Comparative analysis of high-resolution UAV photogrammetry and terrestrial laser scanning for detecting and quantifying urban vegetation changes

Abstract. Extensive urban expansion has significantly impacted green spaces leading to the degradation of urban vegetation. Hence, monitoring variations in vegetation using remote sensing methods is essential. However, 2D remote sensing methods have drawbacks as they lack vertical structures in urban areas, shadows caused by buildings, cloud cover and require substantial preprocessing to encounter these limitations. This study focuses on identifying and quantifying changes in Malminkartano, Helsinki during the leaf-off and leaf-on seasons for the year 2022. The research utilized terrestrial laser scanning (TLS) and UAV-photogrammetry datasets for change detection in urban vegetation and point cloud-based algorithms for seasonal variations such as C2C, C2M, and M3C2. Notably, many existing methods involve rasterizing point clouds as DSM which results in the loss of significant information. Therefore, this paper investigates the potential of utilized datasets in detecting changes directly on point clouds. However, there are uncertainties associated with point clouds including data registration, point density, weather effects, and misalignment therefore this study aims to take these limitations into account. The results from TLS and UAV-photogrammetry demonstrated competence in identifying the maximum growth of urban vegetation up to 2.0 m and 2.8 m respectively. However, the accuracy assessment of data corresponded to a 4 cm difference in both datasets at a 95% confidence threshold and potential vertical height differences accounted for the difference in change detection. This study underscores data processing uncertainties associated with registration, vertical height, and data noise and proposes the integration of point clouds with different sensors for completeness and improved change detection in urban vegetation.