[AGU 2013 Poster] Allometric Scaling and Resource Limitations Model of Total Aboveground Biomass in Forest Stands: Site-scale Test of Model

Shared by Sungho Choi on Jan 10, 2014

Summary

Author(s) :
Sungho Choi, Yuli Shi, Xiliang Ni, Marc Simard, and Ranga Myneni
Abstract

Sparseness in in-situ observations has precluded the spatially explicit and accurate mapping of forest biomass. The need for large-scale maps has raised various approaches implementing conjugations between forest biomass and geospatial predictors such as climate, forest type, soil property, and topography. Despite the improved modeling techniques (e.g., machine learning and spatial statistics), a common limitation is that biophysical mechanisms governing tree growth are neglected in these black-box type models. The absence of a priori knowledge may lead to false interpretation of modeled results or unexplainable shifts in outputs due to the inconsistent training samples or study sites. Here, we present a gray-box approach combining known biophysical processes and geospatial predictors through parametric optimizations (inversion of reference measures). Total aboveground biomass in forest stands is estimated by incorporating the Forest Inventory and Analysis (FIA) and Parameter-elevation Regressions on Independent Slopes Model (PRISM). Two main premises of this research are: (a) The Allometric Scaling and Resource Limitations (ASRL) theory can provide a relationship between tree geometry and local resource availability constrained by environmental conditions; and (b) The zeroth order theory (size-frequency distribution) can expand individual tree allometry into total aboveground biomass at the forest stand level. In addition to the FIA estimates, two reference maps from the National Biomass and Carbon Dataset (NBCD) and U.S. Forest Service (USFS) were produced to evaluate the model. This research focuses on a site-scale test of the biomass model to explore the robustness of predictors, and to potentially improve models using additional geospatial predictors such as climatic variables, vegetation indices, soil properties, and lidar-/radar-derived altimetry products (or existing forest canopy height maps). As results, the optimized ASRL estimates satisfactorily resemble the FIA aboveground biomass in terms of data distribution, overall agreement, and spatial similarity across scales. Uncertainties are quantified (ranged from 0.2 to 0.4) by taking into account the spatial mismatch (FIA plot vs. PRISM grid), heterogeneity (species composition), and an example bias scenario (= 0.2) in the root system extents.

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Publication Name
AGU 2013 Fall Meeting - Poster Session
Publication Location
N/A
Year Published
2013

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Poster_SChoi_AGU2013_final.pdf
AGU2013 Poster
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