Augmenting forest inventory attributes with geometric optical modelling in support of regional susceptibility assessments to bark beetle infestations |
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| Institution: | 1. Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran;2. Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shaherkord University, Shaherkord, Iran;3. Department of Ecosystem Science and Management, The Pennsylvania State University, Forest Resources Building, University Park, PA 16802, USA;1. Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA;2. Department of Oceanography, Texas A&M University, College Station, TX, USA;3. Department of Physics & Astronomy, Texas A&M University, College Station, TX, USA;4. Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, CO 80301, USA;5. CCDC-U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783, USA |
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| Abstract: | Assessment of the susceptibility of forests to mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation is based upon an understanding of the characteristics that predispose the stands to attack. These assessments are typically derived from conventional forest inventory data; however, this information often represents only managed forest areas. It does not cover areas such as forest parks or conservation regions and is often not regularly updated resulting in an inability to assess forest susceptibility. To address these shortcomings, we demonstrate how a geometric optical model (GOM) can be applied to Landsat-5 Thematic Mapper (TM) imagery (30 m spatial resolution) to estimate stand-level susceptibility to mountain pine beetle attack. Spectral mixture analysis was used to determine the proportion of sunlit canopy and background, and shadow of each Landsat pixel enabling per pixel estimates of attributes required for model inversion. Stand structural attributes were then derived from inversion of the geometric optical model and used as basis for susceptibility mapping. Mean stand density estimated by the geometric optical model was 2753 (standard deviation ± 308) stems per hectare and mean horizontal crown radius was 2.09 (standard deviation ± 0.11) metres. When compared to equivalent forest inventory attributes, model predictions of stems per hectare and crown radius were shown to be reasonably estimated using a Kruskal–Wallis ANOVA (p < 0.001). These predictions were then used to create a large area map that provided an assessment of the forest area susceptible to mountain pine beetle damage. |
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| Keywords: | Landsat Forest inventory Mountain pine beetle Susceptibility Geometric optical modelling Western Canada Lodgepole pine Forest health |
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