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528 results for “Land cover”
Land Cover, 2005, for Town of Rowley, Massachusetts - Raster
This is a seven-category land-cover map of Rowley, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Rowley, Massachusetts - Vector
This is a seven-category land-cover map of Rowley, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Tewksbury, Massachusetts - Raster
This is a seven-category land-cover map of Tewksbury, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Tewksbury, Massachusetts - Vector
This is a seven-category land-cover map of Tewksbury, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Topsfield, Massachusetts - Raster
This is a seven-category land-cover map of Topsfield, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Topsfield, Massachusetts - Vector
This is a seven-category land-cover map of Topsfield, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Wenham, Massachusetts - Raster
This is a seven-category land-cover map of Wenham, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Wenham, Massachusetts - Vector
This is a seven-category land-cover map of Wenham, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of West Newbury, Massachusetts - Raster
This is a seven-category land-cover map of West Newbury, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of West Newbury, Massachusetts - Vector
This is a seven-category land-cover map of West Newbury, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Wilmington, Massachusetts - Raster
This is a seven-category land-cover map of Wilmington, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Wilmington, Massachusetts - Vector
This is a seven-category land-cover map of Wilmington, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Woburn, Massachusetts - Raster
This is a seven-category land-cover map of Woburn, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Land Cover, 2005, for Town of Woburn, Massachusetts - Vector
This is a seven-category land-cover map of Woburn, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
VIIRS/NPP Land Cover Dynamics Yearly L3 Global 500m SIN Grid V001
The VNP22Q2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP22Q2](https://doi.org/10.5067/VIIRS/VNP22Q2.002) Version 2 data product.The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index (EVI2) calculated from VIIRS 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.Provided in each VNP22Q2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule. Important information is provided in Sections 5 of the User Guide when comparing VNP22Q2 with the MCD12Q2 data product.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).
VIIRS/NPP Land Cover Dynamics Yearly L3 Global 0.05 Deg CMG V001
The VNP22C2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP22C2](https://doi.org/10.5067/VIIRS/VNP22C2.002) Version 2 data product.The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule. Important information is provided in Section 5 of the User Guide when comparing VNP22C2 with the MCD12C2 data product.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).
Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data
<p>Mastering the evolution of urban land cover is important for urban management and planning. In this paper, a method for analyzing land cover evolution within urban built-up areas based on nighttime light data and Landsat data is proposed. The method solves the problem of inaccurate descriptions of urban built-up area boundaries from the use of single-source diurnal or nocturnal remote sensing data and was able to achieve an effective analysis of land cover evolution within built-up areas. Four main procedures are involved: (1) The neighborhood e<span>xtremum</span> method and maximum likelihood method are used to extract nighttime light data and the urban built-up area boundaries from the Landsat data, respectively; (2) multisource urban boundaries are obtained using boundary pixel fusion of the nighttime light data and Landsat urban built-up area boundaries; (3) the maximum likelihood method is used to classify Landsat data within multisource urban boundaries into land cover classes, such as impervious surface, vegetation and water, and to calculate landscape indexes, such as overall landscape trends, degree of fragmentation and degree of aggregation; (4) the changes in the multisource urban boundaries and landscape indexes were obtained using the abovementioned methods, which were supported by multitemporal nighttime light data and Landsat data, to model the urban land cover evolution. Using the cities of Shenyang, Changchun and Harbin in northeastern China as experimental areas, the multitemporal landscape index showed that the integration and aggregation of land cover in the urban areas had an increasing trend, the natural environment of Shenyang and Harbin was improving, while Changchun laid more emphasis on the construction of artificial facilities. At the same time, the method proposed in this paper to extract built-up areas from multi-source city data showed that the user accuracy, production accuracy, overall accuracy and Kappa coefficient are at least 3%, 1%, 1% and 0.04 higher than the single-source data method.</p>
Correlation Analysis and Simulation Modeling of Land use Land Cover change and its Link with Land surface temperature
<p>The uploaded data is related to LULC modeling. Data consist of driving variables and correlation analysis between LST and NDVI in different LULC classes.</p>
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>
Similarity between agricultural and natural land covers shapes how biodiversity responds to agricultural expansion at landscape scales
<p><span>The impact of agriculture on biodiversity depends on the extent and types of agriculture and the degree to which agricultural land contrasts with the natural ecosystem. Most research on the latter comes from studies on the influence of different agricultural types within a single ecosystem with far less study on how the natural ecosystem context shapes the response of biodiversity to agricultural production. We used citizen science data from agricultural areas in Canada's Eastern Hardwood-Boreal (forest ecosystem, n=108 landscapes) and Prairie Pothole (prairie ecosystem, n=99) regions to examine how ecosystem context shapes the response of avian species diversity, functional diversity and abundance to the amount of arable crop and pastoral agriculture at landscape scales. Avian surveys were conducted along 8km transects of Breeding Bird Survey routes with land cover assembled within a 20km2 landscape around each transect. The amount of agriculture at which species diversity peaked differed between the forest (15%) and prairie (51%) ecosystems, indicating that fewer species tolerated the expansion of agriculture in the former. In both ecosystems, functional diversity initially increased with agriculture and peaked at higher amounts (forest: 42%, prairie: 77 %) than species diversity suggesting that functional redundancy was lost first as agriculture increased. Species turnover with increasing agriculture was primarily among functional groups in forest where a shift from a low to a high agriculture landscape led to a decline in the percent of the community represented by Neotropical migrants, insectivores, upper foliage gleaners and bark foragers, and an increase in the percent of the community represented by short-distance migrants, granivores, omnivores and ground gleaners. There were few distinct shifts in the percent of the community represented by different functional groups in the prairie ecosystem. Total abundance was the least sensitive measure examined in both ecosystems and indicated that species losses with agriculture are likely followed by numerical compensation from agriculture tolerant species. Our results highlight the importance of ecosystem context for understanding how biodiversity is affected by agricultural production with declines in diversity occurring at lower agricultural extents in ecosystems with lower similarity between natural and agricultural land covers. These findings allow for more specific conservation recommendations including managing for species intolerant to agriculture in prairie ecosystems and limiting the expansion of high contrast agriculture and the loss of semi-natural habitat, such as hedge rows, in historically forested ecosystems.</span></p>
ScienceDex guides
Understand access before you commit
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.