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Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense mixed conifer forestAuthor(s): Marek K. Jakubowksi; Qinghua Guo; Brandon Collins; Scott Stephens; Maggi Kelly
Source: Photogrammetric Engineering and Remote Sensing
Publication Series: Scientific Journal (JRNL)
PDF: Download Publication (1.26 MB)
DescriptionWe compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m2), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and "50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management.
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CitationJakubowksi, Marek K.; Guo, Qinghua; Collins, Brandon; Stephens, Scott; Kelly, Maggi. 2013. Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense mixed conifer forest. Photogrammetric Engineering and Remote Sensing. 79(1): 37-49.
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