2012 status and threats on seagrass beds using gis in vietnam

Status and threats on seagrass beds using GIS in Vietnam

Cao Van Luong*
a
, Nguyen Van Thao
a
, Teruhisa Komatsu
b,c
, Nguyen Dac Ve
a
, Dam Duc Tien
a
a
Institute of Marine Environmentand Resources, Vietnam Academy of Science and Technology, 246
Da Nang, Ngo Quyen, Hai Phong, Vietnam;
b
Atmosphere and Ocean Research Institute, The
University of Tokyo, 5-1-5, Kashiwanoha, Kashaiwa, 277-8564, Japan;
c
Japan Science and
Technology Agency, CREST, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan
ABSTRACT
Seagrasses, marine flowering plants, are widely distributed along temperate and tropical coastlines of the world.
Seagrasses have key ecological roles in coastal ecosystems and can form extensive meadows supporting high
biodiversity. Till now, fourteen seagrass species belonging to four families were found in Vietnam: Halophila beccarii,
H. decipiens, H. ovalis, H. minor, Thalassia hemprichii, Enhalus acoroides, Ruppia maritima, Halodule pinifolia, H.
uninervis, Syringodium isoetifolium, Cymadocea rotundata, C. serrulata and Thalassodendron ciliatum. A total area of
seagrass beds in Vietnam is estimated to be approximately 17000 ha by satellite images and GIS technology. In recent
years, the distribution areas and densities of seagrass beds in Vietnam have been serious decreased compared with those

10 – 15 years ago. The decline level depended on the impacts by the natural process, the economical activities and the
conservation awareness of local people. Thus, it is different at each coastal area. Generally speaking, the distribution
areas and densities of seagrass beds were decreased by more than 50%. Seagrasses on tidal flats in some areas such as
Quang Ninh, Hai Phong, Phu Quoc seem to be nearly lost. The distribution areas of seagrass beds in 2009 at Tam Giang
– Cau Hai lagoon and Cua Dai estuary was decreased by 50 – 70% of those in early 1990s.
Keywords: seagrass, Vietnam, GIS, satellite image

1. INTRODUCTION
Ecosystems such as mangroves, seagrasses and coral reefs play important roles in sustaining resources and healthy
coastal environment. These ecosystems have high productivity also providing valuable coastal habitats for animals and
also epiphytic flora. From the ecological point of view, they create proper environments as ecosystem engineers. They
stabilize bottom sediments [1, 2], maintain coastal water quality and clarity, buffer water flow [3]. Additional effects of
seagrass beds seem to be similar to those of seaweed forests such as pH distribution [4], dissolved oxygen distribution
[5], light distribution [6] and water temperature [7, 8, 9]. These functions may be very important for sound coastal
ecosystem of seagrass. Thus, spatial distributions of seagrass beds are an important factor for realizing sustainable
development of coastal waters. However, seagrass beds are threatened by human activities such as reclamation, trawl,
pollution and so on [10]. Since it is needed to identify changes in the distribution areas, it is indispensable to map spatial
distributions of seagrass beds.
Because seagrass beds are distributed over large areas and under water (seagrass and coral ecosystems), they should be
survey in the sea. Diving and observation from the boat are traditionally used for mapping bottom habitats. However,
these methods are laborious and time consuming [11, 12]. These surveying methods are expensive and difficult for areas
without logistic supports. Thus, it is desired to develop efficient mapping and monitoring systems of coastal areas.
Remote sensing data provide an overview over a large area. They are shot regularly to meet the demand of users. Since
the 1970s, numerous studies have been conducted to develop methods of mapping the distribution of seagrass and coral
beds using remote sensing data combined with ground survey. Remote sensing data have been used for mapping seagrass
and coral distributions since 1975 just after Landsat MSS data have been available. Since then, many researches have
been conducted and developed satellite image processing methods for mapping seagrass distribution such as Ackleson,
1987 [13], Armstrong, 1993 [14]; Congalton, 1999 [15], Sagawa et al., [16, 17]. These studies were focused on waters
with high transparancy or very shallow waters. On the other hand, in turbid waters such as lagoons, classification
Remote Sensing of the Marine Environment II, edited by Robert J. Frouin,

Naoto Ebuchi, Delu Pan, Toshiro Saino, Proc. of SPIE Vol. 8525, 852512
© 2012 SPIE · CCC code: 0277-786/12/$18 · doi: 10.1117/12.977277
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accuracies using remote sensing datat are significantly reduced. Today, with ground surveys and support of spectrometer
and ground surveys, algorithms processing remote sensing image for mapping the distribution of seagrass and coral beds
are increasingly innovated and become more precise. However, the use of remote sensing data for mapping coral and
seagrass distributions in Vietnam is still limited up to now.
In this study, the AVNIR – I, ALOS AVNIR – II, Landsat TM and SPOT 5 satellite image data have been combined
together actual survey data is processed to map seagrass distribution and assess changes in their area.

2. MATERIAL AND METHOD
2.1. Material and methods
We analyzed 32 satellite images: AVNIR - I in 1997, ALOS AVNIR - II in 2007 – 2010, Landsat TM in 1990 and 2000,
SPOT 5 in 2009 and Topographic maps by Defense Mapping Agency of VietNam in 2002 with Coordinate system VN-
2000 (Figure 1).

Figure 1. Mozaic of satellite images covering all coastline in Vietnam


This study consits of many researches domestically funded, a number of bilaterally-funded projects relevant to seagrass
management implemented in Vietnamese coastal areas (see Table 1). The results of the field surveys have been used to
classify bottom substrates when satellite images have been analyzed.

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Table 1. Summary of past and ongoing projects related to seagrass in Vietnam
Year Project Title Study Sites
1986 - 1989
Rational utilization of typical ecosystems: coral reefs,
estuaries and lagoons - code KT.03.11
Coastal zones of Quang Ninh, Hai Phong, Nam Dinh,
Thua Thien - Hue and Ba Ria - Vung Tau provinces.
1996
Wise management of the Tam Giang-Cau Hai lagoon,
Thua Thien Hue province – code KT.DL.04-09
Thua Thien Hue coastal zone lagoons (including
seagrass component)
1996 Inventory of seagrass beds in Khanh Hoa province Coastal zone of Khanh Hoa province

1997 - 1999
Surveys of species composition and ecology of
seagrasses in the Vietnam coastal zone
Coastal zone and some islands of Vietnam
1999 - 2000
Survey of seed grounds, spawning grounds of
economic aquatic species of Thua Thien Hue
province and propose solutions to protect
Coastal zone of Thua Thien Hue province
1999 - 2000
Prediction of resilience and recovery of disturbed
coastal communities in the tropics (SE Asia),
PREDICT
Gia Luan tidal flat, Cat Ba island
2001
Initial study on transplantation of seagrasses in Ha
Long Bay
Ha Long Bay
2002 - 2005
Investigation of measures for protecting and restoring
disturbed seagrass and coral ecosystems
Nha Trang bay, Cat Ba, Ha Long bay
2004 - 2006
Assessment of seagrass resources in coastal areas of
Central and South West of
Vietnam proposedsolutions sustainable use
of resources
Coastal zone of Thua Thien Hue, Da Nang, Quang
Nam, Quang Ngai, Phu Yen, Khanh Hoa, Binh Thuan,
Ba Ria – Vung Tau and Kien Giang provinces

2008 - 2010
Assess the degradation of coastal ecosystems of
Vietnam and proposed solutions to the sustainable
management
Coastal zone of Nghe An, Thua Thien Hue, Quang
Nam provinces
2009 - 2011
Study basis scientific, legal to the evaluation and
compensation for oil pollution damage caused by in
Vietnamese sea area
Coastal zone of Thua Thien Hue, Quang Nam, Ba Ria
– Vung Tau province
2009 - 2011
Construction overview of biodiversity status and
using of seagrass ecosystems in coastal Vietnam in
service of sustainable development
Coastal zonea of Vietnam

2.2. Method
Geometric correction
Geometric correction of satellite images is an important step in digital image processing because spatial accuracy of the
results depends on the geometric correction. Satellite images have been often distorted and shown geometric deviation,
caused by technical problems of endogenous and exogenous deviations such as changes in angle from the center to the
edge of the image on coastal area scenes, changes in a height of the satellite above the ground due to topographical
factors, the rotation of the earth and so on. Calibration is the process of assigning geometric coordinates of the map to
image coordinates, and then to project sampled picture to the coordinates of the map with a grid of squares on a plane.
We applied geometric correction of image distortions according to the procedure mentioned above.

Geometric correction is done by specifying the control points on the original image and on the reference map, such as
terrain. The accuracy of the control points is shown by calculating the square root of the average deviation (RMS-error).

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Concerning typical image correction with high accuracy, the RMS-error is less than 1 under the uniform distribution of
the control points.

The UTM maps with a scale of 1 : 50000 was established in Vietnam in 1965 from airplane photos, additional revised
and reprinted in 1998 and 1999. They were used for finding control points for geometric correction of the images. We
selected control points with little changes in surface topography such as intersection of highways and those of rocks,
bridges and culverts spreading over the whole image. The image coordinate system was corrected and adjusted to VN
2000.

RMS-error of the geometric correction of satellite images were within 0.53. To ensure the results of the geometric
correction with high precision, the total RMS-error must be less than or equal to 1 pixel size of the image. Our results
agreed with this standard that is less than 1. Since satellite images have often covered a large area of ea surface, it is
impossible to select control points on the sea surface. In this case, the geometrical correction can not be obtained about
some images. To overcome the problem, road class information of UTM map was superimposed over the image after
editing to assess the accuracy.

Radiometric correction
Process converting DN values of an image to physical ones permits us to separate an object from the others on the
ground. Selection of image bands and their combination is very important for identifying differences of ground objects.
Color combinations make us to observe objects more clearly as well as combinations with those and or other channels
for separating objects with higher precision such as combination of spectral channels from PCA component analysis and
NDVI vegetation index. Two types of color combinations were used for satellite image processing: combination of true
color R:G:B allocating bands 1:2:3 (see Figure 2) and pseudo color R:G:B allocating bands 4:3:2 (see Figure 3). SPOT 4
can not be used in these combinations because blue band was not available. Thus, we used pseudo color combination
(R:G:B = 3:2:1) meaning three bands consisting of near-infrared, red and green, respectively.

Figure 2. True color image on Tam Giang lagoon belonging to Tam Giang – Cau Hai lagoon system.
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Figure 3. Pseudo color image on Tam Giang lagoon belonging to Tam Giang – Cau Hai lagoon system.

Atmospheric correction
Light reflected from the ground to the sensor when passing through the atmosphere is changed in light intensity due to
scattering, absorption or diffusion effects with atmospheric molecules or aerosols. Thus, light spectrum on the satellite
image reflected from the ground is interferred by atmosphere. There are many methods for atmospheric correction. The
method of Armstrong (1993) [14] is simple and easy to apply to satellite image processing while ensuring technical
requirements. This method is based on subtraction of dark pixel from other pixels. A large number of pixels are taken
from deep water where the influence of the bottom is negligible. Thier mean value of each spectral channel is calculated
and subtracted from values of other pixels.

The satellite image processing softwares such as PCI and ENVI comprise atmospheric correction program. They need
additional inflormation on image such as receiving time and date of image and the center coordinates of the image for
use of satellite image processing. We applied PCI 9.1 software to correct an effect of stmosphere in this study.

Water column correction
Water column correction is required for processing satellite imagery to identify and map underwater habitats. When light
passes into the water, its intensity is decreased exponentially with increasing depth. This process is known as the
attenuation of light influencing on remote sensing data to detect deep bottom habitats. Degree of light reduction varies
with wavelength of electromagnetic radiation. In visible light, a red part of the spectrums (longer wavelengths) is
decreased faster than blue one (shorter wavelengths). According to increase in water depth, information of spectrums is
decreased. Consequently, we can not separate bottom habitats based on thier characteristics of spectrums. For example,
the spectrums of sandy bottom and other habitats at 2 m deep comprise more inflormation than those at 20 m deep.
Spectral information of sandy bottom at 20 m in deep can be the same as that of bottom with seagrass cover at 3 m deep.

Therefore, the light received at the sensor depends on bottom substrates and water depth.Spectrums recorded by the
sensor depend on both reflectance of bottom substrates and water column on the bottom. It is necessary to eliminate the
disturbance caused by the water column depth.

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Light intensity is exponentially decreased by increasing depth as a result of absorption and scattering. Absorption
process includes conversion of electromagnetic energy into other forms such as heat or chemical energy, and that by
objects in water such as phytoplankton, suspended organic matter particles, dissolved organic compounds and water
molecules. This absorption depends on the wavelength. Electromagnetic waves interact with particles suspended in water
and change direction. This scattering process mainly caused by organic matter and inorganic particles that increase
turbidity.

To eliminate the influence of water column on reflectance of substrates, it is necessary to measure bottom depth of each
pixel and know characteristics of light attenuation of the water column (dissolved organic matter content). However, it is
very difficult to obtain the information such as spatial distribution of bottom depth due to lack of updating bottom
topographic maps and also precise horaizontal scales. Lyzenga (1997) [18] offered a simple approach based on the image
to compensate for the effects of the water column. This study adopted Lyzenga’s method to remove influences of water
column on satellite images.

Supervise classification
Satellite images combined pseudo color (3:2:1) showed that mangrove and underwater plant areas were fresh red and in
dark green in color. Identification among aquatic grass, seaweed and seagrass was very hard based on the satellite images
because their reflectance spectrums do not differ much. Then, the field survey data and supervise classification were
used to classify these substrates.

GIS methods
A GIS method was used to map habitat distributions of mangrove, seagrass and coral and evaluate their temporal
changes in their spatial distributions. However, it was impossible to detect spatial distribution of seagrass beds in turbid

waters from satellite images with the supervised classification. Therefore, mapping the spatial distribution of seagrass
beds in such areas is based on field survey data of the projects and the results of the previous surveys (Table 1). We used
AcrGIS version 9.3 as the GIS software.

3. RESULT AND DISCUSSION
3.1 Status on seagrass beds in Vietnam
Figure 4 and Table 2 show obtained results on seagrass beds in Vietnam. There are a total of 32 distribution maps of
seagrass beds along the Vietnamese coast. This study indicates that the distribution area and densities of seagrass beds in
Vietnam has been seriously decreased compared with those 10 – 15 years ago. Generally speaking, the distribution areas
and densities of seagrass beds are decreased by above 50%. We present some examples in Figures 5 and 6.

The status on area of seagrass in Tam Giang – Cau hai lagoon was compared between in 1999 and in 2009. Table 3
shows that after 10 years, the are of seagrass in Tam Giang - Cau Hai remaining 1000 ha (decreased 60%).

3.2 Threats against seagrass beds in Vietnam
In Vietnam, a recent decline of seagrasses is due to their natural fluctuations and also human impacts. However,
researches on the natural and human impacts on seagrass ecosystems are still limited in Vietnam. Threats against the
seagrass ecosystems are typhoon, shipping and accidental oil spills, siltation due to soil erosion, over-fishing,
aquaculture, tourism and destructive fishing methods as described in a report on vulnerability and wane of seagrasses in
some key beds in Vietnam, such as: Cua Dai, Tam Giang – Cau Hai, Con Dao, Phu Qui and Thuy Trieu [19].
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Figure 4. Distribution maps of seagrass beds along the Vietnamese coast.
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Table 2. Distribution localities, seagrass species, areas of seagrass in Vietnam [19, 20, 21].

No. Areas Species Area (ha)
1 Nha Mac Warp Halophila beccarii, Ruppia maritime 500
2 Tien Beach Thalassia hemprichii 100
3 Bach Long Vy Islans Halophila decipiews 10
4 Cam Ranh Gulf
Enhalus acoroides, Thalassia hemprichii, Halophila minor, Halodule
uninervis
300
5 Con Dao Island
Enhalus acoroides, Thalassia hemprichii, Cymodocea serrulata,
Halodule uninervis, Halodule pinifolia, Halophila ovalis, Halophila
minor, Syringodium izoetifolium
200
6 Cua Dai Estuary Zostera japonica, Halophila beccarii 162
7 Cu Mong Lagoon
Enhalus acoiroides, Thalassia hemprichii, Halophila ovalis, Halodule
uninervis, Cymodocea rotudata
250
8 Thuy Trieu Lagoon
Enhalus acoroides, Thalassia hemprichii, Halophila minor, Halophila
ovalis, Halodule uninervis, Halophila beccarii, Ruppia maritima
800
9 Ha Lagoon Halophila ovalis 80
10 Nha Phu Lagoon
Enhalus acoroides, Thalassia hemprichii, Halophila minor, Halophila
ovalis, Halodule uninervis, Halophila beccarii, Ruppia maritime
30
11 De Gi Lagoon Enhalus acoroides 50
12 Dinh Vu Zostera japonica 120
13 Dong Hoi Zostera japonica 200

14 Gia Luan Zostera japonica, Halophila ovalis 100
15 Ha Coi Zostera japonica 150
16 Han River Zostera japonica 300
17 Hon Khoi
Enhalus acoiroides, Thalassia hemprichii, Halophila ovalis, Halodule
uninervis, Cymodocea rotudata
100
18 Lach Huyen Zostera japonica 60
19 Lang Co Thalassia hemprichii, Halodule pinifolia 120
20 Long Chau Halophila ovalis 10
21 My Hoa – My Tuong
Enhalus acoiroides, Thalassia hemprichii, Halodule uninervis,
Cymodocea rotudata
15
22
My Giang – Ninh
THuy
Enhalus acoroides, Thalassia hemprichii, Halophila minor, Halophila
ovalis, Halodule uninervis, Cymodocea rotundata
80
23 Gianh River Zostera japonica 500
24
Tam Giang – Cau
Hai Lagoon
Zostera japonica, Halophila ovalis 1000
25 Thi Nai Lagoon Halodule uninervis, Halophila ovalis 200
26 Van Phong Bay
Enhalus acoroides, Thalassia hemprichii, Halophila minor, Halophila
ovalis, Halodule uninervis, Cymodocea rotundata
200

27 My Hao Thalassia hemprichii, Halophila ovalis, Halodule uninervis 15
28 Ha Long Bay Halophila ovalis, Halophila minor 30
29 Phu Quoc Islans
Enhalus acoroides, Thalassia hemprichii, Cydomocea serrulata,
Cymodocea rotundata, Halodule uninervis, Halodule pinifolia, Halophila
ovalis, Halophila minor, Syringodium isoetifolium
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Proc. of SPIE Vol. 8525 852512-8
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Figure 5. Map showing status of seagrass in Tam Giang – Cau Hai lagoon in 1999.

Figure 6. Map showing status of seagrass in Tam Giang – Cau Hai lagoon in 2009.
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Table 3. Comparison of spatial distributions (ha) of seagrass and aquatic grass in brackish water in Tam Giang – Cau Hai lagoon
between 1999 and 2009
Habitat 1999 2009
Seagrass 2450,2 1000
Aquatic-grass 4667,8 1711,5


3.3. Typhoon
Seagrasses growing in coastal waters often suffer disturbances induced by storm that are considered to be major factor in
the dynamics of seagrass meadows. The typhoons with huge waves and strong currents may uproot seagrasses and erode
the sediment. Besides the loss of seagrass, storms also cause sediment redistribution, which results in burying meadows.
In 1997, Linda typhoon brought decrease in seagrass beds in Con Dao Island. Although some increases in seagrasses can
be found after the typhoon, in general the decrease has been continued. After the Linda typhoon, seagrass species
population (Thalassodendron ciliatum) had completely disappeared. Linda typhoon in 1997 has direct negative effects
on seagrasses because the sediment is re-accumulated during the typhoon. The Linda typhoon caused the loss of
approximately 20-30% seagrass area in Con Dao Isalands [22].

3.4. Turbidity
Increase in water turbidity explains loss of seagrasses. Although turbidity of sea water is influenced by the tidal and

seasonal cycles, it demonstrates a trend following monsoon exposure. The run-off from the rivers plays an important role
for the sedimentation in the rainy season, and particularly makes increase in turbidity of water column. Furthermore, the
increased turbidity also occurs due to constructive activities in the land [23].

3.5. Destructive fishing methods
Destructive fishing practices impact seriously to seagrass beds. They include fishing with explosives, trawling, gill net
and using chemical cyanide etc. Trammeling and gleaning animals and plants on intertidal flats affect seagrass beds.
Digging the bottom for taking molluscs and other marine organisms by coastal dwellers caused also the seagrass loss.
Dredging the sea bottom with seagrasses for construction of canals results in disturbance of bottom sediments and
increase in turbidity, which directly affect seagrass meadows. Dredging brings erosion and siltation of the coasts.

3.6. Economic development
The coasts are major economic development zones for the ports, trades and tourisms. Rapid economic growth leads
coastal environment and habitats to deterioration [24]. For instance, infrastructural building activities in Con Dao after
typhoon deteriorated seagrass habitats with pressures of urbanization. The results of annual monitoring from 1998 till
2000 on seagrass cover, density and biomass parameters in Con Dao showed that the recovery of seagrass beds was
slow, whilst some human activities, such as development of building infrastructures, quantity of fishing boats and service
activities etc. have been increased rapidly [23]. All these activities caused environment degradation that brought the loss
of seagrasses.

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3.7. Oil spills
All most seagrass beds are adjacent with anchorage of boats. Oil spills from the ships and fishing boats cause a great
damage to seagrass beds. The oil pollutes seagrasses, especially their younger buds and leaves. The oil also pollutes the
sediment bottom, where seagrasses grow.

3.8. Aquacultural ponds
Seagrasses are lost due to conversion of tidal flats with seagrass to aquaculture ponds. Destroying seagrass beds for

shrimp farming is common in many coastal areas in Khanh Hoa Province. The areas of E. acoroides meadows in Thuy
Trieu Lagoon and Cam Ranh Bay obtained by this study showed reduction down to 20-30% of distribution areas in 1998.
Furthermore, there are no plants of seagrasses that bear flowers and fruits in degraded areas. Hence, the ability of natural
seagrass recovery is very low or seagrasses will be disappeared if the ecosystem is continuously disturbed [23].

3.9. Agricultural reclamation
Marine ecosystems are under increasing stress due to increased agricultural reclamation through reclamation of tidal flats
with seagrasses into rice or food-stuffs trees fields. In Vietnam, thousands of hectares of seagrass beds were lost due to
this kind of reclamation.

We can see seagrasses being destroyed with aforementioned reasons. Main causes of losses are anthropogenic activities
(sedimentation increase or decrease, aquacultures, reclamation, urbanization, land-based pollution etc.). Fundamental
cause of these kinds of harmful human activities is lack of management plans. Otherwise, human impacts on seagrass
beds can be gauged from lack of public awareness. Education and dissemination of information play an important for
local fisheermen and people to conserve seagrass beds. Consequently, the lack of awareness about seagrass resource
among coastal dwellers, users, policy-makers and managers can be recognized as a fatal cause of the habitat loss in
Vietnam. Thus, we propose activities to be carried out are: (1) Prevention – strengthening public awareness and
education, and education and training local developers to conserve seagrass beds; (2) Monitoring – application of the
warning system, strengthening early detection system, and promoting water quality monitoring system.

4. CONCLUSION
A total of 32 distribution maps of seagrass beds were established along the Vietnam coast. The distribution areas status
and cover percentage of seagrass beds are decreased by above 50% in 1999.Fundamental causes of threats against
seagrass beds in Vietnam are typhoon, the turbudity, destructive fishing methods, economic development, oil spills,
aquancutural ponds and agricultural reclamation.
ACKNOWLEGENMENT
A co-author, Teruhisa Komatsu, thanks to JAXA for providing ALOS AVNIR-2 data as one of Principal Investigators of
ALOS project. This study constitutes a part of studies under the Environment Research and Technology Development
Fund (S9) of the Ministry of the Environment in Japan and the Japan Science and Technology Agency (JST/CREST). It
was based on the collaboration networks under JSPS Asian Core Program, “Establishment of research and education

network on coastal marine science in Southeast Asia” and also Ocean Remote Sensing Project for Coastal Habitat
Mapping (WESTPAC-ORSP) of Intergovernmental Oceanographic Commission Sub-Commission for the Western
Pacific supported by Japanese Funds-in-Trust provided by the Ministry of Education, Culture, Sports, Science and
Technology in Japan.
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