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GIS mapping of vector breeding habitats

The survival and longevity of infected mosquitoes and the prevalence of the diseases is spatially determined and controlled by the geo-climatic variables. The remote sensing capabilities of Landsat TM, IRS LISS I, LISS II, IRS CARTOSAT, SPOT, IKONOS, NOVAA – AVHRR etc., have been used to analyse vector habitats areas and mapping vector abundance.1-16 The Indian remote sensing (IRS) resource satellite data is readily available at low cost price for research and education purpose including the study of mosquito breeding habitats in India.7-10 The study has been designed for exploring the utility of IRS resource satellites for mapping vector habitats areas, estimating the vector abundance and vector density across the country. The IRS data product is available to the users with reliable and repetitive coverage. It has been providing repetitive coverage information on the environmental transitions and land use / land cover changes. The indigenous IRS LISS I, LISS II and IRS WiFS have been used to analyse the areas vulnerable to vector habitats suitability and key elements for mapping the vector abundance.7-10 The remote sensing resource satellite data (red and infrared / near infrared spectral data) has been significantly used for mapping the malaria, JE and kala-azar, filariasis, schistosomiasis etc., vector breeding habitats with spatial consistency of 90 per cent accuracy.5,12

The conventional method of mapping vector breeding habitats is laborious, expensive, erroneous and time consuming, whereas, the use of remote sensing and GIS is reliable and accurate. With the help of these techniques vector breeding habitats, vector abundance and vector density can be mapped quickly. Thus, the probability of transmission risk of vector borne diseases can be assessed with respect to space and time.1-16 The rapid sea change of population and the corresponding environmental changes of increasing the agricultural land practices, land use / land cover dynamic changes, urban sprawl and irregular growth of urban development and industrial growths are responsible for a suitable environment for vector borne disease outbreaks.1,3,4,6 Therefore, a rapid and advanced technology is needed for the replacement of conventional methods for predicting the problematic areas, mapping and spatial assessments of transmission risk with reliable and real-time accuracy for vector control and vector borne disease control and management.

Remote sensing data products
The huge numbers of remote sensing resource satellite data products are readily available at low cost for research and education purpose. The visual interpretation and analysis of the Multi Spectral and Multi Temporal satellite data products derived from the earth observation resource satellites (Landsat TM (Thematic Mapper) satellite, French Satellite Systeme Pour l”Observation de la Terre (SPOT), Indian Remote Sensing (IRS) LISS I, LISS-II, LISS III and Panchromatic Imagery, IKONOS) and red and infrared colour aerial photographs and the meteorological satellites, National Oceanic and Atmospheric Administration”s Advanced Very High Resolution Radiometer (NOVA- AVHRR) are used for delineating and mapping of mosquito breeding habitats and mosquito ecology.13-16 Based on the unsupervised digital image processing of remote sensing data and followed by the geo-processing of supervised image analysis, geostatistical analysis of discriminant analysis, cluster analysis and the regression analysis shows the result of statistically significant relationship between the vector abundance, vector borne diseases and the environmental variables.6

GIS software platforms
The GIS software is available at low cost price for research and education purpose in India. A set of spatial analysis (Kriging, Co-Kriging, Universal Kriging, Block Kriging, Buffering, Map overlay analysis, Fussy analysis, K-means analysis, interpolations, etc) using the GIS Platform namely ARC Info/ARC View, Map Info, Map Maker, EPIMAP, Pop Map, Surfer, Atlas GIS, Geo Statistics+, IDRSI, GRASS, Geographical Analysis Support System (GRASS), is available to assess the mosquitogenic conditions, mapping the vector habitats, vector abundance, larvae and adult density with more than 90 per cent accuracy.1-16

The low cost IRS indigenous data
The Indian remote sensing (IRS) resource satellite data is readily available in India at a low cost. The series of IRS 1A,B,C,D and IRS 2A,2B, IRS CARTOSAT, Panchromatic Imagery, IKONOS and IRS WiFS are available at low price in India. (Red and infrared / near infrared spectral data) is significantly used for mapping the malaria, JE, filariasis, kala-azar, and chandipura virus vector breeding habitats with spatial consistency of 90 per cent accuracy.7-10 The remote sensing has reliable and repetitive coverage and has provided the information on land use / land cover changes where the irrigation was practiced. The indigenous IRS LISS I, LISS II and IRS WiFS is used to analyse the areas vulnerable to vector habitats suitability and key elements for mapping the vector abundance.7,9,10 The red and infrared imagery from the multispectral signature of the Indian satellite remote sensing has been used as the baselines for identifying and mapping of potential larval habitats of malaria and JE vectors breeding sources. The results obtained facilitate in assessing the mosquito abundance in the riverside areas, and the area under irrigation wet cultivation was practiced, the results has significant with 85 per cent and increased to 90 per cent accuracy.

The IRS Data for mapping vector breeding habitats in the urban areas
The mosquito nuisance has become a big problem in the urban India. The IRS resource satellite data is used to map the land use / land covers in close association with the potential surface areas of mosquito breeding habitats.7-10 The image processing of indigenous remote sensing satellite data of value added hybrid colour composite imagery of IRS 1D PAN and LISS III with high spatial resolution of 1:25,000 was used for preparing the level-I land use / land cover classifications on the 5m x 5m pixel size byproduct image of Vizagkapattinam city for mapping the potential areas which are supporting for mosquitoes breeding habitats.7 The Arc View 3.2, Arc View Spatial analysis and Arc View image analyst GIS software was used for performing the overlay analysis of thematic maps of Vizagkapattinam urban land use / land cover composite image for mapping the malaria mosquito vector breeding potential surface areas, the filariasis vector mosquitoes potential breeding surface areas and thus, guidelines for enabled to reexamining ward wise mosquitogenic conditions for vector control priority (Fig 1, 2, 3 & 4). GIS was used to create the buffer zones with 2.5 km radius around the breeding habitats describing where the area of maximum adult mosquito flight range of 2.5 km, where the community was at the risk vector borne disease transmission.

Fig.1 Land use/ land cover image of Vizagkapattinam city and its surroundings, using the hybrid IRS PAN and LISS III data with 1:25,000 scale


Fig.2 Malaria vector mosquito (Anopheles genus) potential breeding surface areas


Fig.3 Filariasis vector mosquitoes (Culex genus) potential breeding surface areas


Fig.4 Priority for Malaria and Filariasis vector mosquitoes (Anopheles & Culex genus) control in the ward, based on the quintiles analysis

The IRS indigenous data for mapping the countryside vector breeding habitats
The low cost remote sensing and GIS have been used for ecological modeling of geo-climatic and the environmental variables in association with presents of vectors and vector borne diseases.1,3-5,7-10,13-16 The Normalized Differences Vegetation Index (NDVI) 5,12 value derived from the spectral signature of IRS LISS-I and LISS-II with spatial resolution of 72m and 32.5m respectively is used for mapping at the district level (1:50,000) and the IRS WiFS data with spatial resolution of 188m is used for mapping at the state and the national level with 1:250,000 scale. The IRS WiFS spectral signature of near infrared band (NIR) range between 0.77nm- 0.86nm and red (R) band range between 0.62nm – 0.68nm, is used for deriving the NDVI using the standard formula of [(NIR – R) / (NIR + R)] for mapping the vegetation covers with potential areas of the malaria and JE vector mosquitoes breeding habitats. The calibrated NDVI value has the range between (-) 1.0 and (+) 1.0. The NDVI values of 0.0–0.2 corresponds to bare ground, open scrub, grass land and stressed vegetation, 0.2–0.4 indicates the young and presence of actively photosynthesizing vegetation, 0.4-0.6 indicates the grownup and actively photosynthesizing vegetation, 0.6-0.72 indicates growth stages of healthy vegetation, and the NDVI value > 0.72 measures healthy vegetation, the value near zero and the negative values indicates ice cover, glacier, water bodies and, provides the water depth, colour and chemical compounds of water bodies.5,12 The NDVI value 0.07 – 0.38 and 0.01 to 0.34 during the dry and wet season was found most fitted with the distribution of P. martini and P. orientalis was the dry season respectively,2,11 with accuracy of 93.8 per cent.

The intensive rice irrigation cultivation fields supporting the breeding sites are important variable in determining the abundance of mosquitoes associated with the breeding sites 6 and hence, it is sustaining to identify and mapping the malaria and JE vector mosquito larval habitats. The longevity, survival and the high abundance of maximum breeding of malaria vector (Anopheline genus) mosquitoes and JE vector (Culex genus) mosquitoes was found in the period of 4-6 weeks after wet irrigation cultivation of rice transplantation.13-15 The regional land use / land cover changes were fueled to promote the malaria and JE epidemics in newer areas of the country. The recent years have witnessed growing incidence of malaria epidemics and JE out breaks in different parts of our country, and more frequently in the districts where the water resource projects for irrigation wet cultivation was brought out.8 These changes created conducive environment for mosquito breeding in the buffer zone of 2.5Km radius of water projects (Irrigation Canals, lake, perennial or semi-perennial River / stream, water pools), wetland and cultivation areas (sugarcane, rice and plantain).8 The type of vegetation cover, vegetation types and growth stage which surrounds the breeding sites and thereby provides potential resting sites, sugar-feeding supplies for adult mosquitoes and protection from climatic conditions has been playing the important role in determining vector abundance of mosquitoes associated with the breeding sources. 13-16

It is noted that irrigation rice wet cultivation provides the breeding sites for Anopheles gambiae early in the growth cycle of the plants, this changes as the rice plants mature and form a dense canopy over the water. The spatial agreement between the observed and predicted values of logistic regression model is 0.76 sensitivity and specificity of 0.78 of larval index within a buffer around the trap location of rice fields.13-15 The coefficient model of rainfall and temperature with the mosquito abundance are highly correlated with the NDVI, and it is useful in the estimation of mosquito larval abundance and used to predict adult abundance 7 days.13-15 The transitional swamp and unmanaged pasture were found to be the most important land covers for An. albimanus, and their combined area in the 2.5 km buffer zone was sufficient to predict high and low vector abundance with overall accuracy of 90%. The higher NDVI values in the early growing season were found to have higher larval mosquito densities. Importantly, this distinction was possible two months before the peak in larval anopheline numbers. The Phlebotomus argentipes sandfly was found in the wet alluvial soil dark coloured alkaline in nature (pH 7.2– 8.5), soil moisture between (67mm-108mm) and also where the places having more edible plants, termites mounts, cattle farm, and it was resting in a dark and cool place.2,11

The IRS Linear Imaging and Self Scanning (LISS), IRS LISSI, LISS-II, and IRS WiFS spectral data was found useful for mapping vector breeding habitats.7-10 The NDVI value range between 0.28 and 0.23 of composite DN value between 145 and 158 was found most spatial agreements with supporting for JE and malaria vector breeding habitats suitable for profusion of mosquitoes and it was found in the period of 4-6 weeks immediately after wet irrigation cultivation of rice transplantation.5,8,11,13-16 The logical step towards the mapping land use / land cover changes guide us the possible information on reliable estimates of breeding habitats for mapping ‘mosquitogenic’ conditions,7-10,13-16 and thus, the indigenous low cost remote sensing and GIS tool has been provided to choose appropriate control strategy for vector control operation in the priority areas across the country.

The low cost remote sensing and GIS was found useful in identifying the areas for delineating the mosquito potential breeding surface areas and studying the mosquitogenic conditions in the urban as well as countryside landscape. The integrated hybrid remote sensing and GIS techniques were used to mapping the vector breeding potential areas vulnerable to risk of disease transmission. They also provided information on reliable estimates of breeding habitats. Remote sensing and GIS tools have facilitated in choosing appropriate control strategy for vector control operation and decision making for vector borne disease control across the country.


  1. Dale PE, et al, 1998. An overview of remote sensing and GIS for surveillance of mosquito vector habitats and risk assessment. J Vector Ecol, 23: 54-61
  2. Gebre Michael T, et al, 2004. Mapping the potential distribution of pklebotomus martini and p. Orientalis (diptera: psychodidae), vectors of kala-azar in East Africa by use of geographic information systems, Acta Tropica 90 (2004) 73-86
  3. Hassan AN, and HM Onsi, 2004. Remote sensing as a tool for mapping mosquito breeding habitats and associated health risk to assist control efforts and development plans: a case study in Wadi El Natroun, Egypt. J Egypt Soc Parasit. 34 (2): 367-82
  4. Leonardo LR, et al, 2005. A study of the environmental determinants of malaria and schistosomiasis in Philippines using remote sensing and GIS. Parassitologia 47(1):105-14
  5. Liu J and XP Chen, 2006. Relationship of remote sensing normalized differential vegetation index to Anopheles density and malaria incidence rate. Biomed Environ Sci 19 (2):130-2
  6. Mushinzimana ES, et al., 2006. Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malaria J 5(1):13
  7. Palaniyandi M and T Mariappan, 2012. “Containing the spread of malaria with geospatial tech – a case study, Vizag City, India”, Geospatial World, 27th Feb, 2012 Vol.8 Issue 9, 1-9.
  8. M Palaniyandi, 2004. The Impact of National River Water Projects on Regional Climatic Changes and Vector Borne Disease Outbreaks in India. Paper presented at the National Conference on Climate Change and its Impact on Water Resources in India, Dec- 15-17, School of Earth and Atmospheric Sciences, M K University, Madurai, Tamil Nadu, India.
  9. Sharma VP, B N Nagpal, A Srivastava, S Adiga, and P Manavalan, 1996. Estimation of larval production in Sanjay Lake and its surrounding ponds in Delhi, India using remote sensing technology, Southeast Asian J Trop Med Public Health, 27: 834-40
  10. Sharma VP, et al., 1996. Study on the feasibility of delineating mosquitogenic conditions in and around Delhi using IRS data. Indian J. Malariology 33: 107–125
  11. Sudhakar S, et al, (2006) Mapping of risk prone areas of kala-azar (Visceral leishmaniasis) in parts of Bihar state, India: an RS and GIS approach, J Vector Borne Dis 43:115 (2006)
  12. Tucker CJ, 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing Environ. 8, 127–150
  13. Wood BL, et al., 1991. Distinguishing high and low Anopheline producing rice fields using remote sensing and GIS technologies. Prevent. Vet. Med. 11: 277–288
  14. Wood, B.L. et al. (1991) Spectral and spatial characterization of rice field mosquito habitat. Int. J. Remote Sensing 12, 621–626
  15. Wood, B.L. et al. (1992) Estimating high mosquito-producing rice fields using spectral and spatial data. Int. J. Remote Sensing, 13, 2813–2826
  16. Zou L, SN Miller and ET Schmidtmann, 2006. Mosquito larval habitat mapping using remote sensing and GIS: implications of coalbed methane development and West Nile virus. J Med Entomol 43 (5):1034-41