HR: 1340h
AN: B43C-1442    [Abstracts]
TI: Remote Sensing of Boreal Forest Biophysical and Inventory Parameters: A Review
AU: * Lutz, D A
EM: dal2y@virginia.edu
AF: University Of Virginia Department of Environmental Sciences, 291 McCormick Road Clark Hall P.O. Box 400123, Charlottesville, VA 22903, United States
AU: Washington-Allen, R A
EM: washington-allen@tamu.edu
AF: Department of Ecosystem Science and Management Texas A/&M University, 2138 TAMU, College Station, TX 77843-2138, United States
AB: Vegetation makes up nearly 70 % of the Earth's terrestrial surface and products from vegetated systems are vitally important for human populations. The growing need to manage vegetation resources at regional and global spatial scales has led to the increased use of remote sensing technologies among forestry scientists and managers for use in their investigation and supervision of forested landscapes. With a panoply of extant and developing airborne and satellite sensors, as well as multiple analysis techniques, there is a need to discern the most acceptable methods in which to examine remotely sensed imagery for forest ecosystem parameters. This includes both biophysical and inventory indicators. This study investigates the methods used to examine plant parameters in the boreal forest, and attempts to derive the most appropriate methods for extracting information regarding plant structure and stand information. A suggested methodology is constructed for use by remote sensors and forest managers. Specifically, we reviewed the literature on the remote sensing of boreal forests that featured airborne and satellite optical, passive and active radar, and lidar systems in order to determine if common frameworks for monitoring and assessing change in forest biophysical and inventory parameters could be developed. Other important remote sensing techniques such as change detection and land cover identification were also examined. Our review considered the purpose of each study, the type of sensor(s) used [e.g., Landsat or Lidar], where the study occurred, the methods used, including what vegetation and soil parameters or processes were considered, and the remote sensing indicator developed to measure this parameter [e.g., the normalized difference vegetation index (NDVI) is a surrogate for phytomass, LAI, land cover type, and other plant parameters]. We also investigated how the measured indicators were calibrated and validated as well as the limitations of the sensors that were expressed in conducting a particular study. The most effective methodology for each parameter and/or variable was described. Additionally, we produced and posted in the environment and conservation section of the Google Earth Community (http://bbs.keyhole.com) an annotated spatial bibliography of fifty of these studies.
DE: 0430 Computational methods and data processing
DE: 0476 Plant ecology (1851)
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting