Skip to main content

Advertisement

SpringerLink
Log in

Prevention of vector-borne disease by the identification and risk assessment of mosquito vector habitats using GIS and remote sensing: a case study of Gorakhpur, India

  • Original Paper
  • Published:
Nanotechnology for Environmental Engineering Aims and scope Submit manuscript

Abstract

Vector-borne diseases (VBD) constitute a major portion of diseases spread over the tropical and sub-tropical regions, considerably because of the highly conducive tropical climate. It is the leading cause of viral infections and outbreaks which affects thousands of people annually. It is mostly spread by mosquitoes. The northern part of India is severely affected by VBD. To identify the risk zones of VBD and to take precautionary measures to mitigate the risk of outbreaks in VBD prone area, comprehensive and analytical research involving geographical information system (GIS) and remote sensing is carried out. In this work, the Gorakhpur district of India is taken as the study area. The study area is scrutinized from the Landsat-7 images of the years 2014, 2015 and 2016. These multispectral temporal images have facilitated the identification and delineation of water logged areas and mosquito breeding sites. The VBD risk zones are marked by considering the various parameters that support mosquito breeding. These parameters are water, moisture, vegetation and temperature that are computed through normalized difference water index (NDWI), normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI) and land surface temperature (LST), respectively. NDWI is calculated to identify the shallow and deep water. NDMI identifies the regions with low and high moisture. NDVI finds the sparse and dense vegetation. LST maps the surface temperature using the thermal band data of satellite image. All the layers are reclassified and overlaid that gives the mosquito habitat layer of each year. The ranking of risk zones is then given, and VBD risk layer is subsequently created. The area of 190.15 km2 comes under the risk out of which 39.00 km2 is at high risk, 96.02 km2 is at medium risk, and 55.13 km2 is at low risk. Finally, VBD risk pattern map is generated through spatial interpolation which gives the risk at each coordinate of the study area. This work shows the synergetic use of space-borne satellite images with the GIS for the prevention of VBD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Ramalho-Ortigao M, Gubler DJ (2020) Human diseases associated with vectors (arthropods in disease transmission), 10th edn. Elsevier, Amsterdam

    Google Scholar 

  2. Carter RC, Brook JM, Jewsbury JM (1990) Assessing the impact of small dams on vector borne disease. Irrigat Drain Syst 4:1–16. https://doi.org/10.1007/BF01145969

    Article  Google Scholar 

  3. Dhiman RC, Pahwa S, Dhillon GPS, Dash AP (2010) Climate change and threat of vector-borne diseases in India: are we prepared? Parasitol Res 106:763–773. https://doi.org/10.1007/s00436-010-1767-4

    Article  Google Scholar 

  4. Kumar PSS, Gupta SK, Nongkynrih B (2018) Malaria, dengue and Chikungunya in India: an update. Indian J Med Special 9:25–29. https://doi.org/10.1016/j.injms.2017.12.001

    Article  Google Scholar 

  5. Sharma R, Dutta AK (2011) Malaria and national vector borne disease control programme. Indian J Pediatr 78:1527–1535. https://doi.org/10.1007/s12098-011-0554-2

    Article  Google Scholar 

  6. Yadav K, Nath MJ, Talukdar PK et al (2012) Malaria risk areas of Udalguri district of Assam, India: a GIS-based study. Int J Geogr Inf Sci 26:123–131. https://doi.org/10.1080/13658816.2011.576678

    Article  Google Scholar 

  7. Bonaparte M, Dweik B, Feroldi E et al (2014) Immune response to live-attenuated Japanese encephalitis vaccine (JE–CV) neutralizes japanese encephalitis virus isolates from south-east Asia and India. BMC Infect Dis 14:1–7. https://doi.org/10.1186/1471-2334-14-156

    Article  Google Scholar 

  8. Kabilan L, Rajendran R, Arunachalam N et al (2004) Japanese encephalitis in India: an overview. Indian J Pediatr 71:609–615. https://doi.org/10.1007/BF02724120

    Article  Google Scholar 

  9. Tyagi N, Sahoo S (2019) Geospatial disease risk modeling for the identification of potential areas of encephalitis in a subtropical region of India: a micro-level case study of Gorakhpur tehsil. Appl Geomat. https://doi.org/10.1007/s12518-019-00287-2

    Article  Google Scholar 

  10. Tiwari S, Singh RK, Tiwari R, Dhole TN (2012) Japanese encephalitis: a review of the Indian perspective. Braz J Infect Dis 16:564–573. https://doi.org/10.1016/j.bjid.2012.10.004

    Article  Google Scholar 

  11. Das S, Sarfraz A, Jaiswal N, Das P (2017) Impediments of reporting dengue cases in India. J Infect Public Health 10:494–498. https://doi.org/10.1016/j.jiph.2017.02.004

    Article  Google Scholar 

  12. Dinkar A, Singh J, Prakash P et al (2017) Hidden burden of Chikungunya in North India: a prospective study in a tertiary care centre. J Infect Public Health 11:586–591. https://doi.org/10.1016/j.jiph.2017.09.008

    Article  Google Scholar 

  13. Sabesan S, Raju HKK, Srividya AN, Das PK (2006) Delimitation of lymphatic filariasis transmission risk areas: a geo-environmental approach. Filaria J 5:1–6. https://doi.org/10.1186/1475-2883-5-12

    Article  Google Scholar 

  14. Bhunia GS, Kesari S, Chatterjee N et al (2013) Spatial and temporal variation and hotspot detection of Kala-Azar disease in Vaishali District (Bihar), India. BMC Infect Dis 13:1–12. https://doi.org/10.1186/1471-2334-13-64

    Article  Google Scholar 

  15. Zambrano LI, Rodriguez E, Espinoza-Salvado IA et al (2019) Spatial distribution of dengue in Honduras during 2016–2019 using a geographic information systems (GIS)—dengue epidemic implications for public health and travel medicine. Travel Med Infect Dis 32:101517. https://doi.org/10.1016/j.tmaid.2019.101517

    Article  Google Scholar 

  16. Uusitalo R, Siljander M, Culverwell CL et al (2019) Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya. Int J Appl Earth Obs Geoinf 76:84–92. https://doi.org/10.1016/j.jag.2018.11.004

    Article  Google Scholar 

  17. Burrough PA (1986) Principles of geographical information systems for land resources assessment. Clarendon Press, Oxford

    Book  Google Scholar 

  18. Kumar V, Agrawal S (2019) Agricultural land use change analysis using remote sensing and GIS: a case study of Allahabad, India. In: ISPRS—International archives of the photogrammetry, remote sensing and spatial information sciences, New Delhi, India, pp 397–402. https://doi.org/10.5194/isprs-archives-xlii-3-w6-397-2019

  19. Saha AK, Agrawal S (2020) Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnol Environ Eng 5:1–18. https://doi.org/10.1007/s41204-020-00073-1

    Article  Google Scholar 

  20. Agrawal S, Gupta RD (2020) Development of SOA-based WebGIS framework for education sector. Arab J Geosci 13:1–20. https://doi.org/10.1007/s12517-020-05490-9

    Article  Google Scholar 

  21. Agrawal S, Gupta RD (2017) Web GIS and its architecture: a review. Arab J Geosci 10:1–13. https://doi.org/10.1007/s12517-017-3296-2

    Article  Google Scholar 

  22. Tripathi AK, Agrawal S, Gupta RD (2020) Cloud enabled SDI architecture: a review. Earth Sci Inf 13:211–231. https://doi.org/10.1007/s12145-020-00446-9

    Article  Google Scholar 

  23. Hay SI, Snow RW, Rogers DJ (1998) Predicting Malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 92:12–20. https://doi.org/10.1016/S0035-9203(98)90936-1

    Article  Google Scholar 

  24. Seal M, Pahari D, Saha NC, Chatterjee S (2018) GIS mapping and breeding habitat characterization of anophelines occurring in Malaria endemic areas of Hooghly, WB, India. Proc Natl Acad Sci India Sect B Biol Sci. https://doi.org/10.1007/s40011-018-0981-1

    Article  Google Scholar 

  25. Charoenpanyanet A, Chen X (2008) Satellite-based modeling of anopheles mosquito densities on heterogeneous land cover in Western Thailand. Geogr Inf Sci 14:20–26. https://doi.org/10.1080/10824000809480635

    Article  Google Scholar 

  26. Louis VR, Montenegro Quiñonez CA, Kusumawathie P et al (2016) Characteristics of and factors associated with dengue vector breeding sites in the City of Colombo, Sri Lanka. Pathogens Glob Health 110:79–86. https://doi.org/10.1080/20477724.2016.1175158

    Article  Google Scholar 

  27. Ahmad R, Ali WNWM, Nor ZM et al (2011) Mapping of mosquito breeding sites in malaria endemic areas in Pos Lenjang, Kuala Lipis, Pahang, Malaysia. Malaria J 10:1–12. https://doi.org/10.1186/1475-2875-10-361

    Article  Google Scholar 

  28. Abrha H, Hagos H, Brhane E et al (2019) Spatio-temporal dynamics of malaria expansion under climate change in semi-arid areas of Ethiopia. Environ Hazards 18:400–413. https://doi.org/10.1080/17477891.2019.1609405

    Article  Google Scholar 

  29. Neteler M, Roiz D, Rocchini D et al (2011) Terra and Aqua satellites track tiger mosquito invasion: modelling the potential distribution of Aedes albopictus in north-eastern Italy. Int J Health Geogr 10:1–13. https://doi.org/10.1186/1476-072X-10-49

    Article  Google Scholar 

  30. Ozdenerol E, Bialkowska-Jelinska E, Taff GN (2008) Locating suitable habitats for West Nile Virus-infected mosquitoes through association of environmental characteristics with infected mosquito locations: a case study in Shelby County, Tennessee. Int J Health Geogr 7:1–12. https://doi.org/10.1186/1476-072X-7-12

    Article  Google Scholar 

  31. Bellini R, Michaelakis A, Petrić D et al (2020) Practical management plan for invasive mosquito species in Europe: I. Asian tiger mosquito (Aedes albopictus). Travel Med Infect Dis 35:1–7. https://doi.org/10.1016/j.tmaid.2020.101691

    Article  Google Scholar 

  32. Ali SA, Ahmad A (2019) Mapping of mosquito-borne diseases in Kolkata Municipal Corporation using GIS and AHP based decision making approach. Spat Inf Res 27:351–372. https://doi.org/10.1007/s41324-019-00242-8

    Article  Google Scholar 

  33. Mollalo A, Sadeghian A, Israel GD et al (2018) Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran. Acta Trop 188:187–194. https://doi.org/10.1016/j.actatropica.2018.09.004

    Article  Google Scholar 

  34. Gwitira I, Murwira A, Zengeya FM, Shekede MD (2018) Application of GIS to predict malaria hotspots based on Anopheles arabiensis habitat suitability in Southern Africa. Int J Appl Earth Obs Geoinf 64:12–21. https://doi.org/10.1016/j.jag.2017.08.009

    Article  Google Scholar 

  35. Moise IK, Riegel C, Muturi EJ (2018) Environmental and social-demographic predictors of the southern house mosquito Culex quinquefasciatus in New Orleans, Louisiana. Parasit Vectors 11:1–8. https://doi.org/10.1186/s13071-018-2833-5

    Article  Google Scholar 

  36. Botello G, Golladay S, Covich A, Blackmore M (2013) Immature mosquitoes in agricultural wetlands of the coastal plain of Georgia, U.S.A.: effects of landscape and environmental habitat characteristics. Ecol Ind 34:304–312. https://doi.org/10.1016/j.ecolind.2013.05.018

    Article  Google Scholar 

  37. Bui Q-T, Nguyen Q-H, Pham VM et al (2019) Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers. Geocarto Int 34:1300–1314. https://doi.org/10.1080/10106049.2018.1478890

    Article  Google Scholar 

  38. Gwitira I, Murwira A, Masocha M et al (2019) GIS-based stratification of malaria risk zones for Zimbabwe. Geocarto Int 34:1163–1176. https://doi.org/10.1080/10106049.2018.1478889

    Article  Google Scholar 

  39. Holden J, Howard AJ, West LJ et al (2009) A critical review of hydrological data collection for assessing preservation risk for urban waterlogged archaeology: a case study from the City of York, UK. J Environ Manag 90:3197–3204. https://doi.org/10.1016/j.jenvman.2009.04.015

    Article  Google Scholar 

  40. Dwivedi RS (1994) Study of salinity and waterlogging in Uttar Pradesh (India) using remote sensing data. Land Degrad Rehabil 5:191–199. https://doi.org/10.1002/ldr.3400050303

    Article  Google Scholar 

  41. Runnström MC (2003) Rangeland development of the Mu Us sandy land in Semiarid China: an analysis using landsat and NOAA remote sensing data. Land Degrad Dev 14:189–202. https://doi.org/10.1002/ldr.545

    Article  Google Scholar 

  42. Symeonakis E, Drake N (2004) Monitoring desertification and land degradation over sub-Saharan Africa. Int J Remote Sens 25:573–592. https://doi.org/10.1080/0143116031000095998

    Article  Google Scholar 

  43. Huang D, Liu C, Fang H, Peng S (2008) Assessment of waterlogging risk in Lixiahe region of Jiangsu Province based on AVHRR and MODIS image. Chin Geogr Sci 18:178–183. https://doi.org/10.1007/s11769-008-0178-2

    Article  Google Scholar 

  44. French RH, Miller JJ, Dettling C, Carr JR (2006) Use of remotely sensed data to estimate the flow of water to a Playa Lake. J Hydrol 325:67–81. https://doi.org/10.1016/j.jhydrol.2005.09.034

    Article  Google Scholar 

  45. Grecchi RC, Beuchle R, Shimabukuro YE et al (2017) An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: a case study in Northern Mato Grosso, Brazilian Amazon. Int J Appl Earth Obs Geoinf 61:70–80. https://doi.org/10.1016/j.jag.2017.05.001

    Article  Google Scholar 

  46. Liu W, Huang J, Wei C et al (2018) Mapping water-logging damage on winter wheat at parcel level using high spatial resolution satellite data. ISPRS J Photogramm Remote Sens 142:243–256. https://doi.org/10.1016/j.isprsjprs.2018.05.024

    Article  Google Scholar 

  47. Arnous MO, Green DR (2011) GIS and remote sensing as tools for conducting geo-hazards risk assessment along Gulf of Aqaba Coastal Zone, Egypt. J Coast Conserv 15:457–475. https://doi.org/10.1007/s11852-010-0136-x

    Article  Google Scholar 

  48. Maxwell SK, Schmidt GL, Storey JC (2007) A multi-scale segmentation approach to filling gaps in Landsat ETM + SLC-off images. Int J Remote Sens 28:5339–5356. https://doi.org/10.1080/01431160601034902

    Article  Google Scholar 

  49. El-zeiny A, El-hefni A, Sowilem M (2017) Geospatial techniques for environmental modeling of mosquito breeding habitats at Suez Canal Zone, Egypt. Egypt J Remote Sens Space Sci 20:283–293. https://doi.org/10.1016/j.ejrs.2016.11.009

    Article  Google Scholar 

  50. Zou L, Miller SN, Schmidtmann ET (2006) Mosquito larval habitat mapping using remote sensing and GIS: implications of coalbed methane development and West Nile Virus. J Med Entomol 43:1034–1041. https://doi.org/10.1093/jmedent/43.5.1034

    Article  Google Scholar 

  51. Hassan AN, El Nogoumy N, Kassem HA (2013) Characterization of landscape features associated with mosquito breeding in Urban Cairo using remote sensing. Egypt J Remote Sens Space Sci 16:63–69. https://doi.org/10.1016/j.ejrs.2012.12.002

    Article  Google Scholar 

  52. Downs J, Vaziri M, Deskins G et al (2020) Optimizing arbovirus surveillance using risk mapping and coverage modelling. Ann GIS 26:13–23. https://doi.org/10.1080/19475683.2019.1688391

    Article  Google Scholar 

  53. Madzlan F, Dom NC, Tiong CS, Zakaria N (2016) Breeding characteristics of aedes mosquitoes in dengue risk area. Proc Soc Behav Sci 234:164–172. https://doi.org/10.1016/j.sbspro.2016.10.231

    Article  Google Scholar 

  54. McFeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432. https://doi.org/10.1080/01431169608948714

    Article  Google Scholar 

  55. Jin S, Sader SA (2005) Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens Environ 94:364–372. https://doi.org/10.1016/j.rse.2004.10.012

    Article  Google Scholar 

  56. Townshend JRG, Justice CO (1986) Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int J Remote Sens 7:1435–1445. https://doi.org/10.1080/01431168608948946

    Article  Google Scholar 

  57. Mahmoodi S, Dutta K, Basu D, Agrawal S (2019) Understanding link between land surface temperature and landscape heterogeneity: a spatio-temporal and inter-seasonal variability study on Kabul City, Afghanistan. In: Capacity building and education outreach in advance geospatial technologies and land management. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 57–65. https://doi.org/10.5194/isprs-annals-iv-5-w2-57-2019

  58. Dutta K, Basu D, Agrawal S (2019) Nocturnal and diurnal trends of surface urban heat island intensity: a seasonal variability analysis for smart urban planning. In: Capacity building and education outreach in advance geospatial technologies and land management. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 25–33. https://doi.org/10.5194/isprs-annals-iv-5-w2-25-2019

  59. NASA (2011) Landsat 7 Science Data Users Handbook

  60. Van Hoesen J, Letendre S (2010) Evaluating potential renewable energy resources in Poultney, Vermont: a GIS-based approach to supporting rural community energy planning. Renew Energy 35:2114–2122. https://doi.org/10.1016/j.renene.2010.01.018

    Article  Google Scholar 

  61. Srinivas R, Singh AP, Dhadse K et al (2018) Sustainable management of a river basin by integrating an improved fuzzy based hybridized SWOT model and geo-statistical weighted thematic overlay analysis. J Hydrol 563:92–105. https://doi.org/10.1016/j.jhydrol.2018.05.059

    Article  Google Scholar 

  62. Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34:1044–1055. https://doi.org/10.1016/j.cageo.2007.07.010

    Article  Google Scholar 

  63. de Mesnard L (2013) Pollution models and inverse distance weighting: some critical remarks. Comput Geosci 52:459–469. https://doi.org/10.1016/j.cageo.2012.11.002

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, 211004, India

    Shiv Kumar & Sonam Agrawal

Authors
  1. Shiv Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sonam Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sonam Agrawal.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Agrawal, S. Prevention of vector-borne disease by the identification and risk assessment of mosquito vector habitats using GIS and remote sensing: a case study of Gorakhpur, India. Nanotechnol. Environ. Eng. 5, 19 (2020). https://doi.org/10.1007/s41204-020-00084-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41204-020-00084-y

Keywords

Search

Navigation