The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial mapping products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2018, 2020 and 2022 and Open Street Map (OSM) from 2019. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov/) and Geofabrik GmbH - Open Street Map (https://www.geofabrik.de/geofabrik/openstreetmap.html), respectively. The imagery was classified using Random Forest (RF) Modeling to produce land cover maps with three main classifications - bare, vegetation, and shadows. An updated roads and trails map for the Upper Scotts Creek Watershed, including the BLM Recreational Area, was created to estimate road and trail densities in the watershed. Separate metadata records for each product (Land_Cover_Maps_Scotts_Creek_Watershed_CA_2018_2020_2022_metadata.xml, and Roads_and_Trails_Map_Upper_Scotts_Creek_Watershed_CA _2022_metadata.xml) are provided on the ScienceBase page for each child item. Users should be aware of the inherent errors in remote sensing products.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2020/21 fire year including all wildfires >10ha with a fire start date between July 2020 and June 2021.

Development Maps (2020)Series of Maps and Statistics detailing Proposed, Approved, Under Construction, and Completed Development in the City of Jersey City, Hudson County, New Jersey.

U.S. Geological Survey (USGS) and Virginia Institute of Marine Science (VIMS) scientists conducted field data collection efforts during June 11th - 16th, 2020, using a combination of remote sensing technologies to map riverbank and wetland topography and vegetation at five sites in the Chesapeake Bay Region of Virginia. The five sites are located along the James, Severn, and York Rivers. The work was initiated to evaluate the utility of different remote sensing technologies in mapping river bluff and wetland topography and vegetation for change detection and sediment transport modeling. The USGS team collected Global Navigation Satellite System (GNSS), total station, and ground based lidar (GBL) data while the VIMS team collected aerial imagery using an Unmanned Aerial System (UAS). This data release contains shapefiles of the processed GNSS and total station data, point clouds in the form of lidar data exchange (las) files from the ground lidar data and aerial imagery produced via Structure from Motion (SfM).

The Wildland-Urban Interface (WUI) is the area where houses meet or intermingle with undeveloped wildland vegetation. This makes the WUI a focal area for human-environment conflicts such as wildland fires, habitat fragmentation, invasive species, and biodiversity decline. Using geographic information systems (GIS), we integrated U.S. Census and USGS National Land Cover Data, to map the Federal Register definition of WUI (Federal Register 66:751, 2001) for the conterminous United States from 1990-2020. These data are useful within a GIS for mapping and analysis at national, state, and local levels. Data are available as a geodatabase and include information such as housing densities for 1990, 2000, 2010, and 2020; wildland vegetation percentages for 1992, 2001, 2011, and 2019; as well as WUI classes in 1990, 2000, 2010, and 2020.This WUI feature class is separate from the WUI datasets maintained by individual forest unites, and it is not the authoritative source data of WUI for forest units. This dataset shows change over time in the WUI data up to 2020.Metadata and Downloads

Training data were collected through using a combination of the following sources:Habitat Map of Scotland (ground polygons)2022 National Forest InventoryOrdnance SurveyHigh resolution imageryIn all cases the ground data were not used naively: a careful combination of at least two data sources were used to create each polygon, and checking against recent high resolution imagery to ensure each polygon was ‘pure’ (i.e. included only one class) and up to date (for example, if it was a forest polygon, the trees had not been cleared since the data were collected).Satellite remote sensing datasets used for mapping were Optical Sentinel 2 (S2), Synthetic Aperture Radar (SAR) Sentinel-1, descending and ascending ALOS-PALSAR 2. A complex set of machine learning algorithms were used to produce a Prediction Model, and ultimately a prediction of a class for each pixel.Through the project duration the sophistication of the models used increased, increasing accuracy and efficiency. For commercial reasons the details of the final algorithms used will not be revealed here. This dataset is a change map, which shows the land cover change that is predicted to have occurred between 2020 and 2022. The map is produced through a simple comparison between the 2020 and 2022 maps, where each instance of change identified is interpreted and assigned one of the following descriptors:

(i) Afforestation (ii) Tree removal (iii) Agriculture related (iv) Urban development (v) Forest growth (vi) Water gain (vii) Water loss (viii) Other changes

Please note, we believe these predicted changes, and others, are inaccurate, mainly due to inaccuracies we have identified in the 2020 map, along with improved methodologies and processes developed at Space Intelligence since the creation of the 2020 map.

Final approved map by the 2020 California Citizens Redistricting Commission for California's United States Congressional Districts; the authoritative and official delineations of California's United States Congressional Districts drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State.

Land use map based on LiDAR data and retrieved metrics. Spatial coverage is the LiDAR areas. The map reflects the status of the land use at the LiDAR acquisitions in January/February 2020. The spatial resolution of the maps is 10mx10m and 50mx50m resolution.

Summary:

With funding from the Albemarle-Pamlico National Estuary Partnership (APNEP) and field and technical support from the NC Division of Marine Fisheries (NCDMF), digital data of coastal submerged aquatic vegetation (SAV) was mapped by APNEP for imagery years 2019-2020. In addition to its role as critical habitat for many aquatic fauna species, SAV is an important bio-indicator of environmental health because of its sensitivity to aquatic stressors. The ability to detect SAV is critical in understanding ecosystem health and effects of restoration and protection activities. Because SAV distribution, abundance, and density varies seasonally and annually in response to climatic variability, large-scale SAV changes may occur; thus, due to its dynamic nature, these data need to be continually updated as monitoring continues in the APNEP region.This is the third mapping effort led by APNEP to map the distribution, abundance, and change of SAV in North Carolina; the first and second efforts were mapped for imagery years 2006-2008 and 2012-2014, respectively (those data are also publicly available). Additional SAV mapping for NC coastal waters outside of the APNEP region were led by NCDMF for 2015 and 2021 (those data are also publicly available).

Purpose:

These data were created to assist governmental agencies and others in making resource management decisions through use of a Geographic Information System (GIS) and are intended for research or planning projects that will contribute to better protection for the ecological features involved. APNEP should be contacted prior to use of this dataset to ensure it is the most recent available.

Mapping Extent:

Visible SAV was mapped along the coast of North Carolina. This extent encompasses the high-salinity coastal zone that lies within the APNEP regional boundary (Hwy. 64 Bridge of Roanoke Sound south to Bogue Inlet).

Completeness Report:

These data represent the locations of visible SAV, as could be digitized from remotely-sensed imagery. A substantial portion of SAV beds remain invisible from remote sensing due to environmental factors above (e.g., haze and clouds), on (e.g., white caps), and below (e.g., turbidity) the water's surface.Imagery Acquisition:All imagery was collected with a Vexcel Ultracam Eagle (Mark 3). Aircraft height was 11,300 feet for a final imagery product with a 0.45-foot pixel size.Bogue Sound and Back Sound were collected on June 25, 2019 and May 16, 2020.Core Sound was collected on June 2, 2019 and May 16, 2020.Pamlico Sound from the Hwy. 64 bridge at Roanoke Sound south to Ocracoke Inlet was collected on June 14 and 15, 2019 and June 1, 2020.Onslow Bay, which is outside of the APNEP region and lies between Bogue Inlet and Mason Inlet, was collected on June 21, 2019.Map Digitization:The imagery was loaded into ArcGIS for manual on-screen digitizing using procedures described in Rohman and Monaco (2005). Digitizing scale was set between 1:1,500 and 1:3,000. Habitat boundaries were delineated around benthic habitat features (e.g., areas with visually discernible differences in color and texture patterns). The imagery was occasionally manipulated in terms of brightness, contrast, and color balance to enhance interpretability of subtle features and boundaries. The minimum mapping unit (MMU) is generally defined as the smallest feature (e.g., an individual SAV bed) or aggregate of features (e.g., SAV patches) that is delineated using a given source of imagery. For this study the MMU was approximately 0.2 ha, or in general patches that were at least 15 m across on their longest axis.IMPORTANT – Environmental conditions, including turbidity and cloud cover were determined to be insufficient for accurate delineation of 2019 imagery. Environmental conditions were also insufficient for accurate delineation of some imagery collected in 2020, specifically the mainland side of Core Sound from Marshallberg north to and including Cedar Island and the following areas of Pamlico Sound: Hatteras Island in the vicinity of Rollinson Channel (604 acres of uninterpretable imagery) and Old Rollinson Channel and Kings Channel (151 acres of uninterpretable imagery); Pea Island National Wildlife Refuge (multiple areas totaling 1,156 acres of uninterpretable imagery); spoil islands in the vicinity of Old House Channel (83 acres of uninterpretable imagery); and Roanoke Sound in the vicinity of Bodie Island Lighthouse (2,063 acres of uninterpretable imagery) and just north of Duck Island (140 acres of uninterpretable imagery). Thus, these areas are not included in this mapping project.

Attributes/Values:

CLASS: Type of classification for SAV polygonCONTINUOUS: A polygon with 70-100% SAV coveragePATCHY: A polygon with 5-70% SAV coverageACRES: SAV polygon area in acresHECTARES: SAV polygon area in hectares

Credits:

APNEP / NCDEQ / NCDMF / NCDOT

For more information, please view the metadata and visit the APNEP SAV Monitoring website.

This raster depicts the distribution of 15 species-specific vegetation classes across the island of Lāna‘i at 2m resolution. It represents the final selected neural network model predictions with expert-adjusted posterior probabilities. Each pixel is assigned to the most likely species-specific class based on the model. Overall and class-specific accuracy assessments indicate this map has generally over 95% accuracy. It provides detailed species-level vegetation mapping to support conservation planning and monitoring. Please note that to reduce the inherent 'salt and pepper' noise in the final land cover classification map, we applied a 3x3 pixel moving window majority filter to the final classification results.

This is the land parcel (polygon) dataset for the UKCEH Land Cover Map of 2020 (LCM2020) representing Northern Ireland. It describes Northern Ireland's land cover in 2020 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value and a range of per-parcel pixel statistics to help assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation accompanying this dataset. LCM2020 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2020. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2020. These are one of a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps in 2000, 2007, 2015 and annually since 2017. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

Informed decisions to reduce deforestation, protect biodiversity, and curb carbon emissions require not just knowing where forests are, but understanding their composition. Identifying natural forests, which serve as critical biodiversity hotspots and major carbon sinks, is particularly valuable. We developed a novel global natural forest map for 2020 at 10 m resolution. This map can support initiatives like the European Union's Deforestation Regulation (EUDR) and other forest monitoring or conservation efforts that require a comprehensive baseline for monitoring deforestation and degradation. The globally consistent map represents the probability of natural forest presence, enabling nuanced analysis and regional adaptation for decision-making.The dataset has a spatial resolution of 10 m. It is organized by UTM zones, with one zip file per UTM zone (60 zip files for the 60 longitudinal UTM zones). Each UTM zone contains multiple GEOTIFF files for all patches that contain land (usually up to 100 patches per UTM cell, where each UTM zone can contain up to 20 cells). The single band of each GEOTIFF file contains the probability of the pixel being natural forest in 2020, scaled by a factor of 250, and saved as uint8.

Final approved map by the 2020 California Citizens Redistricting Commission for the California State Senate; the authoritative and official delineations of the California State Senate drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State.

This web map and collection of data layers provides information on the location, extent, quality, and condition of species-rich grassland (SRG) sites in the Cairngorms National Park (CNP) - outputs from a joint project between NatureScot and the Cairngorms National Park Authority. Coverage includes the Spey, Avon, Livet and Dee river catchments.

This dataset contains: - Annual cropland map for the years 2020 and 2021 in Tigray, Ethiopia based on satellite remote sensing data and machine learning classification - Cropland change map for years 2020-2021 based on combining above annual maps - Labeled datasets used for validation (used for model iteration) and testing (used to report final model accuracy) for 2020 and 2021 annual maps

The global map of forest types provides a spatially explicit representation of primary forest, naturally regenerating forest and planted forest (including plantation forest) for the year 2020 at 10m spatial resolution. The base layer for mapping these forest types is the extent of forest cover of version 1 of the …

The Crop Map of England (CROME) is a polygon vector dataset mainly containing the crop types of England. The dataset contains approximately 32 million hexagonal cells classifying England into over 15 main crop types, grassland, and non-agricultural land covers, such as Woodland, Water Bodies, Fallow Land and other non-agricultural land covers. The classification was created automatically using supervised classification (Random Forest Classification) from the combination of Sentinel-1 Radar and Sentinel-2 Optical Satellite images during the period late January 2020 – September 2020. The dataset was created to aid the classification of crop types from optical imagery, which can be affected by cloud cover. The results were checked against survey data collected by field inspectors and visually validated. The data has been split into the Ordnance Survey Ceremonial Counties and each county is given a three letter code. Please refer to the CROME specification document to see which county each CODE label represents.

The 2020 decennial census tracts within Fairfax County. This data was acquired from the US Census Bureau, with fields slightly customized by Fairfax County Department of Management and Budget, Economic, Demographic, and Statistical Research unit.Contact: Department of Management & BudgetData Accessibility: Publicly AvailableUpdate Frequency: As NeededLast Revision Date: 11/2/2022Creation Date: 11/2/2022Feature Dataset Name: DIT_GIS.DSMHSMGR.FEDERAL_CENSUS_2020Layer Name: DIT_GIS.DSMHSMGR.FEDERAL_TRACT_2020