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Prediction of Forest Attributes with Multispectral Lidar Data

Multispectral LiDAR is a recent sensor in the remote sensing domain. In this study the potential of multispectral LiDAR data to model and predict some forest attributes (aboveground biomass per hectare, Gini coefficient of the diameters at breast height, and Shannon diversity index of the tree species) are explored. In particular, Optech Titan LiDAR data characterized by three spectral channels were considered. The results showed that all the three attributes can be accurately predicted with multispectral LiDAR data with high accuracy. Differences emerged on the contribution of the three respective channels of the multispectral LiDAR to the various models.