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528 results for “Land cover”
LBA-ECO ND-01 Primary Forests Land Cover Transition Maps, Rondonia, Brazil: 1975-1999
This data set provides classified land cover transition images (maps) derived from Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery for Ariquemes, Luiza, and Ji-Parana¡ areas in Rondonia, Brazil, at 30-m resolution. Images depict the age relative to the year 2000, of cleared land from the date the land was cut, to the date when primary forests transitioned into nonforest class (for example, 25 = cut by 1975, or 25 years before the year 2000). Temporal changes in three regions are represented by 31 TM scenes acquired between 1984 and 1999, and a pair of MSS scenes from 1975 and 1978. Data are provided as three GeoTiff (*.tif) images, one for each of the three areas.
LBA-ECO LC-09 Land Cover Transitions Maps for Study Sites in Para, Brazil: 1970-2001
This data set includes classified land cover transition maps at 30-m resolution derived from Landsat TM, MSS, ETM+ imagery and aerial photos of Altamira, Santarem, and Ponta de Pedras, in the state of Para, Brazil. The Landsat images were classified into several types of land use (e.g., forest, secondary succession, pasture, annual crops, perennial crops, and water) and subjected to change detection analysis to create transition matrices of land cover change. Dates of acquired images represent the most cloud-free image retrievals from 1970-2001 for each site and are therefore not continuous. There are 3 GeoTIFF files (.tif) with this data set.
CMS: Land Cover Projections (5.6-km) from GCAM v3.1 for Conterminous USA, 2005-2095
The data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled to ~5.6 km (0.05 degree) resolution. For each 5.6 x 5.6 km area, the annual land cover percentage comprised by each of the nineteen different land cover classes/plant functional types (PFTs) of the Community Land Model (CLM) (Table 1) are provided.Results are reported for GCAM runs of three scenarios of future human efforts towards climate mitigation as related to global carbon emissions, radiative forcing, and land cover change. Specific scenario conditions were 1) a reference scenario with no explicit climate mitigation efforts that reaches a radiative forcing level of over 7 W/m2 in 2100, 2) the 2.6 mitigation pathway (MP) scenario which is a very low emission scenario with a mid-century peak in radiative forcing at ~3 W/m2, declining to 2.6 W/m2 in 2100, and 3) the 4.5 MP scenario which stabilizes radiative forcing at 4.5 W/m2 (~ 650 ppm CO2-equivalent) before 2100.These downscaled land cover projections can be used to derive spatially explicit estimates of potential shifts in croplands, grasslands, shrub lands, and forest lands in each future climate scenario.Data are presented as three NetCDF v4 files (.nc4), one for each future climate scenario -- 2.6 MP, 4.5 MP, and GCAM reference).
Land Cover and Vegetation Map, Arctic National Wildlife Refuge
This data set provides a landcover map with 16 landcover classes for the northern coastal plain of the the Arctic National Wildlife Refuge (ANWR) on the North Slope of Alaska. The map was derived from Landsat Thematic Mapper (Landsat TM) data, Digital Elevation Models (DEMs), aerial photographs, existing maps, and extensive ground-truthing. The data used to derive the map cover the period 1982 to 1993.
MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006
The MCD12Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD12Q1 Version 6.1](https://doi.org/10.5067/MODIS/MCD12Q1.061) data product.The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6 data product provides global land cover types at yearly intervals (2001-2020), derived from six different classification schemes listed in the User Guide. The MCD12Q1 Version 6 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes.Layers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property Assessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are provided in each MCD12Q1 Version 6 Hierarchical Data Format 4 (HDF4) file.Known Issues* The "units" field is missing in the metadata, however, this information can be found in the table above or on page 5 of the User Guide.* Known issues are described on pages 3 and 4 of the User Guide.* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).Improvements/Changes from Previous Version* Version 5 used five classification schemes whereas Version 6 uses six classification schemes, including an entirely new classification scheme based on the Land Cover Classification System (LCCS) from the Food and Agricultural Organization (FAO).* New gap-filled spectro-temporal features developed by applying smoothing splines to the Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) time series. This change results in significant changes between land cover classifications in Version 5 and Version 6 data.* Algorithm used in Version 6 includes a post-processing Hidden Markov Model (HMM) that reduces spurious year-to-year variation in class labels.* Algorithm refinements were implemented in upstream MODIS data used as inputs, such as the cloud mask, surface reflectance, and NBAR data products.* The value of "Water" in the IGBP classification scheme has changed from "0" to "17."* The data product should not be used to determine post-classification land cover change between years due to the uncertainty in the land cover labels for any one year. More information can be found on page 2 of the User Guide.* File size is smaller due to HDF internal compression.
ABoVE: Landsat-derived Annual Dominant Land Cover in Boreal North America, 1986-2020
This dataset contains a 30-m resolution time series of annual land cover classifications as the dominant plant functional type class for all of boreal Alaska and Canada from 1986 to 2020. The data were derived from a time series of Landsat Collection 2 Surface Reflectance and processed using the Continuous Change Detection and Classification (CCDC) algorithm. This dataset includes a nine-class land cover scheme. Classification accuracy was assessed using a probability-based random sample, ensuring statistically robust area estimates and uncertainty measures. The classifications were produced using a supervised Random Forest classification model and Canadian National Forest Inventory photo plot data. The data are provided in multiband GeoTIFF file format and distributed by tile in the ABoVE Level B grid.
LBA-ECO ND-30 Fractional Cover of Mixed Land Use Ranches, Para and Rondonia, Brazil
This data set contains images of fractional cover estimates of photosynthetic vegetation (PV) canopy, nonphotosynthetic vegetation (NPV), and exposed soils (S) derived from Landsat images (30-m resolution) obtained for two ranches in the Brazilian Amazon from 1996 to 2002. The Fazenda Vitoria ranch is located in eastern Para near the city of Paragominas and is a mosaic of primary forest, logged forest, secondary forest, and pasture with moderately dissected topography. The Fazenda Nova Vida ranch is located in the state of Rondonia in western Amazonia and is a mosaic of primary forest, logged forest, and pastures. For Fazenda Vitoria, two dry-season Landsat images were obtained, subset, and analyzed. For Nova Vida three dry-season images and one end-of-wet-season image were obtained, subset, and analyzed. Spectral mixture analysis, which decomposes individual satellite pixels into constituent cover fractions of surface materials, was used with a general probabilistic modeling approach to derive subpixel cover fractions of PV, NPV, and S. There are six GeoTIFF (.tif) files with this data set.
LBA Regional Land Cover from AVHRR, 8-km, 1984 (DeFries et al.)
This data set is a subset of an 8-km global land cover product (DeFries et al. 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10° N to 25° S, longitude 30° to 85° W). The data are in ASCII GRID file format.To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies at the University of Maryland employed the NASA/NOAA Pathfinder AVHRR Land (PAL) data set with a spatial resolution of 8 km. The PAL data set has a length of record of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, the data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project's aim was to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data.The global land cover product (Defries et al. 1998) was derived by testing several metrics that describe the temporal dynamics of vegetation over an annual cycle. These metrics were applied to 1984 PAL data at 8-km resolution to derive a global land cover classification product using a decision tree classifier. The final product contains 13 land cover classes. The original 8-km global land cover product is available for download from the University of Maryland's Global Land Cover Facility (GLCF) Web site (http://glcf.umiacs.umd.edu/data/landcover/index.shtml). Additional information and references on this data set can be found at the GLCF Web site, as well as at the LGRSS Web site (http://www.geog.umd.edu/LGRSS/intro.html). More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/comp/land_cover_data_8km/glcf8km_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.
MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006
The MCD12Q2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD12Q2 Version 6.1](https://doi.org/10.5067/MODIS/MCD12Q2.061) data product.The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) Version 6 data product provides global land surface phenology metrics at yearly intervals from 2001 to 2019. The MCD12Q2 Version 6 data product is derived from time series of the 2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. For pixels with more than two valid vegetation cycles, the data represent the two cycles with the largest NBAR-EVI2 amplitudes.Provided in each MCD12Q2 Version 6 Hierarchical Data Format 4 (HDF4) file are layers for the total number of vegetation cycles detected for the product year, the onset of greenness, greenup midpoint, maturity, peak greenness, senescence, greendown midpoint, dormancy, EVI2 minimum, EVI2 amplitude, integrated EVI2 over a vegetation cycle, as well as overall and phenology metric-specific quality information. A low-resolution browse image showing greenup is also available when viewing each MCD12Q2 granule. SDS layers may be multi-dimensional with up to two valid vegetation cycles. For areas where the NBAR-EVI2 values are missing due to cloud cover or other reasons, the data gaps are filled with good quality NBAR-EVI2 values from the year directly preceding or following the product year.Known Issues* Known issues are described on pages 5 and 6 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).Improvements/Changes from Previous Version* Additional Science Dataset (SDS) layers were included in Version 6.* Changes were implemented to better capture phenometrics in systems with multiple vegetation cycles per year. * Version 6 methodological approach increased the reliability of retrieved phenometrics in tropical, arid, and semi-arid ecosystems.* Modifications were designed to more accurately represent phenometrics in systems where NBAR-EVI2 time series do not closely resemble logistic growth patterns.* Version 6 delivers phenometrics in a more usable and intuitive way when vegetation cycles cross calendar boundaries, particularly in the Southern Hemisphere.* Vegetation cycles in a product year are determined by the date of peak NBAR-EVI2 within the product's calendar year.* Date representation changed to days since January 1, 1970. * Overall quality improvements with phenometric-specific quality layers provided.
LBA-ECO LC-01 Landsat TM Land Use/Land Cover, Northern Ecuadorian Amazon: 1986-1999
This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes: Forest, Non-Forest Vegetation, Urban/Barren, and Water; and a fifth class of Clouds/Shadows. The areas of interest were the four Intensive Study Areas (ISA) of the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects: Eastern Intensive Study Area; Northern Intensive Study Area; Southern Intensive Study Area, and Southwestern Intensive Study Area. These areas are in the Northern Ecuadorian Amazon, in the area known as the northern Oriente of Ecuador. The resolution of the data is 30 meters. There are 12 image files (.tif) with this data set.
Data and script:Interactive persistent effects of past land-cover and its trajectory on tropical freshwater biodiversity
<p>This is the unique dataset used to run the analysis of the manuscript: "Interactive persistent effects of past land-cover and its trajectory on tropical freshwater biodiversity". You'll find in the "corumbatai_rb.txt" the catchment's and stream's ID, forest cover (%) within reach contribution areas in 1962, 1972, 1978, 2003 and 2011. The document "aquatic_insects.txt" contains the community data (columns= taxa, rows=sites). We also provide and R code used to analyze the relationship between biodiversity and change in forest cover that occurred across five decades, including landscape trajectories of forest gain and loss.<br> <br> </p>
MONET cost, land cover and CO2 storage capacity data.
<p>This dataset contains cost, land cover and CO<sub>2 </sub>storage capacity data used in the MONET (Modelling and Optimisation of Negative Emissions Technologies) framework. For full MONET model description and dataset see Fajardy (2019) available online at <a href="https://doi.org/10.25560/80691">https://doi.org/10.25560/80691</a>. </p>
Raw datasets for publication: The new Mountain Observatory of the Project "Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification (OCAL)" in the United Arab Emirates: First results on Convection Initiation" - Case studies only (5 and 6 Sept 2018)
<p>Here are zip files containing the raw data sets collected from the Halo Doppler lidar and the Mira Doppler cloud radar from the OCAL Observatory, UAE, on the 5th and 6th September 2018. All RHI and PPI scans at all angles and all time steps from these days are included.</p>
urbisphere_gb-london_UR-5: Gridded land-cover fractions for London, UK
<h2>Files in this archive </h2> <ul> <li>London_landcover.zip <ul> <li>Land-cover fractions in 500-m grid boxes covering Greater London, UK</li> <li>Polygons, ESRI shapefiles (*.shp, *.shx, *.cpg, *.dbf, *.prj)</li> </ul> </li> <li>code.zip <ul> <li>Code to process land cover (Python3)</li> </ul> </li> <li>urbisphere_gb-london_UR-5.pdf <ul> <li>Documentation</li> </ul> </li> </ul> <h2>Data purpose </h2> <p>The data support APEx, <em>urbisphere</em>-London and ASSURE modelling activities, including simulations with the Surface Urban Energy and Water Balance Scheme (<a href="https://suews.readthedocs.io/en/latest/">SUEWS</a>).</p> <h3>Linked with</h3> <ul> <li>Hertwig et al. 2024. urbisphere_gb-london_UR-1: Processing and modelling grid of 500-m horizontal resolution for London, UK. urbisphere–London Data Release and Technical Documentation [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10889756</li> </ul>
Land Cover Aerial Imagery (LICAID) dataset for semantic segmentation
<p><strong>Dataset Highlights:</strong></p> <ul> <li><strong>Title:</strong> Land Cover Aerial Imagery Dataset (LICAID)</li> <li><strong>Focus Area:</strong> Franciacorta wine-growing region, Lombardy, Italy</li> <li><strong>Data Source:</strong> Satellite imagery from Google Earth Pro</li> <li><strong>Classes and Descriptions:</strong> <ol> <li><strong>Grasslands:</strong> Habitats dominated by grasses, with few or no trees, found in various climates from tropical to temperate regions.</li> <li><strong>Arable Land:</strong> Land predominantly used for growing crops.</li> <li><strong>Herb-dominated Habitats:</strong> Areas where non-woody plants (herbs) are the dominant vegetation, including meadows, prairies, marshes, and wetlands.</li> <li><strong>Hedgerows:</strong> Linear strips of vegetation consisting of shrubs, small trees, and grasses, often used to mark boundaries or provide wildlife habitat in agricultural landscapes.</li> <li><strong>Vineyards:</strong> Agricultural landscapes cultivated specifically for growing grapevines, typically for wine production.</li> <li><strong>Tree-dominated Man-made Habitats:</strong> Human-modified landscapes where trees are the predominant vegetation, such as urban parks, orchards, and landscaped gardens.</li> <li><strong>Olea europaea Groves:</strong> Groves or orchards of olive trees, primarily cultivated for the production of olives and olive oil, commonly found in Mediterranean regions.</li> </ol> </li> </ul> <p><strong>gy:</strong></p> <ol> <li> <p><strong>Data Acquisition:</strong></p> <ul> <li>18 orthophoto tiles manually selected from Franciacorta.</li> <li>Satellite imagery and corresponding shape files acquired from Google Earth Pro.</li> <li>Georeferencing of imagery using ArcGIS software.</li> </ul> </li> <li> <p><strong>Data Preparation:</strong></p> <ul> <li>Segmentation using multiresolution segmentation in eCognition software.</li> <li>Validation of segmented images by a plant expert using QGIS software.</li> <li>Manual annotation of seven land cover classes.</li> </ul> </li> </ol>
Code and data used for findings and figures in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region"
<p><span>This is the code and data used in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region" to generate all findings and figures.</span></p>
Figure 1 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 1. The distribution of sample sites on the reclaimed coast.
Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
<p>This dataset comprises processed outputs from an unmanned aerial vehicle (UAV) image acquisition campaign conducted across 27 study sites in Serbia. Each site is organized in a separate folder, labeled by study site name, and includes the following output data for both Object-Based Image Analysis (OBIA) and Convolutional Neural Network (CNN) approaches.</p> <table> <tbody> <tr> <td> <p><span>S.No</span></p> </td> <td> <p><span>Data Alias</span></p> </td> <td> <p><span>File Type</span></p> </td> <td> <p><span>Description</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>name_of_the_study site_multiband (OBIA)</span></p> </td> <td> <p><span>.tif</span></p> </td> <td> <p><span>Five band raster orthomosaic containing RGB, DSM and NDVI layers rescaled from (0-255).</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Vectorized_r3 (OBIA+LSMS)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>The output from the segmentation process and is a basis of preparation for data labeling.</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>train_val_set (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Contain labeled samples of land use classes for training and validation sets for respective study site.</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>ClassifiedVector (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Classified vectorized output in rectangle shape.</span></p> </td> </tr> <tr> <td> <p><span>5</span></p> </td> <td> <p><span>ClassifiedVector_fixed (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Final output of the classified orthomosaic in a vector file containing all the classes in the attribute table.</span></p> </td> </tr> <tr> <td> <p><span>6</span></p> </td> <td> <p><span>RandomForest (OBIA)</span></p> </td> <td> <p><span>.txt</span></p> </td> <td> <p><span>Represents trained model.</span></p> </td> </tr> <tr> <td> <p><span>7</span></p> </td> <td> <p><span>confusion_matrix (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Confusion matrix for each study site</span></p> </td> </tr> <tr> <td> <p><span>8</span></p> </td> <td> <p><span>number_of_polygons (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Contains the number of polygons marked for training and validation.</span></p> </td> </tr> <tr> <td> <p><span>9</span></p> </td> <td> <p><span>class_area_percentage (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Refers to percentage coverage of each class for a given study site.</span></p> </td> </tr> <tr> <td> <p><span>10</span></p> </td> <td> <p><span>name_of_the_study_site_result (OBIA)</span></p> </td> <td> <p><span>.png</span></p> </td> <td> <p><span>Image showing the evaluation metric values for each site.</span></p> </td> </tr> <tr> <td> <p><span>11</span></p> </td> <td> <p><span>train_val_set_CNN</span></p> </td> <td> <p><span>.geojson</span></p> </td> <td> <p><span>Bounding box labeling dataset used for CNN model training for each site.</span></p> </td> </tr> <tr> <td> <p><span>12</span></p> </td> <td> <p><span>train_parameter (CNN)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Hyperparameters used for training the CNN model.</span></p> </td> </tr> <tr> <td> <p><span>13</span></p> </td> <td> <p><span>CNN_models</span></p> </td> <td> <p><span>.h5</span></p> </td> <td> <p><span>CNN models for each site trained with defined hyperparameters.</span></p> </td> </tr> <tr> <td> <p><span>14</span></p> </td> <td> <p><span>CNN_confusion_matrix</span></p> </td> <td> <p><span>.png</span></p> </td> <td> <p><span>Confusion matrix for each study site.</span></p> </td> </tr> <tr> <td> <p><span>15</span></p> </td> <td> <p><span>Classified_rasters_CNN</span></p> </td> <td> <p><span>.tif</span></p> </td> <td> <p><span>Classified rasters through CNN model, both reclassified and colormapped.</span></p> </td> </tr> </tbody> </table>
Data for "LAND COVER CLASSIFICATION FROM A MAPPING PERSPECTIVE: PIXELWISE SUPERVISION IN THE DEEP LEARNING ERA"
<p><strong>Contents</strong></p> <ul> <li>clc_maps.zip contains the dataset.</li> <li>LICENSE.txt describes the usage terms of the maps.</li> </ul> <p>The maps contained in clc_maps.zip follow the naming convention of BigEarthNet [1], i.e. each sample of BigEarthNet has a corresponding pixel-level label map in the dataset.</p> <p>[1] G. Sumbul, M. Charfuelan, B. Demir, and V. Markl, “Bigearthnet: A large-scale benchmark archive for remote sensing image understanding,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019, pp. 5901–5904.</p> <p><strong>Description</strong></p> <p>The original shape file (<a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download">link</a>) was altered by reprojecting the shape file onto the coordinate reference system (CRS) of the respective BigEarthNet sample images to ensure pixel synchronicity. Afterwards, the shapes present in the sample CRS are rasterized by burning a linearly increasing class index which replaces the textual CLC nomenclature. The class IDs and their corresponding class names are presented in the following section. </p> <p><strong>Classes</strong></p> <p>Class ID - Corine Land Cover 2018 class name<br> 1 - Continuous urban fabric<br> 2 - Discontinuous urban fabric<br> 3 - Industrial or commercial units<br> 4 - Road and rail networks and associated land<br> 5 - Port areas<br> 6 - Airports<br> 7 - Mineral extraction sites<br> 8 - Dump sites<br> 9 - Construction sites<br> 10 - Green urban areas<br> 11 - Sport and leisure facilities<br> 12 - Non-irrigated arable land<br> 13 - Permanently irrigated land<br> 14 - Rice fields<br> 15 - Vineyards<br> 16 - Fruit trees and berry plantations<br> 17 - Olive groves<br> 18 - Pastures<br> 19 - Annual crops associated with permanent crops<br> 20 - Complex cultivation patterns<br> 21 - Land principally occupied by agriculture, with significant areas of natural vegetation<br> 22 - Agro-forestry areas<br> 23 - Broad-leaved forest<br> 24 - Coniferous forest<br> 25 - Mixed forest<br> 26 - Natural grasslands<br> 27 - Moors and heathland<br> 28 - Sclerophyllous vegetation<br> 29 - Transitional woodland-shrub<br> 30 - Beaches, dunes, sands<br> 31 - Bare rocks<br> 32 - Sparsely vegetated areas<br> 33 - Burnt areas<br> 34 - Glaciers and perpetual snow<br> 35 - Inland marshes<br> 36 - Peat bogs<br> 37 - Salt marshes<br> 38 - Salines<br> 39 - Intertidal flats<br> 40 - Water courses<br> 41- Water bodies<br> 42 - Coastal lagoons<br> 43 - Estuaries<br> 44 - Sea and ocean<br> 48 - NODATA<br> 49 - UNCLASSIFIED LAND SURFACE<br> 50 - UNCLASSIFIED WATER BODIES </p> <p>More details about the CLC classes and conventions can be found in the CLC nomenclature guide (<a href="https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html">Link</a>).</p> <p><strong>Attribution</strong></p> <p>If you find this work useful please consider citing:</p> <p>Wilhelm, T.; Koßmann, D. LAND COVER CLASSIFICATION FROM A MAPPING PERSPECTIVE: PIXELWISE SUPERVISION IN THE DEEP LEARNING ERA. In Proceedings of the IGARSS 2021—2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 12 – 16 July 2021; to appear.</p> <p><strong>License</strong></p> <p>The generated maps are based on data from the Copernicus program, which are subject to the terms described here:<br> <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata">https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata</a></p>
Coordinates for pairs of neighbouring MODIS pixels containing dense and sparse land cover in New South Wales, Australia
<p>This data set relates to detecting changes in areas of native forest in New South Wales, Australia. Under the Australian government Emissions Reduction Fund (ERF) initiative, land owners are able to generate Australian carbon credit units (ACCUs) in exchange for preventing deforestation of native forest for which a clearing permit has previously been issued. To be eligible the native forest must have at least 20\% canopy coverage with tree height greater than two meters. At the commencement of a project an extensive audit is undertaken by an accredited third party and estimates made of the appropriate number of credits to be allocated throughout the project. A number of subsequent audits are also required to ensure compliance throughout the project lifetime. Projects have a permanence period of either 25 or 100 years. During this time period no clearing, with the exception of minimal thinning (< 5 %), is permitted.</p> <p>At the time of writing there are approximately 400 vegetation projects underway in Australia. A system of automated change alarms based on remotely sensed time series has the potential to significantly reduce the auditing workload and target it to locations where the forest cover has changed and the project may require reassessment. Continuous monitoring is also important to guarantee the integrity of the carbon credit units in circulation.The region considered occupies the western plains of the state of New South Wales. This is an arid region with the majority of land cover characterized by small shrubs. The forested regions protected under the ERF are majority free standing Eucalyptus trees. The western plains also experiences very low cloud cover making it another ideal location for passive remove sensing.</p> <p>This data set was created with the intention that it will also be the subject of future studies. Due to the comparative scarcity of actual land cover change in this region the data set was created for the purpose of synthesizing realistic change time series. D=997 MODIS pixels were identified within regions currently assigned to a project which is currently earning carbon credits in exchange for avoided deforestation. These time series span the years of 2008 to 2018. For each pixel a corresponding pair was identified using high resolution imagery provided by Google Earth, DigitalGlobe. The pair was selected such that it is closely located and contains a similar type of land cover but at a lower density. Using this approach it is possible to generate a synthetic change time series by blending between the two while reducing any possible changes in geology, flora, aspect or climate that might occur over a large distance. Blending between high and low density aims to simulate gradual deforestation over the area of the pixel.</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.