The Bluff GIS Determination Tool is an ArcGIS script that determines if a bluff is present, locates the toe and top of bluff on a map, creates a plot of elevation vs. distance, and produces an Excel spreadsheet showing the data analysis. There are two versions of the tool, one for determining a shoreland bluff (consistent with the shoreland rule bluff definition) and one for determining a Mississippi River Corridor Critical Area bluff (consistent with the MRCCA rule bluff definition).

Technical Requirements
The user will need the following to run this tool:
System Requirements:
- ArcGIS Pro
- Spatial Analyst
Input Data Requirements:
- DEM (You can download 1-meter and 3-meter DEMs from MnTOPO: http://arcgis.dnr.state.mn.us/maps/mntopo )

For step-by-step instructions on how to use the tool, please view Bluff GIS Determination Tool Guide.pptx

The ArcGIS system provides access to both imagery and tools for visualizing and analyzing imagery. Imagery collections from the ArcGIS Living Atlas of the World can be viewed through apps such as the Landsat Explorer app, ArcGIS Online Map Viewer, and ArcGIS Pro, while the Spatial Analyst extension and ArcGIS Image Analyst for ArcGIS Pro, more commonly know as the Image Analyst extension, provide raster functions, classification and change detection tools, and other advanced image interpretation and analysis tools. The tutorials in the Working with Imagery in ArcGIS learning path will introduce you to exploring and selecting imagery in ArcGIS web applications, applying indices and raster functions to imagery in ArcGIS Pro, and performing image classification and change detection in ArcGIS Pro.This ArcGIS Pro project package contains data for Tutorial 3, Performing Image Classification in ArcGIS Pro, and Tutorial 4, Performing Change Detection in ArcGIS Pro, of the learning path. Click Download to download the .ppkx file or click Open in ArcGIS Pro then open the pitemx file to download and open the package.Software Used: ArcGIS Pro 2.8. Project package may be opened in 3.x versions.File Size: 170mbDate Created: November 7, 2022Last Tested: December 5, 2024

This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The Seabed Landform Classification Toolset is a GIS toolbox designed to classify seabed landforms on continental and island shelf settings. The user is guided through a series of classification steps within an ArcGIS toolbox to classify prominent seabed features termed ‘seabed landforms’, which characterise the morphology of the seabed surface. Seabed landforms include reefs/banks, peaks, plains, scarps, channels and depressions. Plain areas can additionally be classified into high and low features at localised and broad scales to capture features within plain surfaces. Common variables for seabed classification are utilised, including slope, bathymetric position index and ruggedness, and a series of procedures are applied to identify reef outcrops and minimise noise. The classification approach applies a whole-seascape classification which is aimed to offer a flexible and user-friendly approach to extract key seabed features from high-resolution shelf bathymetry data.

This toolset was developed using ESRI ArcGIS Desktop 10.8 and requires an Advanced licence with Spatial Analyst and 3D Analyst and extensions. It utilises scripts within the Benthic Terrain Modeler toolset (Walbridge et al. 2018) and Geomorphometry and Gradients Metrics Toolbox (Evans et al., 2014).

Please read the User Guide and supporting documentation for information on how to run the toolset. A web explainer is available at: https://arcg.is/1Tqmv50

The Seabed Landform Classification Toolset is also available for download on GitHub (https://github.com/LinklaterM/Seabed-Landforms-Classification-Toolset/).

The toolset was developed by the Coastal and Marine Team, NSW Department of Climate Change, Energy, the Environment and Water (formerly NSW Department of Planning and Environment), funded by NSW Climate Change Fund through the Coastal Management Funding Package and the Marine Estate Management Authority.

Please cite this toolset as: Linklater, M, Morris, B.D. and Hanslow, D.J. (2023) Classification of seabed landforms on continental and island shelves. Frontiers of Marine Science, 10, https://doi.org/10.3389/fmars.2023.1258556.

Other toolsets utilised by the Seabed Landform Classification Toolset include: Benthic Terrain Modeler: Walbridge, S., Slocum, N., Pobuda, M., and Wright, D. J. (2018). Unified geomorphological analysis workflows with Benthic Terrain Modeler. Geosciences 8, 94. Geomorphometry and Gradients Metrics Toolbox: Evans, J., Oakleaf, J., and Cushman, S. (2014). An ArcGIS Toolbox for Surface Gradient and Geomorphometric Modeling, Version 2.0-0. https://github.com/jeffreyevans/GradientMetrics.

Download In State Plane Projection Here. ** The Street Centerline feature class now follows the NG911/State of Illinois data specifications including a StreetNameAlias table. The download hyperlink above also contains a full network topology for use with the Esri Network Analyst extension ** These street centerlines were developed for a myriad of uses including E-911, as a cartographic base, and for use in spatial analysis. This coverage should include all public and selected private roads within Lake County, Illinois. Roads are initially entered using recorded documents and then later adjusted using current aerial photography. This dataset should satisfy National Map Accuracy Standards for a 1:1200 product. These centerlines have been provided to the United States Census Bureau and were used to conflate the TIGER road features for Lake County. The Census Bureau evaluated these centerlines and, based on field survey of 109 intersections, determined that there is a 95% confidence level that the coordinate positions in the centerline dataset fall within 1.9 meters of their true ground position. The fields PRE_DIR, ST_NAME, ST_TYPE and SUF_DIR are formatted according to United States Postal Service standards. Update Frequency: This dataset is updated on a weekly basis.

This database contains distribution maps of 135 eastern U.S. tree species based on Importance Values (IV) derived from Forest Inventory Analysis (FIA) data and a geographical information system (GIS) database of Elbert L. Jr. Little's published ranges. Between 1971 and 1977, Elbert L. Jr. Little, Chief Dendrologist with the U.S. Forest Service, published a series of maps of tree species ranges based on botanical lists, forest surveys, field notes ad herbarium specimens. These published maps have become the standard reference for most U.S. and Canadian tree species ranges.

The USDA Forest Service's FIA units are charged with periodically assessing the extent, timber potential, and health of the trees in the United States. The investigators have created a spatial database of individual species IV based on the number of stems and basal area of understory and overstory trees using FIA data from more than 100,000 plots in the eastern United States. The IV ranges from 0 to 100 and gives a measure of the abundance of the species. (See the investigator's Climate Change Atlas for 80 Forest Tree Species of the Eastern United States at [http://www.fs.fed.us/ne/delaware/atlas/web_atlas.html] for details). The investigators have aggregated the plot-level IV to 20km cells.

Both sets of maps (Little's ranges and IV based on FIA data) are available for download. The Little's range maps (little.shp) are vector based and are provided as shape files. These maps can span United States or United States and Canada in extent depending on the species. The Importance Value (IV) are raster maps (asciigrid) in asciigrid format. This is an ascii file with header information that can be used to import data into ArcInfo GRID or ArcView's Spatial Analyst GIS software. The spatial resolution is 20km. These raster maps span the eastern U.S. (east of the 100th meridian) in extent.

In spring 2008, the U.S. Geological Survey, as part of its Boston 133 Cities Urban Area mapping program, contracted for true-color imagery covering the metropolitan Boston area and beyond. Image type for the entire region (more than 1.7 million acres) is 24-bit, 3-band (red, green, blue) natural color. Each band has pixel values ranging 0-255. Pixel resolution is 30 cm., or approximately one foot. In spring 2009, USGS continued the project and 4-band 30cm imagery was obtained for the remainder of the state.This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for data development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software.The data are served from MassGIS' ArcGIS Online account as a tiled cached map service for fast display.For full metadata and links to download the imagery visit https://www.mass.gov/info-details/massgis-data-20082009-aerial-imagery.

This dataset represents a water shortage vulnerability analysis performed by DWR using Small Water System boundaries pulled from the SWRCB (State Water Resource Control Board) water system boundary layer (SABL). The water systems were then restricted to only active water systems with under 3000 connections that had SDWIS (Safe Drinking Water Information System) data. This data is from the 2024 analysis.

The spatial data of these feature classes is used as units of analysis for the spatial analysis performed by DWR. These datasets are intended to be authoritative datasets of the scoring tools required from DWR according to Senate Bill 552. Please refer to the source metadata for more information on completeness.

The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.4, dated September 14, 2022. DWR makes no warranties or guarantees — either expressed or implied— as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

Download high-quality, up-to-date shapefile boundaries (SHP, projection system SRID 4326). Our Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov Data Update Frequency: As Needed

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 (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS4_0_VectorAnalysis_Script_Python3.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). Vector Analysis ("PADUS4_0VectorAnalysis_GAP_PADUS_Only_ClipCENSUS.zip") data was created by clipping the PAD-US 4.0 Spatial Analysis and Statistics results to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS4_0_VectorAnalysisFile_OtherExtents_ClipCENSUS2022.zip"). Comma-separated Value (CSV) tables ("PADUS4_0_SummaryStatistics_TabularData_CSV.zip") provided as an alternative format and enable users to explore and download summary statistics of interest from the PAD-US Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 4.0 combined file without other extent boundaries ("PADUS4_0VectorAnalysis_GAP_PADUS_Only_ClipCENSUS.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS4_0VectorAnalysis_State_Clip_CENSUS2022" feature class ("PADUS4_0_VectorAnalysisFile_OtherExtents_ClipCENSUS2022.gdb") is the source of the PAD-US 4.0 Raster Analysis child item. Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the 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.

Moisture Index (MI) for the state of Utah is calculated from a spatial raster of annual actual (ETact) and potential (PET) evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Moisture Index (MI) was created to compare the suitability of settlement locations throughout Utah to explain initial Euro-American settlement of the region. MI is one of two proxies created specifically for Utah for comparison of environmental productivity throughout the state. Moisture index (MI) was originally used by Ramankutty et al. (2002) on a global scale to understand probability of cultivation based on a series of environmental factors. The Ramankutty et al. (2002) methods were used to build a regional proxy of agricultural suitability for the state of Utah. Adapting the methods in Ramankutty et al. (2002), we were able to create a higher resolution dataset of MI specific to the state of Utah. Unlike S, MI only accounts for evapotranspiration rates.The Moisture Index is calculated as: MI = ETact / PET Where ETact is the actual evapotranspiration and PET is the potential evapotranspiration. This calculation results in a zero to one index representing global variation in moisture. MI is calculated for the study area (Utah) using a raster of annual actual (ETact) and potential (PET) evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Using the ArcMap 10.3.1 Raster Calculator (Spatial Analyst), a raster dataset is created at a resolution of 2.6 kilometer square, which contain values representative of the average Moisture Index for Utah over a fourteen year period (ESRI, 2015). The data were collected remotely by satellite (MODIS) and represents reflective surfaces (urban areas, lakes, and the Utah Salt Flats) as null values in the dataset. Areas of null values that were not bodies of water are interpolated using Inverse Distance Weighting (3d Analyst) in ArcMap 10.3.1 (ESRI, 2015). Download the moisture index (MI) data below. If you have any questions or concerns, please contact me at PYaworsky89@gmail.com. Citations ESRI. (2015). ArcGIS Desktop: Release (Version 10.3.1). Redlands, CA: Environmental Systems Research Institute. Mu, Q., Zhao, M., & Running, S. W. (2013). MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3). Algorithm Theoretical Basis Document, Collection, 5. Retrieved from http://www.ntsg.umt.edu/sites/ntsg.umt.edu/files/MOD16_ATBD.pdf Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800. Numerical Terradynamic Simulation Group. (2013, July 29). MODIS Global Evapotranspiration Project (MOD16). University of Montana. Ramankutty, N., Foley, J. A., Norman, J., & Mcsweeney, K. (2002). The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Global Ecology and Biogeography, 11(5), 377–392. http://doi.org/10.1046/j.1466-822x.2002.00294.x

A vector GIS dataset of candidate areas for terrestrial ecological restoration based on landscape context. The dataset was created using NLCD 2011 (www.mrlc.gov) and morphological spatial pattern analysis (MSPA) (http://forest.jrc.ec.europa.eu/download/software/guidos/mspa/). There are 13 attributes for the polygons in the dataset, including presence and length of roads, candidate area size, size of surround contiguous natural areas, soil productivity, presence and length of road, areas suitable for wetland restoration, and others. This dataset is associated with the following publication: Wickham, J., K. Riiters, P. Vogt, J. Costanza, and A. Neale. An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context. RESTORATION ECOLOGY. Blackwell Publishing, Malden, MA, USA, 25(6): 894-902, (2017).

(Link to Metadata) This datalayer contains Vermont forestry estimate data, by county, primarily obtained from the Vermont Forest Inventory and Analysis (FIA), conducted in the years; 1966, 1973, 1983, and 1997 by the USDA Forest Service. Inventory items for Grand Isle and Franklin Counties have been combined. See the 'Attribute Accuracy Report' for reasons. Also included within this database are land-use change figures, reflective of the time periods; 1970s-1980s, 1980s-1990s, and the 1970s-1990s. This data has been made available by the Orton Family Foundation and the UVM School of Natural Resources, Spatial Analysis Lab. One may download Excel spreadsheets or comma-delimited ASCII textfiles of this data from the VGIS indicators webpage - http://vcgi.vermont.gov/indicators/