Evaluation of approaches to estimating aboveground biomass in southern pine forests using SIR-C data
A study was performed to evaluate various techniques for estimating aboveground, woody plant biomass in pine stands found in the southeastern United States, using C- and L- band multiple polarization radar imagery collected by the Shuttle Imaging Radar-C (SIR-C) system. The biomass levels present in the test stands ranged between 0.0 and, 44.5 kg m-2. Two SIR-C data sets were used: one collected in April, 1994, when the soil conditions were very wet and the canopy was slightly wet from dew and a second collected in October, 1994, when the soils and canopy were dry. During the October mission, pine needles were completely flushed and the foliar biomass was twice as great in the forest stands as in April. Four methods were evaluated to estimate total biomass: one including a straight multiple linear correlation between total biomass and the various SIR-C channels; another including a ratio of the L-band HV/C-and HV channels; and two others requiring multiple steps, where linear regression equations for different stand components (height, basal area, and crown or branch biomass) were used as the basis for estimating total biomass. It was shown that the data collected in October (dry soil conditions) were better for estimation of biomass than the data collected in April (wet soil cnjs). Overall, a multistep approach resulted in the lowest root mean square (RMS) errors (5.91 kg m-2) when biomass levels were <20 kg m-2. For all biomass levels, the simple regression technique resulted in the lowest RMS errors (8.1 kg m-2). The multiple-step approaches have the additional advantage of being able to provide estimates of different components of stand structure and biomass, such as average tree height, basal area, branch biomass, canopy biomass, trunk biomass, and foliage biomass. The LHV channel is the critical element in all the biomass equations, as would be expected from the body of literature. The addition of other channels - generally, CHV or CHH - significantly improves biomass estimates, whether as a ratio or as additional terms in a regression equation.
Harrell, PA; Kasischke, ES; Bourgeau-Chavez, LL; Haney, EM; Christensen, NL
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