توضیحات
Abstract
Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3D point data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diameter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Mountains
in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm
detected 86 percent of the trees (“recall”), 94 percent of the segmented trees were correct (“precision”), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data
Introduction
Light Detection and Ranging (lidar) is an active remote sensing technology that measures properties of reflected
light to determine range to a distant object (Lefsky et al.,
2002). The range to an object is calculated by measuring the time delay between transmission of a laser pulse and
detection of the reflected signal (Wehr and Lohr, 1999). Due to its ability to generate 3-dimensional (3D) data with high spatial resolution and accuracy, lidar technology is being increasingly used in ecology (Lefsky et al., 2002; Gaveau and Hill, 2003; Hopkinson et al., 2004a; Hopkinson et al.,2004b), geomorphology (Glenn et al., 2006), seismology (Lee et al., 2009), and remote sensing (Brandtberg et al., 2003). Individual tree segmentations have significant implications in forestry (Chen et al., 2006; Koch et al., 2006; Chenet al., 2007)
Publisher: Photogrammetric Engineering & Remote Sensing
Year:2012
By:Wenkai Li, Qinghua Guo, Marek K. Jakubowski, and Maggi Kelly
File Information:English Language/10Page/Size:752K
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ناشر:Photogrammetric Engineering & Remote Sensing
سال:2012
کاری از:Wenkai Li, Qinghua Guo, Marek K. Jakubowski, and Maggi Kelly
اطلاعات فایل:زبان انگلیسی/10 صفحه/حجم:752K
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