Evaluation of the Tree Identification and Delineation Algorithm (TIDA) using a forest canopy simulation model

Abstract

The automated Tree Identification and Delineation Algorithm (TIDA), developed for application to high spatial resolution digital imagery of Australian native eucalypt forest, was evaluated with respect to forest structural variables and remote sensing conditions using a forest scene simulation model. Results indicate that TIDA is most suited to application in forests with high canopy cover and high crown cover. The structural complexity of forest canopies, represented by the diameter and overlap of tree crowns and tree height, had a relatively small impact on TIDA performance. A small phase angle between the sun and sensor produced optimum TIDA performance when both canopy and crown cover was high. As crown or canopy cover decreased, high solar zenith angles yielded superior TIDA results by decreasing the brightness of the background relative to the canopy and improving the identification of tree peaks. For both dense and sparse canopies, back-scattered radiation from the canopy was preferable. Imagery acquired under an optically thick atmosphere was found to increase TIDA performance by strengthening of the relationship between geometric and radiometric crown shape.