[Metadata] Description: Agricultural Land Use Maps (ALUM) for islands of Kauai, Oahu, Maui, Molokai, Lanai and Hawaii as of 1978-1980. Sources: State Department of Agriculture; Hawaii Statewide GIS Program, Office of Planning. Note: August, 2018 - Corrected one incorrect record, removed coded value attribute domain.For more information on data sources and methodologies used, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/alum.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

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

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2023. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:
Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

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References:

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland
https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

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National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Abstract The Catchment Scale Land Use of Australia – Update December 2023 dataset is the national compilation of catchment scale land use data available for Australia (CLUM), as of December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). Land use is classified according to the Australian Land Use and Management Classification version 8. It has been compiled from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources, through the Australian Collaborative Land Use and Management Program. Catchment scale land use data was produced by combining land tenure and other types of land use information including, fine-scale satellite data, ancillary datasets, and information collected in the field. The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary, reflecting the source data, capture date and scale. Date and scale of mapping are provided in supporting datasets.

Currency Date modified: December 2023 Publication Date: June 2024 Modification frequency: As needed (approximately annual) Data Extent Coordinate reference: WGS84 / Mercator Auxiliary Sphere Spatial Extent North: -9.995 South: -44.005 East: 154.004 West: 112.505 Source information Data, Metadata, Maps and Interactive views are available from Catchment Scale Land Use of Australia - Update 2023 Catchment Scale Land Use of Australia - Update 2023 – Descriptive metadata The data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). ABARES is providing this data to the public under a Creative Commons Attribution 4.0 license. Lineage statement This catchment scale land use dataset provides the latest compilation of land use mapping information for Australia’s regions as at December 2023. It is used by the Department of Agriculture, Fisheries and Forestry, state agencies and regional natural resource management groups to address issues such as agricultural productivity and sustainability, biodiversity conservation, biosecurity, land use planning, natural disaster management and natural resource monitoring and investment. The data vary in date of mapping (2008 to 2023) and scale (1:5,000 to 1:250,000). 2023 updates include more current data and/or reclassification of existing data. The following areas have updated data since the December 2020 version:

New South Wales (2017 v1.5 from v1.2). Northern Territory (2022 from 2020). Tasmania (2021 from 2019). Victoria (2021 from 2017). Data were also added from the Great Barrier Reef Natural Resource Management (NRM) regions in Queensland (2021 from a variety of dates 2009 to 2017). the Australian Tree Crops. Australian Protected Cropping Structures and Queensland Soybean Crops maps as downloaded on 30 November 2023. The capital city of Adelaide was updated using 2021 mesh block information from the Australian Bureau of Statistics. Minor reclassifications were made for Western Australia and mining area within mining tenements more accurately delineated in South Australia.

Links to land use mapping datasets and metadata are available at the ACLUMP data download page at agriculture.gov.au. State and territory vector catchment scale land use data were produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field, as outlined in 'Guidelines for land use mapping in Australia: principles, procedures and definitions, 4th edition' (ABARES 2011). The Northern Territory, Queensland, South Australia, Tasmania, Victoria and Western Australia were mapped to version 8 of the ALUM classification (‘The Australian Land Use and Management Classification Version 8’, ABARES 2016). The Australian Capital Territory was mapped to version 7 of the ALUM classification and converted to version 8 using a look-up table based on Appendix 1 of ABARES (2016). Purpose for which the material was obtained: This catchment scale land use dataset provides the latest compilation of land use mapping information for Australia’s regions as at December 2023. It is used by the Department of Agriculture, Fisheries and Forestry, state agencies and regional natural resource management groups to address issues such as agricultural productivity and sustainability, biodiversity conservation, biosecurity, land use planning, natural disaster management and natural resource monitoring and investment. The data vary in date of mapping (2008 to 2023) and scale (1:5,000 to 1:250,000). Do not use this data to:

Derive national statistics. The Land use of Australia data series should be used for this purpose. Calculate land use change. The Land use of Australia data series should be used for this purpose.

It is not possible to calculate land use change statistics between annual CLUM national compilations as not all regions are updated each year; land use mapping methodologies, precision, accuracy and source data and satellite imagery have improved over the years; and the land use classification has changed over time. It is only possible to calculate change when earlier land use datasets have been revised and corrected to ensure that changes detected are real change and not an artefact of the mapping process. Note: The Digital Atlas of Australia downloaded and created a copy of the source data in October 2024 that was suitable to be hosted through ArcGIS Image Server & Image Dedicated. A copy of the raster was created with RGB fields as a colour map with Geoprocessing tools in ArcPro. Note: The Digital Atlas of Australia downloaded and created a copy of the source data in February 2025 that was suitable to be hosted through ArcGIS Image Server & Image Dedicated. A copy of the raster dataset was created with RGB fields as a colour map with Geoprocessing tools in ArcPro, and the raster dataset was re-projected from 1994 Australia Albers to WGS 1984 Web Mercator (Auxiliary Sphere). Data dictionary

Field name DField description Code values

OID Internal feature number that uniquely identifies each row Integer

Service Pixel value (Date) The year for which land use was mapped in the vector data provided by state and territory agencies or others, Date Range: 2008 to 2023 Integer

Count Count of the number of raster cells in each class of VALUE Integer

Label Reflecting the Date of the source data ranges from 2008 to 2023 Text

Contact Department of Agriculture, Fisheries and Forestry (ABARES), info.ABARES@aff.gov.au

This dataset (2017-2023) 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) 2019-2023.(Alachua 20200102-20200106), (Baker 20200108-20200126), (Bradford 20181020-20190128), (Columbia 20181213-20190106), (Gilchrist 20181020-20190128), (Levy 20181020-20190128), (Suwannee 20181217-20190116), (Union 20181020-20190128).(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) 2020. 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.

This database contains nine land use / land cover datasets collected in a standardized manner between 2013 and 2022 in seven tropical countries within the framework of the international JECAM initiative: Burkina Faso (Koumbia), Madagascar (Antsirabe), Brazil (São Paulo and Tocantins), Senegal (Nioro, Niakhar, Mboro, Tattaguine and Koussanar), Kenya (Muranga), Cambodia (Kandal) and South Africa (Mpumalanga) (cf Study_sites‧kml). These quality-controlled datasets are distinguished by ground data collected at field scale by local experts, with precise geographic coordinates, and following a common protocol. This database, which contains 31879 records (24 287 crop and 7 592 non-crop) is a geographic layer in Shapefile format in a Geographic Coordinates System with Datum WGS84. Field surveys were conducted yearly in each study zone, either around the growing peak of the cropping season, for the sites with a main growing season linked to the rainy season such as Burkina Faso, or seasonally, for the sites with multiple cropping (e‧g. São Paulo site). The GPS waypoints were gathered following an opportunistic sampling approach along the roads or tracks according to their accessibility, while ensuring the best representativity of the existing cropping systems in place. GPS waypoints were also recorded on different types of non-crop classes (e‧g. natural vegetation, settlement areas, water bodies) to allow differentiating crop and non-crop classes. Waypoints were only recorded for homogenous fields/entities of at least 20 x 20 m². To facilitate the location of sampling areas and the remote acquisition of waypoints, field operators were equipped with GPS tablets providing access to a QGIS project with Very High Spatial Resolution (VHSR) images ordered just before the surveys. For each waypoint, a set of attributes, corresponding to the cropping practices (crop type, cropping pattern, management techniques) were recorded (for more informations about data, see data paper being published). These datasets can be used to validate existing cropland and crop types/practices maps in the tropics, but also, to assess the performances and the robustness of classification methods of cropland and crop types/practices in a large range of Southern farming systems.

Global maps of agricultural expansion potential at a 300 m resolution

This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.

Abstract:

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.

Data description:

We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.

Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*". The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.

Repository content:

Full resolution layers: - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds. - "readme.txt" is the text file with the basic description and the metadata for the repository.

Aggregated layers: This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).

File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”

• "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.

• "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.

• "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.

Raster metadata:

Driver: GTiff Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs

Notes on use:

Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.

This dataset contains shape files and supporting files for the most up-to-date (as of the published date) land use map at the UBC Farm. The best uses of these maps are: 1) to visualize locations of field codes in other UBC Farm datasets; 2) to visualize field codes for UBC Farm research projects, and 3) to understand the general layout of the Farm.

The Land Cover dataset demarcates 14 land cover types by area; such as Residential, Commercial, Industrial, Forest, Agriculture, etc. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Geography Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 1994 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: The dataset was created by Chester Environmental through combined image processing and GIS analysis of Landsat TM imagery of October 2, 1992, existing aerial photography, hardcopy and digital mapping sources and Census Bureau demographic data. The original dataset was created in 1993, then updated by Chester in 1994. Other: none Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/1VfUflfki42mpLSkr1R-up_OXGD3mHnv8tqeXf6XS9O0/edit?usp=sharing) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth. Users can select a geographic area of interest or import one, then access acreage statistics for a specific year or view the change from one year to another. The data can be exported or added to the CDL. The information is useful for issues related to agricultural sustainability, biodiversity, and land cover monitoring, especially due to extreme weather events. Resources in this dataset:Resource Title: CropScape and Cropland Data Layer - National Download. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php Downloads available as zipped files at https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php --

National CDL's -- by year, 2008-2020. Cropland Data Layer provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. National Cultivated Layer -- based on the most recent five years (2013-2020). National Frequency Layer -- the 2017 Crop Frequency Layer identifies crop specific planting frequency and are based on land cover information derived from the 2008 through 2020CDL's. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat. National Confidence Layer -- the Confidence Layer spatially represents the predicted confidence that is associated with that output pixel, based upon the rule(s) that were used to classify it. Western/Eastern/Central U.S.

Visit https://nassgeodata.gmu.edu/CropScape/ for the interactive map including tutorials and basic instructions. These options include a "Demo Video", "Help", "Developer Guide", and "FAQ".

The land use legend originates from the CORINE land cover project. It is a tessellation of artificially modeled terrains, agricultural territories, wooded territories and semi-natural environments, wetlands, waters, etc. - Coverage: Entire Regional Territory - Origin: Photo-interpretation and aerial shots in B/W or in color at 1:13000 scale.

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 ...

AbstractCatchment Scale Land Use of Australia (CLUM), depicted as 19 simplified land use classes based on the simplified classes of the Australian Land Use and Management (ALUM) Classification version 8.The classes are Nature conservation, Managed resource protection, Other minimal use, Grazing native vegetation, Production native forests, Grazing modified pastures, Plantation forests (commercial and other), Dryland cropping, Dryland horticulture, Land in transition, Irrigated pastures, Irrigated cropping, Irrigated horticulture, Intensive horticulture and animal production, Rural residential and farm infrastructure, Urban residential, Other intensive uses, Mining and waste, and Water.The Catchment Scale Land Use of Australia – Update December 2023 version 2 dataset is the national compilation of catchment scale land use data available for Australia, as at December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020.It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies).Land use is classified according to the Australian Land Use and Management Classification version 8. It has been compiled from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources, through the Australian Collaborative Land Use and Management Program. Catchment scale land use data was produced by combining land tenure and other types of land use information including, fine-scale satellite data, ancillary datasets, and information collected in the field.The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary, reflecting the source data, capture date and scale. Date and scale of mapping are provided in supporting datasets.CurrencyDate modified: June 2024Modification frequency: As requiredData extentSpatial extentNorth: -8.03°South: -45.5°East: 161.5°West: 105.7°Source informationData, Metadata, Maps and Interactive views are available from Catchment Scale Land Use of Australia - Update 2023Catchment Scale Land Use of Australia - Update 2023 – Descriptive metadataThe data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). ABARES is providing this data to the public under a Creative Commons Attribution 4.0 license.Lineage statementABARES has produced this raster dataset from vector catchment scale land use data provided by state and territory agencies, as follows:Catchment Scale Land Use Mapping for the Australian Capital Territory 20122017 NSW Land Use v1.5Land Use Mapping Project of the Northern Territory, 2016 – 2022 (LUMP)Land use mapping – 2021 – Great Barrier Reef NRM regionsLand use mapping – 1999 to Current – Queensland (June 2019)[South Australia] Land Use (ACLUMP) (2017)Tasmanian Land Use 2022Victorian Land Use Information System [VLUIS] 2021-22Catchment Scale Land Use Mapping for Western Australia 2018Australian Tree Crops, Australian Protected Cropping Structures and Queensland Soybean Crops maps (as at 30 November 2023)Applied Agricultural Remote Sensing Centre (AARSC), University of New England.Links to land use mapping datasets and metadata are available at the ACLUMP data download page at agriculture.gov.au.State and territory vector catchment scale land use data were produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field, as outlined in 'Guidelines for land use mapping in Australia: principles, procedures and definitions, 4th edition' (ABARES 2011). The Northern Territory, Queensland, South Australia, Tasmania, Victoria and Western Australia were mapped to version 8 of the ALUM classification (‘The Australian Land Use and Management Classification Version 8’, ABARES 2016).The Australian Capital Territory was mapped to version 7 of the ALUM classification and converted to version 8 using a look-up table based on Appendix 1 of ABARES (2016).The following agricultural (excluding intensive uses) classes were included from the Queensland Great Barrier Reef NRM Regions 2021 modified ALUM classification schema dataset:2.2.0 Grazing native vegetation3.2.0 Grazing modified pastures3.3.0 Cropping3.3.5 Sugar3.4.0 Perennial horticulture3.4.1 Tree fruits3.5.0 Seasonal horticulture3.6.0 Land in transition4.2.0 Grazing irrigated modified pastures4.3.0 Irrigated cropping4.3.5 Irrigated sugar4.4.0 Irrigated perennial horticulture4.4.1 Irrigated tree fruits4.5.0 Irrigated seasonal horticulture4.6.0 Irrigated land in transitionFixes to known issues include:In Western Australia, ALUM classes 4.0.0 Production from Irrigated Agriculture and Plantations, 5.0.0 Intensive Uses and 6.0.0 Water have been attributed to secondary level by visual interpretation using satellite data.In South Australia, through consultation with the South Australian Department of Environment and Water, the mining area (ALUM class 5.8.0 Mining) within mining tenements is more accurately delineated. The area within mining tenements that is not used for mining is now attributed as grazing of native vegetation (ALUM class 2.1.0) within pastoral areas and residual native cover (ALUM class 1.3.3) outside of pastoral areas.NODATA voids in Adelaide, South Australia were filled with data from mesh block land use attributes (Australian Bureau of Statistics 2021) according to Table 8. All other NODATA voids were filled using the ESRI ArcGIS focal statistics command.For the purposes of web viewing, the data was reprojected to EPSG:3857 - Web Mercator.Land use classificationThe Australian Land Use and Management (ALUM) Classification version 8 is a three-tiered hierarchical structure. There are five primary classes, identified in order of increasing levels of intervention or potential impact on the natural landscape. Water is included separately as a sixth primary class. Primary and secondary levels relate to the principal land use. Tertiary classes may include additional information on commodity groups, specific commodities, land management practices or vegetation information. The primary, secondary and tertiary codes work together to provide increasing levels of detail about the land use. Land may be subject to concurrent uses. For example, while the main management objective of a multiple-use production forest may be timber production, it may also provide conservation, recreation, grazing and water catchment land uses. In these cases, production forestry is commonly identified in the ALUM code as the prime land use.Table 1: Simplified land use classification symbology as RGB and hexadecimal colour valuesVALUESIMPNSIMPRedGreenBlueHex110; 111; 112; 113; 114; 115; 116; 1171Nature conservation150102204#9666CC120; 121; 122; 123; 124; 1252Managed resource protection201190255#C9BEFF130; 131; 132; 133; 1343Other minimal use222135221#DE87DD2104Grazing native vegetation255255229#FFFFE5220; 221; 2225Production native forests4113768#298944310; 311; 312; 313; 314; 410; 411; 412; 413; 4146Plantation forests173255181#ADFFB5320; 321; 322; 323; 324; 3257Grazing modified pastures255211127#FFD37F330; 331; 332; 333; 334; 335; 336; 337; 3388Dryland cropping2552550#FFFF00340; 341; 342; 343; 344; 345; 346; 347; 348; 349; 350; 351; 352; 3539Dryland horticulture171135120#AB8778360; 361; 362; 363; 364; 365; 460; 461; 462; 463; 464; 46510Land in transition000#000000420; 421; 422; 423; 42411Irrigated pastures2551700#FFAA00430; 431; 432; 433; 434; 435; 436; 437; 438; 43912Irrigated cropping20118484#C9B854440; 441; 442; 443; 444; 445; 446; 447; 448; 449; 450; 451; 452; 453; 45413Irrigated horticulture1568446#9C542E510; 511; 512; 513; 514; 515; 520; 521; 522; 523; 524; 525; 526; 527; 52814Intensive horticulture and animal production255201190#FFC9BE542; 543; 544; 54515Rural residential and farm infrastructure178178178#B2B2B2540; 54116Urban residential25500#FF0000530; 531; 532; 533; 534; 535; 536; 537; 538; 550; 551; 552; 553; 554; 555; 560; 561; 562; 563; 564; 565; 566; 567; 570; 571; 572; 573; 574; 57517Other intensive uses15500#9B0000580; 581; 582; 583; 584; 590; 591; 592; 593; 594; 59518Mining and waste71130143#47828F610; 611; 612; 613; 614; 620; 621; 622; 623; 630; 631; 632; 633; 640; 641; 642; 643; 650; 651; 652; 653; 654; 660; 661; 662; 66319Water00255#0000FF Note: Codes refer to the Australian Land Use and Management (ALUM) Classification, version 8.SIMPN 0 = No data is not present in Catchment Scale land Use of Australia 2023Data dictionaryAttribute nameDescriptionOIDInternal feature number that uniquely identifies each row.VALUEALUM code as a three digit integer. First digit is primary code, second digit is secondary code, and third digit is tertiary code.COUNTCount of the number of raster cells in each class of VALUE.LU_CODEV8ALUM code as a string.LU_V8NALUM code as a three digit integer. First digit is primary code, second digit is secondary code, and third digit is tertiary code.TERTV8ALUM tertiary code and description as a string.SECV8ALUM secondary code and description as a string.PRIMV8ALUM primary code and description as a string.SIMPNCode for simplified land use classification.SIMPDescription of the simplified land use classification.AGINDDescription of agricultural industries.Red, Green, BlueRGB values for classification colours ContactDepartment of Agriculture, Fisheries and Forestry (ABARES), info.ABARES@aff.gov.au

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 2001/02 Land Use of Australia, Version 3, is part of a series of land use maps of Australia for the years 1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02. The non-agricultural land uses are based on existing digital maps covering four themes: protected areas, topographic features, tenure and forest. Time series data at relatively high temporal resolution were available for the protected areas and forest themes. The agricultural land uses are based on the Australian Bureau of Statistics' agricultural censuses and surveys for the years mapped. The spatial distribution of agricultural land uses is interpretive and has been determined using Advanced Very High Resolution Radiometer (AVHRR) satellite imagery with ground control data. The maps are supplied as a set of ARC/INFO (Trademark) grids with geographical coordinates referred to GDA94 and 0.01 degree cell size. For each of the years mapped there is a set of probability maps, one for each agricultural land use and a single summary map showing the non-agricultural land uses and a likely arrangement of the agricultural land uses. The arrangement of agricultural land uses in the summary map was determined from the probability maps using some simple rules to make an approximation to a maximum likelihood land use map. As supplied, the probability maps are floating point grids with cell value between 0 and 1 and no value attribute table while the summary map is an integer grid with a value attribute table with attributes defining the agricultural commodity group, irrigation status and land use according to the Australian Land Use and Management Classification (ALUMC), Version 5 (http://www.daff.gov.au). Prospective users of the data should note the caveats and additional metadata, which are included in the document entitled 'User Guide and Caveats: 1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02 Land Use of Australia, Version 3' (BRS, 2006c). The caveats are also available as a separate document entitled 'Caveats: 1992/93, 1993/94, 1996/97, 1998/99, 2000/01 and 2001/02 Land Use of Australia, Version3' (BRS, 2006a).

Dataset History

I. For each year mapped, four thematic layers were constructed in raster form with 0.01 degree pixel size and overlain to determine the non-agricultural land uses and the distribution of agricultural land. The layers were a topographic features layer, a protected areas layer, a tenure layer and a forest type layer. They were based on a 1999 update of TOPO-250K (Series 1) and a 2005 update of TOPO-250K (Series 2), 1:250,000 scale vector topographic data sets published by Geoscience Australia (GA); the Collaborative Australian Protected Areas Database data sets for 1997, 2000 and 2002, 1:250,000 scale vector protected areas data sets published by the Department of Environment and Heritage (DEH); Australian Tenure, a 250m raster tenure data set compiled by BRS in 1997; agricultural land use status information for aboriginal freehold and leasehold land from state and territory agencies; forest extent data compiled by the Department of Environment and Heritage for greenhouse accounting purposes for 1992, 1995, 1998, 2000 and 2002 - 25m raster data; crown cover data from Vegetation: Present (1988) and Vegetation: Pre-European Settlement (1788) published by GA; land use data from the collaborative 'Land Use Mapping at Catchment Scale' project managed by BRS (BRS, 2002) and from the collaborative 'Land Use Data Integration Case Study - Lower Murray NAP Region' project managed by BRS and from the Agricultural Land Cover Change: 1995 Land Cover data set compiled by BRS; and plantation forest data from BRS's Plantations 2001 data set.

II. The spatial distribution of specific agricultural land uses for each of the six years was determined using SPREAD II, developed by Simon Barry of BRS. SPREAD II, like the SPREAD algorithm of Walker and Mallawaarachchi (1998), uses time series NDVI data with control sites (ground control data comprising records of the agricultural land uses that existed at specific points in specific years) to spatially disaggregate agricultural census or survey data. The SPREAD II methodology is statistically based, using a Bayesian technique - a Markov Chain Monte Carlo (MCMC) algorithm. It has been implemented in R. NDVI images were obtained from AVHRR data processed to correct for cloud cover by DEH. Control site data were collected by State and Territory agencies for the Audit (project BRR5) and relate to the years 1996, 1997 and 1998. The irrigation status of most control sites is known and the method was used to determine the distribution, not only of commodity groups, but also of their irrigation status. Agricultural census and survey data reported on Statistical Local Areas (SLAs) were obtained from the Australian Bureau of Statistics. Modifications made to the agricultural census and survey data are the same as carried out during the construction of the 1996/97 Land Use of Australia, Version 2 (Stewart et al, 2001). The irrigation boundaries data set published by the Audit, the Australian Irrigation Areas, Version 1a, with some additional polygons incorporated for irrigation districts in Victoria, served as an irrigation constraint to refine the prior probabilities used in the MCMC algorithm. A horticulture mask constructed using some of the data sets listed in section I served as a horticulture constraint to refine the prior probabilities used in the MCMC algorithm. For each of the six years, SPREAD II generated outputs comprising the 42 probability maps described in the abstract and a summary agricultural land use map (the agricultural component of the summary map described in the abstract).

III. Land uses were assigned to pixels in the summary grids with the aid of a macro, which assigns land use categories from the Australian Land Use Management Classification Version 5 (search the website http://www.daff.gov.au/ for the term 'ALUMC') according to the attributes of the four layers overlaid in step I and of the summary agricultural land use map made in step II.

Dataset Citation

Australian Bureau of Agricultural and Resource Economics - Bureau of Rural Sciences (2013) Bioregional_Assessment_Programme_Land Use of Australia, Version 3 - 2001/2002. Bioregional Assessment Source Dataset. Viewed 21 December 2017, http://data.bioregionalassessments.gov.au/dataset/98cea3eb-61a2-4e77-ad90-c280ec645c3a.

This map features Africa Land Cover at 30m resolution from MDAUS BaseVue 2013, referencing the World Land Cover 30m BaseVue 2013 layer.Land cover data represent a descriptive thematic surface for characteristics of the land's surface such as densities or types of developed areas, agricultural lands, and natural vegetation regimes. Land cover data are the result of a model, so a good way to think of the values in each cell are as the predominating value rather than the only characteristic in that cell.Land use and land cover data are critical and fundamental for environmental monitoring, planning, and assessment.Dataset SummaryBaseVue 2013 is a commercial global, land use / land cover (LULC) product developed by MDA. BaseVue covers the Earth’s entire land area, excluding Antarctica. BaseVue is independently derived from roughly 9,200 Landsat 8 images and is the highest spatial resolution (30m), most current LULC product available. The capture dates for the Landsat 8 imagery range from April 11, 2013 to June 29, 2014. The following 16 classes of land use / land cover are listed by their cell value in this layer: Deciduous Forest: Trees > 3 meters in height, canopy closure >35% (<25% inter-mixture with evergreen species) that seasonally lose their leaves, except Larch.Evergreen Forest: Trees >3 meters in height, canopy closure >35% (<25% inter-mixture with deciduous species), of species that do not lose leaves. (will include coniferous Larch regardless of deciduous nature).Shrub/Scrub: Woody vegetation <3 meters in height, > 10% ground cover. Only collect >30% ground cover.Grassland: Herbaceous grasses, > 10% cover, including pasture lands. Only collect >30% cover.Barren or Minimal Vegetation: Land with minimal vegetation (<10%) including rock, sand, clay, beaches, quarries, strip mines, and gravel pits. Salt flats, playas, and non-tidal mud flats are also included when not inundated with water.Not Used (in other MDA products 6 represents urban areas or built up areas, which have been split here in into values 20 and 21).Agriculture, General: Cultivated crop landsAgriculture, Paddy: Crop lands characterized by inundation for a substantial portion of the growing seasonWetland: Areas where the water table is at or near the surface for a substantial portion of the growing season, including herbaceous and woody species (except mangrove species)Mangrove: Coastal (tropical wetlands) dominated by Mangrove speciesWater: All water bodies greater than 0.08 hectares (1 LS pixel) including oceans, lakes, ponds, rivers, and streamsIce / Snow: Land areas covered permanently or nearly permanent with ice or snowClouds: Areas where no land cover interpretation is possible due to obstruction from clouds, cloud shadows, smoke, haze, or satellite malfunctionWoody Wetlands: Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate periodically is saturated with, or covered by water. Only used within the continental U.S.Mixed Forest: Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. Only used within the continental U.S.Not UsedNot UsedNot UsedNot UsedHigh Density Urban: Areas with over 70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation where constructed materials account for >60%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.Medium-Low Density Urban: Areas with 30%-70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation, where constructed materials account for greater than 40%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.MDA updated the underlying data in late 2016 and this service was updated in February 2017. An improved selection of cloud-free images was used to produce the update, resulting in improvement of classification quality to 80% of the tiles for this service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data across the ArcGIS platform. It can also be used as an analytic input in ArcMap and ArcGIS Pro.This layer has query, identify, and export image services available. The layer is restricted to an 16,000 x 16,000 pixel limit, which represents an area of nearly 300 miles on a side. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

The landscape of the conterminous United States has changed dramatically over the last 200 years, with agricultural land use, urban expansion, forestry, and other anthropogenic activities altering land cover across vast swaths of the country. While land use and land cover (LULC) models have been developed to model potential future LULC change, few efforts have focused on recreating historical landscapes. Researchers at the US Geological Survey have used a wide range of historical data sources and a spatially explicit modeling framework to model spatially explicit historical LULC change in the conterminous United States from 1992 back to 1938. Annual LULC maps were produced at 250-m resolution, with 14 LULC classes. Assessment of model results showed good agreement with trends and spatial patterns in historical data sources such as the Census of Agriculture and historical housing density data, although comparison with historical data is complicated by definitional and methodological differences. The completion of this dataset allows researchers to assess historical LULC impacts on a range of ecological processes.

The AAFC Land Use Time Series is a culmination and curated meta-analysis of several high-quality spatial datasets produced between 2000 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.