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

PEDRO HENRIQUE KARANTINO MILLIKAN, CARLOS ALBERTO SILVA, Luiz Carlos Estraviz Rodriguez, Tupiara Mergen de Oliveira, Mariana Peres de Lima Chaves e Carvalho, Samuel de Pádua Chaves e Carvalho
2019 Cerne  
HIGHLIGHTS Comparative study of four recent methods developed for detecting trees from Airborne Laser Scanning data in the Amazon forest. Automated tree detection algorithms detected 65% of field-referenced trees from LiDAR point cloud. Current raster-based methods are ineffective to detect trees in lower strata. The complexity and heterogeneity of forest formations in the Amazon is certainly a challenge for current tree detection algorithms. Addressing these challenges is essential to improve
more » ... etection and precision rates of individual tree detection in tropical forest. ABSTRACT Light Detection and Ranging (LiDAR) derived individual tree crown attributes can potentially serve as a tool for ecology and forest dynamics studies and reduce field inventory costs. In this study, four methods of individual tree detection (ITD), Watershed, Silva et al. (2016), Coomes (2016), and Coomes et al. (2017) , 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. In order to compare the four methods, results were expressed in recall, precision, and F score. Silva et al. (2016) outperformed the other 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 had multiple detections. Current canopy height model-based methods are ineffective to tropical forests, due to its complexity, which present a challenge for ITD algorithms. We believe that future studies that use complete 3D information from the point cloud, and multi-layer approaches should help to improve the accuracy of individual tree detection.
doi:10.1590/01047760201925032630 fatcat:vvz3ygnawnflxbn7rlglrkqlmy