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Postfire stand structure in a semiarid savanna: cross-scale challenges estimating biomass

Cho-Ying Huang, Stuart E Marsh, Mitchel P McClaran, Steven R Archer
Ecological Applications 2007, 17 (7): 1899-910
17974330
Algorithms relating remotely sensed woody cover to biomass are often the basis for large-scale inventories of aboveground carbon stocks. However, these algorithms are commonly applied in a generic fashion without consideration of disturbances that might alter vegetation structure. We compared field and remote sensing estimates of woody biomass on savannas with contrasting disturbance (fire) histories and assessed potential errors in estimating woody biomass from cover without considering fire history. Field surveys quantified multilayer cover (MLC) of woody and succulent plants on sites experiencing wildfire in 1989 or 1994 and on nearby unburned (control) sites. Remote sensing estimates of the woody cover fraction (WCF) on burned and control sites were derived from contemporary (2005) dry-season Landsat Thematic Mapper imagery (during a period when herbaceous cover was senescent) using a probabilistic spectral mixture analysis model. Satellite WCF estimates were compared to field MLC assessments and related to aboveground biomass using allometry. Field-based MLC and remotely sensed WCFs both indicated that woody cover was comparable on control areas and areas burned 11-16 years ago. However, biomass was approximately twofold higher on control sites. Canopy cover was a strong predictor of woody biomass on burned and control areas, but fire history significantly altered the linear cover-biomass relationship on control plots to a curvilinear relationship on burned plots. Results suggest predictions of woody biomass from "generic" two-dimensional (2-D) cover algorithms may underestimate biomass in undisturbed stands and overestimate biomass in stands recovering from disturbance. Improving the accuracy of woody-biomass estimates from field and/or remotely sensed cover may therefore require disturbance-specific models or detection of vegetation height and transforming 2-D vegetation cover to 3-D vegetation volume.

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