Maximum Canopy Height Estimation Using ICESat GLAS Laser AltimetryMaximum Canopy Height Estimation Using ICESat GLAS Laser Altimetry
- Other Titles
- Maximum Canopy Height Estimation Using ICESat GLAS Laser Altimetry
- Authors
- 박태진; 이우균; 이종열; Masato Hayashi; Yanhong Tang; 곽두안; 곽한빈; 김문일; Guishan Cui; 남기준
- Issue Date
- 2012
- Publisher
- 대한원격탐사학회
- Keywords
- Maximum canopy height; Geoscience Laser Altimeter System (GLAS); Airborne Light Detection and Ranging (LiDAR); Waveform analysis
- Citation
- 대한원격탐사학회지, v.28, no.3, pp.307 - 318
- Indexed
- KCI
- Journal Title
- 대한원격탐사학회지
- Volume
- 28
- Number
- 3
- Start Page
- 307
- End Page
- 318
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/109747
- ISSN
- 1225-6161
- Abstract
- To understand forest structures, the Geoscience Laser Altimeter System (GLAS) instrument have been employed to measure and monitor forest canopy with feasibility of acquiring three dimensional canopy structure information. This study tried to examine the potential of GLAS dataset in measuring forest canopy structures, particularly maximum canopy height estimation. To estimate maximum canopy height using feasible GLAS dataset, we simply used difference between signal start and ground peak derived from Gaussian decomposition method. After estimation procedure, maximum canopy height was derived from airborne Light Detection and Ranging (LiDAR) data and it was applied to evaluate the accuracy of that of GLAS estimation. In addition, several influences, such as topographical and biophysical factors, were analyzed and discussed to explain error sources of direct maximum canopy height estimation using GLAS data. In the result of estimation using direct method, a root mean square error (RMSE) was estimated at 8.15 m. The estimation tended to be overestimated when comparing to derivations of airborne LiDAR. According to the result of error occurrences analysis, we need to consider these error sources, particularly terrain slope within GLAS footprint, and to apply statistical regression approach based on various parameters from a Gaussian decomposition for accurate and reliable maximum canopy height estimation.
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Collections - College of Life Sciences and Biotechnology > Division of Environmental Science and Ecological Engineering > 1. Journal Articles
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