Integration of LiDAR and Pleiades Data in Identifying the Potential of Hazardous Trees

Abstract

Trees beautify the landscape and provide numerous benefits for human well-being. Without proper maintenance and mitigating, the hazardous trees with structural defects can be harmful to the local community. In practice, the hazardous trees can be assessed using visual tree assessment (VTA), expertise interview and questionnaire. The advancement in remote sensing technology provides users with alternative ways to identify the hazardous trees and is very efficient especially in large areas. The intent of this study is to identify the potential of the tree hazard in UiTM Shah Alam from the integration of LiDAR and Pleiades dataset. Tree health condition and tree height were derived from LiDAR and Pleiades dataset. Height of trees were extracted from a digital canopy height model (DCHM) and the condition of tree health were assessed based on the Normalized Difference Vegetation Index (NDVI) values. Both parameters were used to classify the individual into low, moderate and high-risk ratings. The findings demonstrated that the integration of both datasets successfully identified the potential of tree hazard at the study area. Altogether 108 of individual trees were detected. 7 and 67 trees were identified as high and moderate risk respectively. This study derived significant findings that can be used to support the maintenance of tree activities in the campus area and significant to private and government sectors such park management and local authorities.