HR: 1340h
AN: B43C-1449    [Abstracts]
TI: Leaf Area Index Modeling and Mapping in Conifer Forests using Multispectral and Lidar Data
AU: * Jensen, J L
EM: jjensen@uidaho.edu
AF: University of Idaho, Environmental Science Program, Morrill Hall 216 P.O. Box 443006, Moscow, ID 83844-3006, United States
AU: Humes, K S
EM: khumes@uidaho.edu
AF: University of Idaho, Department of Geography, McClure Hall 307A P.O. Box 443021, Moscow, ID 83844-3021, United States
AU: Vierling, L A
EM: leev@uidaho.edu
AF: University of Idaho - Department of Rangeland Ecology and Management, College of Natural Resources P.O. Box 441135, Moscow, ID 83844-1135, United States
AU: Hudak, A T
EM: ahudak@fs.fed.us
AF: USFS Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843, United States
AB: Leaf area index (LAI; the ratio of half the total needle surface area per unit ground area) is an essential conifer forest structural characteristic for quantifying biosphere-atmosphere carbon and water fluxes because it largely controls the fraction of photosynthetically active radiation absorbed by vegetation. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (VIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify with VI/empirical methods due to the asymptotic relationships of VIs and LAI. Traditional methods are also complicated by complex topography, variable species composition, and heterogeneous spatial structure of forest canopies. Here, we identify and compare the functional relationships between field-measured LAI quantities and various SPOT satellite image-derived VIs, height distribution metrics derived from small footprint, discrete-return lidar, and the extent to which integration of both lidar and spectral datasets can accurately estimate LAI over a broad range of coniferous forest stand conditions in the northern Rocky Mountains. We find that LAI models derived from lidar metrics are only incrementally improved with the inclusion of multispectral VI data.
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting