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.

Sonoma County Vegetation Mapping and LiDAR Consortium retained WSI to provide lidar and Orthophoto data and derived products in Sonoma County, CA. A classified LAS format point cloud was collected and developed. Products, such as bare earth DEMs, were derived from the lidar, but are not covered here. The original specified coordinate system for this dataset is California State Plane Zone II (FI...

This survey covers all of Sonoma County as well as two small portions of southern Mendocino County. Data were provided by the University of Maryland and the Sonoma County Vegetation Mapping and Lidar Program under grant NNX13AP69G from NASA's Carbon Monitoring System (Dr. Ralph Dubayah, PI). The Sonoma County Vegetation Mapping and Lidar Program is NASA/UMD's local partner in this project- its members are as follows: 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.

These data will be used for various research projects throughout Sonoma County. They are critical to assessing climate mitigation and adaptation strategies and benefits provided by the landscape, such as the amount of carbon sequestration in forests or the degree to which riparian areas, floodplains, and coastal habitats may buffer extreme weather events. Other research applications include groundwater, ecosystem services valuation, ecosystem resiliency, and wildlife habitat connectivity. Finally, these data sets are key to facilitating good planning and management for watershed protection, flood control, fire and fuels management and wildlife habitat conservation.

The data was collected between September 28 and November 26, 2013 by Watershed Sciences, Inc. (WSI). Lidar was collected at high density greater than 8 pulses per square meter. WSI used two airplanes, one carrying a Leica ALS50 and the other a Leica ALS70; systems were flown at 900 meters above ground level, capturing a scan angle of 15 degrees from nadir (30 degree field of view). 4-band, 6-inch resolution aerial photography was collected simultaneously with the Lidar data.

The Sonoma County fine scale vegetation and habitat map is an 83-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 layer file is just to be used for symbology - no spatial data is included. For the spatial data, download the veg map layer package, file geodatabase, or shapefile. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tDClass 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.

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.

The highest hit digital surface model (DSM) represents the earth's surface elevation with all natural and anthropogenic features included. It was derived from integrated NIR and Green lidar data using the highest hit method. Some elevation values have been interpolated across areas in the ground model where there is no elevation data (e.g. over water). The horizontal datum for this dataset is NAD83 (2011), the vertical datum is NAVD88, Geoid 18, and the data is projected in California State Plane, Zone 2. Units are in US Survey Feet. NV5 Geospatial collected the Sonoma Creek Topo bathymetric Lidar data for Sonoma Water between 02/04/2021 and 02/06/2021.

This intensity raster depicts the aboveground LiDAR return to the total count LiDAR return and provides a ratio of the two from 0.0 to 1.0, where 0.0 represents no canopy and 1.0 very dense canopy. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average intensity 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 all of Sonoma County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Consortium.

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).

This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach. The relationship between field estimated and airborne LiDAR estimated aboveground biomass density is represented as a parametric model that predicts biomass as a function of canopy cover and 50th percentile and 90th percentile LiDAR heights at a 30-m resolution. To estimate uncertainty, the biomass model was re-fit 1,000 times through a sampling of the variance-covariance matrix of the fitted parametric model. This produced 1,000 estimates of biomass per pixel. The 5th and 95th percentiles, and the standard deviation of these pixel biomass estimates, were calculated.

This data set provides estimates of above-ground biomass (AGB), canopy height, and percent tree cover at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha) were generated using a combination of LiDAR data, field plot measurements, and random forest modeling approaches. Estimates of AGB uncertainty are also provided. Maximum canopy height and tree cover were derived from LiDAR data and high-resolution National Agriculture Imagery Program (NAIP) images.

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).

Geospatial data about Sonoma County, California LiDAR Streams. Export to CAD, GIS, PDF, CSV and access via API.

This .tif file represents the intensity values of the Green Lidar laser returns from the Sonoma Creek Topo bathymetric dataset. The horizontal datum for this dataset is NAD83 (2011), the vertical datum is NAVD88, Geoid 18, and the data is projected in California State Plane, Zone 2. Units are in US Survey Feet. NV5 Geospatial collected the Sonoma Creek Topo bathymetric Lidar data for Sonoma Water between 02/04/2021 and 02/06/2021.

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 all of Sonoma County and parts of Mendocino County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Program.Data hosted by Sonoma County Information Systems Department (ISD).

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 all of Sonoma County and parts of Mendocino County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Program.Data hosted by Sonoma County Information Systems Department (ISD).

The highest hit digital surface model (DSM) represents the earth's surface elevation with all natural and anthropogenic features included. It was derived from NIR Lidar data using the highest hit method. Some elevation values have been interpolated across areas in the ground model where there is no elevation data (e.g. over water). The horizontal datum for this dataset is NAD83 (2011), the vertical datum is NAVD88, Geoid 18, and the data is projected in California State Plane, Zone 2. Units are in US Survey Feet. NV5 Geospatial collected the Russian River Lidar data for Sonoma County Water Agency on 01/23/2021.

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. 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 all of Sonoma County and parts of Mendocino County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Program.Data hosted by Sonoma County Information Systems Department (ISD).

This .tif file represents the intensity values of the Lidar laser returns from the Russian River dataset. The horizontal datum for this dataset is NAD83 (2011), the vertical datum is NAVD88, Geoid 18, and the data is projected in California State Plane, Zone 2. Units are in US Survey Feet. NV5 Geospatial collected the Russian River Lidar data for Sonoma County Water Agency on 01/23/2021.

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).