Assessing ensemble models for carbon sequestration and storage estimation in forests using remote sensing data

Abstract

Forests play a crucial role in storing much of the world's carbon (C). Accurately estimating C sequestration is essential for addressing and mitigating the impacts of global warming. While many studies have used machine learning models to estimate carbon storage (CS) in forests based on remote sensing data, this research further examines C sequestration (i.e., the annual carbon uptake by trees; CSE). The objectives of this study are two-fold: firstly, to identify the best models for estimating CSE and CS by testing various methods, and secondly, to examine the effect of climatic data and the canopy height model (CHM) on the estimation of CSE. To achieve the first objective, we will compare the performance of fourteen models, including twelve machine learning models, one deep learning model, and an ensemble model that combines the top four independent models. For the second objective, we study the effect of four input configurations: the first is a baseline configuration based solely on attributes extracted from satellite images (Sentinel-2) and geomorphology; the second combines satellite features with climatic data; the third uses a CHM derived from LiDAR instead of climatic data; and the fourth combines all available features: satellite images, climatic data, and CHM. The results show that adding climatic data does not improve the estimation of CSE and CS. However, adding CHM features significantly improves the models' performance for both targets. The implemented ensemble model demonstrated the best performance across all configurations.