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115 results for “land use and land cover”
Greater Prairie-chicken data used in "Responses to land cover and grassland management vary across life-history stages for a grassland specialist"
<p>Grassland birds have exhibited dramatic and widespread declines since the mid-20th century. Greater Prairie-Chickens (<i>Tympanuchus cupido pinnatus</i>) are considered an umbrella species for grassland conservation and are frequent targets of management, but their responses to land use and management can be quite variable. We used data collected during 2007-2009 and 2014-2015 to investigate effects of land use and grassland management practices on habitat selection and survival rates of Greater Prairie-Chickens in central Wisconsin, USA. We examined habitat, nest-site, and brood-rearing site selection by hens and modeled effects of land cover and management on survival rates of hens, nests, and broods. Prairie-chickens consistently selected grassland over other cover types, but selection or avoidance of management practices varied among life-history stages. Hen, nest, and brood survival rates were influenced by different land cover types and management practices. At the landscape scale, hens selected areas where brush and trees had been removed during the previous year, which increased hen survival. Hens selected nest sites in hay fields and brood-rearing sites in burned areas, but prescribed fire had a negative influence on hen survival. Brood survival rates were positively associated with grazing and were highest when home ranges contained ≈15-20% shrub/tree cover. The effects of landscape composition on nest survival were ambiguous. Collectively, our results highlight the importance of evaluating responses to management efforts across a range of life history stages, and suggest that a variety of management practices are likely necessary to provide structurally heterogeneous, high-quality habitat for Greater Prairie-Chickens. Brush and tree removal, grazing, hay cultivation, and prescribed fire may be especially beneficial for prairie-chickens in central Wisconsin, but trade-offs among life-history stages and the timing of management practices must be considered carefully.</p>
Best Learned Models: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Best learned model learned with the dataset available <a href="http://https://doi.org/10.5281/zenodo.8033058">here</a> for the mTAN-GP, mTAN-MLP, mTAN-LTAE, and raw-LTAE.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_mtan_gp_irregular_sits">open source repository</a>.</p>
Classification Data set: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. This dataset contains irregular and unaligned SITS with their corresponding masks. 9 different random sampling are provided. This data set was used to train mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_mtan_gp_irregular_sits">open source repository</a>.</p>
Land use and land cover in communities along the Bons Sinais estuary, Mozambique
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Data from: Wildfire activity and land use drove 20th-century changes in forest cover in the Colorado front range
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Greater Prairie-chicken data used in "Responses to land cover and grassland management vary across life-history stages for a grassland specialist"
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Integrating stakeholders’ perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania
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Data from: VLUIS, a land use data product for Victoria, Australia, covering 2006 to 2013
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Data from: Contrasting effects of land cover on nesting habitat use and reproductive output for bumble bees
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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.
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
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>
Land use and land cover in China for WRF (Resolution: 500m), 2020
<p>WRF使用的中国2020年下垫面数据。</p>
Figure 1 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil
Figure 1 Phlebotomine sand fly capture sites, land use, and land cover in the municipality of Paracambi, Rio de Janeiro State, Brazil. Detail below: example of the extraction of the annual percentages of land use and land cover in a 200-meter buffer in one of the capture sites (1992-1994 and 2001-2003). Spatial resolution of the land cover data is 30 meters (Source: mapbiomas.org).
Figure 3 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil
Figure 3 Kernel density map of the total number of sand flies captured in Paracambi, RJ, Brazil, 1992-1994 and 2001-2003.
Land cover classification of Central Arizona-Phoenix using Landsat Thematic Mapper (TM) data - year 1985
Land cover classification for the CAP LTER study region using Landsat Thematic Mapper (TM) data - year 1985
Decadal Land Use and Land Cover Classifications across India, 1985, 1995, 2005
This data set provides land use and land cover (LULC) classification products at 100-m resolution for India at decadal intervals for 1985, 1995 and 2005. The data were derived from Landsat 4 and 5 Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multispectral (MSS) data, India Remote Sensing satellites (IRS) Resourcesat Linear Imaging Self-Scanning Sensor-1 or III (LISS-I, LISS-III) data, ground truth surveys, and visual interpretation. The data were classified according to the International Geosphere-Biosphere Programme (IGBP) classification scheme.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.