Object-based mapping of aboveground biomass in tropical forests using LiDAR and very-high-spatial-resolution satellite data

Yasumasa Hirata, Naoyuki Furuya, Hideki Saito, Chealy Pak, Chivin Leng, Heng Sokh, Vuthy Ma, Tsuyoshi Kajisa, Tetsuji Ota, Nobuya Mizoue

研究成果: ジャーナルへの寄稿記事

3 引用 (Scopus)

抄録

Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0%, versus 92.9% for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R2 = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R2 = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus.

元の言語英語
記事番号438
ジャーナルRemote Sensing
10
発行部数3
DOI
出版物ステータス出版済み - 3 1 2018

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aboveground biomass
tropical forest
satellite data
spatial resolution
reflectance
multiple regression
carbon
tropical environment
QuickBird
atmospheric correction
haze
United Nations
segmentation
deforestation
developing world
degradation

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

これを引用

Object-based mapping of aboveground biomass in tropical forests using LiDAR and very-high-spatial-resolution satellite data. / Hirata, Yasumasa; Furuya, Naoyuki; Saito, Hideki; Pak, Chealy; Leng, Chivin; Sokh, Heng; Ma, Vuthy; Kajisa, Tsuyoshi; Ota, Tetsuji; Mizoue, Nobuya.

:: Remote Sensing, 巻 10, 番号 3, 438, 01.03.2018.

研究成果: ジャーナルへの寄稿記事

Hirata, Yasumasa ; Furuya, Naoyuki ; Saito, Hideki ; Pak, Chealy ; Leng, Chivin ; Sokh, Heng ; Ma, Vuthy ; Kajisa, Tsuyoshi ; Ota, Tetsuji ; Mizoue, Nobuya. / Object-based mapping of aboveground biomass in tropical forests using LiDAR and very-high-spatial-resolution satellite data. :: Remote Sensing. 2018 ; 巻 10, 番号 3.
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abstract = "Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0{\%}, versus 92.9{\%} for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R2 = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R2 = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus.",
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AU - Hirata, Yasumasa

AU - Furuya, Naoyuki

AU - Saito, Hideki

AU - Pak, Chealy

AU - Leng, Chivin

AU - Sokh, Heng

AU - Ma, Vuthy

AU - Kajisa, Tsuyoshi

AU - Ota, Tetsuji

AU - Mizoue, Nobuya

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