1 Comparing digital object based approaches for mangrove tree crown delineation using WorldView-2 satellite imagery Muditha K. Heenkenda a, *, Karen E. Joyce a , Stefan W. Maier a a Research Institute for the Environment and Livelihoods, Charles Darwin University, Casuarina Campus, NT 0909, Australia *Corresponding author: mudithakumari.heenkenda@cdu.edu.au; +61-8-8946-7331 Abstract: Mangroves are dense, spatially heterogeneous, and have limited height variations between neighbouring trees. Delineating individual tree crowns is thus very challenging. This study evaluated several different object-based image analysis approaches for isolating mangrove tree crowns using a WorldView-2 high spatial resolution image and a digital surface model generated from true colour aerial photographs. The combination of WorldView-2 imagery, a local maxima filtering algorithm, and a region growing approach provided the best accuracy of the methods tested. The inverse watershed method of tree crown delineation showed the lowest accuracy, demonstrating that this method is only suitable for homogeneous forests with reasonable height variations between trees. Therefore, while isolating mangrove crowns using remote sensing is a challenging task, it is possible to achieve good results with very high spatial resolution imagery and appropriately parameterised object-based algorithms. Keywords: Mangroves, object-based image analysis, local maxima, region growing, WorldView-2 Paper type: Feature article 1. Introduction One of the fundamental processes of tree growth is the expansion of leaf area. Leaves composing tree crowns indicate growth, senescence, and death. To obtain measures of these biophysical parameters, the initial process is to isolate individual trees and delineate tree crown boundaries. Crown shape irregularities present a significant problem for accurate survey, both in the field, and when analysing remotely sensed imagery. Nevertheless, intensive recent research has focussed on detecting individual trees for various vegetation types using remotely sensed data with varying degree of success (Kaartinen et al., 2012; Wannasiri et al., 2013). However, little information is available on detection of mangrove tree crowns from remotely sensed data. It is clear that no single algorithm is universally successful in delineating the crowns of all different vegetation types. Most methods are based on image intensities, elevation, and homogeneity of vegetation with reasonable height variations. Unfortunately, these height variations cannot normally be identified in mangrove forests. Therefore, methods developed for identifying individual tree crowns of terrestrial vegetation are often not suitable for mangrove studies. This study evaluate the potential of using high spatial resolution remotely sensed data and object-based image analysis (OBIA) for outlining mangrove tree crowns around Rapid Creek near Darwin, Australia.