Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images

Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate the profit of urban forestry activities. Earlier studies using high resolution satellite images have shown that automatic tree detection in urban areas using traditional classification techniques remains a very difficult task. This is mainly due to intra-crown spectral variation, heterogeneity of tree species and the complex spatial arrangement of individual trees with respect to other vegetated surfaces and elements of the urban space. This study aims to develop a reproducible object based image analysis (OBIA) methodology to locate and delineate individual tree crowns in urban areas using high resolution imagery and existing topographic maps. We propose an OBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The classification strategy is implemented with classification rules that exploit object features at multiple segmentation levels such as spectral response, texture, size, geometry, roundness, and distance to shadow, which are used to modify the labeling and shape of image objects. The classification procedure was tested in a QuickBird image acquired over a city in The Netherlands with results indicating an identification rate of 75% for individual trees and a commission rate of 39%. For the group of trees the identification rate was 100%. We equally report on the geometrical and positional accuracy of the identified tree crown objects.