (rearrangement by Tino Fauk).</p> Full article ">Figure 5
<p>(<b>A</b>,<b>B</b>) Impressions of macroinvertebrates samples (photo credit: Petra Schneider).</p> Full article ">Figure 6
<p>(<b>A</b>) Simplified planned measures for the restoration of Rathsbruch (manually prepared by Tino Fauk); (<b>B</b>) affected areas that were not included in the life cycle assessment based on DLM 50 [<a href="#B29-land-13-00581" class="html-bibr">29</a>] (manipulation and rearrangement by Fauk; background map: OSM).</p> Full article ">Figure 7
<p>Rewetting scenario schemes based on the current land use, natural carbon, and nitrogen cycles (adapted from [<a href="#B34-land-13-00581" class="html-bibr">34</a>,<a href="#B35-land-13-00581" class="html-bibr">35</a>,<a href="#B36-land-13-00581" class="html-bibr">36</a>]) (figure source: Tino Fauk).</p> Full article ">Figure 8
<p>Organic content of soil for Rathsbruch (planning area) (figure by Tino Fauk).</p> Full article ">Figure 9
<p>Impressions of (<b>A</b>) dig 1 and (<b>B</b>) drilling 1 (photo credit: Petra Schneider).</p> Full article ">Figure 10
<p>Impressions of (<b>A</b>) dig 2 and (<b>B</b>) drilling 2 (photo credit: Petra Schneider).</p> Full article ">Figure 11
<p>Impressions of (<b>A</b>) dig 3 and (<b>B</b>) drilling 3 (photo credit: Petra Schneider).</p> Full article ">Figure 12
<p>(<b>A</b>) Design solution for Boner Nuth; (<b>B</b>) overall planning solution with beaver dams and measures for ditches, along with the rewetting area (rearranged by Tino Fauk).</p> Full article ">Figure 13
<p>Future biotope types for the FFH area after rewetting after a mid-to-long-term succession. Source: Tino Fauk, based on Supplementary Material from [<a href="#B31-land-13-00581" class="html-bibr">31</a>].</p> Full article ">Figure 14
<p>(<b>A</b>) Emissions of various GHG components given in carbon dioxide equivalents for Scenarios 1 to 5; (<b>B</b>) illustration of various scenarios in different time frames after rewetting. Negative outcomes can be interpreted as mitigation of environmental damage.</p> Full article ">Figure A1
<p>(<b>A</b>) <span class="html-italic">Grus grus</span> overflying alder swamp forest; (<b>B</b>) <span class="html-italic">Pyrrhosoma nymphula</span> during mating on <span class="html-italic">Agrostis capillaris</span>; (<b>C</b>) <span class="html-italic">Coenonympha pamphilus</span>; (<b>D</b>) alder–birch swamp forest. Source: Tino Fauk.</p> Full article ">
24 pages, 6655 KiB  
Article
A Coupled InVEST-PLUS Model for the Spatiotemporal Evolution of Ecosystem Carbon Storage and Multi-Scenario Prediction Analysis
by Yan Zhang, Xiaoyong Liao and Dongqi Sun
Land 2024, 13(4), 509; https://doi.org/10.3390/land13040509 - 12 Apr 2024
Cited by 9 | Viewed by 2376
Abstract
In investigating the spatiotemporal patterns and spatial attributes of carbon storage across terrestrial ecosystems, there is a significant focus on improving regional carbon sequestration capabilities. Such endeavors are crucial for balancing land development with ecological preservation and promoting sustainable, low-carbon urban growth. This [...] Read more.
In investigating the spatiotemporal patterns and spatial attributes of carbon storage across terrestrial ecosystems, there is a significant focus on improving regional carbon sequestration capabilities. Such endeavors are crucial for balancing land development with ecological preservation and promoting sustainable, low-carbon urban growth. This study employs the integrated InVEST-PLUS model to assess and predict changes in ecosystem carbon storage under various land use scenarios within the Chengdu urban cluster, a vital region in Central and Western China, by 2050. The results indicate the following. (1) A linkage between land use dynamics and ecosystem carbon storage changes: over two decades, a 7.5% decrease in arable land was observed alongside a 12.3% increase in urban areas, leading to an 8.2% net reduction in ecosystem carbon storage, equating to a loss of 1.6 million tons of carbon. (2) Carbon storage variations under four scenarios—natural development (NDS), urban development (UDS), farmland protection (FPS), and ecological protection (EPS)—highlight the impact of differing developmental and conservation policies on Chengdu’s carbon reserves. Projections until 2050 suggest a further 5% reduction in carbon storage under NDS without intervention, while EPS could potentially decrease carbon storage loss by 3%, emphasizing the importance of strategic land use planning and policy. This research provides a solid theoretical foundation for exploring the relationship between land use and carbon storage dynamics further. In summary, the findings highlight the necessity of incorporating ecological considerations into urban planning strategies. The InVEST-PLUS model not only sheds light on current challenges but also presents a method for forecasting and mitigating urbanization effects on ecosystem services, thus supporting sustainable development goals. Full article
(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas under the Guidance of Ecological Wisdom)
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<p>Location (<b>A</b>) and topographical map (<b>B</b>) of Chengdu Urban Agglomeration.</p> Full article ">Figure 2
<p>Decision framework diagram of this study.</p> Full article ">Figure 3
<p>Spatial–temporal evolution of land use in Chengdu Urban Agglomeration from 2000 to 2020 ((<b>A</b>) 2000; (<b>B</b>) 2010; (<b>C</b>) 2020).</p> Full article ">Figure 4
<p>Sankey diagram of land use evolution in Chengdu Urban Agglomeration from 2000 to 2020.</p> Full article ">Figure 5
<p>Spatial–temporal evolution of carbon storage in Chengdu Urban Agglomeration from 2000 to 2020 ((<b>A</b>) 2000; (<b>B</b>) 2010; (<b>C</b>) 2020).</p> Full article ">Figure 6
<p>Future spatial evolution of land use in Chengdu Urban Agglomeration under different scenarios ((<b>A</b>) NDS; (<b>B</b>) UDS; (<b>C</b>) FPS; (<b>D</b>) EPS).</p> Full article ">Figure 7
<p>Overall changes in land use types in Chengdu Urban Agglomeration under different scenarios.</p> Full article ">Figure 8
<p>Spatial evolution of carbon storage in Chengdu Urban Agglomeration under different scenarios ((<b>A</b>) NDS; (<b>B</b>) UDS; (<b>C</b>) FPS; (<b>D</b>) EPS).</p> Full article ">Figure 9
<p>Contributions of driving factors affecting land use types in Chengdu Urban Agglomeration ((<b>A</b>) biophysical factors; (<b>B</b>) socio-economic factors; (<b>C</b>) climate factors; (<b>D</b>) landscape factors).</p> Full article ">
27 pages, 3129 KiB  
Article
Bridging Sustainable Development Goals and Land Administration: The Role of the ISO 19152 Land Administration Domain Model in SDG Indicator Formalization
by Mengying Chen, Peter Van Oosterom, Eftychia Kalogianni, Paula Dijkstra and Christiaan Lemmen
Land 2024, 13(4), 491; https://doi.org/10.3390/land13040491 - 9 Apr 2024
Cited by 2 | Viewed by 2666
Abstract
This study illustrates the linkages between the ISO’s Land Administration Domain Model (LADM) and the UN’s sustainable development goals (SDGs), highlighting the role of the LADM in promoting effective land administration suitable for efficient computation of land/water (space)-related SDG indicators. The main contribution [...] Read more.
This study illustrates the linkages between the ISO’s Land Administration Domain Model (LADM) and the UN’s sustainable development goals (SDGs), highlighting the role of the LADM in promoting effective land administration suitable for efficient computation of land/water (space)-related SDG indicators. The main contribution of this study is the formalization of SDG indicators by using the ISO standard LADM. This paper proposes several SDG-indicator-related extensions to the multi-part LADM standard that is currently under revision. These extensions encompass the introduction of new procedures for calculating indicators, the integration of blueprints for external classes to fulfil additional information needs and the design of interface classes for presenting indicator values across specific countries and reporting years. In an innovative approach, this paper introduces the Four-Step Method—a powerful framework designed to formalize SDG indicators within the LADM framework. Detailed attention is devoted to specific indicators, including 1.4.2 (secure land rights), 5.a.1 (women’s agricultural land rights), 14.5.1 (protected marine areas) and 11.5.2 (valuation as a basis for direct economic loss). In short, the Four-Step Method is pivotal in eliminating ambiguities, enhancing the efficiency of indicator computation and securing more accurate indicator values that more truly reflect the progress towards SDG realization. This approach is also expected to work with other (ISO) standards for other SDG indicators. Full article
(This article belongs to the Special Issue Land Administration Domain Model (LADM) and Sustainable Development)
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<p>Land administration paradigm and LADM scope ([<a href="#B5-land-13-00491" class="html-bibr">5</a>]; adapted from [<a href="#B18-land-13-00491" class="html-bibr">18</a>]).</p> Full article ">Figure 2
<p>LADM Edition II Parts 1-5 ([<a href="#B19-land-13-00491" class="html-bibr">19</a>]).</p> Full article ">Figure 3
<p>Methodology followed in this paper.</p> Full article ">Figure 4
<p>Keyword extraction for SDG 1.4.2.</p> Full article ">Figure 5
<p>Modeling of SDG Indicator 1.4.2 calculation in a UML class diagram.</p> Full article ">Figure 6
<p>Modeling of SDG Indicator 1.4.2 and 5.a.1 calculation in a UML class diagram.</p> Full article ">Figure 7
<p>Modeling of SDG Indicator 14.5.1 calculation in a UML class diagram.</p> Full article ">Figure 8
<p>Modeling of SDG Indicator 11.5.2 calculation in a UML class diagram.</p> Full article ">
30 pages, 5944 KiB  
Article
A Multi-Scenario Simulation and Dynamic Assessment of the Ecosystem Service Values in Key Ecological Functional Areas: A Case Study of the Sichuan Province, China
by Wei Li, Xi Chen, Jianghua Zheng, Feifei Zhang, Yang Yan, Wenyue Hai, Chuqiao Han and Liang Liu
Land 2024, 13(4), 468; https://doi.org/10.3390/land13040468 - 6 Apr 2024
Cited by 8 | Viewed by 2245
Abstract
The ecosystem service value (ESV) is an important basis for measuring an ecological environment’s quality and the efficient management of ecosystems. It is particularly necessary to explore a proven methodology for assessing and predicting ESV dynamics coupled with policy-oriented scenarios that can provide [...] Read more.
The ecosystem service value (ESV) is an important basis for measuring an ecological environment’s quality and the efficient management of ecosystems. It is particularly necessary to explore a proven methodology for assessing and predicting ESV dynamics coupled with policy-oriented scenarios that can provide a theoretical groundwork for macro decision, particularly in the context of implementing ecological protection and restoration projects. This study selected the land cover (LC) of Sichuan Province at five periods and the spatiotemporal dynamic equivalent factor method to assess the ESVs from 2000 to 2020. Additionally, the study coupled the Markov chain and GeoSOS-FLUS model, and predicted the future pattern of ESVs under four future development scenarios. The results show that (a) the areas of forests, shrubs, waters, wastelands, wetlands, and impervious areas showed a continuous increase from 2000 to 2020, with the most frequent interchanges occurring among croplands, forests, and grasslands. (b) The implementation of ecological protection and restoration projects led to a 13,083.32 × 108 yuan increase in ESV, and barycenter of the ESVs is located in the northeastern part of Ya’an and exhibits a tendency to move towards the northeast. (c) The ESV aggregation pattern of each city has remained unchanged, with Ganzi being the only city with a high aggregation. Overall, there are more conflict cities than coordination cities between economic development and the ecological environment. (d) The total ESV in 2025 will continue to increase under all development scenarios, reaching a maximum of 50,903.37 × 108 yuan under the EP scenario. This study can provide insights for ecological planning decisions and sustainable regional socio-economic development. Full article
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<p>(<b>a</b>) Geographic location of the study area in China, (<b>b</b>) administrative units and elevation of the Sichuan Province.</p> Full article ">Figure 2
<p>The research framework and processes of this study.</p> Full article ">Figure 3
<p>Schematic representation of the hierarchical structure of major ecosystem services.</p> Full article ">Figure 4
<p>Conversion cost matrix (the numbers from I to IX represent cropland, forest, shrub, grassland, water, snow/ice, barren, impervious, and wetland. The horizontal and vertical rows indicate the current and future LC, respectively, with green representing those that are convertible and yellow representing those that are non-convertible).</p> Full article ">Figure 5
<p>Transfers and changes in LC in the study area during different time periods.</p> Full article ">Figure 6
<p>(<b>a</b>–<b>e</b>) Spatial pattern of ESVs in Sichuan Province for 2000, 2005, 2010, 2015, and 2020 at a 1 km grid scale, (<b>f</b>) total and composition of ESVs in different years.</p> Full article ">Figure 7
<p>Trajectories of movement of the barycenters and standard deviational ellipses of ESVs from 2000 to 2020.</p> Full article ">Figure 8
<p>Change in process of time series in ESVs of each ecological service function from 2000 to 2020 (Note: Colors represent different years).</p> Full article ">Figure 9
<p>Results of spatial autocorrelations of total ESV by cities from 2000 to 2020. (<b>a</b>–<b>e</b>) Representations of the Moran Scatter Plots of ESV changes in different periods, by circle and line to represent the city samples and aggregated trends respectively. (<b>f</b>) Representations spatial results.</p> Full article ">Figure 10
<p>Processes of change in the spatiotemporal sequence of economic and environmental harmonization across cities.</p> Full article ">Figure 11
<p>The spatialization results of LC based on the GeoSOS-FLUS model under the NG, CP, EP and DP scenarios in 2025.</p> Full article ">Figure 12
<p>Total values and compositions of ESVs under the NG, CP, EP, and DP scenarios in 2025.</p> Full article ">
18 pages, 251 KiB  
Article
Land Fragmentation and Heirs Property: Current Issues and Policy Responses
by Kurt Smith and Frederick Cubbage
Land 2024, 13(4), 459; https://doi.org/10.3390/land13040459 - 5 Apr 2024
Cited by 1 | Viewed by 2564
Abstract
Land fragmentation continues to be a challenge throughout the world, the United States, and particularly in the rapidly growing Southeast, as well as every state with a metropolitan area that abuts rural lands. With a United States population expected to grow to more [...] Read more.
Land fragmentation continues to be a challenge throughout the world, the United States, and particularly in the rapidly growing Southeast, as well as every state with a metropolitan area that abuts rural lands. With a United States population expected to grow to more than 500 million by 2060, it will present exceptional challenges for planners and policy makers to preserve important agricultural lands for farms and forests to provide both food and fiber, as well as to provide a host of ecosystem services and enhance the quality of life for our growing population. These issues of fragmentation are extremely substantial for African American, other minority, and limited-income landowners in the U.S. South, who often lack wills and have lands that are broken up into small parcels, or have divided ownership rights in one parcel, when passed on to heirs. Existing efforts can be expanded to provide tools and incentives for the owners of hiers property and other working lands to preserve them, and state and municipal planners will need to promote development plans and practices thoughtfully and strategically in order to prevent the projected loss of nearly 18 million acres of working lands by the year 2040. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Socio-Ecological Transformations and Implications for Land Use Change)
22 pages, 8966 KiB  
Article
A Land Administration Data Exchange and Interoperability Framework for Kenya and Its Significance to the Sustainable Development Goals
by Clifford Okembo, Javier Morales, Christiaan Lemmen, Jaap Zevenbergen and David Kuria
Land 2024, 13(4), 435; https://doi.org/10.3390/land13040435 - 29 Mar 2024
Cited by 1 | Viewed by 3233
Abstract
Sharing land data from one department to the other is a continuous process. A solid structure and a set of guidelines on how to share them is to be put in place as a foundation for the development of a land administration data [...] Read more.
Sharing land data from one department to the other is a continuous process. A solid structure and a set of guidelines on how to share them is to be put in place as a foundation for the development of a land administration data exchange and interoperability framework in support of data acquisition, land transactions and distribution of land data. In this research, the application of the ISO Framework for Enterprise Interoperability (FEI) as a standard is the starting point. Utilising the Land Administration Domain Model (LADM) profile for Kenya as a base, an interoperability framework in support of land administration in Kenya is developed that addresses concerns, removes barriers and selects the approach for implementation. Due to the critical nature of land, it fits into the United Nations 2030 sustainability agenda. During the development of the Kenyan profile, four country-specific issues in the context of people-to-land relationships have been identified and modeled. The mapping of those issues relevant to the sustainable development goals supports the achievement of those goals so that all related targets and indicators can be attained. Using GIS tools, the implementing and testing of the new LADM profile for Kenya is not a difficult task. By using existing land data combined with newly collected data in the LADM-compliant database, a complete and accurate workflow is assured. Integration with external databases is useful for improving efficiency and eliminating duplication. Data collection with all stakeholders and validation through public inspection are recommended. Full article
(This article belongs to the Special Issue Land Administration Domain Model (LADM) and Sustainable Development)
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<p>Dutch system of key registers (Source: [<a href="#B12-land-13-00435" class="html-bibr">12</a>]).</p> Full article ">Figure 2
<p>Methodology for the study.</p> Full article ">Figure 3
<p>Workflow for mapping Kenya LADM unique requirements with SDGs.</p> Full article ">Figure 4
<p>ISO 19439:2006 Framework for Enterprise Interoperability [<a href="#B20-land-13-00435" class="html-bibr">20</a>].</p> Full article ">Figure 5
<p>SDGs indicators related to advancing LASs, source [<a href="#B34-land-13-00435" class="html-bibr">34</a>].</p> Full article ">Figure 6
<p>LADM as a base to support SDGs, source [<a href="#B36-land-13-00435" class="html-bibr">36</a>].</p> Full article ">Figure 7
<p>Directly mapping land-management-related parameters of SDGs into identified categories. Source [<a href="#B34-land-13-00435" class="html-bibr">34</a>].</p> Full article ">Figure 8
<p>Field test in Makueni, Kenya, source [<a href="#B21-land-13-00435" class="html-bibr">21</a>].</p> Full article ">Figure 9
<p>Creation of code lists (domains).</p> Full article ">Figure 10
<p>Parcel fabric classes and their relationships.</p> Full article ">Figure 11
<p>Classes (feature classes) and relationship classes (associations) in desktop GIS software.</p> Full article ">Figure 12
<p>Existing data migrated to the parcel fabric.</p> Full article ">Figure 13
<p>Query of spatial unit showing related classes.</p> Full article ">Figure 14
<p>Feature layer and its service definition in online GIS software.</p> Full article ">Figure 15
<p>Feature layer settings to enable editing over the web.</p> Full article ">Figure 16
<p>Field Maps app (formerly Collector app) configuration.</p> Full article ">Figure 17
<p>Mapping parcel polygons and filling attribute values in the field.</p> Full article ">
15 pages, 6600 KiB  
Article
Developing a Wilderness Quality Index for Continental Europe
by Iurii Strus and Stephen Carver
Land 2024, 13(4), 428; https://doi.org/10.3390/land13040428 - 27 Mar 2024
Cited by 2 | Viewed by 2209
Abstract
This paper presents an updated wilderness quality map, WQI 2.0, for Europe, which extends the existing map (WQI 1.0) to include non-EU states in Eastern Europe. The analysis utilizes the Google Earth Engine (GEE) cloud platform and incorporates contemporary datasets to assess wilderness [...] Read more.
This paper presents an updated wilderness quality map, WQI 2.0, for Europe, which extends the existing map (WQI 1.0) to include non-EU states in Eastern Europe. The analysis utilizes the Google Earth Engine (GEE) cloud platform and incorporates contemporary datasets to assess wilderness quality across the continent. WQI 2.0 is compared to the previous version from the EU Wilderness register and global data from the WCS Human Influence Index (HII). Results indicate a high level of consistency between the versions, validating the robustness of the approach and the value of up-to-date datasets. WQI 2.0 serves as a valuable tool for developing a coordinated European policy on wilderness protection, encompassing both EU and non-EU states. By identifying areas outside current protected boundaries, the map helps to identify regions at risk of degradation and loss, due to resource exploitation. While small changes are seen between WQI 1.0 and WQI 2.0, expanding the coverage over the whole of continental Europe provides a foundation for the longer-term monitoring and evaluation of conservation targets. The findings contribute to meeting international commitments, such as the COP15 Kunming–Montreal Agreement and CBD targets, by highlighting the importance of preserving intact wilderness areas and increasing protected areas through restoration and rewilding efforts. Future iterations, such as WQI 3.0+, can track trends and potential threats to wilderness areas, while also identifying opportunities for ecosystem recovery through restoration and rewilding. To ensure comprehensive coverage, there is a need to update the existing Wilderness Register 1.0 and expand its scope to include non-EU states. This can be facilitated through collaboration with national WQI mapping programs, building on the experiences of countries such as Scotland, France, Iceland, and Germany, which have well-established national mapping initiatives. Overall, WQI 2.0 and the proposed updates provide valuable tools for informed decision-making in wilderness conservation and restoration efforts across Europe. Full article
(This article belongs to the Topic Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use)
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<p>Wilderness analysis flowchart.</p> Full article ">Figure 2
<p>Wilderness quality index map WQI 2.0.</p> Full article ">Figure 3
<p>Proportion of available land with a wilderness index value of ≥0.95 in gradients of latitude, longitude, altitude, and distance to oceans or seas across Europe.</p> Full article ">Figure 4
<p>Comparison of wilderness index distribution across different types of protected areas in Europe.</p> Full article ">Figure 5
<p>Focal correlation between the current wilderness map (WQI 2.0) and the map used in the original wilderness register (WQI 1.0). Note that the original wilderness register map covers only the western half of Europe.</p> Full article ">Figure 6
<p>Focal correlation between the current wilderness map (WQI 2.0) and the inverted and scaled WCS Human Influence Index map.</p> Full article ">Figure 7
<p>Difference between the current wilderness map (WQI 2.0) and the map used in the original wilderness register (WQI 1.0). Note that the original wilderness register map covers only the western half of Europe.</p> Full article ">Figure 8
<p>Difference between the current wilderness map (WQI 2.0) and the inverted and scaled WCS Human Influence Index map.</p> Full article ">
19 pages, 24193 KiB  
Article
Exploring Sensitivity of Phenology to Seasonal Climate Differences in Temperate Grasslands of China Based on Normalized Difference Vegetation Index
by Xiaoshuai Wei, Mingze Xu, Hongxian Zhao, Xinyue Liu, Zifan Guo, Xinhao Li and Tianshan Zha
Land 2024, 13(3), 399; https://doi.org/10.3390/land13030399 - 21 Mar 2024
Cited by 1 | Viewed by 1650
Abstract
The affiliation between vegetation phenology and seasonal climate (start and end times of the growing season, or SOS and EOS) provides a basis for acquiring insight into the dynamic response of terrestrial ecosystems to the effects of climate change. Although climate warming is [...] Read more.
The affiliation between vegetation phenology and seasonal climate (start and end times of the growing season, or SOS and EOS) provides a basis for acquiring insight into the dynamic response of terrestrial ecosystems to the effects of climate change. Although climate warming is an important factor affecting the advancement or delay of plant phenology, understanding the sensitivity of phenology to seasonal variation in climate factors (e.g., local air temperature, precipitation) is generally lacking under different climate backgrounds. In this study, we investigated the interannual variability of grassland phenology and its spatial variation in temperate regions of China based on satellite-derived products for the normalized difference vegetation index (NDVI) and weather data acquired from 2001 to 2020. We found that due to differences in local climate conditions, the effects of seasonal warming and precipitation on phenology were divergent or even opposite during the 20 years. The sensitivities of the start of growing season (SOS) to both spring temperature and last-winter precipitation was controlled by mean annual precipitation in terms of spatial variation. The SOS in the semi-humid (200–400 mm) region was most sensitive to spring temperature, advancing 5.24 days for each 1 °C rise in the average spring temperature (p < 0.05), while it was most sensitive to last-winter precipitation in arid regions (<200 mm), with SOS advancing up to 2.23 days for every 1 mm increase in the last-winter precipitation (p < 0.05). The end of growing season (EOS) was sensitive to autumn temperature, being delayed 10.13 days for each 1 °C rise in the average autumn temperature in regions with temperatures between −10 °C and −5 °C (p < 0.05). The uncertainty in the determination of the EOS could conceivably be greater than the determination of the SOS due to the dual effects of pre-autumn climate and growth constraints induced by declining fall temperatures. The effect of atmospheric warming on grassland phenology was lessened with increased atmospheric and soil aridity, suggesting that the interaction of regional drought and climate warming is an important source for local-to-regional differences and uncertainties in grass phenological response. Full article
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<p>Research area and FLUXNET flux stations.</p> Full article ">Figure 2
<p>Comparison and validation of start of the growing season (SOS) extraction using different phenology models based on FLUXNET flux stations and remote sensing NDVI data. The results of pairwise linear fittings of phenological metrics fitted by different models (<b>a</b>–<b>f</b>).</p> Full article ">Figure 3
<p>Comparison and validation of end of the growing season (EOS) extraction using different phenological models based on FLUXNET flux stations and remote sensing NDVI data. The results of pairwise linear fittings of phenological metrics fitted by different models (<b>a</b>–<b>f</b>).</p> Full article ">Figure 4
<p>Spatial distributions of NDVI-derived phenological metrics (SOS and EOS). The SOS (<b>a</b>) and EOS (<b>d</b>) are the start of the growing season and the end of the growing season, respectively. Data in the figure are the mean annual values over years 2001–2020. The histograms are the frequency distribution of the phenological metrics in Julian days. The scatter plot represents over 2000 randomly sampled phenological metric (SOS and EOS) data points in space, and each data point is the mean annual value over years 2001–2020. Phenological metrics of the pixels as a function of both corresponding temperatures (<b>b</b>,<b>e</b>) and precipitation (<b>c</b>,<b>f</b>). Lines are fitted ones.</p> Full article ">Figure 5
<p>Spatial distribution of the trends of phenological metrics (SOS and EOS) over years 2001–2020 and their frequencies. Panel (<b>a</b>,<b>b</b>) were for the start of the growing season (SOS) and end of the growing season (EOS), respectively.</p> Full article ">Figure 6
<p>Spatial distribution of the regression slopes between SOS (start of growing season) and corresponding spring precipitation (<b>a</b>), spring temperature (<b>b</b>), last-winter precipitation (<b>c</b>), and last-winter temperature (<b>d</b>) over years 2001–2020 and between EOS (end of growing season) and corresponding autumn precipitation (<b>e</b>), autumn temperature (<b>f</b>), summer precipitation (<b>g</b>), and summer temperature (<b>h</b>) over years 2001–2020.</p> Full article ">Figure 7
<p>The scatter plot represents over 500 randomly sampled multiple regression slope data points in space, each of which represents the slope of phenological indicators and seasonal climate multiple regression, and all have passed significance tests (<span class="html-italic">p</span> &lt; 0.05). Solid lines are fitted ones. The regression slope is from the linear regression between SOS (start of the growing season) and corresponding spring precipitation (<b>a</b>), spring temperature (<b>b</b>), last-winter precipitation (<b>c</b>), and last-winter temperature (<b>d</b>) over years 2001–2020 and between EOS (end of the growing season) and corresponding autumn precipitation (<b>e</b>), autumn temperature (<b>f</b>), summer precipitation (<b>g</b>), and summer temperature (<b>h</b>) over years 2001–2020.</p> Full article ">Figure 8
<p>The scatter plot represents over 500 randomly sampled multiple regression slope data points in space, each of which represents the slope of phenological indicators and seasonal climate multiple regression, and all have passed significance tests (<span class="html-italic">p</span> &lt; 0.05). Solid lines are fitted ones. The regression slope is from the linear regression between SOS (start of the growing season) and corresponding spring precipitation (<b>a</b>), spring temperature (<b>b</b>), last-winter precipitation (<b>c</b>), and last-winter temperature (<b>d</b>) over years 2001–2020 and between EOS (end of the growing season) and corresponding autumn precipitation (<b>e</b>), autumn temperature (<b>f</b>), summer precipitation (<b>g</b>), and summer temperature (<b>h</b>) over years 2001–2020.</p> Full article ">Figure 9
<p>Comparisons in regression slopes of phenological metrics (SOS and EOS) against climatic factors between different temperature zones (<b>a</b>,<b>b</b>) and between different precipitation zones (<b>c</b>,<b>d</b>). The SOS and EOS are the start of the growing season and the end of the growing season, respectively. Panel (<b>a</b>,<b>c</b>) are the regression slopes of EOS for years 2001–2020 against corresponding precipitations in both summer and autumn, and regression slopes of SOS against precipitation in both spring and last winter. Panels (<b>b</b>,<b>d</b>) are for the regression slopes of SOS against temperature in both spring and last winter and for the regression slopes of EOS against temperature in both summer and autumn. Data are mean values of pixels in the specific zone.</p> Full article ">Figure 10
<p>Spatial distribution of partial correlation coefficients between SOS over years 2001–2020 and both corresponding precipitation (<b>a</b>) and temperature (<b>b</b>) in spring, between SOS of years 2001–2020 and both corresponding precipitation (<b>c</b>) and temperature (<b>d</b>) in last winter, between EOS of years 2001–2020 and both corresponding precipitation (<b>e</b>) and temperature (<b>f</b>) in autumn, and between EOS of years 2001–2020 and both corresponding precipitation (<b>g</b>) and temperature (<b>h</b>) in summer. The blue font (−) and orange font (+) represent the percentage of negatively and positively correlated pixels in the total pixels, respectively. The histograms (<b>i</b>) indicate the average value of all pixels in the graph (<b>a</b>–<b>d</b>), and the histogram (<b>j</b>) indicates the average value of all pixels in the graph (<b>e</b>–<b>h</b>), Error bars indicate the standard deviation among pixels.</p> Full article ">Figure 11
<p>Spatial distribution of major climate controls on phenological metrics (SOS (<b>a</b>) and EOS (<b>b</b>)). It is based on the maximum partial correlation coefficient between phenological metrics and seasonal climate variables over years 2001–2020. The seasonal climatic variables include spring precipitation, spring temperature, later-winter precipitation, and later-winter temperature. Note: the variable is considered as the controlling factor of the pixel SOS or EOS if the maximum partial correlation coefficient is significant and higher than those with other variables.</p> Full article ">
20 pages, 1417 KiB  
Review
Potential Interactions between Climate Change and Land Use for Forest Issues in the Eastern United States
by Brice B. Hanberry, Marc D. Abrams and Gregory J. Nowacki
Land 2024, 13(3), 398; https://doi.org/10.3390/land13030398 - 21 Mar 2024
Cited by 3 | Viewed by 2885
Abstract
Applying an interaction framework, we examined whether climate change and combined land use and disturbance changes were synergistic, antagonistic, or neutral for forest issues of wildfires, tree growth, tree species distributions, species invasions and outbreaks, and deer herbivory, focused on the eastern United [...] Read more.
Applying an interaction framework, we examined whether climate change and combined land use and disturbance changes were synergistic, antagonistic, or neutral for forest issues of wildfires, tree growth, tree species distributions, species invasions and outbreaks, and deer herbivory, focused on the eastern United States generally since the 1800s and the development of instrumental records (1895). Climate largely has not warmed during 1981–2020 compared to 1895–1980, but precipitation has increased. Increased precipitation and land use (encompassing fire exclusion and forestation, with coarse fuel accumulation due to increased tree densities) have interacted synergistically to dampen wildfire frequency in the humid eastern U.S. For overall tree growth, increased precipitation, carbon fertilization, and land use (i.e., young, fast-growing dense stands) likely have been positive, generating a synergistic interaction. Human activities created conditions for expanding native tree species distributions, non-native species invasions, and damaging native species outbreaks. No strong evidence appears to exist for recent climate change or land use influences on deer populations and associated herbivory levels. In the future, a warmer and effectively drier climate may reverse synergistic and neutral interactions with land use, although effects of climate interactions with land use will vary by species. Management can help correct non-climate stressors due to land use and support resilient structures and species against climate change. Full article
(This article belongs to the Special Issue Land Use/Land Cover and Natural Hazards: Interactions, Changes, and Impacts II)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Changes in the mean annual temperature (<b>A</b>), precipitation (<b>B</b>), temperature-precipitation change class ratios (<b>C</b>), and Palmer Modified Drought Index (<b>D</b>) between 1895–1980 and 1981–2020 for the United States. The temperature-precipitation change class ratios were calculated by applying a simple classification to mean temperature and precipitation maps but presented similar changes of stable or decreased evapotranspiration in the eastern U.S. as the Palmer Modified Drought Index, which applied tree-ring reconstructions of available water and instrumental data. Data are modified [<a href="#B4-land-13-00398" class="html-bibr">4</a>,<a href="#B8-land-13-00398" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>Area (hectares) burned and trendlines by region based on fire records provided by the USDA Forest Service, Washington Office, and Short [<a href="#B30-land-13-00398" class="html-bibr">30</a>]. The eastern region is composed of Minnesota, Iowa, Missouri, Arkansas, and Louisiana and all states eastward. The western region is composed of all states west of the eastern region states. In 1930, ten-fold more hectares burned in the east than the west based on long-term USDA Forest Service fire records (19 million vs. 1.9 million hectares, respectively; <a href="#land-13-00398-f002" class="html-fig">Figure 2</a>). Some of this difference might be due to underreporting in the west associated with lower human population densities, hence fewer fire detections due to remoteness. At any rate, annual hectares burned held steady in the west until a noticeable drop occurred around 1950. In contrast, annual hectares burned by wildfire dropped sharply in the east until the mid-1950s, with slow decreases thereafter. The east and the west had similar total area burned by wildfire during the 1960s to the early 1980s, after which trendlines crossed, with the west surpassing the east in the extent of burning by wildfires. This upward trend separates two regions into the future, as the west is currently burning by wildfires at a similar rate as during the 1930s.</p> Full article ">Figure 3
<p>Modeled species distributions of white-tailed deer that show observed range (outlined) from occurrence records, the climate envelope (temperature and precipitation of occurrences) during 1981–2010 (green), and the likely future climate envelopes during 2071–2100, under three general circulation models and high emissions (non-green colors; modeling followed [<a href="#B107-land-13-00398" class="html-bibr">107</a>]).</p> Full article ">
28 pages, 6438 KiB  
Article
Your Favourite Park Is Not My Favourite Park: A Participatory Geographic Information System Approach to Improving Urban Green and Blue Spaces—A Case Study in Edinburgh, Scotland
by Charlotte Wendelboe-Nelson, Yiyun Wang, Simon Bell, Craig W. McDougall and Catharine Ward Thompson
Land 2024, 13(3), 395; https://doi.org/10.3390/land13030395 - 20 Mar 2024
Cited by 2 | Viewed by 2461
Abstract
Access to urban green and blue spaces (UGBSs) has been associated with positive effects on health and wellbeing; however, the past decades have seen a decline in quality and user satisfaction with UGBSs. This reflects the mounting challenges that many UK cities face [...] Read more.
Access to urban green and blue spaces (UGBSs) has been associated with positive effects on health and wellbeing; however, the past decades have seen a decline in quality and user satisfaction with UGBSs. This reflects the mounting challenges that many UK cities face in providing appropriate public facilities, alongside issues such as health inequalities, an ageing population, climate change, and loss of biodiversity. At present, little is known about the preferences of different population subgroups and, specifically, the UGBSs they visit and the spaces they avoid. Using a public participatory geographic information system (PPGIS), the overall aim of the research presented here was to investigate the preferences of different population subgroups in urban areas, and the UGBSs they visit, using Edinburgh, Scotland as a case study. We created a baseline visitor demographic profile for UGBS use, and highlighted how visitors perceive, physically access, use, and engage with UGBSs. The results revealed considerable variation in UGBS preference: one person’s favourite UGBS may be one that someone else dislikes and avoids. It is clear that adapting UGBSs to suit local communities should not be a ‘one-size-fits-all’ approach. The conflicting views and preferences of different groups of respondents point to the importance of developing policies and park management plans that can accommodate a variety of uses and experiential qualities within individual parks. PPGIS approaches, such as those utilised in this study, offer opportunities to address this issue and provide evidence to increase equitable UGBS usage. Full article
(This article belongs to the Special Issue Managing Urban Green Infrastructure and Ecosystem Services)
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<p>Survey respondents’ home location, divided according to Scottish Index of Multiple Deprivation (SIMD) quintile, from one (most deprived 20%) to five (least deprived 20%).</p> Full article ">Figure 2
<p>UGBSs avoided (279) and visited (1629) by the 531 respondents taking part in the survey.</p> Full article ">Figure 3
<p>The visit count of all the UGBS areas the survey respondents prefer to visit; the darker the blue colour, the more people have chosen the area as a place they like to visit. The map gives an overview of the extended Edinburgh area. Two main UGBS ‘hotspots’ were identified: Holyrood Park (No. 6); and the Hermitage of Braid and Blackford Hill (No. 23).</p> Full article ">Figure 4
<p>The visit count of all the UGBSs the survey respondents avoid visiting. The darker red colours reveal distinct areas that are avoided by the survey population. The main UGBS areas respondents avoided: Princes Street Gardens (No. 8); The Meadows (No. 7); Leith Links (No. 18); and Holyrood Park (No. 6).</p> Full article ">Figure 5
<p>Maps showing the SIMD level of the area where the respondents lived: Quintile 1 contains the 20% most deprived data zones in Scotland (yellow), and quintile 5 contains the 20% least deprived data zones (black). (<b>a</b>) The respondents were asked to mark a place close to their home. (<b>b</b>) The five UGBSs that respondents liked to visit. For each participant, their selected pins are coloured according to the SIMD level of their residence.</p> Full article ">Figure 6
<p>(<b>a</b>) shows distance from the respondents’ home to the UGBSs they like visiting, with the visited spaces grouped according to SIMD category of their residence. (<b>b</b>) shows distance from the respondents’ home to the UGBSs they avoid visiting, with the avoided spaces grouped according to SIMD category of their residence.</p> Full article ">Figure 7
<p>The UGBSs visited by respondents according to household income; low (GBP0–GBP26k), moderate (GBP27–GBP45), high (&gt;GBP45k).</p> Full article ">Figure 8
<p>The UGBSs visited by respondents according to age; young (16–34), middle (35–64), old (65+).</p> Full article ">Figure 9
<p>The UGBSs avoided, divided by gender.</p> Full article ">Figure 10
<p>Respondents’ preferences and reasons for visiting a green/blue space (Likert scale, wherein five is most positive and one most negative).</p> Full article ">Figure 11
<p>Responses to questions about facilities and information provided in green/blue spaces visited.</p> Full article ">Figure 12
<p>Images of Saughton Park, illustrating the use of zoning to accommodate the variation in individuals’ preferences for UGBSs (source: the authors).</p> Full article ">Figure 13
<p>Images of Inverleith Park, illustrating the use of zoning to accommodate the variation in individuals’ preferences for UGBSs (source: the authors).</p> Full article ">Figure A1
<p>Green/blue spaces in Edinburgh. 1: Craigmillar Castle Park; 2: Inch Park; 3: The Braid Hills Golf Course; 4: Craiglockhart Hills; 5: Union Canal; 6: Holyrood Park; 7: The Meadows; 8: Princes Street Gardens; 9: Inverleith Park; 10: Harrison Park; 11: Saughton Park; 12: Corstorphine Hill; 13: Cammo Park; 14: Cramond Seafront; 15: Silverknowes Esplanade; 16: Granton Crescent Park; 17: Wardie Bay; 18: Leith Links; 19: Portobello Beach; 20: Pentland Hills; 21: Calton Hills; 22: Victoria Park; 23: Hermitage of Braid and Blackford Hill.</p> Full article ">Figure A2
<p>Opening question of the TGS Maptionnaire survey: ‘Do you ever visit parks or open spaces?’.</p> Full article ">Figure A3
<p>For the map-based part of the TGS Maptionnaire survey, the respondents were asked to mark the area where they live, the UGBSs they visit most often, and the UGBSs they avoid visiting.</p> Full article ">
24 pages, 3335 KiB  
Article
Seminatural Grasslands: An Emblematic Challenge for Nature Conservation in Protected Areas
by Daniela Gigante, Simone Angelucci, Federica Bonini, Federico Caruso, Valter Di Cecco, Domizia Donnini, Luciano Morbidini, Mariano Pauselli, Bernardo Valenti, Andrea Tassi, Marco Vizzari and Luciano Di Martino
Land 2024, 13(3), 386; https://doi.org/10.3390/land13030386 - 18 Mar 2024
Cited by 1 | Viewed by 2056
Abstract
Seminatural grasslands are among the most threatened habitats in Europe and worldwide, mainly due to changes in/abandonment of their traditional extensive use by grazing animals. This study aimed to develop an innovative model that integrates plant biodiversity, animal husbandry, and geo-informatics to manage [...] Read more.
Seminatural grasslands are among the most threatened habitats in Europe and worldwide, mainly due to changes in/abandonment of their traditional extensive use by grazing animals. This study aimed to develop an innovative model that integrates plant biodiversity, animal husbandry, and geo-informatics to manage and preserve seminatural grasslands in protected areas. With this objective, an integrated study was conducted on the seminatural grasslands in the hilly, montane, and (to a minimum extent) subalpine belts of the Maiella National Park, one of Europe’s most biodiversity-rich protected sites. Plant biodiversity was investigated through 141 phytosociological relevés in homogeneous areas; the pastoral value was calculated, and grasslands’ productivity was measured together with the main nutritional parameters. Uni- and multivariate statistical analyses were performed to identify the main grassland vegetation types, their indicator species and ecological–environmental characteristics, and their pastoral and nutritional values’ variability and differences. A total of 17 grassland types, most of which correspond to habitat types listed in Annex I to the 92/43/EEC Directive, were identified and characterised in terms of their biodiversity and potential animal load. To allow for near-real-time analysis of grasslands, an NDVI-based web interface running on Google Earth Engine was implemented. This integrated approach can provide decision-making support for protected-area managers seeking to develop and implement sustainable grassland management practices that ensure the long-term maintenance of their biodiversity. Full article
(This article belongs to the Special Issue High-Altitude Grasslands: Biodiversity Conservation, Management, and Habitat Preference)
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<p>Map of the study area and location of the MNP borders and sampling plots; in the top right insert: location of the MNP (red point) in Italy and Europe. Administrative boundaries: Eurostat, EuroGeographics (available at <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units" target="_blank">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a>, accessed on 3 February 2024).</p> Full article ">Figure 2
<p>Variability in relevant environmental parameters for the 17 identified grassland types, along with the total plant cover: (<b>a</b>) slope, expressed as degrees (°), (<b>b</b>) rockiness and stoniness, expressed as percentage (%), (<b>c</b>) bare soil, expressed as percentage (%), (<b>d</b>) total vegetation cover, expressed as percentage (%). The full names of the grassland types are reported in <a href="#app1-land-13-00386" class="html-app">Table S1</a>.</p> Full article ">Figure 3
<p>Variability in biodiversity parameters for the 17 identified grassland types: (<b>a</b>) number of species per standard survey unit (sampling plot: 4 × 4 m<sup>2</sup>), (<b>b</b>) Shannon index, (<b>c</b>) Simpson index, (<b>d</b>) equitability index. The full names of the grassland types are reported in <a href="#app1-land-13-00386" class="html-app">Table S1</a>.</p> Full article ">Figure 4
<p>Variability in the calculated pastoral value (PV) based on the “visual estimation” method for each identified grassland type (<b>a</b>); statistically significant differences (<b>b</b>) were tested by Kruskal–Wallis one-way non-parametric ANOVA test (H χ2 = 96.65, <span class="html-italic">p</span> &lt; 0.001) and Mann–Whitney pairwise (*** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05, ns: not significant) tests. The full names of the grassland types are reported in <a href="#app1-land-13-00386" class="html-app">Table S1</a>. Data from an additional relevé (“hay_mea”) from the study area are included in the chart for comparison, referring to a hay meadow that is not grazed and is representative of a slightly fertilized lawn not used as pasture.</p> Full article ">Figure 5
<p>Reduced Major Axis Regression between the PVs calculated by the “visual estimation” (PV-ve) and the “point quadrat” (PV-pq) methods (95% bootstrapped confidence intervals, <span class="html-italic">n</span> = 1999, r = 0.8, r<sup>2</sup> = 0.6, <span class="html-italic">p</span> &lt; 0.001) calculated on log-transformed data.</p> Full article ">Figure 6
<p>Nutritional parameters expressed as % (average ± standard deviation) measured on the collected biomass for every grassland type; in brackets is the number of samples per grassland type. Data from an additional relevé (“hay_mea”) from the study area are included in the chart for comparison, referring to a hay meadow that is not grazed and is representative of a slightly fertilized lawn not used as pasture. Legend: ethereal extract (EE), crude protein (CP), non-fibre carbohydrates (NFCs), hemicellulose (HEM), cellulose (CEL), acid detergent fibre (ADF), neutral detergent fibre (NDF). The full names of the grassland types are reported in <a href="#app1-land-13-00386" class="html-app">Table S1</a>.</p> Full article ">
22 pages, 2724 KiB  
Review
A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas
by Odunayo David Adeniyi, Hauwa Bature and Michael Mearker
Land 2024, 13(3), 379; https://doi.org/10.3390/land13030379 - 17 Mar 2024
Cited by 5 | Viewed by 3823
Abstract
Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil [...] Read more.
Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil characteristics. To assess the spatial distribution of soil properties and classes, accurate soil datasets are a prerequisite to facilitate the effective management of agricultural areas. This systematic review explores the DSM approaches in lowland areas by compiling and analysing published articles from 2008 to mid-2023. A total of 67 relevant articles were identified from Web of Science and Scopus. The study reveals a rising trend in publications, particularly in recent years, indicative of the growing recognition of DSM’s pivotal role in comprehending soil properties in lowland ecosystems. Noteworthy knowledge gaps are identified, emphasizing the need for nuanced exploration of specific environmental variables influencing soil heterogeneity. This review underscores the dominance of agricultural cropland as a focus, reflecting the intricate relationship between soil attributes and agricultural productivity in lowlands. Vegetation-related covariates, relief-related factors, and statistical machine learning models, with random forest at the forefront, emerge prominently. The study concludes by outlining future research directions, highlighting the urgency of understanding the intricacies of lowland soil mapping for improved land management, heightened agricultural productivity, and effective environmental conservation strategies. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
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<p>Schematic overview of the screening process applied to the articles examined for this study.</p> Full article ">Figure 2
<p>Trend of the number of articles published.</p> Full article ">Figure 3
<p>Geographic distribution of the number of articles published.</p> Full article ">Figure 4
<p>Percentage of land use from the articles published.</p> Full article ">Figure 5
<p>Percentage of targeted variables in the articles reviewed.</p> Full article ">Figure 6
<p>Percentage of environmental covariates in the articles reviewed.</p> Full article ">Figure 7
<p>Percentage of important variables in the articles reviewed.</p> Full article ">Figure 8
<p>DSM models used in the reviewed articles.</p> Full article ">Figure 9
<p>Evaluation techniques used in the reviewed articles.</p> Full article ">
21 pages, 11576 KiB  
Article
Sample Size Optimization for Digital Soil Mapping: An Empirical Example
by Daniel D. Saurette, Richard J. Heck, Adam W. Gillespie, Aaron A. Berg and Asim Biswas
Land 2024, 13(3), 365; https://doi.org/10.3390/land13030365 - 14 Mar 2024
Cited by 3 | Viewed by 2307
Abstract
In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes [...] Read more.
In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal DJS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of DJS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation. Full article
(This article belongs to the Special Issue Predictive Soil Mapping Contributing to Sustainable Soil Management)
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<p>Flowchart of the workflow utilized in this study showing the sampling of the environmental covariates, the repeated (five times) sample plans of increasing size selected with conditioned Latin hypercube (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS), the calibration and external validation of the random forest models for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC), and the minimization of the Jensen–Shannon divergence. Detailed steps are explained in <a href="#sec2dot3-land-13-00365" class="html-sec">Section 2.3</a>, <a href="#sec2dot4-land-13-00365" class="html-sec">Section 2.4</a>, <a href="#sec2dot5-land-13-00365" class="html-sec">Section 2.5</a>, <a href="#sec2dot6-land-13-00365" class="html-sec">Section 2.6</a> and <a href="#sec2dot7-land-13-00365" class="html-sec">Section 2.7</a>.</p> Full article ">Figure 2
<p>Map of the Ottawa study area in eastern Ontario, Canada, with elevation from the digital terrain model (DTM) and small dots representing the 1791 soil sampling locations. The DTM is draped over a hillshade with 10× exaggeration to highlight the topography and landforms. Inset map shows the location of the study site in relation to southern Ontario, Canada.</p> Full article ">Figure 3
<p>Kriged surfaces of (<b>A</b>) cation exchange capacity (CEC), clay content (<b>B</b>), soil pH (<b>C</b>), and (<b>D</b>) soil organic carbon (SOC) with thinned sample locations (black dots) for the Ottawa study area.</p> Full article ">Figure 4
<p>Exponential decay of the Jensen–Shannon divergence (D<sub>JS</sub>) and cumulative distribution function for determining optimal sample size as a function of sample size for the conditioned Latin hypercube sampling algorithm (<b>A</b>,<b>D</b>), simple random sampling (<b>B</b>,<b>E</b>), and feature space coverage sampling algorithm (<b>C</b>,<b>F</b>). Solid lines in plots (<b>A</b>–<b>C</b>) show the curve fitted uses non-linear least to square the D<sub>JS</sub> to the points that represent the D<sub>JS</sub> at the various sample sizes. Vertical solid lines in plots (<b>D</b>–<b>F</b>) highlight the optimal sample size determined where the cumulative distribution function reached 95%.</p> Full article ">Figure 5
<p>Change in the concordance and root mean square error (RMSE) with increasing sample size from the external validation of the random forest models trained with sample plans developed using conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) for cation exchange capacity. The solid (orange) vertical line, dashed (blue) vertical line, and dotted (green) vertical line identify the optimal sample size based on the unit invariant knee for the cLHS, FSCS, and SRS sampling algorithms, respectively.</p> Full article ">Figure 6
<p>Random forest predictions of (<b>A</b>) cation exchange capacity (CEC), (<b>B</b>) clay content, (<b>C</b>) soil pH, and (<b>D</b>) soil organic carbon (SOC) for the Ottawa Study area using a sample plan created with conditioned Latin hypercube sampling and the overall optimal sample size of 865 sample locations.</p> Full article ">Figure 7
<p>Prediction interval maps (90%) for cation exchange capacity (CEC) generated using quantile regression forest for the optimal sample sizes based on the Jenson-Shannon Divergence for conditioned Latin hypercube sampling (<b>A</b>), feature space coverage sampling (<b>B</b>), and simple random sampling (<b>C</b>) algorithms.</p> Full article ">
24 pages, 5821 KiB  
Article
Identifying the Climatic and Anthropogenic Impact on Vegetation Surrounding the Natural Springs of the Arava Valley Using Remote Sensing Methods
by Ariel Mordechai Meroz, Avshalom Babad and Noam Levin
Land 2024, 13(3), 361; https://doi.org/10.3390/land13030361 - 12 Mar 2024
Viewed by 1334
Abstract
Natural springs, recognized as biodiversity hotspots and keystone ecosystems, exert positive ecological influences beyond their immediate extent, particularly in dryland environments. The water feeding these springs, largely governed by natural climatic conditions, is susceptible to anthropogenic impacts. The objective of this study was [...] Read more.
Natural springs, recognized as biodiversity hotspots and keystone ecosystems, exert positive ecological influences beyond their immediate extent, particularly in dryland environments. The water feeding these springs, largely governed by natural climatic conditions, is susceptible to anthropogenic impacts. The objective of this study was to determine the factors that cause fluctuations in water availability to springs of the hyper-arid Arava Valley (Israel/Jordan). Using the Standard Precipitation Index, we statistically classified the historical record of yearly rainfall for the past four decades into clusters of dry and wet sub-periods. We assessed changes in vegetation cover around the springs using the Landsat-derived Normalized Difference Vegetation Index (NDVI) for each sub-period. To assess the anthropogenic effects, we examined the correlations between vegetation cover, water extraction from the aquifer, and the status of adjacent agricultural plots that share a hydrological connection with the springs. Our findings revealed fluctuations between wet and dry sub-periods over the last four decades. We observed high responsiveness of vegetation cover around the springs to these fluctuating sub-periods. Of the 25 studied springs, 12 were directly influenced by anthropogenic factors—7 experienced a decline in vegetation, which we attributed to water extraction from the aquifers, while vegetation increase in 5 springs was attributed to water seepage from agricultural areas upstream. In conclusion, addressing vital habitats such as natural springs in arid drylands requires a holistic approach that integrates long-term climatic, ecological, and anthropogenic observations. Full article
(This article belongs to the Special Issue Species Vulnerability and Habitat Loss II)
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<p>(<b>a</b>) A broad overview of the Arava Valley research area (purple) exhibiting the main watersheds, each associated with the aquifer it can potentially enrich. The Arava springs are classified based on their contributing aquifers, and rain stations are marked. (<b>b</b>) Inset of the northern Arava; the numbers correspond to the list of springs in <a href="#land-13-00361-t001" class="html-table">Table 1</a>. (<b>c</b>) The southernmost spring studied. The background imagery maps are taken from ESRI World Imagery maps, 2023.</p> Full article ">Figure 2
<p>Evaluating the change in the extent of vegetation cover near two springs by comparing Corona historical satellite imagery taken in 1968 with Planet imagery taken in 2022. (<b>a</b>) Ein Rachel (7 May 1968), (<b>b</b>) Ein Rachel (26 June 2022), (<b>c</b>) Ein Gidron East (7 May 1968), (<b>d</b>) Ein Gidron East (26 June 2022). The purple line marks the vegetation extent which we estimated.</p> Full article ">Figure 3
<p>The figure illustrates the methodological approach used in this research. The vegetation was assessed in three time periods: 1968 (Corona satellite), 2009–2010 according to [<a href="#B24-land-13-00361" class="html-bibr">24</a>], and May 2022 (Planet satellite). Vegetation cover was assessed with the <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> <mo> </mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>M</mi> <mi>a</mi> <mi>y</mi> <mo>−</mo> <mi>J</mi> <mi>u</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> time series. Yearly rainfall anomalies were identified by calculating the SPI in 36-month windows.</p> Full article ">Figure 4
<p>Monthly rainfall measurements recorded at the five meteorological stations of the Arava Valley (blue) and calculated SPI based on a 36-month window (orange). The horizontal green lines represent clusters of months in which the maximum (&gt;1) or minimum (&lt;−1) value within a moving window of 19 months was defined as wet or dry (see <a href="#sec2dot3dot1-land-13-00361" class="html-sec">Section 2.3.1</a>). <a href="#land-13-00361-t003" class="html-table">Table 3</a> depicts the climatic sub-period for each station.</p> Full article ">Figure 5
<p>Time series of NDVI values around each of the natural springs and of the annual water extraction from each aquifer. (<b>a</b>) The Senonian aquifer, (<b>b</b>) the Quaternary aquifer (center), (<b>c</b>), the Quaternary aquifer (south) and (<b>d</b>) the Quaternary aquifer (north). In 5d we present the average, and the lower and upper quartiles of the NDVI time series of the eight springs. The NDVI values shown here were normalized relative to the maximum annual value of each spring, for ease of comparison. The statistical analysis results, as presented in <a href="#land-13-00361-t004" class="html-table">Table 4</a>, are based on the data provided here. The black box at the right side of each graph summarizes the correlation among the springs associated with each aquifer.</p> Full article ">Figure 6
<p>Selected images of springs in the Arava Valley. The aquifers and date taken are indicated in parentheses. All images, apart from (<b>c</b>,<b>j</b>), were taken by Ariel Meroz. (<b>a</b>) Ein Yahav (SA) (30 December 2022), (<b>b</b>) Ein Tamid (SA) (19 October 2023), (<b>c</b>) Ein Shahak (SA) (2012), photo by Roy Galili, (<b>d</b>) Ein Shahak, (SA) (30 December 2022), (<b>e</b>) Ein Hufira (QAC) (19 October 2023), (<b>f</b>) Ein Ofarim (QAC), (19 October 2023) (<b>g</b>) Ein Yamluch (QAC) (19 October 2023), (<b>h</b>) Ein Yotveta (QAS) (19 October 2023), (<b>i</b>) Ein Plutit (QAN) (19 October 2023), (<b>j</b>) Spring # 17 (QAN), Planet NDVI imagery (26 June 2022); the red color emphasizes the dense vegetation around the spring.</p> Full article ">Figure 7
<p>The blue circles encompass the springs that may be influenced by irrigation water leaching from agricultural plots upstream, based on ESRI 2020 LULC maps [<a href="#B57-land-13-00361" class="html-bibr">57</a>], the numbers on the map represent the numbering of each springs as seen in <a href="#land-13-00361-t001" class="html-table">Table 1</a>, <a href="#land-13-00361-t004" class="html-table">Table 4</a> and <a href="#land-13-00361-t006" class="html-table">Table 6</a> and the purple line marks the boundary line of the study area. The background imagery maps are taken from ESRI World Imagery maps, 2023.</p> Full article ">
14 pages, 812 KiB  
Article
Modeling Landscape Influence on Stream Baseflows for Watershed Conservation
by Timothy O. Randhir and Kimberly B. Klosterman
Land 2024, 13(3), 324; https://doi.org/10.3390/land13030324 - 3 Mar 2024
Viewed by 1504
Abstract
Instream flows are vital to the ecology of riverine and riparian systems. The influence of watershed characteristics on these systems is helpful in developing landscape policies to maintain these flows. Watershed characteristics like precipitation, forest cover, impervious cover, soil drainage, and slope affect [...] Read more.
Instream flows are vital to the ecology of riverine and riparian systems. The influence of watershed characteristics on these systems is helpful in developing landscape policies to maintain these flows. Watershed characteristics like precipitation, forest cover, impervious cover, soil drainage, and slope affect baseflows. Spatial analysis using GIS and nonlinear regression analysis is used to analyze spatial and temporal information from gauged watersheds in Massachusetts to quantify the relationship between baseflows and watershed metrics. The marginal functions of landscape factors that reflect changes in baseflow are quantified. This information is then applied to watershed policy toward improving base flows. The interactions of three fixed attributes, soil drainage, rainfall incidence, and slope, are analyzed for the manageable landscape attributes of impervious and forest cover. Developing watershed policy to protect baseflows involves evaluating the complex interactions and functional relationships between these landscape factors and their use in watershed conservation planning. Full article
(This article belongs to the Special Issue Advances in Hydro-Sedimentological Modeling for Simulating LULC)
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<p>Sampled streamflow gage stations and their watershed boundaries.</p> Full article ">Figure 2
<p>Observed and predicted base flow values.</p> Full article ">Figure 3
<p>Influence of landscape factors on baseflows.</p> Full article ">
21 pages, 1281 KiB  
Article
Counteract Soil Consumption through Ecosystem Services and Landscape Restoration for an Efficient Urban Regeneration
by Celestina Fazia, Kh Md Nahiduzzaman, Baqer Al-Ramadan, Adel Aldosary and Francesca Moraci
Land 2024, 13(3), 323; https://doi.org/10.3390/land13030323 - 2 Mar 2024
Cited by 2 | Viewed by 2339
Abstract
Soil consumption, marked by the expansion of artificial land cover for residential, productive, and infrastructural purposes, is a concerning trend in Italy, as revealed by the Copernicus land monitoring program. The issue is exacerbated by agricultural intensification and urbanization, particularly affecting regions like [...] Read more.
Soil consumption, marked by the expansion of artificial land cover for residential, productive, and infrastructural purposes, is a concerning trend in Italy, as revealed by the Copernicus land monitoring program. The issue is exacerbated by agricultural intensification and urbanization, particularly affecting regions like Lombardia and Piemonte. However, Sicilia, Abruzzo, and Lazio experience notable increases in processes of abandonment and re-naturalization. Data from Ispra highlights the need for in-depth study, especially in regions like Sicilia, where contrasting phenomena occur. This study utilizes Ispra data to monitor and formulate strategies for mitigating soil consumption and safeguarding ecosystem services. The research aligns with objectives related to combating climate change and facilitating the ecological transition of territories. The complexity of land consumption, influenced by interdependent factors, is evident in the achieved results. Effective strategies for containment and re-naturalization involve the implementation of town planning regulations and multi-level behavioral pathways. This study aims to identify contextual actions that can reduce land consumption, promote de-impermeabilization, and encourage re-naturalization, focusing on enhancing ecosystem services in land use activities. Thus, it focuses on understanding the contributions of ecosystem services, landscape restoration and green infrastructure on climate mitigation, and a reduction in land consumption in urban regeneration processes. As well, through open-source systems, it is important to monitor in real time the trend of the quantity of factors and variables and the state of the environment, and the reasons to intervene with systemic strategies and actions constitutes another lens of focus. Full article
(This article belongs to the Special Issue Urban Regeneration: Challenges and Opportunities for the Landscape)
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<p>Forest area covered by fires per 1000 square kilometers and in red the number of fires, Source: Figure 13. Elaboration of SDGs 2023 Report on data from the Comando Carabinieri Tutela forestale (accessed on 10 September 2023).</p> Full article ">Figure 2
<p>Methodological steps of the study.</p> Full article ">Figure 3
<p>Percentage share of regional degraded land net of water bodies (UNCCD methodology for SDG 15.3.1 indicator) year 2019. Source: Ispra and SDGs Report 2023, ISTAT, Rome (accessed on 10 September 2023).</p> Full article ">
16 pages, 1146 KiB  
Article
Inequalities and Injustices of Urban Green Regeneration: Applying the Conflict Analysis Perspective
by Annegret Haase
Land 2024, 13(3), 296; https://doi.org/10.3390/land13030296 - 27 Feb 2024
Cited by 1 | Viewed by 2554
Abstract
Green regeneration has become one of the most powerful strategies for improving the quality of life in cities, supporting climate change adaptation, and reducing the carbon footprints of cities. While it is the ambition of most green regeneration projects to create benefits for [...] Read more.
Green regeneration has become one of the most powerful strategies for improving the quality of life in cities, supporting climate change adaptation, and reducing the carbon footprints of cities. While it is the ambition of most green regeneration projects to create benefits for residents and users, reality shows that green regeneration also reinforces existing or even shapes new ‘green inequalities’. These can result from green gentrification and displacement, procedural injustices, and exclusion from participation or barriers to the access and use of newly created urban green spaces. Set against this background, the paper uses a conflict analysis perspective to look at the inequalities and injustices that evolve within the context of green regeneration. Applying social conflict theory, it seeks to understand (1) why and how green regeneration may lead to inequality and justice conflicts and (2) how conflict analysis helps to understand the nature and implications of green regeneration conflicts in more depth. As for its empirical foundation, the paper reanalyses empirical evidence that was examined in earlier projects on a residential area in the city of Leipzig, Germany. Full article
(This article belongs to the Special Issue Public Spaces: Socioeconomic Challenges)
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<p>Analytical framework for the conflict analysis. Source: author’s own work, based on [<a href="#B23-land-13-00296" class="html-bibr">23</a>,<a href="#B31-land-13-00296" class="html-bibr">31</a>,<a href="#B32-land-13-00296" class="html-bibr">32</a>].</p> Full article ">Figure 2
<p>Area of the East Park Circle (EPC) and green regeneration areas/projects that are subjects of inequalities and justice conflicts. Conflict settings: 1 = green gentrification/displacement around Lene-Voigt-Park; 2 = use conflicts with regards to green regeneration; 3 = participation conflicts [<a href="#B42-land-13-00296" class="html-bibr">42</a>].</p> Full article ">
23 pages, 10717 KiB  
Article
Scenario Analysis of Green Infrastructure to Adapt Medium-Size Cities to Climate Change: The Case of Zaragoza, Spain
by Elie Hanna, María R. Felipe-Lucia and Francisco A. Comín
Land 2024, 13(3), 280; https://doi.org/10.3390/land13030280 - 23 Feb 2024
Viewed by 1552
Abstract
Planning a well-structured urban green infrastructure (UGI) is essential for cities to counteract the impacts of climate change. Soil carbon and air temperature differences between open and plant-covered sites were used as proxies of carbon sequestration (CS) and temperature regulation (TR) to evaluate [...] Read more.
Planning a well-structured urban green infrastructure (UGI) is essential for cities to counteract the impacts of climate change. Soil carbon and air temperature differences between open and plant-covered sites were used as proxies of carbon sequestration (CS) and temperature regulation (TR) to evaluate the current conditions of UGI in Zaragoza, a medium-sized city in northeastern Spain. Alternative scenarios were constructed, after a stakeholder consultation, at both city and municipal (city plus peri-urban zone) scales, extrapolating the highest values of CS and TR to two groups of UGI types grouped based on the state of their ecological functioning. We employed analysis of variance to compare mean values of CS and TR across diverse scenarios at both city and municipality scales. Statistically significant differences were found in city-scale and municipality-scale scenarios for both CS and TR. Multiplying CS by area did not show significant variation in city scale. Significant differences were found when multiplying TR by area at both scales, with exceptions in certain scenario combinations. These results suggest favoring the restoration of UGI sites in peri-urban zones (such as forests and steppe zones) to increase CS and those in densely urbanized zones (such as urban parks) to provide TR benefits. Full article
(This article belongs to the Special Issue Urban Green Spaces: An Effective Tool to Regulate Urban Climate and Biodiversity)
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<p>Methodology describing the steps used for the scenario analysis. Ecosystem services (ES), carbon sequestration (CS), temperature regulation (TR), and urban green infrastructure (UGI). Hanna et al. (2023) [<a href="#B9-land-13-00280" class="html-bibr">9</a>]. Copernicus: <a href="https://land.copernicus.eu/" target="_blank">https://land.copernicus.eu/</a> (accessed on 1 October 2023).</p> Full article ">Figure 2
<p>(<b>a</b>) LU/LC map of the study area (Zaragoza) at municipality scale. (<b>b</b>) Functionality map of urban green infrastructure (UGI) sites of Zaragoza at municipality scale. (<b>c</b>) LU/LC map of the study area (Zaragoza) at city scale. (<b>d</b>) Functionality map of UGI of Zaragoza at city scale.</p> Full article ">Figure 3
<p>One-way ANOVA comparing the delivery of carbon sequestration (CS) and temperature regulation (TR) under four types of scenarios. LF: current state of low functioning sites. HF: current state of high-functioning sites. IMLF: improvement/restoration scenario of low-functioning sites. IMHF: improvement/restoration scenario of high functioning sites at city and municipality scale. Hollow dots represent outliers. Black dots represent the mean value.</p> Full article ">Figure 4
<p>(<b>a</b>) Priority map of restoration for temperature regulation (TR) in the city. (<b>b</b>) Priority map of restoration for carbon sequestration (CS) in the municipality.</p> Full article ">
24 pages, 5300 KiB  
Review
Land Use Carbon Emissions or Sink: Research Characteristics, Hotspots and Future Perspectives
by Lina Liu, Jiansheng Qu, Feng Gao, Tek Narayan Maraseni, Shaojian Wang, Suman Aryal, Zhenhua Zhang and Rong Wu
Land 2024, 13(3), 279; https://doi.org/10.3390/land13030279 - 23 Feb 2024
Cited by 2 | Viewed by 4213
Abstract
The land use, land-use change and forestry (LULUCF) sector, as a source and a sink of greenhouse gas (GHG) emissions, is critical for achieving carbon neutrality. Many academic journals have published papers on land use carbon emission or sink (LUCES), but LUCES reviews [...] Read more.
The land use, land-use change and forestry (LULUCF) sector, as a source and a sink of greenhouse gas (GHG) emissions, is critical for achieving carbon neutrality. Many academic journals have published papers on land use carbon emission or sink (LUCES), but LUCES reviews are relatively rare, which poses great challenges in accurately understanding the research progress and future prospects. This work analyzes the research characteristics, hotspots and future perspectives of LUCES research by using a bibliometric analysis (such as DDA, VOSviewer, CiteSpace software) and a review based on the data (6115 scientific papers) during 1991–2023 from the Web of Science (WoS) platform. We found that (1) over the past 33 years, it first presented a steady growth, then fluctuating growth, and finally a rapid growth trend in the yearly number of publications in LUCES research. The USA (17.31%), China (14.96%), and the UK (7.37%) occupy a dominant position in this research field. (2) The related LUCES research is interdisciplinary, which mainly cover science and technology, meteorology and atmospheric sciences, geology, and environmental sciences and ecology disciplines. (3) The research hotspot analysis on LUCES shows that these articles mostly covered the follow three aspects: ecosystem services, climate change, and carbon neutrality. (4) A review of the past LUCES literature suggests that it is mainly focused on exploring the forefront issues in terms of the definition and boundaries, evaluation method and influencing factors, etc. This work suggests that further research could explore the main scientific problems on quantification of land-based carbon neutrality, quantitative analysis of the impact mechanisms, as well as interdisciplinary research and collaborative governance needed for carbon neutrality. Full article
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<p>The framework of this study’s analysis process.</p> Full article ">Figure 2
<p>Number of publications in LUCES research from 1991 to 2023.</p> Full article ">Figure 3
<p>Number of publications in LUCES research by top 10 countries.</p> Full article ">Figure 4
<p>The national cooperation network on LUCES research. (<b>a</b>) Publications of total; (<b>b</b>) publications of “NSP”.</p> Full article ">Figure 5
<p>The institutional cooperation network on LUCES research. (<b>a</b>) Publications of total; (<b>b</b>) publications of “NSP”.</p> Full article ">Figure 6
<p>The journal citation network on LUCES research.</p> Full article ">Figure 7
<p>Top 10 research categories of LUCES research. (<b>a</b>) Publications of total; (<b>b</b>) of “NSP”.</p> Full article ">Figure 8
<p>Keyword co-occurrence network map in LUCES research. (<b>a</b>) Total publications during 1991–2023; (<b>b</b>) publications of “NSP” during 1991–2023.</p> Full article ">Figure 9
<p>The keyword bubble diagram of LUCES research in different periods. Top 10 keywords during (<b>a</b>) 1991–2000; (<b>b</b>) 2001–2010; and (<b>c</b>) 2011–2023.</p> Full article ">Figure 10
<p>Top keywords with the strongest citation bursts in LUCES research. (<b>a</b>) Total publications during 2001–2023; (<b>b</b>) publications of “NSP” during 2001–2023.</p> Full article ">Figure 11
<p>Summary of LUCES research from the following aspects: definition and boundary, evaluation method and influence mechanism.</p> Full article ">
23 pages, 832 KiB  
Article
Integration of Climate Change Strategies into Policy and Planning for Regional Development: A Case Study of Greece
by Stavros Kalogiannidis, Dimitrios Kalfas, Olympia Papaevangelou, Fotios Chatzitheodoridis, Katerina-Navsika Katsetsiadou and Efthymios Lekkas
Land 2024, 13(3), 268; https://doi.org/10.3390/land13030268 - 21 Feb 2024
Cited by 5 | Viewed by 3633
Abstract
Climate change presents a pressing challenge to regional development, impacting economies, environments, and societies across the globe. Europe, with its diverse regions and commitment to sustainability, serves as a unique case study for exploring the integration of climate change strategies into regional policy [...] Read more.
Climate change presents a pressing challenge to regional development, impacting economies, environments, and societies across the globe. Europe, with its diverse regions and commitment to sustainability, serves as a unique case study for exploring the integration of climate change strategies into regional policy and planning. The purpose of this study is to analyze the integration of climate change strategies into policy and planning for regional development in Europe, especially in Greece. Data was collected from 270 environmental experts across Greece using a questionnaire. The results highlight the significance of regional economic growth (gross regional product), infrastructure quality, educational attainment, and a conducive business environment as key measures of regional development. Opportunities arising from climate change strategy integration are explored, revealing economic benefits, environmental opportunities, social enhancements, and technological advancements. These opportunities not only mitigate climate change’s adverse impacts but also foster innovation, economic growth, and community resilience. Successful integration can position regions as global leaders in sustainability and innovation. Correlation and regression analyses reveal that opportunities for integration and common climate change strategies positively influence regional development, while barriers exhibit a counterintuitive positive relationship. However, several barriers hinder integration efforts, including institutional fragmentation, resource constraints, conflicting political and economic priorities, and insufficient stakeholder engagement. This study sheds light on the intricate relationship between climate change, policy integration, and regional development in Greece. It supports the potential for regions to drive sustainability and innovation while navigating the challenges of climate change, ultimately contributing to a more resilient and prosperous future. Full article
(This article belongs to the Special Issue Local and Regional Planning for Sustainable Development)
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<p>Opportunities presented by climate change strategies. Source: Authors’ own work (2023).</p> Full article ">Figure 2
<p>Barriers faced in implementing climate change strategies. Source: Authors’ own work (2023).</p> Full article ">Figure 3
<p>Common climate change strategies employed across Greece. Source: Authors’ own work (2023).</p> Full article ">
21 pages, 50514 KiB  
Article
Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model
by Nikiforos Samarinas, Nikolaos L. Tsakiridis, Eleni Kalopesa and George C. Zalidis
Land 2024, 13(2), 174; https://doi.org/10.3390/land13020174 - 1 Feb 2024
Cited by 6 | Viewed by 4131
Abstract
The existing digital soil maps are mainly characterized by coarse spatial resolution and are not up to date; thus, they are unable to support the physical process-based models for improved predictions. The overarching objective of this work is oriented toward a data-driven approach [...] Read more.
The existing digital soil maps are mainly characterized by coarse spatial resolution and are not up to date; thus, they are unable to support the physical process-based models for improved predictions. The overarching objective of this work is oriented toward a data-driven approach and datacube-based tools (Soil Data Cube), leveraging Sentinel-2 imagery data, open access databases, ground truth soil data and Artificial Intelligence (AI) architectures to provide enhanced geospatial layers into the Revised Universal Soil Loss Equation (RUSLE) model, improving both the reliability and the spatial resolution of the final map. The proposed methodology was implemented in the agricultural area of the Imathia Regional Unit (northern Greece), which consists of both mountainous areas and lowlands. Enhanced soil maps of Soil Organic Carbon (SOC) and soil texture were generated at 10 m resolution through a time-series analysis of satellite data and an XGBoost (eXtrene Gradinent Boosting) model. The model was trained by 84 ground truth soil samples (collected from agricultural fields) taking into account also additional environmental covariates (including the digital elevation model and climatic data) and following a Digital Soil Mapping (DSM) approach. The enhanced layers were introduced into the RUSLE’s soil erodibility factor (K-factor), producing a soil erosion layer with high spatial resolution. Notable prediction accuracy was achieved by the AI model with R2 0.61 for SOC and 0.73, 0.67 and 0.63 for clay, sand, and silt, respectively. The average annual soil loss of the unit was found to be 1.76 ton/ha/yr with 6% of the total agricultural area suffering from severe erosion (>11 ton/ha/yr), which was mainly found in the mountainous border regions, showing the strong influence of the mountains in the agricultural fields. The overall methodology could strongly support regional decision making and planning and environmental policies such as the European Common Agricultural Policy (CAP) and the Sustainable Development Goals (SDGs). Full article
(This article belongs to the Special Issue Global Change in Mediterranean Regions: Potential Impact of Climate Drift and Land Use on Soil Erosion and Land Degradation)
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Full article ">Figure 1
<p>The Soil Data Cube pipeline flow diagram for the generation of the soil erosion map.</p> Full article ">Figure 2
<p>Maps of the study area presenting (<b>a</b>) is the EU-DEM, (<b>b</b>) the soil classes according to FAO, (<b>c</b>) Corine LULC, and (<b>d</b>) LULC by ESA WorldCover.</p> Full article ">Figure 3
<p>The 84 soil samples distribution in the Imathia Regional Unit agricultural area.</p> Full article ">Figure 4
<p>RUSLE formula pipeline [<a href="#B41-land-13-00174" class="html-bibr">41</a>].</p> Full article ">Figure 5
<p>Scatter plot between observed and predicted values in the independent test set for the SOC, clay and sand content using the developed AI models. The predictions for silt content are derived using a mathematical expression from the predicted sand and clay values in order for the particle size distribution to sum up to 100%. The dashed line is the 1:1 line, while the straight line is the least squares fit.</p> Full article ">Figure 6
<p>Feature importance for the SOC, clay, and sand content models using the developed AI models.</p> Full article ">Figure 7
<p>Spatial distribution of SOC and clay content in the agricultural area of the Imathia Regional Unit.</p> Full article ">Figure 8
<p>Spatial distribution of the produced RUSLE factors: (<b>a</b>) C-factor, (<b>b</b>) LS-factor, (<b>c</b>) K-factor and (<b>d</b>) P-factor.</p> Full article ">Figure 9
<p>Average annual soil erosion for agricultural area in Imathia Regional Unit for 2021.</p> Full article ">Figure 10
<p>Portions of total agricultural area per erosion class (from low to extremely high).</p> Full article ">Figure 11
<p>SOC, clay content, K-factor and soil erosion in two different demonstration areas in the Unit.</p> Full article ">Figure 12
<p>Soil erosion maps produced with different spatial resolution.</p> Full article ">
19 pages, 908 KiB  
Article
Key Drivers of Land Use Changes in the Rural Area of Gargano (South Italy) and Their Implications for the Local Sustainable Development
by Nazgul Esengulova, Pasquale Balena, Caterina De Lucia, Antonio Lopolito and Pasquale Pazienza
Land 2024, 13(2), 166; https://doi.org/10.3390/land13020166 - 31 Jan 2024
Cited by 3 | Viewed by 2565
Abstract
This study examines the dynamics of land use and land cover change (LULCC) in the Gargano area (Southern Italy) to reveal crucial insights into the socio-economic and environmental impacts on its unique natural and cultural resources. This analysis was conducted using a mixed [...] Read more.
This study examines the dynamics of land use and land cover change (LULCC) in the Gargano area (Southern Italy) to reveal crucial insights into the socio-economic and environmental impacts on its unique natural and cultural resources. This analysis was conducted using a mixed approach of GIS data and expert interviews to investigate significant changes in the Gargano area, from 2000 to 2018, and their drivers. Artificial surfaces gained 22% of their original surfaces, while heterogeneous areas and pastures lost 25% and 78%, respectively. Urbanization and deforestation emerged as major concerns, reflecting heightened sensitivity to these transformative processes. Agricultural intensification and support policies were perceived as potential pressure sources on specific natural components. Conversely, these drivers counteracted land abandonment. Drivers such as education level and agricultural extensification were seen as levers for a more desirable land cover dynamic. Identified actions include providing targeted support for agriculture within environmental constraints, addressing land ownership fragmentation, supporting agricultural extensification, and promoting environmental awareness. Full article
(This article belongs to the Special Issue Future Scenarios of Land Use and Land Cover Change)
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<p>Representation of land use transformation in the period 2000–2018.</p> Full article ">
26 pages, 38536 KiB  
Article
Integrating Landscape Character Assessment with Community Values in a Scenic Evaluation Methodology for Regional Landscape Planning
by Ata Tara, Gillian Lawson, Wendy Davies, Alan Chenoweth and Georgina Pratten
Land 2024, 13(2), 169; https://doi.org/10.3390/land13020169 - 31 Jan 2024
Cited by 4 | Viewed by 2334
Abstract
The Landscape Character Assessment (LCA) method from the UK has proven effective in identifying landscape values and characteristics through a comprehensive mapping process. However, it is predominantly expert-led and lacks an evaluation of scenery, hindering the inclusion of the broader community’s preferences and [...] Read more.
The Landscape Character Assessment (LCA) method from the UK has proven effective in identifying landscape values and characteristics through a comprehensive mapping process. However, it is predominantly expert-led and lacks an evaluation of scenery, hindering the inclusion of the broader community’s preferences and visual attachment to their landscape. In Australia, the application of the Scenic Amenity Methodology (SAM) using Geographical Information System (GIS) mapping has engaged communities but has often overlooked the importance of landscape character. To overcome these limitations, this study presents an innovative scenic assessment methodology, referred to as modified Scenic Amenity Methodology (modified SAM). The methodology establishes landscape character types (LCTs) to map scenic preference ratings derived from community photo surveys. Simultaneously, it incorporates the visual exposure of the landscape from publicly accessible viewpoints, modelled using a Digital Elevation Model (DEM). The combination of scenic preferences and visual exposure enables mapping of the scenic amenity values held by the community. This methodology was first trialled in Bundaberg, then Cairns, the Whitsunday Islands, and, most recently, Toowoomba in Queensland, Australia. This paper presents the results of the Toowoomba study and reports on the challenges and limitations of informing landscape character type (LCT) values through a public photo survey, developing a scenic preference map from ratings of photos across a region, a map of the visual exposure of landscape elements from key public viewing locations, and, ultimately, a map of scenic amenity values across the Toowoomba Region. It indicates that integrating previous LCA approaches with public participation through community preferences is indeed feasible for regional landscape planning. Full article
(This article belongs to the Special Issue Advances in Landscape Visual Quality Assessment in the Decade of Ecosystem Restoration)
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<p>SEQ Scenic Amenity Methodology (SAM) 2004: (<b>a</b>) scenic preference map; (<b>b</b>) visual exposure map; (<b>c</b>) scenic amenity map; (<b>d</b>) SAM look-up table (source: Queensland Government, 2004).</p> Full article ">Figure 2
<p>Study area: (<b>a</b>) QLD local government areas; (<b>b</b>) Toowoomba Region in relation to SEQ.</p> Full article ">Figure 3
<p>Alternative methodology in relation to SAM (2004) to integrate LCA in scenic preference map.</p> Full article ">Figure 4
<p>Landscape character types (LCTs).</p> Full article ">Figure 5
<p>Survey photo analysis in AutoCAD to classify and calculate ratio of visible LCT subtypes and built elements in each photograph used for correlation analysis with mean SPR.</p> Full article ">Figure 6
<p>Landscape character types (LCTs) with mean scenic preference ratings (SPRs).</p> Full article ">Figure 7
<p>Landscape subtypes with relative SPR.</p> Full article ">Figure 8
<p>Identified scenic lookouts and scenic routes within the Toowoomba Region.</p> Full article ">Figure 9
<p>(<b>a</b>) scenic roads and designated lookouts; (<b>b</b>) 12,600 viewpoints with 500 m intervals along public roads and lookouts across the whole region.</p> Full article ">Figure 10
<p>Preliminary and coarse visual exposure modelling showing the engagement of views beyond the LGA boundary (up to 20 km distance) presented in 3D perspective.</p> Full article ">Figure 11
<p>(<b>a</b>) Distribution of mean scenic preference ratings; (<b>b</b>) age group of survey respondents; (<b>c</b>) primary residence of survey respondents (%).</p> Full article ">Figure 12
<p>Scenic preference mapping: (<b>a</b>) SP mapping process by multiplying LCTs and LCT subtypes; (<b>b</b>) final scenic preference map (1–10) reflecting the combined effect of LCTs and subtypes.</p> Full article ">Figure 13
<p>Visual exposure mapping (1–10): (<b>a</b>) VE including AADT 2009 and land-cover attenuation factors produced by Conics Pty Ltd, Brisbane, Australia.; (<b>b</b>) VE map including AADT 2017 and 2018 only; (<b>c</b>) preferred VE map excluding AADT and land-cover attenuation (visibility only).</p> Full article ">Figure 14
<p>Outcomes: (<b>a</b>) scenic amenity map (1–10); (<b>b</b>) scenic amenity overlay for integration in the planning scheme; (<b>c</b>) landscape features map including scenic routes, lookouts, mountain peaks, gateways, waterways, and unique features.</p> Full article ">
21 pages, 7042 KiB  
Article
Enhancing Sustainability and Yield in Maritime Pine Forests: Evaluating Silvicultural Models for Natural Regeneration
by André Sandim, Maria Emília Silva, Paulo Fernandes and Teresa Fonseca
Land 2024, 13(2), 170; https://doi.org/10.3390/land13020170 - 31 Jan 2024
Cited by 1 | Viewed by 1645
Abstract
The maritime pine (Pinus pinaster Ait.) forest is an essential element of the Portuguese forest landscape due to its social, economic, and environmental importance. The sustainability of these forests in the Mediterranean region faces challenges due to recurrent forest fires and the [...] Read more.
The maritime pine (Pinus pinaster Ait.) forest is an essential element of the Portuguese forest landscape due to its social, economic, and environmental importance. The sustainability of these forests in the Mediterranean region faces challenges due to recurrent forest fires and the absence of or delayed management. The species has a high capacity for regeneration, but the perpetuation of pine forests in sustainable conditions depends on adequate management to achieve high biomass production and assure fire resilience. This study aimed to analyse four management scenarios (C1 to C4) for the natural regeneration of maritime pine in six areas with stand ages ranging from 6 to 16 years and densities varying from 15,000 to circa 93,000 trees per ha. The same four scenarios were implemented in each of the six areas. The scenarios considered the evolution of forest growth according to different management prescriptions and were simulated using Modispinaster and PiroPinus models. Scenario C1 considered no intervention, with only the final cut. Scenario C2 considered a thinning schedule to maintain the stand within the 50–60% range of the Stand Density Index (SDI). Scenario C3 followed the area’s Forest Management Plan (PGF), which typically includes two or three thinning operations throughout the cycle. Scenario C4 was adapted from the MS1 silvicultural model of the National Institute for Nature Conservation and Forests—ICNF, which involves opening strips at earlier ages (3 and 6 years), with the selection of trees to remain in the wooded area carried out between 4 and 10 years of age and performing thinning whenever the Wilson Spacing Factor (FW) reaches 0.21. The final cutting age was assumed to be 45 years but could be lowered to 35 years in Scenario 3 if defined in the plan. Based on the indicators generated by the simulators, the results showed variations in the total volume of timber produced at the time of harvest depending on the silvicultural guidelines. Scenario C4 was the most effective in generating the highest individual tree volume at the end of the cycle and the total volume of timber collected throughout the cycle. The ability of the forest to resist fire was evaluated before and after the first treatment for density reduction. The treatments performed did not decrease the resistance to fire control. Full article
(This article belongs to the Special Issue Forest Ecosystems: Protection and Restoration II)
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<p>Location details of the plots in the municipalities of Vila Pouca de Aguiar and Boticas (<b>a</b>). Geographical location of the study area in relation to mainland Portugal (<b>b</b>).</p> Full article ">Figure 2
<p>Excavator with a shredder head (<b>a</b>), forest shredder (<b>b</b>), tractor with a crushing implement (<b>c</b>).</p> Full article ">Figure 3
<p>Thinning/pruning with a chainsaw (<b>a</b>), thinning/reduction of vegetation with shrub cutter (<b>b</b>). Maritime pine stand before (<b>c</b>) and after treatment (<b>d</b>).</p> Full article ">Figure 4
<p>Number of trees per ha in the sites before and after thinning and intensity of thinning (<b>a</b>); stand characteristics before and after thinning: average total height of trees (<b>b</b>); average diameter at breast height (DBH) (<b>c</b>); basal area (G) (<b>d</b>).</p> Full article ">Figure 5
<p>Percentage of forest cover after the initial density reduction treatment.</p> Full article ">Figure 6
<p>Wood volume in m<sup>3</sup>/ha before and after intervention: total (<b>a</b>), merchantable (<b>b</b>).</p> Full article ">Figure 7
<p>Evolution of the number of trees per ha in areas 1, 2, 4, 5, 6, and 7 in four management scenarios.</p> Full article ">Figure 8
<p>Evolution of volume per ha in areas 1, 2, 4, 5, 6, and 7 in four management scenarios.</p> Full article ">Figure 9
<p>Evolution of individual tree volume in areas 1, 2, 4, 5, 6, and 7 in four management scenarios.</p> Full article ">Figure 10
<p>Thinning volume (TV) removed (wood production) in areas 1, 2, 4, 5, 6, and 7 in four management scenarios.</p> Full article ">Figure 11
<p>Total volume per ha produced in the cycle in areas 1, 2, 4, 5, 6, and 7 in four management scenarios by thinning and final cutting volume (TV).</p> Full article ">Figure 12
<p>Fireline intensity in kW/m, before and after treatment (<b>a</b>). Fire spread rate before and after treatment (<b>b</b>). Flame length before and after treatment (<b>c</b>). Fraction of crown scorch before and after intervention (<b>d</b>).</p> Full article ">
20 pages, 11957 KiB  
Article
Study of Regional Spatial and Temporal Changes of Net Ecosystem Productivity of Crops from Remotely Sensed Data
by Peng Wang, Yong Xue, Zhigang Yan, Wenping Yin, Botao He and Pei Li
Land 2024, 13(2), 155; https://doi.org/10.3390/land13020155 - 30 Jan 2024
Cited by 1 | Viewed by 1481
Abstract
Net ecosystem productivity (NEP) is a crucial indicator of the carbon balance and health of an ecosystem. Until now, few studies have estimated the NEP of crops and analyzed it in space and time. The study of NEP in crops is crucial for [...] Read more.
Net ecosystem productivity (NEP) is a crucial indicator of the carbon balance and health of an ecosystem. Until now, few studies have estimated the NEP of crops and analyzed it in space and time. The study of NEP in crops is crucial for comprehending the carbon cycle of agroecosystems and determining the status of carbon sources and sinks in farmland at the regional scale. In this study, we calculated the net primary productivity (NPP) and NEP of agricultural crops in Jiangsu Province, China, from 2001 to 2022 by using remote sensing data, land cover data and meteorological data. The modified Carnegie Ames Stanford Approach (CASA) model was employed to estimate the NPP, and the soil heterotrophic respiration model was used to calculate the soil heterotrophic respiration (Rh). Then, the availability of the NPP was evaluated. On this basis, the NEP was obtained by calculating the difference between the NPP and Rh. We explored the spatial and temporal changes in the NEP of crops and analyzed the correlation between the NEP and crop cultivation activities and climatic factors under the context of agricultural production information using the NEP datasets of agricultural crops. The study indicated that (1) the NEP of crops in Jiangsu Province showed a north-to-south pattern, being higher in the north and lower in the south. Over the course of 22 years, the average NEP of the crops in Jiangsu Province stands at 163.4 gC/m2, highlighting a positive carbon sink performance. Nonetheless, up to 88.04% of the crops exhibited declining NEP trends. (2) The monthly fluctuations in the NEP of crops in Jiangsu Province exhibited a bimodal pattern, with peaks occurring during spring and summer. The changes in the NEP of the crops were significantly associated with various agricultural production activities. (3) Significant regional differences were observed in the NEP of the crop response to temperature and precipitation, both of which directly impacted the annual performance of the NEP. This study could serve as a reference for research on the carbon cycle in agriculture and the development of policies aimed at reducing emissions and enhancing carbon sinks in local farmland. Full article
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<p>Geographic information about Jiangsu Province (DEM data from SRTMDEMUTM 90 M resolution digital elevation data product).</p> Full article ">Figure 2
<p>Spatial distribution data of arable land and three major crops in Jiangsu Province (Cropland distribution data were derived from CLCD 30 m land cover data).</p> Full article ">Figure 3
<p>Validation statistical indices (r: correlation coefficient, MAE: mean absolute error and RMSE: root mean square error) between the estimated NPP and MOD17A3H in 2005, 2010, 2015 and 2020.</p> Full article ">Figure 4
<p>Comparison of annual NPP results for MOD17A3H and improved CASA models.</p> Full article ">Figure 5
<p>Spatial distribution of annual mean values of crop NEP in Jiangsu Province during 22 years.</p> Full article ">Figure 6
<p>(<b>a</b>) Change in annual mean NEP of crops in Jiangsu Province over 22 years, (<b>b</b>) change in monthly mean NEP of crops in Jiangsu Province over 22 years.</p> Full article ">Figure 7
<p>Month-by-month results of NEP 2018 in the North Jiangsu region.</p> Full article ">Figure 8
<p>Trends in the NEP of crops in Jiangsu Province, 2001–2022.</p> Full article ">Figure 9
<p>Spatial distribution of mean annual precipitation and mean annual temperature in Jiangsu Province, 2001–2022.</p> Full article ">Figure 10
<p>Temporal changes of annual precipitation and annual mean temperature in Jiangsu Province, 2001–2022.</p> Full article ">Figure 11
<p>Partial correlation analysis between crop NEP and mean annual air temperature and annual precipitation in Jiangsu Province, 2001–2022.</p> Full article ">
25 pages, 4304 KiB  
Article
Predicting Soil Properties for Agricultural Land in the Caucasus Mountains Using Mid-Infrared Spectroscopy
by Elton Mammadov, Michael Denk, Amrakh I. Mamedov and Cornelia Glaesser
Land 2024, 13(2), 154; https://doi.org/10.3390/land13020154 - 29 Jan 2024
Viewed by 1674
Abstract
Visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy are increasingly being used for the fast determination of soil properties. The aim of this study was (i) to test the use of MIR spectra (Agilent 4300 FTIR Handheld spectrometer) for the prediction of soil properties [...] Read more.
Visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy are increasingly being used for the fast determination of soil properties. The aim of this study was (i) to test the use of MIR spectra (Agilent 4300 FTIR Handheld spectrometer) for the prediction of soil properties and (ii) to compare the prediction performances of MIR spectra and Vis-NIR (ASD FieldSpecPro) spectra; the Vis-NIR data were adopted from a previous study. Both the MIR and Vis-NIR spectra were coupled with partial least squares regression, different pre-processing techniques, and the same 114 soil samples, collected from the agricultural land located between boreal forests and semi-arid steppe belts (Kastanozems). The prediction accuracy (R2 = 0.70–0.99) of both techniques was similar for most of the soil properties assessed. However, (i) the MIR spectra were superior for estimating CaCO3, pH, SOC, sand, Ca, Mg, Cd, Fe, Mn, and Pb. (ii) The Vis-NIR spectra provided better results for silt, clay, and K, and (iii) the hygroscopic water content, Cu, P, and Zn were poorly predicted by both methods. The importance of the applied pre-processing techniques was evident, and among others, the first derivative spectra produced more reliable predictions for 11 of the 17 soil properties analyzed. The spectrally active CaCO3 had a dominant contribution in the MIR predictions of spectrally inactive soil properties, followed by SOC and Fe, whereas particle sizes and hygroscopic water content appeared as confounding factors. The estimation of spectrally inactive soil properties was carried out by considering their secondary correlation with carbonates, clay minerals, and organic matter. The soil information covered by the MIR spectra was more meaningful than that covered by the Vis-NIR spectra, while both displayed similar capturing mechanisms. Both the MIR and Vis-NIR spectra seized the same soil information, which may appear as a limiting factor for combining both spectral ranges. The interpretation of MIR spectra allowed us to differentiate non-carbonated and carbonated samples corresponding to carbonate leaching and accumulation zones associated with topography and land use. The prediction capability of the MIR spectra and the content of nutrient elements was highly related to soil-forming factors in the study area, which highlights the importance of local (site-specific) prediction models. Full article
(This article belongs to the Special Issue Soils for the Future)
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<p>Location of the study area (Source: Digital Elevation Model, NASA JPL, 2013 [<a href="#B40-land-13-00154" class="html-bibr">40</a>]).</p> Full article ">Figure 2
<p>Histograms and descriptive statistics of the tested soil properties (<span class="html-italic">n</span> = 114). The values of the soil properties were color coded in accordance with three elevation levels: 800–900, 900–1000, &gt;1000 m.</p> Full article ">Figure 2 Cont.
<p>Histograms and descriptive statistics of the tested soil properties (<span class="html-italic">n</span> = 114). The values of the soil properties were color coded in accordance with three elevation levels: 800–900, 900–1000, &gt;1000 m.</p> Full article ">Figure 3
<p>The Spearman’s Rho correlation coefficients between the tested soil constituents (blank cells stand for insignificant correlations at the level of <span class="html-italic">p</span> ≤ 0.05; <span class="html-italic">n</span> = 114).</p> Full article ">Figure 4
<p>DRIFT color-coded spectra of all samples (<b>a</b>–<b>e</b>) based on basic soil properties: (<b>a</b>) CaCO<sub>3</sub>, (<b>b</b>) SOC, (<b>c</b>) sand, (<b>d</b>) silt, and (<b>e</b>) clay contents. (<b>f</b>) Spectra of samples with the highest CaCO<sub>3</sub> (red) and SOC (blue) contents; (<b>g</b>–<b>j</b>) samples grouped based on (<b>g</b>) five levels of CaCO<sub>3</sub>; (<b>h</b>) four levels of SOC; (<b>i</b>) four types of land use; and (<b>j</b>) three levels of elevation.</p> Full article ">Figure 4 Cont.
<p>DRIFT color-coded spectra of all samples (<b>a</b>–<b>e</b>) based on basic soil properties: (<b>a</b>) CaCO<sub>3</sub>, (<b>b</b>) SOC, (<b>c</b>) sand, (<b>d</b>) silt, and (<b>e</b>) clay contents. (<b>f</b>) Spectra of samples with the highest CaCO<sub>3</sub> (red) and SOC (blue) contents; (<b>g</b>–<b>j</b>) samples grouped based on (<b>g</b>) five levels of CaCO<sub>3</sub>; (<b>h</b>) four levels of SOC; (<b>i</b>) four types of land use; and (<b>j</b>) three levels of elevation.</p> Full article ">Figure 4 Cont.
<p>DRIFT color-coded spectra of all samples (<b>a</b>–<b>e</b>) based on basic soil properties: (<b>a</b>) CaCO<sub>3</sub>, (<b>b</b>) SOC, (<b>c</b>) sand, (<b>d</b>) silt, and (<b>e</b>) clay contents. (<b>f</b>) Spectra of samples with the highest CaCO<sub>3</sub> (red) and SOC (blue) contents; (<b>g</b>–<b>j</b>) samples grouped based on (<b>g</b>) five levels of CaCO<sub>3</sub>; (<b>h</b>) four levels of SOC; (<b>i</b>) four types of land use; and (<b>j</b>) three levels of elevation.</p> Full article ">Figure 5
<p>Biplot of PCA F1 and F2 factors based on soil properties and the first two principal components (PC1 and PC2) of the spectral reflectance data.</p> Full article ">Figure 6
<p>Optimal pre-processing spectra color coded based on the correlation coefficients between spectra values and respective soil properties: (<b>a</b>) CaCO<sub>3</sub>, (<b>b</b>) SOC, and (<b>c</b>) clay. The dashed lines represent the absorption feature bands of mineral and organic elements.</p> Full article ">Figure 6 Cont.
<p>Optimal pre-processing spectra color coded based on the correlation coefficients between spectra values and respective soil properties: (<b>a</b>) CaCO<sub>3</sub>, (<b>b</b>) SOC, and (<b>c</b>) clay. The dashed lines represent the absorption feature bands of mineral and organic elements.</p> Full article ">
25 pages, 7367 KiB  
Article
Policy Evaluation and Monitoring of Agricultural Expansion in Forests in Myanmar: An Integrated Approach of Remote Sensing Techniques and Social Surveys
by Su Mon San, Navneet Kumar, Lisa Biber-Freudenberger and Christine B. Schmitt
Land 2024, 13(2), 150; https://doi.org/10.3390/land13020150 - 27 Jan 2024
Cited by 1 | Viewed by 2125
Abstract
Agricultural expansion is the main driver of deforestation in Myanmar. We analyzed the effectiveness of a national policy intervention on agricultural encroachment in state forests in Taungoo District in Myanmar from 2010 to 2020. The policy aims to stop agricultural encroachment and reforest [...] Read more.
Agricultural expansion is the main driver of deforestation in Myanmar. We analyzed the effectiveness of a national policy intervention on agricultural encroachment in state forests in Taungoo District in Myanmar from 2010 to 2020. The policy aims to stop agricultural encroachment and reforest encroached areas through farmers’ participation in an agroforestry community forestry. We applied an integrated approach that involved a land cover change analysis together with a household survey about encroachment behavior. The remote sensing analysis for the years 2010, 2015 and 2020 showed the land cover change pattern and an increase in agricultural encroachment from 9.5% to 18.5%, while forests declined from 62.8% to 51.9%. The survey showed that most farmers (91%) believed that the policy intervention did not lead to a change in their encroachment behavior or farm size. The main reasons that incentivized encroachment were stated to be livelihood needs, immigration due to marriage and increased accessibility due to road construction. The main reason for reducing encroachment was plantation establishment, leading to a loss of land for encroaching farmers. In conclusion, the integrated approach showed that the policy intervention did not decrease encroachment, whereas other factors influenced encroachment behavior. We recommend solving interministerial conflicts of interest related to encroachment in Myanmar and using an integrated approach for future studies. Full article
(This article belongs to the Special Issue Forests in the Landscape: Threats and Opportunities)
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<p>Location of the Taungoo District and the study area (State Forests) (Source: adapted from San et al., 2023 [<a href="#B10-land-13-00150" class="html-bibr">10</a>]; Forest Department, 2020, [<a href="#B25-land-13-00150" class="html-bibr">25</a>]).</p> Full article ">Figure 2
<p>Processing steps of land cover classification and land cover change analysis in ENVI 5.0 and QGIS 3.28.6.</p> Full article ">Figure 3
<p>The extent of different land covers in 2010, 2015 and 2020.</p> Full article ">Figure 4
<p>Net land cover changes among forests, agriculture (Agri) and other wooded lands (OWL) during the period of 2010–2020.</p> Full article ">Figure 5
<p>Comparison of land cover changes in the two study periods (before and after the policy intervention). Positive values represent an “increase/gain”, and negative values represent a “decrease/loss” (Agri = agriculture, OWL = other wooded land).</p> Full article ">Figure 6
<p>(<b>a</b>) Percentage of farmers showing different farm size dynamics (<span class="html-italic">n</span> = 291); (<b>b</b>) number of farmers who expanded their farms in specific years.</p> Full article ">Figure 7
<p>Reasons for increasing numbers of encroaching settlers in the surrounding area during the period of 2010–2020 in relation to frequencies mentioned by the respondents (<span class="html-italic">n</span> = 36) (NTFP = non-timber forest products).</p> Full article ">Figure 8
<p>Reasons for decreasing numbers of encroaching settlers in the surrounding area during the period of 2010–2020 in relation to the frequencies mentioned by the respondents (<span class="html-italic">n</span> = 53).</p> Full article ">Figure A1
<p>Land cover classification map of the state forest areas in Taungoo District in different study years: (<b>a</b>) 2010, (<b>b</b>) 2015 and (<b>c</b>) 2020.</p> Full article ">Figure A1 Cont.
<p>Land cover classification map of the state forest areas in Taungoo District in different study years: (<b>a</b>) 2010, (<b>b</b>) 2015 and (<b>c</b>) 2020.</p> Full article ">
16 pages, 5053 KiB  
Article
Climate Proofing Cities by Navigating Nature-Based Solutions in a Multi-Scale, Social–Ecological Urban Planning Context: A Case Study of Flood Protection in the City of Gothenburg, Sweden
by Colin Hultgren Egegård, Maja Lindborg, Åsa Gren, Lars Marcus, Meta Berghauser Pont and Johan Colding
Land 2024, 13(2), 143; https://doi.org/10.3390/land13020143 - 26 Jan 2024
Cited by 1 | Viewed by 2777
Abstract
Due to unsustainable land management and climate change, floods have become more frequent and severe over the past few decades and the problem is exacerbated in urban environments. In the context of climate-proofing cities, the importance of nature-based solutions (NBSs), obtaining relevant outcomes [...] Read more.
Due to unsustainable land management and climate change, floods have become more frequent and severe over the past few decades and the problem is exacerbated in urban environments. In the context of climate-proofing cities, the importance of nature-based solutions (NBSs), obtaining relevant outcomes in the form of ecosystem services, has been highlighted. Although the role of ecosystem services in building resilience against negative climate change effects is widely recognized and there is an identified need to better integrate ecosystem services into urban planning and design, this has proven difficult to operationalize. A critical limitation is that modeling is a time-consuming and costly exercise. The purpose is to roughly estimate the ecosystem service of water run-off mitigation through simplified, cost-effective, and user-friendly modelling at three nested biophysical scales, under four climate change scenarios. Using the Swedish city of Gothenburg as an example, we propose an approach for navigating NBS-oriented flooding adaptation strategies, by quantifying the ecosystem service of water run-off mitigation at three nested biophysical scales, under four climate change scenarios, hence, proposing an approach for how to navigate nature-based solutions in a multi-scale, social–ecological urban planning context against present and future flooding events. Our findings validate the effectiveness of employing an ecosystem service approach to better comprehend the significant climate change issue of flooding through user-friendly and cost-efficient modeling. Full article
(This article belongs to the Special Issue Urban Green Spaces: An Effective Tool to Regulate Urban Climate and Biodiversity)
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<p>The city of Gothenburg (red) is situated on the west coast of Sweden (green), along the Göta River and situated in the Västra Götaland region (orange). Its biophysical location is characterized by a coastal setting, providing the city with access to the North Sea.</p> Full article ">Figure 2
<p>The local development project, Kärra-Skogome, marked in turquoise and in red (small box), and with the city of Gothenburg in yellow.</p> Full article ">Figure 3
<p>Flowchart of the methodological process. Green areas are presented in the result section.</p> Full article ">Figure 4
<p>The reclassification of 25 land cover classes into four in Kärra-Skogome, follows the guidelines provided by UN-SPIDER (United Nations—Space-based Information for Disaster Management and Emergency Response) and the USDA handbook [<a href="#B50-land-13-00143" class="html-bibr">50</a>,<a href="#B53-land-13-00143" class="html-bibr">53</a>].</p> Full article ">Figure 5
<p>Soil types in Kärra-Skogome reclassified as hydrological soil groups (HSG).</p> Full article ">Figure 6
<p>Spatial distribution per pixel in Kärra-Skogome, of (<b>a</b>) the run-off retention volumes (m<sup>3</sup>) and (<b>b</b>) the run-off volume (m<sup>3</sup>), for a 24 mm rainfall event. High retention volumes are represented by darker blue areas and high run-off volumes are represented by dark red.</p> Full article ">Figure 7
<p>Spatial distribution per pixel of (<b>a</b>) the Run-off Retention Volumes (RRV) and (<b>b</b>) the Run-off Volume (RV) in the sub-drainage basin, encompassing the study area of Kärra-Skogome (in green), for a 24 mm rainfall event.</p> Full article ">Figure 8
<p>Spatial distribution per pixel of (<b>a</b>) the Run-off Retention Volumes (RRV) and (<b>b</b>) the Run-off Volume (RV) in the Västra Götaland Region, for a 24 mm rainfall event.</p> Full article ">
30 pages, 5004 KiB  
Article
The Effects of Vegetation Structure and Timber Harvesting on Ground Beetle (Col.: Carabidae) and Arachnid Communities (Arach.: Araneae, Opiliones) in Short-Rotation Coppices
by Jessika Konrad, Ralph Platen and Michael Glemnitz
Land 2024, 13(2), 145; https://doi.org/10.3390/land13020145 - 26 Jan 2024
Viewed by 1436
Abstract
Landscape complexity is a crucial factor for insect diversity in agricultural landscapes. Short-rotation coppices (SRCs) are characterised by high habitat heterogeneity. The impact of vegetation structure on the composition and diversity of ground beetle and arachnid communities was therefore investigated in four SRCs [...] Read more.
Landscape complexity is a crucial factor for insect diversity in agricultural landscapes. Short-rotation coppices (SRCs) are characterised by high habitat heterogeneity. The impact of vegetation structure on the composition and diversity of ground beetle and arachnid communities was therefore investigated in four SRCs and six reference plots. The study site was located in Hesse, Germany. The invertebrates were surveyed from 2011 to 2014 using pitfall traps, and the vegetation structure was quantified by estimating the percentage cover of 10 structural variables. The impact of the selected structural variables on community composition was analysed during grove growth as well as after a timber harvest. We found correlations between the cover percentages of structural variables and the quantitative and qualitative species composition in both animal groups (p ≤ 0.05). The share of individuals of forest species increased with rising shading and litter cover, while those of open land decreased. The opposite trends were found the year after the timber harvest. The SRCs showed a higher structural diversity compared to the reference biotopes (p ≤ 0.05). This was positively correlated (p ≤ 0.001) with species diversity and the variety of habitat preference groups in both animal groups. The high diversity within the habitat preference groups indicated a functional redundancy among species for both animal groups and, consequently, a high level of resilience within these communities. Little is known about the functional aspects of ground beetles and spiders in ecosystems, and detailed studies are urgently needed. We conclude that SRCs can contribute to the diversification of agricultural landscapes as an alternative to traditional crop cultivation. Full article
(This article belongs to the Special Issue Advances in Land Consolidation and Land Ecology)
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<p>Location of the Haine study area (square) in the federal state of Hesse. Map of the Federal Republic of Germany with the individual federal states marked at a scale of 1:250,000. Adapted with permission from [<a href="#B43-land-13-00145" class="html-bibr">43</a>], 2011, © GeoBasis-DE/BKG, modified. SH = Schleswig–Holstein, HH = Hamburg, HB = Bremen, NI = Lower Saxony, MV = Mecklenburg–Western Pomerania, BE = Berlin, BB = Brandenburg, ST = Saxony–Anhalt, NW = North Rhine–Westphalia, SN = Saxony, TH = Thuringia, RP = Rhineland–Palatinate, SL = Saarland, BY = Bavaria, and BW = Baden–Württemberg.</p> Full article ">Figure 2
<p>Location of the study plots and position of the pitfall traps (one trap point represents five pitfall traps in a linear transect). FIE = arable field, FAL = fallow, HEA = headland, MEA = meadow, GRO = grove, SRC1–SRC4 = short-rotation coppices, and FOR = forest.</p> Full article ">Figure 3
<p>Mean percentage cover of selected structural variables (CovStructVar), calculated from 10 survey squares per study plot and year for the period 2011 to 2014. The year after the timber harvest in the SRCs is framed in black. FIE = arable field, FAL = fallow, GRO = grove, SRC1–SRC4 = short-rotation coppices, HEA = headland, FOR = forest, and MEA = meadow. The total degree of cover of the structural variables in the wooded plots SRC1–SRC4, GRO, and FOR may be &gt;100%, as the shade was added to the cover of the other structural variables.</p> Full article ">Figure 4
<p>Shannon exponential index for vegetation structure diversity (StructDiv) in the individual study years and plots. FIE = arable field, FAL = fallow, HEA = headland, MEA = meadow, GRO = grove, SRC1–SRC4 = short-rotation coppices, and FOR = forest. Circles indicate outliers between 1.5 and 3.0 interquartile range (IQR); asterisks are &gt;3.0 IQR.</p> Full article ">Figure 5
<p>Number of ground beetle species with different habitat preferences in the individual study years and plots. FIE = arable field, FAL = fallow, HEA = headland, MEA = meadow, GRO = grove, SRC1–SRC4 = short-rotation coppices, FOR = forest. Circles indicate outliers between 1.5 and 3.0 interquartile range (IQR); asterisks are &gt;3.0 IQR.</p> Full article ">Figure 6
<p>Shannon exponential indices of species diversity (SpecDiv) for ground beetles (<b>a</b>) and arachnids (<b>b</b>). Median for the whole study period 2011–2014. SRC1–SRC4 = short-rotation coppices (brown), HEA = headland, FIE = arable field, FAL = fallow, MEA = meadow, GRO = grove, and FOR = forest (grey). Circles indicate outliers between 1.5 and 3.0 interquartile range (IQR).</p> Full article ">Figure 7
<p>Shannon exponential indices of habitat preference diversity (HPDiv) for ground beetles (<b>a</b>) and arachnids (<b>b</b>). Median for the study period 2011–2014. SRC1–SRC4 = short-rotation coppices (brown), HEA = headland, FIE = arable field, FAL = fallow, MEA = meadow, GRO = grove, and FAL = forest (grey). Circles indicate outliers between 1.5 and 3.0 interquartile range (IQR); asterisks are &gt;3.0 IQR.</p> Full article ">Figure 8
<p>Ordination diagrams (1st and 2nd ordination axis) of ground beetles based on redundancy analyses (RDAs). Shown are the communities represented by plot symbols (annual sums of individuals from five pitfall traps per plot) in the plots SRC1–SRC4 per year of growth (<b>a</b>–<b>d</b>) as well as the reference biotopes examined in the same period in relation to five structural variables. The plot points were colour-coded and labelled with the corresponding colour.</p> Full article ">Figure 9
<p>Ordination diagrams (1st and 2nd ordination axis) of arachnids based on redundancy analyses (RDAs). Shown are the communities represented by plot symbols (annual sums of individuals from five pitfall traps per plot) in the plots SRC1–SRC4 per year of growth (<b>a</b>–<b>d</b>) as well as the reference biotopes examined in the same period in relation to five structural variables. The plot points were colour-coded and labelled with the corresponding colour.</p> Full article ">Figure 10
<p>Shade cover in the plots SRC1 (<b>a</b>) and SRC3 (<b>d</b>) and percentage of individuals of general forest species (FOR) of the ground beetle (<b>b</b>,<b>e</b>) and arachnid (<b>c</b>,<b>f</b>) communities for the study years 2011–2014. Circles indicate outliers between 1.5 and 3.0 interquartile range (IQR); asterisks &gt;3 IQR. Statistically significant differences between the years can be seen in <a href="#app1-land-13-00145" class="html-app">Tables S22 and S23</a>. The vertical bars indicate timber harvest.</p> Full article ">Figure 11
<p>Percentage of individuals of arable (FIE) (<b>a</b>,<b>b</b>) and grassland species (GRL) (<b>c</b>,<b>d</b>) of the arachnid and ground beetle communities in the plots SRC1–SRC4 for the study years 2011–2014. Circles indicate outliers between 1.5 and 3.0 interquartile range (IQR); asterisks &gt;3 IQR. Statistically significant differences between the years can be seen in <a href="#app1-land-13-00145" class="html-app">Tables S22 and S23</a>. The vertical bars indicate timber harvest.</p> Full article ">Figure 12
<p>Percentage of individuals of stenotopic (green) and eurytopic (brown) forest species for the arachnids in the plots (<b>a</b>) SRC1, (<b>b</b>) SRC2, (<b>c</b>) SRC3, and (<b>d</b>) SRC4. The individuals of the stenotopic (steno) forest species include moist forest (MFO), low mountain forest (LMF), wet forest (WFO), and acidophilous forest (AFO) species, and those of the eurytopic (eury) forest species were preliminary forest species (PFO) and forest species not bound to a specific forest type (FOR) and were totalled for five traps per study year. The time of timber harvesting is indicated by a vertical black line.</p> Full article ">Figure 13
<p>Relation between the degree of shading and the percentage of individuals of xerophilic (x) arachnid species (<b>a</b>) and grassland species (GRL) of ground beetles (<b>b</b>). Linear simple regression for the plots SRC1–SRC4 in the study years 2011–2014 on the basis of sums of individuals from five traps per plot and year (<span class="html-italic">n</span> = 80).</p> Full article ">Figure 14
<p>Relation between the percentage of individuals of arachnid species with the ecological type ‘slightly hygrophilic forests’ ((h)f) and the degree of shade cover (<b>a</b>) and the degree of litter cover (<b>b</b>). Linear simple regression for the plots SRC1–SRC4 in the study years 2011–2014 on the basis of sums of individuals from five traps per plot and year (<span class="html-italic">n</span> = 80).</p> Full article ">Figure 15
<p>Relation between the Shannon indices of vegetation structure diversity and the Shannon indices of species and habitat preference diversity of ground beetles (<b>a</b>,<b>c</b>) and arachnids (<b>b</b>,<b>d</b>) in SRC1 to SRC4 for the study years 2011–2014. Linear regression based on the diversity values per plot and year (<span class="html-italic">n</span> = 40).</p> Full article ">Figure 15 Cont.
<p>Relation between the Shannon indices of vegetation structure diversity and the Shannon indices of species and habitat preference diversity of ground beetles (<b>a</b>,<b>c</b>) and arachnids (<b>b</b>,<b>d</b>) in SRC1 to SRC4 for the study years 2011–2014. Linear regression based on the diversity values per plot and year (<span class="html-italic">n</span> = 40).</p> Full article ">
29 pages, 5124 KiB  
Article
Assessing the Vulnerability of Medicinal and Aromatic Plants to Climate and Land-Use Changes in a Mediterranean Biodiversity Hotspot
by Konstantinos Kougioumoutzis, Maria Tsakiri, Ioannis P. Kokkoris, Panayiotis Trigas, Gregoris Iatrou, Fotini N. Lamari, Dimitris Tzanoudakis, Eleni Koumoutsou, Panayotis Dimopoulos, Arne Strid and Maria Panitsa
Land 2024, 13(2), 133; https://doi.org/10.3390/land13020133 - 24 Jan 2024
Cited by 5 | Viewed by 2969
Abstract
Medicinal and Aromatic Plants (MAPs) play a critical role in providing ecosystem services through their provision of herbal remedies, food and natural skin care products, their integration into local economies, and maintaining pollinators’ diversity and populations and ecosystem functioning. Mountainous regions, such as [...] Read more.
Medicinal and Aromatic Plants (MAPs) play a critical role in providing ecosystem services through their provision of herbal remedies, food and natural skin care products, their integration into local economies, and maintaining pollinators’ diversity and populations and ecosystem functioning. Mountainous regions, such as Chelmos-Vouraikos National Park (CVNP), represent unique reservoirs of endemic MAP diversity that require conservation prioritisation. This study aims to provide insights into the sustainable management of MAPs, contributing to efforts to protect Mediterranean biodiversity amid the dual challenges of climate and land-use change, using a suite of macroecological modelling techniques. Following a Species Distribution Modelling framework, we investigated the vulnerability of endemic and non-endemic MAPs to climate and land-use changes. We examined the potential shifts in MAP diversity, distribution, and conservation hotspots within the CVNP. Our results revealed species-specific responses, with endemic taxa facing severe range contractions and non-endemic taxa initially expanding but eventually declining, particularly under land-use change scenarios. Local biodiversity hotspots are projected to shift altitudinally, with considerable area losses in the coming decades and elevated species turnover predicted throughout the CVNP, leading to biotic homogenization. Climate and land-use changes jointly threaten MAP diversity, calling for adaptive conservation strategies, thus highlighting the importance of proactive measures, such as awareness raising, establishing plant micro-reserves, assisted translocation, and promoting sustainable harvesting to protect these species within the CVNP. Our study offers vital insights for managing biodiversity hotspots amid global change pressures, stressing the need to integrate ecological and socioeconomic factors. Full article
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<p><b>Top</b> panel: The Mediterranean Basin with the Peloponnese highlighted by a red polygon and including ISO-3 country codes. <b>Bottom</b> panel (<b>left</b> to <b>right</b>): The Peloponnese zoomed in, outlined with a blue polygon indicating the wider study area. The study area, Mt. Chelmos, is shown in detail.</p> Full article ">Figure 2
<p>Raincloud plot depicting the median area range change (in percentage) for all medicinal and aromatic plant taxa analysed. This data is presented under three model categories: Climate Change (CC), Land Use Land Cover Change (LULCC), and their interaction (CC-LULCC). The plot aggregates results from selected scenarios and climate models: three Global Circulation Models (GCMs), two Representative Concentration Pathways (RCPs), and three Shared Socioeconomic Pathways (SSPs) across three time periods (2020s, 2050s, and 2080s).</p> Full article ">Figure 3
<p>Raincloud plot of the (<b>A</b>) number of patches and (<b>B</b>) effective mesh size for all the medicinal and aromatic plant taxa we included in our analyses under the baseline period and the Ensemble RCP 8.5 SSP5 combination in the 2080s.</p> Full article ">Figure 4
<p>Mean difference in species richness: This figure displays the average change in future species richness compared to the species richness for the baseline period for all medicinal and aromatic plant (MAP) taxa occurring in Chelmos Vouraikos National Park. The analysis involves subtracting the current species richness from each Global Circulation Model (GCM)/Representative Concentration Pathway (RCP) species richness raster under the Shared Socioeconomic Pathway 5 (SSP5). This process is repeated for three future time periods: (<b>A</b>) the 2020s, (<b>B</b>) the 2050s, and (<b>C</b>) the 2080s. The resulting differences are then averaged to represent the mean change in species richness across all included taxa under the CC-LULCC model.</p> Full article ">Figure 5
<p>From left to right: L1 (top 1%) corrected-weighted—phylogenetic endemism (CWE-PE) hotspots, also known as Priority Hotspots (marked with red cells), for both (<b>A</b>) the baseline period and (<b>B</b>) the future (the Ensemble RCP 8.5 SSP5 combination in the 2080s). Panel (<b>C</b>) depicts the Anthropocene refugia under the strict consensus rule, meaning we only considered cells currently serving and projected to continue serving as Priority Hotspots across every combination of GCM, RCP, SSP, and period for the CC-LULCC model.</p> Full article ">Figure 6
<p>Temporal (<b>A</b>) taxonomic and (<b>B</b>) phylogenetic beta diversity between the baseline period and the Ensemble RCP 8.5 SSP5 combination in the 2080s.</p> Full article ">Figure 7
<p>Spatial median vulnerability for the medicinal and aromatic plant taxa included in our analyses for the (<b>A</b>) 2020s, (<b>B</b>) 2050s, and (<b>C</b>) 2080s.</p> Full article ">
18 pages, 3167 KiB  
Article
A Proposed Typology of Farming Systems for Assessing Sustainable Livelihood Development Pathways in the Tien Shan Mountains of Kyrgyzstan
by Azamat Azarov, Roy C. Sidle, Dietrich Darr, Vladimir Verner and Zbynek Polesny
Land 2024, 13(2), 126; https://doi.org/10.3390/land13020126 - 23 Jan 2024
Cited by 3 | Viewed by 1615
Abstract
In Kyrgyzstan, most farming systems focus on animal husbandry, which depends on mixtures of crops and pastures around settlements and higher-elevation summer pastures. These farms face the problems of insufficient fodder production and pasture degradation due to overgrazing, resulting in low productivity of [...] Read more.
In Kyrgyzstan, most farming systems focus on animal husbandry, which depends on mixtures of crops and pastures around settlements and higher-elevation summer pastures. These farms face the problems of insufficient fodder production and pasture degradation due to overgrazing, resulting in low productivity of livestock and reduced household incomes. The spatial diversity of farms often hampers the development of interventions aimed at improving crop and animal productivity, as well as sustainable grassland management, while the absence of a comprehensive and systematic classification system that effectively encompasses the diverse range of livelihood strategies within farming systems presents a significant obstacle to the advancement of initiatives promoting sustainable livelihoods. This study aimed to develop a consistent typology of smallholder farms in the Tien Shan using multivariate analysis. By analyzing data from 235 farm-households and evaluating key classification variables, we identified two distinct farming systems, upper mountain farms and lower mountain farms, based on socioeconomic and agro-ecological characteristics. Our typology considers elevation, grazing period, cultivated area, and off-farm income and better captures the diversity of farming activities and household income compared to current classification models. These findings will inform and tailor policies and interventions suitable for enhancing sustainable livelihoods in Kyrgyzstan’s mountain farming systems. Full article
(This article belongs to the Special Issue Grassland Ecosystem Services: Research Advances and Future Directions for Sustainability II)
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<p>Study sites and selected villages located in four districts of Chuy and Naryn provinces of Kyrgyzstan.</p> Full article ">Figure 2
<p>Dendrogram showing the range of cluster solutions resulting from Ward’s method. The dashed line represents the cutoff point, which denotes a two-cluster solution: pink—Cluster I and green—Cluster II. The term ‘Height’ shows the distance between the merged clusters at each step.</p> Full article ">Figure 3
<p>Distribution of the classified farming systems across central Tien Shan mountains.</p> Full article ">
16 pages, 2758 KiB  
Article
A Proposed Methodology for Determining the Economically Optimal Number of Sample Points for Carbon Stock Estimation in the Canadian Prairies
by Preston Thomas Sorenson, Jeremy Kiss and Angela Bedard-Haughn
Land 2024, 13(1), 114; https://doi.org/10.3390/land13010114 - 20 Jan 2024
Viewed by 1403
Abstract
Soil organic carbon (SOC) sequestration assessment requires accurate and effective tools for measuring baseline SOC stocks. An emerging technique for estimating baseline SOC stocks is predictive soil mapping (PSM). A key challenge for PSM is determining sampling density requirements, specifically, determining the economically [...] Read more.
Soil organic carbon (SOC) sequestration assessment requires accurate and effective tools for measuring baseline SOC stocks. An emerging technique for estimating baseline SOC stocks is predictive soil mapping (PSM). A key challenge for PSM is determining sampling density requirements, specifically, determining the economically optimal number of samples for predictive soil mapping for SOC stocks. In an attempt to answer this question, data were used from 3861 soil organic carbon samples collected as part of routine agronomic soil testing from a 4702 ha farming operation in Saskatchewan, Canada. A predictive soil map was built using all the soil data to calculate the total carbon stock for the entire study area. The dataset was then subset using conditioned Latin hypercube sampling (cLHS), both conventional and stratified by slope position, to determine the total carbon stocks with the following sampling densities (points per ha): 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. A nonlinear error function was then fit to the data, and the optimal number of samples was determined based on the number of samples that minimized soil data costs and the value of the soil carbon stock prediction error. The stratified cLHS required fewer samples to achieve the same level of accuracy compared to conventional cLHS, and the optimal number of samples was more sensitive to carbon price than sampling costs. Overall, the optimal sampling density ranged from 0.025 to 0.075 samples per hectare. Full article
(This article belongs to the Section Land Innovations – Data and Machine Learning)
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<p>Overview map indicating the location of the study. All soil samples were collected within the areas indicated with the red squares. The dashed black line is the provincial boundary for the province of Saskatchewan. The red squares indicate the specific study areas. The base map is the median Landsat 7 2000 to 2020 May-to-October median surface reflectance. The coordinates are in UTM Zone 13N (EPSG: 26913).</p> Full article ">Figure 2
<p>R2, CCC, relative error, and root-mean-square error for conventional and landscape-stratified conditioned Latin hypercube sampling by the number of sampling points. Locally estimated scatter plot smoothing results for conventional and landscape stratified are presented. The confidence intervals, represented by the shaded areas, for each graph correspond to the 10th and 90th percentile values.</p> Full article ">Figure 3
<p>Average soil organic carbon stock error for conventional and landscape stratified conditioned Latin hypercube sample designs as a function of the number of sample points. The error is determined based on the total carbon stock for each sampling design compared to when the entire dataset is used for the entire study area.</p> Full article ">Figure 4
<p>Soil organic carbon concentrations across slope positions within the study area.</p> Full article ">Figure 5
<p>Optimal sampling density for the conventional conditioned Latin hypercube sampling design based on the cost of sampling and the price of carbon. The shaded grey ribbon corresponds to the 10th and 90th percentile sampling densities for a given sampling density (points ha<sup>−1</sup>). For the top panel, the optimal sampling density is presented as a function of sampling cost, and for the bottom panel, the optimal sampling density is presented as a function of the price of carbon. The grey confidence intervals indicate the variability in optimal sampling density based on variability in carbon price for the top panel and sampling cost for the bottom panel.</p> Full article ">Figure 6
<p>Optimal sampling density for the landscape stratified conditioned Latin hypercube sampling design based on the cost of sampling and the price of carbon. The shaded grey ribbon corresponds to the 10th and 90th percentile sampling densities for a given sampling density (points ha<sup>−1</sup>). For the top panel, the optimal sampling density is presented as a function of sampling cost, and for the bottom panel, the optimal sampling density is presented as a function of the price of carbon. The grey confidence intervals indicate the variability in optimal sampling density based on variability in carbon price for the top panel and sampling cost for the bottom panel.</p> Full article ">
20 pages, 6625 KiB  
Article
A Geospatial Decision Support System for Supporting the Assessment of Land Degradation in Europe
by Piero Manna, Antonietta Agrillo, Marialaura Bancheri, Marco Di Leginio, Giuliano Ferraro, Giuliano Langella, Florindo Antonio Mileti, Nicola Riitano and Michele Munafò
Land 2024, 13(1), 89; https://doi.org/10.3390/land13010089 - 12 Jan 2024
Viewed by 2219
Abstract
Nowadays, Land Degradation Neutrality (LDN) is on the political agenda as one of the main objectives in order to respond to the increasing degradation processes affecting soils and territories. Nevertheless, proper implementation of environmental policies is very difficult due to a lack of [...] Read more.
Nowadays, Land Degradation Neutrality (LDN) is on the political agenda as one of the main objectives in order to respond to the increasing degradation processes affecting soils and territories. Nevertheless, proper implementation of environmental policies is very difficult due to a lack of the operational, reliable and easily usable tools necessary to support political decisions when identifying problems, defining the causes of degradation and helping to find possible solutions. It is within this framework that this paper attempts to demonstrate a new valuable web-based operational LDN tool as a component of an already running Spatial Decision Support System (S-DSS) developed on a Geospatial Cyberinfrastructure (GCI). The tool could be offered to EU administrative units (e.g., municipalities) so that they may better evaluate the state and the impact of land degradation in their territories. The S-DSS supports the acquisition, management and processing of both static and dynamic data, together with data visualization and on-the-fly computing, in order to perform modelling, all of which is potentially accessible via the Web. The land degradation data utilized to develop the LDN tool refer to the SDG 15.3.1 indicator and were obtained from a platform named Trends.Earth, designed to monitor land change by using earth observations, and post-processed to correct some of the major artefacts relating to urban areas. The tool is designed to support land planning and management by producing data, statistics, reports and maps for any EU area of interest. The tool will be demonstrated through a short selection of practical case studies, where data, tables and stats are provided to challenge land degradation at different spatial extents. Currently, there are WEBGIS systems to visualize land degradation maps but—to our knowledge—this is the first S-DSS tool enabling customized LDN reporting at any NUTS (nomenclature of territorial units for statistics) level for the entire EU territory. Full article
(This article belongs to the Section Land – Observation and Monitoring)
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<p>Synthetic workflow of the basic structure of the LANDSUPPORT GCI architecture, functions and technological components.</p> Full article ">Figure 2
<p>SDG 15.3.1 indicator. Assessment for Europe (reference period 2001–2018). Red pixels: areas classified as degraded; green pixels: areas classified as improved; no color: areas classified as stable.</p> Full article ">Figure 3
<p>SDG 15.3.1 indicator. Details of change detection between 2001 and 2018. Red pixels: areas classified as degraded. No color: areas classified as stable.</p> Full article ">Figure 4
<p>Two examples of NDVI trend analysis superimposed over urbanized areas (visible behind) by SDG indicator classes. Legend: green (improvement), yellow (stable), red (degraded). Red dots: centroids of pixels where the GEE codes have been run. (<b>A</b>): positive trend as classified in (<b>B</b>); (<b>C</b>): negative trend as classified in (<b>D</b>).</p> Full article ">Figure 5
<p>The municipality of Rome (white boundaries). Red surfaces represent the urban class (CLC level 1) considered “stable” from 2000 to 2018; The graph shows with respect to red areas, the % of surfaces classified as stable during the period 2001–2018 by both the indicator 15.3.1 in the original version (UNCCD LDN) and the improved version (iLDN).</p> Full article ">Figure 6
<p>The graph shows with respect to the urban areas “stable” (CLC level 1) during the period 2000–2018, the % of surfaces classified as “stable” during the same period by both the indicator 15.3.1 in the original version (UNCCD LDN) and the improved version (iLDN). The data are referred to municipalities with 300 K–3 M range of population (EUROSTAT); iLDN data (orange) are sorted in ascending order.</p> Full article ">Figure 7
<p>On the left, from top to bottom, the UNCCD LDN indicator details for change detection between 2001 and 2018 in the cities of Rome, Naples, Milan and Berlin. On the right, the same cities classified by using the improved indicator (iLDN). Red pixels: areas classified as degraded; green pixels: areas classified as improved; no color: areas classified as stable.</p> Full article ">Figure 8
<p>Graphic User Interface of the LANDSUPPORT S-DSS. (<b>A</b>) Data viewer. (<b>B</b>) Map viewer. (<b>C</b>) Analysis tool. (<b>D</b>) GIS tools. (<b>E</b>) Selection of spatial scales. (<b>F</b>) Model requester for LDN tool.</p> Full article ">Figure 9
<p>Example of the LDN tool output (technical report produced on the fly).</p> Full article ">Figure 10
<p>Graphic representation of zonal statistics by LDN tool applied at the national level.</p> Full article ">Figure 11
<p>Results of the SDG 15.3.1 indicator superimposed over the territory of Campania.</p> Full article ">Figure 12
<p>Zonal statistics indicating the magnitude of degraded/improved lands across the territory and the data aggregated by land use classes.</p> Full article ">Figure 13
<p>Surfaces classified as degraded or improved according to the SDG indicator for the municipality of Naples.</p> Full article ">
24 pages, 25057 KiB  
Article
Participatory Design of Urban Green Spaces to Improve Residents’ Health
by Bram Oosterbroek, Joop de Kraker, Sandra Akkermans, Paola Esser and Pim Martens
Land 2024, 13(1), 88; https://doi.org/10.3390/land13010088 - 11 Jan 2024
Cited by 2 | Viewed by 3978
Abstract
Urban green space (UGS) has important impacts on human health, but an integrated participatory approach to UGS design for improved residents’ health has been lacking to date. The aim of our study was to develop and evaluate such a novel approach to address [...] Read more.
Urban green space (UGS) has important impacts on human health, but an integrated participatory approach to UGS design for improved residents’ health has been lacking to date. The aim of our study was to develop and evaluate such a novel approach to address this gap. The approach was developed following guiding principles from the literature and tested with groups of children and elderly as participants in two neighborhoods of Maastricht (The Netherlands) with a low score in economic and health indicators. The novel aspects of the approach are the inclusion of both positive and negative health effects, the combination of resident self-assessment and model-based assessment of the health effects of UGS designs, and the use of maps to visualize UGS designs and health effects. The participant-generated UGS designs resulted in a considerable (up to fourfold) self-assessed increase in the use of the UGSs for meeting, stress reduction, and leisure-based physical activity as compared to the current situation. The model-assessed positive and negative health effects of the participant-generated UGS designs were limited: heat stress slightly decreased (by 0.1 °C), active transport slightly increased (by 30 m per day), and the perceived unsafety slightly increased (8%). The effects on unattractive views, air pollution, tick bite risk, and traffic unsafety were negligible. The major strength of this approach is that it combines active participation of residents in UGS (re)design with assessment of the health effects of these UGS designs. While in other participatory approaches to UGS design, it often remains unclear whether the resulting designs represent an improvement in terms of health, our combination of computer model-based assessment and a participatory process produced clear outcomes regarding the health benefits and use of UGS designs. A major recommendation for improvement is to involve decision makers already in the initial steps of the approach. Full article
(This article belongs to the Special Issue Sustainable Urban Greenspace Planning, Design and Management)
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<p>Overview maps of Wittevrouwenveld (<b>left</b>) and Pottenberg (<b>right</b>) neighborhoods, showing the neighborhood boundary, buffer zone (of 500 m) around the neighborhood, locations where the participants meet, and the focus areas. Aerial photo: Beeldmateriaal Nederland [<a href="#B59-land-13-00088" class="html-bibr">59</a>].</p> Full article ">Figure 2
<p>Current situation (<b>left</b>) and changes proposed in the final UGS design (<b>right</b>) for the ‘Wittevrouwenveld’ neighborhood.</p> Full article ">Figure 3
<p>Wittevrouwenveld (<b>left</b>) and Pottenberg (<b>right</b>) neighborhoods with health impact hotspot locations.</p> Full article ">
23 pages, 16271 KiB  
Article
Urban Heat Island and Reduced Habitat Complexity Explain Spider Community Composition by Excluding Large and Heat-Sensitive Species
by Valentin Cabon, Hervé Quénol, Vincent Dubreuil, Aurélien Ridel and Benjamin Bergerot
Land 2024, 13(1), 83; https://doi.org/10.3390/land13010083 - 11 Jan 2024
Cited by 7 | Viewed by 2497
Abstract
Along with worldwide urbanization, upheavals in habitat and temperature are major threats for biodiversity. However, due to their interdependence, their relative roles as drivers of animal community composition remain entangled. Here, we investigated how taxonomic and functional compositions of arthropod communities were related [...] Read more.
Along with worldwide urbanization, upheavals in habitat and temperature are major threats for biodiversity. However, due to their interdependence, their relative roles as drivers of animal community composition remain entangled. Here, we investigated how taxonomic and functional compositions of arthropod communities were related to uncorrelated habitat and temperature gradients, and compared landscape (i.e., urbanization, Urban Heat Island (UHI)) to local variables (i.e., vegetation height and cover, near-ground temperature). We sampled 20,499 spiders (137 species) on 36 grasslands in Rennes (northwestern France). Unlike rural areas, urban sites were characterized by short vegetation and intense UHI, hosted species-poor communities, and were composed of small thermophilic species. UHI intensification and local loss of habitat complexity (short and dense vegetation) were associated with declining large and heat-sensitive species. These results highlight the prevalent role of urban warming, rather than land cover change, as an urban filter. Further, we show that landscape-scale UHI, not local temperature, filters species according to their functional attributes. UHI can therefore be considered as a thermal barrier, filtering species according to their physiological capacity to cope with urban thermal conditions. Finally, to counterbalance biotic homogenization, we argue for the importance of implementing complex habitat structures at the local scale within urban green infrastructure. Full article
(This article belongs to the Special Issue Urban Green Spaces: An Effective Tool to Regulate Urban Climate and Biodiversity)
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<p>Location of the 36 sampling sites (black crosses) within and around Rennes (black line). Built surfaces are in grey, grasslands in green, and waterbodies in blue. The red dot on the map at top right indicates the location of the study area.</p> Full article ">Figure 2
<p>Mean scaled values and standard deviations (black segments) of environmental variables after classification of sampling sites into three clusters by hierarchical clustering. Clustering was performed based on the two and three variables measured at the landscape and local scale, respectively.</p> Full article ">Figure 3
<p>Results of NMDS ordination based on 85 species (Bray–Curtis distance) and the four significant environmental variables.</p> Full article ">Figure 4
<p>Community-averaged body size in sites belonging to three categories identified by hierarchical classification. Black dots indicate the exact community-averaged body size in sites, whereas boxes summarize the information per cluster. Differences between clusters are given by non-matching lowercase letters (Tukey tests; <span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 5
<p>Community-averaged thermal affinity based on mean (<b>A</b>), minimum (<b>B</b>), and maximum (<b>C</b>) temperature data, in sites belonging to three clusters identified by the hierarchical classification. Black dots indicate the exact community-averaged thermal affinity in sites, whereas boxes summarize the information per cluster. Differences between clusters are given by non-matching lowercase letters (Tukey tests; <span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 6
<p>Absolute and relative species richness. Upper charts display the total number of species (<b>A</b>), the number of species belonging to the three body size classes (<b>B</b>), and the number of species belonging to three classes of thermal affinity (<b>C</b>). Lower charts display the relative species richness belonging to the three body size classes (<b>D</b>) and relative species richness belonging to the three classes of thermal affinity (<b>E</b>). Differences between clusters are given by non-matching lowercase letters (Tukey tests, <span class="html-italic">p</span> ≤ 0.05) within each single body size or thermal affinity class.</p> Full article ">Figure 7
<p>Map of Rennes (black line) and surrounding area. Symbols represent sampling sites. The shape of the symbols indicates the cluster to which a sampling site belongs (circles = ‘high vegetated rural’, triangles = ‘short vegetated rural’, squares = ‘short vegetated urban’). Symbol size indicates the mean community body size (from 3.30 mm to 5.73 mm). Symbol color indicates the mean community thermal affinity, with white symbols indicating low values (from 9.89 °C to 10.16 °C), light red symbols indicating intermediate values (from 10.17 °C to 10.43 °C), and dark red symbols indicating high values (from 10.44 °C to 10.70 °C). The atmospheric UHI (1 March to 30 September 2022) is illustrated by a color gradient from blue (low intensity; minimum = 0 °C) to red (high intensity; maximum = 3 °C). Impervious surface is shown in grey.</p> Full article ">Figure A1
<p>Map of Rennes (black line) and its surroundings. Areas identified as suitable for sampling (uncorrelated) after spatial correlation analysis are in purple (<span class="html-italic">p</span>-value &gt; 0.05) and associated intersecting grasslands are in green. Impervious surface is displayed in grey. Black crosses display sampling sites. The red dot on the map at top right indicates the location of the study area.</p> Full article ">
17 pages, 2089 KiB  
Article
Research on the Spatial Disparities and Convergence of Guangdong’s Urban Economy Based on Industrial Agglomeration and Industrial Proximity
by Xiaojin Huang, Renzhong Guo, Weixi Wang, Xiaoming Li and Yong Fan
Land 2024, 13(1), 73; https://doi.org/10.3390/land13010073 - 8 Jan 2024
Viewed by 1809
Abstract
Understanding the spatial differences and evolutionary characteristics of urban economy and exploring the impact of industrial agglomeration and industrial proximity on urban economic convergence are the bases for scientifically formulating policies for coordinated regional economic development. This study used QGIS 3.10.10 software and [...] Read more.
Understanding the spatial differences and evolutionary characteristics of urban economy and exploring the impact of industrial agglomeration and industrial proximity on urban economic convergence are the bases for scientifically formulating policies for coordinated regional economic development. This study used QGIS 3.10.10 software and the Theil index to analyze the spatial distribution characteristics and regional disparities of urban economy. Then, a spatial econometric model was constructed to analyze the convergence and influencing factors of Guangdong’s urban economy. The results indicate that from 2006 to 2020, Guangdong’s urban economy grew rapidly and the degree of economic agglomeration gradually weakened, but its economic pattern always maintained the “Core-Edge” structural feature. The interval disparities between the Pearl River Delta Urban Agglomeration (PRD) and the edge area have always been greater than the intra-regional disparities, so they are main source of disparities in Guangdong. In Guangdong’s urban economy, σ-convergence and β-convergence coexist. The conditional β-convergence rate is 0.96~1.53%, and the half-life cycle is 45.4~72.36 years. Compared to the PRD, the economic disparities in the edge area are smaller but the convergence speed is faster and the half-life cycle is shorter. Both industrial agglomeration and industrial proximity have a significant impact on the economic convergence of Guangdong’s cities. Among them, industrial agglomeration has a positive impact, while industrial proximity has a negative impact. There is spatial heterogeneity in the impact of industries on economic development. Industrial agglomeration has a positive impact on the overall economic development of Guangdong, but it is not significant within the regions. Industrial proximity has significant negative externalities in the PRD region, and its impact is not significant in the edge area. Full article
(This article belongs to the Special Issue Feature Papers for 'Land Socio-Economic and Political Issues' Section)
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<p>Spatial distribution of urban per-capita GDP in Guangdong.</p> Full article ">Figure 2
<p>The standard deviation of urban economy in different regions.</p> Full article ">
23 pages, 8381 KiB  
Article
Forest Tales? Unravelling Divergent Land Use and Land Cover Change (LULCC) Maps and State Narratives in Vietnam’s Northern Uplands
by Thinh An Nguyen, Hung Le, Patrick Slack, Margaret Kalacska and Sarah Turner
Land 2024, 13(1), 71; https://doi.org/10.3390/land13010071 - 7 Jan 2024
Viewed by 2342
Abstract
The Vietnamese state has advocated for the sedentarization and market integration of upland northern farmers over the past thirty years, leading to both agrarian and forest transitions. This article presents a comprehensive land use and land cover change (LULCC) analysis of two adjacent [...] Read more.
The Vietnamese state has advocated for the sedentarization and market integration of upland northern farmers over the past thirty years, leading to both agrarian and forest transitions. This article presents a comprehensive land use and land cover change (LULCC) analysis of two adjacent upland borderland districts, Phong Thổ and Bát Xát, in northern Vietnam, spanning two neighboring inland provinces, Lai Châu and Lào Cai. These districts are primarily home to ethnic minority farmers who are encouraged by Vietnamese state officials to not only protect forests but to also transition toward cash crop cultivation from less intensive semi-subsistence agriculture. Our LULCC maps, covering the period from 1990 to 2020, revealed a reduction in the speed by which closed-canopy forests were disappearing. During interviews, state officials were confident that this was due to a range of state policies and state-sponsored initiatives, including the promotion of tree crops and payments for forest environmental services. Our own fieldwork in the region suggests other factors are also supporting this decline in deforestation rates, rooted in ethnic minority farmer livelihood decision making. Some state officials were also able to point to factors hindering a more positive result regarding forest cover, including population pressure and new infrastructure. Interestingly, despite our positive findings on Land use and land cover change (LULCC) related to forest cover, one-third of state officials, upon reviewing our LULCC maps, firmly maintained that errors had occurred. Some even proposed that there was an actual rise in forest cover. Our study shows that these discrepancies raise compelling questions about officials’ political motivations and ongoing pressures to uphold the central state’s reforestation and agrarian transition discourses. Full article
(This article belongs to the Special Issue The Role Played by Agriculture in Inland Areas)
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<p>Map showing the location of the two districts, Phong Thổ and Bát Xát in Lai Châu and Lào Cai Provinces in Northern Vietnam, directly on the Sino-Vietnamese border. Background imagery is from the Planet Labs global cloud-free basemap for the 4th quarter of 2020 (October–December). Inset shows the location of the main map (red box) in Vietnam (beige).</p> Full article ">Figure 2
<p>(<b>A</b>) Mosaic of wet rice terraces, dry rice and/or maize fields (classified as bare soil), and shrubs in Bát Xát; (<b>B</b>) open-canopy forest (undergrowth is black cardamom) in Bát Xát; (<b>C</b>) small built-up area in Bát Xát (ethnic minority Hani houses); (<b>D</b>) mosaic of dry rice or maize fields (classified as bare soil), built up, and shrub classes in Bát Xát; (<b>E</b>) closed-canopy forest patches surrounded by shrubs in Phong Thổ; (<b>F</b>) mosaic of closed-canopy forest, shrubs, and maize fields or dry rice (classed as bare soil) in Phong Thổ; (<b>G</b>) maize fields (classified as bare soil) and shrubs in Phong Thổ; (<b>H</b>) wet rice terraces in Phong Thổ (classified as bare soil). Photographs by S. Turner.</p> Full article ">Figure 3
<p>Flowchart illustrating the generation of the LULCC maps and the integration of these maps with data from state official interviews. The main analytical steps of the thematic coding for the interviews are also shown. The final outcomes of the remote sensing analyses are shown in red, and the final outcomes of the analysis of the interviews are shown in blue.</p> Full article ">Figure 4
<p>Percentage of area covered by each land cover class for the districts of Phong Thổ and Bát Xát at each of the four time periods.</p> Full article ">Figure 5
<p>Ribbon plots of land cover change over the 1990–2020 period for each of the districts separately and combined. The thickness of the colored lines indicates the proportion of the total area of that class.</p> Full article ">Figure 6
<p>LULCC for closed- and open-canopy forests from 1990 to 2000 in Phong Thổ and Bát Xát Districts.</p> Full article ">Figure 7
<p>LULCC for closed- and open-canopy forests from 2000 to 2010 in Phong Thổ and Bát Xát Districts.</p> Full article ">Figure 8
<p>LULCC for closed- and open-canopy forests from 2010 to 2020 in Phong Thổ and Bát Xát Districts.</p> Full article ">
16 pages, 707 KiB  
Article
The Impacts of Climate Change on Tourism Operators, Trail Experience and Land Use Management in British Columbia’s Backcountry
by Courtney W. Mason and Pate Neumann
Land 2024, 13(1), 69; https://doi.org/10.3390/land13010069 - 7 Jan 2024
Cited by 3 | Viewed by 2208
Abstract
Climate change, natural resource industries, and an expanding outdoor tourism sector have recently increased access to sensitive backcountry environments in Western Canada. Trail managers are struggling to manage trail conditions with the mounting effects of smoke, dust, fire, flood, area closures, and beetle [...] Read more.
Climate change, natural resource industries, and an expanding outdoor tourism sector have recently increased access to sensitive backcountry environments in Western Canada. Trail managers are struggling to manage trail conditions with the mounting effects of smoke, dust, fire, flood, area closures, and beetle outbreaks in their regions. Outdoor recreation trail managers are linking these events and are thinking critically about the history and interconnectedness of land use management decisions in the province of British Columbia (BC). As the effects of climate change continue to challenge both trail managers and sport recreationists, guides and trail associations have been identified as key education facilitators in the development and dissemination of environmental consciousness. Guided by a community-based participatory research approach, this study used personal interviews with trail managers across the province to highlight how a connection with local ecosystems can develop a more robust land ethic for recreational trail user communities in BC. Full article
(This article belongs to the Special Issue Impacts of Climate Change on Human Wellbeing and Cultural Ecosystem Services)
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<p>Map by Olea Vandermale.</p> Full article ">
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