Mangrove Tree Crown Delineation from
High-Resolution Imagery
Muditha K. Heenkenda, Karen E. Joyce, and Stefan W. Maier
Abstract
Mangroves are very dense, spatially heterogeneous, and have
limited height variations between neighboring trees. Delineating
individual tree crowns is thus very challenging. This study com-
pared methods for isolating mangrove crowns using object based
image analysis. A combination of WorldView-2 imagery, a digital
surface model, a local maximum filtering technique, and a
region growing approach achieved 92 percent overall accuracy in
extracting tree crowns. The more traditionally used inverse wa-
tershed segmentation method showed low accuracy (35 percent),
demonstrating that this method is better suited to homogeneous
forests with reasonable height variations between trees. The main
challenges with each of the methods tested were the limited
height variation between surrounding trees and multiple upward
pointing branches of trees. In summary, mangrove tree crowns
can be delineated from appropriately parameterized object-
based algorithms with a combination of high-resolution satellite
images and a digital surface model. We recommend partitioning
the imagery into homogeneous species stands for best results.
Introduction
Mangrove forests represent a land-ocean interface ecosys-
tem in tropical and sub-tropical regions of the world. They
provide a wide range of goods and services that have been
acknowledged for decades at local, national, and global levels
(Food and Agriculture Organisation, 2007). Nevertheless, a
rapid rate of destruction of these intertidal forests due to vari-
ous anthropogenic disturbances over the last few decades has
been recorded (Suratman, 2008). While earlier descriptive or
observational studies focused on community usage of man-
groves, more recently there has been an increase in analyti-
cal studies quantifying the diverse value of mangrove forests
(Komiyama
et al
., 2008; Voa
et al
., 2012). To quantitatively
evaluate mangrove forests, estimating individual tree growth,
biomass, and productivity has become important.
The expansion of leaf area or number of leaves available
for photosynthesis is an indicator for the biomass production
of trees, and thus leaves composing a crown indicates indi-
vidual tree growth, senescence, and death (Analuddin
et al
.,
2009). To determine biophysical parameters of trees (height,
diameter at breast height, growth rate, etc.) using remote sens-
ing methods, individual trees must first be isolated and tree
crown boundaries delineated.
Isolating individual trees and extracting tree structure has
a significant contribution to a variety of applications. Indi-
vidual trees and their structure can be used for forest invento-
ries, assessing forest regeneration, quantifying above ground
biomass, and assessing vegetation damage (Chen
et al
., 2006).
Therefore, an investigation of mangrove tree crown foliage
dynamics would be an added value to estimate the productiv-
ity and health of mangrove forests.
Measuring precise crown foliage dynamics manually in the
field is a challenging task as the irregularities of crown shapes
are difficult to capture using standard field survey equipment.
The available field surveying methods for individual tree at-
tribute extraction are labor intensive and time consuming. As
an alternative, very high resolution satellite and aerial images,
as well as laser scanners might provide a viable option for
extracting this information. This is supported by advances in
algorithmic developments towards higher precision, reliabil-
ity, and automation. A number of studies have already been
conducted to extract individual tree crowns for various veg-
etation types using remotely sensed data with varying degree
of success (Blaschke
et al
., 2011; Chen
et al
., 2006; Erikson,
2004; Erikson and Olofsson, 2005; Gougeon and Leckie, 2003;
Hirata
et al
., 2010; Kaartinen
et al
., 2012; Larsen
et al
., 2011;
Vauhkonen
et al
., 2012; Wannasiri
et al
., 2013; Whiteside
et
al
., 2011). However, very little information is available for us-
ing remote sensing for delineating mangrove tree crowns.
A review of methods for delineating individual trees or
groups of trees in optical images identified four main ap-
proaches: local maxima detection algorithm, segmentation
methods, valley following algorithm, and tree modeling and
image template construction (Lopez, 2012). Kaartinen
et al
.
(2012) tested several other tree crown extraction methods us-
ing airborne laser scanning data. However, in both instances,
the most intensively investigated technique is local maxima
detection. It assumes that tree apexes generate intensity peaks
in images, thus local maximum image brightness values relate
to tree top locations (Leckie
et al
., 2005; Pouliot
et al
., 2002).
As this method does not delineate crown boundaries, it has to
be combined with a segmentation method to identify crowns.
A segmentation method involves identification of groups
of similar neighboring pixels that corresponds to objects or
part of objects. There are many approaches for this: grouping
textures, morphological operators, and joining convex-shaped
edges. Hence, construction of regions of the image is based on
homogeneity of image characteristics such as image bright-
ness, texture, shape, and size, etc. Furthermore, in advanced
approaches, semantic information can also be incorporated
during segmentation (Eisank
et al
., 2010).
As an alternative approach for a combination of local
maxima detection algorithm and region growing, the valley
following method considers image intensities as an anal-
ogy for a topographic surface (Gougeon, 1995). It creates a
hill pattern, where the treetops are actually seen as hill tops.
Boundaries of tree crowns show as dark values, shady rings
or ‘valleys’ depending on sun illumination angle (Gougeon,
1995). The valley following algorithm defines a set of semantic
rules to enclose the valleys, leading to the separation of indi-
vidual crowns (Gougeon, 1995). However, this method is more
suitable for sparsely distributed forests or plantations than
Research Institute for the Environment and Livelihoods,
Charles Darwin University, Ellengowan drive, Casuarina,
NT 0909, Australia (
.
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 6, June 2015, pp. 471–479.
0099-1112/15/471–479
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.6.471
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
June 2015
471
