Mapping Tropical Forest Biomass by Combining ALOS-2, Landsat 8, and Field Plots Data
Luong Viet Nguyen,
Ryutaro Tateishi,
Akihiko Kondoh,
Ram C. Sharma,
Hoan Thanh Nguyen,
Tu Trong To,
Dinh Ho Tong Minh
Affiliations
Luong Viet Nguyen
Center for Environmental Remote Sensing, Chiba University, 1–33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Ryutaro Tateishi
Center for Environmental Remote Sensing, Chiba University, 1–33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Akihiko Kondoh
Center for Environmental Remote Sensing, Chiba University, 1–33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Ram C. Sharma
Center for Environmental Remote Sensing, Chiba University, 1–33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Hoan Thanh Nguyen
Institute of Geography, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi 100000, Vietnam
Tu Trong To
Space Technology Institute, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi 100000, Vietnam
Dinh Ho Tong Minh
Institut national de Recherche en Sciences et Technologies pour l’Environnement et l’Agriculture (IRSTEA), UMR TETIS, Maison de la Teledetection, 500 Rue Jean Francois Breton, Montpellier 34000, France
This research was carried out in a dense tropical forest region with the objective of improving the biomass estimates by a combination of ALOS-2 SAR, Landsat 8 optical, and field plots data. Using forest inventory based biomass data, the performance of different parameters from the two sensors was evaluated. The regression analysis with the biomass data showed that the backscatter from forest object (σ°forest) obtained from the SAR data was more sensitive to the biomass than HV polarization, SAR textures, and maximum NDVI parameters. However, the combination of the maximum NDVI from optical data, SAR textures from HV polarization, and σ°forest improved estimates of the biomass. The best model derived by the combination of multiple parameters from ALOS-2 SAR and Landsat 8 data was validated with inventory data. Then, the best validated model was used to produce an up-to-date biomass map for 2015 in Yok Don National Park, which is an important conservation area in Vietnam. The validation results showed that 74% of the variation of in biomass could be explained by our model.
