This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s previous release and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. Data can be accessed directly from the Registry of Open Data on AWS, from the STAC 1.0.0 endpoint, or from the IO Store for a specific Area of Interest (AOI).

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L.

U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km2 in the Northern Basin and Range Ecoregion to a high of 78,782 km2 in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it is collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format. U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 square km in the Northern Basin and Range Ecoregion to a high of 78,782 square km in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it’s collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format.

This dataset is the third (2013) in a series of three 2-kilometer land use land cover (LULC) time-periods datasets (1975, 2000, and 2013) aids in monitoring change in West Africa’s land resources (exception is Tchad at 4 kilometers). To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification” (Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation using Esri’s ArcGIS Desktop ArcMap software. Citation: Trochain, J.-L., 1957, Accord interafricain sur la définition des types de végétation de l’Afrique tropicale: Institut d’études centrafricaines.

This dataset (2020-2023) is a compilation of the Land Use/Land Cover datasets created by the 5 Water Management Districts in Florida based on imagery -- Northwest Florida Water Management District (NWFWMD) 2022.Bay (1/4/2022 – 3/24/2022), Calhoun (1/7/2022 – 1/18/2022), Escambia (11/13/2021 – 1/15/2021), Franklin (1/7/2022 – 1/18/2022), Gadsden (1/7/2022 – 1/16/2022), Gulf (1/7/2022 – 1/14/2022), Holmes (1/8/2022 – 1/18/2022), Jackson (1/7/2022 – 1/14/2022), Jefferson (1/7/2022 – 2/16/2022), Leon (February 2022), Liberty (1/7/2022 – 1/16/2022), Okaloosa (10/31/2021 – 2/13/2022), Santa Rosa (10/26/2021-1/17/2022), Wakulla (1/7/2022 – 1/14/2022), Walton (1/7/2022-1/14/2022), Washington (1/13/2022 – 1/19/2022).Suwannee River Water Management District (SRWMD) 2022-2023.(Alachua (12/27/2022-12/28/2022, Baker (1/6/2023-1/15/2023), Bradford (11/9/2021-11/16/2021), Columbia (12/17/2021-1/29/2022), Gilchrist (12/17/2021-1/29/2022), Levy (12/17/2021-1/29/2022), Suwannee (12/17/2021-1/29/2022), Union (11/9/2021-11/9/2021).(Dixie 12/17/2021-01/29/2022), (Hamilton 12/17/2021-01/29/2022), (Jefferson 01/07/2022-02/16/2022), (Lafayette 12/17/2021-01/29/2022), (Madison 12/17/2021-01/29/2022), (Taylor 12/17/2021-01/29/2022).Southwest Florida Water Management District (SWFWMD) 2023. South Florida Water Management District (SFWMD) 2021-2023.St. John's River Water Management District (SJRWMD) 2020.Year Flight Season Counties:2020 (Dec. 2019 - Mar 2020) Alachua, Baker, Clay, Flagler, Lake, Marion, Osceola, Polk, Putnam.2021 (Dec. 2020 - Mar 2021) Brevard, Indian River, Nassau, Okeechobee, Orange, St. Johns, Seminole, Volusia. 2022 (Dec. 2021 - Mar 2022) Bradford, Union. Codes are derived from the Florida Land Use, Cover, and Forms Classification System (FLUCCS-DOT 1999) but may have been altered to accommodate region differences by each of the Water Management Districts.

A set of three estimates of land-cover types and annual transformations of land use are provided on a global 0.5 x0.5 degree lat/lon grid at annual time steps. The longest of the three estimates spans 1770-2010. The dataset presented here takes into account land-cover change due to four major land-use/management activities: (1) cropland expansion and abandonment, (2) pastureland expansion and abandonment, (3) urbanization, and (4) secondary forest regrowth due to wood harvest. Due to uncertainties associated with estimating historical agricultural (crops and pastures) land use, the study uses three widely accepted global reconstruction of cropland and pastureland in combination with common wood harvest and urban land data set to provide three distinct estimates of historical land-cover change and underlying land-use conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and extent to which different ecosystem have undergone changes. The three estimates use a consistent methodology, and start with a common land-cover map during pre-industrial conditions (year 1765), taking different courses as determined by the land-use/management datasets (cropland, pastureland, urbanization and wood harvest) to attain forest area distributions close to satellite estimates of forests for contemporary period. The satellite based estimates of forest area are based on MODIS sensor. All data uses the WGS84 spatial coordinate system for mapping.

Please note that this file is large, ~550 MB, and may take a substantial amount of time to download especially on slower internet connections.Shapefile (NJ State Plane NAD 1983) download: Click "Open" or Click hereFile Geodatabase (NJ State Plane NAD 1983) download: Click hereThis data represents a "generalized" version of the 2012 LULC. To improve the performance of the web applications displaying the 2012 land use data, it was necessary to create a new simplified layer that included only the minimum number of polygons and attributes needed to represent the 2012 land use conditions. The 2012 LU/LC data set is the fifth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007 and 2012. This present 2012 update was created by comparing the 2007 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2012 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2012 land use directly to the base data layer. All 2007 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2007-2012 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2007. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2012 Land Use/Land Cover (LU/LC) data sets, which were mapped by USGS Subbasin (HU8). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

[Metadata] Description: Land Use Land Cover of main Hawaiian Islands as of 1976

This dataset is the second (circa 2000) in a series of three 2-kilometer land use land cover (LULC) time-periods datasets (1975, 2000, and 2013) aids in monitoring change in West Africa’s land resources (exception is Tchad at 4 kilometers). To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification” (Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation using Esri’s ArcGIS Desktop ArcMap software. Citation: Trochain, J.-L., 1957, Accord interafricain sur la définition des types de végétation de l’Afrique tropicale: Institut d’études centrafricaines.

This web map displays the land use/land cover (LULC) timeseries layer derived from ESA Sentinel-2 imagery at 10m resolution. The visualization uses blend modes and is best used in the new Map Viewer. The time slider can be used to advance through the five years of data from 2017-2021. There are also a series of bookmarks for the locations below:Urban growth examplesOuagadougouCairo/GizaDubai, UAEKaty, Texas, USALoudoun County, VirginiaInfrastructureIstanbul International Airport, TurkeyGrand Ethiopian Renaissance Dam, EthiopiaDeforestationBorder of Acre and Rondonia states, BrazilHarz Mountains, GermanyWetlands lossPantanal, BrazilParana river, ArgentinaVegetation changing after fireNorthern California: Paradise, Redding, Clear Lake, Santa Rosa, Mendocino National ForestKangaroo Island, AustraliaVictoria and NSW, AustraliaYakutia, RussiaHurricane ImpactAbaco Island, BahamasRecent Lava FlowHawaii IslandSurface MiningBrown Coal, Cottbus, GermanyLand ReclamationMarkermeer, NetherlandsEconomic DevelopmentNorth vs South Korea

This dataset shows specific areas of land use/cover conversion in the Chesapeake Bay Watershed during the period 2013/14 to 2017/18. Change in land use/cover from 2013/14 to 2017/18 was interpreted by translating changes in land cover to changes in land use consistent with the 54 unique land use/cover classes in the 2017/18 land use dataset. Changes in land cover were primarily based on multi-date LiDAR imagery if available followed by multi-date NAIP imagery (available for all counties). Similar rules and logic used to classify the 2013/14 land cover data were applied to the change objects to produce a comparable land cover dataset for 2017/18. While some changes in land cover translate directly into changes in land use (e.g., impervious structures), others had to be interpreted based on context (e.g., small fragmented patches of tree canopy reconstituted as forest in 2013/14; turf grass in a newly developed parcel interpreted as cropland prior to development in 2013/14). Transitions between turf grass, cropland, pasture, and natural succession are not evident in the land cover data but are evident in the land use data. For this reason, the extent of land use change is greater than the extent of land cover change. For more information on input data please see: https://docs.google.com/spreadsheets/d/1e0Uy7DVUe_bXY4jJ1TUPUFvwNs9QbyHrSRY8JQs5GxE/edit?usp=sharing For detailed methods and documentation, please see: https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/

The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6.1 data product provides global land cover types at yearly intervals. The MCD12Q1 Version 6.1 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. Land cover types are derived from the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT) classification schemes. The supervised classifications then underwent additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. Additional land cover property assessment layers are provided by the Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) for land cover, land use, and surface hydrology. Layers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property Assessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are also provided. Documentation: User's Guide Algorithm Theoretical Basis Document (ATBD) General Documentation

This dataset consists of raster geotiff outputs of 30-year average annual land use and land cover transition probabilities for the California Central Valley modeled for the period 2011-2101 across 5 future scenarios. The full methods and results of this research are described in detail in “Integrated modeling of climate, land use, and water availability scenarios and their impacts on managed wetland habitat: A case study from California’s Central Valley” (2021). Land-use and land-cover change for California's Central Valley were modeled using the LUCAS model and five different scenarios were simulated from 2011 to 2101 across the entirety of the valley. The five future scenario projections originated from the four scenarios developed as part of the Central Valley Landscape Conservation Project (http://climate.calcommons.org/cvlcp ). The 4 original scenarios include a Bad-Business-As-Usual (BBAU; high water availability, poor management), California Dreamin’ (DREAM; high water avai ...

The MapBiomas annual land use/land cover time series data is the result of a collaborative network of biomes, land use, remote sensing, GIS, and computer science experts working together to monitor change across the country of Brazil. MapBiomas LULC maps are derived using 30-meter Landsat Level-2 cloud-free composite imagery mosaics and machine learning/deep learning classification algorithms. More.Data SummaryGeographic Coverage: BrazilTemporal Coverage: 2015 - 2021Temporal Resolution: AnnualSpatial Resolution: ~30-metersSource Imagery: Landsat Level-2Version: Collection 7.1**The collections represent changes in the coverage periods of the annual map, changes in the legend, and/or corrections to the previous version.Class AttributionCitationMapBiomas Project – Collection 7.1 of the Annual Series of Coverage and Land Use Maps of Brazil, accessed on June 29, 2023 via the link: https://brasil.mapbiomas.org/en/colecoes-mapbiomas/

Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD.

"*_albert.tif" are projected files via proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".

CLCD in 2021 is now available; Filling possible gaps between provinces; Building internal overviews for each file.

The 2015 LU/LC data set is the sixth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012 and 2015. This present 2015 update was created by comparing the 2012 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2015 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2015 land use directly to the base data layer. All 2012 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2012-2015 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2015. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2015 Land Use/Land Cover (LU/LC) data sets, which were mapped by USGS Subbasin (HU8). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

This dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale. The specific land types include: needleleaf evergreen tree-temperate, needleleaf evergreen tree-boreal, needleleaf deciduous tree-boreal, broadleaf evergreen tree-tropical, broadleaf evergreen tree-temperate, broadleaf deciduous tree-tropical, broadleaf deciduous tree-temperate, broadleaf deciduous tree-boreal, broadleaf evergreen shrub-temperate, broadleaf deciduous shrub-temperate, broadleaf deciduous shrub-boreal, C3 arctic grass, C3 grass, C4 grass, and C3 unmanaged rainfed crop. The data were generated by integrating regional LULC projections from GCAM with high-resolution MODIS land cover data and applying two alternative spatial downscaling models: FLUS and Demeter. Data are provided in NetCDF format.

This 54-class land use/land cover data was created by translating land cover into land use using a combination of ancillary data, spatial rules, and regionally-specific parameters. Vector image segments derived from National Agriculture Imagery Program (NAIP) imagery with object-oriented classification software and attributed with land cover classes was intersected with county tax parcel data, creating the base input for field and feature delineation. Additional ancillary data used in the analysis include: landfills, active and abandoned mines, roads, railroads, County land use, NASS Cropland Data Layer, USGS National LAnd Cover Dataset, AI-derived solar fields, timber harvest permits, USFWS National Wetland Inventory, and other data. The classification rules and process was implemented iteratively to allow for review, refinement, and quality assurance by local stakeholders and regional experts. For more information on input data please see: https://docs.google.com/spreadsheets/d/1e0Uy7DVUe_bXY4jJ1TUPUFvwNs9QbyHrSRY8JQs5GxE/edit?usp=sharing For detailed discussion of methods and ancillary data used in the process, please see: https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/

Estimation of Dominant Land Use / Land Cover per DioceseDATA (details below): 1. MODIS Land Cover, Land Cover Type 2: University of Maryland (UMD) scheme2. Global Diocesan Boundaries, 2.0 2019 (1:3M Scale)DATA PROCESSINGZONAL STATISTICS: MODIS Land Cover, Land Cover Type 2: University of Maryland (UMD) scheme --> Global Diocesan Boundaries, 2.0 2019 (1:3M Scale)NOTE:Values for various landuse and land cover (LULC) codes are in pixels. Pixels were 500m sq. Total represents sum of values between 1 and 17 which represented the actual data. Data pixels with center in a diocese can dived each class by the total to get percentage, more accurately this is not simply the percent of LULC per diocese but a percent of pixels representing LULC in diocese. Values could be used to rank by a particular LULC type or could normalize by area also.Data development:Burhans, Molly A., Cheney, David M., Emege, Thomas, Gerlt, R.. . “Land use and land cover per diocese”. 1:3M. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Affiliated Map and Application Development:Molly Burhans, October 2019DATA SET 1: LAND USE LAND COVERGlobal mosaics of the standard MODIS land cover type dataChannan, S., K. Collins, and W. R. Emanuel. 2014. Global mosaics of the standard MODIS land cover type data. University of Maryland and the Pacific Northwest National Laboratory, College Park, Maryland, USA. 2013.ABOUT MODIS LAND COVERINFORMATION QUOTED FROM:URL: https://yceo.yale.edu/modis-land-cover-product-mcd12q1SOURCE: Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., andHuang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168–182.