Remote Sensing Applications and Decision Support

Simple approach to improving the extraction of canopy metrics from airborne laser scanning data for tropical forests

[+] Author Affiliations
Zhengyang Hou, Matti Maltamo, Timo Tokola

University of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences, Borealis Building, Yliopistokatu 7, P.O. Box 111, Joensuu FI-80101, Finland

Qing Xu

University of Illinois at Urbana-Champaign, Department of Geography and Geographic Information Science, 230 Computing Applications Building, MC-150 605 East Springfield Avenue, Champaign, Illinois 61820-6371, United States

Chao Zhang

East China Normal University, Key Laboratory of Geographic Information Science of the Ministry of Education of PRC, 500 Dongchuan Road, Minhang District, Shanghai 200241, China

J. Appl. Remote Sens. 10(1), 016019 (Mar 02, 2016). doi:10.1117/1.JRS.10.016019
History: Received August 4, 2015; Accepted February 11, 2016
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Abstract.  We aim to improve the predictive mapping of stem volume with airborne laser scanning (ALS) data acquired in Laos by adapting the area-based approach (ABA) to a tropical context. Separating laser returns of bushes from main stories with a cut-off threshold is a step very important to the ABA. The adaptation focused here on applying global and plot-adaptive cut-off thresholds to improve the extraction of canopy metrics. In order to select the optimal global cut-off threshold for removing understory bushes and ground objects, a sensitivity analysis of the modeling efficacy to the global cut-off threshold was conducted in the range from 0 to 5 m at 0.1-m intervals. To account for structural variation between plots, a simple plot-adaptive method was proposed for adjusting the threshold of each specific plot. The results showed that the optimal global cut-off threshold, which implicitly assumed the forest structure being homogeneous for all plots was 3.6 m. A model based on the plot-adaptive cut-off thresholds achieved better accuracy (RMSE 28%) than did the optimal global threshold-based model (RMSE 30%). It is concluded that the ALS-based canopy metrics extracted using the plot-adaptive method describe the structural heterogeneity of tropical forests adequately, whereas the global thresholding method is contingent on the forest structure being simple.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Zhengyang Hou ; Qing Xu ; Chao Zhang ; Matti Maltamo and Timo Tokola
"Simple approach to improving the extraction of canopy metrics from airborne laser scanning data for tropical forests", J. Appl. Remote Sens. 10(1), 016019 (Mar 02, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.016019


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