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Journal of Sensors
Volume 2018 (2018), Article ID 1593129, 9 pages
https://doi.org/10.1155/2018/1593129
Research Article

Forest Canopy Height Estimation Using Multiplatform Remote Sensing Dataset

1Earthquake and Volcano Research Division, Korea Meteorological Administration, 61 16-Gil, Yeouidaebang-ro, Dongjak-gu, Seoul 07062, Republic of Korea
2Division of Science Education, 1 Kangwondaehak-gil, Chuncheon, 24341 Gangwon, Republic of Korea

Correspondence should be addressed to Chang-Wook Lee; rk.ca.nowgnak@eelwc

Received 6 February 2017; Accepted 5 February 2018; Published 11 April 2018

Academic Editor: Oleg Lupan

Copyright © 2018 Won-Jin Lee and Chang-Wook Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recently, numerous studies have attempted to determine forest height using remote sensing techniques that not only have the benefits of fast data acquisition, processing, and analysis but are also cost-effective. However, if there was insufficient data to apply the latest remote sensing techniques, we need to consider many kinds of datasets as possible. In this study, we tried to determine forest height using discrete-return LiDAR data, SRTM, satellite L-band SAR data, and Optical data. We experimented with the differences between LiDAR DSM and DTM, as well as SRTM DSM and LiDAR DTM. In addition, we applied an SBAS algorithm and linear regression to the dataset. From the quantitative evaluation, the RMSE and R2 of the LiDAR-derived forest height (3.22 m and 0.43, resp.) and the SRTM-derived forest height (2.90 m and 0.50, resp.) were both reasonably good, especially when we consider data acquisition time differences and measurement errors in mountainous areas. Moreover, we slightly improved the RMSE and R2 from 2.90 m and 0.50, respectively, to 2.75 m and 0.54, respectively, by correcting the SRTM using the SBAS algorithm. Furthermore, we merged the datasets using linear regression and obtained improved forest heights with RMSE and R2 values of 2.68 m and 0.56, respectively. To generate a forest height map, we used NDVI from Optical imagery and masked heights below 2 m from each sensor. Thus, we excluded urban areas, “bare earth surfaces,” and mountain streams from each sensor’s imagery. Finally, we generated a forest height map by overlapping the datasets. The results of this study indicate that each sensor has the potential for not only determining forest height but also extracting complementary forest area information. Furthermore, this study demonstrates the potential for improvement using the SBAS algorithm and linear regression.