HR: 1340h
AN: B43C-1459    [Abstracts]
TI: A Machine Learning Approach to Modeling Old-Growth Using Spectral and Topographic Data
AU: * Evans, J S
EM: jevans02@fs.fed.us
AF: USDA Forest Service, Rocky Mt. Research Station - Forestry Sciences Lab, 1221 South Main St., Moscow, ID 83843, United States
AU: Smith, A
EM: alistair@uidaho.edu
AF: University of Idaho, College of Natural Resources, PO Box 441142, Moscow, ID 83844, United States
AU: Cushman, S A
EM: scushman@fs.fed.us
AF: USDA Forest Service, Rocky Mt. Research Station - Forestry Sciences Lab, 790 Beckwith Ave., Missoula, MT 59801, United States
AU: Mital, J
EM: jmital@fs.fed.us
AF: USDA Forest Service, Clearwater National Forest, 12370 Highway 12, Orofino, ID 83544, United States
AU: Hudak, A T
EM: ahudak@fs.fed.us
AF: USDA Forest Service, Rocky Mt. Research Station - Forestry Sciences Lab, 1221 South Main St., Moscow, ID 83843, United States
AB: Forest land managers and researchers are faced with a myriad of ecological, wildlife, management, and legal issues directly tied to old-growth vegetation structures. However, lack of adequate old-growth inventories has hindered decision making. Spectral remote sensing has previously proved inadequate in filling this gap. Presented is machine learning approach that leverages both spectral and topographic data to describe old- growth niches and accurately predict presence/absence. Two sources of forest inventory plot data were utilized; an operationally derived and a targeted sample. Results show that although the targeted sample provided more accurate results, a strong bias is evident making landscape inference erroneous. The operational sample contained insufficient information to adequately describe the range of old-growth across the study area. The best results were provided by a combination of the data. We provide not only a new modeling framework for old-growth but also recommendations on sample design and utilization of preexisting data to avoid sampling bias and improve predictions.
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0466 Modeling
SC: Biogeosciences [B]
MN: 2007 Fall Meeting