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115 results for “Land use and land cover”
Historical and future land use and land cover data for the STARS4Water river basins
<p>Dataset contains data on historical and future land use and land cover for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average area fraction of five general land use classes (crop, forest, grass, urban and other) within the project river basins was calculated at five-year intervals starting in 2016 and ending in 2051. This dataset was prepared based on the data available in the "LUCAS LUC future land use and land cover change dataset for Europe (Version 1.1)" repository (Hoffmann et al., 2022, DOI: 10.26050/WDCC/LUC_future_EU_v1.1).</p>
North Temperate Lakes LTER Northern Highland Lake District Land Use - Land Cover 1930s
Land use/land cover was interpreted from historical aerial photographs for riparian buffer zones surrounding 50 lakes in Vilas County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
North Temperate Lakes LTER Northern Highland Lake District Land Use - Land Cover 1960s
Land use/land cover was interpreted from historical aerial photographs for riparian buffer zones surrounding 50 lakes in Vilas County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
North Temperate Lakes LTER Northern Highland Lake District Land Use - Land Cover 1990s
Land use/land cover was interpreted from historical aerial photographs for riparian buffer zones surrounding 50 lakes in Vilas County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
North Temperate Lakes LTER Yahara Lakes District Land Use - Land Cover 1930s
Land use/land cover was interpreted from historical aerial photographs for selected watersheds in Dane County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
North Temperate Lakes LTER Yahara Lakes District Land Use - Land Cover 1960s
Land use/land cover was interpreted from historical aerial photographs for selected watersheds in Dane County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
North Temperate Lakes LTER Yahara Lakes District Land Use - Land Cover 1990s
Land use/land cover was interpreted from historical aerial photographs for selected watersheds in Dane County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
MODIS MCD12Q1 Land Cover and Land Use Time Series Global Mosaics 2001-2022 (500 m)
<p><strong>General Description</strong></p> <p>The yearly land use and land cover dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD12Q1"><abbr title="MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m">MCD12Q1 v061</abbr></a>. This data provides an yearly mosaics of land use and land cover data from 2001 to 2022 in cloud optimized Geotiff (COG) format. This dataset includes layers of land cover type 1 (t1), 2 (t2), and 5 (t5), land cover property 1 (p1) and 2 (p2), land cover property assessment 1 (p1a) and 2 (p2a), and land cover quality control (qc). </p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> 2001–2022</li> <li><strong>Type of data:</strong> Land cover and land use</li> <li><strong>How the data was collected or derived:</strong> Derived from MCD12Q1 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>.</li> <li><strong>Statistical methods used:</strong> None</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km)</li> <li><strong>Image size:</strong> 86,400 x 35,849</li> <li><strong>File format:</strong> Cloud optimized Geotiff.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, 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">GitLab Issues</a></li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li> </ul> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: </p> <ol> <li>generic variable name: lc = Land cover</li> <li>variable procedure combination: mcd12q1v061.t1 = MCD12Q1 v061 LC Type1 band</li> <li>Position in the probability distribution / variable type: c = class | p = probability</li> <li>Spatial support: 500m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20010101 = 2001-01-01</li> <li>Time reference end time: 20011231 = 2001-12-31</li> <li>Bounding box: go = global (without Antarctica)</li> <li>EPSG code: epsg.4326 = EPSG:4326</li> <li>Version code: v20230818 = creation date</li> </ol>
Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020
## overview The project extends the long-term, LULC datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six land-use/land-cover (LULC) maps at 30 m resolution were previously created from 1985 to 2010 at five-year intervals (Zhang and Li 2017). This project updates that suite with maps for 2015 and 2020. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The maps comprise 11 land-use/land-cover classes with an overall accuracy of 89.1% for 2015 and 89.6% for 2020. ## literature cited - Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).
A Map of Land Use and Land Cover in Southern Malawi Derived from Sentinel-2 Data (2023)
<h3><strong>Overview</strong></h3> <p>The land use and land cover map comprises the Mulanje and Phalombe districts, in Southern Malawi. It includes five classes: forest, natural vegetation, cropland, wetland, and other lands. The map is derived from Sentinel-2 mosaics, resulting in a spatial resolution of 10 meters, for 2023. </p> <p> </p> <h3><strong>Map Accuracy</strong></h3> <p>The land use and land cover map achieves an overall accuracy of 89%. Details of user and producer accuracies are provided in Table 1.</p> <p>Table 1: Land use and land cover classification validation,including overall, producer (PA) and user (UA) accuracies values for each class.</p> <div> <table> <tbody> <tr> <td> <p><strong>Class </strong></p> </td> <td> <p><strong>Producer Accuracy</strong></p> </td> <td> <p><strong>User Accuracy</strong></p> </td> </tr> <tr> <td> <p>Cropland</p> </td> <td> <p> 93%</p> </td> <td> <p> 85%</p> </td> </tr> <tr> <td> <p>Wetland</p> </td> <td> <p> 100%</p> </td> <td> <p> 100%</p> </td> </tr> <tr> <td> <p>Other Lands</p> </td> <td> <p> 90%</p> </td> <td> <p> 95%</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p> 79%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p>Natural Vegetation</p> </td> <td> <p> 90%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p><strong>Overall Accuracy</strong></p> </td> <td><br> <p><strong> 89%</strong></p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3><strong>Files descripion</strong></h3> <ul> <li>MLW_Sentinel_LULC_2023.tif / .qml: land use and land cover map and QGIS style file</li> <li>training_samples.gpkg: training samples with class labels</li> <li>validation_samples.gpkg: validation samples with class labels</li> </ul>
Navigating deep learning strategies for large-area land cover mapping using very-high-resolution imagery in Senegal: Validation Data
<p><span><span>R</span><span>apid</span><span> advances in deep learning</span><span> for</span> <span>land cover </span><span>classification of </span><span>trees, shrubs and </span><span>very small</span> <span>agricultur</span><span>al</span> <span>fields</span> <span>using</span> <span>very high</span><span>-</span><span>resolution satellite </span><span>data </span><span>(< 2 m</span><span>)</span><span>,</span><span> has tremendous potential</span> <span>for resolving </span><span>current</span><span> challenges </span><span>in </span><span>quantifying</span> <span>land cover </span><span>change </span><span>in</span> <span>sub-</span><span>Saharan</span> <span>African (SSA</span><span>)</span><span>,</span> <span>due to</span> <span>growing </span><span>demand for food resources</span><span>.</span> <span>We</span> <span>conducted experiments </span><span>with</span><span> different training strategies for scaling up </span><span>UNet</span> <span>convolutional neural network </span><span>models for regional land cover mapping with multispectral </span><span>WorldView</span><span> (WV</span><span>)</span><span>-2 and –3,</span><span> imagery</span><span> in</span><span> three distinct regions of Senegal </span><span>which</span> <span>has</span><span> complex </span><span>seasonal wet/dry conditions and </span><span>cropland-savanna mosaics. </span></span></p> <p>The validation exercise of this research consisted in validating more than 70,000 km<sup>2</sup> across Senegal. The infrastructure was setup in the NASA SMCE system with a total of twelve George Mason University (GMU) students participating as operators. These operators validated more than 59 WV-2 and -3 images, each consisting of 200 stratified points in 5,000 x 5,000-pixel images. This effort resulted in a total of ~35,000 aggregated observations that are available through the eo-validation API for public consumption. Each validation point from this dataset has three individual observations.</p>
Supervised land cover classification using Google Earth Engine in Córdoba, Argentina, 2018-2020
Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.
A Land-use/Land Cover Classification of Baltimore City in 1953
Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 113 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County immediately surrounding the city. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures an ecologically and socially important moment in the post-war history of the city. It can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.
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>
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>
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>
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>
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>
Multi-decade land use and land cover samples for Brazil based in a stratified sampling design and visual interpretation of Landsat data (1985 — 2018)
<p>This dataset is composed by 85,152 random points throughout the Brazilian territory selected according to a stratified sampling design, based in 127 regular regions and six slope classes (<a href="https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1-arc?qt-science_center_objects=0#qt-science_center_objects">SRTM</a>). Each sample was visually inspected by three independent interpreters, which associated all the land use and land cover (LULC) changes between 1985 and 2018, on a <strong>yearly basis</strong>, using as reference two <strong>Landsat</strong> images per year, a <strong>MODIS</strong> NDVI time series and high resolution images from <strong>Google Earth</strong>. </p> <p>This process was guided by a <a href="https://www.lapig.iesa.ufg.br/chave/">reference labeling protocol</a> which established the follow LULC classes:</p> <ul> <li><strong>Annual crop:</strong> Areas occupied with short to medium-term crops, usually with a vegetative cycle of less than one year, which after harvest needs to be re-planted. </li> <li><strong>Aquaculture:</strong> Artificial lakes, where aquaculture and/or salt production activities predominate</li> <li><strong>Beach and dune (Other):</strong> Sandy areas, with bright white color, where there is no vegetation predominance of any kind.</li> <li><strong>Forest formation:</strong> Vegetation types with predominance of tree species, with continuous canopy formation</li> <li><strong>Grassland formation:</strong> Grassland formations with predominance of herbaceous stratum</li> <li><strong>Mangrove (Other):</strong> Dense and Evergreen Forest formations, often flooded by tide and associated with the mangrove coastal ecosystem.</li> <li><strong>Mining (Other):</strong> Areas where clear signs of extensive mineral extractions are present, shows clear exposure of the soil by the action of heavy machinery. Only regions surrounding the AhkBrasilien (AHK) and the CPRM digital reference data were considered.</li> <li><strong>Not observed:</strong> Areas blocked by clouds or atmospheric noise, or with absence of ground observation masked out from analysis.</li> <li><strong>Other non-forest natural formations:</strong> Marshes (with fluvio-marine influence).</li> <li><strong>Other non-vegetated area (Other):</strong> Non-permeable surface areas (infrastructure, urban expansion or mining) not mapped into their classes</li> <li><strong>Pasture:</strong> Pasture areas, natural or planted, related with farming activity. In particular in the Pampa and Pantanal biomes part of the area classified as Grassland Formation also includes pasture areas.</li> <li><strong>Perennial crop:</strong> Areas occupied with crops with a long cycle (more than one year), which allow successive harvests without the need for new crop. </li> <li><strong>Rocky outcrop (Other)</strong>: Naturally exposed rocks without soil cover, often with the partial presence of rupicolous vegetation and high slope. </li> <li><strong>Salt flat (Other):</strong> "Apicuns" or Salt flats are formations often without tree vegetation, associated to a higher, hypersaline and less flooded area in the mangrove, generally in the transition between this area and the continent.</li> <li><strong>Savanna formation:</strong> Savanna formations with defined tree and shrub-herbaceous stratum</li> <li><strong>Semi-perennial crop:</strong> Cultivated areas with sugar cane</li> <li><strong>Tree plantation:</strong> Planted tree species for commercial use (e.g. Eucalyptus, Pinus and Araucaria)</li> <li><strong>Urban infrastructure:</strong> Urban areas with predominance of non-vegetated surfaces, including roads, highways and constructions.</li> <li><strong>Water:</strong> Rivers, lakes, dams, reservoir and other water bodies</li> <li><strong>Wetland:</strong> Wetlands with fluvial influence or swampy areas</li> </ul> <p>To enable a proper area estimation and accuracy assessment (<a href="https://www.tandfonline.com/doi/abs/10.1080/01431161.2014.930207">Stehman, 2014</a>) the dataset is provided with the <strong>sampling probability</strong> for each sample (<em>brazil_lulc_samples_1985_2018</em> and <em>brazil_lulc_samples_1985_2018_row_wise</em>) and the <strong>sampling weight</strong> (<em>brazil_lulc_samples_1985_2018_row_wise</em>), which was adjusted to disregard the "<strong>Not observed" </strong>class. The number of votes for the associated LULC class (visual interpretation agreement) and an indication if the sample is between two different LULC<strong> </strong>classes (<strong>border flag</strong>) are also provided.</p> <p>The samples were used to produce several <strong><a href="https://github.com/lapig-ufg/tvi-analysis">area estimation analyses</a></strong>, including land use and land cover dynamics, historical deforestation and agricultural expansion of Brazil. A publication describing in detail the methodology and the analysis is under preparation.</p>
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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.