Augmenting forest inventory attributes with geometric optical modelling in support of regional susceptibility assessments to bark beetle infestations

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• 作者：Sam B. Coggins^a ; Nicholas C. Coops^a ; ^{nicholas.coops@ubc.ca} ; Thomas Hilker^b ; Michael A. Wulder^c
• 关键词：Landsat ; Forest inventory ; Mountain pine beetle ; Susceptibility ; Geometric optical modelling ; Western Canada ; Lodgepole pine ; Forest health
• 刊名：International Journal of Applied Earth Observation and Geoinformation
• 出版年：2013
• 出版时间：April, 2013
• 年：2013
• 卷：21
• 期：Complete
• 页码：444-452
• 全文大小：3103 K

文摘

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 ¡À 308) stems per hectare and mean horizontal crown radius was 2.09 (standard deviation ¡À 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.