study considered the average height difference between tree
top and the crown edge (crown height) as a control parameter,
the investigation of the flow direction map showed the clear
aggregation of neighboring trees with approximately similar
heights. As we understand, the only solution to avoid such a
situation is to deal with homogeneous forest stands separate-
ly. However, Wannasiri
et al
. (2013) stated that the inversed
watershed method performed well in mangrove forests having
low percentage of crown overlap conditioning with a canopy
height model generated from airborne laser scanning data.
The main reasons would be the accuracy of the canopy height
model and relatively large crowns (average diameter is about
3 to15 m). Further, the increasing percentage of crown overlap
decreases the extraction accuracy of individual tree param-
eters from canopy height models. Another drawback of this
method is that it involved creating many intermediate pro-
cessing files, thus the processing itself is complicated. This
method is a good solution for sparsely distributed, homoge-
neous forests with reasonable height variations between trees
(Swamer and Houser, 2012) but less suitable for mangrove
forest applications.
A quantitative accuracy assessment provides the measure
of a goodness of polygon matching with respect to validation
samples. However, highest evaluation results do not neces-
sarily involve a good segmentation (Clinton
et al
., 2010).
The obtained results were validated against 268 tree crowns
manually digitized from the stereo models (Plate 1). The
PAN-
R-NIR1-DSM
method showed the best results having the lowest
closeness index value or 92 percent overall relative accuracy
(Table 3). The completeness error of this method was also
very low indicating almost all reference objects were classi-
fied as tree crowns. When comparing the numerical values of
the accuracy assessment, there was no significant difference
between the
PAN-R-NIR1
and the
PAN-R-NIR1
-
DSM
methods. How-
ever, the crown shape and the number of trees identified were
improved when incorporating the height information rather
than only using spectral information.
Conclusions and Recommendations
This study tested different data layer combinations for
delineating individual tree crowns in a mangrove forest
environment.
WV
2 imagery was used as the primary data
source, supplemented by an aerial photo derived
DSM
. The
combination of a
WV
2 image, a
DSM
and a
GEOBIA
algorithm
was found to successfully delineate mangrove tree crowns.
The achieved overall relative accuracy was 92 percent. Minor
errors were detected where neighboring trees with similar
characteristics were aggregated together and conversely,
where multiple crowns were assigned to a single tree (upward
pointing branches of the same tree identified as different
trees). The
WV
2 image with a
GEOBIA
algorithm also provided
good results compared to the
PAN-R-NIR1-DSM
method. The
statistical values show only minor differences in accuracies
between these two methods though the visual appearance
was dramatically improved when incorporating the
DSM
.
The
IWS
method, which has traditionally been successfully
used in other environments with more sparse canopies, did
not provide data representative of mangrove tree crowns.
Therefore, it can be concluded that the combination of a
WV
2
image, a
DSM
generated from image matching, and a
GEOBIA
is
a relatively low cost, accurate, and robust approach, which
can be applied to heterogeneous forests like mangroves.
The study also confirmed that it is important to classify the
imagery into homogeneous species stands before applying
individual tree detection algorithms. The validation of the
detected locations of trees or treetops is recommended before
applying region-growing algorithm in order to overcome the
structural complexity problems associated with mangroves.
We also recommend using orthorectified
WV
2 images for
detecting treetops to eliminate topographic errors and spatial
mismatches. Further, we suggest testing a combination of
WV
2 image and airborne lidar-derived canopy height model to
delineate mangrove tree crowns.
Acknowledgments
The support of the Northern Territory Government, Australia
in providing aerial photographs for this study is gratefully
acknowledged. Authors appreciate Miguel Tovar Valencia,
Evi Warintan Saragih, Silvia Gabrina Tonyes, Olukemi Ronke
Alaba, and Dinesh Gunawardena for their assistance with
fieldwork. Authors would also like to thank Ian Leiper and
three anonymous reviewers for constructive advice and edito-
rial comments.
References
Analuddin, K., S. Sharma, R. Suwa, and A. Hagihara, 2009. Crown
foliage dynamics of mangrove kandelia obovata in manko
wetland, Okinawa Island, Japan,
Journal of Oceanography
,
65:121–127.
ArcGIS Resources - Esri, 2012. How focal statistics works, URL:
html#//009z000000r7000000
, (last date accessed: 13 April
2015).
Baltsavias, E., A. Gruen, H. Eisenbeiss, L. Zhang, L.T. and Waser,
2008. High-quality image matching and automated generation
of 3D tree models,
Internatioanl Journal of Remote Sensing
, 29
(5):1243–1259.
Beucher, S., and C. Lantuejoul, 1979. Use of watersheds in contour
detection,
Proceedings of the International Workshop on Image
Processing: Real-time Edge and Motion Detection/Estimation
,
17-21 September, Rennes, France, pp. 2.1–2.12.
Blaschke, T., K. Johansen, D. and Tiede, 2011. Object-based image
analysis for vegetation mapping and monitoring,
Advances in
Environmental Remote Sensing
(Q. Weng, editor), CRC Press,
Boca Raton, Florida, pp. 241–272.
Bunting, P., and R. Lucas, 2006. The delineation of tree crowns in
Australian mixed species forests using hyperspectral Compact
Airborne Spectrographic Imager (CASI) data,
Remote Sensing of
Environment
, 101 (2):230–248.
Chavez, J.P.S. and J.A. Bowell, 1988. Comparison of the spectral
information content of landsat thematic mapper and SPOT for
three different sites in the Phoenix, Arizona,
Photogrammetric
Engineering & Remote Sensing
, 54(12):1699–1708.
Chavez, J.P.S., S.C. Sides, J.A. and Anderson, 1991. Comparison
of three different methods to merge multiresolution and
multispectral data: Landsat TM and SPOT Panchromatic,
Photogrammetric Engineering & Remote Sensing
, 57(3):295–303.
Chen, Q., D. Baldocchi, P. Gong, and M. Kelly, 2006. Isolating
individual trees in a savanna woodland using small footprint
lidar data,
Photogrammetric Engineering & Remote Sensing
, 72
(8):923–932.
Clinton, N., A. Holt, L. Lyan, and P. Gong, 2010. An accuracy
asseessment measure for object based image segmentation,
Proceedings of the International Archives of the
Photogrammetry, Remote Sensing and Spatial Information
Sciences
, Beijing, Vol. XXXVII (Part 4B):1189–1194.
DigitalGlobe, 2011,
DigitalGlobe Core Imagery Products Guide
,
DigitalGlobe, URL:
(last
date accessed: 13 April 2015)
Duke, N.C., 2006,
Australia’ Mangroves - The authoritative guide
to Australia’s mangrove plants
, University of Queensland,
University of Queensland, Brisbane, 200 p.
Edson, C., and M.G. Wing, 2011. Airborne light detection and ranging
(LiDAR) for individual tree stem location, height, and biomass
measurements,
Remote Sensing
, 3:24942528.
478
June 2015
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
