A comparative analysis of machine learning techniques for aboveground biomass estimation: A case study of the Western Ghats, India

Abstract

Accurate assessment of aboveground biomass (AGB) in tropical forests, particularly within a biodiversity hotspot, is vital for sustainable resource management and the preservation of ecosystems. However, estimating AGB in tropical forests is complex due to the diverse and intricate nature of vegetation, necessitating the integration of data from multiple sources. To tackle this challenge, our study utilized seven machine learning algorithms to analyze various combination of multisource datasets. We developed seven models/scenarios that incorporated Sentinel-1, Sentinel-2 as well as environmental factors such as topography, soil and climate to identify key variables for accurate estimation of AGB. For optimal performance, hyperparameters of the algorithms were fine-tuned through 10-fold cross-validation and their accuracy were assessed using the testing dataset. We found that the integrated model of satellite datasets, topography, climate, and soil variables exhibited the highest accuracy, where ensemble stacking, that combined multiple MLAs, proved to be reliable and best suited for predicting AGB (mean absolute error-3.97 Mg 0.1 ha−1, root mean square error-5.67 Mg 0.1 ha−1, and coefficient of determination - 0.82). Notably, the top predictor variables included Sentinel-2 bands (near infrared and green), soil properties (pH and soil organic carbon), and topography (elevation). The study emphasizes the significance of incorporating environmental variables (specifically topography and soil properties) along with Sentinel datasets to improve the accuracy of AGB estimation. This approach has the potential for broader applications, specifically in regions where vegetation productivity is governed by diverse environmental conditions.