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 their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image analysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70% and 82% respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards.

• We formulate a GEOBIA workflow for the identification of urban trees in VHR images.
• The method allows identification of individual trees and tree groups.
• It considers the spatial variability and context of urban trees in VHR images.
• Specific GEOBIA rules and object features for tree detection are presented.
• Identification results were acceptable in QB images of two areas in The Netherlands.