The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial mapping products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2018, 2020 and 2022 and Open Street Map (OSM) from 2019. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov/) and Geofabrik GmbH - Open Street Map (https://www.geofabrik.de/geofabrik/openstreetmap.html), respectively. The imagery was classified using Random Forest (RF) Modeling to produce land cover maps with three main classifications - bare, vegetation, and shadows. An updated roads and trails map for the Upper Scotts Creek Watershed, including the BLM Recreational Area, was created to estimate road and trail densities in the watershed. Separate metadata records for each product (Land_Cover_Maps_Scotts_Creek_Watershed_CA_2018_2020_2022_metadata.xml, and Roads_and_Trails_Map_Upper_Scotts_Creek_Watershed_CA _2022_metadata.xml) are provided on the ScienceBase page for each child item. Users should be aware of the inherent errors in remote sensing products.

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2018, 2020 and 2022. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov). The NAIP imagery from 2018, 2020 and 2022 was classified using Random Forest Modeling to produce land cover maps with three main classifications – bare, vegetation, and shadows. A total of 600 independent reference points were used in the accuracy assessment. The overall accuracy for all classes for each dataset is 98%. See attached ScottsCreek_20XX_AccuracyAssessment.csv files (contained within each LandCoverMap_associated_files_20XX.zip for each year respectively) for details. A preview image of the land cover map for 2018 is attached to this data release as an example (see LandCoverMap_RF_ScottsCreekWatershed_USGS2022_CC0.png). The percentage of bare, vegetation and shadow pixels were calculated for the complete watershed and each individual NHDPlus2.1 catchment basins (slightly modified to support hydrological modeling). These metrics can be used to quantify bare and vegetated areas and detect and quantify vegetation changes over time. Users should be aware of the inherent errors in remote sensing products.

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2018, 2020 and 2022. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov). The NAIP imagery from 2018, 2020 and 2022 was classified using Random Forest Modeling to produce land cover maps with three main classifications – bare, vegetation, and shadows. A total of 600 independent reference points were used in the accuracy assessment. The overall accuracy for all classes for each dataset is 98%. See attached ScottsCreek_20XX_AccuracyAssessment.csv files (contained within each LandCoverMap_associated_files_20XX.zip for each year respectively) for details. A preview image of the land cover map for 2018 is attached to this data release as an example (see LandCoverMap_RF_ScottsCreekWatershed_USGS2022_CC0.png). The percentage of bare, vegetation and shadow pixels were calculated for the complete watershed and each individual NHDPlus2.1 catchment basins (slightly modified to support hydrological modeling). These metrics can be used to quantify bare and vegetated areas and detect and quantify vegetation changes over time. Users should be aware of the inherent errors in remote sensing products.

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2022 and Open Street Map (OSM) from 2019. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov/) and Geofabrik GmbH - Open Street Map (https://www.geofabrik.de/geofabrik/openstreetmap.html), respectively. An updated trail map for the Upper Scotts Creek Watershed, including the BLM Recreational Area, was created to estimate trail densities in the watershed. A preview image of the roads and trail maps is attached to this data release (see UpperScottsCreek_Roads_and_Trails_Map_2022_USGS2022_CC0.png).

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2022 and Open Street Map (OSM) from 2019. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov/) and Geofabrik GmbH - Open Street Map (https://www.geofabrik.de/geofabrik/openstreetmap.html), respectively. An updated trail map for the Upper Scotts Creek Watershed, including the BLM Recreational Area, was created to estimate trail densities in the watershed. A preview image of the roads and trail maps is attached to this data release (see UpperScottsCreek_Roads_and_Trails_Map_2022_USGS2022_CC0.png).

This data collection includes spatial and tabular datasets related to the delineation and characterization of surface moisture zones (SMZs) in the vicinity of springs mapped in the National Hydrography Dataset (NHD) in southeastern Oregon using time-series analysis of Normalized Difference Vegetation Index (NDVI) data derived from Landsat Thematic Mapper 5 imagery from 1985-2011. The study area is within and adjacent to the Steens Mountain Cooperative Management and Protection Area (CMPA), which is a protected area of approximately 1,732 km2 managed by the Bureau of Land Management (BLM) in Harney County, Oregon. Within or adjacent to the Steens Mountain CMPA, approximately 1,100 springs are mapped in the NHD, however very little hydrologic data exists for these springs. Data in this data release were produced using a set of scripts written in the R programming language, which are also included in this data release (see ‘larger works citation’ to access R scripts and associated metadata). These data processing scripts, data products, and associated metadata provide documentation for a novel remote-sensing based approach to assess the potential resilience of spring-dependent ecosystems to inter-annual changes in water availability. This approach uses time-series analysis of NDVI to (1) delineate SMZs in the vicinity of mapped springs in a semi-arid sage-steppe landscape, (2) derive quantitative indicators of the relative resilience of these SMZs to inter-annual changes in water availability, and (3) synthesize these indicators into an overall resilience score for each cluster of springs. Specifically, for 39 spring clusters in Harney County, Oregon, USA, these scripts process Landsat-derived NDVI and precipitation data from 1985-2011 to derive 7 potential indicators of SMZ resilience to water-cycle changes. For detailed information on the resilience indicators, including their conceptual basis, methods of calculation, and interpretation, see Cartwright and Johnson (2018) and the R scripts and their associated metadata in this data release. References: Cartwright and Johnson (2018), Springs as hydrologic refugia in a changing climate? A remote sensing approach. Ecosphere X(X).

This data collection includes spatial and tabular datasets related to the delineation and characterization of surface moisture zones (SMZs) in the vicinity of springs mapped in the National Hydrography Dataset (NHD) in southeastern Oregon using time-series analysis of Normalized Difference Vegetation Index (NDVI) data derived from Landsat Thematic Mapper 5 imagery from 1985-2011. The study area is within and adjacent to the Steens Mountain Cooperative Management and Protection Area (CMPA), which is a protected area of approximately 1,732 km2 managed by the Bureau of Land Management (BLM) in Harney County, Oregon. Within or adjacent to the Steens Mountain CMPA, approximately 1,100 springs are mapped in the NHD, however very little hydrologic data exists for these springs. Data in this data release were produced using a set of scripts written in the R programming language, which are also included in this data release (see ‘larger works citation’ to access R scripts and associated metadata). These data processing scripts, data products, and associated metadata provide documentation for a novel remote-sensing based approach to assess the potential resilience of spring-dependent ecosystems to inter-annual changes in water availability. This approach uses time-series analysis of NDVI to (1) delineate SMZs in the vicinity of mapped springs in a semi-arid sage-steppe landscape, (2) derive quantitative indicators of the relative resilience of these SMZs to inter-annual changes in water availability, and (3) synthesize these indicators into an overall resilience score for each cluster of springs. Specifically, for 39 spring clusters in Harney County, Oregon, USA, these scripts process Landsat-derived NDVI and precipitation data from 1985-2011 to derive 7 potential indicators of SMZ resilience to water-cycle changes. For detailed information on the resilience indicators, including their conceptual basis, methods of calculation, and interpretation, see Cartwright and Johnson (2018) and the R scripts and their associated metadata in this data release. References: Cartwright and Johnson (2018), Springs as hydrologic refugia in a changing climate? A remote sensing approach. Ecosphere X(X).

In cooperation with the U.S. Army Corps of Engineers (USACE), the U.S. Geological Survey (USGS) surveyed ground control points and coordinated aerial photograph acquisition of Hills Creek Lake, a multi-purpose reservoir in western Oregon impounded by the 92-meter ([m]; 302-foot [ft]) tall Hills Creek Dam. Aerial photographs were acquired by the Civil Air Patrol (CAP) on December 20, 2023 and December 28, 2023 when water levels were at 443 and 441 m (1453 ft and 1448 ft; National Geodetic Vertical Datum of 1929 [NGVD 29]) elevation, respectively, about 10 m above typical annual “low pool” or minimum pool for flood risk management operations. Photographs were acquired at about the same altitude with a WaldoAir XCAM Ultra 50 camera mounted on a Cessna aircraft and captured the entire reservoir area as defined by full pool (or maximum conservation pool elevation), including major tributaries entering the reservoir such as the Middle Fork Willamette River and Hills Creek, upstream of Hills Creek Dam. Dam operations at the 1,107-hectare (2735-acre) Hills Creek Lake, located about 19 kilometers upstream of the confluence of the Middle Fork Willamette River and the head of Lookout Point Lake, along with other hydrogeomorphic conditions, result in a diverse array of geomorphic processes and landforms within the reservoir. To document reservoir floor geomorphology, the USGS applied structure-from-motion (SfM) techniques to these aerial photographs, following the workflow outlined in Over and others (2021), and generated three-dimensional xyz point clouds, digital surface models (DSMs), and orthomosaics of Hills Creek Lake. This data release includes ground control points, dataset footprints, original aerial photographs, point clouds, DSMs, and orthomosaics of Hills Creek Lake with varying aerial extents and resolutions that were developed from imagery acquired December of 2023: (1) the December 20 model (HillsCreekLake_20231220) covered the entire reservoir area with an average point density of 27.6 points per square meter, DSM resolution of 19 centimeters per pixel, and orthomosaic ground resolution of 9.52 centimeters per pixel; (2) the December 28 model (HillsCreekLake_20231228) covered the entire reservoir area, excluding a portion of the Larison Creek arm, with an average point density of 29.8 points per square meter, DSM resolution of 18.3 centimeters per pixel, and orthomosaic ground resolution of 9.15 centimeters per pixel. All DSMs and orthomosaics are formatted as Cloud Optimized GeoTIFFs (COGs) for enhanced web visualization (GDAL, 2024). This documentation describes a DSM of Hills Creek Lake, Oregon, generated from SfM techniques using aerial photographs acquired on December 20, 2023. References: Agisoft, 2025, Agisoft Metashape User Manual - Professional Edition Version 2.2: Agisoft LLC, 115 p., accessed August 11, 2025, at https://www.agisoft.com/pdf/metashape_2_2_en.pdf. American Society for Photogrammetry and Remote Sensing [ASPRS], 2008, LAS Specification Version 1.2: ASPRS, approved September 2, 2008, 13 p., accessed August 11, 2025, at https://www.asprs.org/wp-content/uploads/2010/12/asprs_las_format_v12.pdf. Geospatial Data Abstraction Library [GDAL], 2024, COG -- Cloud Optimized GeoTIFF generator: GDAL, webpage, accessed August 11, 2025, at https://gdal.org/drivers/raster/cog.html#raster-cog. Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., https://doi.org/10.3133/ofr20211039. Schwid, M.F., Keith, M.K., and Overstreet, B.T., 2025, High-resolution orthoimagery and digital surface models of Fern Ridge Lake, Oregon, during annual low pool, January and February, 2023: U.S. Geological Survey data release, https://doi.org/10.5066/P1Q5K657.

This portion of the data release presents high-resolution Digital Surface Models (DSM) of the Jenkinson Lake upper reservoir delta in El Dorado County, California. The DSMs have resolutions of 10 centimeters per pixel and were derived from structure-from-motion (SfM) processing of aerial imagery collected during surveys with unoccupied aerial systems (UAS). The surveys were on 2021-10-13, 2021-11-04, 2022-10-25, and 2023-11-13, and were generally timed to coincide with low water level in the reservoir to maximize sub-aerial coverage. The raw imagery used to create the orthomosaics was acquired with a UAS quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines, from an approximate altitude of 100 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 2.6 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Survey control was established using temporary ground control points (GCPs) consisting of a combination of small square tarps with black-and-white cross patterns and temporary chalk marks placed on the ground. The GCP positions were measured using dual-frequency real-time kinematic (RTK) GPS with corrections referenced to a static base station operating nearby. The images and GCP positions were used for structure-from-motion (SfM) processing to create topographic point clouds, high-resolution orthomosaic images, and DSMs. The DSMs are provided in a cloud optimized GeoTIFF format with internal overviews and masks to facilitate cloud-based queries and display.

This portion of the data release presents high-resolution Digital Surface Models (DSM) of the Jenkinson Lake upper reservoir delta in El Dorado County, California. The DSMs have resolutions of 10 centimeters per pixel and were derived from structure-from-motion (SfM) processing of aerial imagery collected during surveys with unoccupied aerial systems (UAS). The surveys were on 2021-10-13, 2021-11-04, 2022-10-25, and 2023-11-13, and were generally timed to coincide with low water level in the reservoir to maximize sub-aerial coverage. The raw imagery used to create the orthomosaics was acquired with a UAS quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines, from an approximate altitude of 100 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 2.6 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Survey control was established using temporary ground control points (GCPs) consisting of a combination of small square tarps with black-and-white cross patterns and temporary chalk marks placed on the ground. The GCP positions were measured using dual-frequency real-time kinematic (RTK) GPS with corrections referenced to a static base station operating nearby. The images and GCP positions were used for structure-from-motion (SfM) processing to create topographic point clouds, high-resolution orthomosaic images, and DSMs. The DSMs are provided in a cloud optimized GeoTIFF format with internal overviews and masks to facilitate cloud-based queries and display.