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65 results for “Land cover change”
Data and code from: Breakdown in seasonal dynamics of subtropical ant communities with land-cover change
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Land Use and Land Cover Change 2000-2016 in Mozambique
<p>This repository includes land use and land cover maps of Mozambique for 2000, 2005, 2010 and 2016 years.</p> <p>The methodology is based on remote sensing methodology and include satellite image collection and compositing (annual cloud-free and shadow free Landsat images for 2000, 2005, 2010 and 2016), delineation of a large collection of training plots based on National Land Cover Classification system level 1, supervised classification using a machine learning algorithm (Random Forest) and post-processing steps.</p> <p>The LULCC map for 2016 show area statistics of 45.0% (35.8 Mha) of dry forest, 37.0% (29.3 Mha) of grassland and fallow, 13.7% (10.8 Mha) of cropland 2.0% (1.6 Mha) of wetlands, 1.3% (1 Mha) of other categories (rocks, sands, or bare soils), 0.3% (271,000 ha) of Mangroves, and 0.1% (673.1 ha) of urban areas. The deforestation over the 2000-2016 period is estimated to have been 207,272 ha per year.</p> <p>The methodology and statistics are presented in the report included in this repository. Theses maps are outputs from the study "An Analysis of Land Use Changes and Land Degradation in Mozambique" conducted by Nitidae and CIRAD in the LAUREL project.</p> <p> </p>
30 Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel using Landsat Timeseries
<p>30m resolution historically consistent land cover and cover fraction maps over the Sudano-Sahel for the period 1986-2015. These land cover / cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification / regression model, while historical consistency is achieved using the Hidden Markov Model.</p> <p>Validated land cover / cover fraction maps covering the full Sudano-Sahel are provided for 2015 (2015_Sahel.zip), while historical maps are available for four focus areas. The extent of the areas are displayed in 11_study_area.jpeg</p> <p>Each of the zip files contains 14 GeoTIFF files for the respective period and area:</p> <ul> <li>Landsat_LC30_epochYYYY_AREA_bare-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_crops-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif [# overpasses that are used as input for the creation of the maps for this region / epoch]</li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif [temporally cleaned discrete classification map using the Hidden Markov Model; legend see below] </li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification.tif [original discrete classification map; legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_forest-type-layer.tif [legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_grass-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_moss-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_shrub-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_snow-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_tree-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_urban-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-permanent-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-seasonal-coverfraction-layer.tif [0-100%]</li> </ul> <p>Discrete classification legend:</p> <ul> <li>0: Unknown. No or not enough satellite data available.</li> <li>20: Shrubs. Woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall. The shrub foliage can be either evergreen or deciduous.</li> <li>30: Herbaceous vegetation. Plants without persistent stem or shoots above ground and lacking definite firm structure. Tree and shrub cover is less than 10 %.</li> <li>40: Cultivated and managed vegetation / agriculture. Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type.</li> <li>50: Urban / built up. Land covered by buildings and other man-made structures.</li> <li>60: Bare / sparse vegetation. Lands with exposed soil, sand, or rocks and never has more than 10 % vegetated cover during any time of the year.</li> <li>70: Snow and ice. Lands under snow or ice cover throughout the year.</li> <li>80: Permanent water bodies. Lakes, reservoirs, and rivers. Can be either fresh or salt-water bodies.</li> <li>90: Herbaceous wetland. Lands with a permanent mixture of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water.</li> <li>100: Moss and lichen.</li> <li>111: Closed forest, evergreen needle leaf. Tree canopy >70 %, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>112: Closed forest, evergreen broad leaf. Tree canopy >70 %, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>113: Closed forest, deciduous needle leaf. Tree canopy >70 %, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>114: Closed forest, deciduous broad leaf. Tree canopy >70 %, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>115: Closed forest, mixed.</li> <li>116: Closed forest, not matching any of the other definitions.</li> <li>121: Open forest, evergreen needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>122:Open forest, evergreen broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>123: Open forest, deciduous needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>124: Open forest, deciduous broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>125: Open forest, mixed.</li> <li>126: Open forest, not matching any of the other definitions.</li> <li>200: Oceans, seas. Can be either fresh or salt-water bodies.</li> </ul> <p>Forest type legend:</p> <ul> <li>0: Unknown</li> <li>1: Evergreen needle leaf</li> <li>2: Evergreen broad leaf</li> <li>3: Deciduous needle leaf</li> <li>4: Deciduous broad leaf</li> <li>5: Mix of forest types</li> </ul> <p>More detail on the classification algorithm and the resulting maps can be found in the accompanying paper: </p> <p>Souverijns, N.; Buchhorn, M.; Horion, S.; Fensholt, R.; Verbeeck, H.; Verbesselt, J.; Herold, M.; Tsendbazar, N.-E.; Bernardino, P.N.; Somers, B.; Van De Kerchove, R. Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series. <em>Remote Sens.</em> <strong>2020</strong>, <em>12</em>, 3817. https://doi.org/10.3390/rs12223817</p> <p>Please note that a quality layer is available for each of the historical areas / periods (Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif). In case a value of 4 or lower is achieved here, the discrete land cover classification / cover fraction for this period / area is highly uncertain. Take this into account when analysing the maps. Furthermore, take note that there is a large difference between the temporally cleaned (Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif) and original discrete land cover classification (Landsat_LC30_epochYYYY_AREA_discrete-classification.tif). We recommend to use the temporally cleaned version in combination with the quality layer.</p>
Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
<p>The dataset contains six files from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) land cover transition data derived from LandSat satellite imagery for years 1986 and 2016. The files include data on household and plot features, prices of land use outputs, a diagonal block matrix of variables for mixed effects analysis, beliefs and opinions on land use change, and land cover transitions. The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and is representative of the region. It can be useful for studies on land use policies, environmental protection, and the drivers and impacts of land use land cover change in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17.</p>
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
<p>The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km<sup>2 </sup>of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.</p>
Land Cover Change Hotspots from 2001 to 2021
<p><span>Land cover change hotsposts based on the harmonized global land cover maps from the ESA Climate Change Initiative, WorldCover, and Copernicus Global Land Cover Service from 2001 to 2021 at 0.1x0.1 degree grid</span></p>
GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change
<p>We complied the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) from 632 papers documented in Web of Science till the June 2024. This database comprises 1,187 sites with 5,805 records at multiple sample depths.<br>This dataset (in csv formats) is associated to the "GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change" by Chen et al. (2025). The README file includes the full explanation of all the columns.<br>Manuscript citation: Chen, S., Shuai, Q., Arrouays, D., Chen, Z., Dai, L., Hong, Y., Hu, B., Huang, Y., Ji, W., Li, S., Liang, Z., Ma, Y., Richer-de-Forges, A.C., Schillaci, C., Su, Y., Teng, H., Wang, N., Wang, X., Wang, Y., Wang, Z., Wang, Z., Xu, D., Xue, J., Ye, S., Zhang, X., Zhou, Y., Zhu, P., Shi, Z. , 2025. GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change. In preparation.<br>When using the data, please cite repositories as well as the original manuscript.<br>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Quantifying the impacts of 166 years of land cover change on lowland bird communities
<p><span>Land cover change for agriculture is thought to be a major threat to global biodiversity</span><span>. </span><span>However, </span><span>its ecological impact </span><span>has rarely been quantified in the Northern Hemisphere, as broad-scale conversion to farmland mainly occurred until the 1400s-1700s in the region, limiting the availability of sufficient data.</span><span> </span><span>The Ishikari Lowland in Hokkaido, Japan offers an excellent opportunity to address this issue, as hunter–gatherer lifestyles dominated in this region until the mid-19th century and land cover maps are available for the period of land cover changes, i.e., 1850-2016.</span> <span>Using these maps and a hierarchical community model of relationships between breeding bird abundance and land cover types, we estimated that broad-scale land cover change over a 166 year period was associated with more than 70% decline in both potential species-richness and abundance of avian communities.</span> <span>We estimated that the abundance of wetland and forest species declined by >88%, whereas that of bare-ground species increased by >50%. Our results suggest that broad-scale land cover change for agriculture has led to drastic reductions in wetland and forest species and promoted changes in community composition in large parts of the Northern Hemisphere. This study provides potential baseline information that could inform future conservation policies.</span></p>
Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
<p>Climate change is altering the distribution of woody plants by influencing demographic processes and modifying disturbance regimes. Trailing-edge forests may be particularly vulnerable to these effects because they exist at warm, dry margins of tree distributions. To better understand recent climate-driven changes in trailing-edge forests, we used Landsat time series and 1,558 field reference plots to develop annual land cover maps from 1985 to 2020 in two large, biodiverse landscapes in central Arizona, USA. We then combined annual land cover maps with tree ring records and spatial data describing interannual climate, terrain, bark beetle (Curculionidae: Scolytinae) activity, wildfire, and harvest to quantify drivers of forest change. Throughout the two landscapes, forest extent declined by 0.3% and 0.8% from 1985 to 2020. However, considerable variation occurred within the study period, with abrupt (ca. 1–2 years) declines in forest extent followed by gradual (ca. 10 years) recovery on each landscape. Pinyon-juniper (<em>Pinus</em> <em>edulis</em>, <em>Pinus</em> <em>monophylla</em>, and/or <em>Juniperus</em> spp.) cover increased from 1985 to ca. 2000 but declined after 2000, a period of extreme drought and regional tree die-off. In contrast, pine-oak (<em>Pinus</em> <em>ponderosa</em> and <em>Quercus</em> spp.) cover increased from 2000 to 2020, primarily due to declines in ponderosa pine and mixed conifer cover over the same period. Wildfire was a key driver of transitions from forest to non-forest cover in our study area, with the occurrence of multiple compounded drought years playing an important role in unburned areas. By driving transitions to alternative forest types or non-forest cover, disturbance and drought will increasingly shape forest dynamics and ecosystem transformations throughout the southwestern US.</p>
Global potential invasion maps of traded birds under climate and land-cover change
<p>Biological invasions rank among the top five threatening factors affecting biodiversity, but ongoing changes in climate and land cover might exacerbate risks. We used species distribution models for 609 traded bird species on the CITES list to examine the combined effects of projected climate change and land-cover change worldwide on the potential range expansion of bird species with commercial value as pets. The maps of potential invasion (may be inferred as the invasion risk) have been provided in the main manuscript and here, the potential invasion dataset for the current and future times is provided including the species distribution maps, all as GeoTiff files. The maps for the future time are provided for different future years and over a range of climate scenarios (SSP245, SSP370, and SSP585).</p>
R-script: Deterioration of respiratory health following changes to land cover and climate in Indonesia
<p>This file contains the R-script presented in "Santika, T., Muhidin, S., Haryanto, B. et al. (2023) Deterioration of respiratory health following changes to land cover and climate in Indonesia".</p> <p>** R-script.txt</p> <p>This is the main script used to produce the results of the paper, which contains three parts:</p> <ol> <li>Analysis of the change in rainfall patterns across regencies in Sumatra, Indonesia</li> <li>Analysis of the change in fire patterns across Sumatra by soil type and land cover/degradation</li> <li>Analysis of the link between respiratory illness prevalence and environmental and socio-economic variables</li> </ol>
Quantifying the impacts of 166 years of land cover change on lowland bird communities
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Land use and cover changes and sand fly (Diptera: Psychodidae) assemblages in an emerging focus of leishmaniasis
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Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
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Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
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Global potential invasion maps of traded birds under climate and land-cover change
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Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
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Integrating stakeholders' perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania
<p>Rapid rates of land use and land cover change (LULCC) in eastern Africa and limited instances of genuinely equal partnerships involving scientists, communities and decision makers challenge the development of robust pathways toward future environmental and socioeconomic sustainability. We use a participatory modelling tool, Kesho, to assess the biophysical, socioeconomic, cultural and governance factors that influenced past (1959-1999) and present (2000-2018) LULCC in northern Tanzania and to simulate four scenarios of land cover change to the year 2030. Simulations of the scenarios used spatial modelling to integrate stakeholders' perceptions of future environmental change with social and environmental data on recent trends in LULCC. From stakeholders' perspectives, between 1959 and 2018, LULCC was influenced by climate variability, availability of natural resources, agriculture expansion, urbanization, tourism growth, and legislation governing land access and natural resource management. Among other socio-environmental-political LULCC drivers, the stakeholders envisioned that from 2018 to 2030 LULCC will largely be influenced by land health, natural and economic capital, and political will in implementing land use plans and policies. The projected scenarios suggest that by 2030 agricultural land will have expanded by 8-20% under different scenarios and herbaceous vegetation and forest land cover will be reduced by 2.5-5% and 10-19% respectively. Stakeholder discussions further identified desirable futures in 2030 as those with improved infrastructure, restored degraded landscapes, effective wildlife conservation, and better farming techniques. The undesirable futures in 2030 were those characterized by land degradation, poverty, and cultural loss. Insights from our work identify the implications of future LULCC scenarios on wildlife and cultural conservation and in meeting the Sustainable Development Goals (SDGs) and targets by 2030. The Kesho approach capitalizes on knowledge exchanges among diverse stakeholders, and in the process promotes social learning, provides a sense of ownership of outputs generated, democratizes scientific understanding, and improves the quality and relevance of the outputs.</p>
Cumulative impacts of land cover change and dams on the land-water interface of the Tocantins River
<p>This dataset accompanies the manuscript entitled "Cumulative impacts of land cover change and dams on the land-water interface of the Tocantins River". It contains shapefiles and geoTIFF files.</p>
Data from: Wildfire activity and land use drove 20th-century changes in forest cover in the Colorado front range
Recent shifts in global forest area highlight the importance of understanding the causes and consequences of forest change. To examine the influence of several potential drivers of forest cover change, we used supervised classifications of historical (1938–1940) and contemporary (2015) aerial imagery covering a 2932‐km2 study area in the northern Front Range (NFR) of Colorado and we linked observed changes in forest cover with abiotic factors, land use, and fire history. Forest cover in the NFR demonstrated broad‐scale changes 1938–2015 and overall cover increased 7.8%, but there was notable spatial variability and many sites also experienced Forest Loss. Recent (1978–2015) wildfire was the largest single driver of Forest Loss, with fires burning 14.3% of the total study area. Recently burned areas showed net losses of 36.9% forest cover. Reasons for Forest Gain were more complex, with elevation, past mining density, fire history, and topographic heat load index being the strongest predictors of increases in forest cover. Historical mining activity is one of the dominant anthropogenic impacts in ecosystems in the NFR and it had a complex, non‐linear relationship with 20th‐century changes in forest cover. Subalpine stands originating after stand‐replacing fires circa mid‐1800s to early 1900s showed some of the greatest gains in forest cover, indicative of slow and continuous post‐fire recovery through the 20th century. We also investigated factors such as land ownership, road density, forest management activities, and development intensity, which played detectable, but more minor roles in observed change. Twentieth‐century changes in forest cover throughout the NFR are a result of ecological disturbances and anthropogenic influences operating at varying timescales and overlaid upon variability in the abiotic environment.
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