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spatially complex, and common mangrove communities in
the Northern Territory of Australia.
A WV2 satellite image acquired on 05 June 2010 was used
as the main source of remotely sensed data. The image has
eight multispectral bands with 2.0 m spatial resolution, and a
panchromatic band with 0.5 m spatial resolution. The wave-
length range of the WV 2 satellite images vary from 447 nm to
1043 nm (Table 1). To generate a digital surface model, true
color aerial photographs were used. The date of acquisition of
these photographs is 07 June, 2010 and the sensor is Ultra-
CamD® large format digital camera (Table 1).
Table 1. Spectral Resolution of WV2 Image and Aerial Photographs
(DigitalGlobe, 2011; Northern Territory Government, 2010)
Band Spectral range (nm) Spatial resolution (m)
WorldView-2 satellite image
Panchromatic 447 - 808 0.5
Coastal 396 - 458
2.0
Blue 442 - 515
Green 506 - 586
Yellow 584 - 632
Red 624 - 694
Red-Edge 699 - 749
NIR1 765 - 901
NIR2 856 - 1043
Aerial photographs from UltraCamD camera
Blue 380-600
0.14 Green 480-700
Red 580-720
The WV2 image was radiometrically, atmospherically, and
geometrically corrected according to the method adopted by
Heenkenda et al. (2014). The red and the NIR1 bands were
pan-sharpened to 0.5 m spatial resolution to incorporate edge
information from the high spatial resolution panchromatic
band to the lower spatial resolution multispectral bands. The
high pass filter pan-sharpening method was utilized as it pro-
duces a fused image without distorting the spectral balance of
the original WV2 satellite images (Chavez and Bowell, 1988;
Chavez et al., 1991; Heenkenda et al., 2014; Palubinskas,
2013). Since the spectral range of the panchromatic band does
not cover the spectral ranges of both coastal and NIR2 bands,
they were not used for further processing.
The image was smoothed with a low-pass filtering process.
Low-pass filtering minimizes some of the natural variability
of the data and the noise related to sensor deficiency. It also
helps to reduce difficulties associated with having too many
details within crowns (Gougeon, 1995). The focal statistics
(maximum) algorithm in ArcGIS® was applied to the panchro-
matic, red, and NIR1 bands of the WV2 image to identify the
local maximum reflectance values within the neighborhood
(ArcGIS Resources - Esri). The specified neighborhood was
defined as circular in shape, and three pixels in size. This
threshold value was selected based on the field observations,
assuming that the average radius of a mangrove tree crown
was approximately 1.5 m. These preprocessed panchromatic,
red, and NIR1 bands were then used for further processing.
Extracting Mangrove Coverage from Images
According to Bunting and Lucas (2006), the first step of any
tree crown delineation is to separate trees to be examined and
non-trees of the area (generate a forest mask). Hence, the man-
grove coverage was extracted from the WV2 image, creating an
outline of the mangrove area to be further analyzed. Full de-
scription of the methodology is available in Heenkenda et al.
(2014), but in brief summary, class-specific rules that incor-
porated texture, geometry, location information, and relation-
ships between image objects at different hierarchical levels
from the WV 2 image were used. This mangrove coverage
outline was used to avoid mixing other surrounding features
such as other vegetation types, mudflats within mangroves,
roads and buildings, when isolating individual tree crowns.
Digital Surface Model (DSM) Generation
Aerial photographs captured by an UltraCamD camera were
oriented to ground coordinates following the digital pho-
togrammetric image orientation steps in the Pix4DMapper®
software. The image orientation parameters were extracted
from the UltraCamD camera calibration report. A DSM was
then created with 15 cm resolution. Ground control points
obtained from “National Geospatial Reference System, Aus-
tralia” (Geoscience Australia) were included to minimize the
errors that can arise from image matching. Once the DSM was
created, the mangrove outline was used to extract the region
of interest for further processing.
The DSM was smoothed using a low pass filter to eliminate
unexpected, random height variations (typically noise) and
small gaps, and to enhance crown edges. The focal statistics
(maximum) algorithm in ArcGIS was introduced to the DSM
to identify local maximum values within the neighborhood.
This focal statistics-maximum tool considers each pixel in
the raster, and calculates maximum values with respect to
identified neighborhood (ArcGIS Resources - Esri, 2012).
Therefore, the resulting pixel gets the maximum value of the
given neighborhood. In this study, a circular neighborhood (a
kernel) was applied to select the maximum values within a
range of 10 pixels (0.15 m × 10 m), thus the number of pixels
corresponds the average radius of tree crowns.
Mangrove Tree Crown Delineation
Mangrove tree crown extraction was tested on several dif-
ferent layer combinations using local maxima detection and
a region-growing algorithm. These are explained in greater
detail below.
WV2 Panchromatic, Red, and NIR1 Bands (PAN-R-NIR1 Method)
Individual trees were first extracted using a combination of
panchromatic, red, and NIR1 bands of the WV2 image. The
red and NIR1 multispectral bands were selected based on the
interaction of light at these wavelengths with vegetation. Red
wavelengths are sensitive to chlorophyll absorption, and the
NIR region is useful for analysing vegetation biomass. The
image was initially segmented based on shape and homogene-
ity of the WV 2 bands using the eCognition® software package.
Treetops were detected by investigating local maxima in the
panchromatic and NIR1 bands, assuming they appear brighter
than surrounding shaded areas. The resultant treetops were
one pixel in size. An iterative procedure of region growing
was implemented to expand the identified treetops, and to
draw ovals of tree crowns. The extent of an individual oval
should therefore represent the extent of each crown. As a
control parameter for the region growing, the ratio of the NIR1
band of the tree top object to the neighboring objects was used
assuming that the NIR1 band value is unique for the entire
crown. To remove falsely detected crowns, the mean value
of the Normalized Difference Vegetation Index (NDVI ) and
the mean standard deviation of the red band were used. For
example: if the NDVI value of the selected tree crown object
is less than the mean NDVI value of objects, and the standard
deviation of the red reflectance is less than the mean standard
deviation of the red values of objects, then the object was clas-
sified as the false tree crown. This helped to avoid extending
tree crowns into the gaps between mangrove trees. Finally, the
tree crowns were smoothed (reshaped avoiding jagged edges)
using the morphology algorithm available in eCognition.
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