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528 results for “Land cover”
Capturing land cover and land use with street level imagery
<p>This dataset, collected in September 2018, contains street-level photographs captured by three cameras fixed on the roof of car. A field survey was focused in the Vojvodina, Serbia to more closely examine land cover/land use within croplands monitored by LandSense citizen scientists (March-September 2018).</p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of data collection: Sep 2018</li> <li>Total number of photographs: 26759</li> <li>Region of interest: Vojvodina - Ruma municipality (Serbia)</li> </ul> <p>Associated files: Serbia Streetlevelimagery2018 – Attributes.txt, Serbia Streetlevelimagery2018.csv, Serbia Streetlevelimagery2018.zip</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://ec.europa.eu/jrc/en">Joint Research Centre</a> and <a href="https://inosens.rs/">InoSens</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery
<p>A novel global 30-m land-cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5°×5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile.</p>
A Dataset of European Union Land Cover Validation Samples
<p>A dataset of European Union land cover validation samples in 2015 and 2010 based on the LUCAS micro dataset ( publicly available at <a href="https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid">https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid</a> ) . The dataset provides 9 land cover types of land cover including cropland, forest, grassland, shrubland, wetland, water, bareland, impervious surface and permanent snow/ice. The dataset is provided in .csv format.</p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2018: Globe
<p>Consolidated epoch 2018 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include </p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2017: Globe
<p>Consolidated epoch 2017 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe
<p>Near real time epoch 2019 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Land cover, landscape metrics and typology of European cities for Urban Forest Ecosystem Services (UFES) evaluation
<p>The data refers to the paper "<em>Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities</em>"</p> <p>The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them.</p> <p>The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.</p> <p> </p> <p>The datasets are a csv file (<code>Metrics.csv</code>) and a shapefile (<code>UFES.shp</code>) of polygons with attributes.</p> <ul> <li> <p><code>UFES.shp</code> attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA.</p> </li> <li> <p><code>Metrics.csv</code> contains the FUA codes, the land cover and landscape metrics used in the HCA.</p> </li> </ul> <p> </p>
ELC10: European 10 m resolution land cover map 2018
<p>Refer to preprint here: https://arxiv.org/abs/2104.10922</p> <p>A land cover classification for Europe at 10 m resolution produced with a machine learning workflow driven by Sentinel optical and radar satellite imagery. The classification model was trained on land cover reference data form the LUCAS (Land Use/Cover Area frame Survey) dataset. The map represents conditions in 2018.</p> <p>The methodology is currently under review, but this will be updated as soon as the paper is available online. Please refer to the publication for accuracy estimates and usage guidelines.</p> <p>The map is split up into a number of raster tiles with the coordinate reference system "EPSG:3035 - ERTS89 / LAEA Europe".The filename of each tile is in the form baseFilename-yMin-xMin where xMin and yMin are the coordinates of each tile within the overall bounding box of the entire ELC10 image.</p> <p>The pixel values, their definitions and suggested hex color codes include: 0 (not mapped #000000), 1 (Artificial land, #CC0303), 2 (Cropland, #CDB400), 3 (Woodland, #235123), 4 (Shrubland, #B76124), 5 (Grassland, #92AF1F), 6 (Bare land, #F7E174), 7 (Water/permanent snow/ice, #2019A4), 8 (Wetland, #AEC3D6).</p>
AntarcticaLC2000: The new Antarctic land cover database for the year 2000
<p>Antarctic Land Cover Database for the Year 2000 (AntarcticaLC2000) was produced using Landsat Enhanced Thematic Mapper Plus (ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer (MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100,000-scale. Three land cover types were included in this map, separately, ice-free rocks, blue ice, and snow/firn. This classification legend was determined based on a review of the land cover systems in Antarctica (LCCSA) and an analysis of different land surface types and the potential of satellite data. Image classification was conducted through a combined usage of computer-aided and manual interpretation methods. Results show that the areas and percentages of ice-free rocks, blue ice, and snow/firn are 73,268.81 km<sup>2</sup> (0.537%), 225,937.26 km<sup>2</sup> (1.656%), and 13,345,460.41 km<sup>2</sup> (97.807%), respectively. The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality.</p>
The 30 m annual land cover datasets and its dynamics in China from 1985 to 2024
<p>Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD. </p> <p>"*_albert.tif" are projected files via a proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".</p> <p>CLCD in 2024 is now available.</p> <p>1. Given that the USGS no longer maintains the Landsat Collection 1 data, we are now using the <a href="https://www.usgs.gov/landsat-missions/landsat-collection-2">Collection 2</a> SR data to update the CLCD.</p> <p>2. All files in this version have been exported as Cloud Optimized GeoTIFF for more efficient processing on the cloud. Please check <a href="https://www.cogeo.org/">here</a> for more details.</p> <p>3. Internal overviews and color tables are built into each file to speed up software loading and rendering.</p>
Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt
<p>Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt in 2019/2020. Total of samples: 957. The process to collect them is described in Chaves, M., & Sanches, I. (2023). Improving crop mapping in Brazil's Cerrado from a data cubes-derived Sentinel-2 temporal analysis. Remote Sensing Applications: Society and Environment, 32, 101014. <a href="https://www.sciencedirect.com/science/article/pii/S2352938523000964">https://www.sciencedirect.com/science/article/pii/S2352938523000964</a> and Chaves, M., Soares, A., Mataveli, G., Sánchez, A., & Sanches, I. (2023). A semi-automated workflow for LULC mapping via Sentinel-2 data cubes and spectral indices. Automation, 4(1), 94-109. <a href="https://www.mdpi.com/2673-4052/4/1/7">https://www.mdpi.com/2673-4052/4/1/7</a>.</p>
Europe and China Refined Land cover (ECRLC) (10m)
<p>Europe and China Refined Land cover (ECRLC) V2 is a 10 m Sentinel 1 & 2-based land cover database for Paris Region (Europe), Aarhus (Europe), Velika Gorica (Europe), Beijing (China), Shanghai (China), and Ningbo (China) for 2020.</p>
Multiple Land-use / Land-cover Dataset (MLULC)
<p>This dataset covers the French metropolitan territory (500,000km²). It includes</p> <ul> <li> <p>Six open access land-cover maps from various providers (<a href="https://land.copernicus.eu/pan-european/corine-land-cover">CLC</a>, <a href="https://land.copernicus.eu/global/products/lc">CGLS-LC100</a>, <a href="https://theia.cnes.fr/atdistrib/rocket/#/search?collection=OSO">OSO</a>, <a href="https://geoservices.ign.fr/ocsge">OCS-GE cover</a>, <a href="https://geoservices.ign.fr/ocsge">OCS-GE use</a>, <a href="https://www.data.gou v.fr/en/datasets/mode-doccupation-du-sol-mos-en-11-postes-en-2017/">MOS</a>).</p> </li> <li> <p> A May 2019 Sentinel-2 L3A mosaic ( cloudless image using all maps available during a month) including RVB and NIR. Provided by <a href="https://theia.cnes.fr/atdistrib/rocket/#/search?page=1&collection=SENTINEL2&processingLevel=LEVEL3A">Theia</a>.</p> </li> <li> <p>A Manually built ground truth of 2300 random points annotated with their labels in each map nomenclature.</p> </li> <li> <p>A consolidated ground truth with the original 2300 and 400 non-random points focusing on rare classes.</p> </li> <li> <p>A suggested train/val/test split (60%,5%,35%). Note that all ground truth points belong to patches of the suggested <strong>test</strong> set.</p> </li> </ul> <p>Since this dataset is intended to be used with a deep learning algorithm, the data is split into tiles of 6x6km² following a grid given with the dataset.</p> <p>More information is provided in README.</p> <p>Note that exception made of the ground truth, all the data (Land covers and Sentinel-Images) aren't our property and are only shared as authorized by their respective original license.</p>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (CMG)
<p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset is available at .... After reprojection and aggregation, the global Climate Modeling Grid (CMG) albedo climatology datasets at 0.05° and 0.5° are available here. All of the published datasets include historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached in the CMG files. The International Geosphere-Biosphere Programme (IGBP) and PFT classification results of MCD12Q1 since 2001 were reprojected and aggregated to 0.05° and 0.5° by find mode in each aggregation group. In order to check the heterogeneity of the land cover climatology, the percentage of the dominant type in each aggregation group was also calculated.</p>
Annual maps of cropland abandonment, land cover, and other derived data for time-series analysis of cropland abandonment
<p>This archive contains raw annual land cover maps, cropland abandonment maps, and accompanying derived data products to support:</p> <blockquote> <p>Crawford C.L., Yin, H., Radeloff, V.C., and Wilcove, D.S. 2022. Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate. <em>Science Advances</em> <a href="https://doi.org/10.1126/sciadv.abm8999">doi.org/10.1126/sciadv.abm8999</a><em>.</em></p> </blockquote> <p>An archive of the analysis scripts developed for this project can be found at: <a href="https://github.com/chriscra/abandonment_trajectories">https://github.com/chriscra/abandonment_trajectories</a> (<a href="https://doi.org/10.5281/zenodo.6383127">https://doi.org/10.5281/zenodo.6383127</a>).</p> <p>Note that the label "_2022_02_07" in many file names refers to the date of the primary analysis. "dts” or “dt” refer to “data.tables," large .csv files that were manipulated using the data.table package in R (Dowle and Srinivasan 2021, <a href="http://r-datatable.com/">http://r-datatable.com/</a>). “Rasters” refer to “.tif” files that were processed using the raster and terra packages in R (Hijmans, 2022; <a href="https://rspatial.org/terra/">https://rspatial.org/terra/</a>; <a href="https://rspatial.org/raster">https://rspatial.org/raster</a>).</p> <p>Data files fall into one of four categories of data derived during our analysis of abandonment: <strong>observed</strong>, <strong>potential</strong>, <strong>maximum</strong>, or <strong>recultivation</strong>. Derived datasets also follow the same naming convention, though are aggregated across sites. These four categories are as follows (using “age_dts” for our site in Shaanxi Province, China as an example):</p> <ol> <li><strong>observed</strong> abandonment identified through our primary analysis, with a threshold of five years. These files do not have a specific label beyond the description of the file and the date of analysis (e.g., shaanxi_age_2022_02_07.csv);</li> <li><strong>potential</strong> abandonment for a scenario without any recultivation, in which abandoned croplands are left abandoned from the year of initial abandonment through the end of the time series, with the label “_potential” (e.g., shaanxi_potential_age_2022_02_07.csv);</li> <li><strong>maximum</strong> age of abandonment over the course of the time series, with the label “_max” (e.g., shaanxi_max_age_2022_02_07.csv);</li> <li><strong>recultivation </strong>periods, corresponding to the lengths of recultivation periods following abandonment, given the label “_recult” (e.g., shaanxi_recult_age_2022_02_07.csv).</li> </ol> <p> </p> <p><strong>This archive includes multiple .zip files, the contents of which are described below:</strong></p> <ul> <li><strong>age_dts.zip</strong> - Maps of abandonment age (i.e., how long each pixel has been abandoned for, as of that year, also referred to as length, duration, etc.), for each year between 1987-2017 for all 11 sites. These maps are stored as .csv files, where each row is a pixel, the first two columns refer to the x and y coordinates (in terms of longitude and latitude), and subsequent columns contain the abandonment age values for an individual year (where years are labeled with "y" followed by the year, e.g., "y1987"). Maps are given with a latitude and longitude coordinate reference system. Folder contains observed age, potential age (“_potential”), maximum age (“_max”), and recultivation lengths (“_recult”) for all sites. Maximum age .csv files include only three columns: x, y, and the maximum length (i.e., “max age”, in years) for each pixel throughout the entire time series (1987-2017). Files were produced using the custom functions "cc_filter_abn_dt()," “cc_calc_max_age()," “cc_calc_potential_age(),” and “cc_calc_recult_age();” see "_util/_util_functions.R."</li> <li><strong>age_rasters.zip</strong> - Maps of abandonment age (i.e., how long each pixel has been abandoned for), for each year between 1987-2017 for all 11 sites. Maps are stored as .tif files, where each band corresponds to one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Folder contains observed age, potential age (“_potential”), and maximum age (“_max”) rasters for all sites. Maximum age rasters include just one band (“layer”). These rasters match the corresponding .csv files contained in "age_dts.zip.”</li> <li><strong>derived_data.zip</strong> - summary datasets created throughout this analysis, listed below.</li> <li><strong>diff.zip</strong> - .csv files for each of our eleven sites containing the year-to-year lagged differences in abandonment age (i.e., length of time abandoned) for each pixel. The rows correspond to a single pixel of land, and the columns refer to the year the difference is in reference to. These rows do not have longitude or latitude values associated with them; however, rows correspond to the same rows in the .csv files in "input_data.tables.zip" and "age_dts.zip." These files were produced using the custom function "cc_diff_dt()" (much like the base R function "diff()"), contained within the custom function "cc_filter_abn_dt()" (see "_util/_util_functions.R"). Folder contains diff files for observed abandonment, potential abandonment (“_potential”), and recultivation lengths (“_recult”) for all sites.</li> <li><strong>input_dts.zip</strong> - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 <em>Remote Sensing of Environment </em>(<a href="https://doi.org/10.1016/j.rse.2020.111873">https://doi.org/10.1016/j.rse.2020.111873</a>)<em>. </em>Like “age_dts,” these maps are stored as .csv files, where each row is a pixel and the first two columns refer to x and y coordinates (in terms of longitude and latitude). Subsequent columns contain the land cover class for an individual year (e.g., "y1987"). Note that these maps were recoded from Yin et al. 2020 so that land cover classification was consistent across sites (see below). This contains two files for each site: the raw land cover maps from Yin et al. 2020 (after recoding), and a “clean” version produced by applying 5- and 8-year temporal filters to the raw input (see custom function “cc_temporal_filter_lc(),” in “_util/_util_functions.R” and “1_prep_r_to_dt.R”). These files correspond to those in "input_rasters.zip," and serve as the primary inputs for the analysis.</li> <li><strong>input_rasters.zip</strong> - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 <em>Remote Sensing of Environment. </em>Maps are stored as ".tif" files, where each band corresponds one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Maps are given with a latitude and longitude coordinate reference system. Note that these maps were recoded so that land cover classes matched across sites (see below). Contains two files for each site: the raw land cover maps (after recoding), and a “clean” version that has been processed with 5- and 8-year temporal filters (see above). These files match those in "input_dts.zip."</li> <li><strong>length.zip</strong> - .csv files containing the length (i.e., age or duration, in years) of each distinct individual period of abandonment at each site. This folder contains length files for observed and potential abandonment, as well as recultivation lengths. Produced using the custom function "cc_filter_abn_dt()" and “cc_extract_length();” see "_util/_util_functions.R."</li> </ul> <p><strong>derived_data.zip</strong> contains the following files:</p> <ul> <li>"<strong>site_df.csv</strong>" - a simple .csv containing descriptive information for each of our eleven sites, along with the original land cover codes used by Yin et al. 2020 (updated so that all eleven sites in how land cover classes were coded; see below).</li> <li><strong>Primary derived datasets </strong>for both observed abandonment (“area_dat”) and potential abandonment (“potential_area_dat”). <ul> <li><strong>area_dat</strong> - Shows the area (in ha) in each land cover class at each site in each year (1987-2017), along with the area of cropland abandoned in each year following a five-year abandonment threshold (abandoned for >=5 years) or no threshold (abandoned for >=1 years). Produced using custom functions "cc_calc_area_per_lc_abn()" via "cc_summarize_abn_dts()". See scripts "cluster/2_analyze_abn.R" and "_util/_util_functions.R."</li> <li><strong>persistence_dat</strong> - A .csv containing the area of cropland abandoned (ha) for a given "cohort" of abandoned cropland (i.e., a group of cropland abandoned in the same year, also called "year_abn") in a specific year. This area is also given as a proportion of the initial area abandoned in each cohort, or the area of each cohort when it was first classified as abandoned at year 5 ("initial_area_abn"). The "age" is given as the number of years since a given cohort of abandoned cropland was last actively cultivated, and "time" is marked relative to the 5th year, when our five-year definition first classifies that land as abandoned (and where the proportion of abandoned land remaining abandoned is 1). Produced using custom functions "cc_calc_persistence()" via "cc_summarize_abn_dts()". See scripts "cluster/2_analyze_abn.R" and "_util/_util_functions.R." This serves as the main input for our linear models of recultivation (“decay”) trajectories.</li> <li><strong>turnover_dat</strong> - A .csv showing the annual gross gain, annual gross loss, and annual net change in the area (in ha) of abandoned cropland at each site in each year of the time series. Produced using custom functions "cc_calc_abn_diff()" via "cc_summarize_abn_dts()" (see "_util/_util_functions.R"), implemented in "cluster/2_analyze_abn.R." This file is only produced for observed abandonment.</li> </ul> </li> <li><strong>Area summary files </strong>(for observed abandonment only) <ul> <li><strong>area_summary_df</strong> - Contains a range of summary values relating to the area of cropland abandonment for each of our eleven sites. All area values are given in hectares (ha) unless stated otherwise. It contains 16 variables as columns, including 1) "site," 2) "total_site_area_ha_2017" - the total site area (ha) in 2017, 3) "cropland_area_1987" - the area in cropland in 1987 (ha), 4) "area_abn_ha_2017" - the area of cropland abandoned as of 2017 (ha), 5) "area_ever_abn_ha" - the total area of those pixels that were abandoned at least once during the time series (corresponding to the area of potential abandonment, as of 2017), 6) "total_crop_extent_ha" - the total area of those pixels that were classified as cropland at least once during the time series, 7) "total_area_abn_remaining_2017" - duplicate of "area_abn_ha_2017," the area abandoned as of 2017 (ha), taken from "area_recult_threshold," 8) "total_initial_area_abn" - the sum of the initial area of each cohort of abandonment when it is first classified as "abandoned," i.e., at the 5 year mark (note that this is cumulative, and because it counts those pixels that were abandoned more than once, it is therefore larger than "area_ever_abn_ha"), taken from "area_recult_threshold" 9) "total_area_abn_recultivated_2017" - the area of abandoned land that was recultivated as of 2017 (cumulatively, i.e., "total_initial_area_abn" - "area_abn_ha_2017"), taken from "area_recult_threshold," 10) "proportion_recultivated" - the proportion of all abandoned cropland (including multiple periods per pixel) that was recultivated by 2017, taken from "area_recult_threshold," 11) "area_2017_as_prop_site" - area abandoned as of 2017 as a proportion of the total site area, 12) "area_2017_as_prop_total_crop" - area abandoned as of 2017 as a proportion of the total crop extent, 13) "area_2017_as_prop_crop87" - area abandoned as of 2017 as a proportion of cropland area in 1987, 14) "area_ever_abn_as_prop_site" - area ever abandoned as a proportion of the total site area, 15) "area_ever_abn_as_prop_total_crop" - area ever abandoned as a proportion of the total crop extent, 16) "area_ever_abn_as_prop_crop87" - area ever abandoned as a proportion of cropland area in 1987. See script "1_summary_stats.Rmd."</li> <li><strong>area_recult_threshold</strong> - Contains data on the proportion of observed abandoned cropland area that is recultivated by the end of our time series. This includes the area of abandoned cropland as of 2017 ("total_area_abn_remaining_2017") and the sum of the initial area of each cohort of abandonment when it is first classified as abandoned (at year 5; "total_initial_area_abn"). This "total_initial_area_abn" is cumulative, and allows for pixels that were abandoned multiple times during the time series to be counted multiple times. The difference between these two columns yields the "total_area_abn_recultivated_2017," which in turn is used to calculate the "proportion_recultivated," and the (ascending) "order" of sites based on this proportion. This file includes recultivation stats for each site for three abandonment definitions: 5, 7, and 10 years. See script "1_summary_stats.Rmd."</li> <li><strong>abn_lc_area_2017</strong> - Contains the number of pixels and corresponding area (in ha) of abandoned cropland in the year 2017 at each site, according to the land cover class (either woody vegetation [2], or herbaceous vegetation [4]) and the age in 2017 (5 to 30 years). See script "cluster/6_lc_of_abn.R."</li> <li><strong>abn_prop_lc_2017 </strong>- Contains the number of pixels and corresponding area (ha) of cropland abandoned in the year 2017 in each land cover type (woody vegetation [2], or herbaceous vegetation [4]). It also shows this area as a proportion of the total area abandoned at each site (i.e., in either land cover class: 2 or 4). See script "cluster/6_lc_of_abn.R."</li> </ul> </li> <li><strong>Carbon</strong> <ul> <li><strong>carbon_df </strong>– contains the observed and potential carbon accumulation in abandoned croplands in each site in each year (in Mg C), for two abandonment thresholds: 5 years (our default abandonment definition) and 1 year (i.e., no threshold). Each data point corresponds to one of two scenarios (“type” column), either “observed” or “potential.” Carbon accumulation figures are for both the sum of forest and soil carbon at each site in a given year. Carbon accumulation is listed in three columns: 1) “C_up_to_20” contains the total carbon accumulated in those abandoned croplands with abandonment durations between 5 and 20 years. 2) “C_21_30” contains the total carbon accumulation in croplands with durations between 21 and 30 years, which are differentiated in order to account for non-linear carbon accumulation rates in soils over time, and 3) “total_C_Mg” contains the sum of the previous two columns, representing the total carbon accumulated across all abandoned croplands in each year.</li> <li><strong>soc_mean</strong> – contains mean soil organic carbon accumulation rates for years 1-20 and years 21-80, derived from Sanderman et al. 2020 (in Mg C; <a href="https://doi.org/10.7910/DVN/HA17D3">https://doi.org/10.7910/DVN/HA17D3</a>). These values correspond to accumulation rates in croplands upon abandonment and regeneration to natural vegetation (Sanderman et al. 2020’s “rewilding” scenario). These mean values are calculated across those pixels identified as cropland by Sanderman et al. 2020 at each site. Mean values in year 20 and 80 are contained in columns “mean_soc_20” and “mean_soc_80” respectively, and the annualized rate over the first 20 years and the subsequent years 21 through 80 are contained in columns “mean_annual_soc_1_20” and “mean_annual_soc_21_80” respectively.</li> </ul> </li> <li><strong>Decay model data</strong> – two R data files containing data products for our linear models of abandonment recultivation trajectories. <ul> <li><strong>decay_endpoints_files</strong> – an R data file (.rds) containing seven data products produced as part of our common endpoint analysis, which calculated mean trajectories for each site across a range of common endpoints, ensuring that means were based on coefficient estimates derived from a consistent number of observations for each cohort. These files are: <ul> <li><strong>common_endpoint_dat – </strong>a .csv containing subsets of “persistence_dat” for each “endpoint” (7 through 29).</li> <li><strong>endpoint_n – </strong>a .csv describing, for each endpoint, the corresponding number of observations per cohort (“n_obs”), the number of cohorts (“n_cohorts”), the total number of observations across cohorts included (“total_obs”), and the cohorts that meet the endpoint threshold (“cohorts”).</li> <li><strong>coef_l3_endpoints – </strong>corresponding model coefficients for our primary model (“l3”) parameterized by the range of subsets across endpoints.</li> <li><strong>augment_endpoints – </strong>fitted values (i.e., model predictions) for linear models produced across the full range of endpoint subsets.</li> <li><strong>fitted_endpoints – </strong>a simplified .csv containing the mean linear and log coefficients for each site at each endpoint, and the corresponding predicted proportion remaining abandoned through time (based on the “age,” or duration, of abandonment).</li> <li><strong>time_to_endpoints – </strong>a .csv containing, for mean trajectories for each endpoint at each site, the estimated time required for a given amount of abandoned cropland in a cohort to be recultivated (deciles, 10% through 100%).</li> <li><strong>endpoint_half_lives – </strong>a .csv containing the half-lives calculated for the mean trajectories for each endpoint at each site.</li> </ul> </li> <li><strong>decay_mod_archive</strong> - an R data file (.rds) containing eleven data products derived from linear models of abandonment recultivation ("decay"): <ul> <li><strong>lm_mega_lin_log_lin_l</strong> – the primary linear model produced in our analysis. This model is referred to as “lin_log_lin” (or “l3”) because the model predicts linear persistence (“lin”) as a function of a log term of time (“log”) and a linear term of time (“lin”). “mega” refers to the fact that this model is run for the full dataset, pooled across all 11 sites.</li> <li><strong>coef_l3_mega</strong> – a .csv containing model coefficients for our primary linear model of recultivation (“lin_log_lin”, or “l3”), with a single row each for the linear term of time and the log term of time, for 26 cohorts at 11 sites.</li> <li><strong>mean_coef_l3_mega</strong> – a data frame containing the mean coefficient values for the log and linear terms of time across cohorts at each site. This also contains the mean of the low and high coefficient estimates, based on the 95% confidence interval.</li> <li><strong>half_lives_all_cohorts_l3</strong> – half-lives calculated for each cohort at each site, for our primary model.</li> <li><strong>half_life_mean_coefs_l3</strong> – half-lives calculated based on the mean trajectory for each site (based on the mean log coefficients and mean linear coefficients across all cohorts), for our primary model.</li> <li><strong>mod_AIC_mega</strong> – Akaike Information Criterion (AIC) values for all tested model specifications.</li> <li><strong>fitted_combo</strong> – fitted values (i.e., model predictions) for our primary model (“l3”) and a series of alternative model specifications (“l3_trim” – excluding cohorts with fewer than 5 observations; “lin_log” – a model including only one log time term; “log2_lin” – in which the log of persistence is predicted by log and linear time terms; and “l3_no_cohort” – our primary model, predicting linear persistence as a function of log time and linear time, but without cohort-level fixed effects).</li> <li><strong>time_to_combo</strong> – contains the estimated time required for a certain amount of abandoned cropland in a cohort to be recultivated (deciles, 10% through 100%). See script "2_decay_models.Rmd." These values are calculated for a range of alternative model specifications ("l3_trim", “lin_log”, "log2_lin", and "l3_no_cohort"; see above).</li> </ul> </li> </ul> </li> <li><strong>Length data</strong> – includes “_distill_df” files and “mean_length_df” files for observed, potential, and recultivation. <ul> <li><strong>length_distill_df</strong> - .csvs containing the number ("freq") of abandonment periods of a specific "length" of time (i.e., age) at each site over the course of the entire time series. Derived from the "length" files in "length.zip." See script "cluster/5_distill_lengths.R."</li> <li><strong>mean_length_df</strong> - .csvs with the mean, median, and standard deviation, for each site, for both "all" lengths or just the "max" length per pixel, and for a range of abandonment definitions (1, 3, 5, 7, and 10 years). Derived from "length_distill_df." See script "1_summary_stats.Rmd."</li> </ul> </li> <li><strong>Duration summary files</strong> – includes “summary_stats_all_sites” and “summary_stats_all_sites_pooled,” for observed and potential abandonment, and recultivation periods following abandonment. <ul> <li><strong>“summary_stats_all_sites”</strong> - A simple .csv derived from "mean_length_df" files containing summary stats across the 11 sites. This includes the mean of the mean abandonment duration ("length", in years) for each of our 11 sites ("mean_of_means"), the standard deviation of these site mean abandonment lengths ("sd_of_means"), the mean of the standard deviation at each site ("mean_of_sds"), the mean median ("mean_of_medians"), and the mean number of abandonment periods ("mean_n_abn_periods"). Note that length "all" indicates that these stats account for all periods (including multiple per pixel), rather than just the max duration per pixel. See script "1_summary_stats.Rmd."</li> <li><strong>“summary_stats_all_sites_pooled”</strong> - A summary .csv similar to "summary_stats_all_sites," but calculated by pooling all distinct periods of abandonment across all eleven sites, and then calculating the mean, median, and standard deviation of abandonment duration. See script "1_summary_stats.Rmd."</li> </ul> </li> <li><strong>Comparing annual approach to identifying abandonment to a two-timepoint (“2yr”) approach:</strong> <ul> <li><strong>abn_2yr_ages_df</strong> - Contains the age of former croplands identified as "abandoned" using a two-timepoint method (i.e., 2017 - 1987), where age values (as of 2017) are derived from our map of abandonment identified using the full annual time series. This includes the area in hectares (ha), in each age class (along with the number of pixels), at each of our 11 sites. This dataset is used to calculate the percent of cropland "abandonment" identified using the two-year method that is actually too "young," i.e., less than 5 years old, and therefore not truly abandonment according to our five-year abandonment definition</li> <li><strong>abn_2yr_overestimation</strong> - Compares the area (in hectares) of cropland abandonment at each site identified with our full annual time series (and a five-year abandonment definition) and the "abandonment" identified using a two-timepoint method (2017-1987). This also includes the percent difference in area between the two methods, the Jaccard similarity of the areas identified as abandonment, and the percent of "young" (i.e., <5-year-old) "abandonment" identified by the two-timepoint method.</li> </ul> </li> </ul> <p><strong>Input land cover maps:</strong></p> <p>As noted, the file "input_rasters.zip" contain the raw annual land cover maps for eleven sites generated by:</p> <blockquote> <p>Yin, H., A. Brandão, J. Buchner, D. Helmers, B. G. Iuliano, N. E. Kimambo, K. E. Lewińska, E. Razenkova, A. Rizayeva, N. Rogova, S. A. Spawn, Y. Xie, and V. C. Radeloff. 2020. Monitoring cropland abandonment with Landsat time series. <em>Remote Sensing of Environment</em> 246:111873. https://doi.org/10.1016/j.rse.2020.111873</p> </blockquote> <p>These land cover maps served as raw inputs for this project and form the basis of the analysis.</p> <p>All land cover maps have a resolution of 30-m and exist for each year from 1987 through 2017. The exceptions are Nebraska / Wyoming (1986-2018) and Wisconsin (1987-2018); these additional years were excluded from our analysis of abandonment duration.</p> <p><strong>Land cover categories in these maps are coded as follows:</strong></p> <ol> <li>Non-vegetated area (e.g., water, urban, barren land)</li> <li>Woody vegetation (e.g., forests)</li> <li>Cropland</li> <li>Herbaceous vegetation (e.g., grassland)</li> </ol> <p><strong>Site file names correspond to the following geographic locations:</strong></p> <ul> <li>belarus = Vitebsk, Belarus / Smolensk, Russia</li> <li>bosnia_herzegovina = Bosnia & Herzegovina</li> <li>chongqing = Chongqing, China</li> <li>goias = Goiás, Brazil</li> <li>iraq = Iraq</li> <li>mato_grosso = Mato Grosso, Brazil</li> <li>nebraska = Nebraska / Wyoming, USA</li> <li>orenburg = Orenburg, Russia / Uralsk, Kazakhstan</li> <li>shaanxi = Shaanxi/Shanxi, China</li> <li>volgograd = Volgograd, Russia</li> <li>wisconsin = Wisconsin, USA</li> </ul> <p>This dataset is minimally altered from Yin et al. 2020. However, land cover codes were updated for five sites (Iraq, Nebraska/Wyoming, Orenburg/Uralsk, Volgograd, and Wisconsin) in order to maintain consistency in how land cover was coded across all sites. The original land cover codes (matching Yin et al. 2020) are described in the file "site_df.csv" and are as follows:</p> <ol> <li>Iraq: 1 Non-vegetated; 2 Cropland; 3 Woody; 4 Herbaceous</li> <li>Nebraska / Wyoming (USA): 1 Cropland; 2 Woody; 3 Non-vegetated; 4 Herbaceous</li> <li>Orenburg, Russia / Uralsk, Kazakhstan: 1 Non-vegetated; 2 Cropland; 3 Herbaceous; 4 Woody</li> <li>Volgograd (Russia): 1 Non-vegetated; 2 Cropland; 3 Herbaceous; 4 Woody</li> <li>Wisconsin (USA): 1 Cropland; 2 Herbaceous; 3 Woody; 4 Non-vegetated</li> </ol>
Theia OSO Land Cover Map 2020
<p>Land cover map of France based on Sentinel-2 satellite images with iota² chain (https://framagit.org/iota2-project/iota2/)</p>
Theia OSO Land Cover Map 2019
<p>Land cover map of France based on Sentinel-2 satellite images with iota² chain (https://framagit.org/iota2-project/iota2/)</p>
Theia OSO Land Cover Map 2021
<p>Land cover map of France based on Sentinel-2 satellite images with iota² chain (https://framagit.org/iota2-project/iota2/)</p>
OpenStreetMap+ Land Use / Land Cover classes and administrative regions of Europe
<p>This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from <a href="https://www.openstreetmap.org/">OpenStreetMap</a>, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and <a href="https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness#:~:text=The%20imperviousness%20products%20capture%20the,over%20long%20periods%20of%20time.">Copernicus HRL Imperviousness</a> data.</p> <p>The land use / land cover classes are:</p> <ol> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dcommercial">buildings.commercial</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dindustrial">buildings.industrial</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dresidential">buildings.residential</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dcemetery">cemetery</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dconstruction">construction.site</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dlandfill">dump.site (landfill)</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dfarmland">farmland</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dfarmyard">farmyard</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dforest">forest</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dgrass">grass</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dgreenhouse">greenhouse</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dharbour">harbour</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dmeadow">meadow</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dmilitary">military</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dorchard">orchard</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dquarry">quarry</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:railway%3Drail">railway</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dreservoir">reservoir</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:highway%3Droad">road</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dsalt_pond">salt</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dvineyard">vineyard</a></li> </ol> <p>The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.</p> <p>This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)</p> <p>Each layer can be viewed interactively on the Open Data Science Europe data viewer at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Copernicus-OSM%20buildings&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</p>
Outputs of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory)
<p>The dataset contains the outputs of the notebook "Exploring Land Cover Data (Impact Observatory)" published in The Environmental Data Science Book.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.