Above Ground Biomass Estimation of a Cocoa Plantation using Machine Learning

Abstract

The rapid increase in carbon dioxide in the atmosphere and its associated effects on climate change and global warming has raised the importance of monitoring carbon sequestration levels. Estimating above ground biomass (AGB) is one way of monitoring carbon sequestration in forested areas. Quantifying above ground biomass using direct methods is costly, time-consuming and, in many cases, impractical. However, remote sensing technologies such as LiDAR (Light Detection And Ranging) captures three dimensional information which can be used to perform this estimation. In particular, LiDAR can be used to estimate the diameter of a tree at breast height (DBH) and from this we can estimate its AGB. For this research we used LiDAR data, along with various Machine Learning (ML) algorithms (Multiple Linear Regression, Random Forest, Support Vector Regression and Regression Tree) to estimate DBH of cocoa trees. Various feature selection methods were used to select the most significant features for our model. The best performing algorithm was Random Forest which achieved an R2 value of 0.83 and Root Mean Square Estimate (RMSE) value of 0.062. This algorithm then estimated an AGB value of 28.75 ± 2.34 Mg/ha (Megagram per hectare). We compared this result with that obtained from locally-developed allometric equations for the same cocoa plot. The comparison proved our estimate to be 14.7% lower than the allometric equation. The results demonstrated that using ML with LiDAR measurements for AGB estimation is quite promising.