Three dimensional airborne active remotely sensed data named Light Detection and Ranging (LiDAR) can capture the detail of tree crown shape more directly than any other sensors and techniques. This dissertation, therefore, introduces novel techniques to use the high density LiDAR data for capturing crown formation, to fuse LiDAR data with the other remotely sensed data, and to validate the result obtained by airborne LiDAR using high precision field equipments.

This dissertation is composed of three parts: tree wrapping, data fusion between LiDAR and aerial photography, and field validation technique. (1) Capturing crown formation using implicit surface reconstruction: A graphical approach was utilized to create a wrapped surface over LIDAR points and to identify any irregular tree crown shapes. Using high point density LIDAR data as an input to the wrapped-surface method, unique crown structural attributes that are unrecognized using standard geometrical shape models were determined. (2) True orthophoto creation through fusion between LiDAR and aerial photos. Conventional photogrammetric technique was applied to the data fusion process between aerial photos and LiDAR derived Digital Surface Model (DSM) to make a true orthophoto. During the composition process among respective true ortho photographs, hillshade surface was generated and used for occlusion detection and compensation. This image composition technique is useful and applicable to the data fusion between LiDAR and the other spectral images. (3) Validation of the wrapped tree crown surface with an error mapping technique. The validation technique described here is useful for the comparison analysis among different settings of field equipments. The observation using ground-based LiDAR and the total station is compared with the wrapped surface created by airborne LiDAR data. A new error mapping technique was developed with permutation test to show the systematic error associated with the field equipments.

These new approaches open further opportunities of LiDAR usage, help monitor trees in remote areas without intensive field samplings, and improve the accuracy in detecting tree crown attributes.