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120 results for “land cover data”
Classification Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. Data set are provided for each eco-climatic region. The size corresponds to the data set DS-A. Only one random pixel sampling is provided: seed 0. This data set was used to train Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models.</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>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Korean peninsula's Land use and land cover data, Landsat NDVI data, and CO2 data
<p><strong>Paper: "Greening rate in North Korea doubles South Korea"</strong></p> <p>Data in this repository include Landsat NDVI data, Land use, land cover data, and CO2 data of the Korean Peninsula.</p> <p>Korean Peninsula's <strong>NDVI data</strong> 1986-2017: <a href="https://zenodo.org/record/7221229#.Y07A2HZBxdg">https://zenodo.org/record/7221229#.Y07A2HZBxdg</a></p> <p>Korean Peninsula's <strong>Landuse Data</strong> 1986-2017: <a href="https://zenodo.org/record/7214788#.Y07BD3ZBxdg">https://zenodo.org/record/7214788#.Y07BD3ZBxdg</a></p> <p>Korean Peninsula's <strong>CO2 Data </strong>1986-2017: https://zenodo.org/record/7260091</p> <p>All three types of data are combined and displayed in this repository link. You can view individual data based on the separate link behind the data.</p>
Baltic Sea Region Land Cover Plus - Training and Validation data
<p>Training and validation data used in creating Baltic Sea Region Land Cover Plus (BSRLC+) maps: <a href="https://doi.org/10.5281/zenodo.10653871" target="_blank" rel="noopener">Dataset link</a></p> <ul> <li><strong>landcover_training_data_2006_2018.gpkg</strong>: Points data of consistent land cover from 2006 to 2018</li> <li><strong>crop_training_data_{year}.gpkg</strong>: Points data of crop types derived from <a href="https://doi.org/10.1038/s41597-023-02517-0">EuroCrop dataset </a>in particular year (2019, 2021, 2023)</li> <li><strong>landcover_validation_{year}.gpkg</strong>: Points data of validation data derived from <a href="https://doi.org/10.1038/s41597-020-00675-z">LUCAS points </a>in particular year (2009, 2012, 2015, 2018)</li> <li><strong>Metadata.pdf</strong>: Information of land cover code in each dataset</li> </ul> <p>Version notes:</p> <p>Version 2: Correcting the validation data 2018 and Metadata file</p> <p>Version 1: Original upload</p>
Supplementary File 8; The full data set derived from formal quantitative surveys of land cover types and activities of humans, livestock and wildlife (Section 2.3) that were used for the analyses described in sections 2.4, 2.5 and 2.7
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Data and R code associated to the publication: "Effects of land use, cover and protection on stream and riparian ecosystem services and biodiversity"
<p>This R code and dataset accompany Hanna et al's 2019 publication in Conservation Biology titled "Effects of land use, cover and protection on stream and riparian ecosystem services and biodiversity". Read the "Metadata" tab of the data file and code annotations for more information. </p>
Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization
<h2><strong>INTRODUCTION</strong></h2> <p>This document outlines the creation of a global inventory of reference samples and Earth Observation (EO) / gridded datasets for the Global Pasture Watch (GPW) initiative. This inventory supports the training and validation of machine-learning models for GPW grassland mapping. This documentation outlines methodology, data sources, workflow, and results.</p> <p><strong>Keywords:</strong> Grassland, Land Use, Land Cover, Gridded Datasets, Harmonization</p> <p> </p> <h2><strong>OBJECTIVES</strong></h2> <ul> <li> <p>Create a global inventory of existing reference samples for land use and land cover (LULC);</p> </li> <li> <p>Compile global EO / gridded datasets that capture LULC classes and harmonize them to match the GPW classes;</p> </li> <li> <p>Develop automated scripts for data harmonization and integration.</p> </li> </ul> <p> </p> <h2><strong>DATA COLLECTION </strong></h2> <p>Datasets incorporated:</p> <table> <tbody> <tr> <td><strong>Datasets</strong></td> <td> <p><strong>Spatial distribution</strong></p> </td> <td><strong>Time period</strong></td> <td><strong>Number of individual samples</strong></td> </tr> <tr> <td>WorldCereal</td> <td>Global</td> <td>2016-2021</td> <td>38,267,911</td> </tr> <tr> <td>Global Land Cover Mapping and Estimation (GLanCE)</td> <td>Global</td> <td>1985-2021</td> <td>31,061,694</td> </tr> <tr> <td>EuroCrops</td> <td>Europe</td> <td>2015-2022</td> <td>14,742,648</td> </tr> <tr> <td>GeoWiki G-GLOPS training dataset</td> <td>Global</td> <td>2021</td> <td>11,394,623</td> </tr> <tr> <td>MapBiomas Brazil</td> <td>Brazil</td> <td>1985-2018</td> <td>3,234,370</td> </tr> <tr> <td>Land Use/Land Cover<br>Area Frame Survey (LUCAS)</td> <td>Europe</td> <td>2006-2018</td> <td>1,351,293</td> </tr> <tr> <td>Dynamic World</td> <td>Global</td> <td>2019-2020</td> <td>1,249,983</td> </tr> <tr> <td>Land Change Monitoring,<br>Assessment, and Projection (LCMap)</td> <td>U.S. (CONUS)</td> <td>1984-2018</td> <td>874,836</td> </tr> <tr> <td>GeoWiki 2012</td> <td>Global</td> <td>2011-2012</td> <td>151,942</td> </tr> <tr> <td>PREDICTS</td> <td>Global</td> <td>1984-2013</td> <td>16,627</td> </tr> <tr> <td>CropHarvest</td> <td>Global</td> <td>2018-2021</td> <td>9,714</td> </tr> </tbody> </table> <p><strong>Total:</strong> 102,355,642 samples</p> <p> </p> <h2><strong>WORKFLOW</strong></h2> <h3><strong>Harmonization Process</strong></h3> <p>We harmonized global reference samples and EO/gridded datasets to align with GPW classes, optimizing their integration into the GPW machine-learning workflow.</p> <p>We considered reference samples derived by visual interpretation with spatial support of at least 30 m (Landsat and Sentinel), that could represent LULC classes for a point or region.</p> <p>Each dataset was processed using automated Python scripts to download vector files and convert the original LULC classes into the following GPW classes:</p> <p> 0. Other land cover</p> <p> 1. Natural and Semi-natural grassland</p> <p> 2. Cultivated grassland</p> <p> 3. Crops and other related agricultural practices</p> <p>We empirically assigned a weight to each sample based on the original dataset's class description, reflecting the level of mixture within the class. The weights range from 1 (Low) to 3 (High), with higher weights indicating greater mixture. Samples with low mixture levels are more accurate and effective for differentiating typologies and for validation purposes.</p> <p>The harmonized dataset includes these columns:</p> <table> <tbody> <tr> <td><strong>Attribute Name</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td>dataset_name</td> <td>Original dataset name</td> </tr> <tr> <td>reference_year</td> <td>Reference year of samples from the original dataset</td> </tr> <tr> <td>original_lulc_class</td> <td>LULC class from the original dataset</td> </tr> <tr> <td>gpw_lulc_class</td> <td>Global Pasture Watch LULC class</td> </tr> <tr> <td>sample_weight</td> <td>Sample's weight based on the mixture level within the original LULC class</td> </tr> </tbody> </table> <p> </p> <h2><strong>ACKNOWLEDGMENTS</strong></h2> <p>The development of this global inventory of reference samples and EO/gridded datasets relied on valuable contributions from various sources. We would like to express our sincere gratitude to the creators and maintainers of all datasets used in this project.</p> <p> </p> <h2><strong>REFERENCES</strong></h2> <ul> <li> <p>Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4Van Tricht, K. et al. Worldcereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping. Earth Syst. Sci. Data 15, 5491–5515, 10.5194/essd-15-5491-2023 (2023)</p> </li> <li> <p>Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963</p> </li> <li> <p>d’Andrimont, R. et al. Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union. Sci. data 7, 352, 10.1038/s41597-019-0340-y (2020)</p> </li> <li> <p>Fritz, S. et al. Geo-Wiki: An online platform for improving global land cover, Environmental Modelling & Software, 31, https://doi.org/10.1016/j.envsoft.2011.11.015 (2012)</p> </li> <li> <p>Fritz, S., See, L., Perger, C. et al. A global dataset of crowdsourced land cover and land use reference data. Sci Data 4, 170075 https://doi.org/10.1038/sdata.2017.75 (2017)</p> </li> <li> <p>Schneider, M., Schelte, T., Schmitz, F. & Körner, M. Eurocrops: The largest harmonized open crop dataset across the european union. Sci. Data 10, 612, 10.1038/s41597-023-02517-0 (2023)</p> </li> <li> <p>Souza, C. M. et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote. Sens. 12, 2735, 10.3390/rs12172735 (2020)</p> </li> <li> <p>Stanimirova, R. et al. A global land cover training dataset from 1984 to 2020. Sci. Data 10, 879 (2023) </p> </li> <li>Stehman, S. V., Pengra, B. W., Horton, J. A. & Wellington, D. F. Validation of the us geological survey’s land change monitoring, assessment and projection (lcmap) collection 1.0 annual land cover products 1985–2017. Remot Sensing environment 265, 112646, 10.1016/j.rse.2021.112646 (2021).</li> <li> <p>Tsendbazar, N. et al. Product validation report (d12-pvr) v 1.1 (2021).</p> </li> <li>Tseng, G., Zvonkov, I., Nakalembe, C. L., & Kerner, H. (2021). CropHarvest: A global dataset for crop-type classification. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.</li> </ul>
Data for the practical exercises in the book "Land Use Cover Datasets and Validation Tools"
<p>This dataset contains all the data that is required to carry out the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.</p> <p>The dataset includes data for three different case studies: The Asturias Central Area (Spain), the Ariège Valley (France) and Marqués de Comillas (Mexico). For the Asturias Central Area and the Ariège Valley, the dataset includes Land Use Cover (LUC) maps for several years of reference as well as data (simulation outputs, model drivers) for different modelling exercises. For Marqués de Comillas, the dataset includes a LUC map and a set of reference points used to validate it.</p> <p>The dataset includes a readme file listing all the files it contains and auxiliary files describing the data. For further information on the study area and the files used in the practical exercises, users are referred to Chapter 1 of the book.</p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Mapping past land cover on Poitiers in 1993 at Very High Resolution using GEOBIA approach and open data
<p>This dataset contains a land cover map of Poitiers in 1993 over an area of 225km².</p> <p>The land cover map was achieved using aerial images of the French National Geographic Institute (IGN) and Landsat-5 TM images combined with remote sensing methods. Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classifications produced a reliable land cover map at a 1m of spatial resolution.</p> <p>Orthophotos produced as well as training and validating polygons to achieve the classifications were added into this dataset.</p> <p>As land cover changes is crucial to land management, this map will help to understand changes from 1993 to now for urban, agricultural issues but also their impact on ecological processes. Data will be easily used in GIS applications for any users.</p> <p>This work is part of the thesis of Elie Morin which was funded by la région Nouvelle-Aquitaine and Grand Poitiers Communauté urbaine, among others.</p>
Data from: Land cover, individual’s age and spatial sorting shape landscape resistance in the invasive frog Xenopus laevis
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Data from: Land use type, forest cover, and forest edges modulate avian cross-habitat spillover
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Data from: Sound settlement: noise surpasses land cover in explaining breeding habitat selection of secondary cavity-nesting birds
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Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
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Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
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Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
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Land cover classification using ASTER data - year 2000
Land cover classification for the CAP LTER study region using ASTER imagery acquired September 19, 2000. Current classification is broadly similar to previous classifications using Landsat TM by Stefanov et al (2001). Three visible bands (15m/pixel) of ASTER were used to perfom a multistep classification of the area. The fifteen-class classification is produced by applying the expert system approach and using the initially derived 16-class minimum distance to means (MDM) supervised classification, Normalized Difference Vegetation Index (NDVI), spatial variance texture image, and land use vector coverage. The overall classification accuracy is 88.06%. Although it does not cover the entire CAP LATER, the dataset can be used as higher spatial resolution alternative to Landsat-derived land cover.
Land cover classification using Landsat Enhanced Thematic Mapper (ETM) data - year 2000
This land cover classification map was created using Landsat Enhanced Thematic Mapper (ETM) data from the year 2000. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.
Land cover classification of central Arizona-Phoenix using Landsat Enhanced Thematic Mapper (ETM) data, year 2005
A fundamental dataset required for ecosystem analysis consists of the major types of land cover present in the study area and their areal percentages. Land cover refers to the physical nature of the surficial materials present in a given area such as water, grass, clay-rich soil, asphalt, or concrete. Land cover classification can be used as input into a variety of ecological models, and land cover maps can be constructed to aid in planning field sampling strategy. The land cover types can also be linked to different land use categories to investigate temporal and spatial changes in the urban ecosystem.
Point Count Bird Censusing Data Subset for Paper 'EFFECTS OF LAND USE AND VEGETATION COVER ON BIRD COMMUNITIES' Walker et. al
Animals utilize their environment across a range of scales, which is bounded by their extent, the broadest spatial area which organisms respond to their environment within their lifetime, and the spatial grain, the smallest area they respond to their environment (Kotlier and Wiens 1990). Within this range, organisms likely respond to their environment at a hierarchy of levels. Johnson (1980) recognizes four distinct levels of hierarchical habitat selection. At the very largest scale, first order selection, includes the entire area that an organism utilizes within its lifetime, and is also known as an organisms global home range or extent. In contrast, second order selection is an organisms local home range, or the area that it occupies within a unique ecosystem. This distinction is most apparent with migratory animals who utilize more than one distinct landscape for their survival (i.e. summer vs. winter feeding grounds), and much less so for organisms resident of one specific landscape for their entire life span. Third order selection is the selection of specific habitat patches within an ecosystem. For example, a Monarch butterfly would tend to select patches of milkweed within a prairie. And the lowest level, fourth order selection, involves the physical procurement of food within a selected patch, in our example, specific flowers within a milkweed patch, and is also known as grain. Realizing the importance of hierarchical habitat selection, it has become apparent that single-scale studies of animals responses to their environment may fail to adequately represent how that specific animal is responding to ecological parameter of interest, especially if they are not responding to the landscape at that scale (Holling 1992). The range of scales which an animal of interest is utilizing a landscape is important to determine prior to any further ecological investigation, as inappropriate scalar mismatch between organism and environment can lead to ambiguous or even dece
Land cover classification using Landsat (MSS) data for the Central Arizona-Phoenix area - year 1979
This land cover classification map was created using Landsat MSS data from the year 1979. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.
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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.