[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Issue
Volume 4, June
Previous Issue
Volume 3, December
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 

Land, Volume 4, Issue 1 (March 2015) – 12 articles , Pages 1-254

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
13774 KiB  
Article
High-Precision Land-Cover-Land-Use GIS Mapping and Land Availability and Suitability Analysis for Grass Biomass Production in the Aroostook River Valley, Maine, USA
by Chunzeng Wang, Jason Johnston, David Vail, Jared Dickinson and David Putnam
Land 2015, 4(1), 231-254; https://doi.org/10.3390/land4010231 - 20 Mar 2015
Cited by 5 | Viewed by 10342
Abstract
High-precision land-cover-land-use GIS mapping was performed in four major townships in Maine’s Aroostook River Valley, using on-screen digitization and direct interpretation of very high spatial resolution satellite multispectral imagery (15–60 cm) and high spatial resolution LiDAR data (2 m) and the field mapping [...] Read more.
High-precision land-cover-land-use GIS mapping was performed in four major townships in Maine’s Aroostook River Valley, using on-screen digitization and direct interpretation of very high spatial resolution satellite multispectral imagery (15–60 cm) and high spatial resolution LiDAR data (2 m) and the field mapping method. The project not only provides the first-ever high-precision land-use maps for northern Maine, but it also yields accurate hectarage estimates of different land-use types, in particular grassland, defined as fallow land, pasture, and hay field. This enables analysis of potential land availability and suitability for grass biomass production and other sustainable land uses. The results show that the total area of fallow land in the four towns is 7594 hectares, which accounts for 25% of total open land, and that fallow plots equal to or over four hectares in size total 4870, or 16% of open land. Union overlay analysis, using the Natural Resources Conservation Service (NRCS) soil data, indicates that only a very small percentage of grassland (4.9%) is on “poorly-drained” or “very-poorly-drained” soils, and that most grassland (85%) falls into the “farmland of state importance” or “prime farmland” categories, as determined by NRCS. It is concluded that Maine’s Aroostook River Valley has an ample base of suitable, underutilized land for producing grass biomass. Full article
Show Figures

Figure 1

Figure 1
<p>Map showing location of the study area—the four townships of Presque Isle, Caribou, Fort Fairfield, and Easton in the Aroostook River Valley of Aroostook County of northern Maine, USA.</p>
Full article ">Figure 2
<p>Examples of fallow lands. (<b>a</b>) Fallow-1; (<b>b</b>) fallow-2; (<b>c</b>) forested fallow beyond fallow-2 (dominated by balsam poplar (<span class="html-italic">Populus balsamifera</span>)) for at least 20 years. See the text for detailed explanation.</p>
Full article ">Figure 2 Cont.
<p>Examples of fallow lands. (<b>a</b>) Fallow-1; (<b>b</b>) fallow-2; (<b>c</b>) forested fallow beyond fallow-2 (dominated by balsam poplar (<span class="html-italic">Populus balsamifera</span>)) for at least 20 years. See the text for detailed explanation.</p>
Full article ">Figure 3
<p>Flowchart showing methodological steps of the GIS mapping in this study.</p>
Full article ">Figure 4
<p>Sample images of the same location and extent showing CIR data classification and interpretation of land use. (<b>a</b>) CIR imagery; (<b>b</b>) classified CIR imagery; (<b>c</b>) land-use map (overlaying on classified CIR); (<b>d</b>) visible light, true-color imagery.</p>
Full article ">Figure 4 Cont.
<p>Sample images of the same location and extent showing CIR data classification and interpretation of land use. (<b>a</b>) CIR imagery; (<b>b</b>) classified CIR imagery; (<b>c</b>) land-use map (overlaying on classified CIR); (<b>d</b>) visible light, true-color imagery.</p>
Full article ">Figure 5
<p>LiDAR-derived ground slope raster layer (<b>a</b>) clearly reveals old cultivated land characterized by smooth surface and fine linear features as a result of long-time tilling, some of which are either fallow-2 or completely forested as indicated in (<b>b</b>) and shown in <a href="#land-04-00231-f002" class="html-fig">Figure 2</a>c. The extent of both maps is identical to <a href="#land-04-00231-f004" class="html-fig">Figure 4</a>.</p>
Full article ">Figure 6
<p>Land area (hectares) and percent coverage of current land uses within the four townships of Fort Fairfield (<b>a</b>), Presque Isle (<b>b</b>), Easton (<b>c</b>), and Caribou (<b>d</b>). Percentages are based on total open land (“open land” refers to all land uses except for forests, water bodies, wetlands, wastelands, and developed lands). The last three categories are for plots that are equal to or greater than 10 U.S. acres (or 4.05 hectares).</p>
Full article ">Figure 6 Cont.
<p>Land area (hectares) and percent coverage of current land uses within the four townships of Fort Fairfield (<b>a</b>), Presque Isle (<b>b</b>), Easton (<b>c</b>), and Caribou (<b>d</b>). Percentages are based on total open land (“open land” refers to all land uses except for forests, water bodies, wetlands, wastelands, and developed lands). The last three categories are for plots that are equal to or greater than 10 U.S. acres (or 4.05 hectares).</p>
Full article ">Figure 7
<p>Land area (hectares) and percent coverage within all the four townships combined. Percentages are based on total open land (“open land” refers to all land uses except for forests, water bodies, wetlands, wastelands, and developed lands). The last three categories are for plots that are equal to or greater than 10 U.S. acres (or 4.05 hectares).</p>
Full article ">Figure 8
<p>Land-use maps of Fort Fairfield, Presque Isle, Caribou, and Easton.</p>
Full article ">Figure 8 Cont.
<p>Land-use maps of Fort Fairfield, Presque Isle, Caribou, and Easton.</p>
Full article ">Figure 8 Cont.
<p>Land-use maps of Fort Fairfield, Presque Isle, Caribou, and Easton.</p>
Full article ">Figure 8 Cont.
<p>Land-use maps of Fort Fairfield, Presque Isle, Caribou, and Easton.</p>
Full article ">
17516 KiB  
Article
Detection of Shoreline and Land Cover Changes around Rosetta Promontory, Egypt, Based on Remote Sensing Analysis
by Ali Masria, Kazuo Nadaoka, Abdelazim Negm and Moheb Iskander
Land 2015, 4(1), 216-230; https://doi.org/10.3390/land4010216 - 17 Mar 2015
Cited by 59 | Viewed by 11311
Abstract
Rosetta Promontory, Egypt has been suffering from a continuous erosion problem. The dramatic retreatment was observed during the last century. It is basically due to the construction of Aswan High Dam in 1964, which reduced the flow and sediment discharges. In this paper, [...] Read more.
Rosetta Promontory, Egypt has been suffering from a continuous erosion problem. The dramatic retreatment was observed during the last century. It is basically due to the construction of Aswan High Dam in 1964, which reduced the flow and sediment discharges. In this paper, four Landsat images (two Thematic Mapper and two Enhanced Thematic Mapper) covering the period from 1984 to 2014 were used. These Landsat images were radio-metrically and geometrically corrected, and then, multi-temporal post-classification analysis was performed to detect land cover changes, extracting shoreline positions to estimate shoreline change rates of the Nile delta coast around Rosetta Promontory. This method provides a viable means for examining long-term shoreline changes. Four categories, including seawater, developed (agriculture and urban), sabkhas (salt-flat), and undeveloped areas, were selected to evaluate their temporal changes by comparing the four selected images. Supervised classification technique was used with support vector machine algorithm to detect temporal changes. The overall accuracy assessment of this method ranged from 97% to 100%. In addition, the shoreline was extracted by applying two different techniques. The first method is based on a histogram threshold of Band 5, and the other uses the combination of histogram threshold of Band 5 and two band ratios (Band 2/Band 4 and Band 2/Band 5). For land cover change detection from 1984 to 2014, it was found that the developed area that increased by 9% although the land in the study area has been contracted by 1.6% due to coastal erosion. The shoreline retreat rate has decreased more than 70% from 1984 to 2014. Nevertheless, it still suffers from significant erosion with a maximum rate of 37 m/year. In comparison to ground survey and different remote sensing techniques, the established trend of shoreline change extracted using histogram threshold was found to be closely consistent with these studies rather than combining band ratio with histogram threshold. Full article
(This article belongs to the Special Issue A Land Use Perspective of the Safeguarding Coastal Areas)
Show Figures

Figure 1

Figure 1
<p>Study area, Rosetta Promontory at the terminal of Rosetta branch, SPOT image, 2012.</p>
Full article ">Figure 2
<p>(<b>A</b>) Landsat image (2005) before striping removal; and (<b>B</b>) after striping removal.</p>
Full article ">Figure 3
<p>(<b>A</b>) Landsat image (1990) after clipping; (<b>B</b>) distribution of ground control points at the image(1990); and (<b>C</b>) a rectified digitized road map considered as the base map to geo-reference Landsat (1990), then the Landsat image (1990) was used as the master image for the other images.</p>
Full article ">Figure 4
<p>Flow chart for extracting coastline from Landsat images using threshold histogram, and a combination of threshold histogram with band ratios.</p>
Full article ">Figure 5
<p>Landuse supervised-classification for the different Land-sat images 1984, and 2014.</p>
Full article ">Figure 6
<p>Shoreline changes in Rosetta Promontory between 1984 and 2014.</p>
Full article ">Figure 7
<p>The classified binary images for (<b>A</b>) 1984, and (<b>B</b>) 2014 for Rosetta Promontory.</p>
Full article ">Figure 8
<p>The post-classification change detection image, for (<b>A</b>) 1984–1990, (<b>B</b>) 1990–2005 and (<b>C</b>) 2005–2014 at Rosetta Promontory.</p>
Full article ">
2742 KiB  
Article
Mapping Vegetation Morphology Types in Southern Africa Savanna Using MODIS Time-Series Metrics: A Case Study of Central Kalahari, Botswana
by Niti B. Mishra, Kelley A. Crews, Jennifer A. Miller and Thoralf Meyer
Land 2015, 4(1), 197-215; https://doi.org/10.3390/land4010197 - 10 Mar 2015
Cited by 12 | Viewed by 10147
Abstract
Savanna ecosystems are geographically extensive and both ecologically and economically important; they therefore require monitoring over large spatial extents. There are, in particular, large areas within southern Africa savanna ecosystems that lack consistent geospatial data on vegetation morphological properties, which is a prerequisite [...] Read more.
Savanna ecosystems are geographically extensive and both ecologically and economically important; they therefore require monitoring over large spatial extents. There are, in particular, large areas within southern Africa savanna ecosystems that lack consistent geospatial data on vegetation morphological properties, which is a prerequisite for biodiversity conservation and sustainable management of ecological resources. Given the challenges involved in distinguishing and mapping savanna vegetation assemblages using remote sensing, the objective of this study was to develop a vegetation morphology map for the largest protected area in Africa, the central Kalahari. Six vegetation morphology classes were developed and sample training/validation pixels were selected for each class by analyzing extensive in situ data on vegetation structural and functional properties, in combination with existing ancillary data and coarse scale land cover products. The classification feature set consisted of annual and intra annual matrices derived from 14 years of satellite-derived vegetation indices images, and final classification was achieved using an ensemble tree based classifier. All vegetation morphology classes were mapped with high accuracy and the overall classification accuracy was 91.9%. Besides filling the geospatial data gap for the central Kalahari area, this vegetation morphology map is expected to serve as a critical input to ecological studies focusing on habitat use by wildlife and the efficacy of game fencing, as well as contributing to sustainable ecosystem management in the central Kalahari. Full article
(This article belongs to the Special Issue Ecosystem Function and Land Use Change)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) The location of Botswana, the extent of Kalahari sand deposits and the study area within southern Africa; (<b>b</b>) Land use designations in the central Kalahari; (<b>c</b>) Land cover map of the central Kalahari as represented by the GlobCover 2009 product.</p>
Full article ">Figure 2
<p>Different vegetation morphology types and their physiognomic characteristics considered in this study as derived from field measurements.</p>
Full article ">Figure 3
<p>Flowchart illustrating the methodology followed for mapping vegetation morphology types by combining field data with MODIS time-series derived matrices.</p>
Full article ">Figure 4
<p>NDVI temporal profiles of samples of each vegetation morphology type showing differences in NDVI magnitude and phenological properties for the year 2010–2011.</p>
Full article ">Figure 5
<p>Vegetation morphology map of the central Kalahari produced as a result of this study.</p>
Full article ">Figure 6
<p>Variable importance plot of 20 most significant variables reported as mean decrease in overall accuracy determined during the OOB error calculation in random forest classification. Variables with large decrease in accuracy are considered more important for classification.</p>
Full article ">
716 KiB  
Article
Valuation of Ecosystem Services from Wetlands Mitigation in the United States
by Naveen Adusumilli
Land 2015, 4(1), 182-196; https://doi.org/10.3390/land4010182 - 6 Mar 2015
Cited by 14 | Viewed by 8203
Abstract
Section 404 of the U.S. Clean Water Act includes most wetlands in its jurisdiction and requires wetland mitigation to compensate for permitted wetland losses. These mitigation wetlands can provide ecosystem services similar to original wetlands if properly constructed. Improvement of wetland monitoring requirements [...] Read more.
Section 404 of the U.S. Clean Water Act includes most wetlands in its jurisdiction and requires wetland mitigation to compensate for permitted wetland losses. These mitigation wetlands can provide ecosystem services similar to original wetlands if properly constructed. Improvement of wetland monitoring requirements coupled with economic assessment is critical for effective implementation of the mitigation policy. The economic assessment when left out of evaluation of mitigation policy could result in mitigation wetlands being given too little weight in policy decisions. Under the assumption that mitigation requirements reported in the Army Corps permit files represent actual wetland creation, ecosystem services value is estimated using a wetland benefit‑function transfer approach. Wetland mitigation requirements during 2010–2012 recorded in the Army Corps permit files is used for the analysis. The results indicate that cumulative ecosystem services value per acre per year is in the range of $5000 to $70,000, which translates to a nationwide annual aggregate benefit of $2.7 billion. Given the history of the ecosystem services not fully captured nor adequately quantified, the current analysis is an initial step in understanding the value of wetland mitigation. Full article
(This article belongs to the Special Issue Ecosystem Function and Land Use Change)
1602 KiB  
Article
Examining Social Adaptations in a Volatile Landscape in Northern Mongolia via the Agent-Based Model Ger Grouper
by Julia K. Clark and Stefani A. Crabtree
Land 2015, 4(1), 157-181; https://doi.org/10.3390/land4010157 - 3 Mar 2015
Cited by 19 | Viewed by 7370
Abstract
The environment of the mountain-steppe-taiga of northern Mongolia is often characterized as marginal because of the high altitude, highly variable precipitation levels, low winter temperatures, and periodic droughts coupled with severe winter storms (known as dzuds). Despite these conditions, herders have inhabited [...] Read more.
The environment of the mountain-steppe-taiga of northern Mongolia is often characterized as marginal because of the high altitude, highly variable precipitation levels, low winter temperatures, and periodic droughts coupled with severe winter storms (known as dzuds). Despite these conditions, herders have inhabited this landscape for thousands of years, and hunter-gatherer-fishers before that. One way in which the risks associated with such a challenging and variable landscape are mitigated is through social networks and inter-family cooperation. We present an agent-based simulation, Ger Grouper, to examine how households have mitigated these risks through cooperation. The Ger Grouper simulation takes into account locational decisions of households, looks at fission/fusion dynamics of households and how those relate to environmental pressures, and assesses how degrees of relatedness can influence sharing of resources during harsh winters. This model, coupled with the traditional archaeological and ethnographic methods, helps shed light on the links between early Mongolian pastoralist adaptations and the environment. While preliminary results are promising, it is hoped that further development of this model will be able to characterize changing land-use patterns as social and political networks developed. Full article
(This article belongs to the Special Issue Agent-Based Modelling and Landscape Change)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Figure showing how each individual strategy responds to environmental pressures when no other lineage is present. Each tile is as follows: Columns marked A correspond to the 100% sharing strategy. Columns marked B correspond to the 50% sharing strategy. Columns marked C correspond to the 25% sharing strategy. Columns marked D correspond to 0% sharing strategy. Row 1 is 0% winter patch variability. Row 2 is 5% winter patch variability. Row 3 is 10% winter patch variability. Row 4 is 15% winter patch variability. Row 5 is 20% winter patch variability. Row 6 is 25% winter patch variability. Row 7 is 30% winter patch variability. Thus, tile c3 is the 25% sharing strategy under 10% patch variability. Y-axis goes from 0 to 150 households, X axis goes from 0 to 500 ticks. Red-dotted line corresponds to the standard deviation from the mean, while the gray lines show each strategy. Black central line corresponds to the mean of each strategy.</p>
Full article ">Figure 2
<p>Figure showing how each individual strategy responds to environmental pressures when all other lineages are present. Each tile is as follows: Columns marked A correspond to the 100% sharing strategy. Columns marked B correspond to the 50% sharing strategy. Columns marked C correspond to the 25% sharing strategy. Columns marked D correspond to 0% sharing strategy. Row 1 is 0% winter patch variability. Row 2 is 5% winter patch variability. Row 3 is 10% winter patch variability. Row 4 is 15% winter patch variability. Row 5 is 20% winter patch variability. Row 6 is 25% winter patch variability. Row 7 is 30% winter patch variability. Thus, tile c3 is the 25% sharing strategy under 10% patch variability. Y-axis goes from 0 to 150 households, X-axis goes from 0 to 500 ticks. Red-dotted line corresponds to the standard deviation from the mean, while the gray lines show each strategy. Black central line corresponds to the mean of each strategy.</p>
Full article ">Figure 3
<p>Means of each of the strategies for 0% patch variability. Means correspond to Row 1 of <a href="#land-04-00157-f002" class="html-fig">Figure 2</a>. This figure reflects those runs when all strategies were present in the simulation.</p>
Full article ">Figure 4
<p>Means of each of the strategies for 5% patch variability. Means correspond to Row 2 of <a href="#land-04-00157-f002" class="html-fig">Figure 2</a>. This figure reflects those runs when all strategies were present in the simulation.</p>
Full article ">Figure 5
<p>Means of each of the strategies for 10% patch variability. Means correspond to Row 3 of <a href="#land-04-00157-f002" class="html-fig">Figure 2</a>. This figure reflects those runs when all strategies were present in the simulation.</p>
Full article ">Figure 6
<p>Means of each of the strategies for 15% patch variability. Means correspond to Row 4 of <a href="#land-04-00157-f002" class="html-fig">Figure 2</a>. This figure reflects those runs when all strategies were present in the simulation.</p>
Full article ">Figure 7
<p>Means of each of the strategies for 20% patch variability. Means correspond to Row 5 of <a href="#land-04-00157-f002" class="html-fig">Figure 2</a>. This figure reflects those runs when all strategies were present in the simulation.</p>
Full article ">Figure 8
<p>Means of each of the strategies for 25% patch variability. Means correspond to Row 6 of <a href="#land-04-00157-f002" class="html-fig">Figure 2</a>. This figure reflects those runs when all strategies were present in the simulation.</p>
Full article ">Figure 9
<p>Means of each of the strategies for 30% patch variability. Means correspond to Row 7 of <a href="#land-04-00157-f002" class="html-fig">Figure 2</a>. This figure reflects those runs when all strategies were present in the simulation.</p>
Full article ">
2808 KiB  
Review
Land Use and Wildfire: A Review of Local Interactions and Teleconnections
by Van Butsic, Maggi Kelly and Max A. Moritz
Land 2015, 4(1), 140-156; https://doi.org/10.3390/land4010140 - 25 Feb 2015
Cited by 54 | Viewed by 14860
Abstract
Fire is a naturally occurring process of most terrestrial ecosystems as well as a tool for changing land use. Since the beginning of history humans have used fire as a mechanism for creating areas suitable for agriculture and settlement. As fires threaten human [...] Read more.
Fire is a naturally occurring process of most terrestrial ecosystems as well as a tool for changing land use. Since the beginning of history humans have used fire as a mechanism for creating areas suitable for agriculture and settlement. As fires threaten human dominated landscapes, fire risk itself has become a driver of landscape change, impacting landscapes through land use regulations and fire management. Land use changes also influence fire ignition frequency and fuel loads and hence alters fire regimes. The impact of these changes is often exacerbated as new land users demand alternative fire management strategies, which can impact land cover and management far from where land use change has actually occurred. This creates nuanced land use teleconnections between source areas for fires and economic cores, which demand and fund fire protection. Here we will review the role of fire and fire risk as a driver of land use change, the ways land use changes impact drivers of fire, and suggest that the integration of land use teleconnections into the fire/land use discussion can help us better understand and manage the complex interactions between fire and land use. Full article
(This article belongs to the Special Issue Ecosystem Function and Land Use Change)
Show Figures

Figure 1

Figure 1
<p>Conceptual model of interactions between land use changes and fire risk. Fire risk can drive land use change by creating the need for alternative vegetation management activities, such as type converting flammable fuels and landscape planning, such as laws that dictate suitable areas for subdivision based on wildfire risk. Land use change can in turn impact fire risk by impacting fuel loads and ignitions. Combined, these impacts interact on the landscape and thus inform both future land use change decisions and future fire risk. This creates a feedback-laden system where the actions in one time period may impact future actions.</p>
Full article ">Figure 2
<p>State, Local and Federal, Responsibility Areas, in the state of California (all data provided by CalFire [<a href="#B72-land-04-00140" class="html-bibr">72</a>]).</p>
Full article ">Figure 3
<p>Dynamics of land use change and fire. In box 1, a low-density rural housing in the State Responsibility Area (SRA) is annexed by Local Responsibility Area (LRA) community. In this process SRA fire management loses funds as SRA fees are no-longer collected in this area. In box two SRA land develops at a low enough density so that it does not switch to LRA management. This will increase firefighting revenue for Calfire, but may also increase fire risk as low-density housing is correlated with high fire risk in much of the state. Given the location of this development this may also increase fire risk for areas within the LRA. Box three shows increased housing density near Federal Responsibility Area land. These new homeowners may demand more stringent fuels management within the FRA, although management decisions may be undertaken from within goals of the broader public in mind.</p>
Full article ">
2203 KiB  
Article
How Can Social Safeguards of REDD+ Function Effectively Conserve Forests and Improve Local Livelihoods? A Case from Meru Betiri National Park, East Java, Indonesia
by Kazuhiro Harada, Dede Prabowo, Arif Aliadi, Jun Ichihara and Hwan-Ok Ma
Land 2015, 4(1), 119-139; https://doi.org/10.3390/land4010119 - 24 Feb 2015
Cited by 13 | Viewed by 8208
Abstract
The National REDD+ (Reducing Emissions from Deforestation and Forest Degradation-Plus) Strategy in Indonesia highlights the importance of local participation and the reform of land tenure in the success of forest conservation. National parks are a main target area for REDD+. National parks in [...] Read more.
The National REDD+ (Reducing Emissions from Deforestation and Forest Degradation-Plus) Strategy in Indonesia highlights the importance of local participation and the reform of land tenure in the success of forest conservation. National parks are a main target area for REDD+. National parks in Indonesia have been suffering from forest destruction and conflicts between governments and local communities. This study investigated: (1) the historical process of developing the REDD+ project in collaboration with multiple stakeholders including government authorities, local NGOs, and local people; (2) the social and economic impacts of the REDD+ project on local people; and (3) the local awareness of and motivations to participate in the REDD+ project in Meru Betiri National Park in Indonesia. Interviews of stakeholders including village leaders, NGO staff, and park staff were conducted to obtain an overview of the REDD+ project in the national park. Interviews with a questionnaire were also conducted among randomly selected heads of households who participated or did not participate in the REDD+ project and lived adjacent to the national park. Our analysis revealed that participants in the project obtained the right to use illegally harvested bared lands for intercropping while planting trees to recover forest ecosystems inside the national park. This opportunity could have contributed to a drastic increase in income, particularly for economically disadvantaged people, and to the recovery of forest ecosystems. Although local people did not fully recognize the meaning of REDD+ or carbon credits, they were enthusiastic to join in managing and patrolling forests because of their satisfaction with the income generated by the national park. However, the challenge is how both the recovery of forests and income generation from the project can be maintained in a situation of insufficient funding from donors and unsettled arguments about the benefit of sharing carbon credits with local people. Full article
(This article belongs to the Special Issue Carbon Emission Reductions and Removals in Tropical Forests)
Show Figures

Figure 1

Figure 1
<p>Study site.</p>
Full article ">Figure 2
<p>Local people’s motivations for participating in the rehabilitation program. Note: Respondents chose three statements.</p>
Full article ">Figure 3
<p>Local people’s reasons for not participating in the rehabilitation program. Note: Respondents chose one statement.</p>
Full article ">
2272 KiB  
Review
Carbon Cycling, Climate Regulation, and Disturbances in Canadian Forests: Scientific Principles for Management
by Jean-Sébastien Landry and Navin Ramankutty
Land 2015, 4(1), 83-118; https://doi.org/10.3390/land4010083 - 21 Jan 2015
Cited by 6 | Viewed by 10609
Abstract
Canadian forests are often perceived as pristine and among the last remaining wilderness, but the majority of them are officially managed and undergo direct land use, mostly for wood harvest. This land use has modified their functions and properties, often inadvertently (e.g., age [...] Read more.
Canadian forests are often perceived as pristine and among the last remaining wilderness, but the majority of them are officially managed and undergo direct land use, mostly for wood harvest. This land use has modified their functions and properties, often inadvertently (e.g., age structure) but sometimes purposefully (e.g., fire suppression). Based on a review of the literature pertaining to carbon cycling, climate regulation, and disturbances from logging, fire, and insect outbreaks, we propose five scientific principles relevant for Canadian managed forests. Among these, a principle we wish to highlight is the need to properly account for the management-related fossil fuel emissions, because they will affect the global carbon cycle and climate for millennia unless massive atmospheric carbon dioxide removal becomes a reality. We also use these five principles to address questions of current interest to research scientists, forest managers, and policy makers. Our review focusses on total ecosystem carbon storage and various mechanisms through which forests affect climate, in particular albedo and aerosols forcings—including how disturbances influence all these elements—but also touches on other ecosystem goods and services. Our review underscores the importance of conducting >100-year time horizon studies of carbon cycling, climate regulation, and disturbances in Canadian managed forests. Full article
(This article belongs to the Special Issue Ecosystem Function and Land Use Change)
Show Figures

Figure 1

Figure 1
<p>Current extent of the managed forests within Canada (see text for definitions), along with the percentage of tree cover estimated for year 2001 (based on [<a href="#B39-land-04-00083" class="html-bibr">39</a>]).</p>
Full article ">Figure 2
<p>Net carbon mitigation from a treatment increasing forest carbon storage, based on realistic values for Canadian managed forests (CMF). The dotted gray line gives the initial landscape-level equilibrium carbon, whereas the dashed gray line gives the landscape-level equilibrium carbon with the treatment effect. (<b>a</b>) The treatment started in year 100 and was maintained perpetually. The results account for the forest landscape only; (<b>b</b>) The treatment started in year 100 and was halted in year 300. The results account for the forest landscape only; (<b>c</b>) The treatment started in year 100 and was halted in year 300. The results now also account for the fossil fuel emissions incurred; (<b>d</b>) The treatment started in year 100 and was maintained perpetually. The results now also account for the fossil fuel emissions incurred. Net carbon mitigation became negative in year 1021.</p>
Full article ">
10331 KiB  
Article
Integrating Forest Cover Change with Census Data: Drivers and Contexts from Bolivia and the Lao PDR
by Sébastien Boillat, Hy Dao, Patrick Bottazzi, Yuri Sandoval, Abraham Luna, Sithong Thongmanivong, Louca Lerch, Joan Bastide, Andreas Heinimann and Frédéric Giraut
Land 2015, 4(1), 45-82; https://doi.org/10.3390/land4010045 - 20 Jan 2015
Cited by 12 | Viewed by 10417
Abstract
The aim of this paper is to explore possible links between forest cover change and characteristics of social-ecological systems at sub-national scale based mainly on census data. We assessed relationships between population density, poverty, ethnicity, accessibility and forest cover change during the last [...] Read more.
The aim of this paper is to explore possible links between forest cover change and characteristics of social-ecological systems at sub-national scale based mainly on census data. We assessed relationships between population density, poverty, ethnicity, accessibility and forest cover change during the last decade for four regions of Bolivia and the Lao PDR, combining a parcel-based with a cell-based approach. We found that accessibility is a key driver of forest cover change, yet it has the effect of intensifying other economic and policy-related underlying drivers, like colonization policies, cash crop demand, but also policies that lead to forest gain in one case. Poverty does not appear as a driver of deforestation, but the co-occurrence of poverty and forest loss driven by external investments appears critical in terms of social-ecological development. Ethnicity was found to be a moderate explanatory of forest cover change, but appears as a cluster of converging socio-economic characteristics related with settlement history and land resource access. The identification of such clusters can help ordering communities into a typology of social-ecological systems, and discussing their possible outcomes in light of a critical view on forest transition theory, as well as the relevance and predictive power of the variables assessed. Résumé: L’objectif de cet article est d’explorer les liens entre le changement de la couverture forestière et les caractéristiques des systèmes socio-écologiques à l’échelle nationale, principalement à l’aide de données de recensement. Nous avons évalué les relations entre la densité de population, la pauvreté, l’ethnicité, l’accessibilité et le changement de la couverture forestière pendant la dernière décennie pour quatre régions de Bolivie et du Laos, en combinant des approches par parcelles et par cellules. Nous avons constaté que l’accessibilité est un facteur clé du changement de la couverture forestière, tandis qu’elle a pour effet d'intensifier d'autres facteurs économiques et politiques sous-jacents, comme les politiques de colonisation, la demande de cultures de rente, mais aussi, dans un cas, des politiques conduisant à un accroissement de la forêt. La pauvreté n’apparait pas comme un facteur de déforestation, mais la co-occurrence de la pauvreté et de la perte de forêt entrainée par les investissements extérieurs semble critique en termes de développement socio-écologique. L'ethnicité se révèle être modérément explicative du changement de la couverture forestière, mais elle apparait comme un ensemble de caractéristiques socio-économiques convergentes liées à l'histoire de l’implantation humaine et à l'accès aux ressources foncières. L'identification de tels ensembles peut aider à classer les communautés selon une typologie des systèmes socio-écologiques, et à discuter leurs possibles impacts sur la forêt avec un point de vue critique sur la théorie de la transition forestière, ainsi que la pertinence et la puissance prédictive des variables évaluées. Full article
(This article belongs to the Special Issue Land Change Modeling: Connecting to the Bigger Picture)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overview of the four study areas.</p>
Full article ">Figure 2
<p>Methodological overview.</p>
Full article ">Figure 3
<p>Poverty, ethnic groups and forest cover change in the North of La Paz forest landscape.</p>
Full article ">Figure 4
<p>Forest cover change rate in relation with socio-economic variables in the North of La Paz forest landscape (NLP).</p>
Full article ">Figure 5
<p>Poverty, ethnic groups and forest cover change in the Santa Cruz agroindustrial area.</p>
Full article ">Figure 6
<p>Forest cover change rate in relation with socio-economic variables in the Santa Cruz agroindustrial area (SCA).</p>
Full article ">Figure 7
<p>Poverty, ethnic groups and forest cover change in the Luang Prabang province.</p>
Full article ">Figure 8
<p>Forest cover change rate in relation with socio-economic variables in the Luang Prabang province (LPP).</p>
Full article ">Figure 9
<p>Poverty, ethnic groups and forest cover change in the Luang Prabang province.</p>
Full article ">Figure 10
<p>Forest cover change rate in relation with socio-economic variables in the Luang Namtha province (LNP).</p>
Full article ">Figure 11
<p>Social-ecological contexts and their possible outcomes in the four study areas.</p>
Full article ">
6252 KiB  
Review
Soil and Water Conservation Strategies in Cape Verde (Cabo Verde in Portuguese) and Their Impacts on Livelihoods: An Overview from the Ribeira Seca Watershed
by Isaurinda Baptista, Luuk Fleskens, Coen Ritsema, António Querido, Jacques Tavares, António D. Ferreira, Eduardo A. Reis, Samuel Gomes and Anabela Varela
Land 2015, 4(1), 22-44; https://doi.org/10.3390/land4010022 - 14 Jan 2015
Cited by 9 | Viewed by 11726
Abstract
Severe land degradation has strongly affected both people’s livelihood and the environment in Cape Verde (Cabo Verde in Portuguese), a natural resource poor country. Despite the enormous investment in soil and water conservation measures (SWC or SLM), which are visible throughout the landscape, [...] Read more.
Severe land degradation has strongly affected both people’s livelihood and the environment in Cape Verde (Cabo Verde in Portuguese), a natural resource poor country. Despite the enormous investment in soil and water conservation measures (SWC or SLM), which are visible throughout the landscape, and the recognition of their benefits, their biophysical and socioeconomic impacts have been poorly assessed and scientifically documented. This paper contributes to filling this gap, by bringing together insights from literature and policy review, field survey and participatory assessment in the Ribeira Seca Watershed through a concerted approach devised by the DESIRE project (the “Desire approach”). Specifically, we analyze government strategies towards building resilience against the harsh conditions, analyze the state of land degradation and its drivers, survey and map the existing SWC measures, and assess their effectiveness against land degradation, on crop yield and people’s livelihood. We infer that the relative success of Cape Verde in tackling desertification and rural poverty owes to an integrated governance strategy that comprises raising awareness, institutional framework development, financial resource allocation, capacity building, and active participation of rural communities. We recommend that specific, scientific-based monitoring and assessment studies be carried out on the biophysical and socioeconomic impact of SLM and that the “Desire approach” be scaled-up to other watersheds in the country. Full article
Show Figures

Figure 1

Figure 1
<p>Location of the Ribeira Seca Watershed within Santiago Island and Cape Verde.</p>
Full article ">Figure 2
<p>Mean annual rainfall distribution, evapo-transpiration and temperature for São Jorge Station (subhumid zone of Ribeira Seca Watershed): period 1973–2010.</p>
Full article ">Figure 3
<p>Map of land use types in the Ribeira Seca Watershed showing the dominance of rain-fed farming. Adapted from [<a href="#B18-land-04-00022" class="html-bibr">18</a>].</p>
Full article ">Figure 4
<p>(<b>a</b>) Type, (<b>b</b>) degree, (<b>c</b>) extent and (<b>d</b>) rate of land degradation in the Ribeira Seca Watershed (RSW) on as the dominant type, moderate degree, and slight to moderate rate of degradation. Adapted from [<a href="#B18-land-04-00022" class="html-bibr">18</a>].</p>
Full article ">Figure 5
<p>(<b>a</b>) Existing soil and water conservation measures (<b>b</b>) land conservation group measures and (<b>c</b>) extent as % of mapping unit showing the importance of vegetation measures as the major conservation group at hillslope level. (b) and (c) adapted from [<a href="#B18-land-04-00022" class="html-bibr">18</a>].</p>
Full article ">Figure 6
<p>Vegetation and structural soil and water conservation measures in the Ribeira Seca Watershed: (<b>a</b>) contour rock walls; (<b>b</b>) terraces (São Jorge); (<b>c</b>) Water collecting dam (Poilão) during rainy season, d. check dams on water ways (Godim); <b>(e</b>) Aloe Vera barriers (São Jorge), (<b>f</b>) <span class="html-italic">L. Leucocephala</span> hedges (Godim); (<b>g</b>) Pigeon-pea on terraced fields; and (<b>h</b>) Afforestation with different species (<span class="html-italic">Longueira Baixo</span>).</p>
Full article ">Figure 7
<p>Effectiveness of conservation measures in the Ribeira Seca Watershed, weighted by area, evidencing: (<b>a</b>) the moderate to high effectiveness of the implemented measures at slope level, (<b>b</b>) the increasing conservation efficiency trend on the slopes and (<b>c</b>) expert recommendation on measures. Adapted from [<a href="#B18-land-04-00022" class="html-bibr">18</a>].</p>
Full article ">
155 KiB  
Editorial
Acknowledgement to Reviewers of Land in 2014
by Land Editorial Office
Land 2015, 4(1), 19-21; https://doi.org/10.3390/land4010019 - 8 Jan 2015
Viewed by 3700
Abstract
The editors of Land would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2014:[...] Full article
3030 KiB  
Article
Globalland30 Mapping Capacity of Land Surface Water in Thessaly, Greece
by Ioannis Manakos, Konstantinos Chatzopoulos-Vouzoglanis, Zisis I. Petrou, Lachezar Filchev and Antonis Apostolakis
Land 2015, 4(1), 1-18; https://doi.org/10.3390/land4010001 - 23 Dec 2014
Cited by 22 | Viewed by 11505
Abstract
The National Geomatics Center of China (NGCC) produced Global Land Cover (GlobalLand30) maps with 30 m spatial resolution for the years 2000 and 2009–2010, responding to the need for harmonized, accurate, and high-resolution global land cover data. This study aims to assess the [...] Read more.
The National Geomatics Center of China (NGCC) produced Global Land Cover (GlobalLand30) maps with 30 m spatial resolution for the years 2000 and 2009–2010, responding to the need for harmonized, accurate, and high-resolution global land cover data. This study aims to assess the mapping accuracy of the land surface water layer of GlobalLand30 for 2009–2010. A representative Mediterranean region, situated in Greece, is considered as the case study area, with 2009 as the reference year. The assessment is realized through an object-based comparison of the GlobalLand30 water layer with the ground truth and visually interpreted data from the Hellenic Cadastre fine spatial resolution (0.5 m) orthophoto map layer. GlobCover 2009, GlobCorine 2009, and GLCNMO 2008 corresponding thematic layers are utilized to show and quantify the progress brought along with the increment of the spatial resolution, from 500 m to 300 m and finally to 30 m with the newly produced GlobalLand30 maps. GlobalLand30 detected land surface water areas show a 91.9% overlap with the reference data, while the coarser resolution products are restricted to lower accuracies. Validation is extended to the drainage network elements, i.e., rivers and streams, where GlobalLand30 outperforms the other global map products, as well. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map of the study area.</p>
Full article ">Figure 2
<p>Registration performance visualization between the reference and the GlobalLand30, GlobCover 2009, GlobCorine 2009, and GLCNMO 2008 LSW layers, for two representative sample areas.</p>
Full article ">Figure 3
<p>Methodology workflow (ALT_A: Alternative Approach; for HC, GMEECC, GlobalLand30-Water, GlobCover 2009, GlobCorine 2009, GLCNMO 2008 see <a href="#land-04-00001-t001" class="html-table">Table 1</a>).</p>
Full article ">Figure 4
<p>The width of the selected river transects across the area subdivided in three groups.</p>
Full article ">Figure 5
<p>Accuracy assessment results (for GlobalLand30-Water, GlobCover 2009, GlobCorine 2009, and GLCNMO 2008: see <a href="#land-04-00001-t001" class="html-table">Table 1</a>). LSW: Land surface water detection with the main approach; LSW DN included: Land surface water and Drainage network detection with the main approach; ALT_A: Land surface water detection with the alternative approach; ALT_A DN included: Land surface water and Drainage network detection with the alternative approach.</p>
Full article ">Figure 6
<p>GlobalLand30-Water identified rivers following the categorization after Strahler [<a href="#B61-land-04-00001" class="html-bibr">61</a>], represented by the x-axis. The y-axis draws the number of 1st, 2nd, 3rd, and 4th order rivers.</p>
Full article ">
Previous Issue
Next Issue
Back to TopTop