title: Mapping Regional Distribution of a Single Tree Species: Whitebark Pine in the Greater Yellowstone Ecosystem.
abstract: Moderate resolution satellite imagery traditionally has been thought to be inadequate for mapping vegetation at the species level. This has made comprehensive mapping of regional distributions of sensitive species, such as whitebark pine, either impractical or extremely time consuming. We sought to determine whether using a combination of moderate resolution satellite imagery (Landsat Enhanced Thematic Mapper Plus), extensive stand data collected by land management agencies for other purposes, and modern statistical classification techniques (boosted classification trees) could result in successful mapping of whitebark pine. Overall classification accuracies exceeded 90%, with similar individual class accuracies. Accuracies on a localized basis varied based on elevation. Accuracies also varied among administrative units, although we were not able to determine whether these differences related to inherent spatial variations or differences in the quality of available reference data.
authors.affiliation: Land Resources and Environmental Sciences Department, P.O. Box 173490, Montana State University, Bozeman, MT 59717-3490, U.S.A. lisa_landenburger@usgs.gov.
authors.forename: Lisa
authors.initials: L
authors.lastname: Landenburger
authors.affiliation: Land Resources and Environmental Sciences Department, P.O. Box 173490, Montana State University, Bozeman, MT 59717-3490, U.S.A. rickl@montana.edu.
authors.forename: Rick L
authors.initials: RL
authors.lastname: Lawrence
authors.affiliation: USGS Northern Rocky Mountain Science Center, Bozeman, MT 59717, U.S.A. shannon_podruzny@usgs.gov.
authors.forename: Shannon
authors.initials: S
authors.lastname: Podruzny
authors.affiliation: USGS Northern Rocky Mountain Science Center, Bozeman, MT 59717, U.S.A. chuck_schwartz@usgs.gov.
authors.forename: Charles C
authors.initials: CC
authors.lastname: Schwartz
keywords: Landsat; Remote sensing; See5; Yellowstone; boosting; classification trees