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72 results for “vegetation mapping”
LBA-ECO LC-08 Soil, Vegetation, and Land Cover Maps for Brazil and South America
This data set provides (1) soil maps for Brazil that are digital versions of the MAPA DE SOLOS DO BRASIL (EMBRAPA, 1981) classified at three levels of detail, 19-class, 70-class and 249-class; (2) vegetation maps for Brazil that are digital versions of the MAPA DE VEGETACAO DO BRASIL (IBGE, 1988) classified at three levels of detail, 13-class, 59-class, and an overprint (combination) class; and (3) a land cover map for all of South America that was derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data over the time period 1987 through 1991 (Stone et al., 1994).The seven soil, vegetation, and general land cover classification maps are provided as GeoTIFF files (*.tif) files. There are also three companion files (.pdf), one each, for the soil, vegetation, and land cover maps, with information on map units, class values, codes, and descriptions.
Circumpolar Arctic Vegetation, Geobotanical, Physiographic Maps, 1982-2003
This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the circumpolar Arctic tundra biome for the period 1982-2003. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, Arctic treeline, elevation, and substrate chemistry data. Vegetation indices, trends, and biomass estimate products for the circumpolar Arctic through 2010 are also provided.
Maps of Vegetation Types and Physiographic Features, Kuparuk River Basin, Alaska
This data set provides a collection of vegetation, landscape, geobotanical, elevation, hydrology, and geologic maps for the Kuparuk River Basin, North Slope, Alaska. The maps cover either (1) the entire Kuparuk River Basin, from the headwaters on the north side of the Brooks Range to the Beaufort Sea coast, or (2) the selected Upper Kuparuk River Region including the Toolik Lake and Imnavait Creek research areas. The maps were produced from imagery and existing geobotanical maps covering the period 1976-08-04 to 2008-12-31.
Maps of Vegetation, NDVI, Snow and Thaw Depths: North Slope, Alaska and NWT, Canada
This dataset includes vegetation cover maps, Normalized Difference Vegetation Index (NDVI) maps, snow depth and thaw depth data that were obtained as part of a biocomplexity project on the North Slope of Alaska, USA, and the Northwest Territories (NWT), Canada. In Alaska, seven sites are located along the Dalton Highway and in the Prudhoe Bay Oilfield area, forming a transect across the climate gradient of the North Slope. From South to North, the sites are Happy Valley, Sagwon (an acidic and nonacidic site), Franklin Bluffs, Deadhorse, West Dock and Howe Island. Four sites are in the NWT, forming a latitudinal gradient from South to North; the sites include Inuvik, Green Cabin, Mould Bay, and Isachsen.
Arctic Alaska Vegetation, Geobotanical, Physiographic Maps, 1993-2005
This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the Arctic tundra region of Alaska for the period 1993-2005. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, and substrate chemistry. This data set generally includes areas North and West of the forest boundary and excludes areas that have a boreal flora such as the Aleutian Islands and alpine tundra regions south of treeline.
SAFARI 2000 NBI Vegetation Map of the Savannas of Southern Africa
The National Botanical Institute (NBI) has mapped woody plant species distribution to provide estimates of individual species contribution to peak leaf area index for designated vegetation types in southern Africa (Rutherford et al., 2000). The target was to account for 80% of the woody vegetation leaf area in terms of named species, for 80% of the surface area of Africa south of the equator. The data sources include published and unpublished species lists for vegetation types and individual sample plots, with the species contribution estimated by local experts in terms of dominants and subdominants. Source maps include: Low and Rebelo (1998); Giess (1971); Wild and Barbosa (1968); Barbosa (1970); and White (1983). Each source map delineates a wide variety of land cover categories that differ from region to region. Because vegetation discontinuities exist along some of the regional borders and a perfectly continuous regional map could not be achieved within the timeframe and budget of the project, the final map is made up of six independent sub-regional maps. A cross-referenced database of woody plant species, in order of species dominance, associated with all mapped units is provided.The data set contains six GIS shapefile archives, each containing a shape file for a given region in southern Africa on a 5 x 5 degree grid. An accompanying ASCII file contains the species list associated with the map files. The regional NBI Vegetation Map (a compilation of the 6 independent sub-regional coverages) is provided as a JPEG image.
Land Cover and Vegetation Map Collection for Seward Peninsula, Alaska
This data set provides two landcover and vegetation maps for the Seward Peninsula, Alaska. These maps were produced from existing maps, Landsat imagery, and color infrared aerial photography covering the period 1976-06-01 to 1999-09-01.
Maps of Vegetation Types and Physiographic Features, Imnavait Creek, Alaska
This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.
High-Resolution Vegetation Community Maps, Toolik Lake Area, Alaska, 2013-2015
This dataset contains vegetation community maps at 20 cm resolution for three landscapes near the Toolik Lake research area in the northern foothills of the Brooks Range, Alaska, USA. The maps were built using a Random Forest modeling approach using predictor layers derived from airborne lidar data and high-resolution digital airborne imagery collected in 2013, and vegetation community training data collected from 800 reference field plots across the lidar footprints in 2014 and 2015. Vegetation community descriptions were based on the commonly used classifications of existing Toolik area vegetation maps.
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.
LBA-ECO LC-15 Aerodynamic Roughness Maps of Vegetation Canopies, Amazon Basin: 2000
This data set provides physical roughness maps of vegetation canopies in the Amazon Basin. The images are estimates of aerodynamic roughness length (Z0) and zero plane displacement height (D0) at 1-km spatial resolution. The aerodynamic roughness length (Z0) is an important parameter to determine the vertical gradients of mean wind speed and the conditions for momentum transfer over a vegetated or bare rough surface.The maps were produced from a multivariate regression model algorithm developed from field-measured vegetation structure and remote-sensing data. The data input sources included Shuttle Radar Topography Mission (SRTM) (Saatchi, 2013), JERS-1, MODIS, and field data from vegetation biomass plots over the Amazon basin, as well as tower-based wind profile measurements, and roughness parameters from LBA tower sites. There are two GeoTIFF (.tif) files with this data set.
Pre-LBA CABARE Mapped Land Surface and Vegetation Characteristics, Rondonia, Brazil
Surface parameter digital maps of vegetation, soil, and topography were obtained for Rondonia, Brazil, covering the 5x5 degree region bounded by 13-8 degrees S and 65-60 degrees W. Numerical maps of the natural landscape structure were prepared by digitizing existing 1:1,000,000 maps. Satellite data give information about the most recent modifications of the surface due to human activities. This mapping work was the first step of a mesoscale meteorological modeling program (Calvet et al., 1997) in forested and deforested Southwestern Amazonia (Rondonia, Brazil). This work was performed in the framework of a research program (CABARE) supported by the European Union, CEC Environment Program.Data are provided in ArcGIS ArcInfo grid ascii format for the following surface parameters:Elevation of terrain of the Rondonia region (altitude.txt)LANDSAT-derived vegetation classification of the Rondonia region in 1993-1994 (classify.txt)Soil classification of the Rondonia region (soil.txt)Sand and Clay of the Rondonia region (sand.txt and clay.txt)Vegetation classification of the Rondonia region from RADAMBRASIL (Macedo et al., 1979) (vegetation.txt)
Maps of Vegetation Types and Physiographic Features, Toolik Lake Area, Alaska
This data set provides the spatial distributions of vegetation types, soil carbon, and physiographic features in the Toolik Lake area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.
Genome-wide maps of H3K9me3, H3K27me3 and H3K4me2 in two species of Leptosphaeria maculans during vegetative growth
GEO Series GSE150125. Plenodomus lingam/Leptosphaeria maculans 'brassicae' group; Plenodomus lingam/Leptosphaeria maculans 'lepidii' group. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR
There is a public perception that large high severity wildfires decrease biodiversity and increase fire hazard by homogenising vegetation composition and increasing the cover of mid-story vegetation. But a growing literature suggests that vegetation responses are nuanced. LiDAR technology provides a promising remote sensing tool to test hypotheses about post-fire vegetation regrowth because vegetation cover can be quantified within different height strata at fine-scales over large areas. We assess the usefulness of airborne LiDAR data for measuring post-fire mid-story vegetation regrowth over a range of spatial resolutions (10x10m, 30x30m, 50x50m, 100x100m cell size) and investigate the effect of fire severity on regrowth amount and spatial pattern following a mixed severity wildfire in Warrumbungle National Park, Australia. We predicted that recovery would be more vigorous in areas of high fire severity, because park managers observed dense post-fire regrowth in these areas. Moderate to strong positive associations were observed between LiDAR and field surveys of mid-story vegetation cover between 0.5–3m. Thus our LiDAR survey was an apt representation of on-ground vegetation cover. LiDAR-derived mid-story vegetation cover was 22–40% higher in areas of low and moderate than high fire severity. Linear mixed-effects models showed that fire severity was among the strongest biophysical predictors of mid-story vegetation cover irrespective of spatial resolution. However much of the variance associated with these models was unexplained, presumably because soil seedbanks varied at finer-scales than our LiDAR maps. Dense patches of mid-story vegetation regrowth were small (median size 0.01ha) and evenly distributed between areas of low, moderate and high fire severity, demonstrating that high severity fires do not homogenise vegetation cover. Our results are relevant for ecosystem conservation and fire management because they: indicate that native vegetation are responsive and resilient to high severity fire, and show the usefulness of remote sensing tools such as LiDAR to monitor post-fire vegetation recovery over large area in situ.
Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR
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High spatial resolution Fractional Vegetation Cover maps OAL-DE (Elbe river). Further details can be found in D4.5 of the OPERANDUM project.
<p>In order to reduce the risk posed by flooding, areas of woody vegetation have been removed along the riverbank of Elbe river in order to expedite the inflow and outflow of water from the main channel, and thus contribute to flattening the peak hydrographic response. Maintaining the effectiveness of this clearing requires that there is little or no regrowth of this woody vegetation. The NBS that have been implemented in OAL-Germany sees the use of various animals to graze these areas. NBS is devoted to prevent the re-growth of woody vegetation after an intervention which took place over a period from autumn 2014 to February 2015, when woody vegetation along the riverbank was cut back. Monitoring the effectiveness of the NBS is being performed by means of high spatial resolution remotely sensed data , i.e. Rapideye at 5 m spatial resolution. The preliminary analysis of this experiment consisted in the monitoring of the fractional vegetation cover over four of the seven NBS sites. </p> <p>The green fractional abundance (fc) was calculated by an algorithm based on scaling NDVI in-between the maximum and minimum NDVI values. A semi-empirical method based on the use of NDVI was used following Zeng et al, (2000), to calculate fc. </p> <p>The dataset contains layer stack of fractional abundance calculated for the images calculated by Rapideye images acquired on 18 April 2013, 15 April 2015, 17 March 2016, 9 April 2019. </p>
SMAPVEX12 Vegetation Water Content Map V001
The daily Vegetation Water Content (VWC) maps for the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) were derived by calculating Normalized Difference Vegetation Index (NDVI) from SPOT and RapidEye satellite overpasses and then interpolating it for each day of the campaign. In addition, samples from a range of vegetation types were used to compare ground-based measurements to the satellite-based estimates.
CLASIC07 Vegetation Water Content Map V001
The Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.
SMAPVEX08 Vegetation Water Content Map V001
The Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.
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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.