Annual biomass data (2001-2021) for southern California: above- and below-ground, standing dead, and litter
Creators: name: Schrader-Patton, Charlie C.
name: Underwood, Emma C.
name: Sorenson, Quinn M.
Publisher: name: Zenodo
Year: 2023
Subjects: chaparral
land cover
Landsat
public lands
resource management
USDA Forest Service

description: Biomass estimates for shrubland-dominated ecosystems in southern California have, to date, been limited to national or statewide efforts which can underestimate the amount of biomass; are limited to one-time snapshots; or estimate aboveground live biomass only. We developed a consistent, repeatable method to assess four vegetative biomass pools from 2001-2021 for our southern California study area (totaling 6,441,208 ha), defined by the Level IV Ecoregions (Bailey 2016) that intersect with USDA Forest Service lands (Figure 1). We first generated aboveground live biomass estimates (Schrader-Patton and Underwood 2021), and then calculated belowground, standing dead, and litter biomass pools using field data in the peer-reviewed literature (Schrader-Patton et al. 2022) (Figure 2). Over half (52.3%) of the study area is shrubland, and our method accounts for three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding shrubs. We also generate biomass estimates for trees and herbs, giving a total of five life form/life history types. These data provide an important contribution to the management of shrubland-dominated ecosystems to assess the impacts of wildfire and management activities, such as fuel management and restoration, and for monitoring carbon storage over the long term. The biomass data are a key input into the online web mapping tool SoCal EcoServe, developed for US Department of Agriculture Forest Service resource managers to help evaluate and assess the impacts of wildfire on a suite of ecosystem services including carbon storage. The tool is available at https://manzanita.forestry.oregonstate.edu/ecoservices/ and described in Underwood et al. (2022). REFERENCES Bailey, R.G. 2016. Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Forest Service Research Data Archive. (Fort Collins, Colorado). https://doi.org/10.2737/RDS-2016-0003 Schrader-Patton, C.C. and E.C. Underwood. 2021. New biomass estimates for chaparral-dominated southern California landscapes. Remote Sensing, 13, 1581. https://doi.org/10.3390/rs13081581 Schrader-Patton et al. 2022. "Estimating Wildfire Impacts on the Biomass of Southern California's Chaparral Shrublands." Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Underwood et al. 2022. "Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool." Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA.
description: <strong>USAGE NOTES</strong> The biomass raster layers are packaged in zip files for each year using the following naming structure: WWETAC_UCD_SoCal_Biomass_XXXX.zip Where XXXX is the year of the biomass estimates. Within each zip file are the following files: WWETAC_UCD_ &lt;biomass pool&gt;_XXXX_g_m2.tif Where &lt;biomass pool&gt; is either aboveground, standing dead, litter, or belowground and XXXX designates the year. The dimensions of the geotiff raster files is 21243 columns by 13618 rows and the bounding box coordinates are 36.79, -121.96 (upper left) and 32.47, -115.23 (lower right), in decimal degrees. The rasters are unprojected and in the WGS84 (WKID 4326) geographic coordinate system (decimal degrees). Pixel size is .000317 x .000317 decimal degrees, approximately 35m x 29m depending on latitude. Intended users of this dataset include resource managers, researchers who are carrying out biogeographic studies, and people needing vegetation biomass estimates across this landscape. This dataset is made available under a CC0 license waiver.Funding provided by: U.S. Forest Service<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100006959<br>Award Number: Funding provided by: State of California Department of Forestry and Fire Protection (CalFire)*<br>Crossref Funder Registry ID: <br>Award Number:
description: <strong>METHODS</strong> We generated spatial estimates of above ground live biomass (AGLBM, in kg/m<sup>2</sup>) for 2000-2021 for our southern California study area. The study area, totaling 6,441,208 ha, is defined by the 42 Level IV Ecoregions (Bailey 2016) that intersect the four southern US Department of Agriculture (USDA) National Forests in southern California (Figure 1). We created biomass raster layers (30m spatial resolution) by modeling a set of covariates (Normalized Difference Vegetation Index [NDVI], precipitation, solar radiation, actual evapotranspiration, aspect, slope, climatic water deficit, elevation, flow accumulation, landscape facets, hydrological recharge and runoff, and soil type) to predict AGLBM using 766 field plots of biomass from the USDA Forest Service Forest Inventory and Analysis (FIA); the Landfire Reference Database (LFRDB) plot data; and other research plots. The dates of field data spanned 2001-2012. The NDVI raster data were derived from Landsat TM/ETM+/OLI multispectral satellite data (onboard Landsat 5, 7, and 8, respectively). NDVI data were composited from all available Landsat images for the months of July and August for each year 2001-2021. We also downloaded annual precipitation data for each water year (October 1 - September 30) 2001-2021 from PRISM (http://www.prism.oregonstate.edu/). For each field plot, we extracted the raster values for all covariates; NDVI and precipitation data were matched to the year of plot visit. We predicted AGLBM using the set of 17 covariates (Random Forest [RF] algorithm in R statistical computing software). To create an AGLBM raster surface for each year 2001-2021, we used NDVI and precipitation raster data specific to each year in the RF (using predict function in the R raster module) (see Schrader-Patton and Underwood 2021 for details). To estimate other shrubland biomass pools (standing dead, litter, and below ground) we employed a multi-step process: 1) First, we segregated the study area by community type using the California Wildlife Habitat Relationships (CWHR) data (Mayer and Laudenslayer 1988). The wildland vegetation of the study area (excluding agricultural, urban, water, and barren classes) contains 45 CWHR classes, 14 of which are &gt;=0.75% of the wildland vegetation in the study area. We divided these 14 classes into shrubland dominated versus non-shrubland dominated types (annual grass, oak, conifer, mixed hardwood) (Table 1). Table 1. The Community types (WHR class) that are &gt;= 0.75% of all wildland vegetation in the study area and their % area of the southern California ecoregion <strong>Community type (WHR class)</strong> <strong>Vegetation type</strong> <strong>Percent of wildland vegetation in study area</strong> Mixed Chaparral Shrub 29.2 Annual Grassland Annual grass 15.9 Desert Scrub Shrub 12.7 Coastal Scrub Shrub 12.5 Coastal Oak Woodland Oak 6.4 Chamise-Redshank Chaparral Shrub 6.0 Pinyon-Juniper Conifer 2.5 Montane Hardwood Mixed hardwood 2.3 Blue Oak Woodland Oak 2.0 Sierran Mixed Conifer Conifer 1.2 Juniper Conifer 1.1 Montane Hardwood-Conifer Mixed hardwood-conifer 1.1 Montane Chaparral Shrub 1.0 Sagebrush Shrub 0.9 2) Second, for the shrubland types we determined the per pixel proportion of biomass by three plant life forms: tree, shrub, and herb. We further subdivided the shrub life form into three life history classes based on shrub post-fire regeneration strategies: Obligate Resprouters (OR), obligate seeders (OS), and facultative seeders (FS), providing five plant types in total. We created rasters depicting the proportion of biomass in each of the five plant types by first calculating the proportion of biomass in each type for the plots used in Schrader-Patton and Underwood (2021). The plot data contained individual plant species, crown width and height measurements. Using these measurements, we estimated the biomass for each individual plant within the plot by applying published allometric equations (see Schrader-Patton and Underwood 2021 for details). The individual plants in the plots were classified into the five plant types and the proportion of biomass in each type were calculated for each plot. A multinomial model was used to relate these proportions to a suite of geophysical and remote sensing variables which, in turn, was applied to raster surfaces of these predictors. The final outputs were raster maps of the proportion of biomass by life form (tree, shrub, herb) and, for shrubs, the proportion of biomass by life history type (OR, OS, and FS) (Underwood et al. in review). 3) Third, we estimated the standing dead, litter, and below ground biomass pools by either applying fractions of AGLBM gleaned the available published