The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

The geospatial data reflected in the protected area layer mostly pertain to natural and wilderness areas where development of utility-scale renewable energy is prohibited and were heavily based on RETI 1.0 blackout areas.1 The protected area layer is distinguished for solar PV technology by the BLM greater sage grouse habitat management area which provides separate exclusion areas for the different technology types. Tables 1 and 2 below lists the data sources and precise selection query for each dataset, if applicable, that make up the protected area layer.Table 1: Datasets used in the Protected Area Layer

Dataset

Example Designations

Citation or hyperlink

PAD-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 Easements

California 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=stelprdb5382437

BLM National Landscape Conservation System

Wilderness Areas, Wilderness Study Areas, National Monuments, National Conservation Lands, Conservation Lands of the California Desert, Scenic Rivers

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-areas

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-study-areas

https://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 Areas

Areas 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 Area

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-off-highway-vehicle-designations

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-areas-of-critical-environmental-concern

BLM, personal communication, November 2, 2022.

Mono Basin NFSA

https://pcta.maps.arcgis.com/home/item.html?id=cf1495f8e09940989995c06f9e290f6b#overview

Terrestrial 30x30 Conserved Areas

Gap Status 1 and 2

CA Nature. 30x30 Conserved Areas, Terrestrial. 2021. https://www.californianature.ca.gov/datasets/CAnature::30x30-conserved-areas-terrestrial/ Accessed September 2022.

CPAD

Open Spaces and Parks under city or county level

California Protected Areas Database (CPAD), 2022b. 2022. https://www.calands.org/cpad/. Accessed February 22, 2023.

USFS Special Interest Management Areas

Research Natural Areas, Recreation Areas, National Recreational Trail, Experimental Forest, Scenic Area

https://data-usfs.hub.arcgis.com/datasets/usfs::special-interest-management-areas-feature-layer/about

Proposed Protected Area

Molok Luyuk Extension (Berryessa Mtn NM Expansion)

CalWild, personal communication, January 19, 2023.

Table 2: Query Definition for Components of Protected Areas Dataset SQL Query PAD-US (CBI Edition) p_des_tp IN ('Wild, Scenic and Recreation River', 'Area of Critical Environmental Concern', 'Ecological Reserve', 'National Conservation Area', 'National Historic Site', 'National Historical Park', 'National Monument', 'National Park General Public Land', 'National Preserve', 'National Recreation Area', 'National Scenic Area', 'National Seashore', 'Wilderness Study Area', 'Wilderness Area', 'Wildlife Management Area', 'State Wildlife Management Area', 'State Park', 'State Recreation Area', 'State Nature Preserve/Reserve', 'State Natural Area', 'State Ecological Reserve', 'State Cultural/Historic Area', 'State Beach', 'Special Management Area', 'National Wildlife Refuge', 'Natural Area', 'Nature Preserve', 'Research Natural Area') Or s_des_tp IN ('Natioanal Monument', 'National Monument', 'National Park General Public Land', 'National Preserve', 'National Recreation Area', 'National Scenic Area', 'National Seashore', 'National Conservation Area', 'Area of Critical Environmental Concern', 'National Wildlife Refuge', 'State Park', 'State Wildlife Area', 'State Wildlife Management Area', 'State Wildlife Refuge', 'State Ecological Reserve', 'Wild, Scenic and Recreation River', 'Wilderness Area', 'Wildlife Management Area') Or t_des_tp IN ('National Monument', 'National Park General Public Land', 'National Recreation Area', 'Area of Critical Environmental Concern', 'National Conservation Area', 'State Wildlife Management Area', 'Wild, Scenic and Recreation River', 'Wildlife Management Area') Or p_loc_ds IN ('Ecological Reserve', 'Research and Educational Land') Or gap_sts IN ('1', '2') Or own_type = 'Private Conservation Land' Or (own_type = 'Local Land' And (p_des_tp LIKE '%"Open Space"%' Or p_des_tp LIKE '%Park%' Or p_des_tp LIKE '%Recreation Area%' Or p_des_tp LIKE '%Natural Area%')) Or (p_des_tp = 'Other State Land' And (p_loc_ds IN ('State Vehicular Recreation Area', 'BLM Resource Management Area', 'Resource Management Area') And gap_sts <> '2')) CPAD AGNCY_LEV IN ('City', 'County') And ACCESS_TYP = 'Open Access' And (UNIT_NAME LIKE '%Park%' OR UNIT_NAME LIKE '%Open Space%' OR UNIT_NAME LIKE '%park%' OR UNIT_NAME LIKE '%Recreation Area%' OR UNIT_NAME LIKE '%Natural Area%' OR GAP2_acres > 0 OR GAP1_acres >0) Greater Sage- Grouse Habitat Conservation Areas (BLM) For Solar Technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’) For Wind Technology: BLM_Managm = ‘PHMA’ This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.For a complete description of the creation of this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.[1] Final RETI Phase 2A report, available at https://ww2.energy.ca.gov/2009publications/RETI-1000-2009-001/RETI-1000-2009-001-F-REV2.PDF.

Change Log: Version 1.1 (January 22, 2024 10:29 AM) Layer revised to allow for gaps to remain when combining all components of the protected area layer.

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. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx 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. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The Wildland-Urban Interface (WUI) is the area where houses meet or intermingle with undeveloped wildland vegetation. This makes the WUI a focal area for human-environment conflicts such as wildland fires, habitat fragmentation, invasive species, and biodiversity decline. Using geographic information systems (GIS), we integrated U.S. Census and USGS National Land Cover Data, to map the Federal Register definition of WUI (Federal Register 66:751, 2001) for the conterminous United States from 1990-2020. These data are useful within a GIS for mapping and analysis at national, state, and local levels. Data are available as a geodatabase and include information such as housing densities for 1990, 2000, 2010, and 2020; wildland vegetation percentages for 1992, 2001, 2011, and 2019; as well as WUI classes in 1990, 2000, 2010, and 2020.This WUI feature class is separate from the WUI datasets maintained by individual forest unites, and it is not the authoritative source data of WUI for forest units. This dataset shows change over time in the WUI data up to 2020.Metadata and Downloads