Urban Trees – Detection, Delineation, Quantification, and Characterisation based on VHR Remote Sensing

Abstract

Trees play a vital role in the urban ecosystem, providing benefits for society, ecology and economy. In particular in urban areas, trees mitigate the urban heat island effect, filter air pollution, regulate microclimate and hydrology, bond carbon dioxide, and provide spaces for recreation and leisure, among others. Despite these diverse positive effects, detailed information on the number, location, dimensions and other characteristics of urban trees remains scarce. For this reason, most cities in Germany currently aim to establish a tree information system for efficient and targeted management of their tree inventory. However, traditional terrestrial surveying is time-consuming and costly and therefore only suitable to a limited extent. In addition, the municipal tree cadastre usually only includes urban trees on public propertyand thus does not cover the complete stock. Against this background, remote sensing acquisitions with very-high spatial resolution (VHR) of less than one meter offer promising capabilities for area-wide detection, delineation, and characterization of urban trees. In this study, we use VHR aerial imagery as well as a derived canopy height model (CHM) for detection and delineation of urban trees. Different methods for individual tree detection using local maximum (LM) filtering andLaplacian of Gaussian (LoG) blob detectionare compared and evaluated. For tree crown delineation, marker-controlled watershed segmentation (MCWS), clustering using Voronoi tessellation, and region growing are implemented as segmentationtechniques. The detection of individual trees and delieation of tree crowns are validated against about1,000 reference trees from visual interpretation via stereophotogrammetry.In addition, we relate our results to street tree location data of Munich, which was derived from mobile terrestrial laser scanning (TLS).The characterization of urban trees is realized based on the 3-dimensional shape of individual tree segments as well as auxillary data sets of land use and building density. According to our analyses, there are 1.54 million trees in Munich.Compared to available reference trees, tree detection was evaluated with highest values of F-score, precision, and recall of 0.95, 0.99, and 0.94, respectively. Results of tree crown segmentation revealed an overall accuracy of 88.1 % compared to crowns of reference trees. Based on auxillary land use information, urban trees were categorized into street trees, (public) park trees, as well as trees in (private) residential gardens.In Munich, 9.1 % are characterized as street trees, 38.4 % are allocated in residential gardens and 33.1 % stand in public parks. The remaining 19.4 % oftree segments were found onother land use such as agricultural areas, parking lots, or along railroad tracks. According to these categories, the height and crown area of urban trees are analyzed and related to the distance to the city center. In a more general manner, this analysis was performed in relation to the building density in Munich. As expected, relatively few trees were found close to the city center and generally on areas with high building density. However, these areas are particularly associated with the greatest challenges in the context of sustainable and climate change-adapted urban development.In this study, we demonstrate that information derived from remote sensing contributes new spatial and quantitative knowledge on urban trees, providing the basis for sustainable management and informed decision-making in cities