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* Metric Name: Annual Biomass Data 2001 and2021 Above and Below Ground, Standing Dead, and Litter  

* Tier: 2  

* Data Vintage: 2001 and 2021  

* Unit Of Measure: kg/m2  

* Metric Definition and Relevance: 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. These data were developed using a consistent, repeatable method to assess four vegetative biomass pools from 2001-2021 for the 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). Aboveground live biomass estimates were developed first (Schrader-Patton and Underwood 2021), and then belowground, standing dead, and litter biomass pools were calculated using field data in the peer-reviewed literature (Schrader-Patton et al. 2022). Over half (52.3%) of the study area is shrubland, and the method accounts for different amounts of carbon associated with three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding. Biomass estimates were also generated for trees and herbs, giving a total of five life form post-fire regeneration strategy 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 0f 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).

  

* Creation Method: Researchers generated spatial estimates of above ground live biomass (AGLBM) for 2000-2021 for the southern California area, illustrated in the figure below. 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; Angeles, Cleveland, Los Padres, and San Bernardino:

The researchers 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 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. Annual
precipitation data for each water year (October 1 - September 30) 2001-2021
were downloaded 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. AGLBM was predicted
using the set of 17 covariates in a Random Forest [RF] model in R statistical
computing software. To create an AGLBM raster surface for each year 2001-2021,
NDVI and precipitation raster data specific to each year werre integrated into
the RF model (see [Schrader-Patton and Underwood
2021](https://www.mdpi.com/2072-4292/13/8/1581) for details).

To estimate other shrubland biomass pools (standing dead, litter, and below
ground) a multi-step process was employed:

1) First, the study area was segmented 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 >=0.75% of the
wildland vegetation in the study area. CHWR classes were divided into 14
classes; shrubland-dominated versus non-shrubland-dominated types (annual
grass, oak, conifer, mixed hardwood).

2) For the shrubland types the researchers 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 post-fire regeneration strategies:
Obligate Resprouters (OR), obligate seeders (OS), and facultative seeders
(FS), providing five plant types in total. Rasters depicting the proportion of
biomass in each of the five plant types were created 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, the biomass was
estimated for each individual plant within the plot by applying published
allometric equations (see[ Schrader-Patton and Underwood
2021](https://www.mdpi.com/2072-4292/13/8/1581) 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 post-fire regeneration strategy (OR, OS, and FS)
(Underwood et al. 2023). We used these raster layers to estimate other
vegetative pools of biomass (e.g., below-ground shrub biomass using above- to
below- ground ratios) for each post-fire regeneration strategy type (OR, OS,
and FS) using information found in the published literature.

3) Third, estimates of the standing dead, litter, and below ground biomass
pools by either applying fractions of AGLBM gleaned the available published
literature or by using biomass estimates in existing spatial datasets. The
specific method used was dependent on the percentage of the WHR class in the
study area and the vegetation type (shrub or non-shrub)

  

* Credits: Schrader-Patton, C.C., E.C. Underwood, and Q.M. Sorenson. 2023. Annual biomass spatial data for southern California (2001–2021): Above- and belowground, standing dead, and litter. _Ecology_ e4031. Schrader-Patton, C.C. and E.C. Underwood. 2022. Annual biomass data (2001-2021) for southern California: above- and below-ground, standing dead, and litter. Dryad, Dataset,  Underwood, E.C., Q.M. Sorenson, C.C. Schrader-Patton, N.A. Molinari and H.D. Safford. 2023. Resprouting, seeding, and facultative seeding shrub species in California’s Mediterranean-type climate region. _Frontiers in Ecology and Evolution_ 11:1158265. doi: 10.3389/fevo.2023.1158265 Data available in RRK for 2001 and 2021 (year in file name changes accordingly). The full set of data for intervening years can be downloaded from:[ https://doi.org/10.5061/dryad.qz612jmjt](https://doi.org/10.5061/dryad.qz612jmjt) .