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528 results for “Land cover”
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)
<p>The<strong> User Guide V2.4 </strong>of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <strong><a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a></strong>. A more detailed description of the data can be found in the<strong> <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>.</strong> More related work can be found at my <strong><a href="https://lizhuohong.github.io/lzh/">homepage</a>.</strong></p> <p><a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version"><strong>Click to check all the data versions and download the data (点击查看/下载所有数据版本)</strong></a></p> <p><strong>NOTE: If you have any data needs, questions, or technical issues, contact us at </strong><a href="http://ashelee@whu.edu.cn"><strong>ashelee@whu.edu.cn</strong></a><strong> (Zhuohong Li, 李卓鸿).</strong></p> <p>The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/"><strong>Code link</strong></a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<strong><a href="https://arxiv.org/abs/2403.02746">Paper link</a></strong>).</p> <p><strong>我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。</strong></p> <p><strong>Citation format of the paper:</strong><br>Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. </p> <p>Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. <em>192</em>, pp.244-267, 2022.</p> <p><strong>BibTex format of the paper:</strong></p> <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>
Exploring the Relationship Between Land Cover Classifications and Urban Heat Island Intensity
<p>This dataset is a collection of data and results from a research project conducted by NASA SEES Interns. The research project aimed to study urban heat islands and their relationship with land cover observations. This dataset upload consists of 12 files. One file is a poster pdf that includes all the information needed about the project. The other 11 files are png images of heatmaps, bar graphs, scatter plots, and tables used in the analysis of our project. For quick reference, the abstract to this project is below:</p> <p><strong>The urban heat island (UHI) effect refers to the phenomenon in which urban areas experience higher temperatures compared to their rural counterparts. This research aims to quantify and examine the UHI effect within three areas of interest (AOIs) by utilizing LANDSAT imagery. In addition, this study seeks to explore the relationship between land cover classifications, which represent the most green (rural) and the most urban areas, and the intensity of the UHI effect. To achieve this, temperature data from local weather stations are analyzed, and statistical methods are employed to determine whether a correlation exists between the difference in land cover classifications and the intensity of the UHI effect, as determined by the average temperature difference between urban and rural areas. Google Earth Engine is used to visualize LANDSAT data from 2013 to 2022 in the months of July and August for each AOI. Subsequently, the data is compared with the land cover classifications from Collect Earth Online using statistical models in Microsoft Excel. These tools were used to take data from three pre-selected areas of interest in GLOBE Observer. The data findings from this analysis suggest that the more tree cover and rural an area is according to our classification method, the lower the UHI intensity. On the other hand, the higher the urban area, the higher the UHI intensity. By beginning this research, we have reinforced the validity of land cover classifications, and we now have the capability to generally predict the UHI intensity of locations based on their classifications. Overall, this investigation aims to contribute to a better understanding of the GLOBE land cover classifications and their potential indications of UHI intensity.</strong></p>
Landscape composition and shannon diversity of landuse classes aggregated from CORINE land cover 2012
<p>Based on Copernicus <a href="https://land.copernicus.eu/pan-european/corine-land-cover">CORINE land cover data</a> from 2012, we aggregated the original CORINE land use classes into 8 classes: urban, agriculture, grassland, Broad-leaved forest, Coniferous forest, Mixed forest, natural/seminatural vegetation, and water (see clc_legend.txt). We calculated the landscape composition (percentage of each land use type according to corine land type) and shannon diversity for 100 - 5000 meters (100-1000 meter with 100 meter intervals, 1000 - 5000 meter with 500 meter interval) buffer area around Landklif plots.</p> <p>LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Data and code from: Breakdown in seasonal dynamics of subtropical ant communities with land-cover change
<p><span>Concerns about widespread human-induced declines in insect populations are mounting, yet little is known about how land-use change modifies the dynamics of insect communities, particularly in understudied regions. Here, we examine how the seasonal activity patterns of ants—key drivers of terrestrial ecosystem functioning—vary with anthropogenic land-cover change on a subtropical island landscape, and whether differences in temperature or species composition can explain observed patterns. Using trap captures sampled biweekly over two years from a biodiversity monitoring network covering Okinawa Island, Japan, we processed 1.2 million individuals and reconstructed activity patterns within and across habitat types. Forest communities exhibited greater temporal variability of activity than those in more developed areas. Using time-series decomposition to deconstruct this pattern, we found that sites with greater human development exhibited ant communities with diminished seasonality, reduced synchrony, and higher stochasticity compared to sites with greater forest cover. Our results cannot be explained by variation in regional or site temperature patterns, or by differences in species richness or composition among sites. Our study raises the possibility that disruptions to natural seasonal patterns of functionally key insect communities may comprise an important and underappreciated consequence of global environmental change that must be better understood across Earth's biomes.</span></p>
Land cover data (30m) derived from GlobeLand30 for the China region
<p>This collection contains the land cover map (30m) derived from GlobeLand30 used in our study ‘<strong><em>Forestation at the right time with the right species can generate persistent carbon benefits in China</em></strong>’.</p> <p>Original maps were download from http://www.globallandcover.com/. The map was merged in ArcGIS 10.8 for the region of China (70°E-140°E, 15°N-55°N).</p> <p>Reference:<br> C. Jun, Y. Ban, S. Li, Open access to Earth land-cover map. Nature 514, 434–434 (2014). DOI:10.1038/514434c.</p>
Vegetation Density Across NYC: Analysis of Land Cover Data (2017) within 200 meter Buffers of Points
<p><strong>Summary:</strong></p><p>This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable. </p><p>The contained files were developed by the NY Cities Program of <a href="https://www.nature.org/newyork">The Nature Conservancy</a> and the <a href="https://nyc-eja.org/">NYC Environmental Justice Alliance</a> through the <a href="https://medium.com/gage-nyc/introducing-the-just-nature-nyc-partnership-513612e8c3b4">Just Nature NYC Partnership</a>. Additional context and interpretation of this work is available in a <a href="https://medium.com/gage-nyc/looking-at-cooling-benefits-of-plants-through-nyc-vegetation-data-ccdeb33cbe17">blog post</a>.</p><p> </p><p><i>References:</i></p><p>Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. <i>Urban Climate</i> 31:100583. <a href="https://doi.org/10.1016/j.uclim.2020.100583">https://doi.org/10.1016/j.uclim.2020.100583</a></p><p> </p><p><strong>Files in this Repository:</strong></p><p>Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, <a href="https://epsg.io/2263">EPSG 2263</a>):</p><p>Points with unique identifiers (<i>fid</i>) and data on proportion tree canopy cover (<i>prop_canopy</i>), proportion grass/shrub cover (<i>prop_grassshrub</i>), and proportion total vegetation cover (<i>prop_veg</i>) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage):</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip</i></p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg</i> </p><p>Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data)</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif</i></p><p>Computer Code:</p><p>Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database.</p><p><i>nyc_point_buffer_vegetation_overlay.sql</i></p><p> </p><p><strong>Data Sources and Methods:</strong></p><p>We used two openly available datasets from the City of New York for this analysis:</p><p>Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at <a href="https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page">https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page</a> </p><p>Six-inch resolution land cover data for New York City as of 2017, available at <a href="https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns">https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns</a> </p><p>All data were used in the New York State Plane Coordinate System, Long Island Zone (<a href="https://epsg.io/2263">EPSG 2263</a>). Land cover data were used in a polygonized form for these analyses.</p><p>The general steps for developing the data available in this repository were as follows:</p><p>Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions</p><p>Delete any points that do not overlap the areas in the Borough Boundaries dataset.</p><p>Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020).</p><p>Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles.</p><p>Relate the results from the overlay analysis back to the associated points.</p><p>Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data."</p><p>All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in <i>nyc_point_buffer_vegetation_overlay.sql</i>. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the <a href="https://gdal.org/programs/gdal_rasterize.html">gdal_rasterize</a> function.</p>
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago
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Land cover preferences and spatiotemporal associations of ungulates within a Scottish mammal community
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Differential responses to weather and land-cover conditions explain spatial variation in winter abundance trends in a migratory bird of conservation concern
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Data from: Evapotranspiration is resilient in the face of land cover and climate change in a humid temperate catchment
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Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model
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Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
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North America Holocene land cover: REVEALS-GMRF
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A global synthesis on land-cover changes in watersheds shaping freshwater detrital food webs
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Conservation of woody species in China under future climate and land-cover changes
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Data and code from: Breakdown in seasonal dynamics of subtropical ant communities with land-cover change
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Eight Mile Lake Research Watershed, Thaw Gradient Extended sites: Physical data from land cover classes from an upland watershed undergoing permafrost thaw.
This data set contains meausrements of soil properties (depth to permafrost and depth of organc matter) from sites throughout the wathershed within certain land cover types identified by an unsupervised landcover classification. The purpose was to see how land cover classes differed in soil properties and if we could detect diffences in classes undergoing permafrost thaw that results in thermokarst.
Eight Mile Lake Research Watershed, Thaw Gradient Extended sites: Vegetation data from land cover classes from an upland watershed undergoing permafrost thaw.
This data set contains meausrements ofpercent of ground cover (vegetation, water, bare soil) from sites throughout the wathershed within certain land cover types identified by an unsupervised landcover classification. The purpose was to see how land cover classes differed in soil properties and if we could detect diffences in classes undergoing permafrost thaw that results in thermokarst.
Hubbard Brook National Land Cover Dataset 1992
The National Land Cover Dataset was compiled from Landsat satellite TM imagery (circa 1992) with a spatial resolution of 30 meters and supplemented by various ancillary data (where available). The analysis and interpretation of the satellite imagery was conducted using very large, sometimes multi-state image mosaics (i.e. up to 18 Landsat scenes). Using a relatively small number of aerial photographs for 'ground truth', the thematic interpretations were necessarily conducted from a spatially-broad perspective. Furthermore, the accuracy assessments (see below) correspond to 'federal regions' which are groupings of contiguous states. Thus, the reliability of the data is greatest at the state or multi-State level. The statistical accuracy of the data is known only for the region. Important Caution Advisory With this in mind, users are cautioned to carefully scrutinize the data to see if they are of sufficient reliability before attempting to use the dataset for larger-scale or local analyses. This evaluation must be made remembering that the NLCD represents conditions in the early 1990s. The New Hampshire portion of the NLCD was created as part of land cover mapping activities for Federal Region I that includes the States of Connecticut, Maine, Vermont, Rhode Island, New Hampshire, and Massachusetts. The NLCD classification contains 21 different land cover categories with a spatial resolution of 30 meters. The NLCD was produced as a cooperative effort between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA) to produce a consistent, land cover data layer for the conterminous U.S. using early 1990s Landsat thematic mapper (TM) data purchased by the Multi-resolution Land Characterization (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies that produce or use land cover data. Partners include the USGS (National Mapping, Biological Resources, and Water Resources Divisions), USEPA, the U.S. Forest Service, and the
Land Cover for VCR/LTER Watersheds 1988
This dataset summarizes land cover for each of 65 small watersheds along the Virginia portion of the Atlantic Coast of the Delmarva Peninsula. Watersheds were delineated by Bruce Hayden and John Porter from USGS 1:24,000 scale quadrangle maps and extend down to the 5 ft (1.524) contour. Land cover data comes from the NOAA C-CAP dataset from 1988. To develop this dataset, the watersheds were digitized as ARC/INFO coverages and overlayed with the CCAP data (converted to ARC/INFO polygons from its native ERDAS raster format). The resulting polygon areas were then tabulated using SAS to generate this data file.
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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)
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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.