Remote Sensing of Forest Above-Ground Biomass Dynamics: A Review
Yuzhen Zhang, Yiming Zou, Yiwen Wang
Abstract
Recent studies have primarily focused on estimating forest above-ground biomass (AGB) at single time points, with limited attention to temporal changes. However, time-series remote sensing data offer valuable insights into biomass trends, drivers of change, and forest recovery following disturbance, deepening our understanding of forest dynamics. This review synthesized 166 studies published between 2010 and 2024 (15 years) on forest biomass changes or dynamics monitored through remote sensing, with an emphasis on temporal datasets and both indirect (83.7%) and direct (16.3%) methods for estimating AGB changes, as well as the key drivers of these changes. A meta-analysis of AGB change estimates revealed that 81.5% of studies operated at spatial resolutions below 100 m, while only a few studies addressed coarser scales. Notably, just 11.9% of the studies used independent validation, and 8.8% of studies reported no validation at all, underscoring the need for more rigorous accuracy assessment to ensure methodological reliability and ecological relevance. This review also discusses key challenges, limitations, and future directions for improved remote sensing-based AGB change monitoring.