The seamless, county-wide parcel layer was digitized from official Assessor Parcel (AP) Maps which were originally maintained on mylar sheets and/or maintained as individual Computer Aided Design (CAD) drawing files (e.g., DWG). The CRA office continues to maintain the official AP Maps in CAD drawings and Information Systems Department/Geographic Information Systems (ISD/GIS) staff apply updates from these maps to the seamless parcel base in the County’s Enterprise GIS. The seamless parcel layer is updated and published to the Internet on a monthly basis.The seamless parcel layer was developed from the source data using the general methodology outlined below. The mylar sheets were scanned and saved to standard image file format (e.g., TIFF). The individual scanned maps or CAD drawing files were imported into GIS software and geo-referenced to their corresponding real-world locations using high resolution orthophotography as control. The standard approach was to rescale and rotate the scanned drawing (or CAD file) to match the general location on the orthophotograph. Then, appropriate control points were selected to register and rectify features on the scanned map (or CAD drawing file) to the orthophotography. In the process, features in the scanned map (or CAD drawing file) were transformed to real-world coordinates, and line features were created using “heads-up digitizing” and stored in new GIS feature classes. Recommended industry best practices were followed to minimize root mean square (RMS) error in the transformation of the data, and to ensure the integrity of the overall pattern of each AP map relative to neighboring pages. Where available Coordinate Geometry (COGO) & survey data, tied to global positioning systems (GPS) coordinates, were also referenced and input to improve the fit and absolute location of each page. The vector lines were then assembled into a polygon features, with each polygon being assigned a unique identifier, the Assessor Parcel Number (APN). The APN field in the parcel table was joined to the corresponding APN field in the assessor property characteristics table extracted from the MPTS database to create the final parcel layer. The result is a seamless parcel land base, each parcel polygon coded with a unique APN, assembled from approximately 6,000 individual map page of varying scale and accuracy, but ensuring the correct topology of each feature within the whole (i.e., no gaps or overlaps). The accuracy and quality of the parcels varies depending on the source. See the fields RANK and DESCRIPTION fields below for information on the fit assessment for each source page. These data should be used only for general reference and planning purposes. It is important to note that while these data were generated from authoritative public records, and checked for quality assurance, they do not provide survey-quality spatial accuracy and should NOT be used to interpret the true location of individual property boundary lines. Please contact the Sonoma County CRA and/or a licensed land surveyor before making a business decision that involves official boundary descriptions.

The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. This dataset is also available as a layer package and a file geodatabase.The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels.The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary.The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

Map of Sonoma County Public and Private Schools.

A high resolution LiDAR derived hillshade facilitates the visualization of the topography of a landscape at a variety of scales. This hillshade which was created from a LiDAR derived hydro-flattened bare earth digital elevation model shows the signal returns without any vegetation or human-made structures. In addition to that, bodies of water have been smoothed. This layer may be used on its own or in conjunction with other data.The Sonoma County Vegetation Mapping and LiDAR Program. and the University of Maryland (under grant NNX13AP69G from NASA’s Carbon Monitoring System, Dr. Ralph Dubayah, PI) contracted LiDAR and orthophoto data collection for all of Sonoma County in late 2013. Also included in the data collection were two areas in Mendocino County - the Soda Spring Creek-Dry Creek Watershed and Lake Mendocino. This fine scale data will help provide an accurate, up-to-date inventory of the county’s landscape features, ecological communities and habitats. Project funders include: NASA, the University of Maryland, the Sonoma County Agricultural Preservation and Open Space District, the Sonoma County Water Agency, the California Department of Fish and Wildlife, the United States Geological Survey, the Sonoma County Information Systems Department, the Sonoma County Transportation and Public Works Department, the Nature Conservancy, and the City of Petaluma.The hillshade is a greyscale image showing topography in the landscape. In this case it is created from a LiDAR derived hydro-flattened bare earth digital elevation model illuminated by hypothetical light source shining from the north west. A hydro flattened bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. In addition bodies of waters 2acres or larger have been smoothed.The DEM used to create this hillshade is described as a bare earth digital elevation model (DEM) representing the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from LiDAR data using triangulated irregular network (TIN) processing of the ground point returns. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average elevation for that area.

You are free to: Share - copy and redistribute the data in any medium or format. Adapt - You may make derivative works, transform, and build upon the data for any purpose, even commercial. The licensor cannot revoke these freedoms as long as you follow the license terms.License terms: Attribution - You must give appropriate credit (if supplied, you must provide the name of the creator and attribution parties, a copyright notice, a license notice, a disclaimer notice and a link to the material) and indicate if any changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you, your organization, or your use of the data. ShareAlike - if you modify, transform, or build on the data, you must distribute your contributions under the same license as the original.No additional Restrictions - You may not apply legal terms or technological measures that legally restrict others form doing anything the license permits.Notices: You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the data.EXCEPT TO THE EXTENT REQUIRED BY APPLICABLE LAW, IN NO EVENT WILL LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY FOR ANY SPECIAL, INCIDENTAL, CONSEQUENTIAL, PUNITIVE OR EXEMPLARY DAMAGES ARISING OUT OF THIS LICENSE OR THE USE OF THE DATA, EVEN IF LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.The above is an easily understandable summary of and not a substitute for the license and disclaimer for the Attribution-ShareAlike 3.0 United States (CC BY-SA 3.0 US) the full text is available at creativecommons.org.https://creativecommons.org/licenses/by-sa/3.0/us/legalcode

This shapefile contains tax rate area (TRA) boundaries in Sonoma County for the specified assessment roll year. Boundary alignment is based on the 2018 county parcel map. A tax rate area (TRA) is a geographic area within the jurisdiction of a unique combination of cities, schools, and revenue districts that utilize the regular city or county assessment roll, per Government Code 54900. Each TRA is assigned a six-digit numeric identifier, referred to as a TRA number. TRA = tax rate area number

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands†from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Salt Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and pho... Visit https://dataone.org/datasets/253bc2a5-c3c7-4126-a4a9-d112ab8b6e11 for complete metadata about this dataset.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands†from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Salt Point to Drakes Bay map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth†surveying data are available from the CSMP Video and Photograph Portal at http://dx.doi.org/10.5066/F7J1015K. The “seafloor character†data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats†polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues.

 Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Salt Point to Drakes Bay map area data layers. Data layers are symbolized as shown on the associated map sheets.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands†from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Bodega Head map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and ph... Visit https://dataone.org/datasets/08459c00-1126-4cde-a29e-c0fc2f165b70 for complete metadata about this dataset.

A high resolution LiDAR derived hillshade facilitates the visualization of the topography of a landscape at a variety of scales. This layer may be used on its own or in conjunction with other data. This hillshade which was created from a LiDAR derived highest hit digital elevation model shows the signal returns that were the highest above the ground in a given location. This provides the viewer a hillshade display of the tree canopy or structures at the time of data capture. The Sonoma County Vegetation Mapping and LiDAR Program and the University of Maryland (under grant NNX13AP69G from NASA’s Carbon Monitoring System, Dr. Ralph Dubayah, PI) contracted LiDAR and orthophoto data collection for all of Sonoma County in late 2013. Also included in the data collection were two areas in Mendocino County - the Soda Spring Creek-Dry Creek Watershed and Lake Mendocino. This fine scale data will help provide an accurate, up-to-date inventory of the county’s landscape features, ecological communities and habitats. Project funders include: NASA, the University of Maryland, the Sonoma County Agricultural Preservation and Open Space District, the Sonoma County Water Agency, the California Department of Fish and Wildlife, the United States Geological Survey, the Sonoma County Information Systems Department, the Sonoma County Transportation and Public Works Department, the Nature Conservancy, and the City of Petaluma.Hillshade of the highest hit digital elevation model using the Sonoma Veg Map LiDAR data. The Mosaic hillshade function was applied to generate this hillshade. The default values were used except for the Z value. A value of .4 was used for the Z value. The raster cache was generated from the previous item.The DEM used to create this hillshade is described as a Highest Hit or First Return digital elevation model (DEM) represents the earth’s surface with the base or bare-earth DEM values subtracted from the first returns, with the resulting raster being the height of any vegetation, structure, or the ground for those areas lacking in vegetation or structures for the subject area. Values are in feet. Each cell in the GRID is three feet and has a value that represents an average vegetation height at that location. The purpose of the data is to provide users with a very accurate view of the vegetation height in the subject area for the date of data capture.

This is a vector tile service with labels for the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. Labels appear at scales greater than 1:10,000 and characterize stand height, stand canopy cover, stand map class, and stand impervious cover. This service is mean to be used in conjunction with the vector tile services of the polygon themselves (either the solid symbology service or the hollow symbology service). The key to the labels appears in the graphic below; the key to map class abbreviations can be found here. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

A bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from LiDAR data using triangulated irregular network (TIN) processing of the ground point returns. Hydro-flattened Bare Earth DEMs represent water bodies in a cartographically and aesthetically pleasing manner, and are not intended to accurately map water surface elevations. In a Hydro-flattened DEM, water surfaces are flat and level for lakes with a greater area than two acres, and gradated for rivers or other long impoundments (e.g., reservoirs) that are wider than 100 feet, and tidal areas. Any existing island larger than one acre was be delineated. Water surface edge elevations were at or below the immediately surrounding terrain. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average elevation for that area. The specified coordinate system for this dataset is California State Plane Zone II (FIPS 0402), NAD83 (2011), with units in US Survey Feet for horizontal, and vertical units are NAVD88 (12A) US Survey Feet. The dataset encompasses a portion of Sonoma County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Consortium.

This project aims to understand how the error in mapped fault location and the residual between the modeled and observed coseismic displacements vary with tectonic landform and the surficial lithologic age. We focus on four historical earthquakes: the M6.9 Borah Peak, 2014 M6.0 Napa, 2016 M7.0 Kumamoto, and 2016 M7.8 Kaikoura earthquakes.

The GIS shape file contains information about the tectonic landform, the surficial landscape age, the observed and modelled coseismic displacement, fault location error, and the confidence ranking of the mapped fault trace. Each entry corresponds to a location where a displacement measurement was made following the earthquake of focus. Additional detail is given in the readme.

The entries in the GIS file are collected from the following references:

Chiou, B., Chen, R., Thomas, K., Milliner, C. W. D., Dawson, T., & Petersen, M. D. (2022). Surface Fault Displacement Models for Strike-Slip Faults. Natural Hazards Risk and Resiliency Research Center B. John Garrick Institute for the Risk Sciences University of California, Los Angeles, Report GIRS‐2022‐07, 186. https://doi.org/10.34948/N3RG6X

Crone, A. J., Machette, M. N., Bonilla, M., Lienkaemper, J. J., Pierce, K., Scott, W., & Bucknam, R. (1987). Surface faulting accompanying the Borah Peak earthquake and segmentation of the lost river fault, central Idaho. Bulletin of the Seismological Society of America, 77.

Graymer, R. W., Brabb, E., Jones, D. L., Barnes, J., Nicholson, R. S., & Stamski, R. E. (2007). Geologic Map and Map Database of Eastern Sonoma and Western Napa Counties, California (No. U.S. Geological Survey Scientific Investigations Map 2956). Retrieved from https://doi.org/10.3133/sim2956

Heron, D. W. (2018). Geological Map of New Zealand 1:250 000. GNS Science Geological Map 1 (2nd ed.) Lower Hutt, New Zealand. GNS New Zealand. Retrieved from https://www.gns.cri.nz/data-and-resources/geological-map-of-new-zealand/

Hoshizumi, H., Ozaki, M., Miyazaki, K., Matsuura, H., Toshimitsu, S., Uto, K., et al. (2004). Geological Map of Japan 1:200,000: Kumamoto. Geological Survey of Japan. Retrieved from https://www.gsj.jp/Map/EN/geology2-6.html#Kumamoto

Janecke, S. U., & Wilson, E. (1992). Geologic map of the Borah Peak, Burnt Creek, Elkhorn Creek, and Leatherman Peak 7.5’ quadrangles, Custer County, Idaho, Scale 1:24,000. Idaho Geological Survey Technical Report 92-5. Retrieved from https://www.idahogeology.org/product/T-92-5

Kuehn, Nicolas, Kottke, A., Madugo, C., Sarmiento, A., & Bozorgnia, Y. (2022). Report GIRS 2022-06: UCLA–PG&E Fault Displacement Model. https://doi.org/10.34948/N3X59H

Lewis, R. S., Link, P., Stanford, L. R., & Long, S. P. (2012). Geologic Map of Idaho. Moscow, Boise, Pocatello: Idaho Geologic Survey. Retrieved from https://www.idahogeology.org/maps-pubs-data/state-geologic-map

Ponti, D. J., Blair, J. L., & Rosa, C. M. (2019). Digital Datasets Documenting Fault Rupture and Ground Deformation Features Produced by the Mw 6.0 South Napa Earthquake of August 24, 2014 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7P26W84

Sarmiento, A., Madugo, D., Bozorgnia, Y., Shen, A., Mazzoni, S., Lavrentiadis, G., et al. (2021). Fault Displacement Hazard Initiative Database. Report No. GIRS-2021-08, Revision 3.3 Dated 29 May 2024. Los Angeles, CA: The B. John Garrick Institute for the Risk Sciences at UCLA Engineering. https://doi.org/10.34948/N36P48

Scott, C., Adam, R., Arrowsmith, R., Madugo, C., Powell, J., Ford, J., et al. (2023). Evaluating how well active fault mapping predicts earthquake surface-rupture locations. Geosphere, 19(4), 1128–1156. https://doi.org/10.1130/GES02611.1

Scott, C. P., Arrowsmith, J. R., Nissen, E., Lajoie, L., Maruyama, T., & Chiba, T. (2018). The M 7 2016 Kumamoto, Japan, Earthquake: 3-D Deformation Along the Fault and Within the Damage Zone Constrained From Differential Lidar Topography. Journal of Geophysical Research: Solid Earth, 123, 6138–6155. https://doi.org/10.1029/2018JB015581

Vincent, K. R. (1995). Implications for models of fault behavior from earthquake surface displacement along adjacent segments of the Lost River fault, Idaho: University of Arizona.

Wagner, D., & Gutierrez, C. (2017). Preliminary Geologic Map of the Napa and Bodega Bay 30’ x 60’ Quadrangles, California. California Department of Conservation. Retrieved from https://ngmdb.usgs.gov/Prodesc/proddesc_105819.htm

Zinke, R., Hollingsworth, J., Dolan, J. F., & Van Dissen, R. (2019). Three‐Dimensional Surface Deformation in the 2016 M _W 7.8 Kaikōura, New Zealand, Earthquake From Optical Image Correlation: Implications for Strain Localization and Long‐Term Evolution of the Pacific‐Australian Plate Boundary. Geochemistry, Geophysics, Geosystems, 20(3), 1609–1628. https://doi.org/10.1029/2018GC007951

The Mendocino Pygmy Forest is one of the best-known examples of a rare natural community in California. The unique soil and climatic attributes and the resulting vegetation of the Mendocino coastal terraces described by Jenny et al (1969), Westman (1975), Westman and Whittaker (1975), Sholars (1979), Sholars (1982), Sholars (1984) and others are well- known in the scientific and conservation literature.The mapping and classification process assumed that the unique and biologically significant elements of the pygmy forest ecosystem were definable without a complete inventory of the surrounding regional vegetation and land-use patterns. The boundary of the mapped areas was created using existing geographic information on soils, topography, land use, along with fieldwork from previous efforts. Within that area, an array of vegetation samples were collected and classified representing the full array of vegetation patterns within it. The boundary was refined as part of the mapping process. It was later expanded to include property owned by the Mendocino Coast Park and Recreation District after receiving permission to conduct surveys as part of this project. (Polygons that would not have been mapped for the original project but are within the MCPRD property are marked “MCPRD Additional” in the Notes field.)The map was produced using a classification based on an analysis of surveys taken throughout the range of the oligotrophic areas supporting Pygmy Forest vegetation. This classification has been incorporated into the Manual of California Vegetation Online Database. The map classification is mostly at the Association Level of the NVCS hierarchy (12 types), with some at the Alliance Level (5 types) and Group Level (3 types), and 4 land use and water classes. It was hand-digitized using photointerpretation based on the 2014 NAIP Imagery, with other ancillary data used to help with the identification of vegetation types. The minimum mapping unit was 1 acre for vegetation types, and 0.25 acres for water, developed and agricultural type. The total area mapped was 9782 acres.An accuracy assessment performed on the map. The overall accuracy of each of the 5 most reliably sampled types was between 82 and 92 percent accuracy, meeting minimum accuracy standards.For more information, see the supplemental information below and the report for the map cited in the references. https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=161736ReferencesCalifornia Department of Fish and Wildlife, Vegetation Classification and Mapping Program. Classification and Mapping of Pygmy Forest and Related Mendocino Cypress (Hesperocyparis pygmaea) Vegetation, Mendocino and Sonoma Counties, California. CDFW; 11/2018. A Manual of California Vegetation, Online Edition. http://www.cnps.org/cnps/vegetation/. California Native Plant Society, Sacramento, CA.USNVC [United States National Vegetation Classification]. http://usnvc.org/. 2017. United States National Vegetation Classification Database, V2.01. Federal Geographic Data Committee, Vegetation Subcommittee, Washington DCJenny, H. R.J. Arkley, and A.M. Schultz. 1969. The pygmy forest-podsol ecosystem and its dune associates of the Mendocino coast. Madroño20:60-74.Westman, W.E. 1975. Edaphic climax pattern of the pygmy forest region of California. Ecological Monographs30:279-338.Westman, W.E. and R.H. Whittaker. 1975. The pygmy forest region of northern California: studies on biomass and primary productivity. Journal of Ecology63:493-520.Sholars, R.E. 1979. Water relations in the pygmy forest of Mendocino County. Ph.D. diss. University of California, Davis.Sholars, R.E. 1982. The pygmy forest and associated plant communities of coastal Mendocino County, California; genesis, soils, vegetation. Black Bear Press, Mendocino, CA.Sholars, R.E. 1984. The pygmy forest of Mendocino. Fremontia12(3): 3-8.Bowles, C.J. and E. Cowgill. 2012. Discovering marine terraces using airborne LiDAR along the Mendocino-Sonoma coast, northern California. Geosphere8(2):386''402.Soil Survey Staff, Natural Resources Conservation Service (NRCS), United States Department of Agriculture. Web Soil Survey. Available online at https://websoilsurvey.nrcs.usda.gov/. Accessed [October 13, 2014].National Agriculture Imagery Program (NAIP), United States Department of Agriculture. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index

The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8) The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

Parcels that have changed ownership/title since the October 2017 Tubbs Fire. Based upon .csv data received from Sonoma County Recorder's office on a monthly basis.

https://gis.data.ca.gov/datasets/CaliforniaARB::california-air-district-boundaries/aboutThe air quality control board dataset represents the Northern Sonoma County Air Pollution Control District (a.k.a. NSCAPCD) and the Bay Area Air Quality Management District (a.k.a. BAAQMD). The Northern Sonoma County Air Pollution Control District set forth Rule 110 which states: rules and regulations are set forth to achieve and maintain such levels of air quality as will protect human health and safety; prevent injury to plant and animal life; avoid damage to property; and preserve the comfort, convenience and enjoyment of the natural attractions of the California North Coast Air Basin. The Bay Area Air Quality Management District is committed to achieving clean air to protect the public's health and the environment in the San Francisco Bay region. The Air District aims to: 1) attain and maintain air quality standards, 2) increase public awareness of positive air quality choices, and 3) develop and implement protocol and policies for environmental justice. It is the intent of all air pollution control districts and air quality management districts in the California North Coast Air Basin to adopt and enforce rules and regulations which assure that reasonable provision is made to achieve and maintain state and federal ambient air quality standards for the area under their jurisdiction and to enforce all applicable provisions of State law.