Unveiling urban vegetation monitoring: integrating multitemporal terrestrial laser scanning and UAV photogrammetry datasets for change detection

Urban expansion has led to significant changes in urban green spaces impacting the urban environment and residents’ well-being. Therefore, monitoring changes in urban vegetation using remote sensing techniques is crucial. This study aims to address the limitations of traditional remote sensing techniques by integrating terrestrial laser scanning and UAV photogrammetry for change detection. The study concentrates on change detection within Helsinki's Malminkartano region during the leaf-off and leaf-on seasons for the year 2022. 3D point cloud data are compared using the M3C2-algorithm. The results illustrate their efficacy in detecting changes up to 2.8 meters. Moreover, the accuracy assessment of datasets revealed that 95% confidence threshold corresponded to approximately 4 cm differences in both TLS and UAV photogrammetry datasets. The study emphasizes on data processing uncertainties related to point density, registration, vertical height, and scale differences. Future research should address these uncertainties to ensure an accurate assessment of tree parameters.