Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review

The estimation of aboveground biomass (AGB) in agroforestry systems using remote sensing has proliferated in the last decades. Similarly, machine learning is also being used in AGB assessments. This study reviews the applications of remote sensing and machine learning for AGB estimation in agroforestry systems (AFS). A detailed review was conducted using 33 recent papers by extracting and comparing information on agroforestry type, data sources, methodology, and model accuracy. Statistical tests were performed to evaluate the differences in performances. High- and very-high-resolution imageries (less than 2 m) are widely used for AGB assessment because they helped to delineate heterogeneous features of AFS. Object-based image analysis yielded classification accuracy of up to 90 percent in some cases. Random Forest, Stochastic Gradient Boosting, and Support Vector Regression are the most common algorithms used for AGB estimation. However, there are no statistically significant differences in the performance between machine learning and other models. Similarly, scholars incorporated spectral indices with spectral bands, texture, and biophysical variables as covariate categories into AGB estimation models. The study finds no significant differences in results (R-squared) by adding more covariate categories. The accuracy of AGB estimates depends upon multiple factors, such as the spectral and spatial resolution, number and types of covariates, methods for AFS delineation and AGB estimation, and types and sizes of AFS. Despite some of the methodological challenges around measuring understory vegetation, advancements in cloud computing like Google Earth Engine and the availability of high-resolution datasets present opportunities for wider use of remote sensing for biomass estimation of AFS. Remote sensing and machine learning have the potential to estimate aboveground biomass over a large area with high accuracy and contribute to carbon monitoring.