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Article

Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network

1
Marine Disaster Research Center, Korea Institute of Ocean Science & Technology, Busan 49111, Korea
2
Department of Geoinformatics, University of Seoul, Seoul 02504, Korea
3
Department of Smart Cities, University of Seoul, Seoul 02504, Korea
4
Center for Environmental Assessment Monitoring, Korea Environment Institute (KEI), Sejong-si 30147, Korea
5
Division of Forest Ecology, National Institute of Forest Science, Seoul 02455, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Lin Cao
Remote Sens. 2021, 13(18), 3736; https://doi.org/10.3390/rs13183736
Received: 10 August 2021 / Revised: 11 September 2021 / Accepted: 15 September 2021 / Published: 17 September 2021
The role of forests is increasing because of rapid land use changes worldwide that have implications on ecosystems and the carbon cycle. Therefore, it is necessary to obtain accurate information about forests and build forest inventories. However, it is difficult to assess the internal structure of the forest through 2D remote sensing techniques and fieldwork. In this aspect, we proposed a method for estimating the vertical structure of forests based on full-waveform light detection and ranging (FW LiDAR) data in this study. Voxel-based tree point density maps were generated by estimating the number of canopy height points in each voxel grid from the raster digital terrain model (DTM) and canopy height points after pre-processing the LiDAR point clouds. We applied an unsupervised classification algorithm to the voxel-based tree point density maps and identified seven classes by profile pattern analysis for the forest vertical types. The classification accuracy was found to be 72.73% from the validation from 11 field investigation sites, which was additionally confirmed through comparative analysis with aerial images. Based on this pre-classification reference map, which is assumed to be ground truths, the deep neural network (DNN) model was finally applied to perform the final classification. As a result of accuracy assessment, it showed accuracy of 92.72% with a good performance. These results demonstrate the potential of vertical structure estimation for extensive forests using FW LiDAR data and that the distinction between one-storied and two-storied forests can be clearly represented. This technique is expected to contribute to efficient and effective management of forests based on accurate information derived from the proposed method. View Full-Text
Keywords: forest vertical structure; full-waveform LiDAR; deep neural network; deep learning; forest genetic resource reserve forest vertical structure; full-waveform LiDAR; deep neural network; deep learning; forest genetic resource reserve
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MDPI and ACS Style

Park, S.-H.; Jung, H.-S.; Lee, S.; Kim, E.-S. Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network. Remote Sens. 2021, 13, 3736. https://doi.org/10.3390/rs13183736

AMA Style

Park S-H, Jung H-S, Lee S, Kim E-S. Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network. Remote Sensing. 2021; 13(18):3736. https://doi.org/10.3390/rs13183736

Chicago/Turabian Style

Park, Sung-Hwan, Hyung-Sup Jung, Sunmin Lee, and Eun-Sook Kim. 2021. "Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network" Remote Sensing 13, no. 18: 3736. https://doi.org/10.3390/rs13183736

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