Applied Machine Learning Algorithms and Landsat 8 for Estimating Aboveground Carbon Stock in Evergreen Broadleaf Forest in Binh Phuoc Province

Abstract

Abstract: The assessment of carbon stocks is one of the key measurements to support climate change mitigation policies. The research applied Landsat 8 satellite imagery combined with field-measurements using four machine learning methods (random forest - RF, artificial neural networks - NNET, support vector machines – SVM, and linear regression - LM) to estimate aboveground carbon in evergreen broadleaf forest in Binh Phuoc province. The field sample plots were randomly divided into training (96 plots) and testing (24 plots) data. The results showed that RF yielded the greatest precision with an R2 value above 0,9 and RMSE below 6 ton/ha on the training data, with an R2 value of 0,41 and RMSE of 11,04 ton/ha on the testing data. The estimate of forest carbon stock increased distinctly from the mean value of 59,80 ton/ha in the very poor forest to 87,78 ton/ha in the rich forest. The results found in the present study demonstrated that Landsat 8 imagery in conjunction with RF has the appropriate to estimate aboveground carbon stock in evergreen broadleaf forest-leaved in Binh Phuoc province.
 Keywords: Random forest, aboveground carbon, REDD+, forest carbon estimation.