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• W2907788294 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." @default.
• W2907788294 created "2019-01-11" @default.
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• W2907788294 date "2019-03-01" @default.
• W2907788294 modified "2023-10-14" @default.
• W2907788294 title "Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam" @default.
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• W2907788294 doi "https://doi.org/10.1016/j.ecoinf.2018.12.010" @default.
• W2907788294 hasPublicationYear "2019" @default.
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