Exploring intensity metrics in raw LiDAR data processing for tropical forests

Abstract LiDAR sensing is an active sensor that can produce three-dimensional point clouds. This sensor offers the 3D acquisition and analysis of forest data, providing details on the vertical structures of the forest. This study delved into the processing of raw LiDAR data obtained through laser scanning, employing software tools such as Justin Javad, Pospac MMS, LMS, Terrascan and TerraMatch. The processes involved are mission planning, LiDAR data scanning, trajectory processing and data calibration. This is the crucial part of processing that defines the quality of the raw LiDAR data. The results showed that the standard error recorded for intensity metric ranged from 0.14 to 0.68. It is important to characterize the intensity metrics that provide useful information for identifying specific objects in a LiDAR point cloud. Foresters can leverage this information to interpret both the forest canopy and terrain, aiding in effective forest management. The precision achieved in intensity metrics enhances the utility of LiDAR technology in providing actionable data for forestry applications. This study has resulted in a data processing tool designed to optimize the advantages of utilizing intensity data for object recognition. This tool holds significant importance for users of LiDAR data.