A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

TLDR

Remote sensing–based aboveground biomass estimation has become a major research focus over the past three decades, but optical and radar data suffer from saturation, while lidar can alleviate this issue but is limited in availability. The paper surveys current remote sensing AGB estimation methods, addressing field data collection, variable extraction, algorithm selection, uncertainty analysis, scale effects, and proposes a general estimation procedure, while calling for further research on vertical structure extraction. The authors review methodological aspects of AGB estimation, including data acquisition, variable selection, model development, uncertainty assessment, scale considerations, and suggest integrating vertical vegetation structure from InSAR or optical stereo images into horizontal structure models. The survey shows that single‑sensor approaches are limited and that integrating multi‑sensor and multi‑scale remote sensing data yields more accurate large‑area biomass estimates.

Abstract

Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling.