Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 4.0 Combined Fee, Designation, Easement feature class in the full geodatabase inventory (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to prioritize overlapping designations, avoiding massive overestimation in protected area statistics, and simplified by the following PAD-US attributes to support user needs for raster analysis data: Manager Type, Manager Name, Designation Type, GAP Status Code, Public Access, and State Name. The rasterization process prioritized overlapping designations previously identified (GAP_Prity field) in the Vector Analysis file (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation (e.g. GAP Status Code 1 over 2).The 30-meter Image (IMG) grid Raster Analysis Files area extents were defined by the Census state boundary file used to clip the Vector Analysis File, the data source for rasterization ("PADUS4_0VectorAnalysis_State_Clip_CENSUS2022") feature class from ("PADUS4_0VectorAnalysisFile_OtherExtents_ClipCENSUS2022.gdb"). Alaska (AK) and Hawaii (HI) raster data are separated from the contiguous U.S. (CONUS) to facilitate analyses at manageable scales. Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types (with a legal protection mechanism) represented in some manner, while work continues to maintain updates, improve data quality, and integrate new data as it becomes available (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, protection status represents a point-in-time and changes in status between versions of PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://ngda-portfolio-community-geoplatform.hub.arcgis.com/pages/portfolio ), agencies are the best source of their lands data.

6 inch resolution raster image of New York City, classified by landcover type. High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

Analysis Image Service generated from Extract Raster Data

This submission includes maps of the spatial distribution of basaltic, and felsic rocks in the Oregon Cascades. It also includes a final Play Fairway Analysis (PFA) model, with the heat and permeability composite risk segments (CRS) supplied separately. Metadata for each raster dataset can be found within the zip files, in the TIF images

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

This dataset contains the processing unit for Greenland from the Hydrological Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the sma ...

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

The SWOT Level 2 KaRIn High Rate Raster Product (SWOT_L2_HR_Raster_D) provides rasterized estimates of water surface elevation, inundation extent, and radar backscatter derived from high-resolution radar observations by the Ka-band Radar Interferometer (KaRIn) on the SWOT satellite. This product aggregates the irregularly spaced pixel cloud data from the PIXC and PIXCVec products onto a uniform geographic grid to facilitate spatial analysis of water surface features across inland, estuarine, and coastal domains.

Standard granules cover non-overlapping 128 × 128 km² scenes in the UTM projection at 100 m and 250 m resolution, stored in NetCDF-4 format. Each file contains 2D image layers representing water surface elevation (corrected for geoid, solid Earth, load, and pole tides, as well as atmospheric and ionospheric path delays), surface area, water fraction, and sigma0, along with quality flags and uncertainty estimates. On-demand versions are available at user-specified resolutions and projections, with optional overlapping granules and GeoTIFF output via SWODLR: https://swodlr.podaac.earthdatacloud.nasa.gov/

The raster product offers a gridded alternative to the unstructured pixel cloud, supporting hydrologic and geomorphic analyses in complex flow environments such as braided rivers, floodplains, wetlands, and coastal zones. It enables consistent spatiotemporal sampling while reducing noise through spatial aggregation, making it especially suitable for applications that require map-like continuity or integration with geospatial models.
This dataset is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_D
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_D

The purpose of this acquisition was to provide LiDAR data for portions of the Kaibab National Forest and Grand Canyon National Park on the Kaibab Plateau, in support of ongoing studies of Northern Goshawk demographics. 3Di West, through its subcontractor Watershed Sciences Incorporated (WSI), acquired LiDAR data for over 450,000 acres in the summer of 2012.

Note that raster products are not all at the same spatial resolution:

The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

This submission contains raster files associated with several datasets that include earthquake density, Na/K geothermometers, fault density, heat flow, and gravity. Integrated together using spatial modeler tools in ArcGIS, these files can be used for play fairway analysis in regard to geothermal exploration.

This zip file contains geodatabases with raster mosaic datasets. The raster mosaic datasets consist of georeferenced tiff images of mineral potential maps, their associated metadata, and descriptive information about the images. These images are duplicates of the images found in the georeferenced tiff images zip file. There are four geodatabases containing the raster mosaic datasets, one for each of the four SaMiRA report areas: North-Central Montana; North-Central Idaho; Southwestern and South-Central Wyoming and Bear River Watershed; and Nevada Borderlands. The georeferenced images were clipped to the extent of the map and all explanatory text, gathered from map explanations or report text was imported into the raster mosaic dataset database as ‘Footprint’ layer attributes. The data compiled into the 'Footprint' layer tables contains the figure caption from the original map, online linkage to the source report when available, and information on the assessed commodities accordin ...

MODIS Water Lake Powell Raster Dataset

  Dataset Summary

Raster dataset comprised of MODIS surface reflectance bands along with calculated indices and a label (water/not-water)

  Dataset Structure





  Data Fields

water: Label, water or not-water (binary) sur_refl_b01_1: MODIS surface reflection band 1 (-100, 16000) sur_refl_b02_1: MODIS surface reflection band 2 (-100, 16000) sur_refl_b03_1: MODIS surface reflection band 3 (-100, 16000) sur_refl_b04_1:… See the full description on the dataset page: https://huggingface.co/datasets/nasa-cisto-data-science-group/modis-lake-powell-raster-dataset.

A series of maps at 1:10 000 scale showing base mapping for Northern Ireland. These raster maps can be used with other maps or information to enhance the mapping. Midscale Raster for Northern Ireland can be used as a general background to give context at local and regional level and as a base to overlay data. Includes water bodies, rivers, main roads, town names and townlands.Please Note for Open Data NI Users: Esri Rest API is not Broken, it will not open on its own in a Web Browser but can be copied and used in Desktop and Webmaps

Rasters assocaited with elevation (from the National elevation dataset), slope (created from the elevation dataset using ArcGIS), a Shannon diversity index as a metric of landscape fragmentation (created from the forest/shrub layer using Fragstats), distance to all roads (created in ArcGIS using a road TIGER shapefile), distance to forest/shrubs (created using NLCD 2016 data), human population density (created using data from the US Census Bureau). All rasters are at a 90m resolution.

This data publication contains twenty-four GeoTIFF files for four significant geographic areas (SGAs) in Alabama, Florida, and Georgia. The extent of the SGAs are defined within the America’s Longleaf Range-wide Conservation Plan for Longleaf (2009). A raster grid file is provided for the extent of each SGA within each state and shows the amount of pine basal area per acre (BAA), the amount of all species BAA, the amount of pine trees per acre (TPA), the amount of all species TPA, dominant forest type classification, visually identified classification, the probability of an area being composed primarily of longleaf pine BAA, and the probability of an area being composed primarily of regeneration. These raster surfaces were created using machine learning relationships between FIA plot information (2010-2015) and NAIP imagery (2013) and are intended to be used to help quantify existing conditions of forested ecosystems and help prioritize longleaf restoration efforts across the four SGAs.Intended use for these datasets include: helping quantify existing conditions of forested ecosystems and helping to prioritize Longleaf restoration efforts across four significant geographic areas described in America’s Longleaf Range-wide Conservation Plan for Longleaf (2009).Original metadata date is 03/06/2017. Minor metadata updates made on 9/14/2018, 07/02/2019, and 09/16/2024.

This data set consists of GIS layers that describe the soils of the BOREAS SSA. The original data were submitted as vector layers that were gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection. These data layers include the soil code (which relates to the soil name), modifier (which also relates to the soil name), and extent (indicating the extent that this soil exists within the polygon). There are three sets of these layers representing the primary, secondary, and tertiary soil characteristics. Thus, there is a total of nine layers in this data set along with supporting files. The data are stored in binary, image format files.

An index of TIFs received from the Rural Water Association. The TIFs are comparable to the Unconsolidated Aquifers and the Surficial Geologic Matetrials shapefiles. Data exists for the following towns: Ancram, Austerlitz, Chatham, Claverack, Copake, Germantown, Ghent, Hillsdale, Stuyvesant, and Taghkanic.

TIF Data current as of March 2016.

DOWNLOAD RASTER IMAGERYRS-FRIS Version 5.4 is a remote-sensing based forest inventory for WA DNR State Trust lands. Predictions are derived from three-dimensional photogrammetric point cloud data (DAP), field measurements, and statistical models. RS-FRIS 5.4 was constructed using remote sensing data collected in 2021 and 2022, and incorporates depletions for selected completed harvest types through 2025-08-31.

Statewide Ecopia 3 foot Land Cover (2021-2022)This raster land cover data is based off of high-resolution statewide imagery from 2021-2022. It was used by Ecopia to extract and digitize the entire state into 7 different land cover classes. Download Notes:This service can be entered into ArcGIS Pro where "Download Rasters" can be used to download approximately 20 square miles at a time. (Rt. click layer in TOC > Data > Download Rasters)Alternatively, the entire statewide 3ft dataset is available as a zipped download from here (includes colormap file): Ecopia_Statewide_3ft_Raster_TilesClasses available at bottom of this pages.Data SpecificationImagery Used for Extraction: Pixel resolution: 15 cm (6")Camera sensor: Hexagon Pushbroom (Content Mapper)Date of capture: 06/25/2021 - 08/14/2022Date of Vector Extraction: June 2023 Extraction Methodology:Ecopia uses proprietary extraction and modeling software to process raw images into high-resolution land cover classifications. Quality Measurements:Measure Name - Threshold across Impervious Polygons:False Negatives <= 5% All PolygonsFalse Positives <= 5% All PolygonsValid Interpretation >= 95% All PolygonsMinimum Area 100% All PolygonsValid Geometry 100% All PolygonsMeasure Name - Threshold across Natural Polygons:False Negatives <=5% All Polygons False Positives <=5% All Polygons Valid Interpretation >=90% All Polygons Minimum Area 100% All Polygons Valid Geometry 100% All PolygonsLand Cover Classes:UnclassifiedImperviousImpervious, covered by treesShrub/low vegetationTree/forest/high vegetationOpen waterRailroadVegetation (Canopy Mapping)Tree canopy will be captured as a unique polygon layer. It can therefore overlap impervious layers. High vegetation is distinguished from low vegetation based on crown, texture, and derived height models. Leveraging stereo imagery produces results using 3D elevation models used to aid the distinction of vegetation categories. Distinguishing low from high vegetation is based on a 5m threshold, but this is not always feasible, especially in areas where heavy canopy prevents a visualization of the ground. In these circumstances, high vegetation will be given the priority over low vegetation. For more information visit: www.ecopiatech.com Classes: 0: No data - Null, clear1: Unclassified2: Impervious3: Impervious, Covered by Tree Canopy6: Shrub/Low Vegetation7: Tree/Forest/High Vegetation8: Open Water12: Railroad