Learn to appropriately symbolize rasters based on their attributes and intended use, modify raster properties to support better visualization and interpretation, and apply out-of-the-box appearance functions to enhance the viewing experience.GoalsChoose appropriate tools to help with better visualization and interpretation of rasters and imagery.

Vegetative Difference Image gives an easy to interpret visual representation of vegetative increase/decrease across 2 time periods.This raster function template is used to generate a visual product. The results cannot be used for analysis. This templates generates an NDVI in the backend, hence it requires your imagery to have the red and near infrared bands. In the resulting image, greens indicate increase in vegetation, while the magenta indicates decrease in vegetationReferences:Raster functionsWhen to use this raster function templateThis template is particularly useful when trying to intuitively visualize the increase or decrease in vegetation over two time periods. How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. This index supports many satellite sensors, such as Landsat-8, Sentinel-2, Quickbird, IKONOS, Geoeye-1, and Pleiades-1.Applicable geographiesThe template uses a standard vegetation which is designed to work globally.

This template is used to compute urban growth between two land cover datasets, that are classified into 20 classes based on the Anderson Level II classification system. This raster function template is used to generate a visual representation indicating urbanization across two different time periods. Typical datasets used for this template is the National Land Cover Database. A more detailed blog on the datasets can be found on ArcGIS Blogs. This template works in ArcGIS Pro Version 2.6 and higher. It's designed to work on Enterprise 10.8.1 and higher.References:Raster functionsWhen to use this raster function templateThe template is useful to generate an intuitive visualization of urbanization across two images.Sample Images to test this againstNLCD2006 and NLCD2011How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual representation of urban sprawl across two images. Applicable geographiesThe template is designed to work globally.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

Zipped raster dataset of 2018 Land-Use-Land-Cover (named pima_landcover_noroads.zip)Download this zipped dataset here by clicking the download button at top right.https://gis.pima.gov/data/contents/metadet.cfm?name=lulc18

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

Aligning rasters such that their bounding extent and cell sizes match precisely is a tedious, time consuming, and challenging task. East-to-use tools have been lacking up until now. Many modeling approaches require rasters to be perfectly aligned. For example, a common workflow using R would be to stack rasters and then do subsequent predictive modeling using the stacked rasters as covariates. The Align Rasters Toolbox allows users to quickly and easily align rasters. It has options for working with rasters of differing cell sizes and extents. The Align Rasters without Expansion tool is suitable for situations in which the template raster is smaller than all inputs.

This geodatabase includes spatial datasets that represent the Mississippian aquifer in the States of Alabama, Illinois, Indiana, Iowa, Kentucky, Maryland, Missouri, Ohio, Pennsylvania, Tennessee, Virginia and West Virginia. The aquifer is divided into three subareas, based on the data availability. In subarea 1 (SA1), which is the aquifer extent in Iowa, data exist of the aquifer top altitude and aquifer thickness. In subarea 2 (SA2), which is the aquifer extent in Missouri, data exist of the aquifer top and bottom aquifer surface altitudes. In subarea 3 (SA3), which is the aquifer area of the remaining States, no altitude or thickness data exist. Included in this geodatabase are: (1) a feature dataset "ds40MSSPPI_altitude_and_thickness_contours that includes aquifer altitude and thickness contours used to generate the surface rasters for SA1 and SA2, (2) a feature dataset "ds40MSSPPI_extents" that includes a polygon dataset that represents the subarea extents, a polygon dataset that represents the combined overall aquifer extent, and a polygon dataset of the Ft. Dodge Fault and Manson Anomaly, (3) raster datasets that represent the altitude of the top and the bottom of the aquifer in SA1 and SA2, and (4) georeferenced images of the figures that were digitized to create the aquifer top- and bottom-altitude contours or aquifer thickness contours for SA1 and SA2. The images and digitized contours are supplied for reference. The extent of the Mississippian aquifer for all subareas was produced from the digital version of the HA-730 Mississippian aquifer extent, (USGS HA-730). For the two Subareas with vertical-surface information, SA1 and SA2, data were retrieved from the sources as described below. 1. The aquifer-altitude contours for the top and the aquifer-thickness contours for the top-to-bottom thickness of SA1 were received in digital format from the Iowa Geologic Survey. The URL for the top was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_topography.zip. The URL for the thickness was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_isopach.zip Reference for the top map is Altitude and Configuration, in feet above mean sea level, of the Mississipian Aquifer modified from a scanned image of Map 1, Sheet 1, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 1 Reference for the thickness map is Distribution and isopach thickness, in feet, of the Mississipian Aquifer, modified from a scanned image of Map 1, Sheet 2, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 2 The altitude contours for the top and bottom of SA2 were digitized from georeferenced figures of altitude contours in U.S. Geological Survey Professional Paper 1305 (USGS PP1305), figure 6 (for the top surface) and figure 9 (for the bottom surface). The altitude contours for SA1 and SA2 were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derive the altitude from the thickness (depth from the land surface), and merge the subareas into a single surface. The primary tool was an enhanced version of "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. ArcGIS Desktop: Release 10.2 Redlands, CA: Environmental Systems Research Institute. The raster surfaces were corrected in areas where the altitude of the top of the aquifer exceeded the land surface, and where the bottom of an aquifer exceeded the altitude of the corrected top of the aquifer.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

This resource contains the test data for the GeoServer OGC Web Services tutorials for various GIS applications including ArcGIS Pro, ArcMap, ArcGIS Story Maps, and QGIS. The contents of the data include a polygon shapefile, a polyline shapefile, a point shapefile, and a raster dataset; all of which pertain to the state of Utah, USA. The polygon shapefile is of every county in the state of Utah. The polyline is of every trail in the state of Utah. The point shapefile is the current list of GNIS place names in the state of Utah. The raster dataset covers a region in the center of the state of Utah. All datasets are projected to NAD 1983 Zone 12N.

2015 3in Cupertino Aerial Photo Tile Info: Height: 256 Width: 256 DPI: 96 Levels of Detail: 9 Full Extent: XMin: 6097999.999999999 YMin: 1926999.9999999995 XMax: 6127999.999999999 YMax: 1950999.9999999995 Spatial Reference: PROJCS["NAD_1983_California_zone_3_ftUS",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["false_easting",6561666.667],PARAMETER["false_northing",1640416.667],PARAMETER["central_meridian",-120.5],PARAMETER["standard_parallel_1",37.06666666666667],PARAMETER["standard_parallel_2",38.43333333333333],PARAMETER["latitude_of_origin",36.5],UNIT["Foot_US",0.3048006096012192]] Pixel Size X: 0.25 Pixel Size Y: 0.25 Band Count: 3 Pixel Type: U8 Raster Type Infos: Name: Raster Dataset Description: Supports all ArcGIS Raster Datasets

This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [> 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention). The dataset can be downloaded from the following website: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.phpNote: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option. An image service that depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands.

The Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format is required by most, grid or raster, spatial modelling tools such as the Multi-criteria Analysis Shell for Spatial Decision Support (MCAS-S). Grid Garage contains 36 tools designed to save you time by batch processing repetitive GIS tasks as well diagnosing problems with data and capturing a record of processing step and any errors encountered.

Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to:

• List, describe and manage very large volumes of geodata.
• Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models.
• Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed.
• Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs.
• Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets.

The Grid Garage is intended for use by anyone with an understanding of GIS principles and an intermediate to advanced level of GIS skills. Using the Grid Garage tools in ArcGIS ModelBuilder requires skills in the use of the ArcGIS ModelBuilder tool.

Download Instructions: Create a new folder on your computer or network and then download and unzip the zip file from the GitHub Release page for each of the following items in the 'Data and Resources' section below. There is a folder in each zip file that contains all the files. See the Grid Garage User Guide for instructions on how to install and use the Grid Garage Toolbox with the sample data provided.

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.

The Terrain Ruggedness Index (TRI) is used to express the amount of elevation difference between adjacent cells of a DEM. This raster function template is used to generate a visual representation of the TRI with your elevation data. The results are interpreted as follows:0-80m is considered to represent a level terrain surface81-116m represents a nearly level surface117-161m represents a slightly rugged surface162-239m represents an intermediately rugged surface240-497m represents a moderately rugged surface498-958m represents a highly rugged surface959-4367m represents an extremely rugged surfaceWhen to use this raster function templateThe main value of this measurement is that it gives a relatively accurate view of the vertical change taking place in the terrain model from cell to cell. The TRI provides data on the relative change in height of the hillslope (rise), such as the side of a canyon.How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual TRI representation of your imagery. This index supports elevation data.References:Raster functionsApplicable geographiesThe index is a standard index which is designed to work globally.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

This dataset holds the map “Carte du recouvrement ligneux de la réserve de Lamto" published by Gautier, L. in 1990. We georeferenced the scanned paper map using ground control points derived from Google Maps. The dataset contains the scanned map, the ground control points and the raster layer of the georeferenced map.

Calculates zonal statistics on polygons from many categorical rasters for multiple attributes

The University of Rhode Island has conducted several studies of habitat use of Scolopax minor (American Woodcock) in Rhode Island, USA. In 2020 we developed a new species distribution model (SDM) tool to identify sites in the Rhode Island where forest clearcutting to create young forest habitat would have the most positive effect for American woodcock. A typical SDM predicts the probability of presence (POP) of a species at any location based on an analysis of known occurrences and environmental variables, but it cannot predict how much the POP of a species would change after a new patch of young forest is created in any location. We believe that our new tool is effective, and that it will help landowners identify the best locations on their properties to improve woodcock habitat. We also believe that similar tools can be developed for other wildlife species of conservation concern. We created the new tool by modifying the existing 2018 SDM raster for American Woodcock in Rhode Island. Creating the tool involved creating four new ArcGIS raster datasets. The existing 2018 SDM raster and the four new rasters are now publicly available in a geodatabase in the Dryad repository.