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Remote Sensing for Detecting and Mapping Whitefly (Bemisia tabaci) Infestations

  • Chenghai Yang
  • James H. Everitt
Chapter

Abstract

Remote sensing technology has long been used for detecting insect infestations on agricultural crops. With recent advances in remote sensing sensors and other spatial information technologies such as Global Position Systems (GPS) and Geographic Information Systems (GIS), remote sensing is finding more and more practical applications for the detection and management of insect pests, including sweetpotato whitefly, Bemisia tabaci (Gennadius). This chapter begins with an extended overview of remote sensing principles and systems that can be used for entomological studies. Properties and behavior of electromagnetic energy, major divisions of the electromagnetic spectrum (i.e., ultraviolet, visible, infrared and microwave), and the interactions between radiation and ground targets are ­discussed. Major types of remote sensing systems are described, including ground-based spectroradiometers, aerial photographic cameras, airborne digital multispectral and hyperspectral imaging systems, and moderate and high resolution satellite imaging systems. The second part of the chapter provides a brief review on the use of remote sensing for detecting whitefly infestations and presents an application example to illustrate how remote sensing can be integrated with GPS and GIS technologies for detecting and mapping whitefly infestations in cotton fields. The methodologies for ground reflectance and airborne image acquisition and for the integration of image data with GPS and GIS are discussed.

Keywords

Geographic Information System Global Position System Cotton Plant Sooty Mold Multispectral Imagery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Avery TE, Berlin GL (1992) Fundamentals of remote sensing and airphoto interpretation, 5th edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  2. Brown JK, Bird J (1992) Whitefly-transmitted geminiviruses and associated disorders in the Americas and the Caribbean Basis. Plant Dis 76:220–225CrossRefGoogle Scholar
  3. Byrne DN, Bellows TS, Parrella MP (1990) Whiteflies in agricultural systems. In: Gerling D (ed.) Whiteflies: their bionomics, pest status and management. Intercept, AndoverGoogle Scholar
  4. Campbell JB (2002) Introduction to remote sensing, 3rd edn. The Guilford Press, New YorkGoogle Scholar
  5. ERDAS (2008) ERDAS field guide, vol 1 and 2. ERDAS, NorcrossGoogle Scholar
  6. Escobar DE, Everitt JH, Noriega JR, Davis MR, Cavazos I (1997) A true digital imaging system for remote sensing applications. In: Proceedings of the16th biennial workshop on color photography and videography in resource assessment, American Society for Photogrammetry and Remote Sensing, BethesdaGoogle Scholar
  7. Everitt JH, Escobar DE, Summy KR, Davis MR (1994) Using airborne video, global positioning system, and geographic information system technologies for detecting and mapping citrus blackfly infestations. Southwest Entomol 19:129–138Google Scholar
  8. Everitt JH, Escobar DE, Cavazos I, Noriega JR, Davis MR (1995) A three-camera multispectral digital video imaging system. Remote Sens Environ 54:333–337CrossRefGoogle Scholar
  9. Everitt JH, Escobar DE, Summy KR, Alaniz MA, Davis MR (1996) Using spatial information technologies for detecting and mapping whitefly and harvester ant infestations in south Texas. Southwest Entomol 21:421–432Google Scholar
  10. Fitzgerald GJ, Maas SJ, Detar WR (2004) Spider mite detection in cotton using hyperspectral imagery and spectral mixture analysis. Precision Agric 5:275–289CrossRefGoogle Scholar
  11. Gao J (2002) Integration of GPS with remote sensing and GIS: reality and prospect. Photogramm Eng Remote Sens 68:447–453Google Scholar
  12. Gausman HW, Hart WG (1974) Reflectance of four levels of sooty mold deposits produced from the honeydew of three insect species. J Rio Grande Valley Hortic Soc 28:131–136Google Scholar
  13. Harris MK, Hart WG, Davis MR, Ingle SJ, Van Cleave HW (1976) Aerial photographs show ­caterpillar infestation. The Pecan Quarterly 10:12–18Google Scholar
  14. Hart WG (1990) Remote sensing. In: Rosen D (ed.) The armored scale insects, their biology, ­natural enemies and control, vol B. Elsevier Science, AmsterdamGoogle Scholar
  15. Hart WG, Ingle SJ (1969) Detection of arthropod activity on citrus foliage with aerial infrared color film as a method of surveying for citrus blackfly. J Econ Entomol 66:190–194Google Scholar
  16. Hart WG, Myers VI (1968) Infrared aerial photography for detection of populations of brown soft scale in citrus groves. J Econ Entomol 61:617–624Google Scholar
  17. Hart WG, Ingle SJ, Davis MR, Mangum C, Higgins A, Boling JC (1971) Some uses of infrared aerial photography in entomology. In: Proceedings of the 3rd biennial workshop color aerial photography in the plant sciences, American Society of Photograrnmetry, Falls ChurchGoogle Scholar
  18. Hart WG, Ingle SJ, Davis MR, Mangum C (1973) Aerial photography with infrared color film as a method of surveying for citrus blackfly. J Econ Entomol 66:190–194Google Scholar
  19. Hendrix DL, Wei Y (1992) Detection and elimination of honeydew excreted by the sweetpotato whitefly feeding upon cotton. In: Proceedings of the beltwide cotton Conference, National Cotton Council, MemphisGoogle Scholar
  20. Kennedy M (2002) The global positioning system and GIS: an introduction, 2nd edn. Taylor & Francis, New YorkGoogle Scholar
  21. Lillesand TM, Kiefer RW, Chipman JW (2007) Remote sensing and image interpretation, 6th edn. Wiley, HobokenGoogle Scholar
  22. Madden M (2009) Manual of geographic information systems. American Society of Photogrammetry and Remote Sensing, BethesdaGoogle Scholar
  23. Mao C (1999) Hyperspectral imaging systems with digital CCD cameras for both airborne and laboratory application. In: Proceedings of 17th biennial workshop on videography and color photography in resource assessment. American Society for Photogrammetry and Remote Sensing, BethesdaGoogle Scholar
  24. Mausel PW, Everitt JH, Escobar DE, King DJ (1992) Airborne videography: current status and future perspectives. Photogramm Eng Remote Sens 58:1189–1195Google Scholar
  25. Meisner DE, Lindstrom OM (1985) Design and operation of a color-infrared aerial video system. Photogramm Eng Remote Sens 51:555–560Google Scholar
  26. Morales FJ, Jones PG (2004) The ecology and epidemiology of whitefly-transmitted viruses in Latin America. Virus Res 100:57–65PubMedCrossRefGoogle Scholar
  27. Moran MS, Inoue Y, Barnes EM (1997) Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens Environ 61:319–346CrossRefGoogle Scholar
  28. Myers VI, Bauer ME, Gausman HW, Hart WG, Heilman JL, McDonald RB, Park AB, Ryerson RA, Schmugge TJ, Westin FC (1983) Remote sensing in agriculture. In: Colwell RN (ed.) Manual of remote sensing. American Society of Photogrammetry, Falls ChurchGoogle Scholar
  29. Myhre RJ (1992) Use of color airborne videography in the U.S. Forest Service. In: Proceedings of the Resource Technology 92. Symposium, American Society of Photogrammetry and Remote Sensing, BethesdaGoogle Scholar
  30. Nixon PR, Escobar DE, Menges RM (1985) Use of a multi-band video system for quick ­assessment of vegetal condition and discrimination of plant species. Remote Sens Environ 17:203–208CrossRefGoogle Scholar
  31. Nuessly GS, Meyerdirk DE, Hart WG, Davis MR (1987) Evaluation of color-infrared aerial ­photography as a tool for the identification of sweetpotato whitefly induced fungal and viral infestations of cotton and lettuce. In: Proceedings of the 11th biennial workshop on color aerial photography and videography in the plant sciences and related fields, American Society of Photogrammetry and Remote Sensing, Falls ChurchGoogle Scholar
  32. Payne JA, Hart WG, Davis MR, Jones LS, Weaver DJ, Horton BD (1971) Detection of peach and pecan pests and diseases with color infrared aerial photography. In: Proceedings of the 3rd biennial workshop on color aerial photography in the plant sciences, American Society of Photogrammetry, Falls ChurchGoogle Scholar
  33. Pearson R, Mao C, Grace J (1994) Real-time airborne monitoring. Remote Sens Environ 49:304–310CrossRefGoogle Scholar
  34. Pinter PJ Jr, Hatfield JL, Schepers JS, Barnes EM, Moran MS, Daughtry CST, Upchurch DR (2003) Remote sensing for crop management. Photogramm Eng Remote Sens 69:647–664Google Scholar
  35. Reisig D, Godfrey L (2006) Remote sensing for detection of cotton aphid- (Homoptera: Aphididae) and spider mite- (Acari: Tetranychidae) infested cotton in the San Joaquin Valley. Pest Manag 35:1635–1646Google Scholar
  36. Richards JA, Jia X (2005) Remote sensing digital image analysis: an introduction, 4th edn. Springer, BerlinGoogle Scholar
  37. Richardson AJ, Summy KR, Davis MR, Gomez A, Williams DW (1993) The use of 1990 Tiger/Line™ Census files for monitoring the Rio Grande Valley cotton stalk destruction program. In: Proceedings of the application advanced information technology Symposium, StevensGoogle Scholar
  38. Riley JR (1989) Remote sensing in entomology. Annu Rev Entomol 34:247–271CrossRefGoogle Scholar
  39. Ryerson RA, Curran PJ, Stephens PR (1997) Applications: agriculture. In: Philipson WR (ed.) Manual of photographic interpretation. American Society for Photogrammetry and Remote Sensing, BethesdaGoogle Scholar
  40. Summy KR, Everitt JH, Escobar DE, Alaniz MA, Davis MR (1997) Use of airborne digital video imagery to monitor damage caused by two honeydew-excreting insects on cotton. In: Proceedings of the 16th biennial workshop on color photography and videography in resource assessment, American Society for Programmetry and Remote Sensing, BethesdaGoogle Scholar
  41. Yang C (2010) A high resolution airborne four-camera imaging system for agricultural applications. ASABE paper no. 1008856, American Society of Agricultural and Biological Engineers, St. JosephGoogle Scholar
  42. Yang C, Everitt JH (2005) Remote sensing, GPS and GIS technologies for agricultural insect pest detection. In: Liu TX, Kang L (eds.) Entomological research: progress and prospects. Science Press, BeijingGoogle Scholar
  43. Yang C, Everitt JH, Davis MR, Mao C (2003) A CCD camera-based hyperspectral imaging system for stationary and airborne applications. Geocarto Int J 18:71–80CrossRefGoogle Scholar
  44. Yang C, Fernandez CJ, Everitt JH (2010) Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosyst Eng 107:131–139CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.United States Department of Agriculture, Agricultural Research ServiceKika de la Garza Subtropical Agricultural Research CenterWeslacoUSA

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