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32 results for “land use mapping”
POI-based land use map for Africa
Open the record for dataset details and reuse information.
Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm
<p>This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.</p>
Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
Land Use Map of Vietnam (2018) used in the making of Vietnam Biomass Atlas
<p>Albertine Potter van Loon. (2018). <em>Land Use Map of Vietnam used in the making of Vietnam Biomass Atlas </em>[Raster file]. Available from Energydata.info Website: <a href="https://energydata.info/dataset/vietnam-biomass-atlas-2017">https://energydata.info/dataset/vietnam-biomass-atlas-2017</a> #VIETSE</p>
The Threatened Species No-Go Mapping Tool: An online open-access land-use decision support tool that identifies areas of importance for highly sensitive species of conservation concern
Open the record for dataset details and reuse information.
Land use map of Maricopa County, central Arizona: period 1995
The 1995 Land Use coverage was created as a joint effort of MAG (Maricopa Association of Governments) and MAG member agency staff. Land Use components were classified into 24 categories. The 1995 Land Use coverage is used for a variety of planning purposes including socioeconomic forecasting and air quality modeling.
Input dataset for gap filling and land-cover mapping using eumap Library - 2000 to 2020
<p>Benchmark dataset containing slope, elevation, Landsat temporal composites and night light raster layers, and the training samples (<a href="https://land.copernicus.eu/imagery-in-situ/lucas">LUCAS</a> and <a href="https://land.copernicus.eu/pan-european/corine-land-cover">CORINE</a> samples compilation) to map the land-cover in different areas of the European Union-EU.</p> <p>The slope and elevation refers to <a href="https://zenodo.org/record/4057883#.X3MJ0Nkmz0q">Digital Terrain Model for Continental Europe</a>, and the night light images are from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP46A1/">VNP46A1 product (VIIRS/NPP Daily Gridded Day Night Band 500m)</a>. The temporal composites were based on <a href="https://glad.geog.umd.edu/ard/glad-landsat-ard">GLAD Landsat ARD</a>, considering the 4 seasons and 3 percentiles per season (25, 50 and 75), for 6 spectral (blue, green, red, NIR, SWIR1, SWIR2) and 1 thermal band, resulting at end in 88 Landsat composites per year. The images for each season were selected using the same calendar dates for all period:</p> <ul> <li>Winter: December 2 of previous year until March 20 of current year</li> <li>Spring: March 21 until June 24 of current year</li> <li>Summer: June 25 until September 12 of current year</li> <li>Fall: September 13 until December 1 of current year</li> </ul> <p>The temporal composites were generated to <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> for 2018, 2019 and 2020, using the same approach (4 seasons x 3 percentiles x 6 spectral bands).</p> <p>The benchmark areas were selected according to the EU tiling system, which consists of 7,042 regular tiles with 30 x 30 km. The dataset uses the ETRS89-extended / LAEA Europe as spatial reference system (<a href="https://epsg.io/3035">EPSG:3035</a>), and all the raster layers have 1,000 x 1,000 pixels and 30m of spatial resolution.</p> <p>For all the EU the training samples will have 32 land-cover classes, varying according to the benchmark area:</p> <ul> <li>111: Urban fabric</li> <li>122: Road and rail networks and associated land</li> <li>123: Port areas</li> <li>124: Airports</li> <li>131: Mineral extraction sites</li> <li>132: Dump sites</li> <li>133: Construction sites</li> <li>141: Green urban areas</li> <li>211: Non-irrigated arable land</li> <li>212: Permanently irrigated arable land</li> <li>213: Rice fields</li> <li>221: Vineyards</li> <li>222: Fruit trees and berry plantations</li> <li>223: Olive groves</li> <li>231: Pastures</li> <li>311: Broad-leaved forest</li> <li>312: Coniferous forest</li> <li>321: Natural grasslands</li> <li>322: Moors and heathland</li> <li>323: Sclerophyllous vegetation</li> <li>324: Transitional woodland-shrub</li> <li>331: Beaches, dunes, sands</li> <li>332: Bare rocks</li> <li>333: Sparsely vegetated areas</li> <li>334: Burnt areas</li> <li>335: Glaciers and perpetual snow</li> <li>411: Inland wetlands</li> <li>421: Maritime wetlands</li> <li>511: Water courses</li> <li>512: Water bodies</li> <li>521: Coastal lagoons</li> <li>522: Estuaries</li> <li>523: Sea and ocean</li> </ul> <p>The gap filling validation data was generated by creating a mask of all nodata pixels (gaps) for each temporal composite, and then transposing that mask. All valid pixels covered by the transposed nodata mask are considered validation pixels. This method was chosen to retain the diversity of spatiotemporal nodata patterns that occur in the data. Each gap filling validation file contains 3 directory:</p> <ul> <li>raw: original temporal composite</li> <li>validation: transposed data</li> <li>filled_tmwm8: the best gap filling method that was tested</li> </ul> <p>See the <a href="https://gitlab.com/geoharmonizer_inea/eumap">eumap library</a> for more information about the gapfiling approach and land-cover mapping using this dataset.</p>
Land use maps Bonaire (2020 and 2050)
<p>Land use GIS files of Bonaire at a resolution of 10x10 meters. This publication contains two geo-tif files and an accompanying QGIS style. The '2020' GIS file represents the current land use. The '2050' file represents the normative 'Nature Inclusive' possible future. The latter was produced based on stakeholders consultation in combination with participatory GIS. .</p><p>For more information, see Verweij et al (2022) - <a href="https://doi.org/10.18174/571039">https://doi.org/10.18174/571039</a> or <a href="https://edepot.wur.nl/571039">https://edepot.wur.nl/571039</a></p>
Land Use and Cover Maps from Landsat, Mawas, Central Kalimantan, Indonesia, 1994-2019
This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019. An additional file for 2015 was created for accuracy assessment. A high-quality and low-cloud coverage image from Landsat 5 or Landsat 8 over each 5-year period was selected or composited for the January-August timeframe. Investigators used their knowledge to manually identify training polygons in these images for five LUC classes: peat swamp forest, tall shrubs/ secondary forest, low shrubs/ferns/grass, urban/bare land/open flooded areas, and river. Pixel values of Landsat Tier 1 surface reflectance products and selected indices were extracted for each LUC and used to predict LUC classes across the Mawas study area using the Classification and Regression Trees (CART) method. These data can be used to evaluate the relationship between fire occurrence and land cover type in the study site.
LBA-ECO CD-06 Ji-Parana River Basin Land Use and Land Cover Map, Brazil: 1999
This data set provides a land use/land cover map of the Ji-Parana River Basin in the state of Rondonia, Brazil produced from the digital classification of eight Landsat 7-ETM+ scenes from 1999 acquired from the Tropical Rain Forest Information Center (TRFIC) at Michigan State University. Nine land cover classes covering the Ji-Parana Basin were identified. There is one GeoTiff file with this data set.
2060 Proposed Land Use Map
<p>2060 Proposed Land Use Map for Marilao, Bulacan</p>
Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing
<p>This dataset contains continental (Africa) land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. The dataset contains seven classes, prepared annually from 2000 to 2015, using high‐resolution Landsat 7 images (ETM+) and analyzed by Google Earth Engine cloud computing method. The model that generated the LULC classification was evaluated for predictive accuracy across classes as well as overall accuracy. The model achieved an overall accuracy of 88% with class-specific user’s and producer’s accuracies ranged from 84-94% and 79-96% respectively (Midekisa et al., 2017).</p> <p> </p>
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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.