Abstract
Old-growth forests have complex structure variability that provides critical habitat for endangered species, enhancing biodiversity and ecosystem services, but old-growth has become rare due to historical forest harvesting. In order to support the recovery process of old-growth forests in British Columbia (B.C.), Canada, recent studies believe it is valuable to identify the old-growth characteristics in recovering forests for conservation purposes. This study attempted to predict the distribution of old-growth attributes in the Coastal Western Hemlock (CWH) zone in British Columbia using area-based Light Detection and Ranging (LiDAR) metrics. LiDAR point clouds of 61 forestry inventory plots were extracted to generate LiDAR metrics to create multilinear regression models for four old-growth attributes: standard deviation of diameter at breast height (DBH), maximum tree DBH, average live crown percentage, and the sum of understory plants percentage. The results showed the ability of LiDAR to predict varied by old-growth attributes and were highest for the standard deviation of DBH (R2=0.6766), followed by Maximum Tree DBH (R2=0.5322), the Sum of understory plants percentage (R2=0.4218) and lowest for average live crown percentage (R2=0.242). An old-growth index was derived from four old-growth attributes for mapping the potential locations of old-growth. However, the validation results of 19.08% from vegetation resource inventory (VRI) illustrate that the old-growth index does not successfully identify old growth. Despite the challenges encountered, the prediction results can still be used to identify old-growth attributes and enhance knowledge of old-growth landscapes. Also, this study has potential applications in old-growth forest restoration in the Western Hemlock Ecosystem and supports the old-growth management plan of the government.
MGEM Student: Wan Xilin
Community Partner: Dr. Travis Heckford, BC Government
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Wan, Xilin, 2023, “Old-Growth Attributes Prediction in the Coastal Western Hemlock Ecosystem, British Columbia using LiDAR”, https://doi.org/10.5683/SP3/CFRGAQ, Borealis, V1