This raster file represents land within the Mountain Home study boundary classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by use of Random Forest, a supervised machine learning algorithm. Classification models often employ Random Forest due to its accuracy and efficiency at labeling large spatial datasets. To build a Random Forest model and supervise the learning process, IDWR staff create pre-labeled data, or training points, which are used by the algorithm to construct decision trees that will be later used on unseen data. Model accuracy is determined using a subset of the training points, otherwise known as a validation dataset. Several satellite-based input datasets are made available to the Random Forest model, which aid in distinguishing characteristics of irrigated lands. These characteristics allow patterns to be established by the model, e.g., high NDVI during summer months for cultivated crops, or consistently low ET for dryland areas. Mountain Home Irrigated Lands 2023 employed the following input datasets: US Geological Survey (USGS) products, including Landsat 8/9 and 10-meter 3DEP DEM, and European Space Agency (ESA) Copernicus products, including Harmonized Sentinel-2 and Global 30m Height Above Nearest Drainage (HAND). For the creation of manually labeled training points, IDWR staff accessed the following datasets: NDVI derived from Landsat 8/9, Sentinel-2 CIR imagery, US Department of Agriculture National Agricultural Statistics Service (USDA NASS) Cropland Data Layer, Active Water Rights Place of Use data from IDWR, and USDA’s National Agriculture Imagery Program (NAIP) imagery. All datasets were available for the current year of interest (2023). The published Mountain Home Irrigated Lands 2023 land classification raster was generated after four model runs, where at each iteration, IDWR staff added or removed training points to help improve results. Early model runs showed poor results in riparian areas near the Snake River, concentrated animal feeding operations (CAFOs), and non-irrigated areas at higher elevations. These issues were resolved after several model runs in combination with post-processing masks. Masks used include Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data. These data were amended to exclude polygons overlying irrigated areas, and to expand riparian area in specific locations. A manually created mask was primarily used to fill in areas around the Snake River that the model did not uniformly designate as irrigated. Ground-truthing and a thorough review of IDWR’s water rights database provided further insight for class assignments near the town of Mayfield. Lastly, the Majority Filter tool in ArcGIS was applied using a kernel of 8 nearest neighbors to smooth out “speckling” within irrigated fields. The masking datasets and the final iteration of training points are available on request. Information regarding Sentinel and Landsat imagery:All satellite data products used within the Random Forest model were accessed via the Google Earth Engine API. To find more information on Sentinel data used, query the Earth Engine Data Catalog https://developers.google.com/earth-engine/datasets) using “COPERNICUS/S2_SR_HARMONIZED.” Information on Landsat datasets used can be found by querying “LANDSAT/LC08/C02/T1_L2” (for Landsat 8) and “LANDSAT/LC09/C02/T1_L2” (for Landsat 9).Each satellite product has several bands of available data. For our purposes, shortwave infrared 2 (SWIR2), blue, Normalized Difference Vegetation Index (NDVI), and near infrared (NIR) were extracted from both Sentinel and Landsat images. These images were later interpolated to the following dates: 2023-04-15, 2023-05-15, 2023-06-14, 2023-07-14, 2023-08-13, 2023-09-12. Interpolated values were taken from up to 45 days before and after each interpolated date. April-June interpolated Landsat images, as well as the April interpolated Sentinel image, were not used in the model given the extent of cloud cover overlying irrigated area. For more information on the pre-processing of satellite data used in the Random Forest model, please reach out to IDWR at gisinfo@idwr.idaho.gov.

This layer depicts polygons representing land within the Mountain Home plateau study area boundary and are classified as either "irrigated", "non-irrigated" or "semi-irrigated", where the semi-irrigated classification typically depicts residential land. Attribute assignments for irrigation status fall within three classes - irrigated, non-irrigated, and semi-irrigated - and are determined using available Landsat imagery as background reference. Landsat imagery is typically 30-meter resolution for Landsat7 and Landsat8. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery as appropriate and relevant is used for determining irrigation status and for refining the polygon geometry. The interpretation and classification process is described in detail in an internal (IDWR)document and may be made available upon request.

This layer is a modified version of the Esri "USA Summits and Peaks" data (https://www.arcgis.com/home/item.html?id=6706f7e6712b4b479dcb4fce4b7b3172). Data within the corridor of interest may have been updated spatially. Some elevation data has been updated to reflect the Alaska IFSAR dataset (https://maps.dggs.alaska.gov/arcgis/services/elevation/IFSAR_DTM/ImageServer).

Areas with Elevated Nitrate in Ground Water Thirty-five areas in Idaho have been identified as nitrate priority areas (NPAs) with elevated levels of nitrate in ground water, according to a study prepared by the Idaho Department of Environmental Quality (DEQ).Nitrate is a chemical form of nitrogen found in soil and water. In elevated levels, nitrates can present a risk to human health. NPAs are areas where 25% of the sampled wells contain water with nitrate levels of 5 milligrams per liter (mg/L) or greater. The drinking water standard is 10 mg/L.DEQ conducted the study in conjunction with the Idaho Ground Water Monitoring Technical Committee, a panel of scientists from state and federal agencies, and health districts.The 2020 Nitrate Priority Area Delineation and Ranking Process document ranks the 35 areas based on the following three primary criteria:• the number of people living in an area that are potentially drinking nitrate-degraded water• the percentage of sampled wells with elevated ground water nitrate concentrations• water quality trends indicating whether nitrate levels are increasing, staying the same, or decreasing.The potential impact of nitrate on other beneficial uses of ground water besides water supply was also considered.The NPA's are located across Idaho, primarily along the Snake River Plain. The top five NPAs are: NE Star, Mountain Home AFB, Minidoka, Fort Hall, and Marsh Creek..The study consisted of compiling data from over 14,000 wells statewide. Of these, over 4,300 are located within the boundaries of the NPAs. Approximately 414,000 people are estimated to live within the NPAs.The document, which contains a complete listing of all 35 NPAs, can be accessed on DEQ's website (download at bottom). Also available online is a summary of comments received on the document during a 60-day public comment period and DEQ responses.The nitrate priority area ranking is used to prioritize the development and implementation of strategies to help reduce nitrate loading from land-use activities. In coordination with other agencies, DEQ assists local ground water quality advisory groups in developing ground water quality management strategies for NPAs.2020 Nitrate Priority Online Map Application (ArcGIS Enterprise 10.9.1)Idaho DEQ Nitrate in Ground WaterIdaho DEQ 2020 Nitrate Priority Area Delineation and Ranking Process (PDF)2020 Nitrate Priority Area Ranking Summary (PDF)

Arizona National Scenic Trail from Mexico north to Utah Border (Official, Authoritative) This references the original maintained feature service from the Arizona Trail Association's ArcGIS Online Organization at https://aztrail.maps.arcgis.com/May 1, 2015 - This is a newly digitized data set at 1:1200 scale using Statewide aerial imagery, GPS data, LiDar and 10M USGS DEM for 3D values that represents the Arizona National Scenic Trail. For further details about the the trail and to download GPX files for each passage that fit onto GPS devices please visit www.aztrail.orgSince updates to the Arizona Trail is constant, and this feature service occur at least quarterly, we encourage you to use this feature service to have the most up to date layer. However if you would like to download layer packages I encourage you to join the Arizona Trail Association group here: https://azgeo.maps.arcgis.com/home/group.html?id=5acd173735354e789ddd3f66412c9ddb#overview so that I can update members when there are small or large updates to re-download datasets.September 2017 recalculated mileage due to reroutes in the trailApril 2021 recalculated mileage due to reroutes in the trailFebruary 2023 recalculated mileage due to reroutes in the trailJanuary 2025 recalculated mileage du to reroutes in the trail

Map created to support Volcanic Landscapes—Integrative Studies of Volcanism in the Western United States geonarrative: http://arcg.is/0Te4rKMammoth Mountain mafic flows:Hildreth, Wes, and Fierstein, Judy, 2016, Eruptive history of Mammoth Mountain and its mafic periphery, California: U.S. Geological Survey Professional Paper 1812, 128 p., 2 plates, scale 1:24,000, https://doi.org/10.3133/pp1812.Mammoth Mountain silicic flows:Hildreth, Wes, and Fierstein, Judy, 2016, Eruptive history of Mammoth Mountain and its mafic periphery, California: U.S. Geological Survey Professional Paper 1812, 128 p., 2 plates, scale 1:24,000, https://doi.org/10.3133/pp1812.Bishop Tuff:Bailey, R.A., 1989, Geologic map of Long Valley caldera, Mono-Inyo Craters volcanic chain, and vicinity, Mono County, California: U.S. Geological Survey Miscellaneous Investigations Map I–1933, scale 1:62,500, in Battaglia, M., Williams, M.J., Venezky, D.Y., Hill, D.P., Langbein, J.O., Farrar, C.D., Howle, J.F., Sneed, M., and Segall, P., 2003, The Long Valley Caldera GIS Database: U.S. Geological Survey Digital Data Series DDS–81, https://pubs.er.usgs.gov/publication/ds81.Long Valley caldera boundary:Bailey, R.A., 1989, Geologic map of Long Valley caldera, Mono-Inyo Craters volcanic chain, and vicinity, Mono County, California: U.S. Geological Survey Miscellaneous Investigations Map I–1933, scale 1:62,500, in Battaglia, M., Williams, M.J., Venezky, D.Y., Hill, D.P., Langbein, J.O., Farrar, C.D., Howle, J.F., Sneed, M., and Segall, P., 2003, The Long Valley Caldera GIS Database: U.S. Geological Survey Digital Data Series DDS–81, https://pubs.er.usgs.gov/publication/ds81.Basemap web service:Esri, 2017, World Imagery: Esri map service, accessed June 11, 2017, at http://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9.

This scanned historical map from the University of Idaho Experimental Forest archives shows school district boundaries, schoolhouses, occupied houses, and vacant house on Moscow Mountain and vicinity. The printed paper map is dated April 23, 1934.This map was scanned on a Contex HD 5450 wide format scanner at 300 dpi with 24-bit color. The original file was scanned on April 7, 2023 and created as an uncompressed TIF file. Subsequently, a 300 dpi JPEG file was created from the archival TIF file using Adobe Photoshop 2023. The JPG file was rotated, de-skewed, and cropped to make the documents as usable as possible.The JPG file was georeferenced using georeferencing tools in ArcGIS Pro 3.0.1 in June, 2023. Twenty-two (22) control points were placed to align the image to the NAD 1983 UTM Zone 11N coordinate system. The public land survey system was used as the target layer. A first order polynomial transformation (affine) was selected to transform the image without deforming it and the georeferencing information was saved with the image. Total RMS error was less than 100.

This map was created using the USDA future snow residence time data layer for displaying on the Riparian and Aquatic Ecosystem Vulnerability EvaluatioN (RAEVEN) Data Library Experience as a visual comparison between historical and future climate. The data layer here is publicly available and presented "as-is" based on the settings from the original data layer available on AGOL. All credit goes to the USDA Forest Service Rocky Mountain Research Station Air Water and Aquatic Environments program.Copied from the future snow residence time data layer details: Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.For more information, please visit the original data layer details page: https://usfs.maps.arcgis.com/home/item.html?id=2b82c333a6094600aa6e4720d551638b Information is also available from the USDA Forest Service Rocky Mountain Research Station Air Water and Aquatic Environments website https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html