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120 results for “land cover data”
Fig. 3. Land use and land cover data for 2014 in Population trends and conservation status of proboscis monkeys (Nasalis larvatus) in the face of habitat change in the Klias Peninsula, Sabah, Borneo, Malaysia
Fig. 3. Land use and land cover data for 2014/2015 within the 1-km buffer distance from surveyed rivers, overlaid with proboscis monkey sightings from the 2004/2005 and 2014 surveys, Protected Areas, and Production Forest Reserve boundaries.
Data for "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover"
<p>Dataset for the "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover". Dataset 01 includes site locations, basin area, dissolved organic carbon (DOC), dissolved lignin concentration and relevant references. Dataset 03 includes mean/discharge-weighted DOC, mean/discharge-weighted dissolved lignin concentrations. Dataset 03 includes geomorphological, climatic, hydrological and land-cover data for the 25 rivers. Dataset 04 includes the reconstructed yield of dissolved lignin and basin area of the 79 rivers.</p>
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
<p><span>These data on bird species abundance and environmental variables were used in testing and comparing two different species distribution model validation methods that are applied to models which are used to predict the effects of climate change on species' distributions. The aim of the study was to investigate whether different validation methods give different results of the model's predictive performance and to demonstrate that validation methods based on measuring and validating a "static" pattern in distribution can assess model performance over-optimistically compared to methods based on measuring and validating a "change" in the distribution, which can assess the predictive performance more critically. </span></p>
Land cover classification data for the first Chinese wetland cities in 2015 and 2020
<p>Land cover classification data for the first Chinese wetland cities in 2015 and 2020</p> <p>A land cover dataset, which had a resolution of 10 m and included four wetland types and five non-wetland types.</p> <p>The first Chinese wetland cities include Yinchuan, Changde, Haikou, Harbin, Dongying and Changshu.</p>
Supplementary Material 7 including Salamandra salamandra occurrence data, topographic, geological and land cover data and node-based resistances
<p>Supplementary material for the article "Habitat connectivity supports the local abundance of fire salamanders (Salamandra salamandra) but also the spread of Batrachochytrium salamandrivorans" by Bolte <em>et al</em>. (2023) published in Landscape Ecology (DOI: 10.1007/s10980-023-01636-8)</p> <p>This folder comprises a .shp file with fire salamander occurrences, topographic and land cover data (GeoTiff) from the northern Eifel region as well as the R Code used for the statistical analysis of salamander habitat suitability and connectivity.</p>
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>
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>
Data from: Evapotranspiration is resilient in the face of land cover and climate change in a humid temperate catchment
Open the record for dataset details and reuse information.
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
Open the record for dataset details and reuse information.
Data and code from: Breakdown in seasonal dynamics of subtropical ant communities with land-cover change
Open the record for dataset details and reuse information.
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.
Satellite Data for Corn and Soybean Fields + Other Land Cover: Illinois, US, 2017-2019
<p>These are the datasets associated with the paper:<br> Hannah Kerner, Ritvik Sahajpal, Sergii Skakun, Inbal Becker-Reshef, Brian Barker, Mehdi Hosseini, Estefania Puricelli, and Patrick Gray. 2020. Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization. In <em>KDD ’20: ACM Special Interest Group (SIG) on Knowledge Discovery and Data Mining Conference Workshops</em>, August 23–27, 2020, San Diego, CA. </p> <p>The code that uses these datasets can be found at: <a href="https://github.com/nasaharvest/croptype-mapping-gsn/tree">https://github.com/nasaharvest/croptype-mapping-gsn</a></p>
Data from: Sound settlement: noise surpasses land cover in explaining breeding habitat selection of secondary cavity-nesting birds
Birds breeding in heterogeneous landscapes select nest sites by cueing in on a variety of factors from landscape features and social information to the presence of natural enemies. We focus on determining the relative impact of anthropogenic noise on nest site occupancy, compared to amount of forest cover, which is known to strongly influence the selection process. We examine chronic, industrial noise from natural gas wells directly measured at the nest box as well as site-averaged noise, using a well-established field experimental system in northwestern New Mexico. We hypothesized that high levels of noise, both at the nest site and in the environment, would decrease nest box occupancy. We set up nest boxes using a geospatially paired control and experimental site design and analyzed four years of occupancy data from four secondary cavity-nesting birds common to the Colorado Plateau. We found different effects of noise and landscape features depending on species, with strong effects of noise observed in breeding habitat selection of Myiarchus cinerascens, the Ash-throated Flycatcher, and Sialia currucoides, the Mountain Bluebird. In contrast, the amount of forest cover less frequently explained habitat selection for those species or had a smaller standardized effect than the acoustic environment. Although forest cover characterization and management is commonly employed by natural resource managers, our results show that characterizing and managing the acoustic environment should be an important tool in protected area management.
Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
<p>The dataset contains six files from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) land cover transition data derived from LandSat satellite imagery for years 1986 and 2016. The files include data on household and plot features, prices of land use outputs, a diagonal block matrix of variables for mixed effects analysis, beliefs and opinions on land use change, and land cover transitions. The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and is representative of the region. It can be useful for studies on land use policies, environmental protection, and the drivers and impacts of land use land cover change in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17.</p>
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
<p>The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km<sup>2 </sup>of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.</p>
The gridded 2-m air temperature data produced by Yao et al. (2021),the depth of major lakes in Wuhan, and and the land use/land cover data in 2000, 2010, and 2020
<p>The gridded 2-m air temperature data produced by Yao et al. (2021), the depth of major lakes in Wuhan, and the land use/land cover data in 2000, 2010, and 2020 used in the manuscript </p>
Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
<p>Climate change is altering the distribution of woody plants by influencing demographic processes and modifying disturbance regimes. Trailing-edge forests may be particularly vulnerable to these effects because they exist at warm, dry margins of tree distributions. To better understand recent climate-driven changes in trailing-edge forests, we used Landsat time series and 1,558 field reference plots to develop annual land cover maps from 1985 to 2020 in two large, biodiverse landscapes in central Arizona, USA. We then combined annual land cover maps with tree ring records and spatial data describing interannual climate, terrain, bark beetle (Curculionidae: Scolytinae) activity, wildfire, and harvest to quantify drivers of forest change. Throughout the two landscapes, forest extent declined by 0.3% and 0.8% from 1985 to 2020. However, considerable variation occurred within the study period, with abrupt (ca. 1–2 years) declines in forest extent followed by gradual (ca. 10 years) recovery on each landscape. Pinyon-juniper (<em>Pinus</em> <em>edulis</em>, <em>Pinus</em> <em>monophylla</em>, and/or <em>Juniperus</em> spp.) cover increased from 1985 to ca. 2000 but declined after 2000, a period of extreme drought and regional tree die-off. In contrast, pine-oak (<em>Pinus</em> <em>ponderosa</em> and <em>Quercus</em> spp.) cover increased from 2000 to 2020, primarily due to declines in ponderosa pine and mixed conifer cover over the same period. Wildfire was a key driver of transitions from forest to non-forest cover in our study area, with the occurrence of multiple compounded drought years playing an important role in unburned areas. By driving transitions to alternative forest types or non-forest cover, disturbance and drought will increasingly shape forest dynamics and ecosystem transformations throughout the southwestern US.</p>
ScienceDex guides
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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)
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.