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528 results for “Land cover”
Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
<p>This dataset consists of raw RGB and NIR images captured using the DJI Inspire 1 UAV equipped with interchangeable RGB and NDVI-modified cameras. Data were collected across 27 diverse study sites in Serbia, representing a variety of ecological and landscape features. The UAV flights followed pre-defined grid missions, capturing high-resolution imagery with a ground sampling distance (GSD) of 3–4 cm. The collected images were processed to produce georeferenced orthomosaics, which serve as the basis for detailed land cover classification and analysis.</p>
ALCC: Temporally consistent annual land cover maps over China from 1985 to 2022 based on an ensemble change detection method
<p><span><span>We develope <span><span> </span>a consistent annual land cover product for China (ALCC) from 1985 to 2022. First, change areas for each year was detected baesd on an ensemble change detection method combing CCDC, BFASTm, and Chow Test. Then, stable training samples were derived from both CLCD and CLUDs. The Random Forest classifier was locally trained and used to classify change areas year by year. Finally, ALCC was generated by updating land cover classification results for change areas and remaining class label the same as the base map forthe unchanged areas. ALCC achieved a mean overall accuracy of 81.11±0.67% nationwide and exceeded 72.00% across seven geographical regions.</span></span></span></p>
Datasets of "The Onset of a Globally Ice-covered State for a Land Planet"
<p>This dataset contains results to create figures in the paper "The Onset of a Globally Ice-covered State for a Land Planet" by T. Kodama et al.</p> <p>It contains data from our GCM calculations to create figures. We used Gtool3-dcl5 which is developed by the GFD-DENNOU Club for analysis (<a href="https://www.gfd-dennou.org/">https://www.gfd-dennou.org</a>).</p>
Land use and land cover in communities along the Bons Sinais estuary, Mozambique
<p><span>The Bons Sinais estuary (BSE) is one of the most important estuaries in the central region of the Mozambican coast. The BSE plays an important role as main source of food and income for many people living along the estuary. The present data contain information on the land use and cover of eight communities located along the Bons Sinais estuary, Mozambique, over the time period 2019-2020. The dataset was created by drawing shape files over high-resolution satellite imagens obtained from Bing and Google Satellites. All images were analysed using QGIS software. A total of 101 shapefiles were created. These files will help local managers and researchers to better understand the human use of the natural resources and landscape, as well as to predict impacts of the community's growth on natural resources in the BSE.</span></p>
Boundary Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Boundary data set used to evaluate the continuity predictions for different models in boundary zones. It is composed of labelled and unlabelled pixels for a boundary size of 100m and 200m.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>To compute the predictions in the boundary zones with different models (GP, RF, MLP, LTAE), the code is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Results in raster format of Land Cover in the Mendoza and Tunuyán River Basins, Argentina (1986–2018) published in Rojas et al. 2020
<p>Results in raster format of "Land Use Changes" in the Mendoza and Tunuyán River Basins, Argentina (1986-2018) published in Rojas et al. 2020, available at: </p> <p>Resultados en formato raster de "Cambios de Uso del Suelo" en las cuencas de los ríos Mendoza y Tunuyán, Argentina (1986-2018) publicados en Rojas et al. 2020, disponible en: </p> <p>https://doi.org/10.1007/s12061-020-09335-6<br>https://link.springer.com/article/10.1007/s12061-020-09335-6</p> <p>Rojas, F., Rubio, C., Rizzo, M., Bernabeu, M., Akil, N., & Martín, F. (2020). Land Use and Land Cover in Irrigated Drylands: a Long-Term Analysis of Changes in the Mendoza and Tunuyán River Basins, Argentina (1986–2018). Applied Spatial Analysis and Policy, 13(4), 875–899. doi:10.1007/s12061-020-09335-6 </p>
Losing Ground 6, Annual Land Cover Products for Massachusetts.
<p>This dataset consists of a set of annual land cover products for 1985-2017 produced using the CCDC algorithm as part of Mass Audubon's Losing Ground 6 analysis. </p> <p>The <a href="https://www.massaudubon.org/content/download/41477/1007612/file/Losing-Ground-VI_2020_final.pdf">sixth edition of Mass Audubon’s Losing Ground series of reports</a> builds on the methods first employed in <a href="https://www.massaudubon.org/content/download/12561/197565/file/MassAudubon_LosingGround5_FINAL_lores.pdf">Losing Ground 5</a>. Following <a href="https://www.massaudubon.org/content/download/13242/207580/file/Mass%20Audubon-Losing%20Ground%205-Technical%20Document.pdf">LG5 analysis</a>, the Continuous Change Detection and Classification (CCDC) algorithm, a harmonic modeling approach, was used to identify land cover changes and characterizes the spectral-temporal properties of stable land cover “segments” based on dense time series of all high-quality Landsat observations (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425712000387">Zhu et al. 2012</a>; <a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425714000248">Zhu and Woodcock 2014</a>). However, the LG6 analysis improves on the training data and land cover labeling system used in LG5. For LG6, we developed a modified version of the MassGIS 2005 Land Use/Land Cover polygons and legend in an effort to provide greater thematic detail as well as increase the ecological relevancy of the mapped results. Integrating the CCDC approach and our detailed land cover training dataset, we produced a 30-year time series of land cover maps for the Massachusetts Mainland and Islands as well as offshore waters. The original maps generated by the CCDC algorithms were then manually post-processed to improve spatial accuracy of change detection results, and final results including post-processing steps are available here.</p> <p>The Losing Ground 6 Classification includes the following land cover classes adapted from the <a href="https://docs.digital.mass.gov/dataset/massgis-data-land-use-2005">2005 MassGIS Land Use</a> and <a href="https://docs.digital.mass.gov/dataset/massgis-data-impervious-surface-2005">1m Impervious surface</a> datasets:</p> <p>> Man-made Built -- Greater than 25% constructed surfaces such as buildings, roads, parking lots, brick, asphalt, concrete </p> <p>> Man-made Bare -- Areas of man-made compacted soil or material such as mining or unpaved parking lots</p> <p>> Natural Barren -- Natural occurring barren areas (i.e. rocky shores, sand, bare soil)</p> <p>> Forest -- Pixels dominated by closed-canopy forest in leaf-on imagery, as well as mixed-cover pixels spanning forest edges that do not meet criteria for Built. </p> <p>> Forested Wetland -- Persistently vegetated wetlands dominated by tree cover; often found adjacent to rivers and in low-lying areas; Reference MassDEP Wetlands </p> <p>> Plantation -- Planted tree crops, includes orchards and tree farms</p> <p>> Cultural grassland -- Dominated by herbaceous cover, includes pastures, fields, and row crops</p> <p>> Irrigated herbaceous -- Heavily managed grass cover, includes golf courses and (non-synthetic) athletic fields</p> <p>> Successional/transitional forest -- Forest recovering after stand-replacing disturbance; Predominantly (> 25%) shrub cover, and some immature trees not large or dense enough to be classified as forest. It also includes areas that are more permanently shrubby, such as heath areas, wild blueberries or mountain laurel.</p> <p>> Managed shrubland -- Woody vegetation maintained in a mid-successional state, e.g. powerline corridors; includes "self-managed" stable shrublands, e.g. coastal shrub/heathland (e.g. scrub oak)</p> <p>> Open water -- Dominated by open-water conditions, little to no seasonal vegetation cover</p> <p>> Non-forested wetland -- Persistently or seasonally vegetated wetlands with little to no tree cover; Includes freshwater emergent and shrub swamp wetland types; Reference MassDEP Wetlands</p> <p>> Saltwater wetland -- Coastal wetlands, often herbaceous-dominated marsh and may show signs of ditching; Reference MassDEP Wetlands</p> <p>> Active cranberry bog -- active and recently active cranberry bogs; notable red color and geometric patch shapes</p> <p> </p> <p>A more complete description of training sources, class labels and codes, and definitions is available <a href="https://docs.google.com/spreadsheets/d/1Ld9VO9TlEFC5M5XEXsYEj6G-3JA0iC7a_ntT3ghCXhY/edit?usp=sharing">here</a>.</p> <p> </p> <p>Please contact Dr. Valerie Pasquarella (valpasq@bu.edu) with any questions regarding these data and potential use cases.</p> <p> </p> <p> </p>
Hcropland30: A hybrid 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model
<p><strong>Hcropland30</strong><strong>:</strong><strong>A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model</strong></p> <p><strong>***Please note this dataset is undergoing peer review***</strong></p> <p><strong>Version</strong>: <strong>1.0</strong></p> <p><strong>Authors</strong>: Qiong Hu <sup>a, 1</sup>, Zhiwen Cai<sup> b, 1</sup>, Liangzhi You<sup> c, d</sup>, Steffen Fritz<sup> e</sup>, Xinyu Zhang<sup> c</sup>, He Yin<sup> f</sup>, Haodong Wei<sup>c</sup>, Jingya Yang<sup> g</sup>, Zexuan Li<sup> a</sup>, Qiangyi Yu<sup> g</sup>, Hao Wu<sup> a</sup>, Baodong Xu<sup> b *</sup>, Wenbin Wu<sup> g, *</sup></p> <p><em><sup>a</sup></em><em> Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China</em></p> <p><em><sup>b</sup></em><em> College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>c</sup></em><em> Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>d </sup></em><em>International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA</em></p> <p><em><sup>e </sup></em><em>Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</em></p> <p><em><sup>f </sup></em><em>Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA</em></p> <p><a name="_Hlk166672711"></a><em><sup>g </sup></em><em>State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China</em></p> <p><strong> </strong></p> <p><strong>Introduction</strong></p> <p>We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.</p> <p> </p> <p><strong>Dataset</strong></p> <p><strong><em><u>1. Hcropland30</u></em></strong><strong>:</strong> A hybrid 30-m global cropland map in 2020</p> <p>****<strong>Data format</strong>: GeoTiff</p> <p>****<strong>Spatial resolution</strong>: 30 m</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Values</strong>: 1 denotes cropland and 0 denotes non-cropland</p> <p>The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.</p> <p><strong><em><u>2. </u></em></strong><strong><em><u>1°×1° </u></em></strong><strong><em><u>Grids</u></em></strong><strong>:</strong> This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>Id:</strong> The grid’s ID number.</p> <p><strong>area:</strong> The area of the grid.</p> <p><strong>mode:</strong> Indicates the representative sample grid.</p> <p><strong>climate:</strong> The climate type the grid belongs to.</p> <p><strong>dem: </strong>Average DEM value of the grid.</p> <p><strong>ndvi_s1 to ndvi_s4:</strong> Average NDVI values for four seasons within the grid.</p> <p><strong>esa, esri, fcs30, fromglc, glad, globeland30:</strong> Proportion of cropland pixels of different publicly available cropland products.</p> <p><strong>inconsistent:</strong> Proportion of inconsistent pixels within the grid according to different public cropland products.</p> <p><strong>hcropland30:</strong> Proportion of cropland pixels of our Hcropland30 dataset.</p> <p><strong><em><u>3. Samples</u></em></strong>: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>type:</strong> 1 denotes cropland sample and 0 denotes non-cropland sample.</p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite the following paper:</p> <p>Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.</p> <p><strong>License</strong></p> <p>The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).</p> <p><strong>Disclaimer</strong></p> <p>This dataset is provided as-is, without any warranty, express or implied. The dataset author is not</p> <p>responsible for any errors or omissions in the data, or for any consequences arising from the use</p> <p>of the data.</p> <p><strong>Contact</strong></p> <p>If you have any questions or feedback regarding the dataset, please contact the dataset author</p> <p>Qiong Hu (huqiong@ccnu.edu.cn)</p>
Combined effect of water stress and increased carbon dioxide on land cover establishment and development at mining sites - Field measurement results
<p>Excel files containing field measurement results of plant species growing in open top chambers support the research presented in the manuscript titled 'Combined Effect of Water Stress and Increased Carbon Dioxide on Land Cover Establishment and Development at Mining Sites'. The data include measurements of physiological attributes, height, diameter, dry matter, and the number of emerged individuals of plants commonly used for land cover in post-mining sites. These data were collected in Parauapebas-PA, Brazil, from July to November 2022.</p>
Carbon storage response to land use/land cover changes and SSP-RCP scenarios simulation: A case study in Yunnan Province, China
Open the record for dataset details and reuse information.
Improved cross-scale snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model
<p>Noah-MP data used to support the analyses conducted by Abolafia-Rosenzweig et al.: <strong>Improved </strong><strong>cross-scale </strong><strong>snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model</strong></p>
CR2LUC: Historical (1950-2020) land use and land cover in continental Chile
<p>The CR2LUC product was created to track long-term land-use changes in Chile since 1950, using a method that combines historical data with satellite information. Unlike recent land use/cover estimates that rely heavily on satellite data, CR2LUC integrates various sources, including agricultural censuses and other national statistics. This reconstruction involves extensive data preprocessing, such as digitizing historical documents. Key data sources include Agricultural Censuses (1955, 1965, 1976, 1997, 2007), annual crop statistics (1997-2021), and cadastres for fruits, viticulture, and vegetation.</p> <p>This dataset has been developed within the framework of the Center for Climate and Resilience Research (CR2, ANID/FONDAP/1523A0002) and the research project ANID/FSEQ210001.</p> <p> </p>
Modified land cover georeference data based on ESA Worldcover 2021
Open the record for dataset details and reuse information.
Datasets and code used to generate the figures in the article "Influence of Forest Cover Loss on Land Surface Temperature Differs by Drivers in China"
<p>We have provided the data and code used to generate the figures in the article "Influence of Forest Cover Loss on Land Surface Temperature Differs by Drivers in China" for reference and further reading. These data can be used to replicate the analyses presented in the paper. If you wish to use the data for other purposes, please contact the authors for permission. Thank you.</p>
Annual 30 m land cover dataset on the Tibetan Plateau from 1990 to 2023
<p><span>TPLUCD provide</span><span>d</span><span> an annually updated land use and cover dataset for the Tibetan Plateau from 1990 to 2023, with a spatial resolution of 30 m, using the WGS-84 as the spatial reference, and publicly available in GeoTIFF format. The values range</span><span>d</span><span> from 1 to 10, corresponding to the following land use types: 1 for cropland, 2 for forest, 3 for shrubland, 4 for alpine steppe, 5 for alpine meadow, 6 for water bodies, 7 for bare land, 8 for impervious surfaces, 9 for wetlands, and 10 for snow/ice.</span></p>
Towards the conservation of Brazilian legumes: Summary, land cover and use analytics, and a comparative analysis of locations count methods (curated vs. automated [buffer-dissolution])
<p>This dataset presents key findings from the study "<strong>Automating and Enhancing Species Extinction Risk Assessments with Historical Land Use and Land Cover Data</strong>."</p> <p>The file `<em>summary-threatened-legume-species.ods</em>` provides a summary of all threatened species of the Leguminosae family native to Brazil, including IUCN category and criteria, location counts, and the year of the latest assessment, sourced from official records. Additionally, it includes calculated values for Area of Occupancy (AOO) and Extent of Occurrence (EOO), along with trend data indicating natural area change rates (decline [positive number, red] or growth [negative number, green]) within both AOO and EOO. Location counts are further detailed across various buffer radii (1-5 km), utilizing a buffer-dissolution method for automated location counting. Each buffer radius is analyzed to assess AOO and EOO decline, where '1' indicates a species is threatened and '0' indicates it is not. This data provides an efficient method for screening threatened species under criterion B of the IUCN Red List guidelines.</p> <p>The file `<em>overlay-analysis.ods</em>` contains overlay analysis results for AOO and EOO of each species using MapBiomas land use and land cover (LULC) data (specifically MapBiomas Brazil, collection 7.1) from 1985 to 2021, covering all threatened legume species. This file provides both absolute area in square kilometers and percentages for each LULC class. The overlay analysis results support estimates of growth and decline trends for each LULC class.</p> <p>The file `<em>trend-analysis.ods</em>` presents results of annual rate estimates from trend analysis across LULC classes, and including both natural and anthropic groupings. A complete JSON database with p-values and R² values is provided in `<em>trend-analysis.json</em>`.</p> <p>This approach, combining all results for each species in a comprehensive, merged dataset, allows for effective filtering and ranking of the most threatened species as well as identification of their primary threats.</p> <p>We recommend opening the ODS files with LibreOffice, as Microsoft Excel may experience issues parsing decimal formats accurately.</p> <p>More detailed maps and graphs are available at <a title="LULC-MapBiomas-Leguminosae" href="https://github.com/lsbjordao/LULC-MapBiomas-Leguminosae" target="_blank" rel="noopener">https://github.com/lsbjordao/LULC-MapBiomas-Leguminosae</a>.</p>
Data from: Contrasting effects of land cover on nesting habitat use and reproductive output for bumble bees
<p><span><span><a name="_Hlk64216363">Understanding habitat quality is central to understanding the distributions of species on the landscape, as well as to conserving and restoring at-risk species. Although it is well-known that many species require different resources throughout their life cycles, pollinator conservation efforts focus almost exclusively on forage resources. In this study, we evaluate nesting habitat for bumble bees by locating nests directly on the landscape. We compared colony density and colony reproductive output for <i>Bombus impatiens, </i>the common eastern bumble bee, across three different land cover types (hay fields, meadows, and forests). We also assessed nesting habitat associations for all <i>Bombus</i> nests located during surveys to tease apart species-specific patterns of habitat use. We found that <i>B. impatiens</i> nested under the ground in two natural land cover types, forests and meadows, but found no <i>B. impatiens</i> nests in hay fields. Though <i>B. impatiens</i> nested at similar densities in both meadows and forests, colonies in forests had much higher reproductive output<i>. </i></a>In contrast, <i>B. griseocollis</i> tended to nest on the surface of the ground and was almost always found in meadows. <i>B. perplexis</i> was the only species to nest in all three habitat types, including hay fields. For some bumble bee species in this system, meadows, the habitat type with abundant forage resources, may be sufficient to maintain them throughout their life cycles. However, <i>B. impatiens</i> might benefit from heterogeneous landscapes with forests and meadows. Results for <i>B. impatiens</i> emphasize the longstanding notion that habitat use is not always positively correlated with habitat quality (as measured by reproductive output). Our results also show that habitat selection by bumble bees at one spatial scale may be influenced by resources at other scales. Finally, we demonstrate the feasibility of direct nest searches for understanding bumble bee distribution and ecology. </span></span></p>
Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters
<p>The datasets archived here include simulation results shown in the paper, “Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters”, published in Hydrology and Earth System Sciences (Maertens et al., 2021). The simulations were conducted over the South-American Dry Chaco and output on the different water budget components was produced with three land surface models (CLM2.0, CLSM-F2.5, and Noah3.6) embedded in the NASA Land Information System (LIS). The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction), and (iii) yearly land cover information. For each experiment described in the manuscript, we provide daily netcdf files (0.125° resolution, period Jan 1992 – Dec 2015). The conducted experiments are simulations with (i) default LIS parameters (BL), (ii) updated soil parameters (REV<sub>s</sub>) and (iii) interannualy varying vegetation and land cover parameters (REV<sub>SV</sub>).<br> For details, please refer to</p> <p>Maertens, M., De Lannoy, G. J. M., Apers, S., Kumar, S. V., and Mahanama, S. P. P.: Land Surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters, Hydrology and Earth System Sciences.</p> <p>Please contact Michiel Maertens (michiel.maertens@kuleuven.be) or Gabriëlle De Lannoy (gabrielle.delannoy@kuleuven.be) for any questions.</p>
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>
Adopt a Pixel 3 km: A Multiscale Data Set Linking Remotely Sensed Land Cover Imagery with Field Based Citizen Science Observation
<p>These datasets were used in an article submitted to the journal Frontiers in Climate in 2021: <a href="https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full">https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full</a></p> <p>Further supplemental links (including general information about GLOBE data) can be accessed at <a href="https://observer.globe.gov/get-data/mosquito-habitat-data">https://observer.globe.gov/get-data/mosquito-habitat-data</a>.</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.