Shrublands have seen large changes over time due to factors such as fire and drought. As the climate continues to change, vegetation monitoring at the county scale is essential to identify large-scale changes and to develop sampling designs for field-based vegetation studies. This dataset contains two raster files that each depict the height of vegetation. The first layer is restricted to actively growing vegetation and the second is restricted to dormant/dead vegetation. Both layers cover open space areas in San Diego County, California. Height calculations were derived from Lidar data collected in 2014 and 2015 for the western two-thirds of San Diego County. Lidar point clouds were pre-classified into ground and non-ground. Rasters for the Digital Elevation Model (DEM) and Digital Surface Model (DSM) were calculated using ArcGIS software using ground classified points and last returns for the natural surface (DEM) and non-ground first returns for the surface model (DSM). The spatial resolution for both layers is 1 meter and aligns with 2014 National Agriculture Imagery Program (NAIP) imagery. Object height was calculated by subtracting the DEM from the DSM in meters. To remove structures or non-natural objects from the imagery, layers were clipped to open space areas using the National Land Cover Database, building footprints, roads, and railways. This ensures that objects above the natural surface are vegetation, even when Normalized Difference Vegetation Index (NDVI) numbers are very low. NDVI measures the amount of photosynthetically active vegetation in the raster cell. Healthy vegetation reflects high levels of near-infrared and low levels of red electromagnetic radiation. NDVI ranges from -1 to 1 with low values indicating little or no presence of healthy vegetation and higher values indicating the presence of healthy vegetation. The NDVI was calculated from the 2014 NAIP imagery and a cutoff of 0.1 was used to separate photosynthetically active vegetation from non-vegetated or dormant/dead vegetation areas. The imagery was collected during 2014, an exceptional drought year. It is not possible to separate extremely water-stressed plants from truly dead plants using only NDVI. The natural surface was verified using established National Geodetic Survey (NGS) benchmarks and exceeded 98 percent accuracy. Vegetation structure was validated using visual assessments of high-resolution aerial imagery to verify the vegetation form and greenness. Vegetation form and health (NDVI) had an accuracy of 82 percent.

The zip file contains a large tiff mosaic stitched together from a series of aerial photographs of the Calhoun CZO area taken in 1933, when the area was being acquired by the US Forest Service. USFS archaeologist Mike Harmon delivered the black-and-white photographs, known to him as the 'Sumter National Forest Purchase Aerials', to us in a box. The photographs include most of the Enoree District of the Sumter National Forest, including the entirety of the Calhoun CZO, not just the long-term plots and small watersheds. The photographs were scanned and georectified, then color-balanced and stitched together following 'seams' - high-contrast features such as rivers and roads ('seamlined'). In addition to the main tiff are four files that can be used to properly geolocate the composite image in ArcGIS.

The multilayer pdf file includes a smaller version of the seamlined 1933 aerial photography mosaic raster layer, as well as this aerial mosaic transparent over slope map (for a 3D-like 1933 image raster). Other layers include contours, roads, boundaries, sampling locations, 1.5 m DEM, 1.5m slope, 1m 2013 NAIP aerial imagery, and 2014 canopy height. The pdf file includes both 'interfluve order' and 'landshed order.' These two layers mean the same thing, but the landshed is the area unit around the interfluve that is used for statistics; this dataset has been QC'ed. The Interfluve Order network was used to delineate the landsheds and agrees with it >95% of the time, but has a few inaccuracies (it was automated by the computer) that were fixed manually. Use the network for viewing and considering the landscape at large, but for the specific interfluve order, check the color of the 'Landshed Order' dataset to verify its accuracy.

Date Range Comments: The exact date these photos were taken is unknown, but the year is thought to be 1933.The flight date is prior to the USFS land purchases for the Enoree District of the Sumter National Forest; the photos are thus known as the "pre-purchase photos").

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.

These watershed and sub-watershed/catchment boundaries were delineated by Sue Parsons (Reynolds Creek Critical Zone Observatory, Idaho State University, Pocatello Idaho) in May 2020 using a 2007 3m resolution LiDAR-derived DEM of Reynolds Creek Experimental Watershed (Owyhee County, Idaho) published by Dr. Nancy Glenn’s Boise Center Aerospace Laboratory group (http://doi.org/10.18122/B27C77). Polygons were generated using ESRI ArcMap 10.5 Spatial Analyst tools, Hydrology toolset (Fill-Flow Direction-Flow Accumulation-Snap Pour Point, Watershed), and then converted to polygons and smoothed using ArcMap raster to polygon conversion tool, with the simplified polygons option. Pour points were placed directly over weir locations using a combination of reported coordinates from the USDA-ARS Northwest Watershed Research Center (Boise, ID), and basemap imagery provided by ESRI ArcGIS Online:

ArcGIS Online streaming World Imagery (Clarity): World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (591M down to 72k) and 2.5m SPOT Imagery (288k to 72k) for the world, and USGS 15m Landsat imagery for Antarctica. The map features 0.3m resolution imagery in the continental United States and 0.6m resolution imagery in parts of Western Europe from Digital Globe. Recent 1m USDA NAIP imagery is available in select states of the US. In other parts of the world, 1 meter resolution imagery is available from GeoEye IKONOS, AeroGRID, and IGN Spain. Additionally, imagery at different resolutions has been contributed by the GIS User Community. For more information on this map, including the terms of use, visit us online at http://goto.arcgisonline.com/maps/World_Imagery

Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User CommunityWeir locations were used to constrain watershed boundaries to the topographic areas captured by experimental measurements, and to resemble previously used watershed boundaries derived, mostly likely, from 10m NED DEMs. This set of watershed and sub-watershed delineations may be most suitable for use with the 2007 3m LiDAR-derived DEM.

These watershed and sub-watershed/catchment boundaries were delineated by Sue Parsons (Reynolds Creek Critical Zone Observatory, Idaho State University, Pocatello Idaho) in May 2020 using a 2007 3m resolution LiDAR-derived DEM of Reynolds Creek Experimental Watershed (Owyhee County, Idaho) published by Dr. Nancy Glenn’s Boise Center Aerospace Laboratory group (http://doi.org/10.18122/B27C77). Polygons were generated using ESRI ArcMap 10.5 Spatial Analyst tools, Hydrology toolset (Fill-Flow Direction-Flow Accumulation-Snap Pour Point, Watershed), and then converted to polygons and smoothed using ArcMap raster to polygon conversion tool, with the simplified polygons option. Pour points were placed directly over weir locations using a combination of reported coordinates from the USDA-ARS Northwest Watershed Research Center (Boise, ID), and basemap imagery provided by ESRI ArcGIS Online:

ArcGIS Online streaming World Imagery (Clarity): World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (591M down to 72k) and 2.5m SPOT Imagery (288k to 72k) for the world, and USGS 15m Landsat imagery for Antarctica. The map features 0.3m resolution imagery in the continental United States and 0.6m resolution imagery in parts of Western Europe from Digital Globe. Recent 1m USDA NAIP imagery is available in select states of the US. In other parts of the world, 1 meter resolution imagery is available from GeoEye IKONOS, AeroGRID, and IGN Spain. Additionally, imagery at different resolutions has been contributed by the GIS User Community. For more information on this map, including the terms of use, visit us online at http://goto.arcgisonline.com/maps/World_Imagery

Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User CommunityWeir locations were used to constrain watershed boundaries to the topographic areas captured by experimental measurements, and to resemble previously used watershed boundaries derived, mostly likely, from 10m NED DEMs. This set of watershed and sub-watershed delineations may be most suitable for use with the 2007 3m LiDAR-derived DEM.

This raster file represents land within the ESPA 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 using random forest, a supervised machine learning algorithm. 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. ESPA Irrigated Lands 2020 employed the following input datasets: US Geological Survey (USGS) products, including Landsat 8/9 and 10-meter 3DEP DEM, European Space Agency (ESA) Copernicus products, including Harmonized Sentinel-2, and Global 30m Height Above Nearest Drainage (HAND) (Donchyts et al., 2016). Evapotranspiration data from the METRIC model (Mapping Evapotranspiration at high Resolution with Internalized Calibration) was provided by IDWR and used as an input. IDWR staff used the following datasets to aid in the labeling of training data: 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 and METRIC data from IDWR, and USDA’s National Agriculture Imagery Program (NAIP) imagery. NAIP imagery from 2019 was used as a reference; all other datasets were available for 2020.ESPA Irrigated Lands 2020 model runs were processed on six separate subregions covering the study boundary. This was done to reduce processing time and better train the model on specific climatic regions. Each subregion may undergo 1-4 model iterations, where at each iteration IDWR staff added or removed training points to help improve results. The northeast section of the study boundary, spanning from Kilgore, Island Park, and Ashton, was largely hand-delineated due to a lack of quality training data or inaccuracy of modeled results. Post-processing of model output included a wetland mask derived from the Fish and Wildlife Service’s National Wetlands Inventory wetlands dataset, as well as a manually created mask specific to issues found in the ESPA 2020 model results. The masking datasets and pre-labeled training points are available on request.References:Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T., Gao, H., Savenije, H., & van de Giesen, N. (2016). Global 30m height above the nearest drainage (HAND). Geophysical Research Abstracts, 18, EGU2016-17445-3. EGU General Assembly 2016.