Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
528
datasets available to search
ShareScore release 0.7.1
Dataset results
528 results for “Land cover”
Data from: Differential vulnerability of key threatened mammals to climate and land cover changes in the Central Himalayas
Open the record for dataset details and reuse information.
Preserving the woody plant tree of life in China under future climate and land-cover changes
Open the record for dataset details and reuse information.
Data from: Land cover diversity increases predator aggregation and consumption of prey
Open the record for dataset details and reuse information.
Data from: Contrasting effects of land cover on nesting habitat use and reproductive output for bumble bees
Open the record for dataset details and reuse information.
GIS Shapefile, Spatial boundaries and land cover summaries for eight sub-watersheds of the Baltimore Ecosystem Study LTER
Watershed boundaries for eight sub-watersheds within the Baltimore Ecosystem Study LTER were delineated at 1-meter and 30-meter spatial resolutions. Watershed boundaries were used to calculate total area and extract and summarize existing land cover data (1m 2016 Chesapeake Conservancy Land Cover Data Project; 30m 2011 USGS National Land Cover Database). Two spatial resolutions are included to accommodate the needs of studies with different input requirements. In addition, providing data at both spatial scales highlights the importance of spatial resolution on study results.
Land Cover, Baltimore County
High resolution land cover dataset for Baltimore County, MD. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 8 square meters. The primary sources used to derive this land cover layer were color infrared aerial imagery acquired in 2007 as part of the National Agricultural Imagery Program (NAIP), a normalized Digital Surface Model (nDSM) derived from 2005 LiDAR data, LiDAR intensity data resulting from the 2005 acquisition, building footprints, road polygons, and water polygons. This land cover dataset is considered current as of August, 2007. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. Over 16,000 corrections were made to the classification.
Land Cover, Baltimore City
High resolution land cover dataset for Baltimore City, MD. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 8 square meters. The primary sources used to derive this land cover layer were color infrared aerial imagery acquired in 2007 as part of the National Agricultural Imagery Program (NAIP), a normalized Digital Surface Model (nDSM) derived from 2006 LiDAR data, LiDAR intensity data resulting from the 2006 acquisition, building footprints, road polygons, and water polygons. This land cover dataset is considered current as of August, 2007. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. 10,184 corrections were made to the classification.
Land cover classification of the central Arizona-Phoenix area using Landsat (MSS) data - year 1973
These data represent a land use classification of the central Arizona-Phoenix area. They were created using a Landsat MSS image for the year 1973.
Land use and land cover (LULC) classification of the CAP LTER study area using 2010 Landsat imagery
The land use and land cover (LULC) mapping generated from the 30 meter resolution Landsat TM5 is prepared for CAP LTER analyses. The series of products includes three levels LULC classifications, from coarser land-cover types to finer, hybrid LULC types, arranged in three thematic maps with contrasting numbers of LULC categories: (1) 12, (2) 15, and (3) 21. The percent of vegetation cover (vegetation fraction) in the residential area is provided. The image has a resampled spatial resolution of 15 meters due to the image classification procedure.
CAP LTER land cover classification using 2010 National Agriculture Imagery Program (NAIP) Imagery
Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science at large, and increasingly for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER). This project provides the first fine-resolution land-cover mapping of the CAP LTER using 2010 NAIP four-band data. It demonstrates a new object-based method capable of delivering robust outputs for the range of research activities not only undertaken by the program of study but for such studies that appear to be emerging worldwide. The classification process incorporates cadastral GIS data to assist the land-cover type extraction at the parcel scale, and a hierarchical network approach that balances computation time with classification accuracy. Decision rules that may prove useful for other high resolution image classification in other arid-land metropolitan areas are provided.
Evaluating water and energy fluxes across three distinct land cover types in a desert urban environment
Urbanization impacts surface energy and water balances across multiple spatial and temporal scales, which can be particularly important in desert cities where resources are limited. Urban climate observations are limited, especially over a variety of locations that represent urban land cover. To help address the lack of observations over different urban land cover types, a mobile eddy covariance tower (ECT) was deployed at three different locations in the Phoenix metropolitan area, representing a xeric landscape (drip irrigated palo verde trees with gravel), a parking lot, and a mesic landscape (sprinkler irrigated turf grass). In this project, data obtained from the mobile ECT deployments will be coupled with data from an eddy covariance tower managed by CAP LTER in the Maryvale suburb of Phoenix, Arizona. Data is processed to obtain energy and water fluxes, which are controlled by land surface characteristics, over the four distinct land cover types.
Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1993
Land cover classification for the Central Arizona-Phoenix CAP LTER study region using Landsat Thematic Mapper (TM) data - for the year 1993
Land Cover, 2005, for Town of Danvers, Massachusetts - Raster
This is a seven-category land-cover map of Danvers, 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 Danvers, Massachusetts - Vector
This is a seven-category land-cover map of Danvers, 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 Essex, Massachusetts - Raster
This is a seven-category land-cover map of Essex, 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 Essex, Massachusetts - Vector
This is a seven-category land-cover map of Essex, 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 Georgetown, Massachusetts - Raster
This is a seven-category land-cover map of Georgetown, 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 Georgetown, Massachusetts - Vector
This is a seven-category land-cover map of Georgetown, 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 Groveland, Massachusetts - Raster
This is a seven-category land-cover map of Groveland, 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 Groveland, Massachusetts - Vector
This is a seven-category land-cover map of Groveland, 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.
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.