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Article

Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches

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Department of Environmental Science, Policy, and Management, University of California, Berkeley, 130 Mulford Hall #3114, Berkeley, CA 94720, USA
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School of Engineering, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343, USA
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Author to whom correspondence should be addressed.
Remote Sens. 2013, 5(9), 4163-4186; https://doi.org/10.3390/rs5094163
Received: 28 June 2013 / Revised: 22 August 2013 / Accepted: 22 August 2013 / Published: 26 August 2013
Light detection and ranging (lidar) data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA) of a canopy height model (CHM). The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m2), discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r2: 0.93–0.96). Tree detection rates increased for more dominant trees (8–100 percent). The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure. View Full-Text
Keywords: lidar; individual tree; segmentation; object based; DEM; raster; vector lidar; individual tree; segmentation; object based; DEM; raster; vector
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MDPI and ACS Style

Jakubowski, M.K.; Li, W.; Guo, Q.; Kelly, M. Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches. Remote Sens. 2013, 5, 4163-4186. https://doi.org/10.3390/rs5094163

AMA Style

Jakubowski MK, Li W, Guo Q, Kelly M. Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches. Remote Sensing. 2013; 5(9):4163-4186. https://doi.org/10.3390/rs5094163

Chicago/Turabian Style

Jakubowski, Marek K., Wenkai Li, Qinghua Guo, and Maggi Kelly. 2013. "Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches" Remote Sensing 5, no. 9: 4163-4186. https://doi.org/10.3390/rs5094163

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