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65 results for “Land cover change”
Land-Cover Change Scenarios for Massachusetts 2010-2060
Working with a panel of practitioners and regional experts, we developed and analyzed four plausible but divergent land-use scenarios that depict the future of Massachusetts from 2010 to 2060. We simulated the land-use scenarios and their interactions with anticipated climate change by coupling statistical models of land use to the LANDIS-II landscape model and then evaluated the outcomes in terms of the magnitude and spatial distribution of (1) direct human uses of the landscape (residential and commercial development, agricultural, timber harvest), (2) ecosystem services (carbon storage, flood regulation, nutrient retention), and (3) habitat quality (forest tree species composition, interior forest habitat). Across all scenarios, conflicts occurred between dispersed residential development and the supply of ecosystem services and habitat quality. In all but the scenario that envisioned a significant agricultural expansion, forest growth resulted in net increases in aboveground carbon storage, despite the concomitant forest clearing and harvesting. One scenario, called Forests as Infrastructure, showed the potential for synergies between increased forest harvest volume through the sustainable practices that encouraged the maintenance of economically and ecologically important tree species, and carbon storage. This scenario also showed trade-offs between development density and water quantity and quality at the watershed scale. The process of integrated scenario analysis led to important insights for land managers and policymakers in a populated forested region where there are tensions among development, forest harvesting, and land conservation. More broadly, the results emphasize the need to consider the consequences of contrasting land-use regimes that result from the interactions between human decisions and spatially heterogeneous landscape dynamics.
US Atlantic and Gulf Coast Annual Wetland Land Cover and Change Maps, 1985 to 2022
<h3>This dataset is associated with the following article published in Remote Sensing Applications: Society and Environment, which can be accessed here: https://doi.org/10.1016/j.rsase.2024.101392</h3> <p>Shortly after publishing version 2, errors in the map projections were identified and corrected. Please use version 3 instead of version 2.</p> <p>Updates to version 2 were as follows:</p> <ul> <li>Includes watersheds in Texas that were not included in Version 1.</li> <li>A color map (using ArcPro) was added for improved interpretation.</li> <li>A sub-pixel scale offset in the change type map, related to map projection errors, was corrected.</li> </ul> <h2><strong>Mapping Coastal Wetland Changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat Time Series and National Wetland Inventories</strong></h2> <p>Courtney A. Di Vittorio<sup>1</sup>, Melita Wiles<sup>2</sup>, Yasin W. Rabby<sup>2</sup>, Saeed Movahedi<sup>2</sup>, Jacob Louie<sup>1</sup>, Lily Hezrony<sup>1</sup>, Esteban Coyoy Cifuentes<sup>1</sup>, Wes Hinchman<sup>1</sup>, Alex Schluter<sup>1</sup></p> <p><sup>1</sup>Department of Engineering, Wake Forest University, Winston-Salem, North Carolina, USA.</p> <p><sup>2</sup>Department of Statistics, Wake Forest University, Winston-Salem, North Carolina, USA.</p> <h3>Abstract</h3> <p>The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), the widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution, and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses Google Earth Engine and the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing imagery with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The average overall accuracy of the algorithm is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsat-derived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the magnitude of severe changes matches that of the existing inventory and the magnitude of the moderate changes matches that of the more comparable product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.</p>
Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes
<p>Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..30cm = vertical reference: standard layer 0-30 cm below surface,</li> <li>2014 = time reference: year 2014,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Associating Land Cover Changes with Climate Sensitive Infection in Fennoscandia, as part of the CLINF project: Example on Tick-Borne Diseases
<p>The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts used for the analysis.</p> <p>Leibovici DG, Bylund H, Björkman C, Tokarevich N, Thierfelder T, Evengård B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. <strong><em>International Journal of Environmental Research and Public Health, 18(20):10963. <a href="https://doi.org/10.3390/ijerph182010963">doi:10.3390/ijerph182010963</a></em></strong></p> <p>Special Issue: <a href="https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects">https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects</a></p> <p> </p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</p>
The 30 m land cover dataset for capturing land cover changes induced by ecological restoration from 1990 to 2022 on the Chinese Loess Plateau
<p>Continuous time-series of land cover is critical for attributing runoff, sediment and carbon changes on the Chinese Loess Plateau (CLP). However, current land cover products with annal temporal resolution lack spatial identification accuracy, particularly in capturing authentic changes of cropland, forest and grassland. To address these issues, a 30 m annual land cover dataset was proposed by the Yellow River Conservancy Commission (YRCC_LPLC) for the CLP from 1990 to 2022. Different levels of land cover were classified using different combinations of spectral, monthly and annual temporal and topographic features and Random Forest classifier. Compared to other land cover products (45.64%–73.38%), the accuracy of YRCC_LPLC has a better performance with an overall accuracy of 85.16%. The YRCC_LPLC is capable of capturing not only the explicit spatial variation but also the change direction and change time of land cover, especially for the most critical conversion of cropland into forest and grassland induced by implementation of Grain to Green Program on the CLP.</p>
Validation data set on land cover changes for RapidAI4EO project
<p>This is a reference data set collected for validation of the monthly land cover maps at a 3m and at a 10m resolution produced in the WP5. The reference data set has been collected by using Geo-Wiki toolbox for visual interpretation of very high-resolution images, including Planet data and Google maps. The data set has been collected over 3 AOIs. Each reference sample site corresponds to a 30m-by-30m box and includes information about monthly land cover type over the period 2018-2020. Land cover legend is the same as in ESA WorldCover map at a 10m resolution (https://worldcover2021.esa.int/).</p> <p>Fields:</p> <p>"rowid" – unique row identifier;</p> <p>"sampleid" – unique sample site identifier in the Geo-Wiki database;</p> <p>"samplegroupid" – group id with values 257(Portugal), 258 (Belgium), 259(Sicily);</p> <p>"x_min","x_max","y_min","y_max" – bounding box coordinates of each sample site (30m x 30m), in WGS84</p> <p>"X2018_1","X2018_2",…, "X2020_12" – dominant land cover class in each sample site in each month from January 2018 to December 2020;</p> <p>Land cover codes:</p> <p>10 – Tree cover</p> <p>20 - Shrubland</p> <p>30 - Grassland</p> <p>40 - Cropland</p> <p>50 – Urban/built-up</p> <p>60 - Bare/Sparse vegetation</p> <p>80 - Water</p> <p>90 - Wetland</p> <p>110 - Burnt</p> <p>120 – Not sure</p>
High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes
<p>A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.</p> <p>The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv <em>et al.</em> (2023).</p> <p><strong>Data description</strong></p> <p>Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes.</p> <p>To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover.</p> <p><strong><em>Land surface category (LSC)</em></strong></p> <p>These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories.</p> <p>Discrete LSC classification legend:</p> <table> <tbody> <tr> <td> <p>Map code</p> </td> <td> <p>Land cover class</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>Tree (leaf-on)</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>Shrubland (leaf-on)</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>Woody vegetation (leaf-off)</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>Wilted herbaceous vegetation</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>Water</p> </td> </tr> <tr> <td> <p>24</p> </td> <td> <p>Built-up</p> </td> </tr> </tbody> </table> <p>In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype.</p> <p><strong><em>Land use land cover</em></strong></p> <p>After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system.</p> <p>The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions.</p> <p>Discrete LC classification legend:</p> <table> <tbody> <tr> <td> <p><strong>Map code</strong></p> </td> <td> <p><strong>Land cover class</strong></p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>Tree cover</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Shrubland</p> </td> </tr> <tr> <td> <p>30</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>40</p> </td> <td> <p>Cropland</p> </td> </tr> <tr> <td> <p>50</p> </td> <td> <p>Built-up</p> </td> </tr> <tr> <td> <p>60</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>80</p> </td> <td> <p>Permanent water bodies</p> </td> </tr> <tr> <td> <p>90</p> </td> <td> <p>Herbaceous wetland</p> </td> </tr> </tbody> </table> <p><strong><em>Land use land cover change </em></strong></p> <p>Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change.</p> <p><strong><em>Files</em></strong></p> <p>The zip files contain the following data:</p> <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> <p>These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “<em>tile</em>-<em>year</em>-<em>month</em>.tif”.</p> <p><strong><em>References</em></strong></p> <p>Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963 </p> <p>Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.</p> <p><em>Zanaga, D., </em><em>et al.</em><em>., 2021. ESA WorldCover 10 m 2020 v100. </em><a href="https://doi.org/10.5281/zenodo.5571936 "><em>https://doi.org/10.5281/zenodo.5571936 </em></a></p>
Database of a study on long-term land cover changes in Thừa Thien Huế Province, Central Vietnam
<p>Data provided here forms the basis of a study which is published in the journal Land Use Policy (year 2023) under the title 'The nature of a ‘forest transition’ in Thừa Thien Huế Province, Central Vietnam – A study of land cover changes over five decades'. The database contains the image files of the maps (and associated legends) shown in the publication. Further data (currently under use for other related research) will eventually be added to the repository.</p>
Data set: Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces
<p>Tropical mountain ecosystems are threatened by land use pressures, compromising their capacity to provide multiple ecosystem services. The analysis of landscape changes and their proximate driving forces is often qualitative and sectorial oriented, although local patterns and numerous interactions among socio-economic, demographic, and biophysical factors shape these socio-ecological systems. We characterized land use land cover (LULC) dynamics using Markov-chain probabilities by elevation and geographic settings and then, implementing the DPSIR holistic approach, we integrated them with a variety of freely available geospatial and temporal data into a Generalized Additive Model (GAM) to uncover the factors driving such landscape dynamics in a sensitive region of the northern Ecuadorian Andes. Our results demonstrated a dynamic and clear geographical pattern of distinct LULC transitions through time, explained by different combination of socio-economic factors, demographic and infrastructure variables and environmental parameters, from which topographic variables were the main drivers of change in this landscape. We found that deforestation of remnant native forest and agricultural expansion still occur in higher elevations, while land conversion toward anthropic environments, particularly significant expansion of floriculture and urban areas were observed in lower elevations to the east of the studied territory. Our findings also revealed an unexpected stability trend of paramo and a successional recovery of previous agricultural land to the west and center of the territory, which could be explained by agricultural land abandonment. However, the very low probability of persistence of montane forests found overall, highlights the greater threat to permanently lose the already vulnerable mountain native biodiversity. The methodological approach and our findings, demonstrating dynamic patterns through space and time and their explanatory drivers, could help local authorities and stakeholder to improve sustainably resource land management in vulnerable landscapes such as the tropical Andes in northern Ecuador.</p>
Land use and land cover changes in the contiguous United States from 1630 to 2020
<p>Through integrating multi-source data including high-resolution remote sensing image-based land use and land cover (LULC) data, model-based land use products, and historical land archives, we reconstructed historical LULC at an annual time scale and 1 km x 1 km resolution in the contiguous United States (CONUS) from 1630 to 2020. Compared to other historical LULC datasets, our data can capture the major characters of LULC as well as provide more accurate information with higher spatial and temporal resolution. The LULC data can be used for regional studies in a wide range of topics including LULC impacts on the ecosystem, biodiversity, water resource, carbon and nitrogen cycles, and greenhouse gas emissions.</p>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
NLCD_INEGI: Harmonized US-Mexico Land Cover Change Dataset, 1992/2001/2011
<p>We have taken the Uso del Suelo y Vegetacion land cover classification products for Mexico (courtesy of Mexico's Instituto Nacional de Estadistica y Geografia, or INEGI) for years 1985, 1993, 2002, 2007, and 2011 (INEGI, 2015); harmonized their classes with the classes of the Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) (Homer et al., 2015), and merged the two datasets to form a single land cover product covering the continental US and Mexico, for years 1992/3, 2001/2, and 2011. Details of processing, along with the processing scripts, are archived in GitHub in the <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.5">NLCD_INEGI</a> project (Bohn, 2019).</p> <p>Output files are ESRI ascii-format raster files, geographic projection, 0.000350884 degree cellsize. Each file contains a 1x1 degree box or "tile", named nlcd_inegi.<em>lat0</em>_<em>lat1</em>n.<em>lon0</em>_<em>lon1</em>w.asc, where <em>lat0</em> and <em>lat1</em> are the south and north boundaries of the box and <em>lon0</em> and <em>lon1</em> are the west and east boundaries (west = positive).</p> <p>This project contains the following g-zipped tar files:</p> <ul> <li>1992.tgz - land cover from 1992 (NLCD) and 1993 (INEGI)</li> <li>2001.tgz - land cover from 2001 (NLCD) and 2002 (INEGI)</li> <li>2011.tgz - land cover from 2011 (both NLCD and INEGI)</li> <li>stable_2001-2011.tgz - land cover from those pixels that had the same class in 2001 and 2011 ("stable" pixels)</li> </ul> <p>On LINUX, the contents of these files can be extracted via "tar":</p> <p>tar -xvzf 1992.tgz >& log.tar.txt</p> <p>On Windows, applications such as "7-zip" can extract the contents.</p> <p>Each of these .tgz files contain a folder with the same name but without the ".tgz". Within each of these folders is a sub-folder called "asc.clip.1deg". This folder contains the 1x1 tiles.</p>
Fig. 3. Land use and land cover data for 2014 in Population trends and conservation status of proboscis monkeys (Nasalis larvatus) in the face of habitat change in the Klias Peninsula, Sabah, Borneo, Malaysia
Fig. 3. Land use and land cover data for 2014/2015 within the 1-km buffer distance from surveyed rivers, overlaid with proboscis monkey sightings from the 2004/2005 and 2014 surveys, Protected Areas, and Production Forest Reserve boundaries.
Conservation of woody species in China under future climate and land-cover changes
<ol> <li>Climate and land-cover changes are major threats to biodiversity, and their impacts are expected to intensify in the future. Protected areas (PAs) are crucial for biodiversity conservation. However, their effectiveness under future climate and land-cover changes remains to be evaluated. Moreover, the impacts of climate and land-cover changes on multi-dimensions of biodiversity are rarely considered when expanding PAs.</li> <li>Using distributions of 8732 woody species in China and species distribution models, we identified species that will be threatened by future climate and land-cover changes (i.e. species with significant projected loss of suitable habitats by the 2070s) under different dispersal scenarios. We then estimated the geographical patterns in species richness (SR) and phylogenetic diversity (PD) of these threatened species, evaluated the effectiveness (i.e. the changes in SR and PD) of Chinese PAs, and identified conservation priorities for future PA expansion.</li> <li>Approximately 12-38% of woody species will be threatened under different scenarios. These species tend to be clustered in the tree of life, and their SR and PD show consistent spatial patterns, being highest at low latitudes. PAs currently protect 90% of these threatened species. However, their SR and PD of threatened species within PAs will decrease by 30-40% by the 2070s, which reduces the PA effectiveness, especially for PAs at low elevations and those with low topographic heterogeneity and high natural vegetation loss.</li> <li>The conservation priorities identified from the SR and PD of the threatened species are mainly in mountains in southern China, the Yunnan-Guizhou Plateau, and Taiwan Island. PA expansion and ecological corridors in these regions are needed to conserve these threatened species.</li> <li> <i>Synthesis and applications.</i> We present a systematic study of the impacts of future climate and land-cover changes on the conservation status of woody species and PA effectiveness in China. Our results suggest that future climate and land-cover changes will reduce PA effectiveness, and the spatial prioritization of biodiversity conservation should consider the influences of future global changes on biodiversity. These results shed new light on the conservation priorities for the post-2020 expansion of PAs in China.</li> </ol>
Datasets for Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China
<p>These datasets are the processed and refined data that support and lead to the described results and allow other readers to assess the conclusions in the paper, entitled “<strong>Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China </strong> ”.</p>
Data and code from: Breakdown in seasonal dynamics of subtropical ant communities with land-cover change
<p><span>Concerns about widespread human-induced declines in insect populations are mounting, yet little is known about how land-use change modifies the dynamics of insect communities, particularly in understudied regions. Here, we examine how the seasonal activity patterns of ants—key drivers of terrestrial ecosystem functioning—vary with anthropogenic land-cover change on a subtropical island landscape, and whether differences in temperature or species composition can explain observed patterns. Using trap captures sampled biweekly over two years from a biodiversity monitoring network covering Okinawa Island, Japan, we processed 1.2 million individuals and reconstructed activity patterns within and across habitat types. Forest communities exhibited greater temporal variability of activity than those in more developed areas. Using time-series decomposition to deconstruct this pattern, we found that sites with greater human development exhibited ant communities with diminished seasonality, reduced synchrony, and higher stochasticity compared to sites with greater forest cover. Our results cannot be explained by variation in regional or site temperature patterns, or by differences in species richness or composition among sites. Our study raises the possibility that disruptions to natural seasonal patterns of functionally key insect communities may comprise an important and underappreciated consequence of global environmental change that must be better understood across Earth's biomes.</span></p>
Data from: Evapotranspiration is resilient in the face of land cover and climate change in a humid temperate catchment
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A global synthesis on land-cover changes in watersheds shaping freshwater detrital food webs
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Conservation of woody species in China under future climate and land-cover changes
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.