Land cover (Australia - 2008)

NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, Oceania, and Africa are available. This dataset includes global land cover and land cover change data for the period 2001 to 2019. Africa is extended through 2021. GLanCE is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule. Known Issues * Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * All GLnanCE granules should contain 7 products except the year 2001, which contain 4, as there are no change products (chgDate, EVI2chg, prevClass) for the initial year of data. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. * High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs. * The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

The FAOSTAT domain on Land Cover data under the Agri-Environmental Indicators section contains land cover information organized by the land cover classes of the international standard system for Environmental and Economic Accounting Central Framework (SEEA CF). The land cover information is compiled from publicly available Global Land Cover (GLC) maps: a) MODIS land cover types based on the Land Cover Classification System, LCCS; b) The European Spatial Agency (ESA) Climate Change Initiative (CCI) annual land cover maps produced by the Université catholique de Louvain (UCL)-Geomatics and now under the European Copernicus Program; c) The annual land cover maps which were produced under the European Copernicus Global Land Service (CGLS) (CGLS land cover, containing discrete land cover categorization), with spatial resolution 100m; and d) the WorldCover maps of the European Space Agency, produced at 10m resolution.

This map represents the Ethiopia Land Cover map for the development of Green House Gas Inventory. These products were derived from LandSat Imagery Data through a supervised classification method. The Ancillary data was provided by Ethiopia Mapping Agency. **Contact points:** Resource Contact: Ethiopia Mapping Agency Resource Contact: [USAID](mailto:https://www.usaid.gov/) Resource Contact: [RCMRD](mailto:https://www.rcmrd.org/en/) Resource Contact: [NASA](mailto:https://www.nasa.gov/) Resource Contact: [US EPA](mailto:https://www.epa.gov/) Metadata Contact: [Patrick Kabatha. ](mailto:pkabatha@rcmrd.org) **Data lineage:** Data quality information not available **Resource constraints:** This Ethiopia Land Cover 2003 Scheme I is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ **Online resources:** Ethiopia_LandCover_2008_Scheme_i

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 – 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meter) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data.

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the MYD13A2 products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

This raster dataset represents land cover classifications for Napa County, California, for the year 2008. It is derived from the Annual National Land Cover Database (NLCD) Collection 1, Version 1.1 (June 2025), produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The Annual NLCD product combines methodologies from the National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP) programs with modern geospatial deep learning techniques to produce annual, high-resolution land cover maps. The dataset provides annual land cover classifications for the conterminous United States (CONUS) from 1985 through 2024 at a 30-meter spatial resolution using a modified Anderson Level II classification system consisting of 16 land cover classes. Source data are derived from Landsat Analysis Ready Data (ARD) processed through the Land Cover Artificial Mapping System (LCAMS), a multi-stage deep learning architecture. This derivative dataset was prepared by the Napa County Planning, Building and Environmental Services (PBES) GIS Division for the Baseline Data Report (BDR) and General Plan Update (GPU) Land Cover Change Analysis. The source raster was reprojected from the native Annual NLCD Albers Equal Area CONUS coordinate system (EPSG:5070) to NAD83 California State Plane Zone II (WKID 2226, US Survey Feet) using Nearest Neighbor resampling to preserve the original land cover classifications, then clipped to the Napa County administrative boundary. Napa County encompasses approximately 507,000 acres in the North San Francisco Bay Area of California. This dataset is one of a 20-year annual time series (2005–2024) used to evaluate land cover change at the countywide scale and within the 13 Geographic Evaluation Areas (GEAs) established for the Napa County General Plan Update. The analysis documents changes associated with agricultural expansion, urban development, and major wildfire events, including the 2017 Atlas and Nuns Fires and the 2020 LNU Lightning Complex Fire.