HR: 08:45h
AN: B41E-03    [Abstracts]
TI: Using LiDAR Metrics to Characterize Forest Structural Complexity at Multiple Scales
AU: * Kane, V R
EM: vkane@u.washington.edu
AF: College of Forest Resources, University of Washington Box 352100, Seattle, WA 98195, United States
AU: McGaughey, R J
EM: bmcgaughey@fs.fed.us
AF: USDA Forest Service Pacific Northwest Research Station, University of Washington Box 352100, Seattle, WA 98195, United States
AU: Gersonde, R
EM: Rolf.Gersonde@Seattle.Gov
AF: Watershed Services Division Seattle Public Utilities, 19901 Cedar Falls Road SE, North Bend, WA 98045, United States
AU: Franklin, J F
EM: jff@u.washington.edu
AF: College of Forest Resources, University of Washington Box 352100, Seattle, WA 98195, United States
AB: Forest structure - the size and arrangement of trees and foliage - reflects a stand's history of initiation, growth, disturbance, and mortality. Because of this, studying the structure of forests can provide key insights into ecological processes, guides to silvicultural prescriptions to improve habitat, and assessments of forested landscapes. This study tested LiDAR metrics to characterize stands based on canopy structure. The study site was the 34,591 ha of forests in the Cedar River Watershed in western Washington State, USA. Stands ranged in age from <25 years old to >350 years old (including old-growth). Study sites spanned the western hemlock- Douglas fir (Tsuga heterophylla-Pseudotsuga menziesii), Pacific silver fir (Abies amabilis), and mountain hemlock (Tsuga mertansiana) forest zones. Eighty sample plots were used to ground truth the LiDAR data. A variety of structural indices were used to study canopy structural variations at the plot, stand, and landscape scales. The two most successful indices used the exposed geometry of the canopy surface: (1) the ratio of the canopy surface area to ground surface area (rumple index), and (2) the ratio of the volume beneath the canopy surface to maximum volume beneath the 95th percentile height (modified canopy volume method). These two indices integrated the spatial effects of tree heights, foliage distribution, and tree arrangement within 15m pixels. Variation between pixels revealed structural complexity at larger scales. Results: At the plot scale (~4 pixels), correlations with standard plot metrics (e.g., diameter at breast height) were similar to those reported by other studies. Comparison of structural complexity with age and height revealed a diversity of development pathways. The relationship between height and complexity allowed stands to be classified by the degree to which they have achieved their potential structural complexity, a new way to examine forest development. At the stand scale, the indices allowed spatial analysis of the patterns of structural variance. Analyzing these patterns would assist managers in designing forest management regimes based on natural stand complexity patterns. At the landscape scale, the indices allowed classification of stands based on structural complexity, which better differentiated stands than using only age or simpler structural element classifications. Old-growth stands, for example, were classified into four distinct groups based on their canopy structural complexity.
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0452 Instruments and techniques
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting