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65 results for “Land Cover Classification”
Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020
## overview The project extends the long-term, LULC datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six land-use/land-cover (LULC) maps at 30 m resolution were previously created from 1985 to 2010 at five-year intervals (Zhang and Li 2017). This project updates that suite with maps for 2015 and 2020. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The maps comprise 11 land-use/land-cover classes with an overall accuracy of 89.1% for 2015 and 89.6% for 2020. ## literature cited - Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).
OEMC Hackathon 2023: EU Land Cover Classification Dataset
<p>Dataset organized by the <a href="https://earthmonitor.org/">Open-Earth-Monitor (OEMC) project</a> within the context of <a href="http://www.kaggle.com/competitions/oemc-hackathon-eu-land-cover-classification/overview">Hackathon 2023</a>.</p> <p>The dataset (both train and test) was produced by stratified sampling of the <strong>ground-truth</strong> data provided by LUCAS Survey, funded by the European Commission. The target land cover considered <strong>level-3</strong> classes from the harmonized legend, resulting in <strong>72 classes</strong> distributed over <strong>5 years </strong>(<code>2006</code>, <code>2009</code>, <code>2012</code>, <code>2015</code>, <code>2018</code>):</p> <p>All samples were overlaid with <strong>416</strong> raster spatial layers, including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation, air temperature), accessibility and distance maps (highways, water bodies, burned areas), digital terrain model (slope and elevation) and other existing maps (population count and snow covering). The result values were organized in columns, one for each spatial layers, which combined represent the feature space available for ML modeling.</p> <p><strong>Column names:</strong></p> <p>The columns are formed by six metadata fields separated by <code>_</code>:</p> <ul> <li>Example: <strong>red_landsat.glad.ard_p50_30m_jun25_sep12</strong></li> <li>Metadata fields: <ul> <li>F1 - Variable name: <strong>red</strong></li> <li>F2 - Variable procedure including product name: <strong>landsat.glad.ard</strong></li> <li>F3 - Position in the probability distribution: <strong>p50</strong></li> <li>F4 - Spatial resolution: <strong>30m</strong></li> <li>F5 - Start date: <strong>jun25</strong></li> <li>F6 - End date: <strong>sep12</strong></li> </ul> </li> </ul> <p><strong>Column description:</strong></p> <p>All the columns can be aggregated in six thematic groups according to F1 and F2:</p> <ul> <li><strong>Satellite images (spectral reflectance & vegetation indices):</strong> <ul> <li><code>blue_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat blue band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>blue_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 blue band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>green_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat green band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>mir_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 mid-infrared band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>ndvi_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 normalized vegetation index (NDVI) (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>nir_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat near-infrared band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>nir_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 near-infrared band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>red_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat red band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>red_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 red band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>swir1_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat short-wave infrared-1 band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>swir2_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat short-wave infrared-1 band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> </ul> </li> <li><strong>Temperature images:</strong> <ul> <li><code>lst_mod11a2.daytime_{..}</code>: Monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime_{..}</code>: Monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>thermal_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat thermal band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> </ul> </li> <li><strong>Climate layers:</strong> <ul> <li><code>accum.precipitation_chelsa.annual_{..}</code>: Accumulated precipitation over the entire year according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.annual.3years.dif_{..}</code>: 3-years difference considering the yearly accumulated precipitation according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.annual.log.csum_{..}</code>: Cumulative sum, in logarithmic space, consdering the yearly accumulated precipitation according to CHELSA timeseries (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.montlhy_{..}</code>: Accumulated precipitation for each month according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>bioclim.var_chelsa.{variable_code}_{..}</code>: Bioclimatic variables derived variables from the monthly mean, max, mean temperature, and mean precipitation values. For <code>variable_code</code> descriptions see <a href="https://chelsa-climate.org/bioclim/">chelsa-climate.org</a> (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> </ul> </li> <li><strong>Accessibility & distance maps:</strong> <ul> <li><code>accessibility.to.ports_map.ox.{variable_code}_{..}</code>: Time-required to access ports of different size according to <a href="https://doi.org/10.1038/s41597-019-0265-5">Nelson et al., 2019</a></li> <li><code>burned.area.distance_global.fire.atlas_{..}</code>: Distance to burned areas mapped by <a href="https://doi.org/10.3334/ORNLDAAC/1642">Global Fire Atlas</a></li> <li><code>cost.distance.to.coast_gedi.grass.gis_{..}</code>: Cumulative cost of moving (derived by <a href="https://grass.osgeo.org/grass83/manuals/r.cost.html">r.cost</a>) to the coast</li> <li><code>road.distance_osm.highways.high.density_{..}</code>: Distance to high density of roads according to <a href="https://www.openstreetmap.org/#map=8/52.154/5.295">OpenStreetMap</a></li> <li><code>road.distance_osm.highways.low.density_{..}</code>: Distance to low density of roads according to <a href="https://www.openstreetmap.org/#map=8/52.154/5.295">OpenStreetMap</a></li> <li><code>water.distance_glad.interanual.dynamic.classes_{..}</code>: Distance to permanent / seasonal water bodies according to<br> <a href="https://doi.org/10.1016/j.rse.2020.111792">Pickens et al., 2020</a></li> </ul> </li> <li><strong>Digital terrain model (DTM):</strong> <ul> <li><code>elev.lowestmode_gedi.eml_{..}</code>: Mean estimate of the terrain elevation in <code>dm</code> filtered using <a href="https://saga-gis.sourceforge.io/saga_tool_doc/6.2.0/grid_filter_1.html">SAGA GIS Gaussian filter</a> (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>slope.percent_gedi.eml_{..}</code>: Mean slope in <code>%</code> derived from terrain elevation ([Witjes et al., 2023]</li> </ul> </li> <li><strong>Other existing maps:</strong> <ul> <li><code>pop.count_ghs.jrc_{..}</code>: Annual time-series of population count in number of people mapped by <a href="https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">Schiavina et al., 2023</a></li> <li><code>snow.duration_global.snowpack_{..}</code>: Annual duration of snow occurrence mapped by <a href="https://www.dlr.de/eoc/desktopdefault.aspx/tabid-8297/14218_read-37938/">Global SnowPack</a></li> </ul> </li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>train.csv</strong>: Training set with 42,237 rows and 420 columns, including sample id (<code>sample_id</code> - index column), land cover code (<code>land_cover</code>), land cover label (<code>land_cover_label</code>), reference year (<code>year</code>) and 416 features / covariates</li> <li><strong>test.csv</strong>: Test set with 42,271 rows and 418 columns, including sample id (<code>sample_id</code> - index column), reference year (<code>year</code>) and 416 features / covariates</li> <li><strong>sample_submission.csv</strong>: a sample submission file with 42,271 rows and 2 columns, including sample id (<code>sample_id</code> - index column) and predicted land cover code (<code>land_cover</code>)</li> </ul>
Supervised land cover classification using Google Earth Engine in Córdoba, Argentina, 2018-2020
Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.
A Land-use/Land Cover Classification of Baltimore City in 1953
Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 113 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County immediately surrounding the city. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures an ecologically and socially important moment in the post-war history of the city. It can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.
Green Lakes Valley land cover classification, Niwot Ridge LTER, Colorado
Land cover data generated by Don Cline (graduate student, CU Boulder Geography), as part of suite of spatial maps made for Green Lakes Valley (see Williams et al. 1999).
GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery
<p>A novel global 30-m land-cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5°×5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile.</p>
INEGI Uso del Suelo y Vegetacion Land Cover Classifications for Mexico (1985, 1993, 2002, 2007, 2011), Harmonized with NLCD 2011 Legend
<p>We have taken the Uso del Suelo y Vegetacion land cover classification products for Mexico (courtesy of Mexico's Instituto Nacional de Estadistica y Geografia, or INEGI) for years 1985, 1993, 2002, 2007, and 2011 (INEGI, 2015); and harmonized their classes with the classes of the Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) (Homer et al., 2015). Details of processing, along with the processing scripts, are archived in GitHub in the <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.5">NLCD_INEGI</a> project (Bohn, 2019).</p> <p>This project contains the following g-zipped tar files:</p> <ul> <li>SERIE_I.tgz - land cover from 1985</li> <li>SERIE_II.tgz - land cover from 1993</li> <li>SERIE_III.tgz - land cover from 2002</li> <li>SERIE_IV.tgz - land cover from 2007</li> <li>SERIE_V.tgz - land cover from 2011</li> </ul> <p>On LINUX, the contents of these files can be extracted via "tar":</p> <p>tar -xvzf SERIE_I.tgz >& log.tar.txt</p> <p>On Windows, applications such as "7-zip" can extract the contents.</p> <p>Each of these .tgz files contain a folder with the same name but without the ".tgz". Within each of these folders are the following sub-folders:</p> <ul> <li>For SERIE_I to SERIE_IV: <ul> <li>metatiles/ - original land cover shapefiles, with Mexico divided into "metatiles" along UTM zones, as documented in <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.0/docs/Processing_of_INEGI_USOSV_dataset.docx">Processing_of_INEGI_USOSV_dataset.docx</a></li> <li>geo/ - shapefiles from "metatiles", reprojected into geographic</li> <li>entire/ - shapefiles from "geo" merged into a single file for the entire country</li> </ul> </li> <li>For SERIE_V: <ul> <li>entire/ - original land cover shapefile in Lambert Conical projection, covering all of Mexico</li> <li>geo/ - shapefile from "entire" reprojected into geographic</li> </ul> </li> <li>SERIE_I to SERIE_V: <ul> <li>cve_union/ - shapefiles covering all of Mexico, in geographic projection, with land cover reclassified to NLCD 2011 legend</li> <li>rasters/ - files from "cve_union", rasterized at 0.000350884 degree resolution</li> <li>ascii/ - raster files from "rasters", exported to ascii ESRI grid file format</li> </ul> </li> </ul> <p>Output files (in the "ascii" folders) are ESRI ascii raster grid files, in geographic projection, with cellsize = 0.000350884 degrees.</p>
Land-Cover Classification of Mesoamerica's Cimate Hubs
<p>This project presents land use classification maps obtained through satellite image analyses for 10 study areas in Mesoamerica. A classification algorithm was adapted specifically for the tropics and developed using the latest satellite images available. We applied Object Based Image Analysis method & Random Forest algorithm on 10m Sentinel 1 (S1) & Sentinel 2 (S2) image collections of years 2022/2023. The study area encompassed 10 landscapes spanning 8 countries across Mesoamerica. These areas cover a total of 256 971 km² and were previously defined by Osa Conservation as potential climate adaptation hubs.</p>
A Land-use/Land Cover Classification of Baltimore City in 1927
Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 93 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 585 km2 (226 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures a moment of dynamic expansion in the city, just prior to the Great Depression and can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.
GLC_FCS30-2020:Global Land Cover with Fine Classification System at 30m in 2020
<p>The new GLC_FCS30-2020 products were produced based on Global 30-m land-cover product with fine classification system in 2015 (GLC_FCS30-2015) and combined with the 2019-2020 time series Landsat surface reflectance data, Sentinel-1 SAR data, DEM terrain elevation data, global thematic auxiliary dataset and prior knowledge dataset. </p>
GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022
<div> <p>GLC_FCS30D is the first global fine land cover dynamic product at a 30-meter resolution that adopts continuous change detection. It utilizes a refined classification system containing 35 land-cover categories and covers the time span from 1985 to 2022. Before the year 2000, the update cycle was every 5 years, while after 2000, it is updated annually. In specific, it developed by combining the continuous change detection method, local adaptive updating models and the spatiotemporal optimization algorithm from dense time-series Landsat imagery, and was validated to achieve an overall accuracy of 80.88% (±0.27%) for the basic classification system 10 major land-cover types) and 73.24% (±0.30%) for the LCCS level-1 validation system (17 LCCS land-cover types).</p> <p>The GLC_FCS30D has been compressed into 36 zip files, and<strong> <em>the details about the GLC_FCS30D can be found in the User's guides.</em></strong></p> </div>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</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>
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago
<p>The use of remote sensing for developing land cover maps in the Arctic has grown considerably in the last two decades, especially for monitoring the effects of climate change. The main challenge is to link information extracted from satellite imagery to ground covers due to the fine-scale spatial heterogeneity of Arctic ecosystems. There is currently no commonly accepted methodological scheme for high-latitude land cover mapping, but the use of remote sensing in Arctic ecosystem mapping would benefit from a coordinated sharing of lessons learned and best practices. Here, we aimed to produce a highly accurate land cover map of the surroundings of the Canadian Forces Station Alert, a polar desert on the northeastern tip of Ellesmere Island (Nunavut, Canada) by testing different predictors and classifiers. To account for the effect of the bare soil background and water limitations that are omnipresent at these latitudes, we included as predictors soil-adjusted vegetation indices and several hydrological predictors related to waterbodies and snowbanks. We compared the results obtained from an ensemble classifier based on a majority voting algorithm to eight commonly used classifiers. The distance to the nearest snowbank and soil-adjusted indices were the top predictors allowing the discrimination of land cover classes in our study area. The overall accuracy of the classifiers ranged between 75 and 88%, with the ensemble classifier also yielding a high accuracy (85%) and producing less bias than the individual classifiers. Some challenges remained, such as shadows created by boulders and snow covered by soil material. We provide recommendations for further improving classification methodology in the High Arctic, which is important for the monitoring of Arctic ecosystems exposed to ongoing polar amplification.</p>
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>
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago
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Land cover maps: Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Land cover maps obtained with Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see section VII-A-2 (Results-Performance results in the Southfrance area-Qualitative results) of the 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>
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>
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
<p>EuroSAT is a land use and land cover classification dataset. The dataset is based on Sentinel-2 satellite imagery covering 13 spectral bands and consists of 10 LULC classes with a total of 27,000 labeled and geo-referenced images. The dataset is associated with the publications "<a href="https://ieeexplore.ieee.org/abstract/document/8519248">Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification</a>" and "<a href="https://ieeexplore.ieee.org/abstract/document/8736785">EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification</a>".</p> <p>EuroSAT_RGB.zip contains the RGB version of the dataset, which includes the optical R, G and B frequency bands encoded as JPEG images.</p> <p>EuroSAT_MS.zip contains the multi-spectral version of the EuroSAT dataset, which includes all 13 Sentinel-2 bands in the original value range.</p>
Land cover maps: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Land cover maps obtained with mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p>
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.