Journal of Global Ecology and Environment

Malawi’s Miombo Woodlands provide critical wood products and ecosystem services, including carbon storage through above ground-biomass (AGB). However, accurate and efficient estimation of AGB and C remains a huge challenge due to the complexity of heterogeneous natural and anthropogenic environmental factors exacerbated by choice of data collection and analysis techniques. A study was conducted to explore the potential of Machine Learning (ML) algorithmic on modelling AGB in Malawi’s Miombo Woodlands using remotely sensed data. A combination of AGB field measurements and Sentinel 2 Multi-Spectral Instrument (S2 MSI) imagery, and environmental data were employed to train and evaluate the performance of Random Forest (RF) regression ML model, thus against the backdrop of traditional simple linear and multiple linear regression models used in the past. The randomForest package in Rstudio R-3.6.1 software was used to model the S2 median seasonal composite band imagery datasets acquired in 2019 over Ntchisi Forest. Results demonstrate an outstanding performance of the RF with Mean Decrease in Accuracy (MDA) importance of >80 acquired from the Green, Blue and Near-Infra-Red (NIR) bands of the post-rainy season imagery while the dry season scene registered a MDA importance of >52 obtained from the Red, Blue and NIR bands. These findings underscore the superiority of the RF, revealing its ability to model AGB using different spectral bands, and more importantly, in different seasons. The outcomes have also revealed the better accuracy that the RF render in generating AGB maps (providing a minimum range of 25-33 tCha^-1, which is in line with the ground reference estimates). Overall, the study has shown that RF Regression is a better technique to estimate AGB in Miombo Woodlands using the S2 MSI imagery under the given conditions. In conclusion, the research has further shown that the optical band combination of NIR and the Red regions of the Electro-Magnetic Spectrum are indispensable variables for successful modelling of AGB as well as delineating forest cover from non-forest attributes in the Miombo Woodlands. However, the study recommends exploring modelling the AGB using averaged vegetation indices i.e., NDVI, SAVI, and MSAVI, for robust results.