Coral Reef Mapping in Vietnam’s Coastal Waters from High-spatial Resolution Satellite
and Field Survey Data
Van Dien TRAN, Stuart PHINN, Chris ROELFSEMA
Centre for Spatial Environmental Research,
School of Geography, Planning and Environmental Management,
The University of Queensland, Australia
Phone: +61 7 336 54370; Fax: +61 7 336 56899
Email: dien.tran@uqconnect.edu.au
Title: Coral Reef Mapping in Vietnam’s Coastal Waters from High-spatial Resolution Satellite
and Field Survey Data
Abstract: A process for mapping the benthic cover of coral reefs at selected sites in Vietnam’s
coastal waters from high-spatial resolution, multi-spectral satellite data was developed and
tested. GeoEye-1, IKONOS, QuickBird satellite images in Dam Mon, My Giang and Nha Trang
respectively were used and field data was collected in 2009. Images were corrected for
radiometric, atmospheric, and depth effects. Band combination of three atmospheric corrected
bands, second band of PCA, and depth-invariant index of spectral band 1, 2 were used for
supervised classification at fine level of description. Calibration and validation field data were
acquired through georeferenced photo-transect method and photos were analysed for benthic
cover. The overall accuracy of Maximum Likelihood, Minimum Distance to Means and
Mahalanobis Distance classifiers were compared for each study site derived from the individual
confusion matrix. Overall accuracy of the classified image using Maximum Likelihood classifier
were higher than other classifiers in Dam Mon and My Giang, but Mahalanobis Distance
classifier was highest in Nha Trang. Overall accuracy of classified image increased when
reduced number of benthic classes. Variable environmental conditions, including water clarity
and depth, along with reef structures were identified as main factors causing mis-classification
and reducing the overall accuracy.
Key Words: benthic cover, coral reef mapping, GeoEye-1, IKONOS, QuickBird, snorkelling
survey, Vietnam
Introduction
Vietnam’s coastal waters contain a wide range of reef diversity and structures. These reefs
support over 350 species of hard corals and cover an estimated area of 1,122 km2. The condition
of 60% of Vietnam’s reefs can be described as fair, 20% as poor, 17% as good, and only 3% as
excellent (Chou et al. 2002). These coral reefs continue to be stressed by a variety of threats,
particularly in areas of dense human populations. For protection and conservation of the coral
reef ecosystem in Vietnam, their distribution and status should be investigated and monitored
(Tuan 2001). Recently, the Vietnamese government has become interested in the study of coral
reefs, demonstrated by the investigation of reefs in the Cat Ba - Ha Long coastal area, the
monitoring of reefs in Nha Trang Bay, Con Dao Archipelago and Phu Quoc Islands (Tuan et al.
2005; Tuan et al. 2008), towards development of a network of marine protected areas. With
support of government and international organizations, several research projects on coral
ecosystems were implemented and data was collected by diving surveys (Long et al. 2004; Yet
2003). However, these studies only assessed coral species identification, reef status, and stress
level at a limited number of specific reef sites. Information on coral cover and status is important
for coastal management programs for sustainable development yet our knowledge of this for
Vietnam’s coral reefs is still limited (Tuan et al. 2008; Tun et al. 2004).
Satellite remote sensing provides a means by which to map the composition, condition and
dynamics of shallow tropical coral reefs from the scale of coral patches (several square metres)
to regional and global scales (Mumby et al. 2004). Most studies mapping ecological properties of
coral reefs from satellite and airborne images have been implemented in clear, shallow (depth
less than 10 metres) water environments (Andréfouët et al. 2003; Mumby et al. 2004). Coral
reefs cover areas that range in size from fringing reefs at less than 100 metres wide to barrier reef
systems that extend over millions of square kilometres. The development of techniques to deliver
accurate and appropriately detailed maps in a cost-effective manner for this range of areas
remains a challenge. The accuracy of derived reef maps depends on spatial, radiometric and
spectral resolutions of remote sensing data, optical properties of the water environment, type of
mapping classes used, and type of mapping approaches applied (Phinn et al. 2006). The optical
properties of coastal waters are locally dependent and highly dynamic, which makes mapping
coral reefs in optically complex coastal water bodies very challenging.
Marine benthic habitat mapping in Vietnam has attracted attention in recent years. Several
studies have applied moderate spatial resolution remote sensing images for benthic habitat
mapping at small scales and coarse descriptive levels (Dien 2007; Dien et al. 2008; Son 2004;
Tripathi et al. 2008; Tuan 2004; Vinh 2000). These studies present the potential for remote
sensing to map and monitor coral reefs in Vietnam. However, there is a lack of systematic work
to define image-based techniques suitable for mapping and monitoring coral reefs in Vietnam's
coastal waters (Dien et al. 2008; Tuan et al. 2005). The composition of typical and non-typical
coastal fringing reefs in Vietnam was difficult to discriminate on moderate-spatial resolution
imagery, only reef and non-reef areas or broad benthic cover types were identified (Dien et al.
2008). At rough estimation, less than ten percent of the coral reefs were mapped at broad
descriptive level. There are no detailed benthic cover maps of most coral reefs in Vietnam and a
lack of accurate information of the benthic cover and status of those reefs countered over time.
Therefore, the knowledge of the extent, composition, and condition of coral reefs in Vietnam is
very limited.
This study developed and tested a sequence for mapping the benthic cover of coral reefs at
selected sites in Vietnam’s coastal waters from high-spatial resolution, multi-spectral satellite
image data. Van Phong Bay and Nha Trang Bay of Khanh Hoa Province were selected for
mapping benthic cover of coral reefs. Three reef sites in the bays, representing different coastal
reef structures, environmental conditions, and development pressures were studied (Figure 1).
The My Giang site is in the southern part of the Van Phong Bay, which is characterised by clear
water, steep slope, and high wave action. The Dam Mon site is in the eastern part of the Van
Phong Bay where water is clear and wave action is low. The Nha Trang site is in the southern
part of the Nha Trang Bay, which is characterised by fairly clear water, steep slope reefs, and
high tidal dynamic and wave action. This bay has been a Marine Protected Area (MPA) since
2002. No major river flows to the My Giang and Dam Mon study areas, yet water clarity in the
Nha Trang study sites is influenced by two rivers flow to Nha Trang Bay (Dau 2002).
Methodology
Coral reefs in Vietnam's coastal waters are mostly fringing and patch reefs, less than 200 metres
in width, with sparse cover and are limited in size compared to barrier reefs and atolls. To detect
and map benthic cover of these reefs, high-spatial resolution multi-spectral remote sensing data
are required. These satellite image types have been only available since the launch of IKONOS
satellite in 1999 and QuickBird satellite in 2001. In this paper, high-spatial resolution satellite
images (Geo Ortho preprocessing level) of IKONOS captured on 13 July 2008 in My Giang,
QuickBird captured on 26 August 2006 in Nha Trang, and GeoEye-1 captured on 24 March
2009 in Dam Mon and processing techniques were evaluated for mapping benthic cover at finer,
moderate and broad descriptive levels.
A field survey was conducted in April and May 2009 to collect data on benthic cover and cover
status in the study areas. This was done for calibration and validation purposes. These data were
input to the image processing sequence to map benthic cover of coral reefs and to check the
accuracy of the benthic cover maps. The belt photo transect field survey method (Green et al.
2000) was used in this study. Snorkelling equipment was used in this survey method. Divers
swam freely on the water surface to observe, take photos and record data on coverage of bottom
features along transects at set elevation above substrate and benthos. A total of 38 phototransects that cover most benthic cover types in three study areas were conducted (Figure 2).
Among them, 14 transects were in My Giang (a), 13 transects were in Dam Mon (b), 11 transects
were in Nha Trang (c). Under-water photos were taken perpendicular to the sea bottom at a
height of one metre (one square metre per photo) with intervals of about two metres. A
handheld Garmin Map76CSX GPS with location accuracy of 2-3 metres in a water-proof bag
was placed to float on the surface and was towed by a snorkeller to record the position of
survey transects and photos taken. GPS Photo-Link software was used to link the taken
photos to their position recorded by GPS.
Photos of benthic cover were processed using Coral Point Count with Excel Extensions
(CPCe 3.6) (Kohler and Gill 2006) to quantify the benthic cover of each photographed area. A
stratified random distribution of 24 points was overlaid on a photo and benthic cover lying
beneath each point was visually identified. Percentage cover of benthic components was
calculated as a percentage of the 24 points per photo. Each feature observed on the photo
(Figure 3) was assigned to a benthic class using the fine classification scheme in Table 1. The
lower thresholds of dominant benthic classes are of 30% benthic cover. The benthic cover
amounts and classification results for each photo were linked to their position and overlaid on
satellite images. These data were used for selection of the training areas for supervised
classification and accuracy assessment of classified images.
Classification schemes of marine habitats have been established with geophysical, chemical
and biological attributes by Mumby and Harborne (1999). In this paper, a hierarchical
classification scheme (Table 1) of benthic cover in the study sites was generated based on
existing classification schemes and benthic habitats in the study area. Observed habitats or
benthic cover types in study locations were input into the classification scheme in a
hierarchical order. Major classes such as bare substrates, coral, algae, and seagrass were
divided on the basis of geo-morphological and ecological classification. Descriptive levels of
classes in these sites were divided into broad (4 classes), moderate (8 classes) and finer (15
classes).
Based on the review of existing coral reef mapping approaches, and published coral reef
mapping flowcharts by Andréfouët (2008), an image processing sequence for mapping of
benthic cover in Vietnam's coastal waters has been developed (Figure 4). The basic processing
steps in image processing and validation sequence include pre-processing corrections
(radiometric, atmospheric, geometric, principal components analysis, and water column
corrections), classifications (supervised classification), post-classification, and accuracy
assessment (error matrix). The FLAASH model was used for atmospheric correction of GeoEye1 and QuickBird images. The ATCOR model was used for atmospheric correction of IKONOS
imagery because corrected IKONOS images using FLAASH contained some bright strips.
Visibility used in atmospheric correction models was calculated from MODIS-derived aerosol
optical depth. The depth-invariant index proposed by Lyzenga (1978; 1981) and tested in several
studies (Arce 2005; Benfield et al. 2007; Mumby et al. 1998; Mumby and Edwards 2002) was
used for correction of water column effects. Principal Components Analysis was used to produce
uncorrelated output bands, to segregate noise components, and to reduce the dimensionality of
data sets. In coastal waters, the first PCA band is correlated to deep variation, and the second
PCA band contains information of benthic features (Mishra et al. 2006).
Benthic survey points were classified to benthic cover classes based on quantification of benthic
features on field photos. Nine benthic classes were identified in the Dam Mon study site, 14
classes in My Giang and 13 classes in Nha Trang. Training areas of each benthic cover class
were selected over areas containing survey points of the same benthic class and image pixels
with similar colour and texture. Six to eight training areas for each benthic class were created in
shape file format with similar size. These training areas were divided into classification training
and validation areas with the same number of training areas for each benthic class. These training
areas were converted to region of interest (ROI) in ENVI for supervised classification and
accuracy assessment of satellite images. Band combination of optimal differentiability of benthic
feature which include three atmospheric corrected spectral bands (RGB), depth-invariant index,
and second band of PCA was selected for supervised classification. Land, deep waters (where
benthic features invisible on satellite image), and cloud areas were masked out to enhance
contrast of reef areas. Three decision rules of supervised classification (Maximum Likelihood,
Mahalanobis Distance, and Minimum Distance) were applied. The accuracies of these decision
rules were evaluated to determine the most accurate classified images for benthic cover mapping
in these study areas. Several classes in the detailed classification scheme were combined to
create a major benthic cover type in moderate and broad classification schemes. Validation
training areas were used to compare with benthic cover on classified images. The output of this
process was the error matrices of the benthic cover maps. These error matrices were analysed to
calculate the producer and user accuracies, overall accuracy and Kappa coefficient of respective
maps (Congalton and Green 1999). The methods and processing sequence that produced the
most accurate maps was selected as the suitable image processing sequence for mapping of coral
reefs from high-spatial resolution satellite data in Vietnam's coastal waters.
Results and Discussion
Benthic cover mapping in Dam Mon from GeoEye-1 satellite image
Nine benthic cover types were identified on field survey photos and satellite image in Dam Mon.
They were brown algae, living coral, dead coral, rock and pavement, rubble, shallow sand,
medium sand, deep sand, and deep waters. The optimal band combination was used for
supervised classification. Three decision rules were applied to produce output benthic cover
maps of nine classes. Three sandy classes at different depth were combined to produce benthic
cover maps at moderate level of description (seven classes). In broad classification level (four
classes), the living coral and dead coral classes were grouped to coral class and sand, rubble,
rock and pavement classes were grouped to substrate class. Validation ROIs were grouped into
seven classes and four classes to assess the accuracy of classified images at moderate and broad
schemes. Accuracies of classified images using Maximum Likelihood Classifier were higher
than accuracies of classified images using other classifiers. Confusion matrix of classified image
using Maximum Likelihood Classifier at moderate classification scheme is presented in Table 2.
These images were selected to map benthic cover in the Dam Mon study area from GeoEye-1
satellite image (Figure 5) at moderate descriptive level and broad descriptive level. Confusion
matrix shows that the accuracies of living coral, brown algae, and sand were quite high (>80%)
as a results of the homogenous spectral property of brown algae and sand. However, accuracies
of substrates and dead coral were quite low due to high variance within a training area and
spectral mixing within an image pixel. Rock and pavement was misclassified as dead coral and
sand. Rubble was misclassified as sand and dead coral. Dead coral was misclassified as rock and
pavement and sand. These misclassifications may be due to similar spectral reflectance
properties of substrates and dead coral.
Benthic cover mapping in My Giang from IKONOS satellite image
Fourteen benthic cover types were identified on field survey photos and satellite images in My
Giang. They were branching coral, massive and encrusting coral, dead coral, reef slope, shallow
brown algae, deep brown algae, sparse seagrass, medium seagrass, dense seagrass, rock and
pavement, rubble, shallow sand, medium sand, and deep sand. The optimal band combination
was used for supervised classification. Three decision rules were applied to produce benthic
cover map of 14 classes. To produce benthic cover maps at moderate level of description (eight
classes), branching coral and massive and encrusting coral were combined to living coral, deep
brown algae and shallow brown algae were grouped to brown algae, and three sandy classes at
different depth were combined to sand. In broad classification level (five classes), the living coral
and dead coral classes were grouped to coral class and sand, rubble, rock and pavement classes
were grouped to substrate class. Validation ROIs were grouped into eight classes and five classes
to assess the accuracy of classified images at moderate and broad schemes. Accuracies of
classified images using Maximum Likelihood Classifier were higher than accuracies of classified
images using other classifiers. However, these accuracies were less than those reported by
Andréfouët et al. (2003) at 10 tropical coral reefs worldwide. When similar classes were merged
to broader benthic classes, the overall accuracy of classified images increased. The high variance
of benthic cover in My Giang was a region of lower accuracy when using Minimum Distance
and Mahalanobis Distance Classifiers. A confusion matrix of eight classes on classified image
using Maximum Likelihood Classifier is presented in Table 3. These images were selected to
map benthic cover in the My Giang study area from IKONOS satellite image (Figure 6) at
moderate descriptive level and broad descriptive level. Confusion matrix shows that accuracy of
reef slope and sand was very high in both user and producer accuracy (>80%) as result of
homogenous and wide distribution of sandy areas. However, accuracy of rock and pavement and
rubble were quite low because of spectral mixing with other classes. Accuracy of living coral and
brown algae was fair. Living coral were misclassified as reef slope and brown algae due to
mixing of these benthic types within an image pixel and a training area.
Benthic cover mapping in Nha Trang from QuickBird satellite image
Thirteen benthic cover types were identified on field survey photos and satellite image in Nha
Trang. They were brown algae, reef slope, sheet and table coral, branching coral, soft and fire
coral, massive and encrusting, dead coral, rock and pavement, rubble, shallow sand, medium
sand, deep sand, and deep water. The optimal band combination was used for supervised
classification. Three decision rules were applied to produce benthic cover maps of 13 classes. To
produce benthic cover maps at moderate level of description (eight classes), branching coral,
massive and encrusting coral, sheet and table coral and soft and fire coral were combined to
living coral and three sandy classes at different depth were combined to sand. In broad
classification level (five classes), the living coral and dead coral classes were grouped to coral
class and sand, rubble, rock and pavement classes were grouped to substrate class. Validation
ROIs were grouped into eight classes and five classes to assess the accuracy of classified images
at moderate and broad schemes. Accuracies of classified images using Mahalanobis Distance
Classifier were higher than accuracies of classified images using other classifiers. Overall
accuracy increased when the number of classes decreased by grouping certain classes. High
variation in water clarity and depth in Nha Trang Bay may be the regions of low accuracy of
classified images using Maximum Likelihood and Minimum Distance Classifiers. The accuracy
of classified images using Mahalanobis Distance Classifier was almost similar to the accuracy of
classified QuickBird images in Roatan Island, Honduras with six classes conducted by Mishra et
al. (2006) using ISOData unsupervised classification. A confusion matrix of eight classes in
classified image using Mahalanobis Distance Classifier is presented in Table 4. These images
were selected to map benthic cover in the Nha Trang study area at moderate descriptive level and
broad descriptive level (Figure 7). Producer accuracies of living coral, deep waters, and sand
were quite high due to homogeneity and wide distribution of sandy bottom and deep waters.
Producer accuracy of brown algae, reef slope was fair. Brown algae and reef slope were
misclassified as living coral because of mixed distribution of these benthic types. Producer and
user accuracies of rock and pavement, rubble, and dead coral were very low as a result of limited
distribution. They were misclassified as living coral and brown algae. User accuracies of reef
slope, sand, and deep waters were high. User accuracy of living coral, brown algae, deep waters
were fair. Living coral was misclassified as brown algae and reef slope as a result of mixed
distribution of these benthic types.
Overall accuracies of classified images were compared between three study sites, high-mediumlow detail benthic classification, three satellite image types, and three classification algorithms
(Figure 8). Band combination of three atmospheric corrected spectral RGB bands, 2nd PCA, and
depth-invariant index was considered as optimal band combination for classification of benthic
cover because this combination selected the highest contrast bands to distinguish the benthic
features. Spatial resolution of satellite image and reef geomorphology were the main factors
affecting overall accuracy of image classification, as was indicated by high accuracy of classified
GeoEye-1 and QuickBird images and lower accuracy of classified IKONOS satellite imagery. The
overall accuracy of classified images increased when merging similar benthic classes. Accuracy of
classified images was acceptable for management planning (Green et al. 2000), but only classified
QuickBird and GeoEye-1 imagery at broad descriptive levels was suitable for habitat monitoring
(>80%). Combination of QuickBird sensor with Mahalanobis Distance classification algorithm
yielded the best combination of sensor and algorithm for benthic cover mapping in Vietnam’s
coastal waters. The pixel size of QuickBird is about the same size of benthic features which
reduced spectral mixing within an image pixel. This sensor provided good accuracy for mapping
coral reefs in Roatan Island as indicated by Mishra et al. (2006). Mahalanobis Distance
classification algorithms consider the covariance and variance of each class that suits more
complex and high variation of pixel value within a training area as in coral reefs. This algorithm
provided good accuracy when mapping benthic cover in Florida Keys from Landsat TM and ETM
data (Palandro et al. 2008).
Conclusions and Recommendations
A suitable image processing and validation sequence for mapping benthic cover of coral reefs in
some of Vietnam’s coastal waters from high-spatial resolution satellite imagery was achieved by
combining field survey techniques, image processing and validation methods. The benthic cover
maps derived from satellite images in the three study reef sites provided information of the
benthic cover of those reefs and contributed to the knowledge of the extent, composition and
condition of coral reefs in the study areas. A critical assessment of the accuracy of derived maps
from three satellite sensors using three classification methods at three descriptive levels
identified a suitable processing and classification approach for coral reef mapping in the coastal
waters of Vietnam. Acuracy of derived benthic cover maps is directly proportional to spatial
resolution of satellite imagery and inversely proportional to the number of benthic classes. The
outputs and findings of this study contribute to the study of coastal ecosystems, sustainable
management of coral reefs, and remote sensing of coral reef science in Vietnam, Asia and
globally. More coral reefs and seagrass beds in Vietnam’s coastal waters should be mapped from
high-spatial satellite data to cover a range of benthic cover and environmental conditions in
Vietnam.
Acknowledgements
I appreciate the funding of field survey and data collection in Vietnam by the Institute of Marine
Environment and Resources in Vietnam and School of Geography, Planning and Environmental
Management, University of Queensland. We would like to thank Mr Cao Van Luong and Mr
Tran Quoc Hung for their assistance in conducting the snorkelling survey. I am grateful to
scholarship support from Vietnamese Government through the Ministry of Education and
Training (MOET).
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Table 1. Benthic cover classification schemes for field data analysis and satellite image
classification in Vietnam's coastal waters
Table 2. Confusion matrix of 7 classes (Brown Algae, Dead Coral, Deep Waters, Living Coral, Rock
& Pavement, Rubble, Sand) using Maximum Likelihood Classifier in the Dam Mon study area
from GeoEye-1 satellite image acquired on 24 March 2009 (Overall accuracy of 76.98% and
Kappa coefficient of 0.6618).
Table 3. Confusion matrix of 8 classes (Brown Algae, Dead Coral, Living Coral, Reef Slope, Rock &
Pavement, Rubble, Sand, Seagrass) using Maximum Likelihood Classifier in the My Giang study
area from IKONOS satellite image acquired on 13 July 2008 (Overall accuracy of 65.09% and
Kappa coefficient of 0.5837).
Table 4. Confusion matrix of 8 classes (Brown Algae, Dead Coral, Deep Waters, Living Coral, Reef
Slope, Rock & Pavement, Rubble, Sand) using Mahalanobis Distance Classifier in the Nha Trang
study area from QuickBird satellite image acquired on 26 August 2006 (Overall accuracy of
79.50% and Kappa coefficient of 0.7247).
Table 1
Broad classification scheme Moderate classification
scheme
Substrate
- Sand
- Rock and Pavement
- Rubble
Coral
- Living Coral
- Dead Coral
Seagrass
- Seagrass
Macroalgae
- Green Algae
- Brown and Red Algae
Finer classification scheme
- Sand
- Rock and Pavement
- Rubble
- Branching Coral
- Table Coral
- Sheet Coral
- Massive and Encrusting Coral
- Soft and Fire Coral
- Dead Coral
- Sparse Seagrass (30-50%)
- Medium Seagrass (50-70%)
- Dense Seagrass (>70%)
- Green Algae
- Brown Algae
- Red Algae
Table 2
Image (pixels)
Brown Algae
Rock & Pavement
Rubble
Living Coral
Sand
Dead Coral
Deep Waters
Total
Producer Accuracy (%)
1120
13
3
63
44
109
0
1352
82.84
169
70
310
0
281
261
0
1091
6.42
1
0
535
0
0
132
0
668
80.09
0
0
0
461
0
0
0
461
100
0
0
696
0
5543
99
102
6440
86.07
122
47
0
20
236
220
0
645
34.11
0
0
0
0
17
0
1165
1182
98.56
1412
130
1544
544
6121
821
1267
11839
User Accuracy (%)
Total
Deep Waters
Dead Coral
Sand
Living Coral
Rubble
Brown Algae
Class
Rock & Pavement
Reference (pixels)
79.32
53.85
34.65
84.74
90.56
26.8
91.95
Table 3
User Accuracy (%)
Total
Rubble
Seagrass
Reef Slope
Sand
Brown Algae
Dead Coral
Living Coral
Image (pixels)
Living Coral
Dead Coral
Brown Algae
Sand
Reef Slope
Seagrass
Rock &
Pavement
Rubble
Total
Producer Accuracy (%)
Rock & Pavement
Reference (pixels)
Class
199
0
105
0
18
5
17
94
0
0
0
0
44
30
319
2
10
34
4
0
34
696
0
0
56
0
0
3
282
0
15
44
16
3
0
235
0
3
17
0
0
25
0
13
26
55
0
0
335
184
517
759
310
299
59.4
51.09
61.7
91.7
90.97
78.6
0
0
327
60.86
0
7
118
79.66
10
152
601
53.08
0
0
734
94.82
0
0
341
82.7
67
115
495
47.47
5
61
111
4.5
0
18
112
16.07
82
353
2839
6.1
5.1
Table 4
25
0
0
245
57.14
12
51
0
0
41
0
1
105
11.43
0
9
6
0
32
0
0
86
10.47
8
22
10
0
7
0
0
88
11.36
0
0
1
119
137
0
27
284
41.9
15
0
8
8
340
0
2
398
85.43
0
0
0
0
5
646
59
710
90.99
0
0
0
4
0
0
1338
1342
99.7
53
83
35
131
668
646
1427
3288
22.64
10.84
28.57
90.84
50.9
100
93.76
Total
Deep Waters
0
Living Coral
41
Reef Slope
39
Dead Coral
0
Rubble
Sand
Rock & Pavement
Brown Algae
Image (pixels)
Brown Algae
140
Rock &
Pavement
18
Rubble
1
Dead Coral
10
Reef Slope
0
Living Coral
106
Sand
0
Deep Waters
0
Total
275
Producer Accuracy (%) 50.91
User Accuracy (%)
Reference (pixels)
Class
Figure 1. Location of the Van Phong Bay and Nha Trang Bay (a) and the study areas (b) on
ALOS AVNIR-2 image on 26 July 2008 (A- Dam Mon, B- My Giang, C- Nha Trang).
Figure 2. Location of the field survey transects on GeoEye-1 satellite image in Dam Mon (a),
IKONOS satellite image in My Giang (b), and QuickBird datellite image in Nha Trang (c).
Figure 3. Field photos show an overview of the mapped features in three study areas. Several
benthic features can be observed and mapped such as branching coral (b), table coral (b),
sheet coral (d), massive coral (a), soft coral (a), brown algae (d), seagrass (c), rock (b), rubble
(d), and sand (d).
Figure 4. Image processing and validation sequence for mapping benthic cover of coral reefs in
Vietnam’s coastal waters from high-spatial resolution satellite imagery.
Figure 5. Benthic cover map of the Dam Mon study area from GeoEye-1 satellite image
acquired on 24 March 2009 in moderate classification scheme (7 classes) (a) and broad
classification scheme (4 classes) (b) using Maximum Likelihood Classifier.
Figure 6. Benthic cover map of the My Giang study area from IKONOS satellite image acquired
on 13 July 2008 in moderate classification scheme (8 classes) (a) and broad classification scheme
(5 classes) (b) using Maximum Likelihood Classifier.
Figure 7. Benthic cover map of the Nha Trang study area from QuickBird satellite image
acquired on 26 August 2006 in moderate classification scheme (8 classes) (a) and broad
classification scheme (5 classes) (b) using Mahalanobis Distance Classifier.
Figure 8. Overall accuracy (%) of benthic cover maps derived from classified satellite images at
broad (4-5 classes), moderate (7-8 classes) and finer (9-14 classes) schemes using three
classification algorithms in three study areas.
a
b
A
B
C
Figure 1.
a
c
Figure 2.
b
a
b
Branching Coral
Table Coral
Soft Coral
Massive Coral
Rock
d
c
Rubble
Sand
Brown Algal
Seagrass
Sheet Coral
Figure 3.
High-spatial resolution
satellite images
Radiometric and atmospheric
correction (FLAASH, ATCOR)
Map and GPS
Geometric correction
Principal Component Analysis
(PCA)
Cloud, cloud-shadow, land, depth
water masking
Water column correction
(Depth-invariant Index)
Band combination
Field survey data
Classification
(supervised classification)
Post-classification
(combination and smoothing)
Accuracy assessment
and validation
Benthic cover maps
Figure 4.
a
b
a
b
Figure 5
Figure 6
a
Figure 7
b
Figure 8