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

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).