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Land
Volume 10
Issue 5
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Land, Volume 10, Issue 5 (May 2021) – 107 articles

Cover Story (view full-size image): Virtual reality offers new forms of representation and geovisualization. In the cover photo, Las Tereseitas Beach (Tenerife, Spain) is displayed using a game engine VR environment, in which twenty-five architecture students performed landscape design tasks. The 3D environment perception was analyzed through the Questionnaire on User eXperience in Immersive Virtual Environments. The motivational factor was part of the Intrinsic Motivation Inventory. Results showed a high 3D environment perception during geovisualization in the nine subcategories (sense of presence, engagement, immersion, flow, usability, emotion, judgment, experience consequence, and technology adoption) analyzed. The game engine-based teaching approach carried out has been motivating for students, with values over 5 (1–7 Likert scale) in the five subscales considered. View this paper
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16 pages, 5115 KiB  
Article
Major United States Land Use as Influenced by an Altering Climate: A Spatial Econometric Approach
by Sung Ju Cho and Bruce McCarl
Land 2021, 10(5), 546; https://doi.org/10.3390/land10050546 - 20 May 2021
Cited by 11 | Viewed by 3889
Abstract
Climate and socioeconomic and policy factors are found to stimulate land use changes along with changes in greenhouse gas emissions and adaption behaviors. Most of the studies investigating land use changes in the U.S. have not considered potential spatial effects explicitly. We used [...] Read more.
Climate and socioeconomic and policy factors are found to stimulate land use changes along with changes in greenhouse gas emissions and adaption behaviors. Most of the studies investigating land use changes in the U.S. have not considered potential spatial effects explicitly. We used a two-step linearized multinomial logit to examine the impacts of various factors on conterminous U.S. land use changes including spatial lag coefficients. The estimation results show that the spatial dependences have existed for cropland, pastureland, and grasslands with a negative dependence on forests but weakened in most of the land uses except for croplands. Temperature and precipitation were found to have nonlinear impacts on the land use shares in the succeeding years by exerting opposite effects on crop versus pasture/grass shares. We also predicted land use changes under different climate change scenarios. The simulation results imply that the southern regions of the U.S. would lose cropland shares with further severity under the business-as-usual climate scenarios, while the land use shares for pasture/grass and forest would increase in those regions. As land use plays an important role in the climate system and vice versa, the results from this study may help policymakers tackle climate-driven land use changes and farmers adapt to climate change. Full article
(This article belongs to the Special Issue Agricultural Land Use, Economics and Climate Change)
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<p>Weighted average of land capability classification (non-irrigated). The class values from gSSURGO [<a href="#B38-land-10-00546" class="html-bibr">38</a>] are averaged at the cell level. Here, lower classes indicate those more suitable for cultivation. Numbers in brackets indicate the range of each category, with the lands in each category having a class greater than the first number and less than or equal to the second one.</p> Full article ">Figure 2
<p>Percentage changes of cropland share between 2016 and select time periods under alternative representative concentration pathways. Numbers in brackets indicate the range of each category, with the change greater than the first number and less or equal to the second one.</p> Full article ">Figure 3
<p>Percentage changes of pastureland share between 2016 and select time periods under alternative representative concentration pathways.</p> Full article ">Figure 4
<p>Percentage changes of grassland share between 2016 and select time periods under alternative representative concentration pathways.</p> Full article ">Figure 5
<p>Percentage changes of forest share between 2016 and select time periods under alternative representative concentration pathways.</p> Full article ">Figure 6
<p>Percentage changes of urban land share between 2016 and select time periods under alternative representative concentration pathways.</p> Full article ">
28 pages, 2736 KiB  
Review
A Comprehensive Review of Urban Regeneration Governance for Developing Appropriate Governance Arrangements
by Fangyun Xie, Guiwen Liu and Taozhi Zhuang
Land 2021, 10(5), 545; https://doi.org/10.3390/land10050545 - 20 May 2021
Cited by 26 | Viewed by 10271
Abstract
Urban regeneration governance (URG) has become a popular issue in academia, politics and civil society because it has a significant influence on the success of urban regeneration activities. However, a comprehensive review on URG has yet to be produced, which hinders providing references [...] Read more.
Urban regeneration governance (URG) has become a popular issue in academia, politics and civil society because it has a significant influence on the success of urban regeneration activities. However, a comprehensive review on URG has yet to be produced, which hinders providing references to developing appropriate governance arrangements. Therefore, this study selected 88 relevant literatures from 1990 to 2019 to conduct a critical review. The goal of this review is to conceptualize URG, refine the signature elements of URG, compare the main modes of URG, and analyze the influential factors of URG. As a decision-making mode or a partnership, URG consists of three elements—partner, power and procedure—and influenced by three factors, the plan, place and person. There are three main modes of URG and each has pros and cons. Based on a comprehensive review, this paper concludes some findings and draws an 8p model that can provide an analysis framework for decision makers. Finally, four avenues for future research are proposed. Full article
(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The definition of urban regeneration governance.</p> Full article ">Figure 2
<p>The flow of the screening process.</p> Full article ">Figure 3
<p>The publication year of selected papers.</p> Full article ">Figure 4
<p>Research framework.</p> Full article ">Figure 5
<p>The categories of URG modes.</p> Full article ">Figure 6
<p>An 8p model of URG.</p> Full article ">Figure 7
<p>The connection among the “8p”.</p> Full article ">
19 pages, 3750 KiB  
Article
A Comparison, Validation, and Evaluation of the S-world Global Soil Property Database
by Jetse J. Stoorvogel and Vera L. Mulder
Land 2021, 10(5), 544; https://doi.org/10.3390/land10050544 - 20 May 2021
Cited by 5 | Viewed by 2851
Abstract
Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon [...] Read more.
Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies. Full article
(This article belongs to the Special Issue Indicators Engineering for Sustainable Land Transformation and Soil Conservation)
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<p>Terrestrial ecosystems as defined by Olson et al. [<a href="#B26-land-10-00544" class="html-bibr">26</a>].</p> Full article ">Figure 2
<p>Selected soil profiles (<span class="html-italic">n</span> = 83,393) from the WoSIS database for the validation of the S-world soil property database.</p> Full article ">Figure 3
<p>Global maps of soil organic carbon contents in the topsoil from different sources.</p> Full article ">Figure 4
<p>Global maps of soil organic carbon contents in the subsoil from different sources.</p> Full article ">Figure 5
<p>Global maps of clay contents in the soil profile from different sources.</p> Full article ">Figure 6
<p>The comparative analysis of S-world against an ensemble of different soil property maps.</p> Full article ">Figure 7
<p>The ranges of topsoil SOC for the 50 most important soil types of the HWSD as estimated by the WISE (blue bars) and WoSIS (orange bars) databases.</p> Full article ">
18 pages, 8553 KiB  
Article
The Spatial Effect of Administrative Division on Land-Use Intensity
by Pengrui Wang, Chen Zeng, Yan Song, Long Guo, Wenping Liu and Wenting Zhang
Land 2021, 10(5), 543; https://doi.org/10.3390/land10050543 - 20 May 2021
Cited by 15 | Viewed by 3451
Abstract
Land-use intensity (LUI) is one of the most direct manifestations of regional land use efficiency. The study of cross-administrative LUI in urban agglomerations is of great importance for the sustainable development of land, new urbanization, and territorial spatial planning. In this study, the [...] Read more.
Land-use intensity (LUI) is one of the most direct manifestations of regional land use efficiency. The study of cross-administrative LUI in urban agglomerations is of great importance for the sustainable development of land, new urbanization, and territorial spatial planning. In this study, the urban agglomeration in the middle reaches of the Yangtze River in China was used as the case study area to explore the spatial spillover effect through the administrative division, underlying driving mechanism, and spatial interactions or constraints of LUI. First, LUI was measured using the index of the proportion of construction land to the total area of the administrative region. Second, the adjacency relationship of the county-level administrative units was identified on the basis of the queen-type adjacency criterion under the county-level administrative division system. Thereafter, spatial weight matrix for spatial modeling was constructed. Last, a spatial model using the “Spatial adjacency matrix” was devised to examine the influencing factors and the potential spatial interactions or constraints of administrative units. Results revealed that the level of LUI of different county-level administrative units were quite different, and the gap of LUI among county-level administrative units widened from 2010 to 2017. The fixed asset investment per land (FAIL), gross domestic product per capital (PGDP), and proportion of tertiary sector (PTS) are the driving factors of LUI. County-level administrative units not only had a significant and increasing spatial interaction effect based on the relationship of cooperation, but also had an influence of restraint mutually which was caused by the competition. The direct spatial spillover effect was remarkable. In the future, the effect of interaction among administrative units under the administrative division should be considered to promote the reasonable use and optimal layout of regional urban land to realize the optimal allocation of land resources. Full article
(This article belongs to the Section Land Planning and Landscape Architecture)
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<p>The theoretical framework.</p> Full article ">Figure 2
<p>Study area.</p> Full article ">Figure 3
<p>Conceptual model of the spatial adjacency matrix. Note: Chibi City is a county-level city administered by Xianning city of Hubei province. Linxiang City is a county-level city administered by Yueyang City of Hunan province. Jiayu County, Chongyang County, and Tongshan County are counties of Xianning city. Jiangxia District is a municipal district of Wuhan City. Xianan District is a municipal district of Xianning City. In the <a href="#land-10-00543-f003" class="html-fig">Figure 3</a>, C1, C2, C3, C4, C5, C6, and C7 are Linxiang City, Jiayu County, Chibi City, Chongyang County, Jiangxia District, Xianan District, and Tongshan County, respectively. S1 is the adjacent scenario of county-level city and county-level city. S2 is the adjacent scenario of county-level city and county. S3 is the adjacent scenario of county-level city and urban district. S4 is the adjacent scenario of county and urban district. S5 is the adjacent scenario of urban district and urban district. S6 is the adjacent scenario of county and county.</p> Full article ">Figure 4
<p>Spatial and hotspot patterns of LUI in the urban agglomeration of the middle reaches of the Yangtze River in 2010 and 2017. (<b>a</b>) Shows the spatial and hotspot patterns in the urban agglomeration in the middle reaches of the Yangtze River in 2010. (<b>b</b>) Refers to the spatial and hotspot patterns in the urban agglomeration in the middle reaches of the Yangtze River in 2017.</p> Full article ">Figure 5
<p>Coefficient changes of indicators (FAIL, PGDP, and PTS) and spatial coefficient (γ) under different hypothesis coefficients λ of administrative barriers in 2010 and 2017. (<b>a</b>) Shows the different spatial coefficient (γ) of LUI under different hypothesis coefficients λ of administrative barriers in 2010 and 2017. (<b>b</b>) Refers to the coefficient changes of FAIL under the different hypothesis coefficients λ of administrative barriers in 2010 and 2017. (<b>c</b>) Presents the coefficient changes of PGDP under the different hypothesis coefficients λ of administrative barriers in 2010 and 2017. (<b>d</b>) Indicates the coefficient changes of PTS under the different hypothesis coefficients λ of administrative barriers in 2010 and 2017. Notes: The main axis, which is on the left, represents the year of 2010 and the secondary axis, which is on the right, represents the year of 2017.</p> Full article ">
14 pages, 3581 KiB  
Article
Attribution Analysis of Seasonal Runoff in the Source Region of the Yellow River Using Seasonal Budyko Hypothesis
by Guangxing Ji, Leying Wu, Liangdong Wang, Dan Yan and Zhizhu Lai
Land 2021, 10(5), 542; https://doi.org/10.3390/land10050542 - 19 May 2021
Cited by 31 | Viewed by 2911
Abstract
Previous studies mainly focused on quantifying the contribution rate of different factors on annual runoff variation in the source region of the Yellow River (SRYR), while there are few studies on the seasonal runoff variation. In this study, the monthly water storage and [...] Read more.
Previous studies mainly focused on quantifying the contribution rate of different factors on annual runoff variation in the source region of the Yellow River (SRYR), while there are few studies on the seasonal runoff variation. In this study, the monthly water storage and monthly actual evaporation of SRYR were calculated by the monthly ABCD model, and then a seasonal Budyko frame was constructed. Finally, the contribution rate of climatic and anthropic factors on the seasonal runoff variation in Tangnaihai hydrological station were quantitatively calculated. It turned out that: (1) The changing point of runoff data at Tangnaihai hydrological station is 1989. (2) The ABCD monthly hydrological model could well simulate the monthly runoff variation of Tangnaihai hydrological station. (3) Anthropic factors play a major role in runoff change in spring, summer, and winter, while climatic factors play a major role in runoff change in autumn. Full article
(This article belongs to the Special Issue Impact of Land-Use Change on Water Resources)
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<p>Location of hydrological and weather stations in and around the SRYR.</p> Full article ">Figure 2
<p>Curve of possible evapotranspiration and effective water quantity in the “ABCD” model.</p> Full article ">Figure 3
<p>Schematic of the decomposition method for quantifying the climatic and anthropic factors on runoff.</p> Full article ">Figure 4
<p>Result of Mann–Kendall mutation analysis in the Tangnaihai station from 1967–2016.</p> Full article ">Figure 5
<p>Result of cumulative anomaly mutation analysis in the Tangnaihai station from 1967–2016.</p> Full article ">Figure 6
<p>Monthly runoff simulation results in the pre-change period using the monthly ABCD model.</p> Full article ">Figure 7
<p>Monthly runoff simulation results in the post-change period using the monthly ABCD model.</p> Full article ">Figure 8
<p>Parameter fitting results of the Budyko curve in spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>).</p> Full article ">
33 pages, 4645 KiB  
Article
Spatial Chaos as a Result of War Damage and Post-War Transformations. Example of the Small Town of W?gorzewo
by Łukasz Musiaka, Paweł Sudra and Tomasz Spórna
Land 2021, 10(5), 541; https://doi.org/10.3390/land10050541 - 19 May 2021
Cited by 10 | Viewed by 5133 | Correction
Abstract
World War II’s military activities and the post-war devastation period destroyed many European cities and towns. One of the areas that was struck the most was former East Prussia, currently located in Poland and the Kaliningrad Region (the Russian Federation). In addition to [...] Read more.
World War II’s military activities and the post-war devastation period destroyed many European cities and towns. One of the areas that was struck the most was former East Prussia, currently located in Poland and the Kaliningrad Region (the Russian Federation). In addition to the destruction of cities, which are strategically and economically important, small towns have also suffered. An example of such a town is W?gorzewo, where the scale of destruction of the pre-war urban tissue exceeded 80%, and the old town’s built-up area practically ceased to exist. This town magnifies most of the processes and spatial problems characteristic of Central and Eastern Europe’s towns of the “metamorphic” type. Post-war zoning during the Polish People’s Republic period, in the spirit of constructing a socialist town and bypassing the original spatial arrangement, brought about irreversible changes in the urban tissue. This was reflected in the break with the town’s original layout and the creation of modernist buildings. The changes were solidified or even deepened during the economic and political transition of the 1990s in Poland. Today, decades after the end of World War II, despite taking corrective measures, the town is still facing the problem of spatial chaos. Its morphological and physiognomic manifestations in the lack of a central public space, the loss of its historic character, the disharmonization of the urban landscape, and the dispersed development are the main subjects of this article’s analysis. This study uses a diverse methodological apparatus consisting of an analysis of the town’s morphological transformations, an analysis of the physiognomy of the urban landscape and architecture, in situ studies, and an analysis of municipal documents and expert interviews. In the discussion, the study results are embedded in the context of the cases of other European cities and towns. The conclusions indicate the risks to the formation of spatial order in W?gorzewo and possible paths of action. Full article
(This article belongs to the Special Issue Conditions, Effects and Costs of Spatial Chaos)
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<p>(<b>A</b>) Land use in Węgorzewo in 2020; (<b>B</b>) the location of Węgorzewo within present administrative and political borders; and (<b>C</b>) the location of Węgorzewo within historical and political borders (1939). Source: (<b>A</b>–<b>C</b>) own study; (<b>A</b>) using the Topographic Objects Database BDOT [<a href="#B68-land-10-00541" class="html-bibr">68</a>].</p> Full article ">Figure 2
<p>Panoramic view of the central part of Węgorzewo: (<b>A</b>) before World War II; (<b>B</b>) today. Source: (<b>A</b>) Kreis und Stadt Angerburg [<a href="#B69-land-10-00541" class="html-bibr">69</a>] and (<b>B</b>) Google Maps [<a href="#B70-land-10-00541" class="html-bibr">70</a>].</p> Full article ">Figure 3
<p>View of the town center, 1945. Source: Photo Archive—East Prussia [<a href="#B105-land-10-00541" class="html-bibr">105</a>].</p> Full article ">Figure 4
<p>Graphs showing the morphological changes in Węgorzewo during the examined period. Source: own studies based on archival and present plans for Węgorzewo [<a href="#B71-land-10-00541" class="html-bibr">71</a>,<a href="#B73-land-10-00541" class="html-bibr">73</a>,<a href="#B77-land-10-00541" class="html-bibr">77</a>,<a href="#B79-land-10-00541" class="html-bibr">79</a>].</p> Full article ">Figure 5
<p>Changes in the development cover of the central area of Węgorzewo during the period 1944–2019. Source: own studies based on archival and present plans for Węgorzewo [<a href="#B71-land-10-00541" class="html-bibr">71</a>,<a href="#B73-land-10-00541" class="html-bibr">73</a>,<a href="#B77-land-10-00541" class="html-bibr">77</a>,<a href="#B79-land-10-00541" class="html-bibr">79</a>].</p> Full article ">Figure 6
<p>Comparison of archival and contemporary views of the former Altmarkt and Holzmarkt and the present Grunwald Square, 1910–1915. Source: [<a href="#B105-land-10-00541" class="html-bibr">105</a>,<a href="#B106-land-10-00541" class="html-bibr">106</a>].</p> Full article ">Figure 7
<p>Examples of spatial chaos in Węgorzewo on an architectural and urban scale (status: September 2020). From left to right in the rows: (<b>A</b>). the lack of development on the northern side of Freedom Square (formerly Neuer Markt), (<b>B</b>). the castle and its fence from the side of Zamkowa Street, (<b>C</b>). ‘temporary’ commercial and food facilities at Zamkowa Street, (<b>D</b>). the building of the former Railway Station requiring revitalization, (<b>E</b>). the deteriorating infrastructure of the industrial district from the side of Jaracza Street, (<b>F</b>). chaotic service and municipal buildings from the Armii Krajowej Street side. Source: own photos.</p> Full article ">
19 pages, 12186 KiB  
Review
Pollination in Agroecosystems: A Review of the Conceptual Framework with a View to Sound Monitoring
by Manuela Giovanetti, Sergio Albertazzi, Simone Flaminio, Rosa Ranalli, Laura Bortolotti and Marino Quaranta
Land 2021, 10(5), 540; https://doi.org/10.3390/land10050540 - 19 May 2021
Cited by 8 | Viewed by 4898
Abstract
The pollination ecology in agroecosystems tackles a landscape in which plants and pollinators need to adjust, or be adjusted, to human intervention. A valid, widely applied approach is to regard pollination as a link between specific plants and their pollinators. However, recent evidence [...] Read more.
The pollination ecology in agroecosystems tackles a landscape in which plants and pollinators need to adjust, or be adjusted, to human intervention. A valid, widely applied approach is to regard pollination as a link between specific plants and their pollinators. However, recent evidence has added landscape features for a wider ecological perspective. Are we going in the right direction? Are existing methods providing pollinator monitoring tools suitable for understanding agroecosystems? In Italy, we needed to address these questions to respond to government pressure to implement pollinator monitoring in agroecosystems. We therefore surveyed the literature, grouped methods and findings, and evaluated approaches. We selected studies that may contain directions and tools directly linked to pollinators and agroecosystems. Our analysis revealed four main paths that must come together at some point: (i) the research question perspective, (ii) the advances of landscape analysis, (iii) the role of vegetation, and (iv) the gaps in our knowledge of pollinators taxonomy and behavior. An important conclusion is that the pollinator scale is alarmingly disregarded. Debate continues about what features to include in pollinator monitoring and the appropriate level of detail: we suggest that the pollinator scale should be the main driver. Full article
(This article belongs to the Special Issue Landscape Controls of Insect Biodiversity in Agricultural Environments)
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<p>Research perspectives. Opposite but complementary perspectives of research topics addressing pollination in agroecosystems (<a href="#sec3-land-10-00540" class="html-sec">Section 3</a>). Although the waves of factors may proceed by different paths (green and orange in the figure), the factors themselves are part of a more general context and are placed according to the perspective adopted in a given study.</p> Full article ">Figure 2
<p>The landscape ecology approach. Innovation in landscape ecology enables generalization and introduces new definitions for methodological approaches (<a href="#sec4-land-10-00540" class="html-sec">Section 4</a>). We grouped them according to three spatial units: the study area (SA), the specific spatial unit (SSU), the advanced spatial unit (ASU). Level of detail can vary but can be resumed and compared according to spatial context. The examples were inspired by (<b>1</b>) [<a href="#B26-land-10-00540" class="html-bibr">26</a>]; (<b>2</b>) [<a href="#B14-land-10-00540" class="html-bibr">14</a>]; (<b>3</b>) [<a href="#B21-land-10-00540" class="html-bibr">21</a>]; (<b>4</b>) [<a href="#B32-land-10-00540" class="html-bibr">32</a>]; (<b>5</b>) [<a href="#B30-land-10-00540" class="html-bibr">30</a>]; (<b>6</b>) [<a href="#B13-land-10-00540" class="html-bibr">13</a>]; (<b>7</b>) [<a href="#B36-land-10-00540" class="html-bibr">36</a>].</p> Full article ">Figure 3
<p>Levels of complexity of vegetation. The different ways of describing vegetation, with increasing levels of detail, as addressed in <a href="#sec5-land-10-00540" class="html-sec">Section 5</a>.</p> Full article ">Figure 4
<p>Plots: a space to describe vegetation. Complexity of plot size and spatial arrangements. Plot size coincided in some studies, e.g., 1 × 1 m [<a href="#B56-land-10-00540" class="html-bibr">56</a>,<a href="#B57-land-10-00540" class="html-bibr">57</a>]. Examples of plot arrangement were inspired by (<b>1</b>) [<a href="#B28-land-10-00540" class="html-bibr">28</a>]; (<b>2</b>) [<a href="#B56-land-10-00540" class="html-bibr">56</a>]; (<b>3</b>) [<a href="#B55-land-10-00540" class="html-bibr">55</a>]; (<b>4</b>) [<a href="#B57-land-10-00540" class="html-bibr">57</a>].</p> Full article ">Figure 5
<p>Study of pollinators. The two tasks for pollinator collection and specimen identification, as explained in <a href="#sec6-land-10-00540" class="html-sec">Section 6</a>.</p> Full article ">
31 pages, 7791 KiB  
Article
Informed Geoheritage Conservation: Determinant Analysis Based on Bibliometric and Sustainability Indicators Using Ordination Techniques
by Boglárka Németh, Károly Németh and Jon N. Procter
Land 2021, 10(5), 539; https://doi.org/10.3390/land10050539 - 19 May 2021
Cited by 8 | Viewed by 4001
Abstract
Ordination methods are used in ecological multivariate statistics in order to reduce the number of dimensions and arrange individual variables along environmental variables. Geoheritage designation is a new challenge for conservation planning. Quantification of geoheritage to date is used explicitly for site selection, [...] Read more.
Ordination methods are used in ecological multivariate statistics in order to reduce the number of dimensions and arrange individual variables along environmental variables. Geoheritage designation is a new challenge for conservation planning. Quantification of geoheritage to date is used explicitly for site selection, however, it also carries significant potential to be one of the indicators of sustainable development that is delivered through geosystem services. In order to achieve such a dominant position, geoheritage needs to be included in the business as usual model of conservation planning. Questions about the quantification process that have typically been addressed in geoheritage studies can be answered more directly by their relationships to world development indicators. We aim to relate the major informative geoheritage practices to underlying trends of successful geoheritage implementation through statistical analysis of countries with the highest trackable geoheritage interest. Correspondence analysis (CA) was used to obtain information on how certain indicators bundle together. Multiple correspondence analysis (MCA) was used to detect sets of factors to determine positive geoheritage conservation outcomes. The analysis resulted in ordination diagrams that visualize correlations among determinant variables translated to links between socio-economic background and geoheritage conservation outcomes. Indicators derived from geoheritage-related academic activity and world development metrics show a shift from significant Earth science output toward disciplines of strong international agreement such as tourism, sustainability and biodiversity. Identifying contributing factors to conservation-related decisions helps experts to tailor their proposals for required evidence-based quantification reports and reinforce the scientific significance of geoheritage. Full article
(This article belongs to the Special Issue Geoparks as a Form of Tourism Space Management)
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<p>Flowchart of research process.</p> Full article ">Figure 2
<p>Background fields of the 47 authors most influencing the conceptual evolution of geoheritage conservation.</p> Full article ">Figure 3
<p>Cumulative citations (Y-axis) of the 47 authors since 1991. The citation number includes citations for all their published and co-published material available in the Scopus online database.</p> Full article ">Figure 4
<p>The three columns display the countries extracted through the bibliometric analysis. All columns are rankings according to their scientific output (1) in all scientific subject areas from SCImago, (2) in Earth science and (3) in geoheritage (third column). The width of the arrows is proportional to the flow rate. For detailed explanation, please refer to the main text.</p> Full article ">Figure 5
<p>The chart depicts relationships between the main background subjects of citing articles and the four main concepts of geoheritage conservation. Concept 1 = Earth science focus, Concept 2 = Geodiversity–biodiversity-aligned conservation, Concept 3 = Geotourism, Concept 4 = Sustainability.</p> Full article ">Figure 6
<p>This figure provides information on the relation of the origin countries of citing articles and the four different concepts. The dots indicate the normalized value of citations generated by the country for the different concepts. For detailed explanation, please refer to the main text.</p> Full article ">Figure 7
<p>(<b>a</b>) Number of geoparks per country in the year 2020. (<b>b</b>) Percentage of protected areas per total area of each country in the year 2020.</p> Full article ">Figure 8
<p>The matrix is built up by the correlation coefficient values of each indicator to detect the level to which they affect one another. The coefficient is based on their variance for all the studied countries. Indicators of academic output are marked with (A), geographic data are marked with (G), world development statistics are marked with (W).</p> Full article ">Figure 9
<p>Correspondence analysis ordination diagram of the first two axes with countries (gray) and indicators (red); ellipsoids illustrate the three identified indicator bundles.</p> Full article ">Figure 10
<p>Individual factor map. The chart breaks down the relationship of values with the origin countries of citing articles separately for each indicator. None of the indicators create well-distinguished clusters of the countries (represented by dots), meaning none of the indicators alone influenced the course of geoheritage conservation.</p> Full article ">Figure 11
<p>Biplot of the first two axes of the multiple correspondence analysis (MCA) illustrating the categories of the three identified indicator bundles with their associations with the four geoheritage concepts. The categories show the highest variation within the first concept.</p> Full article ">Figure 12
<p>Cumulative number of geoparks in the world. The X-axis represents the year the geopark was established.</p> Full article ">Figure 13
<p>MCA factor map of the concepts and their positions in New Zealand. The angle between row points and the central point of confidence ellipsoids gives a measure of their correlation. The farther the angle is from 90°, the closer the relations are.</p> Full article ">
16 pages, 491 KiB  
Article
Transforming Land Administration Practices through the Application of Fit-For-Purpose Technologies: Country Case Studies in Africa
by Danilo Antonio, Solomon Njogu, Hellen Nyamweru and John Gitau
Land 2021, 10(5), 538; https://doi.org/10.3390/land10050538 - 19 May 2021
Cited by 5 | Viewed by 4605
Abstract
Access to land for many people in Africa is insecure and continues to pose risks to poverty, hunger, forced evictions, and social conflicts. The delivery of land tenure in many cases has not been adequately addressed. Fit-for-purpose spatial frameworks need to be adapted [...] Read more.
Access to land for many people in Africa is insecure and continues to pose risks to poverty, hunger, forced evictions, and social conflicts. The delivery of land tenure in many cases has not been adequately addressed. Fit-for-purpose spatial frameworks need to be adapted to the context of a country based on simple, affordable, and incremental solutions toward addressing these challenges. This paper looked at three case studies on the use of the Social Tenure Domain Model (STDM) tool in promoting the development of a fit-for-purpose land administration spatial framework. Data gathering from primary and secondary sources was used to investigate the case studies. The empirical findings indicated that the use and application of the STDM in support of the fit-for-purpose land administration framework is quite effective and can facilitate the improvement in land tenure security. The findings also revealed that the tool, together with participatory and inclusive processes, has the potential to contribute to other frameworks of Fit-For-Purpose Land Administration (FFP LA) toward influencing changes in policy and institutional practices. Evidently, there was a remarkable improvement in the institutional arrangements and collaboration among different institutions, as well as a notable reduction in land conflicts or disputes in all three case studies. Full article
(This article belongs to the Special Issue Fit-for-Purpose Land Administration-Providing Secure Land Rights at Scale)
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<p>The conceptual model for the Social Tenure Domain Model (STDM).</p> Full article ">Figure 2
<p>STDM case studies area in the three countries.</p> Full article ">
18 pages, 1783 KiB  
Article
Spatial-Temporal Changes and Driving Force Analysis of Green Space in Coastal Cities of Southeast China over the Past 20 Years
by Huayan Weng, Yongchao Gao, Xinyi Su, Xiaodong Yang, Fangyan Cheng, Renfeng Ma, Yanju Liu, Wen Zhang and Liwen Zheng
Land 2021, 10(5), 537; https://doi.org/10.3390/land10050537 - 19 May 2021
Cited by 22 | Viewed by 4409
Abstract
The purpose of this study is to reveal the spatial-temporal change and driving factors of green space in coastal cities of southeast China over the past 20 years. A supervised classification method combining support vector machines (SVMs) and visual interpretation was used to [...] Read more.
The purpose of this study is to reveal the spatial-temporal change and driving factors of green space in coastal cities of southeast China over the past 20 years. A supervised classification method combining support vector machines (SVMs) and visual interpretation was used to extract the green space from Landsat TM/OLI imageries from 2000–2020. The landscape pattern index was used to calculate geospatial information of green space and analyze their spatial-temporal changes. The hierarchical partitioning analysis was then used to determine the influences of anthropogenic and geographic environmental factors on the spatial-temporal changes in green space. The results indicated that the total area of green space remained constant over the past 20 years in coastal cities of southeast China (1% reduction). The spatial change of green space mainly occurred in the area near the ocean and the southern region. 41.37% of forest land was transferred from cultivated land, while 44.56%, 41.83%, 43.20%, 46.31%, 41.98% and 40.20% of shrub land, sparse woodland, other woodland, high-coverage grassland, moderate-coverage grassland and low-coverage grassland were transferred from forest land. The number of patches, patch density, edge density, landscape shape index and Shannon’s diversity index increased from 2000–2015, and then decreased to the minimum in 2020, while largest patch index continued to decline from 2000–2020. The contribution of anthropogenic factors (0.53–0.61) on the spatial-temporal changes of green space continually increased over the past 20 years, which was also higher than geographical environment factors (0.39–0.41). Our study provides a new perspective to distinguish the impact of anthropogenic activities and geographical environmental factors on the change of green space area, thereby providing a theoretical support for the construction and ecological management of green space. Full article
(This article belongs to the Special Issue Dynamic of Natural Ecosystems under Anthropogenic Disturbances)
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<p>The coastal cities of southeast China.</p> Full article ">Figure 2
<p>Spatial-temporal change of green space in coastal cities of southeast China over the past 20 years.</p> Full article ">Figure 3
<p>The change of landscape pattern index of green space in coastal cities of southeast China over the past 20 years. NP, PD, LPI, ED, LSI and SHDI are number of patches, patch density, the largest patch index, edge density, landscape shape Index and Shannon’s diversity index, respectively.</p> Full article ">Figure 4
<p>The contributions of socio-economic determinants and geographical environmental factors to the spatial-temporal change of green space over the past 20 years. The explanations of REI, FAT, LSPP, PNAP, GAPC, WRPC, AE, AT, AP, PTI, PSI and PPI are given in <a href="#land-10-00537-t001" class="html-table">Table 1</a>. (<b>a</b>) The contributions of all influencing factors; (<b>b</b>) The contributions of socio-economic determinants and geographical environmental factors.</p> Full article ">
31 pages, 13984 KiB  
Article
Prototyping a Methodology for Long-Term (1680–2100) Historical-to-Future Landscape Modeling for the Conterminous United States
by Jordan Dornbierer, Steve Wika, Charles Robison, Gregory Rouze and Terry Sohl
Land 2021, 10(5), 536; https://doi.org/10.3390/land10050536 - 19 May 2021
Cited by 8 | Viewed by 4110
Abstract
Land system change has been identified as one of four major Earth system processes where change has passed a destabilizing threshold. A historical record of landscape change is required to understand the impacts change has had on human and natural systems, while scenarios [...] Read more.
Land system change has been identified as one of four major Earth system processes where change has passed a destabilizing threshold. A historical record of landscape change is required to understand the impacts change has had on human and natural systems, while scenarios of future landscape change are required to facilitate planning and mitigation efforts. A methodology for modeling long-term historical and future landscape change was applied in the Delaware River Basin of the United States. A parcel-based modeling framework was used to reconstruct historical landscapes back to 1680, parameterized with a variety of spatial and nonspatial historical datasets. Similarly, scenarios of future landscape change were modeled for multiple scenarios out to 2100. Results demonstrate the ability to represent historical land cover proportions and general patterns at broad spatial scales and model multiple potential future landscape trajectories. The resulting land cover collection provides consistent data from 1680 through 2100, at a 30-m spatial resolution, 10-year intervals, and high thematic resolution. The data are consistent with the spatial and thematic characteristics of widely used national-scale land cover datasets, facilitating use within existing land management and research workflows. The methodology demonstrated in the Delaware River Basin is extensible and scalable, with potential applications at national scales for the United States. Full article
(This article belongs to the Special Issue Land Change Assessment Using Remote Sensing and Geographic Information Science)
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<p>Clockwise from top-left: Delaware River Basin (DRB) study area in the conterminous US; the study area intersects the states of New York, Delaware, New Jersey, Pennsylvania, and Maryland; subset region referenced below (<a href="#sec2dot4dot2-land-10-00536" class="html-sec">Section 2.4.2</a> and <a href="#sec3dot2-land-10-00536" class="html-sec">Section 3.2</a>).</p> Full article ">Figure 2
<p>54 km × 54 km region (introduced <a href="#land-10-00536-f001" class="html-fig">Figure 1</a>; axes units are projected coordinates in meters) demonstrating (<b>a</b>) presence of the category Corn in the starting land cover; (<b>b</b>) corresponding likelihood/suitability values; (<b>c</b>) TOC curve derived from presence and suitability over the entire region; (<b>d</b>) histogram (pixel counts vs. values) for suitability values corresponding with initial presence; (<b>e</b>) histogram showing pixels selected from all available (green) for potential landscape expansion of Corn under old (pink) and new (brown) methods.</p> Full article ">Figure 3
<p>(<b>a</b>) Urban land cover logistic growth curves fit HISDAC, Census ACS, and Census population data. Urban areas for HISDAC and Census ACS series are given by the right vertical axis. The population series uses the left vertical axis. Logistic curve growth rates provided in parentheses are not dependent on y-units. (<b>b</b>) Harmonized, weighted growth curve with remote-sensing-based urban land cover estimates and historical urban estimates from digitized topographic maps.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Urban land cover logistic growth curves fit HISDAC, Census ACS, and Census population data. Urban areas for HISDAC and Census ACS series are given by the right vertical axis. The population series uses the left vertical axis. Logistic curve growth rates provided in parentheses are not dependent on y-units. (<b>b</b>) Harmonized, weighted growth curve with remote-sensing-based urban land cover estimates and historical urban estimates from digitized topographic maps.</p> Full article ">Figure 4
<p>Modeled quantities for the aggregated land cover categories Agriculture (Hay/Alfalfa, Corn, Soybeans, Wheat, Fallow/Idle, Fruits/Vegetables, Perennial Grass, and Other Crops, top), Urban (Developed Low Intensity and Developed High Intensity, middle), and Forest (Deciduous, Evergreen, and Mixed, bottom). Backcasting/forecasting (left) panels cover the entire time series—1680 to 2100. Forecasting (right) panels reduce time series to projections—2018 to 2100. The “no change” baselines represent initial (2018) quantities. Respectively, the aggregated categories Agriculture, Urban, and Forest represent 14%, 20%, and 49% of the total region in 2018.</p> Full article ">Figure 5
<p>From top-left to bottom-right: Backcasting at 1680, 1900, and 1970, starting land cover (boxed in black), and all forecast scenarios (BAU, BTU, GCAM) at the year 2100 for the same 54 km × 54 km area from <a href="#land-10-00536-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 6
<p>Measures of difference (quantity, exchange, and shift) for all modeled forecasting scenario combinations and all forecasting years.</p> Full article ">Figure 7
<p>Difference between modeled scenario combinations for the year 2100 for (<b>a</b>) all components of difference (quantity, exchange, and shift); (<b>b</b>) spatial allocation difference (exchange and shift); (<b>c</b>) spatial allocation difference by land cover category; (<b>d</b>) cumulative (2020–2100) absolute (positive and negative) prescribed change by scenario and land cover category.</p> Full article ">Figure 8
<p>Comparisons of modeled (solid orange) quantities and prescribed demand (dashed black) for all categories and years of historical backcasting. (Note: demand for “flexible” categories is represented by initial (2018) quantities.).</p> Full article ">Figure 9
<p>(<b>a</b>) Counties with majority area inside the study area; (<b>b</b>) time series for model runs both without (A) and with (B) incorporation of historical dataset plotting the absolute difference in area (hectares) between modeled and historical land-in-agriculture summed for counties with majority area inside the study region; (<b>c</b>) county-level disagreement with historical cropland; (<b>d</b>–<b>f</b>) modeled agriculture vs. historical for the three counties with the most disagreement with historical cropland data.</p> Full article ">Figure 10
<p>Urban land cover footprint modeled for Philadelphia area for the year 1900 (<b>a</b>) without incorporation of BUI and (<b>b</b>) with incorporation of BUI; (<b>c</b>) reference BUI for Philadelphia area, year 1900; (<b>d</b>) time series for model run (C), which did not use BUI in the urban module, and model run (D), which did use BUI in the urban module; (<b>e</b>) time series of BUI autocorrelation; (<b>f</b>) time series demonstrating rural population proportion change for the DRB.</p> Full article ">Figure 11
<p>Comparison of FORE-SCE and LUH2 modeled LULC from 1680–2100 in the DRB for major categories (<b>a</b>,<b>b</b>) Agriculture; (<b>c</b>,<b>d</b>) Urban; and (<b>e</b>,<b>f</b>) Forest.</p> Full article ">Figure 11 Cont.
<p>Comparison of FORE-SCE and LUH2 modeled LULC from 1680–2100 in the DRB for major categories (<b>a</b>,<b>b</b>) Agriculture; (<b>c</b>,<b>d</b>) Urban; and (<b>e</b>,<b>f</b>) Forest.</p> Full article ">
15 pages, 867 KiB  
Article
Spontaneous Cities: Lessons to Improve Planning for Housing
by Nikos Angelos Salingaros
Land 2021, 10(5), 535; https://doi.org/10.3390/land10050535 - 19 May 2021
Cited by 3 | Viewed by 3480
Abstract
The world can learn two key lessons from spontaneous settlements: (i) design so as to adapt to human biology; and (ii) design to save energy. Timeless processes of urban growth and sustainability have forced societies to conserve energy. Yet, [...] Read more.
The world can learn two key lessons from spontaneous settlements: (i) design so as to adapt to human biology; and (ii) design to save energy. Timeless processes of urban growth and sustainability have forced societies to conserve energy. Yet, nowadays, a profession focused on design ideology and short-term profit discredits many economical and effective long-term design methods. Decision-makers, politicians, and urbanists talk of energy conservation while continuing to use failed notions of industrial urbanity in place of documented solutions that work. Most damaging is the myopic academic elite’s fixation on an unsustainable industrial-modernist visual vocabulary of minimalist forms. By promoting typologies based on images dating from the 1920s, instead of using scientific analysis, the industry serves extractive global imperialism rather than satisfying the world’s population needs. We should instead learn from how self-builders adapt form, geometry, materials, surfaces, and ornament to maximize the user’s emotional experience in an otherwise extremely challenging environment. Full article
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<p>Unacceptable plan composed of monotonous low-rise buildings with no fractal qualities nor any useful urban space, following the industrial-modernist ideology.</p> Full article ">Figure 2
<p>Roughly radial plan with a good distribution of urban spaces, where building footprints adapt to the street flows instead of sacrificing the urban space to maintain some imaginary abstract plan for the buildings’ position.</p> Full article ">Figure 3
<p>Acceptable plan, despite the formal rectangular grid, includes a connected distribution of urban spaces in a sequence of different sizes.</p> Full article ">
20 pages, 505 KiB  
Article
Introducing Collaborative Governance in Decentralized Land Administration and Management in South Africa: District Land Reform Committees Viewed through a ‘System of Innovation’ Lens
by Evert Waeterloos
Land 2021, 10(5), 534; https://doi.org/10.3390/land10050534 - 18 May 2021
Cited by 3 | Viewed by 3332
Abstract
A Fit-for-Purpose (FFP) land administration system strives for a more flexible, inclusive, participatory, affordable, reliable, realistic, and scalable approach to land administration and management in developing countries. The FFP finds itself thus at the interface with the coordination and governance challenges of the [...] Read more.
A Fit-for-Purpose (FFP) land administration system strives for a more flexible, inclusive, participatory, affordable, reliable, realistic, and scalable approach to land administration and management in developing countries. The FFP finds itself thus at the interface with the coordination and governance challenges of the mainstream promotion of democratic decentralization of the past decades in general, and collaborative systems for decentralized and participatory land governance in Africa, in particular. One recent example of such collaborative systems for decentralized land governance is the introduction in South Africa between 2015 and 2019 of District Land Reform Committees (DLRCs). We analyze this official experiment in collaborative land governance from a ‘system of innovation’ (SI) perspective. An adapted SI framework is developed and applied in three DLRCs. This study points out that for the innovation of collaboration to be effective, DLRCs require a firm operational and institutional backup. This is an important lead for the general discussion on inclusion, participation, and collaboration in FFP. We not only need these innovations to be well-supported and -resourced; they also require the explicit adoption of a systemic perspective in which various technical and social dimensions are interlinked. Full article
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<p>SI Framework for Collaborative Governance.</p> Full article ">
15 pages, 32133 KiB  
Article
Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)
by Sheng Li, Yi Jiang, Shuisong Ke, Ke Nie and Chao Wu
Land 2021, 10(5), 533; https://doi.org/10.3390/land10050533 - 18 May 2021
Cited by 26 | Viewed by 7429
Abstract
The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has [...] Read more.
The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods. Full article
(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)
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<p>Overall methodological framework.</p> Full article ">Figure 2
<p>The study area: Shenzhen and the locations of the communities.</p> Full article ">Figure 3
<p>Examples of the data used to calculate influential factors for housing prices: (<b>a</b>) community location and 1 km buffer area; (<b>b</b>) land-use data in buffer area of sample; (<b>c</b>) road network and street view sampling points in buffer area; and (<b>d</b>) POI data in buffer area of sample.</p> Full article ">Figure 4
<p>Example of a street view image in a given sample location from four angles.</p> Full article ">Figure 5
<p>A list of the relative importance of influential factors on housing prices.</p> Full article ">
21 pages, 1395 KiB  
Article
Livelihood Capital and Land Transfer of Different Types of Farmers: Evidence from Panel Data in Sichuan Province, China
by Huanxin Yang, Kai Huang, Xin Deng and Dingde Xu
Land 2021, 10(5), 532; https://doi.org/10.3390/land10050532 - 17 May 2021
Cited by 68 | Viewed by 5601
Abstract
Farmers’ livelihood and land have been the focus of academic and political attention for a long time. In the process of rapid urbanization in China, as farmers change their livelihood strategies and livelihood capital allocation driven by economic interests, farmland abandonment increases, which [...] Read more.
Farmers’ livelihood and land have been the focus of academic and political attention for a long time. In the process of rapid urbanization in China, as farmers change their livelihood strategies and livelihood capital allocation driven by economic interests, farmland abandonment increases, which is not conducive to the guarantee of food security. This study aims to explore the characteristics of livelihood capital and land transfer of farmers under different livelihood strategies and the effect of livelihood capital on land transfer. Based on the data obtained from Sichuan Province in 2012, 2016 and 2019 by the China Rural Development Survey Group, this paper divides farmers into pure farmers, part-time farmers and non-farmers according to the proportion of non-agricultural income in total income, and constructed the panel binary Logit model and panel Tobit model. The analysis points to the following results: (1) pure farmers tend to shift other capitals toward natural capital, so their livelihood capital total index value decreased. The part-time farmers have different shift characteristics but their livelihood capital total index value both increased first and then decreased. Non-farmers tend to shift natural capital towards other livelihood capitals, so their livelihood capital total index value increased. (2) The higher the natural capital and human capital, the higher the probability of land transfers in. The higher the natural capital, the larger the area of land transfers in. The higher the financial capital, the higher the probability of land transfers out. The higher the financial capital and social capital, the larger the area of land transfers out. It is expected to provide suggestions for the policy of farmers’ land transfer under different livelihood capital endowments. Full article
(This article belongs to the Special Issue Land Use Transitions under Rapid Urbanization)
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<p>Analysis frame diagram of household livelihood capital and land transfer.</p> Full article ">Figure 2
<p>Changes in livelihood capital allocation of different types of farmer households. Note: (<b>a</b>) <a href="#land-10-00532-t003" class="html-table">Table 3</a>. (<b>b</b>) The maximum value of the coordinate axes in (<b>a</b>–<b>d</b>) is 0.15, the scale unit is 0.05; the maximum value of the coordinate axis in (<b>b</b>) is 0.3, the scale unit is 0.1; and the minimum value of the axes of all graphs is 0.</p> Full article ">
29 pages, 9124 KiB  
Article
Effects of Forestry Intensification and Conservation on Green Infrastructures: A Spatio-Temporal Evaluation in Sweden
by Per Angelstam and Michael Manton
Land 2021, 10(5), 531; https://doi.org/10.3390/land10050531 - 17 May 2021
Cited by 15 | Viewed by 5656
Abstract
There is a rivalry between policies on intensification of forest management to meet the demands of a growing bioeconomy, and policies on green infrastructure functionality. Evaluation of the net effects of different policy instruments on real-world outcomes is crucial. First, we present data [...] Read more.
There is a rivalry between policies on intensification of forest management to meet the demands of a growing bioeconomy, and policies on green infrastructure functionality. Evaluation of the net effects of different policy instruments on real-world outcomes is crucial. First, we present data on final felling rates in wood production landscapes and stand age distribution dynamic in two case study regions, and changes in dead wood amounts in Sweden. Second, the growth of formally protected areas was compiled and changes in functional connectivity analysed in these regions, and the development of dead wood and green tree retention in Sweden was described. The case studies were the counties Dalarna and Jämtland (77,000 km2) representing an expanding frontier of boreal forest transformation. In the wood production landscape, official final felling rates averaged 0.84%/year, extending the regional timber frontier. The amount of forest <60 years old increased from 27–34% in 1955 to 60–65% in 2017. The amounts of dead wood, a key forest naturalness indicator, declined from 1994 to 2016 in north Sweden, and increased in the south, albeit both at levels far below evidence-based biodiversity targets. Formal forest protection grew rapidly in the two counties from 1968 to 2020 but reached only 4% of productive forests. From 2000 to 2019, habitat network functionality for old Scots pine declined by 15–41%, and Norway spruce by 15–88%. There were mixed trends for dead wood and tree retention at the stand scale. The net result of the continued transformation of near-natural forest remnants and conservation efforts was negative at the regional and landscape levels, but partly positive at the stand scale. However, at all three scales, habitat amounts were far below critical thresholds for the maintenance of viable populations of species, let alone ecological integrity. Collaboration among stakeholder categories should reject opinionated narratives, and instead rely on evidence-based knowledge about green infrastructure pressures, responses, and states. Full article
(This article belongs to the Special Issue Planning Effective Conservation Landscapes for Nature and People: What Have We Learned?)
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<p>In Sweden, the boreal forest biome extends from the Limes Norrlandicus, the country’s steepest socio-ecological transition zone [<a href="#B31-land-10-00531" class="html-bibr">31</a>], and to the Scandinavian mountain range in the northwest. Access to the boreal biome’s wood resources was provided by a suite of rivers used for driving logs from upland areas to industries along the coast [<a href="#B39-land-10-00531" class="html-bibr">39</a>]. This study focuses on the counties marked 20 (Dalarna) and 26 (Jämtland), which are outlined in red.</p> Full article ">Figure 2
<p>Overview of policies and policy instruments in Sweden 1990–2020, and previous assessments of outcomes regarding the development of forest habitat networks. See text for details.</p> Full article ">Figure 3
<p>Overview of the approach to assess the net effects of pressures and responses on the states of green infrastructures for forest biodiversity conservation at stand, landscape, and regional scales.</p> Full article ">Figure 4
<p>Graph showing the annual proportion of tree cover loss in the Dalarna and Jämtland counties for the period 2001–2019. (SFA) represents the Swedish Forest Agency data on forest clear-cuts, and (Hansen) represents the forest canopy loss data created by Hansen et al. [<a href="#B58-land-10-00531" class="html-bibr">58</a>].</p> Full article ">Figure 5
<p>Proportions of productive forests harvested through final fellings during the period 2001–2019, and the proportions of the forest land owned, by three categories of forest owners (<a href="#land-10-00531-f001" class="html-fig">Figure 1</a>) in Dalarna and Jämtland Counties.</p> Full article ">Figure 6
<p>Maps showing the mean annual forest canopy loss in Dalarna and Jämtland counties of Sweden in 5 × 5 km raster cells for four time periods using two individual forest change databases, viz. following Hansen et al., [<a href="#B58-land-10-00531" class="html-bibr">58</a>] (<b>bottom</b>), and the Swedish Forest Agency (<b>top</b>).</p> Full article ">Figure 7
<p>Stand age distribution below and above the mountain forest border in Dalarna and Dalarna Counties 1955–2017 (5-year running means). Note that mountain forests make up only 1.4% in Dalarna County, but 9.1% in Jämtland County. This explains the latter 5-year mean graph’s erratic performance.</p> Full article ">Figure 8
<p>Trends in dead wood among five Swedish forest regions (see <a href="#land-10-00531-f001" class="html-fig">Figure 1</a>). Dalarna County is located in region 3 and Jämtland in region 4. The highest value on the y-axis is half of the lowest threshold value for sufficient amounts of dead wood [<a href="#B63-land-10-00531" class="html-bibr">63</a>].</p> Full article ">Figure 9
<p>Proportions of formally protected productive forest totally, both above and below the sub-alpine mountain forest border, in Dalarna and Jämtland Counties.</p> Full article ">Figure 10
<p>Assessment of the development of green infrastructure functionality in two Swedish regions Dalarna County (Angelstam and Andersson 2013 [<a href="#B43-land-10-00531" class="html-bibr">43</a>]) and Jämtland (Lindgren and Olssson 2019 [<a href="#B48-land-10-00531" class="html-bibr">48</a>]) (see map in <a href="#land-10-00531-f001" class="html-fig">Figure 1</a>).</p> Full article ">Figure 11
<p>Loss of habitat functionality for four specialised Scots pine (<b>left</b>) and Norway spruce (<b>right</b>) forest species 2000–2019.</p> Full article ">Figure 12
<p>Spatial distribution of functional habitat networks in 2019 with patches of sufficient quality and size which satisfy the requirements of the Scots pine focal species bird <span class="html-italic">Tetrao urogallus</span> and the beetle <span class="html-italic">Tragosoma depsarium</span> (<b>left</b>), and Norway spruce focal species like a complete guild of resident passerine birds and the woodpecker <span class="html-italic">Picoides tridactylus</span> (<b>right</b>).</p> Full article ">Figure 13
<p>Development over time in the amount of dead wood of different decay stages on areas subject to final fellings that remains after 5 years in southern Sweden and 7 years in northern Sweden. Ten m<sup>3</sup> ha<sup>−1</sup> is half of the lowest evidence-based threshold value for the conservation of species dependent on dead wood; see also Figure 18 and the associated text.</p> Full article ">Figure 14
<p>Trends for the amounts of four types of retention structures left in forest stands subject to final felling in Sweden. Ten m<sup>3</sup> ha<sup>−1</sup> is half of the lowest evidence-based threshold value for the conservation of species dependent on dead wood; see also Figure 18 and the associated text.</p> Full article ">Figure 15
<p>Maps of the core region of Gåsberget/Ore Skogsrike area (latitude 61.5 N and longitude 15.2E), located in the northernmost part of Rättvik municipality in Dalarna County, showing final fellings 2000–2020 according to the Swedish Forest Agency with the forest mask of the national land cover data (<b>left</b>), and the accumulated forest canopy loss 2000–2020 [<a href="#B58-land-10-00531" class="html-bibr">58</a>] with the corresponding forest mask (<b>right</b>).</p> Full article ">Figure 16
<p>Maps of Björkvattnet (the small lake in the southwest) area (latitude 64.6 N and longitude 13.58 E), which is located in the westernmost part of Strömsund municipality in Jämtland County, along the border to Norway, showing final fellings 2000–2020 according to the Swedish Forest Agency with the forest mask of the national land cover data (<b>left</b>), and the accumulated forest canopy loss 2000–2020 [<a href="#B58-land-10-00531" class="html-bibr">58</a>] with the corresponding forest mask (<b>right</b>).</p> Full article ">Figure 17
<p>Annual gains and losses of dead wood (&gt;10 cm dbh) on productive forest land in Sweden. Data from NFI 2012–2017, covering the period 2011–2016 [<a href="#B40-land-10-00531" class="html-bibr">40</a>].</p> Full article ">Figure 18
<p>Environmental conditions can range from degraded to benchmark reference conditions, and anthropogenic disturbances or transformations can range from none to severe [<a href="#B15-land-10-00531" class="html-bibr">15</a>]. Monitoring of any indicator needs to be compared with a threshold interval, analogous to tipping points and planetary boundaries, to determine its status, e.g., being unsustainable, sustainable in the short term, or even with long-term ecological integrity [<a href="#B81-land-10-00531" class="html-bibr">81</a>].</p> Full article ">
21 pages, 1609 KiB  
Article
Causal Analysis of Ecological Impairment in Land Ecosystem on a Regional Scale: Applied to a Mining City Daye, China
by Kai Guo, Yiyun Chen, Min Chen, Chaojun Wang, Zeyi Chen, Weinan Cai, Renjie Li, Weiming Feng and Ming Jiang
Land 2021, 10(5), 530; https://doi.org/10.3390/land10050530 - 17 May 2021
Cited by 5 | Viewed by 3514
Abstract
We adopted a weight of evidence approach to establish a causal analysis of an impaired land ecosystem on a regional scale; namely, Daye, a traditional mining city in China. Working processes, including problem statements, a list of candidate causes, and a conceptual model [...] Read more.
We adopted a weight of evidence approach to establish a causal analysis of an impaired land ecosystem on a regional scale; namely, Daye, a traditional mining city in China. Working processes, including problem statements, a list of candidate causes, and a conceptual model were developed to represent a causal hypothesis for describing land degradation. Causal criteria were applied to integrate multiple lines of evidence. Then, various pieces of evidence were scored to either strengthen or weaken our causal assumptions. Results showed that habitat alteration, heavy metal accumulation, organic pollutants, water eutrophication, and nutrient runoff were the probable causes of land ecosystem impairment in Daye. Meanwhile, noxious gas, toxicants, altered underground runoff, atmospheric deposition, and acid rain were identified as possible causes. The most unlikely causes were altered hydrology, altered earth surface runoff, and soil erosion. Soil salinization, soluble inorganic salts, biological species invasion, and pathogens were deferred as delayed causes due to lack of adequate information. The causal analysis approach was applied to identify the primary causes of land degradation and implement accurate protective measures in an impaired land ecosystem. Full article
(This article belongs to the Special Issue Managing and Restoring of Degraded Land in Post-mining Areas)
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<p>Map showing location of Daye City and its mining distribution.</p> Full article ">Figure 2
<p>The procedure for regional causal analysis of land degradation in Daye area.</p> Full article ">Figure 3
<p>Conceptual model of land degradation in Daye area. The nodes were assigned to three groups: risk sources (orange), causes (green), and hazardous effects (blue) [<a href="#B1-land-10-00530" class="html-bibr">1</a>,<a href="#B42-land-10-00530" class="html-bibr">42</a>].</p> Full article ">
24 pages, 29077 KiB  
Article
Initiating Research into Adapting Rural Hedging Techniques, Hedge Types, and Hedgerow Networks as Novel Urban Green Systems
by Lisa Höpfl, Defne Sunguroğlu Hensel, Michael Hensel and Ferdinand Ludwig
Land 2021, 10(5), 529; https://doi.org/10.3390/land10050529 - 15 May 2021
Cited by 8 | Viewed by 5176
Abstract
This article seeks to initiate research into traditional rural hedging techniques, hedge types, and hedgerow networks for the purpose of their potential adaptation as urban green systems (UGS). The research involves three scales: (1) the plant scale and related manipulation techniques; (2) hedgerows [...] Read more.
This article seeks to initiate research into traditional rural hedging techniques, hedge types, and hedgerow networks for the purpose of their potential adaptation as urban green systems (UGS). The research involves three scales: (1) the plant scale and related manipulation techniques; (2) hedgerows and their context-specific types, ecosystem function, and ecosystem services; and (3) hedgerow networks as continuous green systems that characterize and support specific landscapes. This research required an interdisciplinary approach. The analysis was conducted by applying different modes of research including: (a) an extensive literature review, (b) analysis and systematization of hedge types and manipulation methods, (c) field experiments, (d) design experiments, and (e) examination of real-life projects that use hedges or hedging techniques as distinct design features. The initial research indicates that traditional hedges can be adapted to vitally contribute to UGS by providing a broad range of urban ecosystem services. Furthermore, the research includes initial proposals on future applications of adapted rural hedge types and techniques. On the larger scale, anticipated difficulties regarding implementation, such as land allocation in cities and resource-intensive planting, management, and maintenance, are discussed and further research questions are outlined. Full article
(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)
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<p>Classification of hedging techniques (L. Höpfl).</p> Full article ">Figure 2
<p>Forms of coppicing and pollarding (L. Höpfl).</p> Full article ">Figure 3
<p>Timeline of shaping: from the planting stage, over the first pruning step during growth, to a densified and pruned hedge (L. Höpfl).</p> Full article ">Figure 4
<p>A huge and well-maintained domestic hedge in Höfen, along the property boundary, protecting the building from wind. (photography: Caronna, CC BY-SA 3.0. [<a href="#B20-land-10-00529" class="html-bibr">20</a>])</p> Full article ">Figure 5
<p>Timeline of laying, shown on a single tree: a chosen tree is partly cut and laid towards the ground, where it resprouts and thickens vertically. From there two steps are possible: another laying process (above) or a maturing process (below) (L. Höpfl).</p> Full article ">Figure 6
<p>Timeline of inosculation of a diagonal hedge: from planting as a diagonal pair, shoots are fixed at the crossing points to inosculate through thickening processes. Over time the diagonal hedge is either maintained to keep its height (above) or grows out vertically (below) (L. Höpfl).</p> Full article ">Figure 7
<p>Test field of the Research Group Baubotanik (TUM) in Bad Zwischenahn, Germany, where inosculation methods and connection stability is investigated by joining pairs of young trees. (photography: Ferdinand Ludwig).</p> Full article ">Figure 8
<p>Map of the selected 53 historical rural hedge types from all over the world used as the initial dataset for building a hedge database for design. “Hedge Database for selection” by Senta Badovinac Bajuk and Nouhaila Karroum, GTLA at TUM, winter semester 2019, Urban Hedges Seminar. Numbers in square brackets in the figure are codes of the hedges (see <a href="#land-10-00529-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 9
<p>Comparative study of the three traditional rural hedge types, the related planting, manipulation, management, and maintenance decisions mapped along a timeline, planned and associated biodiversity, and potentials for transfer of solutions to improve urban hedge performance; GTLA at TUM, winter term 2019, Urban Hedges Seminar, work by Arda Cosan and Carling Sioui, including work of Kianu Goedemond and Marco Alonso Hsu.</p> Full article ">Figure 10
<p>Analysis of urban hedges and urban boundary conditions found in Munich (top left column: types of land use indexed and top row: numerical combinations denoting border conditions on site) and design exercises with focus on plant selection and distribution for urban hedge interventions informed by historical case studies; GTLA at TUM, winter term 2019, Urban Hedges Seminar, works of Elementary Application of Rural Hedging into Urban Functionality by Arda Cosan and Carling Sioui.</p> Full article ">Figure 11
<p>Basic system variability to cope with diverse needs and conditions based on tree pruning techniques for beech and hornbeam and the espalier method; GTLA at TUM, winter term 2019, Urban Hedges Seminar, works of Community Green Densification by Carlos Martinez, Pablo Giobellina, and Andres López, and Fruit Production in the City with Espalier Hedges by Alice Lahourde and Simon Ochott.</p> Full article ">Figure 12
<p>Analysis of rural hedges and urban boundary conditions found in Munich and translation as input for modeling and simulating such systems using L-system. Potential timeline takes into consideration the hedge-laying angle established according to the predominant direction of sunlight (second panel from the top); GTLA at TUM, winter term 2019, Urban Hedges Seminar, work of Context Specific L-System: Colonization of Hedge Under Solar Analysis by Dafni Filippa and Fan Wan.</p> Full article ">Figure 13
<p>A tree hedge as a living façade for a holiday retreat. Design by Duncan Lewis and Edouard Francois. (photography: © Maison Edouard François)</p> Full article ">Figure 14
<p>Plane Tree Cube, Nagold, Germany. The design by ludwig.schönle is based on the hedging technique of inosculation. (photography: © Ferdinando Iannone)</p> Full article ">Figure 15
<p>The bocage landscape in France as an example of a well-connected hedgerow network. (Aerial photo from 1944, <a href="https://www.flickr.com/photos/photosnormandie/2994957914" target="_blank">https://www.flickr.com/photos/photosnormandie/2994957914</a> CC BY-SA 2.0) (accessed on 3 March 2021).</p> Full article ">
13 pages, 3751 KiB  
Article
Effects of Land-Use Intensity and Land Management Policies on Evolution of Regional Land System: A Case Study in the Hengduan Mountain Region
by Le Yin, Erfu Dai, Guopan Xie and Baolei Zhang
Land 2021, 10(5), 528; https://doi.org/10.3390/land10050528 - 15 May 2021
Cited by 13 | Viewed by 2784
Abstract
In the last few decades, land use/land cover (LULC) has changed significantly under the influence of local planning and policy implementation, and this has had a profound impact on the regional ecological environment. By taking the Hengduan Mountain region as the study area, [...] Read more.
In the last few decades, land use/land cover (LULC) has changed significantly under the influence of local planning and policy implementation, and this has had a profound impact on the regional ecological environment. By taking the Hengduan Mountain region as the study area, this study considered the demands of various commodities and services and applied the CLUMondo model to predict the trajectory of change in the land system for the years 2010–2030. The results indicate that the forest system expands significantly in this time, while the grassland and cropland systems are projected to develop intensively under the three scenarios. The high demand for livestock products is the main cause of the intensification of the grassland system under the TREND scenario, the demand for forests leads to the expansion of the forest land system under the FOREST scenario, and the significant intensification of the cropland system under the CONSERVATION scenario is closely related to an increase in the area of ecological land. The results of this study can provide a scientific reference for the optimal management of land systems in other mountainous areas. Full article
(This article belongs to the Section Land Systems and Global Change)
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<p>Location and land-use (2015) map of the Hengduan Mountain region.</p> Full article ">Figure 2
<p>Research framework of this study.</p> Full article ">Figure 3
<p>Approaches to land system delineation.</p> Full article ">Figure 4
<p>Results of land system delineation in (<b>a</b>) 2000, (<b>b</b>) 2010.</p> Full article ">Figure 5
<p>Spatial distribution of the land system in 2010 and different scenarios.</p> Full article ">Figure 6
<p>Relative change rates of land system under different scenarios compared with their state in 2010.</p> Full article ">Figure 7
<p>Spatial distribution of changes to the land system under different scenarios.</p> Full article ">Figure 8
<p>Validation of results of cropland classification. The histogram represents the ratios of different types in the cropland system; the green dots show total crop production at the county level, and the black dotted line shows the results of linear fitting.</p> Full article ">Figure 9
<p>Validation of the results of classification of the forest land and grassland systems. The red point represents the mean NDVI, and the five horizontal lines from top to bottom represent the upper limit, upper quartile, middle value, lower quartile, and lower limit of the NDVI.</p> Full article ">
16 pages, 2757 KiB  
Article
Can Famine Be Averted? A Spatiotemporal Assessment of The Impact of Climate Change on Food Security in The Luvuvhu River Catchment of South Africa
by Geoffrey Mukwada, Sabelo M. Mazibuko, Mokhele Moeletsi and Guy M. Robinson
Land 2021, 10(5), 527; https://doi.org/10.3390/land10050527 - 14 May 2021
Cited by 9 | Viewed by 3921
Abstract
Climate change has proved to be a threat to food security the world over. Using temperature and precipitation data, this paper examines the differential effects climate change has on different land uses in the Luvuvhu river catchment in South Africa. The paper uses [...] Read more.
Climate change has proved to be a threat to food security the world over. Using temperature and precipitation data, this paper examines the differential effects climate change has on different land uses in the Luvuvhu river catchment in South Africa. The paper uses the Normalised Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI), which were calculated from Landsat images, and the Standardised Precipitation Index (SPI) for a sample of years between 1980 and 2016 to assess how drought and flood frequency have affected the agricultural environment. The results indicate that the lowest SPI values were recorded in 1996/1997, 2001/2002 and 2014/2015, suggesting the occurrence of drought during these years, while the highest SPI values were recorded in 1997/1998, 2002/2003 and 2004/2005. The relationship between three-month SPI (SPI_3) and VCI was strongest in grassland, and subsistence farming areas with the correlation coefficients of 0.8166 (p = 0.0022) and ?0.6172 (p = 0.0431), respectively, indicating that rainfall variability had a high negative impact on vegetation health in those land uses with shallow-rooted plants. The findings of this study are relevant to disaster management planning in South Africa, as well as development of farming response strategies for coping with climate hazards in the country. Full article
(This article belongs to the Special Issue Land Use and Climate Change Effects on Food Security in Africa)
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<p>Location of the Study Area: Luvuvhu Catchment Area of South Africa.</p> Full article ">Figure 2
<p>Mean SPI_3 in in the LRCA for the 1980–2016 period.</p> Full article ">Figure 3
<p>Spatial and temporal pattern of SPI_3, NDVI and VCI during the dry seasons at different croplands.</p> Full article ">Figure 3 Cont.
<p>Spatial and temporal pattern of SPI_3, NDVI and VCI during the dry seasons at different croplands.</p> Full article ">Figure 4
<p>Spatiotemporal variation of NDVI in different croplands for the period 1991 to 2015 in LRCA.</p> Full article ">Figure 5
<p>Relationship between SPI and VCI on different land usages.</p> Full article ">Figure 5 Cont.
<p>Relationship between SPI and VCI on different land usages.</p> Full article ">
21 pages, 4939 KiB  
Review
The Application of Genetic Algorithm in Land Use Optimization Research: A Review
by Xiaoe Ding, Minrui Zheng and Xinqi Zheng
Land 2021, 10(5), 526; https://doi.org/10.3390/land10050526 - 14 May 2021
Cited by 31 | Viewed by 5393
Abstract
Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has [...] Read more.
Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources. Full article
(This article belongs to the Special Issue Land Use Optimisation)
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<p>Distribution of the literature in the timeline.</p> Full article ">Figure 2
<p>The framework of this research.</p> Full article ">Figure 3
<p>The knowledge map of cooperative countries.</p> Full article ">Figure 4
<p>Authors’ geographical distribution by kernel density estimation.</p> Full article ">Figure 5
<p>Distribution pattern of spatiotemporal hotspot trends.</p> Full article ">Figure 6
<p>Top 15 research institutions in the last 5 years (2015–2020).</p> Full article ">Figure 7
<p>The time-zone map of keywords from 1995 to 2020. The time corresponding to each keyword is the time when the keyword appears in document for the first time; the size of the node represents the frequency of occurrence of the keyword, and the larger the node, the higher the frequency of occurrence of the keyword.</p> Full article ">
29 pages, 4724 KiB  
Article
Quantifying the Relative Contribution of Climate Change and Anthropogenic Activities on Runoff Variations in the Central Part of Tajikistan in Central Asia
by Nekruz Gulahmadov, Yaning Chen, Aminjon Gulakhmadov, Moldir Rakhimova and Manuchekhr Gulakhmadov
Land 2021, 10(5), 525; https://doi.org/10.3390/land10050525 - 14 May 2021
Cited by 10 | Viewed by 3522
Abstract
Quantifying the relative contribution of climate change and anthropogenic activities to runoff alterations are essential for the sustainable management of water resources in Central Asian countries. In the Kofarnihon River Basin (KRB) in Central Asia, both changing climate conditions and anthropogenic activities are [...] Read more.
Quantifying the relative contribution of climate change and anthropogenic activities to runoff alterations are essential for the sustainable management of water resources in Central Asian countries. In the Kofarnihon River Basin (KRB) in Central Asia, both changing climate conditions and anthropogenic activities are known to have caused changes to the hydrological cycle. Therefore, quantifying the net influence of anthropogenic contribution to the runoff changes is a challenge. This study applied the original and modified Mann–Kendall trend test, including the Sen’s slope test, Pettitt’s test, double cumulative curve, and elasticity methods. These methods were applied to determine the historical trends, magnitude changes and change points of the temperature, precipitation, potential evapotranspiration, and runoff from 1950 to 2016. In addition, the contributions of climate change and anthropogenic activities to runoff changes in the KRB were evaluated. The trend analysis showed a significant increasing trend in annual temperature and potential evapotranspiration, while the annual precipitation trend showed an insignificant decreasing trend during the 1950–2016 time period. The change point in runoff occurred in 1986 in the upstream region and 1991 in the downstream region. Further, the time series (1950–2016) is separated into the prior impacted period (1950–1986 and 1950–1991) and post impacted period (1987–2016 and 1992–2016) for the upstream and downstream regions, respectively. During the post impacted period, climate change and anthropogenic activities contributed to 87.96% and 12.04% in the upstream region and 7.53% and 92.47% in the downstream region of the KRB. The results showed that in runoff changes, the anthropogenic activities played a dominant role in the downstream (97.78%) and the climate change impacts played a dominant factor in the upstream region (87.96%). In the land-use type changes, the dominant role was played by construction land, which showed that the area from 248.63 km2 in 1990 increased to 685.45 km2 (175.69%) in 2015. These findings suggest that it is essential to adopt effective steps for the sustainable development of the ecological, hydrological, and social order in the KRB in Central Asia. Full article
(This article belongs to the Special Issue Advances in Hydrologic and Water Quality Modeling of Water Systems)
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<p>(<b>a</b>) Location of the study area; (<b>b</b>) digital elevation model, hydrological, and meteorological stations.</p> Full article ">Figure 2
<p>The mean monthly values of the runoff and mean monthly precipitation in the Kofarnihon River Basin, Central Asia.</p> Full article ">Figure 3
<p>Monthly potential evapotranspiration (PET) in the upstream region (<b>figures on the left side</b> (<b>a</b>–<b>e</b>)) and monthly PET in the downstream region (<b>figures on the right side</b> (<b>f</b>–<b>j</b>)) for 2000 at the Hushyori (in the upstream region) and Isambay (in the downstream region) climate stations in the Kofarnihon River Basin, Central Asia.</p> Full article ">Figure 4
<p>The dynamics of the main crop area in the last decade in Tajikistan, Central Asia.</p> Full article ">Figure 5
<p>The proposed framework. T, PET, P, and R, respectively, are temperature, potential evapotranspiration, precipitation, and runoff.</p> Full article ">Figure 6
<p>(<b>a</b>) The trend of annual average temperature; (<b>b</b>) the trend of annual precipitation; and (<b>c</b>) the trend of annual potential evapotranspiration during the 1950–2016 time period.</p> Full article ">Figure 7
<p>The trend of annual runoff during the period of 1950–2016 in the (<b>a</b>) upstream and (<b>b</b>) downstream of the Kofarnihon River Basin, Central Asia.</p> Full article ">Figure 8
<p>The double cumulative curve (DCC) of the annual runoff and precipitation in the (<b>a</b>) upstream region and (<b>b</b>) downstream region of the Kofarnihon River Basin in Central Asia.</p> Full article ">Figure 9
<p>(<b>a</b>) Mean monthly runoff for the prior impacted period (1950–2003) and post impacted period (2004–2016) and (<b>b</b>) changes in flow duration curve between the prior impacted period and post impacted period in the upstream of the Kofarnihon River Basin.</p> Full article ">Figure 10
<p>Land use category of the year 1990, 2000, 2010, and 2015 in Kofarnihon River Basin, Central Asia.</p> Full article ">Figure A1
<p>Population growth rates per annum by the intercensal period 1996–2020, Tajikistan, Central Asia.</p> Full article ">
21 pages, 8385 KiB  
Article
Emergence of Centralized (Collective) and Decentralized (Individual) Environmentally Friendly Solutions during the Regeneration of a Residential Building in a Post-Socialist City
by Josef Navrátil, Petr Klusáček, Stanislav Martinát and Petr Dvořák
Land 2021, 10(5), 524; https://doi.org/10.3390/land10050524 - 13 May 2021
Cited by 2 | Viewed by 3457
Abstract
Our paper deals with a micro-study of one residential building in the city center of Brno (Czech Republic) where we strived to identify and better understand the main factors behind the successful implementation of environmentally friendly solutions during the regeneration process. We followed [...] Read more.
Our paper deals with a micro-study of one residential building in the city center of Brno (Czech Republic) where we strived to identify and better understand the main factors behind the successful implementation of environmentally friendly solutions during the regeneration process. We followed the unique, complicated, and often conflictual story of the regeneration (conducted during the years 2010–2020) of the residential building, which was originally built in the 1930s. In total, 18 solutions were discussed—all four solutions on the state level of centralization were realized, only two of six solutions on the building level of centralization were materialized, and six of eight decentralized solutions were realized during the regeneration process. In the field of energy savings requiring high investments, a significant dominance of centralized solutions (on the state level) was identified. Centralized solutions on the building level such as heat pumps or solar panels were not realized. In the area of waste management and care for community greenery (that did not require large investments), we see as the most beneficial the promotion of decentralized solutions in the form of community-funded communal composting or the planting of new greenery. The formation of various regeneration options, which is discussed in detail, appeared as an integral instrument for dealing with conflicts among residents during the planning phase. Full article
(This article belongs to the Special Issue Quality of Urban Space versus Quality of Urban Life)
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<p>Research conceptualization.</p> Full article ">Figure 2
<p>Location of the studied residential building in the urban space of Brno. Author’s elaboration.</p> Full article ">Figure 3
<p>A eastern view (<b>A</b>) and an western view (<b>B</b>) of the neglected and underfinanced residential building before its reconstruction in 2010. Source: Vojtěch Šíp.</p> Full article ">Figure 4
<p>A eastern view (<b>A</b>) and an western view (<b>B</b>) of the modernized and regenerated residential building at the end of the studied period in June 2020. Source: Petr Klusáček (<b>A</b>) and Vojtěch Šíp (<b>B</b>).</p> Full article ">Figure 5
<p>Subsidized urban plastic building composters (<b>A</b>) were replaced due to the reproduction of rats by a two-chamber sheet metal insulated composter used by a minority of owners (<b>B</b>). Author: Petr Klusáček.</p> Full article ">Figure 6
<p>Greenery formed by ivy and willows combined with a place for a garbage dump. Author: Petr Klusáček.</p> Full article ">Figure 7
<p>The fig tree and roses east of the building as part of the adaptation to global climate change. Author: Petr Klusáček.</p> Full article ">Figure 8
<p>Combination of leisure activities (swing for children) and composting and garbage cans, which are separated by fast-growing greenery (ivy and willows). Author: Petr Klusáček.</p> Full article ">Figure 9
<p>Overview of centralized and decentralized solutions discussed and factors affecting implementation in the studied area during the 2010–2020 period.</p> Full article ">Figure A1
<p>Timeline of regeneration activities.</p> Full article ">
14 pages, 2632 KiB  
Article
Study on the Spatial Classification of Construction Land Types in Chinese Cities: A Case Study in Zhejiang Province
by Lin Dong, Jiazi Li, Yingjun Xu, Youtian Yang, Xuemin Li and Hua Zhang
Land 2021, 10(5), 523; https://doi.org/10.3390/land10050523 - 13 May 2021
Cited by 10 | Viewed by 2975
Abstract
Identifying the land-use type and spatial distribution of urban construction land is the basis of studying the degree of exposure and the economic value of disaster-affected bodies, which are of great significance for disaster risk predictions, emergency disaster reductions, and asset allocations. Based [...] Read more.
Identifying the land-use type and spatial distribution of urban construction land is the basis of studying the degree of exposure and the economic value of disaster-affected bodies, which are of great significance for disaster risk predictions, emergency disaster reductions, and asset allocations. Based on point of interest (POI) data, this study adopts POI spatialization and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to accomplish the spatial classification of construction land. Zhejiang province is selected as a study area, and its construction land is divided into 11 land types using an accurate spatial classification method based on measuring the area of ground items. In the research, the POI dataset, which includes information, such as spatial locations and usage types, was constructed by big data cleaning and visual interpretation and approximately 620,000 pieces in total. The overall accuracy of the confusion matrix is 76.86%, which is greatly improved compared with that constructed with EULUC data (61.2%). In addition, compared with the official statistical data of 11 cities in Zhejiang Province, the differences between the calculated spatial proportions and statistics are not substantial. Meanwhile, the spatial characteristics of the studied land-use types are consistent with the urban planning data but with higher accuracy. The research shows that the construction land in Zhejiang Province has a high degree of land intensity, concentrated assets, and high economic exposure. The approach proposed in this study can provide a reference for city management including urbanization process, risk assessment, emergency management and asset allocation. Full article
(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)
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<p>Methodological procedures of the research.</p> Full article ">Figure 2
<p>The classification process of the construction land use type based on road network blocks.</p> Full article ">Figure 3
<p>Comparison of classification results and remote sensing images of typical regions. (<b>a</b>) Comparison map of sample 1 in Jianggan District, Hangzhou City; (<b>b</b>) Comparison map of sample 2 in Jianggan District, Hangzhou.</p> Full article ">Figure 4
<p>Classification results of 11 cities in Zhejiang Province (covering nonconstruction land).</p> Full article ">Figure A1
<p>Sampling area distribution of industrial land after logarithm.</p> Full article ">Figure A2
<p>Box plots.</p> Full article ">
17 pages, 3452 KiB  
Article
Driving Factor Analysis of Ecosystem Service Balance for Watershed Management in the Lancang River Valley, Southwest China
by Shiliang Liu, Yongxiu Sun, Xue Wu, Weiqiang Li, Yixuan Liu and Lam-Son Phan Tran
Land 2021, 10(5), 522; https://doi.org/10.3390/land10050522 - 13 May 2021
Cited by 8 | Viewed by 2794
Abstract
Revealing the spatio-temporal change of the supply, demand and balance of ecosystem services (ESs) associated with human activities and land-use changes is of great significance for watershed ecosystem management. Taking the Lancang river valley as a case, we explicitly studied the ES spatial [...] Read more.
Revealing the spatio-temporal change of the supply, demand and balance of ecosystem services (ESs) associated with human activities and land-use changes is of great significance for watershed ecosystem management. Taking the Lancang river valley as a case, we explicitly studied the ES spatial characteristics, using the land use/land cover (LULC) matrix model, Optimized Hot Spot Analysis and landscape pattern analysis. Furthermore, we screened out the dominant explanatory variables that had significant influence on the ES supply, demand and balance by means of the Geographical Weighted Regression (GWR) method at pixel scale. The results showed that the ES demand intensity varied little throughout the watershed, while the downstream ES supply capacity and balance values were greater than upstream ones. Meanwhile, the hotspots of ES supply and demand were mainly distributed in the south part with coldspots in the north part. Human activity factors integrating landscape pattern variables were verified to have a negative impact on the ES balance in general. Among them, the Largest Patch Index (LPI) had a negative influence on the majority of pixels, while the Gross Domestic Product (GDP), cultivated land ratio and Area Weighted Average Patch Fractal Dimension (AWAPFD) had positive effects on a few pixels. This study will provide scientific support for regional ecosystem service trade-off and regulation at multiple scales. Full article
(This article belongs to the Section Landscape Ecology)
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<p>Location and land use type distribution of study area.</p> Full article ">Figure 2
<p>Assessment matrix of ES supply and demand for different land use types.</p> Full article ">Figure 3
<p>Research framework and corresponding methods.</p> Full article ">Figure 4
<p>Spatial distributions of ESs in the Lancang river valley.</p> Full article ">Figure 5
<p>Local spatial patterns of ES supply and demand.</p> Full article ">Figure 6
<p>Spatial differentiation of dominant explanatory variables.</p> Full article ">
21 pages, 2781 KiB  
Article
Relative Contribution of the Xiaolangdi Dam to Runoff Changes in the Lower Yellow River
by Qinghe Zhao, Shengyan Ding, Xiaoyu Ji, Zhendong Hong, Mengwen Lu and Peng Wang
Land 2021, 10(5), 521; https://doi.org/10.3390/land10050521 - 13 May 2021
Cited by 15 | Viewed by 2819
Abstract
Human activities are increasingly recognized as having a critical influence on hydrological processes under the warming of the climate, particularly for dam-regulated rivers. To ensure the sustainable management of water resources, it is important to evaluate how dam construction may affect surface runoff. [...] Read more.
Human activities are increasingly recognized as having a critical influence on hydrological processes under the warming of the climate, particularly for dam-regulated rivers. To ensure the sustainable management of water resources, it is important to evaluate how dam construction may affect surface runoff. In this study, using Mann–Kendall tests, the double mass curve method, and the Budyko-based elasticity method, the effects of climate change and human activities on annual and seasonal runoff were quantified for the Yellow River basin from 1961–2018; additionally, effects on runoff were assessed after the construction of the Xiaolangdi Dam (XLD, started operation in 2001) on the Yellow River. Both annual and seasonal runoff decreased over time (p < 0.01), due to the combined effects of climate change and human activities. Abrupt changes in annual, flood season, and non-flood season runoff occurred in 1986, 1989, and 1986, respectively. However, no abrupt changes were seen after the construction of the XLD. Human activities accounted for much of the reduction in runoff, approximately 75–72% annually, 81–86% for the flood season, and 86–90% for the non-flood season. Climate change approximately accounted for the remainder: 18–25% (annually), 14–19% (flood season), and 10–14% (non-flood season). The XLD construction mitigated runoff increases induced by heightened precipitation and reduced potential evapotranspiration during the post-dam period; the XLD accounted for approximately 52% of the runoff reduction both annually and in the non-flood season, and accounted for approximately ?32% of the runoff increase in the flood season. In conclusion, this study provides a basic understanding of how dam construction contributes to runoff changes in the context of climate change; this information will be beneficial for the sustainable management of water resources in regulated rivers. Full article
(This article belongs to the Special Issue The Role of Water Management: Feedback between Water and Land Degradation)
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<p>Location of the Xiaolangdi Dam, the Huayuankou hydrological station, and select meteorological stations in the Yellow River basin.</p> Full article ">Figure 2
<p>Variation in precipitation annually (<b>a</b>), in the flood season (<b>b</b>), and in the non-flood season (<b>c</b>) from 1961–2018 in the Yellow River basin.</p> Full article ">Figure 3
<p>Variation in air temperature annually (<b>a</b>), in the flood season (<b>b</b>), and in the non-flood season (<b>c</b>) from 1961–2018 in the Yellow River basin.</p> Full article ">Figure 4
<p>Variation in runoff annually (<b>a</b>), in the flood season (<b>b</b>), and in the non-flood season (<b>c</b>) from 1961–2018 at the Huayuankou hydrological station.</p> Full article ">Figure 5
<p>Abrupt changes in runoff annually (<b>a</b>,<b>d</b>), during the flood season (<b>b</b>,<b>e</b>), and during the non-flood season (<b>c</b>,<b>f</b>) from 1961–2018 at the Huayuankou hydrological station; change point assessments were made using Mann-Kendall tests and the double mass curve method.</p> Full article ">Figure 6
<p>Double mass curves for cumulative precipitation-runoff measured annually (<b>a</b>), in the flood season (<b>b</b>), and in the non-flood season (<b>c</b>) in the Yellow River basin from 1961–2018.</p> Full article ">
21 pages, 32967 KiB  
Article
Radiation Effect of Urban Agglomeration’s Transportation Network: Evidence from Chengdu–Chongqing Urban Agglomeration, China
by Zhangfeng Yao, Kunhui Ye, Liang Xiao and Xiaowei Wang
Land 2021, 10(5), 520; https://doi.org/10.3390/land10050520 - 13 May 2021
Cited by 18 | Viewed by 3563
Abstract
Recent years have seen the global proliferation and integration of transportation systems in urban agglomeration (UA), suggesting that transportation networks have become more prominent in the sustainable development of UA. Core cities play a radiating and driving role in affecting their adjacent cities [...] Read more.
Recent years have seen the global proliferation and integration of transportation systems in urban agglomeration (UA), suggesting that transportation networks have become more prominent in the sustainable development of UA. Core cities play a radiating and driving role in affecting their adjacent cities to formulate transportation networks. Such a phenomenon is called the radiation effect of transportation networks and can be imaged using a field strength model as proposed in the study. The field strength model was verified using the Chengdu–Chongqing urban agglomeration (CCUA) as a case. Case data concerning transportation routes and traffic volume were collected for the past 20 years. The data analyses results indicate a relatively stable pattern of transportation networks in the UA. UA cities’ radiation effects follow the same compactness trend. The core cities’ radiation spheres go beyond their territories, and they can envelop the surrounding cities, highlighting the core cities’ dominance in the entire transportation network. Moreover, two development stages of UA transportation—focus and spillover—are also identified. This study contributes to the literature by providing an innovative quantitative method to detect the interaction between a city’s transportation system and peripheral cities or regions. The radiation effect of cities’ transportation systems should be considered in the UA transportation development plan, so as to meet the needs of spatial structure planning and coordinated development of the UA. Full article
(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)
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<p>A cost surface.</p> Full article ">Figure 2
<p>Transportation Networks of the CCUA. Source: <sup>a</sup> <span class="html-italic">Atlas of Traffic Mileage China</span> (2000). Beijing, CHN: People’s Communications Publishing House. <sup>b</sup> <span class="html-italic">Transport Atlas of China</span> (2010). Shaanxi, CHN: Xi‘an Map Publishing House. <sup>c</sup> <span class="html-italic">Transport Atlas of China</span> (2000). Beijing, CHN: Sinomap press.</p> Full article ">Figure 2 Cont.
<p>Transportation Networks of the CCUA. Source: <sup>a</sup> <span class="html-italic">Atlas of Traffic Mileage China</span> (2000). Beijing, CHN: People’s Communications Publishing House. <sup>b</sup> <span class="html-italic">Transport Atlas of China</span> (2010). Shaanxi, CHN: Xi‘an Map Publishing House. <sup>c</sup> <span class="html-italic">Transport Atlas of China</span> (2000). Beijing, CHN: Sinomap press.</p> Full article ">Figure 3
<p>Indexes for CCUA cities’ transportation field source mass.</p> Full article ">Figure 4
<p>CCUA’s traffic isochronous rings.</p> Full article ">Figure 4 Cont.
<p>CCUA’s traffic isochronous rings.</p> Full article ">Figure 5
<p>Overall field strength map of the CCUA.</p> Full article ">Figure 5 Cont.
<p>Overall field strength map of the CCUA.</p> Full article ">Figure 6
<p>Radiation sphere of the CCUA.</p> Full article ">Figure 6 Cont.
<p>Radiation sphere of the CCUA.</p> Full article ">Figure 7
<p>Deviation range indexes of radiation sphere in the CCUA.</p> Full article ">Figure 8
<p>Evolution of deviation in the CCUA. Note: the locations of cities are plotted on the <span class="html-italic">x</span>, <span class="html-italic">y</span> axis. The height of a stick gives the deviation range index in the z dimension. Deviation range indexes are projected onto the <span class="html-italic">x, z</span> axis and the <span class="html-italic">y, z</span> axis as scatter plots. Polynomials are fit through the scatter plots on the projected axis. Polynomial curves in the <span class="html-italic">x, z</span> axis, and <span class="html-italic">y, z</span> axis show the deviation range in the east-west direction and north-south direction, respectively.</p> Full article ">
22 pages, 4903 KiB  
Article
Stream Temperature and Environment Relationships in a Semiarid Riparian Corridor
by Nicole Durfee, Carlos G. Ochoa and Gerrad Jones
Land 2021, 10(5), 519; https://doi.org/10.3390/land10050519 - 13 May 2021
Cited by 4 | Viewed by 4240
Abstract
This study examined the relationship between stream temperature and environmental variables in a semiarid riparian corridor in northcentral Oregon, USA. The relationships between riparian vegetation cover, subsurface flow temperature, and stream temperature were characterized along an 800 m reach. Multiple stream temperature sensors [...] Read more.
This study examined the relationship between stream temperature and environmental variables in a semiarid riparian corridor in northcentral Oregon, USA. The relationships between riparian vegetation cover, subsurface flow temperature, and stream temperature were characterized along an 800 m reach. Multiple stream temperature sensors were located along the reach, in open and closed canopy areas, with riparian vegetation cover ranging from 4% to 95%. A support vector regression (SVR) model was developed to assess the relationship between environmental characteristics and stream temperature at the larger valley scale. At the reach scale, results show that air temperature was highly correlated with stream temperature (Pearson’s r = 0.97), and no significant (p < 0.05) differences in stream temperature levels were found among sensor locations, irrespective of percent vegetation cover. Channel subsurface temperature levels from an intermittent flow tributary were generally cooler than those in the perennial stream in the summer and warmer during winter months, indicating that the tributary may have a localized moderating effect on stream temperature. At the valley scale, results from the SVR model showed that air temperature, followed by streamflow, was the strongest variable influencing stream temperature. Also, riparian area land cover showed little effect on stream temperature along the entire riparian corridor. This research indicates that air temperature, subsurface flow, and streamflow are important variables affecting the stream temperature variability observed in the study area. Full article
(This article belongs to the Special Issue Feature Papers for Land–Climate Interactions Section)
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<p>Schematic illustrating the location of instrumentation installed at the 800 m reach along 15-MC and an intermittent tributary.</p> Full article ">Figure 2
<p>Stream temperature monitoring locations along the Fifteenmile Creek longitudinal profile in north-central Oregon, USA. Elevation is in meters above sea level (mASL).</p> Full article ">Figure 3
<p>Map of the 15-MC study area showing streamflow gauging stations and stream and air temperature monitoring locations. The 15-MC HUC-10 watershed is outlined in red. Locations of the riparian stream study sensors are labeled in order of decreasing elevation: Ramsey Creek valley site (A), the 15-MC upstream site (B), the 15-MC valley site (C), and the 15-MC downstream site (D). Red circles indicate locations of stream temperature sensors used courtesy of the Oregon Department of Fish and Wildlife (ODFW): 15-MC upstream (1), Ramsey Creek (2), and 15-MC valley (3).</p> Full article ">Figure 4
<p>Flowchart showing the SVR process used in this study.</p> Full article ">Figure 5
<p>Box diagram showing the 17-sensor daily averaged stream temperature by month from October 2015 to September 2016. Each box shows the mean (red line) and median (black line). The upper and lower ends of the boxes represent the 25th and 75th percentiles. Lower and upper error bars represent the 10th and 90th percentiles.</p> Full article ">Figure 6
<p>Shallow groundwater temperature fluctuations in two monitoring wells at the 15-MC valley site from 1 October 2015 to 30 September 2016. SW-1 is a shallow groundwater well located along 15-MC before the confluence with the intermittent tributary. TW-1 is a shallow groundwater well located along the tributary approximately 20 m before the confluence.</p> Full article ">Figure 7
<p>Daily averaged stream and riparian air temperature for all sensors in the 800 m reach of 15-MC.</p> Full article ">Figure 8
<p>Seven-day running average (7DA) stream temperature at the four sites from October 2015 to September 2016.</p> Full article ">Figure 9
<p>Streamflow level variability at the four gauging stations located within the 15-MC watershed from January 2015 to October 2016.</p> Full article ">Figure 10
<p>Scaled importance of each environmental variable. Values can range from −1 to 1, with positive values indicating that an increase in the parameter (e.g., increased air temperature) results in an increase in stream temperature and vice versa. The closer the value is to zero, the lower the importance. Scenarios shown are (<b>a</b>) 7DADM, (<b>b</b>) _Q+A, (<b>c</b>) 7DADM_Oct–May, (<b>d</b>) 7DADM_Jun–Sept, (<b>e</b>) 7DA, (<b>f</b>) 7DA_Q+A, (<b>g</b>) 7DA_Oct–May, (<b>h</b>) 7DA_Jun–Sept, (<b>i</b>) max, (<b>j</b>) max_Q+A, (<b>k</b>) max_Oct–May, (<b>l</b>) max_Jun–Sept, (<b>m</b>) mean, (<b>n</b>) mean_Q+A, (<b>o</b>) mean_Oct–May, and (<b>p</b>) mean_Jun–Sept.</p> Full article ">Figure 11
<p>Comparison of modeled and observed temperatures (°C) for each scenario. The observed temperatures are plotted on the x-axis and the modeled temperatures are plotted on the y-axis. Scenarios shown are (<b>a</b>) 7DADM, (<b>b</b>) _Q+A, (<b>c</b>) 7DADM_Oct–May, (<b>d</b>) 7DADM_Jun–Sept, (<b>e</b>) 7DA, (<b>f</b>) 7DA_Q+A, (<b>g</b>) 7DA_Oct–May, (<b>h</b>) 7DA_Jun–Sept, (<b>i</b>) max, (<b>j</b>) max_Q+A, (<b>k</b>) max_Oct–May, (<b>l</b>) max_Jun–Sept, (<b>m</b>) mean, (<b>n</b>) mean_Q+A, (<b>o</b>) mean_Oct–May, and (<b>p</b>) mean_Jun–Sept.</p> Full article ">Figure 12
<p>Residuals for selected sites for 7DADM_Jun–Sep (<b>top</b>) and 7DADM (<b>bottom</b>). Sites are listed in order from most upstream to most downstream. Sensor location can be found in <a href="#land-10-00519-f003" class="html-fig">Figure 3</a>.</p> Full article ">
21 pages, 696 KiB  
Article
How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis
by Hongjie Bao, Ling Shan, Yufei Wang, Yuehua Jiang, Cheonjae Lee and Xufeng Cui
Land 2021, 10(5), 518; https://doi.org/10.3390/land10050518 - 13 May 2021
Cited by 15 | Viewed by 3491
Abstract
Real estate investment has been an important driving force in China’s economic growth in recent years, and the relationship between real estate investment and PM2.5 concentrations has been attracting widespread attention. Based on spatial econometric modelling, this paper explores the relationships between [...] Read more.
Real estate investment has been an important driving force in China’s economic growth in recent years, and the relationship between real estate investment and PM2.5 concentrations has been attracting widespread attention. Based on spatial econometric modelling, this paper explores the relationships between real estate investment and PM2.5 concentrations using multi-source panel data from 30 provinces in China between 1987 and 2017. The results demonstrate that compared with static spatial panel modelling, using a dynamic spatial Durbin lag model (DSDLM) more accurately reflects the influences of real estate investment on PM2.5 concentrations in China, and that PM2.5 concentrations show significant superposition effects and spillover effects. Moreover, there is an inverted U-shaped relationship between real estate investment and PM2.5 concentrations in the Eastern and Central Regions of China. At the national level, the impacts of real estate investment on land urbanization and PM2.5 concentrations first increased and then decreased over time. The key implications of this analysis are as follows. (1) it highlights the need for a unified PM2.5 monitoring platform among Chinese regions; (2) the quality of population urbanization rather than land urbanization should be given more attention; and (3) the speed of construction of green cities and building of green transportation systems and green town systems should be increased. Full article
(This article belongs to the Special Issue Sustainable Land Management and Land Tenure: Experiences for the Future)
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<p>The location of the study area.</p> Full article ">
20 pages, 4592 KiB  
Article
A Two-Stage Approach to the Estimation of High-Resolution Soil Organic Carbon Storage with Good Extension Capability
by Sunwei Wei, Zhengyong Zhao, Qi Yang and Xiaogang Ding
Land 2021, 10(5), 517; https://doi.org/10.3390/land10050517 - 13 May 2021
Cited by 2 | Viewed by 2270
Abstract
Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability [...] Read more.
Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m?2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy. Full article
(This article belongs to the Special Issue Digital Agriculture for Sustainable Food Systems: Implications for Land-Resource Use and Management)
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<p>DEM and field samples of study areas.</p> Full article ">Figure 2
<p>1:1,000,000 scale map of soil subtype with Chinese Soil Taxonomy.</p> Full article ">Figure 3
<p>The soil organic carbon density (SOCD) estimation results of the ANN, STM, OK, and RBF methods in Luoding (the error bar represent ± one standard error; ANN: ANN estimation method; STM: soil type method; OK: ordinary Kriging; RBF: radial basis function).</p> Full article ">Figure 4
<p>The soil organic carbon storage (SOCS) estimation results of the ANN, STM, OK, and RBF methods in Luoding. (ANN: ANN estimation method; STM: Soil type method; OK: Ordinary Kriging; RBF: Radial basis function).</p> Full article ">Figure 5
<p>SOCD spatial distribution maps of the four methods at five soil layers. ANN estimation method (<b>a</b>), soil type method (<b>b</b>), ordinary Kriging (<b>c</b>), radial basis function (<b>d</b>).</p> Full article ">Figure 5 Cont.
<p>SOCD spatial distribution maps of the four methods at five soil layers. ANN estimation method (<b>a</b>), soil type method (<b>b</b>), ordinary Kriging (<b>c</b>), radial basis function (<b>d</b>).</p> Full article ">Figure 6
<p>(<b>a</b>) Spatial distribution map of Xining at L1, which extended from the Luoding-based ANN model; (<b>b</b>) CSOM (existing coarse-resolution soil organic matter) map (Level 3: 20–30 g/kg; Level 4: 10–20 g/kg; Level 5:6–10 g/kg); (<b>c</b>) spatial distribution map of Xinxing at L1 modified by the linear models. (The linear models were built based on (<b>a</b>), (<b>b</b>), and 20% of the soil samples from Xinxing).</p> Full article ">
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