The Tamalpais Lands Collaborative (One Tam; https://www.onetam.org/), the network of organizations that manage lands on Mount Tamalpais in Marin County, initiated the countywide mapping project with their interest in creating a seamless, comprehensive map depicting vegetation communities across the landscape. With support from their non-profit partner the Golden Gate National Parks Conservancy (https://www.parksconservancy.org/) One Tam was able to build a consortium to fund and implement the countywide fine scale vegetation map.Development of the Marin fine-scale vegetation map was managed by the Golden Gate National Parks Conservancy and staffed by personnel from Tukman Geospatial (https://tukmangeospatial.com/) Aerial Information Systems (AIS; http://www.aisgis.com/), and Kass Green and Associates. The fine-scale vegetation map effort included field surveys by a team of trained botanists. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the California Native Plant Society (CNPS) Vegetation Program (https://www.cnps.org/vegetation) with support from the California Department of Fish and Wildlife Vegetation Classification and Mapping Program (VegCAMP; https://wildlife.ca.gov/Data/VegCAMP) to develop a Marin County-specific vegetation classification.High density lidar data was obtained countywide in the early winter of 2019 to support the project. The lidar point cloud, and many of its derivatives, were used extensively during the process of developing the fine-scale vegetation and habitat map. The lidar data was used in conjunction with optical data. Optical data used throughout the project included 6-inch resolution airborne 4-band imagery collected in the summer of 2018, as well as 6-inch imagery from 2014 and various dates of National Agriculture Imagery Program (NAIP) imagery.In 2019, a 26-class lifeform map was produced which serves as the foundation for the much more floristically detailed fine-scale vegetation and habitat map. The lifeform map was developed using expert systems rulesets in Trimble Ecognition®, followed by manual editing.In 2019, Tukman Geospatial staff and partners conducted countywide reconnaissance fieldwork to support fine-scale mapping. Field-collected data were used to train automated machine learning algorithms, which produced a fully automated countywide fine-scale vegetation and habitat map. Throughout 2020, AIS manually edited the fine-scale maps, and Tukman Geospatial and AIS went to the field for validation trips to inform and improve the manual editing process. In the spring of 2021, draft maps were distributed and reviewed by Marin County's community of land managers and by the funders of the project. Input from these groups was used to further refine the map. The countywide fine-scale vegetation map and related data products were made public in June 2021. In total, 107 vegetation classes were mapped with a minimum mapping size of one fifth to one acre, varying by class.Accuracy assessment plot data were collected in 2019, 2020, and 2021. Accuracy assessment results were compiled and analyzed in the summer of 2021. Overall accuracy of the lifeformmap is 95%. Overall accuracy of the fine-scale vegetation map is 77%, with an overall 'fuzzy' accuracy of 81%.The Marin County fine-scale vegetation map was designed for a broad audience for use at many floristic and spatial scales. At its most floristically resolute scale, the fine-scale vegetation map depicts the landscape at the National Vegetation Classification alliance level - which characterizes stands of vegetation generally by the dominant species present. This product is useful to managers interested in specific information about vegetation composition. For those interested in general land use and land cover, the lifeform map may be more appropriate. Tomake the information contained in the map accessible to the most users, the vegetation map is published as a suite of GIS deliverables available in a number of formats. Map products are being made available wherever possible by the project stakeholders, including the regional data portal Pacific Veg Map (http://pacificvegmap.org/data-downloads).

This datasheet describes a set of 5 lidar derived, 5-meter resolution rasters that cover the entire extents of Santa Cruz and Santa Clara Counties. The rasters are slope (Degrees), aspect, elevation, canopy height, and canopy cover. These rasters were derived from the early-2020 Quality Level 1 (QL1) points clouds for Santa Cruz and Santa Clara County. As such, these rasters represent the state of the landscape in 2020 before the CZU and SCU complex fires. The horizontal coordinate system of these rasters is UTM zone 10 NAD 83.
Higher resolution, single-county versions of each of these rasters exist and are available on https://pacificvegmap.org. These 5-meter versions were produced for the entire 2 county area and are used – along with the 5-meter Scott and Burgan fuel model – as landscape (.LCP) file rasters to accompany the Santa Cruz / Santa Clara 5-meter fuel model.
Table 1 provides links to download these lidar derived rasters.
Table 1. lidar derivatives for Santa Clara County

  Dataset


  Description


  Link to Data


  Link to Datasheet




  Slope (Degrees)


  Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each 5m x 5m cell to its neighbors. 


  https://vegmap.press/scc_scz_5_meter_slope_degrees


  https://vegmap.press/scc_scz_5_meter_datasheet




  Aspect


  Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each 5m x 5m cell to its neighbors. 


  https://vegmap.press/scc_scz_5_meter_aspect


  https://vegmap.press/scc_scz_5_meter_datasheet




  Elevation


  Elevation above sea level (in feet) for each 5m x 5m cell. 


  https://vegmap.press/scc_scz_5m_elevation


  https://vegmap.press/scc_scz_5_meter_datasheet




  Canopy Height


  Pixel values represent the aboveground height of vegetation and trees.


  https://vegmap.press/scc_scz_5_meter_can_height


  https://vegmap.press/scc_scz_5_meter_datasheet




  Canopy Cover


  Pixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.


  https://vegmap.press/scc_scz_5_meter_can_cov


  https://vegmap.press/scc_scz_5_meter_datasheet

Dataset Summary: This datasheet describes a suite of 5-meter resolution rasters that cover the entire extents of Santa Cruz and Santa Clara Counties. The rasters include a 5-meter fuel model, the associated landscape files, and each landscape file component as a standalone geotiff. Table 1 lists these data products and provides links to the data and the datasheet for each one. The Santa Cruz and Santa Clara County Fuel Model is a 5-meter spatial resolution fuel model that adheres to Scott and Burgan’s classification (Scott and Burgan, 2005). The fuel model provides a fine scale map of fuel conditions on the landscape and is a required input for fire behavior and fire spread models. The fuel model provides a higher spatial resolution than the existing, publicly available fuel models, which are based on the LANDFIRE data derived from 30-meter Landsat data. The fuel model was updated to post CZU and SCU fire conditions using Sentinel-derived burn severity data. For a more in-depth technical report on the methods used to create this fuel model, visit this report (link will be live by April 30th, 2022): https://fuelsmapping.com/santa_clara_fuels_full_report The associated Landscape File (.LCP) provide the fuel model and associated raster inputs in a format required for common fire behavior and fire spread models. Note that the landscape file is a large (4.4 GB) countywide stack of rasters that may be too large for some fire behavior software models to use. In this case, use the tools that come with the fire behavior software to resize the landscape files to the area that you are modeling.
The other rasters in table 1 (besides the fuel model and LCP file) were derived from the early-2020 Quality Level 1 (QL1) points clouds for Santa Cruz and Santa Clara County. As such, these rasters represent the state of the landscape in 2020 before the CZU and SCU complex fires. The horizontal coordinate system of these rasters is UTM zone 10 NAD 83. These 5-meter versions were produced for the entire 2 county area and are used – along with the 5-meter Scott and Burgan fuel model – as landscape (.LCP) file rasters. Higher resolution, single-county versions of some of these rasters (elevation, canopy height, canopy cover) exist and are available on https://pacificvegmap.org. Table 1. Standalone 5-meter fuel related products for Santa Cruz and Santa Clara Counties

  Dataset


  Description


  Link to Data






  LCP File


  LCP file containing all of the rasters listed in this table. The LCP file is a direct input to fire behavior and fire spread models.


  https://fuelsmapping.com/santa_cruz_santa_clara_LCP





  5m Fuel Model


  5-meter Scott and Burgan Fuel Model with Value Attribute Table, which contains fields for each component of the crosswalk. These are enhanced lifeform class (MapClass), canopy cover (AbsCover), ladder fuel (LadderFuel), canopy height (CanHeight), burn severity (BurnSeveri), pyrome (EastWest), fuel model as a numeric code (FuelModel) and fuel model as a string (FuelModTxt).


  https://vegmap.press/scc_scz_5_meter_fuel_model




  Slope (Degrees)


  Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each 5m x 5m cell to its neighbors. 


  https://vegmap.press/scc_scz_5_meter_slope_degrees




  Aspect


  Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each 5m x 5m cell to its neighbors. 


  https://vegmap.press/scc_scz_5_meter_aspect




  Elevation


  Elevation above sea level (in feet) for each 5m x 5m cell. Elevation is also available as a bare earth DEM at higher resolution on pacificvegmap.org.


  https://vegmap.press/scc_scz_5m_elevation




  Canopy Height


  Pixel values represent the aboveground height of vegetation and trees. Canopy height is also available as a higher resolution raster on pacificvegmap.org.


  https://vegmap.press/scc_scz_5_meter_can_height




  Canopy Cover


  Pixel values represent the density of vegetation greater than or equal to 10 feet tall. Canopy cover is also available as a higher resolution raster on pacificvegmap.org.


  https://vegmap.press/scc_scz_5_meter_can_cov




  Canopy Base Height


  Canopy base height (values in feet) was calculated as per Moran et al., 2020 using the following adaptation of his formula:
  Mean lidar return height minus the standard deviation of lidar returns * .35 CBH is capped at 60 feet.


  https://vegmap.press/scc_scz_cbh




  Canopy Bulk Density


  Canopy bulk density was derived from a 10-meter resolution raster from SALO Sciences provided in February 2022.


  https://vegmap.press/scc_scz_cbd

Uses: The fuel model and the landscape files provided by this project are necessary inputs for fire behavior and fire prediction models.
With spatial fire behavior prediction modeling using FlamMap software from the USDA Forest Service (https://www.firelab.org/project/flammap), a prioritization of treatment areas can be made based on how the extent of predicted fires overlap with values at risk, access routes, or significant topographic features (such as ridgetops). Fire behavior prediction can also be used for prioritizing fuel treatments and for pre-attack planning, as it identifies areas of hazard as well as potential containment opportunities that could be enhanced. Hazards can be analyzed spatially by ownership, adjacency to access corridors, and used for planning fuel treatments.
The landscape files can be used for evacuation planning as inputs to fire growth models via USDA’s FARSITE fire growth software (https://www.firelab.org/project/farsite). Results of fire growth simulations are overlaid with access routes, populations served by the access routes, and typical egress and response times. Even without the landscape files, the fuel model can be used to prioritized treatments via development of a spreadsheet or decision tree that describes the type of concern, recommended treatment options, and the benefits of treatments associated with each fire behavior fuel model. Table 3 illustrates this type of approach for using the fuel model to prioritize treatments and management recommendations.
An example of using the fuel model for prioritizing treatments

  Fire Behavior Fuel Model


  Concern


  Level of Concern


  Treatment


  Proximity/Location of treatment


  Benefits of Treatment






  GR1, 2, 3


  Ignition, high rate of spread


  Mod


  Mow


  Near access routes, structures


  Ignition prevention, easier containment




  GR1, 2, 3


  Ignition, high rate of spread


  Mod


  Graze, prescribed burn


  Large areas


  Containment potential




  TU5


  Torching, Spotting, high fire intensity


  High


  Remove ladder fuels via hand labor, mechanical


  Ridgetops, around structures, 


  Shaded fuelbreak, reduced fire intensity to lessen structure ignition & firebrand production 




  TU5


  Torching, Spotting


  High


  Prescribed burn, graze with goats


  Large areas


  Containment potential, reduced fire intensity to lessen structure ignition, firebrand production




  TU1


  Minimal


  Low


  Low priority


  Near access routes, structures


  Ignition prevention, Structure protection




  TL9


  Torching, Spotting, high fire intensity


  High


  Remove Surface fuels via Hand labor, mechanical


  Near access routes, structures


  Containment potential, reduced fire intensity to lessen structure ignition, firebrand production




  TL9


  Torching, Spotting, high fire intensity


  High


  Remove Surface fuels via prescribed burns


  Large areas


  Containment potential, reduced fire intensity to lessen structure ignition, firebrand production

References:

Scott, J. and Burgan, R. (2005). Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-153, 72 pp. Moran, C. J., Kane, V. R., & Seielstad, C. A. (2020). Mapping Forest Canopy Fuels in the Western United States with lidar–Landsat Covariance. Remote Sensing, 12(6), 1000.