Advances in forest characterisation, mapping and monitoring through integration of LiDAR and other remote sensing datasets

Abstract

The diversity of scales and modes in which ground, airborne and spaceborne LiDAR operate has increased opportunities for quantitatively assessing forest structure, biomass and species composition and obtaining more general information on dynamics and ecological/commercial value. However, the level of information extracted can be increased even further by integrating data from other sensor types, including hyperspectral and Synthetic Aperture Radar (SAR). Examples include the generation of species-specific tree and stand level maps of biomass through inclusion of fine spatial resolution hyperspectral data and the use of LiDAR data and derived products for better interpreting the information content of SAR and optical data and parameterising models that simulate and assist understanding of the interaction of electromagnetic energy with forest components. Applications where synergistic use of LiDAR and other remote sensing data are advantageous include commercial forest inventory, quantifying carbon dynamics and biodiversity, and detecting change at scales from individual trees to landscapes. Recognition of the value of integrating other forms of remote sensing data with LiDAR is leading to the development of techniques for data fusion and also new synergistic sensors on platforms ranging from Unmanned Airborne Vehicles (UAVs) to satellites (e.g., DESDynI).