TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery

Abstract

The sustainable management of forests for multiple uses requires fine-scale resource information for a range of attributes. Remotely sensed imagery, if appropriately interpreted, can provide detailed, quantitative data for deriving forest information. This paper describes the tree identification and delineation algorithm (TIDA), an image analysis tool designed to delineate tree crowns automatically in high spatial resolution digital imagery. The (local) radiometric maxima and minima are the primary image features used for the crown delineation process, being indicative of crown centroids and boundaries, respectively. TIDA was developed for application to imagery of native Eucalypt forests in Australia, and uses a ‘top–down’ spatial clustering approach involving key steps designed to reduce the effects of crown segmentation. The assumptions and fundamental processes of the algorithm are described, examples of the output and performance considerations are given, and possible limitations are discussed.