AUTOMATED INDIVIDUAL TREE DETECTION IN AMAZON TROPICAL FOREST FROM AIRBORNE LASER SCANNING DATA

Abstract

Light Detection and Ranging (LiDAR) derived individual tree crown attributes have the potential of progressing ecology and forest dynamics studies and reduce field inventory costs. In this study seven methods for individual tree detection (ITD) were evaluated in a tropical forest under sustainable forest management, situated in the State of Rondônia, Brazil. An automated tree matching procedure was developed in order to minimize the error when matching individual tree count from LiDAR and field data. The ITD results were expressed in recall, precision, and F score, for the purpose of comparing methods. A local maxima-based algorithm showed best performance among the four methods, detecting 48% of trees with 46% of precision. Omission of trees was the leading source of error, caused primarily by overlapped trees in lower vegetation. However, errors of over-segmentation were relevant, caused by large and heterogeneous crowns that were considered as more than one individual. The complexity of tropical forests in the Amazon is certainly a challenge for current tree detection algorithms. We suggested that the future studies should consider testing multi-layer ITD workflows for improving the accuracy of tree detection.