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Open AccessArticle

Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest

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Institute of Decision Science for a Sustainable Society, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581, Japan
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Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581, Japan
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Faculty of Agriculture, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-8580, Japan
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Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581, Japan
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Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Japan
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Hokkaido Research Center, Forestry and Forest Products Research Institute, 7 Hitsujigaoka, Toyohiraku, Sapporo 062-8516, Japan
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Asia Air Survey Co., Ltd., Shinyuri 21 Building, 1-2-2 Manpukuji, Asao-ku, Kawasaki 215-0004, Japan
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Forestry Administration, Khan Sen Sok, Phnom Penh 12157, Cambodia
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Tano Forest Science Station of Field Science Center, Faculty of Agriculture, University of Miyazaki, 11300 Tano-Otsu, Miyazaki, Miyazaki 889-1712, Japan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Joanne C. White
Forests 2015, 6(11), 3882-3898; https://doi.org/10.3390/f6113882
Received: 11 September 2015 / Revised: 18 October 2015 / Accepted: 26 October 2015 / Published: 30 October 2015
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
We investigated the capabilities of a canopy height model (CHM) derived from aerial photographs using the Structure from Motion (SfM) approach to estimate aboveground biomass (AGB) in a tropical forest. Aerial photographs and airborne Light Detection and Ranging (LiDAR) data were simultaneously acquired under leaf-on canopy conditions. A 3D point cloud was generated from aerial photographs using the SfM approach and converted to a digital surface model (DSMP). We also created a DSM from airborne LiDAR data (DSML). From each of DSMP and DSML, we constructed digital terrain models (DTM), which are DTMP and DTML, respectively. We created four CHMs, which were calculated from (1) DSMP and DTMP (CHMPP); (2) DSMP and DTML (CHMPL); (3) DSML and DTMP (CHMLP); and (4) DSML and DTML (CHMLL). Then, we estimated AGB using these CHMs. The model using CHMLL yielded the highest accuracy in four CHMs (R2 = 0.94) and was comparable to the model using CHMPL (R2 = 0.93). The model using CHMPP yielded the lowest accuracy (R2 = 0.79). In conclusion, AGB can be estimated from CHM derived from aerial photographs using the SfM approach in the tropics. However, to accurately estimate AGB, we need a more accurate DTM than the DTM derived from aerial photographs using the SfM approach. View Full-Text
Keywords: aboveground biomass; aerial photograph; Airborne LiDAR; seasonal tropical forest; SfM aboveground biomass; aerial photograph; Airborne LiDAR; seasonal tropical forest; SfM
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Ota, T.; Ogawa, M.; Shimizu, K.; Kajisa, T.; Mizoue, N.; Yoshida, S.; Takao, G.; Hirata, Y.; Furuya, N.; Sano, T.; Sokh, H.; Ma, V.; Ito, E.; Toriyama, J.; Monda, Y.; Saito, H.; Kiyono, Y.; Chann, S.; Ket, N. Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest. Forests 2015, 6, 3882-3898.

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