Remote Sensing of Forest Biomass

Abstract

Forest biomass reflects sequestration or release of carbon between terrestrial ecosystems and the atmosphere. Measuring the size and complexity of forest biomass over large areas can enable us to better understand the environmental processes, availability of renewable energy, and global carbon cycle. This chapter reviews recent progress in measuring forest biomass from remote sensing. In quantifying forest biomass, forest properties are often characterized from three types of remote sensing data. Passive optical spectral reflectances are sensitive to vegetation structure (leaf area index, crown size and tree density), texture and shadow. Radar data measure dielectric and geometrical properties of forests. Lidar data characterize vegetation vertical structure and height. Because these instruments have their advantages and disadvantages in reflecting forest properties, data fusion techniques can combine data from multiple sensors and related information from associated databases to achieve improved accuracy in biomass estimation. The remote sensing data or derived forest attributes are commonly correlated to forest biomass using empirical regression models, non-parametric methods, and physically-based allometric models. Although forest biomass is widely estimated at various scales from remote sensing data, models tend to underestimate large biomass densities and overestimate small ones because of saturation issues. Finally, the assessment and validation of forest biomass obtained from remote sensing is critical because current biomass estimates at large area are of large uncertainties.