Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

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 2018-2024 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 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source 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 ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas 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.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.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.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. 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 Victorian Land Use Information System (VLUIS) 2016/17 dataset has been created by the Spatial Information Sciences Group of the Agriculture Victoria Research in the Department of Economic …Show full descriptionThe Victorian Land Use Information System (VLUIS) 2016/17 dataset has been created by the Spatial Information Sciences Group of the Agriculture Victoria Research in the Department of Economic Development, Jobs, Transport, and Resources. It covers the entire landmass of Victoria and separately describes the land tenure, land use and land cover across the state at the cadastral parcel level. The methodology for creating the VLUIS is described in Morse-McNabb et al. (2015) with the following notable changes: Land use data provided by the Office of the Valuer-General of Victoria for the 2014 year has been used as a base input. Readily available sources of land use information from government and industry have been used to provide updates to the land tenure and land use components of the 2016/17 dataset. The source dataset and source date are recorded for each parcel. The land cover mapping method remains unchanged to previous versions of the VLUIS. The Australian Land Use and Management (ALUM) Classification, version 8, has been added to the attribute table. The VLUIS land use code fields have been translated across to the ALUM classification. Version 8 has been used you can find the ALUM Classification on the Department of Agriculture and Water Resources ABARES ALUM page: http://www.agriculture.gov.au/abares/aclump/land-use/alum-classification. Land parcels within urban areas, mapped in previous versions, have been masked out and have been renamed as Built Up Areas vastly reducing the size of the 2016/17 dataset. Land cover for Built Up Areas (LC_CODE = BUILT) is listed as null. Road reserves and road parcels have been merged together and renamed Voids. Land cover for Voids (LC_CODE = VOID) is listed as null. Parcels <12.5 hectares: land cover has not been attributed as the resolution of MODIS cannot support classifications of polygons smaller than 12.5 hectares. The data is in the form of an ESRI feature class. To use the VLUIS data correctly it is important to understand the difference between the three components of VLUIS. The Guidelines for land use mapping in Australia: principles, procedures and definitions, Edition 3 published in 2006 by the Commonwealth of Australia, defines them as follows: Land tenure is the form of an interest in land. Some forms of tenure (such as pastoral leases or nature conservation reserves) relate directly to land use and land management practice. Land use means the purpose to which the land cover is committed. Some land uses, such as agriculture, have a characteristic land cover pattern. These usually appear in land cover classifications. Other land uses, such as nature conservation, are not readily discriminated by a characteristic land cover pattern. For example, where the land cover is woodland, land use may be timber production or nature conservation. Land cover refers to the physical surface of the earth, including various combinations of vegetation types, soils, exposed rocks and water bodies as well as anthropogenic elements, such as agriculture and built environments. Land cover classes can usually be discriminated by characteristic patterns using remote sensing. A metadata statement, for the VLUIS product, and ESRI symbology files for the data can be freely downloaded from the VLUIS project page on the Victorian Resources Online website: http://vro.agriculture.vic.gov.au/dpi/vro/vrosite.nsf/pages/vluis DOI 10.26279/5b96043f7bd02

The wildland-urban interface (WUI) is the area where urban development occurs in close proximity to wildland vegetation. We generated WUI maps for the conterminous U.S. using building point locations (Carlson et al. 2022), offering higher spatial resolution compared to previously developed WUI maps based on U.S. Census Bureau housing density data (Radeloff et al., 2017). Building point locations were obtained from a Microsoft product released in 2018, which classified building footprints based on high-resolution satellite imagery. Maps were also based on wildland vegetation mapped by the 2016 National Land Cover Dataset (Yang et al., 2018). The mapping algorithm utilized definitions of the WUI from the U.S. Federal Register (USDA & USDI, 2001) and Radeloff et al. (2005). According to these definitions, two classes of WUI were identified: 1) the intermix, where there is at least 50% vegetation cover surrounding buildings, and 2) the interface, where buildings are within 2.4 km of a patch of vegetation at least 5 km2 in size that contains at least 75% vegetation. Both classes required a minimum building density of 6.17 buildings per km2. Maps of intermix and interface WUI were generated using a range of circular neighborhood sizes, based on radius distances from 100 – 1,500 m, to determine building density and vegetation cover on a pixel-by-pixel basis (Bar Massada et al., 2013). A composite map was also generated by combining the combined WUI maps (both interface and intermix WUI) for all neighborhood sizes, with field values indicating the radius distances for which pixels are included in the WUI classification. For each of the 6 neighborhood sizes, the data include rasters indicating the vegetation density threshold for intermix WUI, building density, the building density threshold, and the building-based WUI classification. Additional rasters are included indicating the vegetation proximity threshold for interface WUI and the combined WUI composite map. References: Bar Massada, A., S.I. Stewart, R.B. Hammer, M.H. Mockrin, and V.C. Radeloff. 2013. Using structure locations as a basis for mapping the wildland urban interface. Journal of Environmental Management 128:540–547; https://doi.org/10.1016/j.jenvman.2013.06.021 Carlson, A.R., Helmers, D.P., Hawbaker, T.J., Mockrin, M.H., Radeloff, V.S. 2022. The wildland-urban interface in the United States based on 125 million building locations. Ecological Applications. https://doi.org/10.1002/eap.2597 Radeloff, V. C., R. B. Hammer, S. I. Stewart, J. S. Fried, S. S. Holcomb, and J. F. McKeefry. 2005. The wildland-urban interface in the United States. Ecological Applications 15:799-805; https://doi.org/10.1890/04-1413 Radeloff, V. C., D.P. Helmers, H.A. Kramer, M.H. Mockrin, P.M. Alexandre, A. Bar Massada, V. Butsic, T.J. Hawbaker, S. Martinuzzi, A.D. Syphard, and S.I. Stewart. 2017. The 1990-2010 wildland-urban interface of the conterminous United States (2nd ed.) [Geospatial data]. Forest Service Research Data Archive; https://doi.org/10.2737/RDS-2015-0012-2. USDA and USDI. 2001. Urban wildland interface communities within vicinity of Federal lands that are at high risk from wildfire. Federal Register 66:751-777. Yang, L., S. Jin, P. Danielson, C. Homer, L. Gass, S.M. Bender, A. Case, C. Costello, J. Dewitz, J. Fry, M. Funk, B. Granneman, G.C. Liknes, M. Rigge, and G. Xian. 2018. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS Journal of Photogrammetry and Remote Sensing 146:108–123; https://doi.org/10.1016/j.isprsjprs.2018.09.006

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Land Use of Australia, Version 4, 2005-06, is a land use map of Australia for the year 2005-06. The non-agricultural land uses are drawn from existing digital maps covering six themes: topographic features, catchment scale land use, protected areas, world heritage areas, tenure and forest cover. Time series data at relatively high temporal resolution are available for the protected areas and forest themes. Only intensive land uses (e.g. built-up areas, mining) and plantation forestry were drawn from the catchment scale land use data.

The types of agricultural land uses to be mapped and their abundances were based on the 2005-06 agricultural census data collected by the Australian Bureau of Statistics (ABS); the spatial distribution of the agricultural land uses was modelled and has largely been determined using Advanced Very High Resolution Radiometer (AVHRR) satellite imagery with training data. Irrigation status has also been mapped. Existing digital maps also contributed to the classification of grazing land as native or modified pastures. The maps are supplied as ArcInfo (Trademark) grids with geographic coordinates referred to GDA94 and 0.01 degree cell size.

The distribution and abundance of most of the agricultural land uses is described by a set of floating point grids serving as probability surfaces for specific agricultural land uses. There is also a single integer grid serving as a categorical summary land use map, which has a value attribute table (VAT) with columns defining the mapped agricultural commodity groups, their irrigation status and the land use according to the Australian Land Use and Management Classification (ALUMC), Version 6 (http://www.daff.gov.au - search site for ALUM).

Prospective users of the data should note that caveats and additional metadata are included in a document entitled 'User Guide and Caveats: Land Use of Australia, Version 4, 2005-06' (ABARES, 2010) and that the Version 4 map differs significantly from the Version 3 maps.

http://adl.brs.gov.au/anrdl/metadata_files/pa_luav4g9abl07811a00.xml#metadataMetadata

Users of the data set are asked to acknowledge, in any visual or published material, that it was derived and compiled by ABARES and to make known to ABARES any errors, omissions or suggestions for improvement.

Dataset History

Lineage

Catchment scale mapping is produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field. Mapping from many State and Territory agencies are combined and gridded at 50m Positional Accuracy The scale of the source data varies greatly. See individual landuse mapping dataset metadata for specific measures of accuracy. Attribute Accuracy The methods for mapping and classifying land use adhere to the standards outlined in the document ""Land Use Mapping at Catchment Scale - Principles, Procedures and Definitions, Edition 2"", published by the BRS. Specifically, the attributes adhere to the ALUM (Australian Land Use and Management) classification. Logical Consistency Not applicable Completeness Some areas of Australia are not yet complete.

Dataset Citation

Australian Bureau of Agricultural and Resource Economics â Bureau of Rural Sciences (2010) Land use of Australia, Version 4 2005/2006 (September 2010 release, BRS). Bioregional Assessment Source Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/a18dbf2b-c0fe-4a26-b39a-e553bf6c39b5.

Contained within the Atlas of Canada's Various Map Series, 1965 to 2006, is map which shows the distribution of land cover types across Canada. The images are Advanced Very High Resolution Radiometer data, which means very precise detail, is shown on the map. The land cover map contains 31 land cover classes: 12 forest; 3 shrub land; 6 barren land and grassland; 7 developed land types including cropland; mosaic and built-up areas; and 2 non vegetable land cover types. Base data is limited to a small selection to populated places and major roads. The right hand panel of the map gives pictures and descriptions for each of the 31 land cover classes. The imagery data are from 1995.

According to many previous studies, application of remote sensing for the complex and heterogeneous urban environments in Sub-Saharan African countries is challenging due to the spectral confusion among features caused by diversity of construction materials. Resorting to classification based on spectral indices that are expected to better highlight features of interest and to be prone to unsupervised classification, this study aims (1) to evaluate the effectiveness of index-based classification for Land Use Land Cover (LULC) using an unsupervised machine learning algorithm Product Quantized K-means (PQk-means); and (2) to monitor the urban expansion of Luanda, the capital city of Angola in a Logistic Regression Model (LRM). Comparison with state-of-the-art algorithms shows that unsupervised classification by means of spectral indices is effective for the study area and can be used for further studies. The built-up area of Luanda has increased from 94.5 km2 in 2000 to 198.3 km2 in 2008 and to 468.4 km2 in 2018, mainly driven by the proximity to the already established residential areas and to the main roads as confirmed by the logistic regression analysis. The generated probability maps show high probability of urban growth in the areas where government had defined housing programs.

The Urban Atlas provides pan-European comparable land use and land cover data for 6 Functional Urban Areas (FUA) in Lithuania territory for the 2012 reference year. Additional information (product description, mapping guidance and class description) can be found: https://land.copernicus.eu/user-corner/technical-library/urban-atlas-2012-mapping-guide-new The Urban Atlas is mainly based on the combination of (statistical) image classification and visual interpretation of Very High Resolution (VHR) satellite imagery. Multispectral SPOT 5 & 6 and Formosat-2 pan-sharpened imagery with a 2 to 2.5m spatial resolution is used as input data. The built-up classes are combined with density information on the level of sealed soil derived from the High Resolution Layer imperviousness to provide more detail in the density of the urban fabric. Finally, the Urban Atlas product is complemented and enriched with functional information (road network, services, utilities etc…) using ancillary data sources such as local city maps or online map services. Additional codes are given according to EAGLE matrix: https://land.copernicus.eu/eagle/content-documentation-of-the-eagle-concept/manual/content-documentation-of-the-eagle-concept/b-thematic-content-and-definitions-of-eagle-model-elements/part-ii-land-use-attributes Minimum Mapping Unit: Class 1: 0.25 ha Class 2-5: 1ha Minimum Mapping Width: 10m

Data was produced with funding by the European Union. Copyright Copernicus Programme

DISCLAIMER: Construction Sector Development Agency has undertaken to distribute the data on behalf of EEA under Specific Contract No 3436/R0-Copernicus/EEA.56943 implementing Framework service contract No EEA/IDM/R0/16/009/Lithuania. SE «GIS-Centras» accepts no responsibility or liability whatsoever with regard to the content and use of these data.

"Due to the size of this dataset, both Shapefile and Spreadsheet download options will not work as expected. The File Geodatabase is an alternative option for this data download"This is SCAG's 2019 Annual Land Use (ALU v. 2019.1) at the parcel-level, updated as of February 2021. This dataset has been modified to include additional attributes in order to feed SCAG's Housing Element Parcel Tool (HELPR), version 2.0. The dataset will be further reviewed and updated as additional information is released. Please refer to the tables below for data dictionary and SCAG’s land use classification.Field NameData TypeField DescriptionPID19Text2019 SCAG’s parcel unique IDAPN19Text2019 Assessor’s parcel numberCOUNTYTextCounty name (based on 2016 county boundary)COUNTY_IDDoubleCounty FIPS code (based on 2016 county boundary)CITYTextCity name (based on 2016 city boundary)CITY_IDDoubleCity FIPS code (based on 2016 city boundary)MULTIPARTShort IntegerMultipart feature (the number of multiple polygons; '1' = singlepart feature)STACKLong IntegerDuplicate geometry (the number of duplicate polygons; '0' = no duplicate polygons)ACRESDoubleParcel area (in acreage)GEOID20Text2020 Census Block Group GEOIDSLOPEShort IntegerSlope information1APN_DUPLong IntegerDuplicate APN (the number of multiple tax roll property records; '0' = no duplicate APN)IL_RATIODoubleRatio of improvement assessed value to land assessed valueLU19Text2019 existing land useLU19_SRCTextSource of 2019 existing land use2SCAGUID16Text2016 SCAG’s parcel unique IDAPNText2016 Assessor’s parcel numberCITY_GP_COText2016 Jurisdiction’s general plan land use designationSCAG_GP_COText2016 SCAG general plan land use codeSP_INDEXShort IntegerSpecific plan index ('0' = outside specific plan area; '1' = inside specific plan area)CITY_SP_COText2016 Jurisdiction’s specific plan land use designationSCAG_SP_COText2016 SCAG specific plan land use codeCITY_ZN_COText2016 Jurisdiction’s zoning codeSCAG_ZN_COText2016 SCAG zoning codeLU16Text2016 existing land useYEARLong IntegerDataset yearPUB_OWNShort IntegerPublic-owned land index ('1' = owned by public agency)PUB_NAMETextName of public agencyPUB_TYPETextType of public agency3BF_SQFTDoubleBuilding footprint area (in square feet)4BSF_NAMETextName of brownfield/superfund site5BSF_TYPETextType of brownfield/superfund site5FIREShort IntegerParcel intersects CalFire Very High Hazard Local Responsibility Areas or State Responsibility Areas (November 2020 version) (CalFIRE)SEARISE36Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)1 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)SEARISE72Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)2 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)FLOODShort IntegerParcel intersects with a FEMA 100 Year Flood Plain data from the Digital Flood Insurance Rate Map (DFIRM), obtained from Federal Emergency Management Agency (FEMA) in August 10, 2017EQUAKEShort IntegerParcel intersects with an Alquist-Priolo Earthquake Fault Zone (California Geological Survey; 2018)LIQUAFAShort IntegerParcel intersects with a Liquefaction Susceptibility Zone (California Geological Survey; 2016)LANDSLIDEShort IntegerParcel intersects with a Landslide Hazard Zone (California Geological Survey; 2016)CPADShort IntegerParcel intersects with a protected area from the California Protected Areas Database(CPAD) – www.calands.org (accessed April 2021)RIPARIANShort IntegerParcel centroid falls within Active River Areas(2010)or parcel intersects with a Wetland Area in the National Wetland Inventory(Version 2)WILDLIFEShort IntegerParcel intersects with wildlife habitat (US Fish & Wildlife ServiceCritical Habitat, Southern California Missing Linkages, Natural Lands & Habitat Corridors from Connect SoCal, CEHC Essential Connectivity Areas,Critical Coastal Habitats)CNDDBShort IntegerThe California Natural Diversity Database (CNDDB)includes the status and locations of rare plants and animals in California. Parcels that overlap locations of rare plants and animals in California from the California Natural Diversity Database (CNDDB)have a greater likelihood of encountering special status plants and animals on the property, potentially leading to further legal requirements to allow development (California Department of Fish and Wildlife). Data accessed in October 2020.HCPRAShort IntegerParcel intersects Natural Community & Habitat Conservation Plans Reserve Designs from the Western Riverside MHSCP, Coachella Valley MHSCP, and the Orange County Central Coastal NCCP/HCP, as accessed in October 2020WETLANDShort IntegerParcel intersects a wetland or deepwater habitat as defined by the US Fish & Wildlife Service National Wetlands Inventory, Version 2.UAZShort IntegerParcel centroid lies within a Caltrans Adjusted Urbanized AreasUNBUILT_SFDoubleDifference between parcel area and building footprint area expressed in square feet.6GRCRY_1MIShort IntegerThe number of grocery stores within a 1-mile drive7HEALTH_1MIShort IntegerThe number of healthcare facilities within a 1-mile drive7OPENSP_1MIShort IntegerQuantity of open space (roughly corresponding to city blocks’ worth) within a 1-mile drive7TCAC_2021TextThe opportunity level based on the 2021 CA HCD/TCAC opportunity scores.HQTA45Short IntegerField takes a value of 1 if parcel centroid lies within a 2045 High-Quality Transit Area (HQTA)JOB_CTRShort IntegerField takes a value of 1 if parcel centroid lies within a job centerNMAShort IntegerField takes a value of 1 if parcel centroid lies within a neighborhood mobility area.ABS_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within an absolute constraint area. See the Sustainable Communities Strategy Technical Reportfor details.VAR_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within a variable constraint area. See the Sustainable Communities Strategy Technical Reportfor details.EJAShort IntegerField takes a value of 1 if parcel centroid lies within an Environmental Justice Area. See the Environmental Justice Technical Reportfor details.SB535Short IntegerField takes a value of 1 if parcel centroid lies within an SB535 Disadvantaged Community area. See the Environmental Justice Technical Reportfor details.COCShort IntegerField takes a value of 1 if parcel centroid lies within a Community of Concern See the Environmental Justice Technical Reportfor details.STATEShort IntegerThis field is a rudimentary estimate of which parcels have adequate physical space to accommodate a typical detached Accessory Dwelling Unit (ADU)8.SBShort IntegerIndex of ADU eligibility according to the setback reduction policy scenario (from 4 to 2 feet) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SMShort IntegerIndex of ADU eligibility according to the small ADU policy scenario (from 800 to 600 square feet ADU) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)PKShort IntegerIndex of ADU eligibility according to parking space exemption (200 square feet) policy scenario (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SMShort IntegerIndex of ADU eligibility according to both the setback reduction and small ADU policy scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_PKShort IntegerIndex of ADU eligibility according to both the setback reduction and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SM_PKShort IntegerIndex of ADU eligibility according to both the small ADU policy and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SM_PKShort IntegerIndex of ADU eligibility according to the setback reduction, small ADU, and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)1. Slope: '0' - 0~4 percent; '5' - 5~9 percent; '10' - 10~14 percent; '15' = 15~19 percent; '20' - 20~24 percent; '25' = 25 percent or greater.2. Source of 2019 existing land use: SCAG_REF- SCAG's regional geospatial datasets;ASSESSOR- Assessor's 2019 tax roll records; CPAD- California Protected Areas Database (version 2020a; accessed in September 2020); CSCD- California School Campus Database (version 2018; accessed in September 2020); FMMP- Farmland Mapping and Monitoring Program's Important Farmland GIS data (accessed in September 2020); MIRTA- U.S. Department of Defense's Military Installations, Ranges, and Training Areas GIS data (accessed in September 2020)3. Type of public agency includes federal, state, county, city, special district, school district, college/university, military.4. Based on 2019 building footprint data obtained from BuildingFootprintUSA (except that 2014 building footprint data was used for Imperial County). Please note that 2019 building footprint data does not cover the entire SCAG region (overlapped with 83% of parcels in the SCAG Region).5. Includes brownfield/superfund site whose address information are matched by SCAG rooftop address locator. Brownfield data was obtained from EPA's Assessment, Cleanup and Redevelopment Exchange System (ACRES) database, Cleanups in my community (CIMC), DTSC brownfield Memorandum of Agreement (MOA). Superfund site data was obtained from EPA's Superfund Enterprise Management System (SEMS) database.6. Parcels with a zero value for building footprint area are marked as NULL to indicate this field is not reliable.7. These values are intended as a rudimentary indicator of accessibility developed by SCAG using 2016 InfoUSA business establishment data and 2017 California Protected Areas data. See documentation for details.8. A detailed study conducted by Cal Poly Pomona (CPP) and available hereconducted an extensive review of state and local requirements and development trends for ADUs in the SCAG region and developed a baseline set of assumptions for estimating how many of a jurisdiction’s parcels

This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2019), processed by mundialis Processing was performed for blocks of federal states and individual maps were mosaicked afterwards. For each class 100,000 pixels from the potential training areas were extracted as training data. An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results: overall accurary: 91.9% class: user's accuracy / producer's accurary (number of reference points n) forest: 98.1% / 95.9% (1410) low vegetation: 76.4% / 91.5% (844) water: 98.4% / 92.8% (69) built-up: 99.2% / 97.4% (983) bare soil: 35.1% / 95.1% (41) agriculture: 95.9% / 85.3% (1653) Incora report with details on methods and results: pending

Abstract The article proposes the adaptation and application of the German methodology for the construction of climatic maps for the city of Belo Horizonte. Land use data, geographic aspects and wind information were analyzed, producing different layers of thematic maps. Their combination allowed a definition of eight classes of climatopes, making an analytical urban climatic map. It was verified that the edges of the city at south, southeast and northeast, which the green areas are concentrated, have greater dynamic potential and lower thermal load, considered an advantage for nocturnal cooling. However, the densely built-up areas located in the city center have low nocturnal cooling capacity, due to thermal load storage and the lowest dynamic potential that favor the heating of the surfaces. Concerning to the distribution of climatope classes, it was observed that about half of the city area presents a negative thermal load and a good dynamic potential as an atmospheric response. This result indicates that the negative impact of the urban elements on the surface thermal load can be considered still low. The results are also the basis for urban planning recommendations in order to preserve and expand urban areas that can contribute to the improvement of the local climate of Belo Horizonte.

Important Note: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 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 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. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas 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.2. TreesAny significant clustering of tall (~15-m 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).4. Flooded 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.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built 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.8. Bare 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.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open 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.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.For questions please email environment@esri.com

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission to show generalized land cover for regional planning with a land use component used for forecasts and modeling at ARC.LandPro2012 should not be taken out of its regional context, though county-level or municipal-level analysis may be useful for transportation, environmental and land use planning. LandPro2012 is ARC's land use/land cover GIS database for the 21-county Atlanta Region (Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Fulton, Gwinnett, Henry, Rockdale, the EPA non-attainment (8hr standard) counties of Carroll, Coweta, Barrow, Bartow, Forsyth, Hall, Newton, Paulding, Spalding and Walton and Dawson which will become a part of the 2010 Urbanized Area). LandPro2012 was created by on-screen photo-interpretation and digitizing of ortho-rectified aerial photography. The primary source for this GIS database were the local parcels and the 2009 true color imagery with 1.64-foot pixel resolution, provided by Aerials Express, Inc. 2010 is the first year we have used parcel data to help more accurately delineate the LandPro categories.For ArcGIS 10 users: See full metadata by enabling FGDC metadata in ArcCatalog Customize > ArcCatalog Options > Metadata (tab)Though the terms are often used interchangeably, land use and land cover are not synonymous. Land cover generally refers to the natural or cultivated vegetation, rock, or water covering the land, as well as the developed surface which can be identified on aerial photography. Land use generally refers to the way that humans use or will use the land, regardless of its apparent land cover. Collateral data for the land cover mapping effort included the Aero Surveys of Georgia street atlas, the Georgia Department of Community Affairs (DCA) Community Facilities database and the USGS Digital Raster Graphics (DRGs) of 1:24,000 scale topographic maps. The land use component of this database was added after the land cover interpretation was completed, and is based primarily on ownership information provided by the 21 counties and the City of Atlanta for larger tracts of undeveloped land that meet the land use definition of "Extensive Institutional" or "Park Lands" (refer to the Code Descriptions and Discussion section below). Although some of the boundaries of these tracts may align with visible features from the aerial photography, these areas are generally "non-photo-identifiable," thus require other sources for accurate identification. The land use/cover classification system is adapted from the USGS (Anderson) classification system, incorporating a mix of level I, II and III classes. There are a total of 25 categories in ARC's land use/cover system (described below), 2 of which are used only for land use designations: Park Lands (Code 175) and Extensive Institutional (Code 125). The other 23 categories can describe land use and/or land cover, and in most cases will be the same. The LU code will differ from the LC code only where the Park Lands (Code 175) and Extensive Institutional (Code 125) land holdings have been identified from collateral sources of land ownership.Although similar to previous eras of ARC land use/cover databases developed before 1999 (1995, 1990 etc.), "LandPro" differs in many significant ways. Originally, ARC's land use and land cover database was built from 1975 data compiled by USGS at scales of 1:100,000 and selectively, 1:24,000. The coverage was updated in 1990 using SPOT satellite imagery and low-altitude aerial photography and again in 1995 using 1:24,000 scale panchromatic aerial photography. Unlike these previous 5-year updates, the 1999, 2001, 2003, 2005 2007, 2008 and 2009 LandPro databases were compiled at a larger scale (1:14,000) and do not directly reflect pre-1999 delineations. In addition, all components of LandPro were produced using digital orthophotos for on-screen photo-interpretation and digitizing, thus eliminating the use of unrectified photography and the need for data transfer and board digitizing. As a result, the positional accuracy of LandPro is much higher than in previous eras. There have also been some changes to the classification system prior to 1999. Previously, three categories of Forest (41-deciduous, 42-coniferous, and 43-mixed forest) were used; this version does not distinguish between coniferous and deciduous forest, thus Code 40 is used to simply designate Forest. Likewise, two categories of Wetlands (61-forested wetland, and 62-non-forested wetland) were used before; this version does not distinguish between forested and non-forested wetlands, thus Code 60 is used to simply designate Wetlands. With regard to Wetlands, the boundaries themselves are now based on the National Wetlands Inventory (NWI) delineations along with the CIR imagery. Furthermore, Code 51 has been renamed "Rivers" from "Streams and Canals" and represents the Chattahoochee and Etowah Rivers which have been identified in the land use/cover database. In addition to these changes, Code 52 has been dropped from the system as there are no known instances of naturally occurring lakes in the Region. Finally, the land use code for Park Lands has been changed from 173 to 175 so as to minimize confusion with the Parks land cover code, 173. There has been a change in the agriculture classification for LandPro2005 and any LandPro datasets hereafter. Previously, four categories of agriculture (21- agriculture-cropland and pasture, 22 - agriculture - orchards, 23 - agriculture - confined feeding operations and 24 - agriculture - other) were used; this version does not distinguish between the different agricultural lands. Code 20 is now used to designate agriculture. Due to new technology and the enhancements to this database, direct comparison between LandPro99, LandPro2001, LandPro2003 and landPro2005 and all successive updates are now possible, with the 1999 database serving as ARC's new baseline. Please note that as a result of the 2003 mapping effort, LandPro2001 has been adjusted for better comparison to LandPro2003 and is named "LandPro01_adj." Likewise, LandPro99 was previously adjusted when LandPro2001 was completed, but was not further adjusted following the 2003 update. Although some adjustments were originally made to the 1995 land use/cover database for modeling applications, direct comparisons to previous versions of ARC land use/cover before 1999 should be avoided in most cases.The 2010 update has moved away from using the (1:14,000) scale, as will any future updates. Due to the use of local parcels, we have begun to snap LandPro boundaries to the parcel data, making a more accurate dataset. The major change in this update was to make residential areas reflect modern zoning codes more closely. Due to these changes you will no longer be able to compare this dataset to previous years. High density (113) has changed from lots below .25 to lots .25 and smaller. Medium density (112) has changed from .25 to 2 acre lots, to .26 to 1 acre lots. Low density has changed from 2 to 5 acre lots to 1.1 to 2 acre lots. It must be noted that in the 2010 update, you still have old acreage standards reflected in the low density. This will be corrected in the 2011 and 2012 updates. The main focus of the 2010 update was to make sure the LandPro' residential areas reflected the local parcels and change LandPro based on the parcel acreage. DeKalb is the only county not corrected at this time because no parcels were available. The future updates will consist of but are not limited to, reclassifying areas in 111 that do not meet the new acreage standards, delineating and reclassifying Cell Towers, substations and transmission lines/power cuts from TCU (14) to a subset of this (142), reclassifying airports as 141 form TCU, and reclassifying landfills form urban other (17) to 174. Other changes are delineating more roads other than just Limited Access Highways, making sure parks match the already existing Land use parks layer, and beginning to differentiate office from commercial and commercial/industrial.Classification System:111: Low Density Single Family Residential - Houses on 1.1 - 2 acre lots. Though 2010 still reflects the old standard of lots up to 5 acres.112: Medium Density Single Family Residential - These areas usually occur in urban or suburban zones and are generally characterized by houses on .26 to 1 acre lots. This category accounts for the majority of residential land use in the Region and includes a wide variety of neighborhood types.113: High Density Residential - Areas that have predominantly been developed for concentrated single family residential use. These areas occur almost exclusively in urban neighborhoods with streets on a grid network, and are characterized by houses on lots .25 acre or smaller but may also include mixed residential areas with duplexes and small apartment buildings.117: Multifamily Residential - Residential areas comprised predominantly of apartment, condominium and townhouse complexes where net density generally exceeds eight units per acre. Typical apartment buildings are relatively easy to identify, but some high rise structures may be interpreted as, or combined with, office buildings, though many of these dwellings were identified and delineated in downtown and midtown for the first time with the 2003 update. Likewise, some smaller apartments and townhouses may be interpreted as, or combined with, medium- or high-density single family residential. Housing on military bases, campuses, resorts, agricultural properties and construction work sites is not included in this or other residential categories.119: Mobile Home Parks - Areas that have been developed for single family mobile home use. These residential areas may occur in urban, suburban, or rural zones throughout the Region, with or without a significant mix of forested land cover. Due to their sparse distribution, individual mobile homes are

This Existing Vegetation (Eveg) polygon feature class is a CALVEG (Classification and Assessment with LANDSAT of Visible Ecological Groupings) map product at a scale of 1:24,000 for CALVEG Zone 7, the South Coast . Source imagery for this layer ranges from the year 2002 to 2010.The CALVEG classification system was used for vegetation typing and crosswalked to other classification systems in this database. USGS Land Use / Land Cover Anderson 1 classification system is included in the database to meet national standard requirements. Mapping standards meet requirements of the USDA Forest Service as defined by the FS GIS data dictionary, FGDC Vegetation standards and the FS Existing Vegetation Classification and Mapping Technical Guide. Regional add-ons are retained for crosswalking to the California Wildlife Habitat Relationship System (CWHR). For a description of CALVEG and a data dictionary for codes in this database, go to the Existing Vegetation Layer Description at http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5365219. For an index of CALVEG zones, go to http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192 and select the link called Existing Vegetation Tiles Index. For a CALVEG mapping status by scale and year, go to http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192 and select the "Existing Vegetation Mapping Status by Year, Scale and Project" link. *******Note: This layer is comprised of "multi-part" features, spatially separate polygons sharing the same attributes and stored as a single feature. A group of islands could be represented as a multi-part polygon feature. This allows for reduction in the size of the database and portability across a network. For analysis purposes however, it is wise to select a smaller area of interest and break apart features using the "Multipart To Singlepart" tool in ArcGIS. In its entirety, a "single-part" format of this feature class can potentially be more than one million polygons.

The product presents the breakdown of productive and unproductive forest land within what is mapped as forest land in the base layer of NMD. Forest land, productive forests and unproductive forests developed in the NMD are in accordance with the definitions in the Forestry Act (and FAO), except that NMD also maps forests in agricultural and built-up areas as forest land.

This GIS database is a generalized land cover database designed for Regional Planning with a land use component used for forecasts and modeling at ARC. LandPro should not be taken out of its Regional context, though county-level or municipal-level analysis may be useful for transportation, environmental and land use planning.

Description This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission and is a generalized land cover database designed for regional planning with a land use component used for forecasts and modeling at ARC. LandPro2012 should not be taken out of its regional context, though county-level or municipal-level analysis may be useful for transportation, environmental and land use planning. LandPro2012 is ARC's land use/land cover GIS database for the 21-county Atlanta Region (Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Fulton, Gwinnett, Henry, Rockdale, the EPA non-attainment (8hr standard) counties of Carroll, Coweta, Barrow, Bartow, Forsyth, Hall, Newton, Paulding, Spalding and Walton and Dawson which will become a part of the 2010 Urbanized Area). LandPro2012 was created by on-screen photo-interpretation and digitizing of ortho-rectified aerial photography. The primary source for this GIS database were the local parcels and the 2009 true color imagery with 1.64-foot pixel resolution, provided by Aerials Express, Inc. 2010 is the first year we have used parcel data to help more accurately delineate the LandPro categories.For ArcGIS 10 users: See full metadata by enabling FGDC metadata in ArcCatalog Customize > ArcCatalog Options > Metadata (tab)Though the terms are often used interchangeably, land use and land cover are not synonymous. Land cover generally refers to the natural or cultivated vegetation, rock, or water covering the land, as well as the developed surface which can be identified on aerial photography. Land use generally refers to the way that humans use or will use the land, regardless of its apparent land cover. Collateral data for the land cover mapping effort included the Aero Surveys of Georgia street atlas, the Georgia Department of Community Affairs (DCA) Community Facilities database and the USGS Digital Raster Graphics (DRGs) of 1:24,000 scale topographic maps. The land use component of this database was added after the land cover interpretation was completed, and is based primarily on ownership information provided by the 21 counties and the City of Atlanta for larger tracts of undeveloped land that meet the land use definition of "Extensive Institutional" or "Park Lands" (refer to the Code Descriptions and Discussion section below). Although some of the boundaries of these tracts may align with visible features from the aerial photography, these areas are generally "non-photo-identifiable," thus require other sources for accurate identification. The land use/cover classification system is adapted from the USGS (Anderson) classification system, incorporating a mix of level I, II and III classes. There are a total of 25 categories in ARC's land use/cover system (described below), 2 of which are used only for land use designations: Park Lands (Code 175) and Extensive Institutional (Code 125). The other 23 categories can describe land use and/or land cover, and in most cases will be the same. The LU code will differ from the LC code only where the Park Lands (Code 175) and Extensive Institutional (Code 125) land holdings have been identified from collateral sources of land ownership.Although similar to previous eras of ARC land use/cover databases developed before 1999 (1995, 1990 etc.), "LandPro" differs in many significant ways. Originally, ARC's land use and land cover database was built from 1975 data compiled by USGS at scales of 1:100,000 and selectively, 1:24,000. The coverage was updated in 1990 using SPOT satellite imagery and low-altitude aerial photography and again in 1995 using 1:24,000 scale panchromatic aerial photography. Unlike these previous 5-year updates, the 1999, 2001, 2003, 2005 2007, 2008 and 2009 LandPro databases were compiled at a larger scale (1:14,000) and do not directly reflect pre-1999 delineations. In addition, all components of LandPro were produced using digital orthophotos for on-screen photo-interpretation and digitizing, thus eliminating the use of unrectified photography and the need for data transfer and board digitizing. As a result, the positional accuracy of LandPro is much higher than in previous eras. There have also been some changes to the classification system prior to 1999. Previously, three categories of Forest (41-deciduous, 42-coniferous, and 43-mixed forest) were used; this version does not distinguish between coniferous and deciduous forest, thus Code 40 is used to simply designate Forest. Likewise, two categories of Wetlands (61-forested wetland, and 62-non-forested wetland) were used before; this version does not distinguish between forested and non-forested wetlands, thus Code 60 is used to simply designate Wetlands. With regard to Wetlands, the boundaries themselves are now based on the National Wetlands Inventory (NWI) delineations along with the CIR imagery. Furthermore, Code 51 has been renamed "Rivers" from "Streams and Canals" and represents the Chattahoochee and Etowah Rivers which have been identified in the land use/cover database. In addition to these changes, Code 52 has been dropped from the system as there are no known instances of naturally occurring lakes in the Region. Finally, the land use code for Park Lands has been changed from 173 to 175 so as to minimize confusion with the Parks land cover code, 173. There has been a change in the agriculture classification for LandPro2005 and any LandPro datasets hereafter. Previously, four categories of agriculture (21- agriculture-cropland and pasture, 22 - agriculture - orchards, 23 - agriculture - confined feeding operations and 24 - agriculture - other) were used; this version does not distinguish between the different agricultural lands. Code 20 is now used to designate agriculture. Due to new technology and the enhancements to this database, direct comparison between LandPro99, LandPro2001, LandPro2003 and landPro2005 and all successive updates are now possible, with the 1999 database serving as ARC's new baseline. Please note that as a result of the 2003 mapping effort, LandPro2001 has been adjusted for better comparison to LandPro2003 and is named "LandPro01_adj." Likewise, LandPro99 was previously adjusted when LandPro2001 was completed, but was not further adjusted following the 2003 update. Although some adjustments were originally made to the 1995 land use/cover database for modeling applications, direct comparisons to previous versions of ARC land use/cover before 1999 should be avoided in most cases.The 2010 update has moved away from using the (1:14,000) scale, as will any future updates. Due to the use of local parcels, we have begun to snap LandPro boundaries to the parcel data, making a more accurate dataset. The major change in this update was to make residential areas reflect modern zoning codes more closely. Due to these changes you will no longer be able to compare this dataset to previous years. High density (113) has changed from lots below .25 to lots .25 and smaller. Medium density (112) has changed from .25 to 2 acre lots, to .26 to 1 acre lots. Low density has changed from 2 to 5 acre lots to 1.1 to 2 acre lots. It must be noted that in the 2010 update, you still have old acreage standards reflected in the low density. This will be corrected in the 2011 and 2012 updates. The main focus of the 2010 update was to make sure the LandPro' residential areas reflected the local parcels and change LandPro based on the parcel acreage. DeKalb is the only county not corrected at this time because no parcels were available. The future updates will consist of but are not limited to, reclassifying areas in 111 that do not meet the new acreage standards, delineating and reclassifying Cell Towers, substations and transmission lines/power cuts from TCU (14) to a subset of this (142), reclassifying airports as 141 form TCU, and reclassifying landfills form urban other (17) to 174. Other changes are delineating more roads other than just Limited Access Highways, making sure parks match the already existing Land use parks layer, and beginning to differentiate office from commercial and commercial/industrial.Classification System:111: Low Density Single Family Residential - Houses on 1.1 - 2 acre lots. Though 2010 still reflects the old standard of lots up to 5 acres.112: Medium Density Single Family Residential - These areas usually occur in urban or suburban zones and are generally characterized by houses on .26 to 1 acre lots. This category accounts for the majority of residential land use in the Region and includes a wide variety of neighborhood types.113: High Density Residential - Areas that have predominantly been developed for concentrated single family residential use. These areas occur almost exclusively in urban neighborhoods with streets on a grid network, and are characterized by houses on lots .25 acre or smaller but may also include mixed residential areas with duplexes and small apartment buildings.117: Multifamily Residential - Residential areas comprised predominantly of apartment, condominium and townhouse complexes where net density generally exceeds eight units per acre. Typical apartment buildings are relatively easy to identify, but some high rise structures may be interpreted as, or combined with, office buildings, though many of these dwellings were identified and delineated in downtown and midtown for the first time with the 2003 update. Likewise, some smaller apartments and townhouses may be interpreted as, or combined with, medium- or high-density single family residential. Housing on military bases, campuses, resorts, agricultural properties and construction work sites is

Table of development assumptions by land use zoning category to support the City of Seattle Zone Development Capacity Model.Assumptions include floor-area-ratio, residential density, split between residential and commercial floor area in mixed use zones, redevelopment ratio thresholds and conversions between floor area and housing units and jobs.Supporting Resources:Complete Data Dictionary

VITAL SIGNS INDICATOR Greenfield Development (LU5)

FULL MEASURE NAME The acres of construction on previously undeveloped land

LAST UPDATED November 2019

DESCRIPTION Greenfield development refers to construction on previously undeveloped land and the corresponding expansion of our region’s developed footprint, which includes the extent of urban and built-up lands. The footprint is defined as land occupied by structures, with a building density of at least 1 unit to 1.5 acres.

DATA SOURCE Department of Conservation: Farmland Mapping and Monitoring Program GIS Data Tables/Layers (1990-2016) https://www.conservation.ca.gov/dlrp/fmmp

U.S. Census Bureau: Decennial Census Population by Census Block Group (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey (5-year) Population by Census Block Group (2000-2017) http://factfinder.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) For regional and local data, FMMP maps the extent of “urban and built-up” lands, which generally reflect the developed urban footprint of the region. The footprint is defined as land occupied by structures with building density of at least 1 unit to 1.5 acres. Uses include residential, industrial, commercial, construction, institutional, public administration, railroad and other transportation yards, cemeteries, airports, golf courses, sanitary landfills, sewage treatment, water control structures, and other developed purposes.

To determine the amount of greenfield development (in acres) occurring in a given two-year period, the differences in urban footprint are computed on a county-level. FMMP makes slight refinements to urban boundaries over time, so changes in urban footprint +/- 100 acres are not regionally significant. The GIS shapefile represents the 2016 urban footprint and thus does not show previously urbanized land outside of the footprint (i.e. Hamilton Air Force Base).

For metro comparisons, a different methodology had to be used to avoid the geospatial limitations associated with FMMP. U.S. Census population by census block group was gathered for each metro area for 2000, 2010, and 2017. Population data for years 2000 and 2010 come from the Decennial Census while data for 2018 comes from the 2017 5-year American Community Survey. The block group was considered urbanized if its average/gross density was greater than 1 housing unit per acre (a slightly higher threshold than FMMP uses for its definition). Because a block group cannot be flagged as partially urbanized, and non-residential uses are not fully captured, the urban footprint of the region calculated with this methodology is smaller than in FMMP. The metro data should be primarily used for looking at comparative growth rate in greenfield development rather than the acreage totals themselves.

The Land Cover Database of the Islamic Republic of Afghanistan has been created as part of the land cover mapping component of the project on “Strengthening Agricultural Economics, Market Information and Statistics Services” formulated upon request from the Government of the Islamic Republic of Afghanistan and funded by the European Commission. The Food and Agriculture Organization of the United Nations (FAO) provided technical assistance as the executing agency in close cooperation with all parties. The Land Cover database provides information on land cover distribution. It has been created using the FAO/GLCN methodology and tools. The main data sources include satellite imagery from SPOT-4 (2009-2011) and Global Land Survey (GLS-2011) Landsat satellites, high resolution satellite imagery and very hisgh resolution aerial photographs, ancillary data. The national legend was prepared using the Land Cover Classification System (LCCS). FAO’s Mapping Device Change Analysis Tools (MADCAT) software was used to create the database using object based classification methodology. The full resolution land cover legend has 25 classes. As result, more that 500,000 polygons were delineated. To refine the interpretation, high resolution images from various sources are used. The 25 original land cover classes were aggregated into 11 generalized and self-explicative classes as following: Built-Up (URB); Fruit Trees (AGT); Vineyard (AGV); Irrigated Agricultural Land (AGI); Rainfed Agricultural Land (AGR); Forest and Shrubs (NFS); Rangeland (NHS); Barren land (BRS); Sand Cover (BSD); Water Body and Marshland (WAT); Permanent Snow (SNW). The database is distributed in shapefile format in UTM zone 42 North WGS-84 datum. Each shapefile is included in a geodatabase. The tabular attributes contains 4 fields: -AGGCODE is the aggregated class name; -LCCSPERC is the percentage share of each code in the land cover unit as following:100 means that there is only one single land cover class present; 60/40 means that this is a mixed unit land cover class with two classes; the distribution of the land cover classes inside the land cover unit is 60 percent for the first class and 40 percent for the second class; 40/30/30 means that this is a mixed unit land cover class with three classes; the distribution of the land cover classes inside the land cover unit is 40 percent for the first class, 30 percent for the second class and 30 percent for the third class; -DIST_NAME is the second administrative unit level (District level) name based on the administrative layer provided by the Counterpart Agency in Afghanistan; -PROV_NAME is the first administrative unit level (Provincial level) name based on the administrative layer provided by the Counterpart Agency in Afghanistan.

The Land Cover Database of the Islamic Republic of Afghanistan has been created as part of the land cover mapping component of the project on “Strengthening Agricultural Economics, Market Information and Statistics Services” formulated upon request from the Government of the Islamic Republic of Afghanistan and funded by the European Commission. The Food and Agriculture Organization of the United Nations (FAO) provided technical assistance as the executing agency in close cooperation with all parties. The Land Cover database provides information on land cover distribution. It has been created using the FAO/GLCN methodology and tools. The main data sources include satellite imagery from SPOT-4 (2009-2011) and Global Land Survey (GLS-2011) Landsat satellites, high resolution satellite imagery and very hisgh resolution aerial photographs, ancillary data. The national legend was prepared using the Land Cover Classification System (LCCS). FAO’s Mapping Device Change Analysis Tools (MADCAT) software was used to create the database using object based classification methodology. The full resolution land cover legend has 25 classes. As result, more that 500,000 polygons were delineated. To refine the interpretation, high resolution images from various sources are used. The 25 original land cover classes were aggregated into 11 generalized and self-explicative classes as following: Built-Up (URB); Fruit Trees (AGT); Vineyard (AGV); Irrigated Agricultural Land (AGI); Rainfed Agricultural Land (AGR); Forest and Shrubs (NFS); Rangeland (NHS); Barren land (BRS); Sand Cover (BSD); Water Body and Marshland (WAT); Permanent Snow (SNW). The database is distributed in shapefile format in UTM zone 42 North WGS-84 datum. Each shapefile is included in a geodatabase. The tabular attributes contains 4 fields: -AGGCODE is the aggregated class name; -LCCSPERC is the percentage share of each code in the land cover unit as following:100 means that there is only one single land cover class present; 60/40 means that this is a mixed unit land cover class with two classes; the distribution of the land cover classes inside the land cover unit is 60 percent for the first class and 40 percent for the second class; 40/30/30 means that this is a mixed unit land cover class with three classes; the distribution of the land cover classes inside the land cover unit is 40 percent for the first class, 30 percent for the second class and 30 percent for the third class; -DIST_NAME is the second administrative unit level (District level) name based on the administrative layer provided by the Counterpart Agency in Afghanistan; -PROV_NAME is the first administrative unit level (Provincial level) name based on the administrative layer provided by the Counterpart Agency in Afghanistan.