3D Change Detection of Urban Vegetation Using Integrated TLS and UAV Photogrammetry Point Clouds

Abstract

Urbanization has brought about notable transformations in urban green areas within cities, affecting both environmental quality and the well-being of inhabitants. As a result, it is essential to monitor variations in urban vegetation through remote sensing methods. This research aims to overcome the shortcomings of conventional remote sensing approaches by integrating terrestrial laser scanning (TLS) with UAV-based photogrammetry for effective vegetation monitoring using change detection methods. For instance, the traditional remote sensing limitations include cloud coverage in remote sensing images, illumination issues, vertical shadows, and sensor-specific issues such as geometric and radiometric distortions that restrict the spatiotemporal availability of the ground surface information and limit the change detection analysis. This research focuses on detecting changes in the Malminkartano area of Helsinki during the leaf-off and leaf-on periods of 2022. 2D point cloud data were analyzed using the Multiscale Model-to-Model Cloud Comparison algorithm. The findings demonstrate the method’s capability to identify growth in urban vegetation up to 2.8 m. Additionally, accuracy evaluations indicated that the 95% confidence interval corresponded to a difference of approximately 4 cm for both TLS and UAV photogrammetric datasets. The study highlights processing-related uncertainties, including point density, alignment, vertical accuracy, and scale variation. Addressing these sources of error in future studies is essential for reliable estimation of tree attributes.