HR: 1340h
AN: B43C-1461    [Abstracts]
TI: Evaluating the Potential of Waveform Lidar and Hyperspectral Data Fusion for Species Level Biomass Mapping.
AU: * Swatantran, A
EM: aswatan@umd.edu
AF: Geography Department, University of Maryland, College Park, MD 20742, United States
AU: Dubayah, R
EM: dubayah@umd.edu
AF: Geography Department, University of Maryland, College Park, MD 20742, United States
AU: Hofton, M
EM: mhofton@umd.edu
AF: Geography Department, University of Maryland, College Park, MD 20742, United States
AU: Blair, J B
EM: James.B.Blair@nasa.gov
AF: Laser Remote Sensing Branch, Goddard Space Flight Center, Greenbelt, MD 20771, United States
AB: Many studies have demonstrated the ability of waveform lidar to map forest structural metrics such as canopy height, canopy cover and above ground biomass with high accuracies over different forest cover and types. Hyperspectral data provides forest attributes complementary to lidar such as vegetation stress, moisture content and land cover at species level. This study explores and evaluates the combined potential of waveform lidar (LVIS) and AVIRIS hyperspectral imagery for species level biomass mapping in the Sierra Nevada spotted owl habitat.LVIS quartile heights and canopy cover along with spectral metrics and endmember fractions from AVIRIS were compared with field biomass measures using linear and stepwise regression.Water band indices and shade fractions from AVIRIS show moderate to strong correlation with LVIS canopy height and biomass for certain species. LVIS variables were found to be consistently good predictors of total biomass as well as species level biomass. The inclusion of AVIRIS metrics in combination with lidar added little explanatory value for biomass. However, biomass prediction at species level lowered residual error by 20% or more in comparison to total biomass estimates, suggesting that its main value for biomass mapping is through species-level stratification.
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting