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154 results for “Raster”
SWOT Level 2 Water Mask Raster Image 100m Data Product, Version D
The SWOT Level 2 KaRIn High Rate Raster Product (SWOT_L2_HR_Raster_D) provides rasterized estimates of water surface elevation, inundation extent, and radar backscatter derived from high-resolution radar observations by the Ka-band Radar Interferometer (KaRIn) on the SWOT satellite. This product aggregates the irregularly spaced pixel cloud data from the PIXC and PIXCVec products onto a uniform geographic grid to facilitate spatial analysis of water surface features across inland, estuarine, and coastal domains.<br><br>Standard granules cover non-overlapping 128 × 128 km² scenes in the UTM projection at 100 m and 250 m resolution, stored in NetCDF-4 format. Each file contains 2D image layers representing water surface elevation (corrected for geoid, solid Earth, load, and pole tides, as well as atmospheric and ionospheric path delays), surface area, water fraction, and sigma0, along with quality flags and uncertainty estimates. On-demand versions are available at user-specified resolutions and projections, with optional overlapping granules and GeoTIFF output via SWODLR: https://swodlr.podaac.earthdatacloud.nasa.gov/<br><br>The raster product offers a gridded alternative to the unstructured pixel cloud, supporting hydrologic and geomorphic analyses in complex flow environments such as braided rivers, floodplains, wetlands, and coastal zones. It enables consistent spatiotemporal sampling while reducing noise through spatial aggregation, making it especially suitable for applications that require map-like continuity or integration with geospatial models.<br><br>This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_D
BOREAS TGB-12 Soil Carbon and Flux Data of NSA-MSA in Raster Format
The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites. This data set provides: (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drainage, and soil C inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; (4) a raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated. This data set was created from raster files, soil polygon data files, and detailed lab analysis of soils data that were received from Hugo Veldhuis who did the original mapping in the field during 1994. Also used were soils data from Susan Trumbore and Jennifer Harden (BOREAS TGB-12). The binary raster file covers a 733 km^2 area within the NSA-MSA.
Raster files catalog to forest growth simulation performed by r.recovery module (Grass Gis).
<p>This repository contains all publicly available raster files to calibrate/validate the parameters of the Diffusive-logistic growth (DLG) model and generate prognostics by means of the GRASS-GIS module r.recovery (Richit et al., 2019).</p> <p>Three .tiff files are needed for perform calibration/validation: two are EVI raster maps (two time-lapsed conditions of forest density) and a soil use file. In the repository the example files are:</p> <p>Calibration_EVI_2000;</p> <p>Calibration_EVI_2011 and</p> <p>Calibration_soil_use_2000, respectively.</p> <p>The others files in the repository are four EVI .tiff files and their respectively soil use .tiff files that were used to perform simulations by the means of calibrated parameters of the DGL model. The files are:</p> <p>AMNP_EVI_2016 and AMNP_soil_use_2016;</p> <p>FPSP_EVI_2016 and FPSP_soil_use_2016;</p> <p>MDRB_EVI_2016 and MDRB_soil_use_2016;</p> <p>MNPTS_EVI_2016 and MNPTS_soil_use_2016.</p> <p>For more details on r.recovery module please check </p> <p>Richit, L.A., Bonatto, C., da Silva, R.V., Grzybowski, J.M.V., 2019. Prognostics of forest recovery with r.recovery grass-gis module: an open source forest growth simulation model based on the diffusive-logistic equation. Environmental modelling & software 111, 108–120.</p>
Urban Green Raster Germany 2018
<p><strong>Abstract</strong></p> <p>The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset.<br> The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation).<br> The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.</p> <p><strong>Data acquisition</strong></p> <p>The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.</p> <p><strong>Data collection</strong></p> <p>Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).</p> <p>The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).</p> <p>The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.</p> <p>GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.</p> <p><strong>Value of the data</strong></p> <p>The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].</p> <p>Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].</p> <p>Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).</p> <p><strong>Data description</strong></p> <p>A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).</p> <p>A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.</p> <p><strong>Experimental design, materials and methods</strong></p> <p>The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].</p> <p>Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.</p> <p>To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.</p> <p>Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.</p> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).</p> <p><strong>References</strong></p> <p>[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: <a href="https://ec.europa.eu/eurostat/web/lucas/data/database">https://ec.europa.eu/eurostat/web/lucas/data/database</a></p> <p>[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32</p> <p>[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39</p> <p>[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: <a href="https://doi.org/10.1016/j.jag.2020.102065">https://doi.org/10.1016/j.jag.2020.102065</a></p> <p>[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030</p> <p>[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687</p>
Fast raster-scan optoacoustic mesoscopy enables assessment of human melanoma microvasculature in vivo
<p>The folder contains raw optoacoustic imaging data and the reconstruction code.</p> <p>1. raw data to compare the motion effects;</p> <p>2. waw data to compare the two ultrasound transducers.</p> <p>3. the main function of the image reconstruction algorithm.</p>
Developing a Wilderness Quality Index for Continental Europe - WQI raster
<p>Wilderness quality raster used in the publication - Strus, I.; Carver, S. Developing a Wilderness Quality Index for Continental Europe. <em>Land</em> <strong>2024</strong>, <em>13</em>, 428. <a title="Developing a Wilderness Quality Index for Continental Europe - Land" href="https://doi.org/10.3390/land13040428" target="_blank" rel="noopener">https://doi.org/10.3390/land13040428</a></p>
Arquivos Raster de Unidades de Resposta Hidrologica (HRU) para aplicação em Modelagem Hidrológica (MGB) no Estado do Espirito Santo, Brasil
<p><strong>Descrição em português:</strong></p> <p>Os arquivos raster (HRU_output_ES_<ano>) contém o mapeamento de Unidades de Resposta Hidrológica para aplicação na modelagem hidrológica (MGB) para o estado do Espírito Santo, Brasil.</p> <p>Um script python (hru_maker_mapbiomas.py) utilizando na reclassificação.</p> <p>Um arquivo de estilo QGIS (HRU_pastagem_raster.qml) com simbologia for the categories.</p> <p>Elaborado a partir do cruzamento dos Mapa de Pedologia do Brasil, na escala 1:500.000 (ANA/IBGE, 2001) e mapas de cobertura da terra dos anos de 2000, 2010 e 2020 (resolução de 30m) do "Projeto MapBiomas - Coleção 8 da Série Anual de mapas de Uso e Cobertura da Terra do Brasil, acessado em 4 de setembro de 2023, através do link: https://storage.googleapis.com/mapbiomas-public/initiatives/brasil/collectio_9/lclu_coverage/brasil_coverage_<ano>.tif"</p> <p> </p> <p><strong>Description in english:</strong></p> <p>The Raster files (HRU_output_ES_<year>) contains the Hydrological Response Units (HRU) for applications in hydrological modeling for the state of Espírito Santo, Brazil.</p> <p>A python script (hru_maker_mapbiomas.py) utilizando na reclassificação.</p> <p>A QGIS style file (HRU_pastagem_raster.qml) with symbology for the categories.</p> <p>Developed through the integration of the Brazilian Pedology Map, scale 1:500,000 (ANA/IBGE, 2001), and land-cover for years 2000, 2010, and 2020 (30-meter resolution )from do "Projeto MapBiomas - Coleção 8 da Série Anual de mapas de Uso e Cobertura da Terra do Brasil, acessado em 4 de setembro de 2023, using the https://storage.googleapis.com/mapbiomas-public/initiatives/brasil/collectio_9/lclu_coverage/brasil_coverage_<year>.tif"</p> <p> </p>
Georeferenced Population Datasets of Mexico (GEO-MEX): Raster Based GIS Coverage of Mexican Population
The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).
NASA Shuttle Radar Topography Mission Water Body Data Shapefiles & Raster Files V003
The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) SRTM, which includes the Water Body Data Shapefiles and Raster Files (~30 m) product. Version 3.0 contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data (SWBD), in shapefile and rasterized formats.The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the NGA (previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days.The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth's total landmass. Known Issues* Known issues in the NASA SRTM are described in the following publication: * Rodriguez, E., C. S. Morris, and J. E. Belz (2006), A global assessment of the SRTM performance, Photogramm. Eng. Remote Sens., 72, 249–260. https://doi.org/10.14358/PERS.72.3.249Improvements/Changes from Previous Version* Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED)
SWOT Level 2 Water Mask Raster Image 250m Data Product, Version 2.0
The SWOT Level 2 Water Mask Raster Image 250m Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025.\r\n<br> Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at 250 meter horizontal resolution in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats.\r\n<br> <br>This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_2.0
SWOT Level 2 Water Mask Raster Image Data Product, Version 2.0
The SWOT Level 2 Water Mask Raster Image Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. <br> Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at resolutions of 100 m and 250 m in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats. <br> This dataset is the parent collection to the following sub-collections: <br> https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_2.0 <br> https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_2.0 <br>
SWOT Level 2 Water Mask Raster Image 100m Data Product, Version 2.0
The SWOT Level 2 Water Mask Raster Image 100m Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025.\r\n<br> Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at 100 meter horizontal resolution in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats.\r\n<br> <br>This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_2.0
High Mountain Asia Rasterized PyGEM Glacier Projections with RCP Scenarios V001
This data set comprises a rasterized (gridded) version of the of glacier point data from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.
Raster representing the probability of the upper forest-cover in the near future in the Romanian Carpathians (selected mountain units)
<p>The dataset represents the estimated probability for the upper forest-cover across 11 selected mountain units in the Romanian Carpathians, merged into a single map to show the regional differences induced by several biophysical and anthropogenic factors. </p>
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