RS, GIS, AND B REEDING H ABITATS OF A N. MINIMUS A AND C IN THAILAND

USE OF A REMOTE SENSING-BASED GEOGRAPHICINFORMATION SYSTEM IN THE CHARACTERIZING SPATIALPATTERNS FOR ANOPHELES MINIMUS A AND C BREEDING
HABITATS IN WESTERN THAILAND
Pornpimol Rongnoparut 1, Donald M Ugsang 2, Visut Baimai 3, Kiyoshi Honda 2
and Ratana Sithiprasasna 41

Department of Biochemistry, Faculty of Sciences, Mahidol University, Bangkok; 2RS & GIS FoS,
Asian Institute of Technology, Pathum Thani; 3Center for Vectors and Vector-borne Diseases,
Faculty of Sciences, Mahidol University, Bangkok; 4Department of Entomology,
US Army Medical Component, Armed Forces Research Institute of Medical Sciences,
Bangkok, Thailand
Abstract. A remote sensing (RS)-based Geographic Information System (GIS) was used to characterize the breeding habitats of Anopheles minimus species A and C in five different districts ofKanchanaburi Province in western Thailand. The GIS and RS were used to monitor the area for thepresence and absence of An. minimus A and C in five major land areas, forest, agriculture, urban,
water and bare land. The results show that An. minimus A survives both in dense canopy forest andin open fields where agriculture is dominant. A scatter plot of land-use/land-cover for An. minimus,
considering proximities to the forest and proximities to agriculture, suggests that An. minimus A has
a wider habitat preference, ranging from dense canopy forest to open agricultural fields. A scatterplot for An. minimus C, on the other hand, showed a narrow habitat preference. A scatter plot for
proximities performed on separate populations of An. minimus species A, one in the north and theother in the south, showed that there was an association in the northern population with the forest
and in the southern population with agricultural areas. There were no statistically significant differences in the scatter plot of proximities to urban areas and water bodies with the An. minimus A
north, south, and An. minimus C. LANDSAT TM satellite data classification was used to identify
larval habitats that produce An. minimus A and C and analyze proximities between land-use/landcover classes and locations of larval habitats. An. minimus A has a wide habitat preference, fromdense canopy forest to open agricultural fields, while An. minimus C has a narrow habitat preference.

INTRODUCTION
Thailand, one of the developing countriesof Southeast Asia being confronting with endemic malaria, needs a reliable surveillance program to understand and manage its malaria
problem. Understanding the spatial and temporal changes in anopheline mosquito abundance,
quantification of the transmission potential ofvector populations, and description of the disCorrespondence: Ratana Sithiprasasna, Departmentof Entomology, US Army Medical Component, Armed
Forces Research Institute of Medical Sciences, 315/6
Rajvithi Road, Bangkok 10400, Thailand.
Tel: 66 (0) 2644-4888 ext 2724; (0) 2354-7885
E-mail: ratanas@afrims.org
Vol 36 No. 5 September 2005

tribution of host (human) populations are necessary prerequisites for predicting high-risk malaria areas. Modern tools, such as Remote Sensing (RS) and the Geographic Information System (GIS), are increasingly being used in studies
of disease transmission and vector ecology.
The GIS is a powerful tool for studying andmapping the spatial relationships of objects.
Such computer programs are designed to collect, store, manipulate, and display spatially referenced data (Liebhold et al, 1993). The spatial
data managed by a GIS program may include
items such as localities where mosquitoes havebeen collected, mosquito densities, distributionof mosquitoes relative to environmental parameters, such as climate or vegetation, human ar1145

S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTH

tifacts, such as political boundaries or roads,
boundaries of crop fields, and geologic information, such as topography and soil type. Similar
data are typically grouped into individual layers
or themes. The display of these layers can beturned on or off and modified to display various
data. An important strength of the GIS is that it
can simultaneously relate layers of data at thesame points in space and can analyze and map
out the results (Noonan, 2003).
RS is the science of collection of information about an object without physical contact.
RS technology uses the visible, infrared red andmicrowave regions of solar radiation to collectinformation about the various objects on theearth’s surface. The responses of the objects tothe different regions of the electromagnetic spectrum are different, and are used to distinguishobjects (Lillesand and Kiefer, 1987). RS technology has now reached a state where the application of satellite RS has been made operational
in many countries in different disciplines. Existing satellite-borne sensors, for which data are
available to the scientific research community,
range from those in low altitude orbits with an
infrequent repeat time to those in high altitude
orbits which correspondingly low spatial resolutions but more repeat times. These contrasting
categories of sensors provide information ofpotential use to epidemiology. These RS and GIS
tools greatly enhance our abilities to analyze land
use level relationships of vectors and diseases.
Yet they can only be used successfully in combination with a thorough understanding of ecological and epidemiological processes of diseasetransmission.
Anopheles minimus sensu lato (subgenus
Cellia, Myzomyia Series and Minimus Group) isa major malaria vector throughout Southeast
Asia. In Thailand, its distribution is reduced on
the peninsula and was not observed in the central plains, but remains abundant in forested hilly
areas (Harrison, 1980). This species plays an important role in malaria transmission during thedry season, and during much of the rainy season when populations of Anopheles dirus are low
along the Thai-Myanmar border (Sithiprasasna
et al, 2003a). It is regarded as a species complex comprised of two species, with informal
1146

names of An. minimus A and C (Harbach, 1994).
In Thailand, species A has been found predominantly, while species C is only seen in three provinces in wester n and norther n Thailand(Kanchanaburi, Tak, and Chiang Mai) (Green etal, 1990; Sharpe et al, 1999, Kengluecha et al,2005). Success of the approach to select thetargeted interventions for vector control requires
a good stratification of control areas in time andspace, accurate information on vector biology
and ecology, detailed information on vectorial
capacity, and malaria transmission and epidemiology. Therefore, both correct species identification and real-time monitoring of the geographical distribution are needed to provide accurate information on elucidating the nature of
the malaria vector species complex. In Thailand,
the potential applications of RS/GIS technologies have been demonstrated in studying the epidemiology of dengue hemorrhagic fever(Sithiprasasna et al, 1997), to identify the breeding habitats of major malaria vectors and theirdistribution (Sithiprasasna et al, 2003b), to survey for dengue virus-infected Aedes mosquitoes(Sithiprasasna et al, 2004), and to predict malaria transmission risk (Sithiprasasna et al, 2005).
To predict areas with critical densities at theland use level is to: 1) develop an understanding of the vector ecology and define the environmental determinants of its presence andabundance (this step is based on field studies),2) construct a database that characterizes theland use elements associated with the important aspects of vector biology and human habitation (RS and GIS are suitable tools for thisstep), 3) formulate and verify predictions of vector abundance. The malaria control program in
Thailand requires an efficient monitoring and surveillance system. With the availability of affordable computers, information and telecommunication systems, a more effective monitoring andsurveillance system for malaria in Thailand using GIS and RS should be developed. The aimof this study was to detect the environmental
determinants of the presence and absence ofAn. minimus A and C in five different districts ofKanchanaburi Province. Properly used, GPS, RS
and GIS should allow the location and quantification of malaria risk to be determined in a much
Vol 36 No. 5 September 2005

RS, GIS, AND B REEDING H ABITATS OF A N. MINIMUS A AND C IN THAILAND

more time effective and cost-effective way, and probably moreaccurately in many situations,than was previously possible.

MATERIALS AND METHODS
Study area
The study area was in amalaria-endemic area of western
Thailand near the Myanmar border. It covers the 5 districts of
Mueang, Sangkhla Buri, Si
Sawat, Sai Yok, and Thong Pha
Phum in Kanchanaburi Province.
Most of the area is covered with
forest (Ministry of Agriculture andCooperatives, 2002) and with
agricultural lands mainly locatedalong rivers and highways(Kusabe and Higuche, 1992).
Generally, the climate of thestudy area falls into 2 categories:
wet, from May to October, anddry, from November to April.
Heavy rain in May marks thestart of cropping season.
Larvae collection
Anopheles larvae were collected from different sites in fiveFig 1–Thematic map showing localities of breeding habitats of An. minimus
districts of Kanchanaburi ProvA (blue dots), An. minimus C (red dots), both A and C (yellow dots)
ince during February to Junearound the five districts, red flag represents location of each village.
2004. Larvae were collectedfrom breeding habitats and
UTM Zone 47, WGS 84 datum (Sarawut N, unreared to adults for species identification by morpublished project report, 2004). A subset was
phological and molecular techniques. Geomade from the full scene of LANDSAT TM datagraphical and ecological data were recorded forcovering Kanchanaburi Province, Thailand.
each of the collections. The coordinates for eachlarval habitat were recorded using a Global PoLand-use/land-cover classification
sitioning System unit (Garmin, model: III Plus,
Supervised classification was performed to
Olathe, KS, USA).
cluster pixels in the subset image data into landSatellite data
use/land-cover classes. This was done by grouping homogenous pixels into regions of interest
A LANDSAT Thematic Mapper (Path 130(ROI) that represent the desired land-use/landand Row 50) acquired on 21 December 1989
cover classes in the output image. Topographic
with a spatial resolution of 30 x 30 m was acmaps (1:50,000 scale) were used as references
quired and analyzed for land-use/land-coverin defining ROIs, along with the description inclassifications using ENVI 3.4 (ENVI, 2000) im(Kusabe and Higuche, 1992) of the main locaage processing software. The LANDSAT datation of agricultural lands. Utmost attention was
was radiometrically corrected and projected into
Vol 36 No. 5 September 2005

1147

S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTH

made in selecting ROIs that were homogeneousby exporting them to the n-D Visualizer functionof ENVI 3.4 (ENVI, 2000) image processing software and correcting for overlaps between
classes. After the ROIs were finalized, the maximum likelihood classification was performed on
FCC bands 4, 3 and 2 to assign each pixel inthe subset image data to the class that has thehighest probability.
Five major land-uses were defined: forest,
agriculture, urban, water, and bare land. The
separability of agricultural fields with standing
crops, forest, and water classes is good. From
the false color composite (FCC) of LANDSAT TM,
agricultural fields appear pink (or light red) andcan be easily identified from forests, which appears dark red, and from blue to dark-color water classes. The bare land class may include harvested agricultural fields that have a similar spectral response as bare lands.
Since most of the area in Kanchanaburi
Province is reserved forest and national park, primary land uses do not change drastically. Forest land covered 58% of the total land area in1988 (Ministry of Agriculture and Cooperatives,
1990) and remains relatively intact at 54% in
1999; farm land changed from 22% in 1988 to
16% in 1999 (Ministry of Agriculture and Cooperatives, 2002). The rates of primary land usechange in the study area could be safely assumed to be smaller than theprovincial figures, consideringthat the 5 districts in the study
area had mostly forest cover.
The use of multi-temporal satellite images should allow more detailed classification of landuse/landcover, andis recommended for futurestudies.
Proximity analysis
One of the strengths of ageographic information system
is its ability to analyze spatial
arrangements of information in
one or more data layers. The
ability to identify items in proximity to each other is one of
the classic functions of a GIS(Stan and Estes, 1990). Proximity analysis allows determining which map features are located near or within the neighborhood of target map features, and is widely used forenvironmental planning andimpact assessment (Landis,1993).

Fig 2–Locations of breeding habitats of An. minimus A (blue dots), An. minimus
C (red dots), both A and C (yellow dots) from Global Positioning Systemoverlaid on LANDSAT 5 Thematic Mapper (spatial resolution 30 x 30 m)
satellite image. Yellow line represents district boundary.

Proximity analysis wasmade on all major land coverclasses except bare land.
Proximity was calculated bysumming up 1/(R*R) for each1148

Vol 36 No. 5 September 2005

RS, GIS, AND B REEDING H ABITATS OF A N. MINIMUS A AND C IN THAILAND

ForestAgriculture
Built-up
BarelandWater
An. minimus A (north)
An. minimus A (south)
An. minimus C

5 Thematic Mapper (spatial
resolution 30 x 30 m) satellite image dated 21 December 1989 was used as thebase map for Fig 2 as a yellow line representing a district boundary. As shown in
Fig 2, the northern districtsin Kanchanaburi Province
are mostly forested areas
while the middle and southern areas are agriculturalareas and bare land. Fig 3
depicts the land-use/landcover classification of thestudy area. The depicted
area displays forest, agriculture, urban, bare land, andwater (depicted by green,
yellow, white, gray, and blue,
respectively).

As shown in Fig 4A, thetotal land-use area within the
1.5 km buffer breeding habitat for An. minimus A washigher than for An. minimus
Fig 3–Classified LANDSAT image showing land-use/land-cover of the study
C. Since the habitat of An.
area.
minimus C is confined to the
middle region, we asked
whether the area of land use for An. minimus A
concerned land class, where R is the distancein the northern area is different from that of thefrom the target pixel (center of the window) tosouthern area. As shown in the same figure, forother pixels in the window. Proximity is comest land use was predominantly occupied by An.
monly measured in units of length but can also
minimus A in the north, while both species C
be measured in other units (Arnoff, 1993). In thisand A in the south occupied the agricultural arstudy, proximity was normalized in the window,
eas and bare land. The total areas correspondand thus was unitless. If all pixels in the window
ing to land-use, as shown in Fig 4A, depend on
were paddy, the proximity becames 1. A winthe number of samples where the buffers were
dow size of 1.5 km by 1.5 km was used based
generated. Fig 4B shows the proportion of landon the estimated flight range of An. minimus.
use for the total buffer area in each series and
RESULTS
the proportion of land-use for An. minimus A in
Figs 1 and 2 depict the localities of thethe north, An. minumus A in the south, and An.
breeding habitats from the GPS units for An.
minimus C. The results show that An. minimus
minimus A in blue dots, An. minimus C in red
A thrives both in dense canopy forest and in open
dots, and both A and C in yellow dots aroundfields where agriculture is dominant. Scatter plots
the five districts (Mueang, Sangkhla Buri, Siof total forest and agricultural areas for each
Sawat, Sai Yok, and Thong Pha Phum districts,
buffer case clearly illustrate the results. Fig 5A
Kanchanaburi Province). Red flags in Fig 1 repshows that buffer cases of An. minimus A in the
resent the location of each village. A LANDSAT
north had the highest forest area per case, folVol 36 No. 5 September 2005

1149

S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTH

An. minimus C were compared(without segmenting An.
3000.00
minimus A to the north and2500.00
south) by one-sided statistical
2000.00
test of their mean proximity to
Area (ha)the forest. The results show a1500.00
significant difference in their
1000.00
means at 5% significancelevel, meaning An. minimus A
500.00
had a closer proximity (bigger
0.00
Urban
ForestAgriculture
Water
Barelandproximity mean) to forest land
24.37
2980.15686.84329.121204.81An. minimus A north (ha)use than An. minimus C. The
83.42
505.711512.49
16.81
2157.34
An. minimus A south (ha)results show a highly signifi107.79
3485.85
2199.33
345.943362.15
An. minimus A (ha)cant difference, at a 5% sig88.13
1353.94
1170.2954.42
2539.17An. minimus C (ha)nificance level, when meanLand use
proximities to the forest for An.
Fig 4A–Land-use within 1.5 Km buffer of An. minimus locations.
minimus A in the north arecompared to An. minimus C.
Fig 6B shows the proximity toagriculture for all the An.
60.00
minimus species. The proxim50.00
ity means for An. minimus A
and
An. minimus C (to agricul40.00
ture) were not significantly dif% 30.00
ferent. We found the same results when testing mean
20.00
proximities between An.
10.00
minimus A south and An.
minimus C. However, mean
0.00
proximities to agriculture were
Urban
ForestAgriculture
Water
Barelandsignificantly different between
0.47
57.03
13.14
6.30
23.06
An.minimus A north (%)
1.95
11.83
35.37
0.39
50.45
An. minimus A north and An.
An.minimus A south (%)
1.13
36.69
23.15
3.64
35.39
An.minimus A (%)
minimus C. Fig 7 shows
1.69
26.01
22.48
1.05
48.77
An. minimus C (%)
proximities to forest and agriLand use
culture. The figure shows
Fig 4B–Proportion of each land-use to the total buffer area of An. minimus
clearly that An. minimus A has
locations.
a wider habitat, from dense
canopy forest in the north toopen agricultural fields in the south. An. minimus
lowed by An. minimus C, then by An. minimus A
C, on the other hand, has narrow habitat preferin the south. The scatter plot in Fig 5B showsence.
that the total agriculture area for each buffer casewas highest for An. minimus A in the south, folDISCUSSION
lowed by An. minimus C, then by An. minimus A
in the north.
RS-based GIS was used to characterize
thebreeding
habitats of Anopheles minimus speAn. minimus A in the north had the closest
cies
A
and
C in five different districts ofproximity to forest, followed by An. minimus C,
Kanchanaburi
Province, western Thailand.
then An. minimus A in the south (Fig 6A). BasedLANDSATsatellite
data classification was usedon earlier results showing an association of An.
toidentify
larvalhabitats
that produce An.
minimus A to forest land use, An. minimus A and3500.00

1150

Vol 36 No. 5 September 2005

RS, GIS, AND B REEDING H ABITATS OF A N. MINIMUS A AND C IN THAILAND
700.001.2
An. minimus A south

600.00

Proximity to forest land-use

An. minimus C

500.00Forest area (ha)An. minimus A south

1

An. minimus A north

400.00300.00200.00100.00
0.00
5

10

15

20

Buffer case

0.6
0.4
0.2

450.00An. minimus A south

400.00An. minimus A north350.00

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Proximity to agricultural land-use

Fig 5A–Scatter plot of the total forest area in each buffer
case.

Agricultural area (ha)An. minimus C

0.8

0
0

An. minimus C

300.00

Fig 7–Scatter plot of proximities to forest and proximities
to agriculture. The figure shows clearly that An.
minimus A has a wider habitat adaptation, fromdense canopy forest (in the North) to open agricultural fields (in the South). An. miminus C, on
the other hand, has a narrow habitat preference.

250.00200.00150.00100.00
50.00
0.00
0

5

10

15

20

Buffer caseFig 5B–Scatter plot of the total agriculture area in eachbuffer case.
1.2
An. minimus A south

1

Proximity to forest

An. minimus A northAn. minimus A northminimus A and C. Proximities between land-use/
land-cover classes and location of larval habitats were analyzed. An. minimus A has a wide
habitat preference, from dense canopy forest to
open agricultural fields, while An. minimus C has
a narrow habitat preference. These systems arevaluable tools, allowing for information regarding malaria transmission and malaria risk to beprocessed and used to guide the managementof malaria control campaigns.

An. minimus C

0.8
0.6

ACKNOWLEDGEMENTS

0.4
0.2
0
0

5

10

15

20

Anopheles cases

Fig 6A–Scatter plot of proximity to forest land-use for
all An. minimus.

The authors would like to thank Mr Sarawut
Ninsawat of the RS & GIS FoS, Asian Institute ofTechnology, Pathum Thani, Thailand for providing the LANDSAT TM data of the study area. This
work was supported by the TRF/BIOTEC Special Program for Biodiversity Research and Training and CNRS (France) grant BRT R_245006.

0.7Proximity to agriculture

0.6

REFERENCES

An. minimus A south

0.5

An. minimus A northAronoff S. Geographic information systems: a management perspective. Ottawa: WDL Publications,1993.

An. minimus C

0.4
0.3
0.2
0.1
0
0

5

10

15

20

Anopheles cases

Fig 6B–Scatter plot of proximity to agriculture land-use
for all An. minimus.
Vol 36 No. 5 September 2005

ENVI Research Systems I: user’s guide version 3.4.
Boulder, Colorado: ENVI Research Systems,
2000.
Green CA, Gass RF, Munstermann LE, Baimai V. Population-genetic evidence for two species in Anopheles minimus in Thailand. Med Vet Entomol 1990;
1151

S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTH
4: 25-34.
Harbach RE. Review of the internal classification of thegenus Anopheles (Diptera: Culicidae): the foundation for comparative systematics and phylogenetic research. Bull Entomol Res 1994; 84: 33142.
Harrison BA. The Myzomyia Series of Anopheles(Cellia) in Thailand, with emphasis on intra-interspecific variations (Diptera: Culicidae). Medical
entomology studies - XIII. USA: American Entomological Institute, 1980.
Kengluecha A, Rongnoparut P, Rodpradit P,
Sithiprasasna R, Boonsuepsakul S, Baimai V.
Geographical distribution of Anopheles minimus
species A and C in Western Thailand. J Vector
Ecol 2005 (in press).
Kusabe K, Higuche Y. A case study of discontinuance:cotton contract farming in Kanchanaburi Province, Thailand. HSD Monograph, 24. Bangkok:
Asian Institute of Technology, 1992.
Landis JD. GIS capabilities, uses, and organizational
issues. In: Castle GH, ed. Profiting from a Geographic Information System. Colorado: GISWorld, 1993: 23-55.
Liebhold AM, Rossi RE, Kemp WP. Geostatistics andgeographic information systems in applied insect
ecology. Annu Rev Entomol 1993; 38: 303-27.
Lillesand TM, Kiefer RM. Remote sensing and image
interpretation. New York: John Wiley and Sons,
1987.
Ministry of Agriculture and Cooperatives. Agricultural
statistics of Thailand. Crop Year 1989/90.
Bangkok, 1990.
Ministry of Agriculture and Cooperatives. Agricultural
statistics of Thailand. Crop Year 2001/02.
Bangkok, 2002.
Noonan GR. GIS technology. A powerful tool for ento-

1152

mologists. Insight A Milwaukee Public Museum
Series in Natural History 2003; 1: 1-98.
Sharpe RG, Hims MM, Harbach RE, Butlin RK. PCRbased methods for identification of species of theAnopheles minimus group: allele-specific amplification and single-strand conformation polymorphism. Med Vet Entomol 1999; 13: 265-73.
Sithiprasasna R, Linthicum KJ, Lerdthusnee K, Brewer
BG. Use of geographic information system tostudy the epidemiology of dengue hemorrhagic
fever in Thailand. Dengue Bull 1997; 21: 68-72.
Sithiprasasna R, Linthicum KJ, Liu GJ, Jones JW,
Singhasivanon P. Some entomological observations on temporal and spatial distribution of malaria vectors in three villages in northwestern
Thailand using a geographic information system.
Southeast Asian J Trop Med Public Health.
2003a; 34: 505-16.
Sithiprasasna R, Linthicum KJ, Liu GJ, Jones JW,
Singhasivanon P. Use of GIS-based spatial modeling approach to characterize the spatial patterns of malaria mosquito vector breeding habitats in northwestern Thailand. Southeast Asian J
Trop Med Public Health 2003b; 34: 517-28.
Sithiprasasna R, Patpoparn S, Attatippaholkun W,
Suvannadabba S, Srisuphanunt M. Geographic
Information System as epidemiological tool insurveillance of dengue virus-infected Aedes
mosquitoes. Southeast Asian J Trop Med Public
Health 2004; 35: 918-26.
Sithiprasasna R, Ugsang DM, Honda K, Jones JW,
Singhasivanon P. IKONOS-derived malaria transmission risk in northwestern Thailand. Southeast
Asian J Trop Med Public Health 2005; 36: 14-22.
Stan J, Estes J. Geographic Information Systems: anintroduction. Englewood Cliffs: Prentice Hall,1990.

Vol 36 No. 5 September 2005