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Journal of Applied Remote Sensing • Open Access

Extraction of forest structural parameters based on the intensity information of high-density airborne light detection and ranging
Author(s): Chunxiang Cao; Yunfei Bao; Wei Chen; Rong Tian; Yongfeng Dang; Lin Li; Guanghe Li

Paper Abstract

The quantitative description of forest canopy structure is significant for the investigation of a forest, which serves as an important component of the terrestrial ecosystem. Light detection and ranging (LIDAR), as a new technical means that can acquire high-precision vertical information, plays a crucial role in forest monitoring and management. Choosing Dayekou forest experimental area in the Heihe watershed as a study area, we separated the ground points from the vegetation points using the skewness-change algorithm based on the intensity information from airborne LIDAR data. After that, digital terrain model (DTM) and digital surface model (DSM) were generated, respectively, based on which the canopy height model (CHM) was acquired. Finally, using the variational window, the local maximum filter method was used to extract individual tree heights and crown widths from CHM. The determination coefficients of tree heights and crown widths were 0.8568 and 0.3923, respectively. The validation results indicated that the tree heights could be effectively extracted from intensity information of airborne LIDAR, while the accuracy of extracted crown widths needed to be improved. In the future work, aerial photos and other high-resolution images would be combined to improve the accuracy.

Paper Details

Date Published: 21 June 2012
PDF: 13 pages
J. Appl. Remote Sens. 6(1) 063533 doi: 10.1117/1.JRS.6.063533
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Chunxiang Cao, Institute of Remote Sensing Applications (China)
Yunfei Bao, Institute of Remote Sensing Applications (China)
Wei Chen, Institute of Remote Sensing Applications (China)
Rong Tian, Institute of Remote Sensing Applications (China)
Yongfeng Dang, State Forestry Administration (China)
Lin Li, China Agricultural Univ. (China)
Guanghe Li, Beijing Univ. of Technology (China)


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