Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam
Corresponding Author
Date
2018-12-31ISI journal
Impact factor: 1.820 (Year: 2018)
Author(s)
Citation
An Thi Ngoc Dang, Subrata Nandy, Ritika Srinet, Nguyen Viet Luong, Surajit Ghosh, A. Senthil Kumar. (31/12/2018). Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam. Ecological Informatics, 50, pp. 24-32.
Abstract
Forest biomass is one of the key measurement for carbon budget accounting, carbon flux monitoring, and climate
change studies. Hence, it is essential to develop a credible approach to estimate forest biomass and carbon
stocks. Our study applied Sentinel-2 satellite imagery combined with field-measured biomass using Random
Forest (RF), a machine learning regression algorithm, to estimate forest aboveground biomass (AGB) in Yok Don
National Park, Vietnam. A total of 132 spectral and texture variables were extracted from Sentinel-2 imagery
(February 7, 2017) to predict AGB of the National Park using RF algorithm. It was found that a combination of
132 spectral and texture variables could predict AGB with an R2 value of 0.94, RMSE of 34.5 Mgha−1 and %
RMSE of 18.3%. RF regression algorithm was further used to reduce the number of variables in such a way that a
minimum number of selected variables can be able to estimate AGB at a satisfactory level. A combination of 11
spectral and texture variables was identified based on out-of-bag (OOB) estimation to develop an easy-to-use
model for estimating AGB. On validation, the model developed with 11 variables was able to predict AGB with
R2=0.81, RMSE=36.67 Mg ha−1 and %RMSE of 19.55%. The results found in the present study demonstrated
that Sentinel-2 imagery in conjunction with RF-based regression algorithm has the potential to effectively
predict the spatial distribution of forest AGB with adequate accuracy.
DSpace URI
https://hdl.handle.net/20.500.11766/9262Other URI
https://mel.cgiar.org/dspace/limitedhttps://www.sciencedirect.com/science/article/pii/S1574954118301894
Collections
- Agricultural Research Knowledge [12844]
Author(s) ORCID(s)
Ghosh, Surajithttps://orcid.org/0000-0002-3928-2135
Subject(s)
AGROVOC Keywords
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