Skip to main content
SpringerLink
Log in

Remote Sensing, Public Health & Disaster Mitigation

  • Chapter
  • First Online:
Geospatial Technologies in Environmental Management

Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 3))

Abstract

The authors review advances in applications for geotechnologies, specifically earth-observing satellite remote sensing, geo-positioning (i.e. USA’s Global Positioning System (GPS), Russia’s Global’naya Navigatsionnaya Sputnikovaya Sistema (GLONASS), Europe’s Galileo and China’s Beidou/Compass) and selected geo-spatial modeling software for public health and disaster management applications, with an emphasis on environmental health and environmental sustainability. Specific applications addressed include the use of remote sensing for infectious disease vector habitat identification and ecologically sustainable disease vector population mitigation, as well as the integration of GPS into mobile CD4 testing devices for HIV/AIDS. Public domain software models described include the Spatio-Temporal Epidemiological Modeler (STEM) and the Hydrologic Engineering River Analysis System (HEC-RAS) for flood modeling. Examples of regional, national and global real-time data acquisition and near-real-time data product development and distribution for time-critical events are offered, specifically through the Purdue Terrestrial Observatory (PTO), the United States Geological Survey (USGS) supported AmericaView and the International Charter – Space & Major Disasters.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abdo-Salem S, Gerbier G, Bonnet P, Al-Qadasi M, Tran A, Al-Eryni G, Roger F (2006) Descriptive and spatial epidemiology of Rift Valley fever outbreak in Yemen 2000–2001. Ann NY Acad Sci 1081:240–242

    Article  Google Scholar 

  • Achee NL, Grieco JP, Penny M, Richard AG, Donald RR, James T, Ireneo B, Russell K, Eliska R (2006) Use of remote sensing and geographic information systems to predict locations of Anopheles darlingi-positive breeding sites within the Sibun River in Belize, Central America. J Med Entomol 43(2):382–392

    Article  Google Scholar 

  • Ahmed SA, Diffenbaugh NS, Hertel TW (2009) Climate volatility deepens poverty vulnerability in developing countries. Environ Res Lett 4(3):004.1–8. doi:10.1088/1748-9326/4/3/034004.8 pp

    Google Scholar 

  • Anyamba A, Linthicum KJ, Tucker CJ (2001) Climate-disease connections: Rift Valley fever in Kenya. Cadernos Saude Publ 17:133–140

    Article  Google Scholar 

  • Barnes C, Cibula W (1979) Some implications of remote sensing technology in insect control programs including mosquitoes. Mosq News 39:271–282

    Google Scholar 

  • Baylis M, Meiswinkel R, Venter GJ (1999) A preliminary attempt to use climate data and satellite imagery to model the abundance and distribution of Culicoides imicola (Diptera: Ceratopogonidae) in Southern Africa. Tydskr South Afr Vet Ver 70(2):80–89

    Google Scholar 

  • Beck LR, Lobitz BM, Wood BL (2000) Remote sensing and human health: new sensors and new opportunities. Emerg Infect Dis 6:217–226

    Article  Google Scholar 

  • Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Washino RK, Roberts DR, Spanner MA (1997) Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico. Am J Trop Med Hyg 56(1):99–106

    Google Scholar 

  • Boone JD, McGwire KC, Otteson EW, DeBaca RS, Kuhn EA, Villard P, Brussard PF, St. Jeor SC (2000) Remote sensing and geographic information systems: Charting Sin Nombre Virus infections in deer mice. Emerg Infect Dis 6:248–258

    Article  Google Scholar 

  • Brooker S (2007) Spatial epidemiology of human schistosomiasis in Africa: risk models, transmission dynamics and control. Trans R Soc Trop Med Hyg 101(1):1–8

    Article  Google Scholar 

  • Brooker S, Hay SI, Tchuenté LT, Ratard R (2002) Using NOAA-AVHRR data to model human helminth distributions in planning disease control in Cameroon, West Africa. Photogramm Eng Remote Sens 68(2):175–179

    Google Scholar 

  • Brooker S, Clarke S, Njagi JK, Polack S, Mugo B, Estambale B, Muchiri E, Magnussen P, Cox J (2004) Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya. Trop Med Int Health 9(7):757–766

    Article  Google Scholar 

  • Brown HE, Diuk-Wasser MA, Guan Y, Caskey S, Fish D (2008) Comparison of three satellite sensors at three spatial scales to predict larval mosquito presence in Connecticut wetlands. Remote Sens Environ 12(5):2301–2308

    Article  Google Scholar 

  • Carbajo AE, Schweigmann N, Curto SI, de Garin A, Bejarán R (2001) Dengue transmission risk maps of Argentina. Trop Med Int Health 6:170–183

    Article  Google Scholar 

  • Casimiro E, Calheiros J, Santos FD, Kovats S (2006) National assessment of human health effects of climate change in Portugal: approach and key findings. Environ Health Perspect 114(12):1950–1956

    Google Scholar 

  • Chauhan AJ, Johnston SL (2003) Air Pollution and Infection in Respiratory Illness. Br Med Bull 68:95–112

    Article  Google Scholar 

  • Cross ER, Newcomb WW, Tucker CJ (1996) Use of weather data and remote sensing to predict the seasonal distribution of Phlebotomus papatasi in southwestern Asia. Am J Trop Med Hyg 54:530–536

    Google Scholar 

  • Cuevas LE, Jeanne I, Molesworth A, Bell M, Savory EC et al (2007) Risk mapping and early warning systems for the control of meningitis in Africa. Vaccine 25:A12–A17

    Article  Google Scholar 

  • Daniel M, Zitek K, Danielová V, Kríz B, Valter J, Kott I (2006) Risk assessment and prediction of Ixodes ricinus tick questing activity and human tick-borne encephalitis infection in space and time in the Czech Republic. Int J Med Microbiol 296(1):41–47

    Article  Google Scholar 

  • Danson FM, Giraudoux P, Craig PS (2006) Spatial modeling and ecology of Echinococcus multilocularis transmission in China. Parasitol Int 55(1):S227–S231

    Article  Google Scholar 

  • De La Rocque S, Michel V, Plazaneta D, Pinc R (2004) Remote sensing and epidemiology: examples of applications for two vector-borne diseases. Comp Immunol Microbiol Infect Dis 27(5):331–341

    Article  Google Scholar 

  • Ebi KL (2007) Using health models to prepare for and cope with climate change. Clim Change 88(1):1–3

    Article  Google Scholar 

  • Elnaiem DA, Connor SJ, Thomson MC, Hassan MM, Hassan HK, Aboud MA, Ashford RW (1998) Environmental determinants of the distribution of Phlebotomus orientalis in Sudan. Ann Trop Med Parasitol 92(8):877–887

    Article  Google Scholar 

  • Estrada-Peña A, Venzal JM (2006) High-resolution predictive mapping for Boophilus annulatus and B. microplus (Acari: ixodidae) in Mexico and Southern Texas. Vet Parasitol 142(3–4):350–358

    Article  Google Scholar 

  • Flanary BE, Kletetschka G (2005) Analysis of telomere length and telomerase activity in tree species of various life-spans, and with age in the bristlecone pine Pinus longaeva. Biogerontol 6(2):101–111

    Article  Google Scholar 

  • Gallardo C, Arribas A, Prego JA, Gaertner MA, de Castro M (2001) Multi-year simulations using a regional-climate model over the Iberian Peninsula: current climate and double CO2 scenario. Quat J R Meteorol Soc 127:1659–1681

    Google Scholar 

  • Gilbert M, Xiao X, Chaitaweesub P, Kalpravidh W, Premashthira S, Boles S, Slingenbergh J (2007) Avian influenza, domestic ducks and rice agriculture in Thailand. Agric Ecosyst Environ 119:409–415

    Article  Google Scholar 

  • Goetz S, Prince S, Small J (2000) Advances in satellite remote sensing of environmental variables for epidemiological applications. Adv Parasitol 47:289–307

    Article  Google Scholar 

  • Goossens B, Mbwambo H, Msangi A, Geysen D, Vreysen M (2006) Trypanosomosis prevalence in cattle on Mafia Island (Tanzania). Vet Parasitol 139:74–83

    Article  Google Scholar 

  • Graham AJ, Atkinson PM, Danson FM (2004) Spatial analysis for epidemiology. Acta Trop 91(3):219–225

    Article  Google Scholar 

  • Green RM, Hay SI (2002) The potential of pathfinder AVHRR data for providing surrogate climatic variables across Africa and Europe for epidemiological applications. Remote Sens Environ 79(2–3):166–175

    Article  Google Scholar 

  • Guo J, Vounatsou P, Cao C, Jürg U, Zhu H, Daniel A, Zhu R, He Z, Li D, Hu F, Chen M, Marcel T (2005) A geographic information and remote sensing based model for prediction of Oncomelania hupensis habitats in the Poyang Lake area, China. Acta Trop 96(2–3):213–222

    Article  Google Scholar 

  • Hajat S, Kosatsky T (2009) Heat-related mortality: a review and exploration of heterogeneity. J Epidemiol Community Health. doi:10.1136/jech.2009.087999 (Accepted for publication). Available as Online First article: http://jech.bmj.com/content/early/2009/09/01/jech.2009.087999.abstract

  • Hay SI, Tatem AJ, Graham AJ, Goetz SJ, Rogers DJ (2006) Global environmental data for mapping infectious disease distribution. Adv Parasitol 62:37–77

    Article  Google Scholar 

  • Hay SI, Snow RW, Rogers DJ (1998) Prediction of malaria seasons in Kenya using multi-temporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 92:12–20

    Article  Google Scholar 

  • Hay SI (2000) An overview of remote sensing and geodesy for epidemiology and public health application. Adv Parasitol 47:1–35

    Article  Google Scholar 

  • Hay SI, Randolph SE, Rogers DJ (2000) Remote sensing and geographical information systems in epidemiology. Academic, London

    Google Scholar 

  • Herbreteau V, Salem G, Souris M, Hugot JP, Gonzalez JP (2007) Thirty years of use and improvement of remote sensing applied to epidemiology: from early promises to lasting frustration. Health Place 13:400–403

    Article  Google Scholar 

  • Hugh-Jones M (1989) Applications of remote sensing to the identification of the habitats of parasites and disease vectors. Parasitol Today 5(8):244–251

    Article  Google Scholar 

  • Hugh-Jones M, Barre N, Nelson G, Wehnes K, Warner J, Garvin J, Garris G (1992) Landsat-TM identification of Amblyomma variegatum (Acari: Ixodidae) habitats in Guadeloupe. Remote Sens Environ 40:43–55

    Article  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2007) Climate change 2007: impacts, adaptation, and vulnerability. Cambridge University Press, Cambridge

    Google Scholar 

  • Jones RG, Murphy JM, Noguer M, Keen AB (1997) Simulation of climate change over Europe using a nested regional-climate model. II: comparison of driving and regional model responses to a doubling of carbon dioxide. Quat J R Meteorol Soc 123:265–292

    Google Scholar 

  • Jovanovic P (1987) Remote sensing of environmental factors affecting health. Adv Space Res 7(3):11–18

    Article  Google Scholar 

  • Kalluri S, Gilruth P, Rogers D, Szczur M (2007) Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathog 3(10):1361–1371

    Article  Google Scholar 

  • Kashiwaya K, Ochiai S, Sakai H, Kawai T (2001) Orbit-related long-term climate cycles revealed in a 12-Myr continental record from Lake Baikal. Nat 410:71–74

    Article  Google Scholar 

  • Kaya S, Sokol J, Pultz TJ (2004) Monitoring environmental indicators of vector-borne disease from space: a new opportunity for RADARSAT 2. Can J Remote Sens 30:560–565

    Article  Google Scholar 

  • Kitron U, Kazmierczak JJ (1997) Spatial analysis of the distribution of Lyme disease in Wisconsin. Am J Epidemiol 145:558–566

    Article  Google Scholar 

  • Kitron U, Otieno LH, Hungerford LL, Odulaja A, Brigham WU, Okello OO, Joselyn M, Mohamed-Ahmed MM, Cook E (1996) Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, using Landsat TM satellite imagery and GIS. J Anim Ecol 65(3):371–380

    Article  Google Scholar 

  • Knebl MR, Yang ZL, Hutchinson K, Maidment DR (2005) Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event. J Environ Manag 75(4):325–336

    Article  Google Scholar 

  • Lacaux JP, Tourre YM, Vignolles C, Ndione JA, Lafaye M (2007) Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley fever epidemics in Senegal. Remote Sens Environ 106(1):66–74

    Article  Google Scholar 

  • Leblond A, Sandoz A, Lefebvre G, Zeller H, Bicout DJ (2007) Remote sensing based identification of environmental risk factors associated with West Nile disease in horses in Camargue, France. Prev Vet Med 79(1):20–31

    Article  Google Scholar 

  • Leonardo LR, Rivera PT, Crisostomo BA, Sarol JN, Bantayan NC, Tiu WU, Bergquist NR (2005) A study of the environmental determinants of malaria and schistosomiasis in the Philippines using Remote Sensing and Geographic Information Systems. Parassitologia 47(1):105–114

    Google Scholar 

  • Linthicum KJ, Bailey CL, Davies FG, Tucker CJ (1987) Detection of Rift Valley fever viral activity in Kenya by satellite remote sensing imagery. Sci 235(4796):1656–1659

    Article  Google Scholar 

  • Liu J, Chen X (2006) Relationship of remote sensing normalized differential vegetation index to Anopheles density and malaria incidence rate. Biomed Environ Sci 19(2):130–132

    Google Scholar 

  • Lobitz B, Beck L, Huq A, Wood B, Fuchs G, Faruque AS, Colwell R (2000) Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proc Natl Acad Sci USA 97(4):1438–1443

    Article  Google Scholar 

  • Malone JB, Yilma JM, McCarroll JC, Erko B, Mukaratirwa S, Zhou X (2001) Satellite climatology and the environmental risk of Schistosoma mansoni in Ethiopia and East Africa. Acta Trop 79:59–72

    Article  Google Scholar 

  • Manguin S, Boussinesq M (1999) Remote sensing in public health: applications to malaria and other diseases. Med Mal Infect 29(5):318–324

    Article  Google Scholar 

  • McLin SG, Springer EP, Lane LJ (2001) Predicting floodplain boundary changes following the Cerro Grande wildfire. Hydrological Processes 15(15):2967–2980

    Google Scholar 

  • McMichael AJ, Campbell-Lendrum D, Kovats S, Edwards S, Wilkinson P, Wilson T, Nicholls R, Hales S, Tanser F, LeSueur D, Schlesinger M, Andronova N (2004) Global climate change. In: Ezzati M, Lopez A, Rodgers A, Murray C (eds) Comparative quantification of health risks: global and regional burden of disease due to selected major risk factors. World Health Organization, Geneva

    Google Scholar 

  • Mendelsohn J, Dawson T (2008) Climate and cholera in KwaZulu-Natal, South Africa: The role of environmental factors and implications for epidemic preparedness. Int J Hyg Environ Health 211(1–2):156–162

    Article  Google Scholar 

  • Mushinzimana E, Munga S, Minakawa N, Li L, Feng C, Bian L, Kitron U, Schmidt C, Beck L, Zhou G, Githeko AK, Yan G (2006) Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malar J 5:13

    Article  Google Scholar 

  • Nihei N, Hashida Y, Kobayashi M, Ishii A (2002) Analysis of malaria endemic areas on the Indochina Peninsula using remote sensing. Jpn J Infect Dis 55(5):160–166

    Google Scholar 

  • Odiit M, Bessell PR, Fèvre EM, Robinson T, Kinoti J, Coleman PG, Welburn SC, McDermott J, Woolhouse ME (2005) Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda. Trans R Soc Trop Med Hyg 100(4):354–362

    Article  Google Scholar 

  • Patz JA, McGeehin MA, Bernard SM, Ebi KL, Epstein PR, Grambsch A, Gubler DJ, Reiter P, Romieu I, Rose JB, Samet JM, Trtanf J (2000) The potential health impacts of climate variability and change for the United States: executive summary of the report of the health sector of the U.S. national assessment. Environ Health Perspect 108(4):367–376

    Article  Google Scholar 

  • Patz JA, Campbell-Lendrum D, Holloway T, Foley JA (2005) Impact of regional climate change on human health. Nat 438(17):310–317

    Article  Google Scholar 

  • Pavlin JA, Mostashari F, Kortepeter MG, Hynes NA, Chotani RA, Mikol YB, Ryan MAK, Neville JS, Gantz DT, Writer JV, Florance JE, Culpepper RC, Henretig RM, Kelley PW (2003) Innovative surveillance methods for rapid detection of disease outbreaks and bioterrorism: results of an interagency workshop on health indicator surveillance. Am J Public Health 93(8):1230–1235

    Article  Google Scholar 

  • Peterson AT, Martínez-Campos C, Nakazawa Y, Martínez-Meyer E (2005) Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases. Trans R Soc Trop Med Hyg 99(9):647–655

    Article  Google Scholar 

  • Petit JR, Jouzel J, Raynaud D, Barkov NI, Barnola J, Basile I, Bender M, Chappellaz J, Davisk M, Delaygue G, Delmotte M, Kotlyakov VM, Legrand M, Lipenkov VY, Lorius C, Pépin L, Ritz C, Saltzmank E, Stievenard M (1999) Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nat 399(3):429–436

    Article  Google Scholar 

  • Pope KO, Sheffner EJ, Linthicum KJ, Bailey CL, Logan TM, Kasischke ES, Birney K, Njogu AR, Roberts CR (1992) Identification of central Kenyan Rift Valley fever virus vector habitats with Landsat TM and evaluation of their flooding status with airborne imaging radar. Remote Sens Environ 40:185–196

    Article  Google Scholar 

  • Porter K, Eeri M (2009) Cracking an open safe: HAZUS vulnerability functions in terms of structure-independent spectral acceleration. Earthq Spectra 25(2):361–378

    Article  Google Scholar 

  • Rahman A, Kogan F, Roytman L (2006) Short report: Analysis of malaria cases in Bangladesh with remote sensing data. Am J Trop Med Hyg 74(1):17–19

    Google Scholar 

  • Randolph SE (2000) Ticks and tick-borne disease systems in space and from space. Adv Parasitol 47:217–243

    Article  Google Scholar 

  • Raso G, Vounatsou P, Singer BH, N’Goran EK, Tanner M, Utzinger J (2006) An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni–hookworm coinfection. Proc Natl Acad Sci USA 103(18):6934–6939

    Article  Google Scholar 

  • Ratana S, Lee WJ, Ugsang DM, Linthicum KJ (2005) Identification and characterization of larval and adult anopheline mosquito habitats in the Republic of Korea: potential use of remotely sensed data to estimate mosquito distributions. Int J Health Geogr 4(17)

    Google Scholar 

  • Rejmankova E, Roberts DR, Pawley A, Manguin S, Polanco J (1995) Predictions of adult Anopheles albimanus densities in villages based on distances to remotely-sensed larval habitats. Am J Trop Med Hyg 53:482–488

    Google Scholar 

  • Robinson JP, Rochon HS, Rochon GL (2007) Cytometry for life (C4L) CD4 diagnostic device for HIV/AIDS. Co-sponsors: Purdue University Cytometry Laboratories, ICYT, Parker Life Sciences, Wealthy Consults and convoy of Hope International. Transcorp Hilton Hotel, Abuja, Nigeria, 14 March 2007

    Google Scholar 

  • Rochon GL (2009) Space-based technologies and high performance computing in support of environmental sustainability in developing countries. Clean Technol Environ Policy 11(3):251–252

    Article  Google Scholar 

  • Rochon GL, Niyogi D, Fall S, Quansah JE, Biehl L, Araya B, Maringanti C, Valcarcel AT, Rakotomalala L, Rochon HS, Mbongo BH, Thiam T (2010) Best management practices (BMPS) for corporate, academic and governmental transfer of sustainable technology to developing countries. Clean Technologies & Environmental Policy 12(1):19–30, February, 2010. Springer. doi:10.1007/s10098-009-0218-3. http://www.springerlink.com/content/103074/?Content+Status=Accepted&sort=p_OnlineDate&sortorder=desc&v=expanded

  • Rochon GL, Niyogi D, Chatturvedi A, Madhavan K, Arangarasan R, Biehl L, Quansah J, Fall S (2008) Adopting multisensor remote sensing datasets and coupled models for disaster management. In: Nayak S, Zlatanova S (eds) Remote sensing and GIS technologies for monitoring and prediction of disasters. Springer, Heidelberg

    Google Scholar 

  • Rochon GL, Johannsen C, Landgrebe D, Engel B, Harbor J, Majumder S, Biehl L (2004) Remote sensing for monitoring sustainability. In: Sikdar SK, Glavič P, Jain R (eds) Technological choices for sustainability. Springer, Berlin, Heidelberg

    Google Scholar 

  • Rogers DJ, Myers MF, Tucker CJ, Smith PF, White DJ, Backenson PB, Eidson M, Kramer LD, Bakker B, Hay SI (2002) Predicting the distribution of West Nile fever in North America using satellite sensor data. Photogramm Eng Remote Sens 68:112–114

    Google Scholar 

  • Rogers DJ, Randolph SE (1991) Mortality rates and population density of tsetse flies correlated with satellite imagery. Nat 351:739–741

    Article  Google Scholar 

  • Rogers DJ (2000) Satellites, space, time and the African trypanosomiases. Adv Parasitol 47:129–171

    Article  Google Scholar 

  • Rogers DJ, Randolph SE (1994) Satellite imagery, tsetse flies and sleeping sickness in Africa. Sistema Terra Year III:40–43

    Google Scholar 

  • Rogers DJ, Randolph SE, Snow RW, Hay SI (2002) Satellite imagery in the study and forecast of malaria. Nat 415:710–715

    Article  Google Scholar 

  • Rogers DJ, Hay SI, Packer MJ (1996) Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Ann Trop Med Parasitol 90:225–241

    Google Scholar 

  • Rotela C, Florence F, Mario L, Phillipe S, Virginia I, Mario Z, Scavuzzo C (2007) Space–time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop 103(1):1–13

    Article  Google Scholar 

  • Scawthorn C, Blais N, Seligson H, Tate E, Mifflin E, Thomas W, Murphy J, Jones C (2006) HAZUS-MH flood loss estimation methodology. I: Overview and flood hazard characterization. Nat Hazards Rev 7(2):60–71

    Article  Google Scholar 

  • Spear RC, Gong P, Seto E, Zhou Y, Xu B, Liang S, Davis D, Gu X (1998) Remote Sensing and GIS for schistosomiasis control in mountainous areas in Sichuan, China. Geogr Inf Syst 4(1–2):14–22

    Google Scholar 

  • Segerström U, von Stedingk H (2003) Early-Holocene spruce, Picea abies (L.) Karst., in west central Sweden as revealed by pollen analysis. Holocene 13(6):897–906

    Article  Google Scholar 

  • Srivatsava A, Nagpal BN, Saxena R, Subbarao SK (2001) Predictive habitat modeling for forest malaria vector species An. Dirus in India – A GIS based approach. Curr Sci 80:1129–1134

    Google Scholar 

  • Sudhakar S, Srinivas T, Palit A, Kar SK, Battacharya SK (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 43:115–122

    Google Scholar 

  • Šumilo D, Bormane A, Asokliene L, Lucenko I, Vasilenko V, Randolph S (2006) Tick-borne encephalitis in the Baltic States: identifying risk factors in space and time. Int J Med Microbiol 296(1):76–79

    Article  Google Scholar 

  • Tappan G, Hadj A, Wood E, Lietzow R (2000) Use of argon, corona, and Landsat imagery to assess 30 years of land resource changes in west-central Senegal. Photogramm Eng Remote Sens 6:727–735

    Google Scholar 

  • Tatem AJ, Goetz SJ, Hay SI (2004) Terra and Aqua: new data for epidemiology and public health. Int J Appl Earth Obs Geoinf 6(1):33–46

    Article  Google Scholar 

  • Thomson MC, Obsomer V, Dunne M, Connor SJ, Molyneux DH (2000) Satellite mapping of Loa Loa prevalence in relation to ivermectin use in West and Central Africa. The Lancet 356(9235):1077–1078

    Article  Google Scholar 

  • Thomson MC, Connor SJ (2000) Environmental information systems for the control of arthropod vectors of disease. Med Vet Entomol. 14:227–244

    Article  Google Scholar 

  • Thomson MC, Connor SJ, Milligan PJM, Flasse SP (1996) The ecology of malaria seen by earth-observation satellites. Ann Trop Med Parasitol 90:243–264

    Google Scholar 

  • Thomson MC, Molesworth AM, Djingarey MH, Yameogo KR, Belanger F, Cuevas LE (2006) Potential of environmental models to predict meningitis epidemics in Africa. Trop Med Int Health 11(6):781–788

    Article  Google Scholar 

  • Tran A, Ponçon N, Toty C, Linard C, Guis H, Ferré J-B, Seen DL, Roger F, Rocque S, Fontenille D, Baldet T (2008) Using remote sensing to map larval and adult populations of Anopheles hyrcanus (Diptera: Culicidae) a potential malaria vector in Southern France. Int J Health Geogr 7:9 doi:10.1186/1476-072X-7-9. Open access: http://www.ij-healthgeographics.com/content/7/1/9

  • Tralli DM, Blom RG, Fielding EJ, Donnellan A, Evans DL (2007) Conceptual case for assimilating interferometric synthetic aperture radar data into the HAZUS-MH earthquake module. IEEE Trans Geosci Remote Sens 45(6):1595–1604

    Article  Google Scholar 

  • Tran A, Gordon J, Weber S, Polidori L (2002) Mapping disease incidence in suburban areas using remotely sensed data. Am J Epidemiol 156:662–668

    Article  Google Scholar 

  • US Army Corps of Engineers (USACE) (2002) HEC-RAS: river analysis system. hydraulic reference manual, version 3.1. Hydrologic Engineering Center, Davis, CA

    Google Scholar 

  • Verdin J, Funk C, Senay G, Choularton R (2005) Climate science and famine early warning. Philos Trans R Soc Lond Biol Sci 360(1463):2155–2163

    Article  Google Scholar 

  • Vickery PJ, Lin J, Skerlj PF, Twisdale LA Jr, Huang K (2006) HAZUS-MH hurricane model methodology. I: hurricane hazard, terrain, and wind load modeling. Nat Hazards Rev 7(2):82–93

    Article  Google Scholar 

  • Vickery PJ, Skerlj PF, Lin J, Twisdale LA Jr, Young MA, Lavelle FM (2006) HAZUS-MH hurricane model methodology. II: damage and loss estimation. Nat Hazards Rev 7(2):94–103

    Article  Google Scholar 

  • Wagner VE, Hill-Rowley R, Narlock SA, Newson HD (1979) Remote sensing: a rapid and accurate method of data acquisition for a newly formed mosquito control district. Mosq News 39:282–287

    Google Scholar 

  • Welch JB, Olson JK, Hart WG, Ingle SG, Davis MR (1989) Use of aerial color-infrared photography as a survey technique for Psorophora columbiae oviposition habitats in Texas ricelands. J Am Mosq Control Assoc 5:147–160

    Google Scholar 

  • Willyard C (2007) Simpler tests for immune cells could transform AIDS care in Africa. Nat Med 13(10):1131

    Article  Google Scholar 

  • World Health Organization (2007) Fact sheet N°266, Geneva

    Google Scholar 

  • Wu W, Davis GM, Liu H, Seto E, Lu S, Zhang J, Hua Z, Guo J, Lin D, Chen H, Gong P, Feng Z (2002) Application of remote sensing for surveillance of snail habitats in Poyang Lake, China. Chin J Parasitol 20:205–208

    Google Scholar 

  • Xiao X, Gilbert M, Slingenbergh J, Lei F, Boles S (2007) Remote sensing, ecological variables, and wild bird migration related to outbreaks of highly pathogenic H5N1 Avian Influenza. J Wildl Dis 43(3):S40–S46

    Google Scholar 

  • Zhou X, Hu X, Sun N, Hong Q, Sun L, Lu G, Fuentes M, Malone JB (1999) Application of geographic information systems on schistosomiasis surveillance II. Predicting transmission intensity. Chin J Schistosomiasis Control 11:66–70

    Google Scholar 

  • Zhou XN, Hu XS, Sun NS, Hong QB, Sun LP, Fuentes M, Malone JB (1998) Application of geographic information systems on schistosomiasis surveillance I. Application possibility of prediction model. Chin J Schistosomiasis Control 10:321–324

    Google Scholar 

Download references

Acknowledgments

The authors wish to acknowledge support from AmericaView, Inc., NATO Science for Peace Program, and Purdue University’s Information Technology at Purdue (ITaP) – Rosen Center for Advanced Computing.

Author information

Authors and Affiliations

  1. Purdue Terrestrial Observatory, Rosen Center for Advanced Computing, Information Technology at Purdue (ITaP), Purdue University, Lafayette, IN, 47907, USA

    Gilbert L. Rochon

  2. USGS National Wetland Research Center (NRWC), IAPWS, Leetown Science Center, Kearneysville, WV, 25430, USA

    Joseph E. Quansah

  3. Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, IN, 47907, USA

    Souleymane Fall & Angel Torres Valcarcel

  4. Purdue Terrestrial Observatory, Rosen Center for Advanced Computing, Information Technology at Purdue (ITaP), Purdue University, and School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Bereket Araya & Abdur Rahman M. Maud

  5. Purdue Terrestrial Observatory, Rosen Center for Advanced Computing, Information Technology at Purdue (ITaP), Purdue University, West Lafayette, IN, 47907, USA

    Larry L. Biehl & Thierno Thiam

  6. Purdue Terrestrial Observatory, Rosen Center for Advanced Computing, Information Technology at Purdue (ITaP) and School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Sohaib Ghani

  7. Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, 08544, USA

    Lova Rakotomalala

  8. Purdue University Cytometry Lab. (PUCL), Cytometry for Life (C4L)-Africa, Bindley Bioscience Center, West Lafayette, IN, 47907, USA

    Lova Rakotomalala & Hildred S. Rochon

  9. Alpert School of Medicine, Brown University, Providence, RI, 02912, USA

    Hildred S. Rochon

  10. Purdue Terrestrial Observatory, Rosen Center for Advanced Computing, Information Technology at Purdue (ITaP), and Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Bertin Hilaire Mbongo

  11. Purdue Terrestrial Observatory, Rosen Center for Advanced Computing, Information Technology at Purdue (ITaP), and School of Civil Engineering, Purdue University, Lafayette, IN, 47907, USA

    Jinha Jung

  12. Purdue Terrestrial Observatory, Rosen Center for Advanced Computing, Information Technology at Purdue (ITaP), and School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Darion Grant & Wonkook Kim

  13. Purdue Terrestrial Observatory, Rosen Center for Advanced Computing, Information Technology at Purdue (ITaP), and Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Chetan Maringanti

Authors
  1. Gilbert L. Rochon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joseph E. Quansah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Souleymane Fall
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bereket Araya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Larry L. Biehl
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thierno Thiam
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sohaib Ghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lova Rakotomalala
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hildred S. Rochon
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Angel Torres Valcarcel
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Bertin Hilaire Mbongo
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jinha Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Darion Grant
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Wonkook Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Abdur Rahman M. Maud
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Chetan Maringanti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gilbert L. Rochon .

Editor information

Editors and Affiliations

  1. Dept. Geography & Anthropology, Kennesaw State University, Chastain Road 1000, Kennesaw, 30144, Georgia, USA

    Nancy Hoalst-Pullen

  2. Dept. Geography & Anthropology, Kennesaw State University, Chastain Road 1000, Kennesaw, 30144, Georgia, USA

    Mark W. Patterson

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Netherlands

About this chapter

Cite this chapter

Rochon, G.L. et al. (2010). Remote Sensing, Public Health & Disaster Mitigation. In: Hoalst-Pullen, N., Patterson, M. (eds) Geospatial Technologies in Environmental Management. Geotechnologies and the Environment, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9525-1_11

Download citation

Publish with us

Policies and ethics

Search

Navigation