This polygon feature class represents the spatial extent of historical BLM Administrative Unit Boundaries (at the State, District, and Field Office levels).This state dataset may have published a dataset that is more current than the National dataset; there may be geometry variations between the state and national dataset which may have different results. The national dataset is updated following the data standard schedule

This polygon feature class represents the spatial extent and boundaries for anticipated, in-progress, existing and historic BLM Land Use Planning Area (LUPA) polygons. Land Use Planning Areas are geographic areas within which the BLM will make decisions during a land use planning effort. Land Use Planning Area Boundaries shift from an "in-progress" status and become Existing Land Use Planning Areas when the Land Use Plan has been approved and a Record of Decision Date has been established. At this point, these LUPAs are officially "existing" and the previous plan is moved to a "historic" status.

This polygon feature class represents the spatial extent of historical BLM Administrative Unit Boundaries (at the State, District, and Field Office levels).This state dataset may have published a dataset that is more current than the National dataset; there may be geometry variations between the state and national dataset which may have different results. The national dataset is updated following the data standard schedule

Types of recreation decisions include Special Recreation Management Areas (SRMA) and Extensive Recreation Management Areas (ERMA). There are also sub-designations of open, closed, or limited for the OHV areas. The name of each area along with what field office it is managed by is also in the attribute table. The Comments field contains the attributes 'High', 'Transition', and 'Remote'. Recreation opportunities for SRMAs are organized into three major categories: High use areas: opportunities for high levels of social interaction (high levels of use with people in close proximity). Transition areas: opportunities for moderate levels of social interaction (moderate levels of use with people in close to moderate proximity). Remote areas: opportunities for low levels of social interaction, with a focus on appreciation for and a sense of solitude or remoteness.

"West Mojave Route Network Project Travel Management Area 1 - Map 8 of 21 (Shows Route Designations Decisions for Alternatives 1, 2, 3 and 4,Planning Area Boundary, Travel Management Area Boundary, Subregion Boundaries, Field Office Boundaries, Surface Management Agencies, Land Ownership, Route Designations, Areas of Critical Environmental Concern, Sensitive Resource Receptors, Restricted Areas, Wilderness Study Areas, Off-highway Vehicle Open Areas, National Monument Boundaries, Special Recreation Management Areas, Desert Linkage Network, Soil Erosion, Air Quality Management Districts, California Air Basins, and Unusual Plant Assemblages)

TMA_01_Map_08_West of Broadwell Mesa

1. Travel Management Area 1 - Map 8 of 21
2. WEMO Map Index Figure (Locator Map)

3. Map and Resource Data

   a. Labels i. Route Designation • Motorized • Non-BLM • Non-Mechanized • Non-Motorized • Transportation Linear Disturbance • Route with Subdesignation • WEMO Planning Area • WEMO Travel Management Area • WEMO Subregion • BLM Field Office Boundary ii. Land Ownership • Bureau of Land Management • Forest Service • National Park Service • Fish and Wildlife Service • Bureau of Reclamation • Bureau of Indian Affairs • Department of Defense • Other Federal • State • Local Government • Private iii. Resource Data • Area within 1/4 mile of a sensitive receptor • Area within 1 mile of a sensitive receptor • Wilderness Study Area • Area of Critical Environmental Concern • OHV Open Areas • National Conservation Lands • National Monument • Extensive Recreation Management Area • Special Recreation Management Area • Desert Linkage Network • Area Prone to Erosion Due to Slopes Greater than 10 Percent iv. Air Quality Management Districts • Mojave Desert v. California Air Basins • Mojave Desert vi. Unusual Plant Assemblage • None

   b. Base Data i. City or Town (Data Source: USGS Geographic Names Inventory System) ii. Major Roads (Data Source: US Census TIGER/Line) iii. County Boundary (Data Source: ESRI) iv. BLM Field Office Boundary (Data Source: BLM State Office)

   c. WEMO Planning Boundaries i. WEMO Planning Area (Data Source: BLM State Office) ii. Travel Management Area 1 (Data Source: BLM Barstow Field Office) iii. Subregions - (Data Source: BLM Barstow Field Office)

   d. Project Alternatives i. Alternative 1 - No Action (Data Source: BLM Barstow Field Office) ii. Alternative 2 – Conservation (Data Source: BLM Barstow Field Office) iii. Alternative 3 – Increased Access (Data Source: BLM Barstow Field Office) iv. Alternative 4 - Preferred (Data Source: BLM Barstow Field Office)

   e. Non-BLM Routes or routes not under BLM jurisdiction (Data Source: BLM State Office)

   f. Resource Descriptions and Data Sources

   i. Residential Area are areas near residences (Data Source: BLM State Office) ii. Wilderness Areas are areas that include federally designated wildernesses (Data Source: BLM State Office) iii. Area of Critical Environmental Concern are federally protected areas with special natural resources (Data Source: BLM State Office) iv. National Monuments are federally designated through Presidential Proclamation (Data Source: BLM State Office) v. National Conservation Lands are lands that the Bureau of Land Management for conservation purposes federally designates these (Data Source: BLM State Office) vi. Areas Prone to Erosion are areas that are likely to experience erosion (Data Source: BLM State Office) vii. Air Quality Management Districts are federally designated air quality districts with boundaries (Data Source: CA Air Resource Board) viii. California Air Basins are designated by California with boundaries (Data Source: CA Air Resource Board) "

This data layer contains geothermal resource areas and their technical potential used in long-term electric system modeling for Integrated Resource Planning and SB 100. Geothermal resource areas are delineated by Known Geothermal Resource Areas (KGRAs) (Geothermal Map of California, 2002), other geothermal fields (CalGEM Field Admin Boundaries, 2020), and Bureau of Land Management (BLM) Geothermal Leasing Areas (California BLM State Office GIS Department, 2010). The fields that are considered in our assessment have enough information known about the geothermal reservoir that an electric generation potential was estimated by USGS (Williams et al. 2008) or estimated by a BLM Environmental Impact Statement (El Centro Field Office, 2007). For the USGS identified geothermal systems, any point that lies within 2 km of a field is summed to represent the total mean electrical generation potential from the entire field. Geothermal field boundaries are constructed for identified geothermal systems that lie outside of an established geothermal field. A circular footprint is assumed with a radius determined by the area needed to support the mean resource potential estimate, assuming a 10 MW/km2 power density. Several geothermal fields have power plants that are currently generating electricity from the geothermal source. The total production for each geothermal field is estimated by the CA Energy Commission’s Quarterly Fuel and Energy Report that tracks all power plants greater than 1 MW. The nameplate capacity of all generators in operation as of 2021 were used to inform how much of the geothermal fields are currently in use. This source yields inconsistent results for the power plants in the Geysers. Instead, an estimate from the net energy generation from those power plants is used. Using these estimates, the net undeveloped geothermal resource potential can be calculated. Finally, we apply the protected area layer for geothermal to screen out those geothermal fields that lie entirely within a protected area. The protected area layer is compiled from public and private lands that have special designations prohibiting or not aligning with energy development. This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library. Change Log:Version 1.1 (January 18, 2024)ProtectedArea_Exclusion field was updated to correct for the changes to the Protected Area Layer. A Development Focus Area on Bureau of Land Management (BLM) land that overlays the Coso Hot Springs allows its resource potential to be considered in the statewide estimate. Data Dictionary: Total_MWe_Mean: The estimated resource potential from each geothermal field. All geothermal fields, except for Truckhaven, was given an estimate by Williams et al. 2008. If more than one point resource intersects (within 2km of) the field, the sum of the individual geothermal systems was used to estimate the magnitude of the resource coming from the entire geothermal field. Estimates are given in MW. Total_QFER_NameplateCapacity: The total nameplate capacities of all generators in operation as of 2021 that intersects (within 2 km of) a geothermal field. The resource potential already in use for the Geysers is determined by Lovekin et al. 2004. Estimates are given in MW. ProtectedArea_Exclusion: Binary value representing whether a field is excluded by the land-use screen or not. Fields that are excluded have a value of 1; those that aren’t have a value of 0. NetUndevelopedRP: The net undeveloped resource potential for each geothermal field. This field is determined by subtracting the total resource potential in use (Total_QFER_NameplateCapacity) from the total estimated resource potential (Total_MWe_Mean). Estimates are given in MW. Acres_GeothermalField: This is the geodesic acreage of each geothermal field. Values are reported in International Acres using a NAD 1983 California (Teale) Albers (Meters) projection. References: Geothermal Map of California, S-11. California Department of Conservation, 2002. https://www.conservation.ca.gov/calgem/geothermal/maps/Pages/index.aspx CalGEM Field Admin Boundaries, 2020. https://gis.conservation.ca.gov/server/rest/services/CalGEM/Admin_Bounds/MapServerCalifornia BLM State Office GIS Department, California BLM Verified and Potential Geothermal Leases in California, 2010. https://databasin.org/datasets/5ec77a1438ab4402bf09ef9bfd7f04d9/ Williams, Colin F., Reed, Marshall J., Mariner, Robert H., DeAngelo, Jacob, Galanis, S. Peter, Jr. 2008. "Assessment of moderate- and high-temperature geothermal resources of the United States: U.S. Geological Survey Fact Sheet 2008-3082." 4 p. https://certmapper.cr.usgs.gov/server/rest/services/geothermal/westus_favoribility_systems/MapServer/0 El Centro Field Office, Bureau of Land Management (2007). Final Environmental Impact Statement for the Truckhaven Geothermal Leasing Area (Publication Index Number: BLM/CA/ES-2007-017+3200). United States Department of the Interior Bureau of Land Management. Lovekin, James W., Subir K. Sanyal, Christopher W. Klein. 2004. “New Geothermal Site Identification and Qualification.” Richmond, California: California Energy Commission: Public Interest Energy Research Program. Accessed September 14, 2022.

This ownership dataset utilizes a methodology that results in a federal ownership extent that matches the Federal Responsibility Areas (FRA) footprint from CAL FIRE's State Responsibility Areas for Fire Protection (SRA) data. FRA lands are snapped to county parcel data, thus federal ownership areas will also be snapped. Since SRA Fees were first implemented in 2011, CAL FIRE has devoted significant resources to improve the quality of SRA data. This includes comparing SRA data to data from other federal, state, and local agencies, an annual comparison to county assessor roll files, and a formal SRA review process that includes input from CAL FIRE Units. As a result, FRA lands provide a solid basis as the footprint for federal lands in California (except in the southeastern desert area). The methodology for federal lands involves:

The California Department of Transportation (Caltrans) and California Department of Fish and Game (CDFG) commissioned the California Essential Habitat Connectivity Project because a functional network of connected wildlands is essential to the continued support of Californias diverse natural communities in the face of human development and climate change. The Essential Connectivity Map depicts large, relatively natural habitat blocks that support native biodiversity (Natural Landscape Blocks) and areas essential for ecological connectivity between them (Essential Connectivity Areas). This coarse-scale map was based primarily on the concept of ecological integrity, rather than the needs of particular species. Essential Connectivity Areas are placeholder polygons that can inform land-planning efforts, but that should eventually be replaced by more detailed Linkage Designs, developed at finer resolution based on the needs of particular species and ecological processes. It is important to recognize that even areas outside of Natural Landscape Blocks and Essential Connectivity Areas support important ecological values that should not be written off as lacking conservation value. Furthermore, because the Essential Habitat Connectivity Map was created at the statewide scale, based on available statewide data layers, and ignored Natural Landscape Blocks smaller than 2,000 acres squared, it has errors of omission that should be addressed at regional and local scales.

This polygon feature class represents the spatial extent and boundaries for BLM Grazing Allotments.This state dataset may have published a dataset that is more current than the National dataset; there may be geometry variations between the state and national dataset which may have different results.The national dataset is updated following the data standard schedule.

This polygon feature class represents the spatial extent and boundaries of the BLM National Landscape Conservation System (NLCS) Wilderness Study Areas. This state dataset may have published a dataset that is more current than the National dataset; there may be geometry variations between the state and national dataset which may have different results. The national dataset is updated following the data standard schedule.

The datasets that are included in the composite layer making up the protected area layer are given below:

DatasetExample DesignationsCitation or hyperlinkPAD-US (CBI Edition)National Parks, GAP Status 1 and 2, State Parks, Open Spaces, Natural Areas“PAD-US (CBI Edition) Version 2.1b, California”. Conservation Biology Institute. 2016. https://databasin.org/datasets/64538491f43e42ba83e26b849f2cad28.Conservation EasementsCalifornia Conservation Easement Database (CCED), 2022a. 2022. www.CALands.org. Accessed December 2022. Inventoried Roadless Areas“Inventoried Roadless Areas.” US Forest Service. Dec 12, 2022. https://www.fs.usda.gov/detail/roadless/2001roadlessrule/maps/?cid=stelprdb5382437BLM National Landscape Conservation SystemWilderness Areas, Wilderness Study Areas, National Monuments, National Conservation Lands, Conservation Lands of the California Desert, Scenic Rivershttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-areashttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-study-areashttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-national-monuments-nca-forest-reserves-other-poly/Greater Sage Grouse Habitat Conservation Areas (BLM)For solar technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’)For wind technology: BLMP_Managm = ‘PHMA’“Nevada and Northeastern California Greater Sage-Grouse Approved Resource Management Plan Amendment.” US Department of the Interior Bureau of Land Management Nevada State Office. 2015. https://eplanning.blm.gov/public_projects/lup/103343/143707/176908/NVCA_Approved_RMP_Amendment.pdf Other BLM Protected AreasAreas of Critical Environmental Concern (ACECs), Recreation Areas (SRMA, ERMA, OHV Designated Areas), including Vinagre Wash Special Recreation Management Area, National Scenic Areas, including Alabama Hills National Scenic Areahttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-off-highway-vehicle-designations

Change Log: Version 1.1 (January 22, 2024 11:05 AM) Layer edited to reflect the Bureau of Land Management (BLM) Land Use Plan Amendment (LUPA) Development Focus Area (DFA), Variance Process Land (VPL) and General Public Land (GPL) areas within the DRECP that allow for geothermal energy development applications.Layer revised to allow for gaps to remain when combining all components of the protected area layer.

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.

This dataset is a modified version of the FWS developed data depicting “Highly Important Landscapes”, as outlined in Memorandum FWS/AES/058711 and provided to the Wildlife Habitat Spatial analysis Lab on October 29th 2014. Other names and acronyms used to refer to this dataset have included: Areas of Significance (AoSs - name of GIS data set provided by FWS), Strongholds (FWS), and Sagebrush Focal Areas (SFAs - BLM). The BLM will refer to these data as Sagebrush Focal Areas (SFAs). Data were provided as a series of ArcGIS map packages which, when extracted, contained several datasets each. Based on the recommendation of the FWS Geographer/Ecologist (email communication, see data originator for contact information) the dataset called “Outiline_AreasofSignificance” was utilized as the source for subsequent analysis and refinement. Metadata was not provided by the FWS for this dataset. For detailed information regarding the dataset’s creation refer to Memorandum FWS/AES/058711 or contact the FWS directly. Several operations and modifications were made to this source data, as outlined in the “Description” and “Process Step” sections of this metadata file. Generally: The source data was named by the Wildlife Habitat Spatial Analysis Lab to identify polygons as described (but not identified in the GIS) in the FWS memorandum. The Nevada/California EIS modified portions within their decision space in concert with local FWS personnel and provided the modified data back to the Wildlife Habitat Spatial Analysis Lab. Gaps around Nevada State borders, introduced by the NVCA edits, were then closed as was a large gap between the southern Idaho & southeast Oregon present in the original dataset. Features with an area below 40 acres were then identified and, based on FWS guidance, either removed or retained. Finally, guidance from BLM WO resulted in the removal of additional areas, primarily non-habitat with BLM surface or subsurface management authority. Data were then provided to each EIS for use in FEIS...

With funding from the Bureau of Land Management (BLM), the California Native Plant Society (CNPS) Vegetation Program has collected and compiled vegetation data, produced a vegetation classification, and produced a fine-scale vegetation map of select BLM land units in the inner Central Coast of California. During this effort, CNPS completed vegetation sampling across the southern portion of the Ciervo-Panoche Natural Area (CPNA), including 21 relevé plots and 52 rapid assessment surveys, that represent 28 vegetation alliances. Building upon previous vegetation sampling and mapping efforts, we also compiled and quality-controlled greater than1,800 surveys of vegetation data from BLM to develop a robust regional vegetation classification of the inner Central Coast. All new and compiled classification data are stored within a geodatabase and a standardized Access database.CNPS additionally produced a fine-scale vegetation map and monitoring data for the Hubbard Hill, Anderson Canyon, and Freeborn Mt allotments in San Luis Obispo County in conjunction with the US Bureau of Land Management and The Nature Conservancy. This vegetation map encompasses 7,000 acres and is based upon 2020 NAIP imagery. It includes 15 map unit codes for natural vegetation. All areas of natural vegetation were mapped at the floristic association/alliance level. The vegetation classification follows Survey of California Vegetation (SCV) standards. The classification is based on about 1,900 regional vegetation surveys along the inner Central Coast from Ciervo-Panoche Natural Area to Carrizo Plain National Monument, using classification techniques such as clustering. The map was produced applying heads-up digitizing techniques, using a base of 2020 NAIP imagery. Map polygons were assessed for Vegetation Type, Percent Cover, Exotics, etc. The minimum mapping unit (MMU) is 1 acre. The average producers’ map accuracy across all types was 77 percent and the average users’ map accuracy was 73 percent , these scores fall below the state standard of overall accuracy at 80 percent. Upon scoring the accuracy assessments, CNPS staff have reviewed all polygons where the field verification name and map unit did not agree, to correct issues in photo interpretation and attribution for the final map. A total of 720 map polygons representing 15 vegetation map classes were developed.For detailed information, please refer to the following report:J. Buck-Diaz, K. Sikes, S. Vu, A. LaFever-Jackson and J.M. Evens. 2023. Vegetation Sampling, Classification, and Mapping Report for Ciervo-Panoche Natural Area and the Hubbard Hill Unit. Final Report prepared for the U.S. Bureau of Land Management. California Native Plant Society, Vegetation Program, Sacramento, CA. https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=220225. Appendix D, Vegetation Descriptions: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=220226.

These life history accounts and range maps represent updated versions of the species information in the three-volume set Californias Wildlife edited by Zeiner, D.C. et al 1988-1990. There are also accounts for 48 more species here than in the original publication, bringing the total up to 694.

The information was prepared under contract with the best available experts for various taxonomic groups. Authors and reviewers names appear on the individual life history accounts. Accounts were initially edited by Marshall White and, in later years, by California Wildlife Habitat Relationships (CWHR) Program staff with the California Department of Fish and Game. Update dates are noted on individual life history accounts. Revision histories are also noted on individual range maps.

The life history accounts and range maps are designed to support the computerized species-habitat relationships database models in the CWHR System. The system is continually being revised. Users are encouraged to submit corrections and additional information to:

CWHR Program Coordinator California Department of Fish and Game 1807 13th Street, Suite 202 Sacramento, CA 95811

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.

This GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi season satellite imagery (Landsat ETM Plus) from 1999 to 2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi natural vegetation. Vegetation classes were drawn from NatureServes Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServes Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.