#TITLE_ALTERNATIVE# - KANDIA (NIM 15108048); Pembimbing: Prof. Ketut Wikantika, P.hD. dan Dr. Ir. Agung Budi Harto,, PRISKE
One of the remote sensing technology that is currently growing very rapidly is LiDAR, LiDAR data utilization is being more developed in lots of fields, one of them is for plantation and forestry application. LiDAR technology can offer a very fast measurement process and produce varying accuracy for large area. Indonesia as the largest oil palm producer in the world needs to develop a method which can effectively provide detailed structural information of oil palm trees that can be used for plantation and forestry monitoring and management. This final project would assess on the data processing LiDAR, point clouds to be formed as DTM (Digital Terrain Model), DSM (Digital Surface Model) and CHM (Canopy Height Model). CHM will be used to establish model and parameter of oil palm trees to detect individual oil palm trees in a study area. Automatic detection of individual oil palm trees would provide the estimates amount of trees in study area complete with the height and diameter canopy width informations of each individual detected trees. This research showed that in the oil palm tree plantation area of 20 hectares, in Kabupaten Prabumulih, South Sumatra, a number of 2618 trees successfully detected automatically. After validation process on some tile samples in the study area of Ã‚Â± 20 Ha was obtained accuracy of 93% and 7% of error estimation. The height standard deviation is 0.150 meters and the width canopy standard deviatiom is 0.790 meters. This study hopefully can be an input that is useful for the utilization of remote sensing technology especially LiDAR for plantation management and forestry monitoring in Indonesia.