HR: 1340h
AN: B43C-1458    [Abstracts]
TI: Predicting tree-level forest structure from LiDAR data
AU: * Falkowski, M J
EM: falk4587@uidaho.edu
AF: Department of Forest Resources University Of Idaho, PO Box 441133, Moscow, ID 83844-1133, United States
AU: Gessler, P E
EM: paulg@uidaho.edu
AF: Department of Forest Resources University Of Idaho, PO Box 441133, Moscow, ID 83844-1133, United States
AU: Hudak, A T
EM: ahudak@fs.fed.us
AF: US Forest Service Moscow Forestry Sciences Laboratory, 1221 South Main Street, Moscow, ID 83843, United States
AU: Crookston, N L
EM: ncrookston@fs.fed.us
AF: US Forest Service Moscow Forestry Sciences Laboratory, 1221 South Main Street, Moscow, ID 83843, United States
AB: This research evaluates the efficacy of statistical imputation models (e.g., the k-nearest neighbor algorithm) incorporating LiDAR data to predict and map tree-level forest structure data (individual tree height, diameter at breast height, and species) across an 88,000 ha study area in Northern Idaho, USA. The primary objective is to provide spatially explicit data to parameterize the Forest Vegetation Simulator (FVS), a forest growth model that operates at the individual tree level, so that forest growth can be modeled across the entire study area. In addition to FVS parameterization, the imputed forest structure data could be used for many purposes including forest commodity assessment, carbon accounting, wildlife habitat modeling, etc. The final imputation models utilize LiDAR derived intensity and height measurements as well as LiDAR DEM topographic variables, to predict tree- level forest structure data. The imputed forest structure data are compared to independent ground-based forest inventory data via statistical equivalence models. Initial results indicate that the imputed data are equivalent (± 15 %) to the independent forest inventory data. Further development of this method will provide tree- level forest inventory data across large spatial extents.
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0452 Instruments and techniques
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting