We’re excited to announce the release of a suite of hydrologic data products.
Clockwise from upper left – flow direction, flow accumulation, stream centerlines, and orthophotos (image by Quantum Spatial)
These Sonoma County hydrologic data products were produced in fall 2015 and winter 2016 from the countywide 2013 LiDAR data. The hydrologic products include a set of vector deliverables and a set of raster deliverables. Vector products include stream centerlines, confluence points, hydroenforcement burn locations, and watersheds. Raster products include flow direction, flow accumulation, and a hydroenforced bare earth digital elevation model (DEM). Hydroenforcement of a DEM imparts the true elevations of culverts, pipelines, and other buried passages for water into a Digital Elevation Model, creating a DEM suitable for modeling the flow of surface water.
The extent of the deliverables is all of Sonoma County, the Lake Sonoma watershed in Mendocino County, and the Lake Mendocino area.
These hydrologic datasets are a mostly-automated first step in the eventual development of a ‘localized’ or ‘LiDAR enhanced’ National Hydrography Dataset (NHD). They are suitable for landscape level planning and hydrologic modeling. These data products do not contain a guarantee of accuracy or precision and – without site specific validation and/or refinement – should not be relied upon for engineering level or very fine scale decision making.
The individual hydrologic data products are described briefly below.
Vector Hydrologic Products (available here as a file geodatabase)
Stream Centerlines – Centerlines of streams in Sonoma County. An area of flow concentration is considered a stream if its flow accumulation (upstream catchment area) exceeds 5 acres and a clearly defined channel exists. Where possible, stream centerline names (GNIS_Name) are consistent with the NHD.
Hydroenforcement Burn Locations – Line features that represent locations where hydroenforcement occurred.
Confluence Points – Points that represent stream intersections (confluences).
Watersheds (HUC2 through HUC14) – Watershed boundaries for nested hydrologic units from HUC 2 (region) to HUC 14 (sub-watershed). Where possible, watershed names are consistent with the NHD. Watershed mapping conventions follow those for NHD’s Watershed Boundary Dataset (http://nhd.usgs.gov/wbd.html).
Hydroenforced Digital Elevation Model – The Hydroenforced DEM is the LiDAR derived (2013) bare earth DEM with contours, pipelines and other buried passages to water ‘burned in’, so that the DEM correctly models surface water flow.
Flow Direction Rasters – Values in a flow direction raster represent one of eight directions (pixel values range from 1 to 8); No Data represents areas where there is no flow off of the pixel (sinks).
Flow Accumulation Rasters – Flow accumulation is a measure of upstream catchment area. Pixel values in a flow accumulation raster represent the cumulative number of upstream pixels (in other words, the count of pixels that contribute flow to a given pixel).
The webmap below shows the HUC 12 watersheds and the stream centerlines.
Explore this Story Map to See How the LiDAR Hillshade Helps with Road Mapping
Sonoma County has benefited from the NASA-funded 2013 LiDAR data in many ways. One recent example is the County’s GIS Group’s use of the LiDAR-derived hillshade for improving road centerline delineations. Road centerlines have many uses: they form the base reference layer for navigation, they are critical for the county’s emergency response services, they serve as a key input for natural resource conservation planning, and they are integral to the operations of county and state agencies that maintain the county’s roads and transportation network.
Until now, road centerline delineation relied solely on orthophoto interpretation and occasionally GPS data collection, which can be costly. Since dense tree canopy sometimes covers roads, which renders them invisible on orthophotographs, the road centerline data is prone to accuracy problems. Additionally, GPS error increases under tree canopy, which can lead to accuracy problems even for road centerlines mapped with GPS.
Now with the LiDAR bare earth hillshade, which provides a three dimensional depiction of the ground even under canopy, we have a way to significantly improve road centerline accuracy. The image below shows a heavily forested section of Fort Ross Road (the road is shown in dotted yellow). The top pane shows the 2011 orthophotography. Because of the high tree density in this area, locating the road with the photo would be guesswork. However, in the bare earth hillshade (bottom pane), the road is clearly visible in darker tones than the surrounding landscape.
Fort Ross Road (dotted yellow) – Orthos Above; Bare Earth Hillshade Below
Check out this story map to see explore some areas that illustrate the benefits of using the LiDAR hillshade for mapping road centerlines.
Using the LiDAR bare earth hillshade, County of Sonoma Information Services Department (ISD) GIS Group has made significant strides in correcting misaligned centerlines under canopy in the countywide roads dataset – the result is a greatly improved and much more reliable roads layer. The Sonoma Veg Map team is using similar techniques to contribute improvements to the roads layer for use in impervious surface mapping.
The committee advises the mapping team on state-of-the art mapping techniques and methods. Dr. Golinkoff is an expert at using LiDAR to help map, measure, and manage forestland in Northern California. A Forest Carbon Analyst for the Conservation Fund, Dr. Golinkoff will add to the Advisory Committee’s LiDAR and forest mapping expertise.
Dr. Golinkoff is one of the nation’s leading experts on forest carbon project development, monitoring, and verification. He joined The Conservation Fund in 2005 and has extensive experience in forest inventory and projection methodologies, statistical analysis, and carbon accounting practices and standards compliance. A trained forester and Climate Action Reserve and California Air Resources Board approved verifier, Jordan is responsible for forest inventory design and carbon offset registration for The Conservation Fund, the nation’s largest seller of verified forest carbon offsets. Jordan has a B.A. in Computer Science from Yale University, a Masters of Forestry from Duke University, and a Ph.D. in forest mensuration and biometrics from the University of Montana.
On May 21st, the Open Space District and the Sonoma County Water Agency hosted the LiDAR and Environmental Data Forum. The forum included a number of mapping and remote sensing experts to discuss the benefits and applications of LiDAR and other high-resolution environmental data products being developed for the Sonoma County Vegetation Mapping and LiDAR Program. Speakers discussed LiDAR’s role in providing next generation data products for engineering, feature extraction, vegetation mapping, and land management.
The table below lists the speakers and provides links to their presentations. For general information on LiDAR’s benefits, visit this blog post.
Tom Robinson, Conservation Planner – SCAOSD
Mr. Robinson is project manager for the Sonoma County Vegetation Mapping and LiDAR program. Tom has many years of conservation planning, GIS, and mapping experience.
Jeremy Kobor, Senior Hydrologist – O'Conner Environmental
Mr. Kobor is a registered geologist and holds and MS in geology. He is an expert in the development and application of numerical hydrologic, hydraulic, and sediment transport models and has experience in geomorphic mapping.
Laurel Marcus, Executive Director – California Land Stewardship Institute
Ms. Marcus has over 30 years of experience in watershed, river and wetland restoration work in California. She is the author of the Fish Friendly Farming Environmental (FFF) Environmental Certification Program (also known as the Napa Green Land Certification) that she directs in seven counties for over 15 different crops and grazing land.
Benefits and Applications The benefits of LiDAR for land and water management, planning and engineering are wide ranging. The following discussion demonstrates some of these benefits as they apply to Sonoma County.
Forest Management and Measuring Forest Carbon LiDAR data is used to create automated forest structure metrics, such as tree height and canopy density. Forest structure metrics can be combined with forest plot data to produce accurate models of forest biomass and aboveground carbon sequestration. Read this 2010 New York Times article titled “New Map Shows Measuring Carbon in Amazon Is Feasible.” See the videos at the bottom of this post for more on using LiDAR for forest management, forest inventory and biomass mapping.
Vegetation and Habitat Mapping One of the main products of the Sonoma County Vegetation Mapping and LiDAR program will be a fine scale vegetation and habitat map of the entire county. Mapping vegetation involves developing a classification of the county’s vegetation types, interpreting aerial photographs and imagery using human interpreters and computer algorithms, and verifying vegetation characteristics in the field. LiDAR data is extremely useful for vegetation & habitat mapping, as well as riparian area mapping and crop type mapping.
PG&E and other utilities periodically collect LiDAR data for their transmission lines. One major application of this data is to target danger trees for removal. If a danger tree falls in the direction of a high voltage line, it could hit and damage the line or start a wildfire. Danger trees are identified by combining LiDAR derived tree height & location information with LiDAR derived line height & location information.
Floodplain Mapping, Flood and Tsunami Inundation Modeling Highly accurate digital elevation data is a prerequisite for accurate floodplain mapping and flood inundation modeling (from river flooding, tsunami, or storm surge). High resolution LiDAR-based DEMS greatly improve the accuracy of flood mapping.
Using LiDAR data, a team of researchers from the College of Staten Island successfully predicted coastal inundation extent resulting from Hurricane Sandy. An article in the February 13, 2013 New Yorker Magazine states “The C.S.I. study had predicted the extent of flooding uncannily-the projected flood zone proved to be accurate to the street, and almost to the house.” Click here for the New Yorker article.
Read this article about flood management and planning in the Red River Basin of the North Dakota.
Calculating Solar Power Potential for a Roof Top or Land Parcel SCEIP (the Sonoma County Energy Independence Program) has funded 1750 residential and 57 commercial installations of solar energy since it started in the late 2000’s. It provides low cost loans that pay for the system; the homeowner or business owner pays the loan back as part of their property taxes. LiDAR data provides very accurate roof pitch and roof aspect information for entire areas, as well as information that can be used to predict roof shadedness. The City and County of LA’s Solar Map and Green Planning Tool is underpinned by LiDAR data. Estimating solar potential of roofs and vacant lots using LiDAR data has become a big business. Read the May, 2011 article from Forbes Magazine titled “How Much Energy Can Your Roof Generate.” See the second half of the article for the LiDAR related discussion. New York City has also recently collected LiDAR data and used the data to map rooftop solar potential. This effort is described in a New York Times article where the NYC Deputy Mayor of Operations is quoted as saying, “the quality of the LiDAR information is so remarkable that it will much more rapidly unlock usable sites.”
Precision Agriculture and Crop Planning LiDAR is used to create very accurate DEMs and contours that characterize the landscape in a much more refined and detailed way than 10-meter USGS DEMs. The ability to map micro topography is important for crop planning. For example, vineyard managers use topographic maps (and other data) to divide a vineyard into zones based on physical site properties, climate, and soil type. LiDAR data will produce far more accurate depictions of topography than what is available today.
Elevation Contours and DEMs Digital Elevation Models (DEMS) are produced from LiDAR data at a much higher resolution than USGS DEMS. LiDAR produces elevation models of the terrain at ground level as well as at the highest surface (buildings & tree canopy). LiDAR data is capable of producing 1 foot elevation contours.
Potential beneficiaries of high resolution DEMs and 1 foot contours in Sonoma County include city and county planning departments, land managers, farmers, builders, and engineering firms.
Monitoring the Effects of Sea Level Rise Sea level rise will affect Sonoma County along the Pacific Coast and in areas adjacent to the tidal wetlands of Southern Sonoma County. LiDAR data (combined with accurate bathymetric data and advanced modeling techniques) produces very accurate projections of coastal inundation as a result of storm surge and/or sea level rise. County planners and the California Department of Transportation (CalTrans) will benefit from LiDAR data when planning for the effects of sea level rise on the Sonoma County baylands and on Highway 37 in southern Sonoma County.
Landslide & Fault Detection and Mapping Since LiDAR penetrates the tree canopy, creating a very detailed model of the bare earth surface, LiDAR is a valuable tool for mapping faults, landslides, and debris flows – especially those obscured by vegetation. LiDAR is also used to produce landslide susceptibility maps. See the video at the end of this post from the Southern California Earthquake Center – it provides an overview of LiDAR and how it is used to identify and map earthquake faults.
Engineering Planning LiDAR produces very detailed contours and elevation models useful for engineering planning. Uses include transmission corridor and pipeline planning, storm water modeling, drainage analysis, cut and fill analysis, master planning, grading, erosion control, and roadway design. LiDAR is very effective for and for evaluating the relative merits of alternative sites for a project.
LiDAR is used in numerous water related engineering planning activities such as diversion channel routing, hydraulic analysis, flood control impoundment planning, cut and fill analysis, and 3D project visualization. High resolution topographic data (usually derived from LiDAR) is necessary for flood inundation mapping. Dam breach analysis and resulting flood inundation mapping often relies on LiDAR data, which provides much more accurate results than analysis using 10 meter USGS DEMs.
Pervious/Impervious Surface Mapping
LiDAR data aids in the effective mapping of pervious and impervious areas across a landscape. Maps of pervious and impervious surfaces are useful for managing and modeling stormwater runoff, hydrologic modeling, habitat modeling, and urban planning.
Fuel Mapping and Fire Hazard Modeling
Because it depicts the forest in three dimensions, LiDAR data improves the accuracy of fuel maps and increases the accuracy of fire risk and fire behavior models. LiDAR is increasingly used by state and federal agencies to assist in mapping fuels, fire risk, and fire behavior.
Videos that Demonstrate Some of LiDAR’s Benefits
Watch the ESRI Video Below, entitled “Improving Operational Planning Using Forest Inventories and GIS”
The video below from the Southern California Earthquake Center provides an overview of LiDAR and how it is used to identify and map earthquake faults.
Watch the video below from the United States Forest Service titled “Modeling Biomass and Canopy Fuel Attributes Using LiDAR”
Watch the “Introduction to LiDAR” video below from ASPRS (American Society of Photogrammetry and Remote Sensing)
Course information (including lecture materials) for Penn State’s “LiDAR Technology and Applications” Course (Geography 497D). See the course outline (bottom left) for access to lecture materials and readings.
NOAA Coastal Service’s Center Digital Coast web site for downloading LiDAR data and other coastal datasets. The web site includes access to the 2009-2011 LiDAR collection (2 pts./square meter) that includes coastal areas of Sonoma County.