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. This layer is a partial view of the Information Sales System (ISS) extract, a report of property characteristics taken from the County’s Megabyte Property Tax System (MPTS). This layer may be missing some attributes (e.g., Owner Name) which may not be published to the Internet due to privacy conditions under the California Public Records Act (CPRA). Please contact the Clerk-Recorder-Assessor (CRA) office at (707) 565-1888 for information on availability, associated fees, and access to other versions of Sonoma County parcels containing additional property characteristics.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.

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 EnviroAtlas dataset describes the breakdown of the land cover classes with each Census Block Group. In this community, forest is defined as Trees & Forest, Orchards, and Woody Wetlands. Green space is defined as Trees & Forest, Shrubs, Grass & Herbaceous, Agriculture, Orchards, Woody Wetlands, and Emergent Wetlands. Agriculture is defined as Agriculture and Orchards. Wetlands are defined as Woody Wetlands and Emergent Wetlands. This dataset also includes the area per capita for each block group for some land cover types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset shows the Sonoma County, CA EnviroAtlas community boundary. It represents the outside edge of all the block groups included in the EnviroAtlas Community. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset describes the percentage of forested, vegetated, and impervious land within 15- and 50-meters of hydrologically connected streams, rivers, and other water bodies within the EnviroAtlas community area. In this community, tree cover is defined as Trees & Forest, and Woody Wetlands. and In this community, vegetated land is defined as Trees & Forest, Shrubs, Grass & Herbaceous, Woody Wetlands, and Emergent Wetlands. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Public and protected lands of Sonoma County for use in identifying individual parks, public and private conservation easements, mitigation banks, public lands, and easements. Originally adapted from California Protected Areas Database (CPAD). Last modified May 2025 per the 2024b release of the California Conservation Easement Database (CCED), California Protected Areas Database (CPAD), and including the latest Ag + Open Space acquisitions. *These GIS data are for representational purposes only and are not intended to depict definitive property boundaries or feature locations. The Sonoma County Agricultural Preservation and Open Space District permanently protects the diverse agricultural, natural resource, and scenic open space lands of Sonoma County for future generations. Visit us at www.sonomaopenspace.org.

Last Publication Date: December 10, 2015Legacy county boundary believed to be created from a 1:100000 scale depiction of the County Boundary on file at Permit Resource Management Department.

The Sonoma County lifeform map is an 19-class land use and land cover map of Sonoma County, reflecting the state of the landscape in fall, 2013. The lifeform map classifies forested areas with less detail than the forest lifeform map, but includes more detail than the forest lifeform map for built up and agricultural map classes. For fully detailed floristic classes, use the 83-class fine-scale vegetation and habitat map, from which this product is derived.The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/oNBzTIr1T8 Appropriate Scale Range for Use: 1:5,000 and smaller The lifeform map has 19 classes (listed below) - detailed class definitions, as well as a dichotomous key for the lifeform classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8). Lifeform Map Classes: Urban Window (urban core areas) Water

Barren & Sparsely Vegetated Major Road Developed Orchard or Grove Vineyard Vineyard Replant Annual Cropland Perennial Agriculture Irrigated Pasture Intensively Managed Hayfield Nursery or Ornamental Horticultural Area Native Forest Non-Native Forest Forest Sliver Shrub Non-Native Shrub Herbaceous

Napa County has used a 2004 edition vegetation map produced using the Manual of California Vegetation classification system (Thorne et al. 2004) as one of the input layers for land use decision and policy. The county decided to update the map because of its utility. A University of California, Davis (UCD) group was engaged to produce the map. The earlier map used black and white digital orthophoto quadrangles from 1993, with a pixel resolution of 3 meters. This image was delineated using a heads up digitization technique produced by ASI (Aerial Services Incorporated). The resulting polygons were the provided vegetation and landcover attributes following the classification system used by California State Department of Fish and Wildlife mappers in the Manual of California Vegetation. That effort included a brief field campaign in which surveyors drove accessible roads and verified or corrected the dominant vegetation of polygons adjacent to roadways or visible using binoculars. There were no field relevé or rapid assessment plots conducted. This update version uses a 2016 edition of 1 meter color aerial imagery taken by the National Agriculture Imagery Program (NAIP) as the base imagery. In consultation with the county we decided to use similar methods to the previous mapping effort, in order to preserve the capacity to assess change in the county over time. This meant forgoing recent data and innovations in remote sensing such as the use LiDAR and Ecognition’s segmentation of imagery to delineate stands, which have been recently used in a concurrent project mapping of Sonoma County. The use of such technologies would have made it more difficult to track changes in landcover, because differences between publication dates would not be definitively attributable to either actual land cover change or to change in methodology. The overall cost of updating the map in the way was approximately 20% of the cost of the Sonoma vegetation mapping program.Therefore, we started with the original map, and on-screen inspections of the 2004 polygons to determine if change had occurred. If so, the boundaries and attributes were modified in this new edition of the map. We also used the time series of imagery available on Google Earth, to further inspect many edited polygons. While funding was not available to do field assessments, we incorporated field expertise and other map data from four projects that overlap with parts of Napa Count: the Angwin Experimental Forest; a 2014 vegetation map of the Knoxville area; agricultural rock piles were identified by Amber Manfree; and parts of a Sonoma Vegetation Map that used 2013 imagery.The Angwin Experimental Forest was mapped by Peter Lecourt from Pacific Union College. He identified several polygons of redwoods in what are potentially the eastern-most extent of that species. We reviewed those polygons with him and incorporated some of the data from his area into this map.The 2014 Knoxville Vegetation map was developed by California Department of Fish and Wildlife. It was made public in February of 2019, close to the end of this project. We reviewed the map, which covers part of the northeast portion of Napa County. We incorporated polygons and vegetation types for 18 vegetation types including the rare ones, we reviewed and incorporated some data for another 6 types, and we noted in comments the presence of another 5 types. There is a separate report specifically addressing the incorporation of this map to our map.Dr Amber Manfree has been conducting research on fire return intervals for parts of Napa County. In her research she identified that large piles of rocks are created when vineyards are put in. These are mapable features. She shared the locations of rock piles she identified, which we incorporated into the map. The Sonoma Vegetation Map mapped some distance into the western side of Napa County. We reviewed that map’s polygons for coast redwood. We then examined our imagery and the Google imagery to see if we could discern the whorled pattern of tree branches. Where we could, we amended or expanded redwood polygons in our map.The Vegetation classification systems used here follows California’s Manual of California Vegetation and the National Vegetation Classification System (MCV and NVCS). We started with the vegetation types listed in the 2004 map. We predominantly use the same set of species names, with modifications/additions particularly from the Knoxville map. The NVCS uses Alliance and Association as the two most taxonomically detailed levels. This map uses those levels. It also refers to vegetation types that have not been sampled in the field and that has 3-6 species and a site descriptor as Groups, which is the next more general level in the NVCS classification. We conducted 3 rounds of quality assessment/quality control exercises.

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

This layer can be used for watershed analysis and planning in the Russian River region of California.

This polygon shapefile contains areas of important farmland in Sonoma County, California for 2010. Important Farmland Maps show the relationship between the quality of soils for agricultural production and the land's use for agricultural, urban, or other purposes. A biennial map update cycle and notation system employed by FMMP captures conversion to urban land while accommodating rotational cycles in agricultural use. The minimum land use mapping unit is 10 acres unless specified. Smaller units of land are incorporated into the surrounding map classifications. In order to most accurately represent the NRCS digital soil survey, soil units of one acre or larger are depicted in Important Farmland Maps. For environmental review purposes, the categories of Prime Farmland, Farmland of Statewide Importance, Unique Farmland, Farmland of Local Importance, and Grazing Land constitute 'agricultural land' (Public Resources Code Section 21060.1). The remaining categories are used for reporting changes in land use as required for FMMP's biennial farmland conversion report. This layer is part of the 2010 California Farmland Mapping and Montoring Project.

The California Association Local Agency Formation Commissions defines a sphere of influence (SOI) as "a planning boundary outside of an agency’s legal boundary (such as the city limit line) that designates the agency’s probable future boundary and service area." This feature set represents the SOIs of the incorporated jurisdictions for the San Francisco Bay Region. The Metropolitan Transportation Commission (MTC) updated the feature set in late 2019 as part of the jurisdiction review process for the BASIS data gathering project. Changes were made to the growth boundaries of the following jurisdictions based on BASIS feedback and associated work: Antioch, Brentwood, Campbell, Daly City, Dublin, Fremont, Hayward, Los Gatos, Monte Sereno, Newark, Oakland, Oakley, Pacifica, Petaluma, Pittsburg, Pleasanton, San Bruno, San Francisco (added to reflect other jurisdictions whose SOI is the same as their jurisdiction boundary), San Jose, San Leandro, Santa Clara, Saratoga, and Sunnyvale. Notes: With the exception of San Mateo and Solano Counties, counties included jurisdiction (city/town) areas as part of their SOI boundary data. San Mateo County and Solano County only provided polygons representing the SOI areas outside the jurisdiction areas. To create a consistent, regional feature set, the Metropolitan Transportation Commission (MTC) added the jurisdiction areas to the original, SOI-only features and dissolved the features by name.Because of differences in base data used by the counties and the MTC, edits were made to the San Mateo County and Solano County SOI features that should have been adjacent to their jurisdiction boundary so the dissolve function would create a minimum number of features. Original sphere of influence boundary acquisitions:Alameda County - CityLimits_SOI.shp received as e-mail attachment from Alameda County Community Development Agency on 30 August 2019 Contra Costa County - BND_LAFCO_Cities_SOI.zip downloaded from https://gis.cccounty.us/Downloads/Planning/ on 15 August 2019Marin County - 'Sphere of Influence - City' feature service data downloaded from Marin GeoHub on 15 August 2019Napa County - city_soi.zip downloaded from their GIS Data Catalog on 15 August 2019 City and County of San Francisco - does not have a sphere of influence San Mateo County - 'Sphere of Influence' feature service data downloaded from San Mateo County GIS open data on 15 August 2019 Santa Clara County - 'City Spheres of Influence' feature service data downloaded from Santa Clara County Planning Office GIS Data on 15 August 2019 Solano County - SphereOfInfluence feature service data downloaded from Solano GeoHub on 15 August 2019 Sonoma County - 'SoCo PRMD GIS Spheres Influence.zip' downloaded from County of Sonoma on 15 August 2019

The EnviroAtlas Sonoma County, CA Meter-scale Urban Land Cover (MULC) data were generated from four-band (red, green, blue, and near infrared) aerial photography and LiDAR data at 6-inch spatial resolution, collected in late 2013, as well as ancillary vector data (e.g., roads, agriculture, wetlands) provided by the Sonoma County Vegetation Mapping and LiDAR Program (http://Sonomavegmap.org). Ten land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, shrub, grass-herbaceous non-woody vegetation, agriculture, orchards, as well as woody wetlands and emergent herbaceous wetlands. An accuracy assessment of 629 completely random and 176 stratified random photo-interpreted reference points yielded an overall MAX accuracy of 79 percent and an overall RIGHT accuracy of 80 percent (See overview section for more details on accuracy assessment). The area mapped is Sonoma County, CA. This dataset was produced by the Sonoma County Vegetation Mapping and LiDAR Program and the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This data represents a land use survey of Sonoma County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during July - September 2012 by staff visiting each field and noting what was grown. The county was divided into five survey areas using major road as centerlines and other geographic features for boundaries. The county was surveyed with two teams. The linework was heads up digitized in ArcGIS 10.0 with 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Field Boundaries were reviewed with ArcGIS 10.2 and NAIP 2012 imagery when it became available. The data was recombined after it was finished. The Virtual Basic Landuse Attributor was used for the survey and to start the post survey process; after converting to ArcGIS 10.2, the domain file geodatabase structure was used to attribute and help finish facilitating the post survey process. Tables were run through a Python script to put the data in the standard landuse format. ArcGIS geoprocessing tools and topology rules were used to locate errors and for quality control and assurance. Horse pastures were designated either S2 or S6. The special condition 'G' was used to denote vineyards that had sprinklers for frost protection rather than representing a cover crop as stated in the February 2009 Standard Land Use Legend used for this survey. Field Boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries.Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. GPS units connected to the laptops were used to confirm surveyor's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land use. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0.Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

OWTS Community Clusters were grouped based on parcels intersecting the APMP boundary and presence adjacent to a city sphere of influence or sanitation distraction, or those that share the same supervisor district, MAC, or resource conservation district (RCD). The RCD boundary was modified to include parcels which were excluded from the original RCD dataset.Fields:OWTSCategory: known or suspectedAPN: from parcel dataUseCode: from parcel dataUseCodeDescription: from parcel dataUseCodeType: from parcel dataSitusFormatted: from parcel dataBuildingPrimaryUnitCount: from parcel dataBuildingPrimaryYearBuilt: from parcel dataBuildingSecondaryUnitCount: from parcel dataVacationRental: vacation rental informationGMResult: GAMA wells nitrate levels that exceed 10 mg/LGMUnits: units for GMResultSystemType: concatenated from OWTS permitsImplications: whether system type implies a standard or non-standard OWTSOWTSCount: assumed number of OWTSOWTSPermits: number of OWTS permitsAcres: parcel acresinOWTSDensityID: cluster ID for OWTS parcel density categories (see criteria report)ClusterDensityExceeded: is maximum allowable density exceeded for density cluster?GovCity: city majority of the parcel is withinGovWaterSys: water system majority of parcel is withinGovResourceConserv: resource conservation district majority of parcel is withinGovSupervisorMAC: supervisor MAC majority of parcel is withinGovSupervisorDist: supervisor district majority of parcel is withinGovTribal: tribal area majority of parcel is withinGovDACBlock: DAC block group majority of parcel is withinGovSDACBlock: SDAC block group majority of parcel is withinGovDACTract: DAC tract majority of parcel is withinGovSDACTract: SDAC tract majority of parcel is withinIntersectsAPMP: does the parcel intersect an APMPStillWater200ft: is the parcel within 200-ft of still waterFlowingWater100ft: is the parcel within 200-ft of flowing waterGWBasin: is the parcel in a groundwater basin?WWTreatmentPlantHalfMi: is there a wastewater treatment plant within a half mile of the parcel?StreamHighPointBuffer: intersects with 200 or 400ft buffer from stream's high point depending on distance to intake pointWells100ft: is there a drinking water well within 100-ft of the parcel?UtilityLineDistance: distance to closest utility line based on available data (see criteria report)ElecEsmntPrcl: distance to closest electric easement parcelGasEsmntPrcl: distance to closest gas easement parcelMeanSlope: average slope (percent rise) on the parcelRtngSepTnkDC: Rating for Septic Tank Absorption Fields - Dominant ConditionF2Floodplain: is the parcel in an F2 floodplainF1Floodway: is the parcel in an F1 floodwayHUC12Name: name of the HUC12 watershed the parcel falls. Uses majority for parcels that fall in multiple.OWTSCommunityID: community cluster IDCommunityCluster: is the parcel in a community cluster?CommunityGroup: what criteria was used to create the groupCommunityGrouping: the jurisdictions the parcel was grouped according toTechRating: technical score rating for the parcelCreatedDate: date analysis was run