Remote Sens. 2013, 5(9), 4163-4186; doi:10.3390/rs5094163
Article

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

1 Department of Environmental Science, Policy, and Management, University of California, Berkeley, 130 Mulford Hall #3114, Berkeley, CA 94720, USA 2 School of Engineering, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343, USA
* Author to whom correspondence should be addressed.
Received: 28 June 2013; in revised form: 22 August 2013 / Accepted: 22 August 2013 / Published: 26 August 2013
PDF Full-text Download PDF Full-Text [3993 KB, uploaded 26 August 2013 11:52 CEST]
Abstract: 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.
Keywords: lidar; individual tree; segmentation; object based; DEM; raster; vector

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

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.

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.

Chicago/Turabian Style

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

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert