Skip to main content

Geographical Approaches for Integrated Pest Management of Arthropods in Forestry and Row Crops

  • Chapter
  • First Online:
Precision Crop Protection - the Challenge and Use of Heterogeneity

Abstract

With the proper technology and access to geographical information, it is more important to spend time developing an excellent classification scheme of a remotely sensed attribute of crop and forest vigor than to spend that time collecting multiple samples of insect counts . The ability to define zones from remote sensing images of crop or forest systems provides a vastly improved capacity to assess the sample variability of insect counts. Perspectives on defining zones from remote sensing information, including an examination of some relationships between these zones and insect sample counts, are discussed.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
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

  • Bugayevskiy LM, Snyder JP (1995) Map projections. A reference manual. Taylor and Francis, Philadelphia

    Google Scholar 

  • Campbell PKE, Rock BN, Martin ME et al (2004) Detection of initial damage in Norway spruce canopies using hyperspectral airborne data. Int J Remote Sens 25:5557–5584

    Article  Google Scholar 

  • Campenella R (2000) Testing components toward a remote-sensing-based decision support system for cotton production. Photogramm Eng Remote Sens 66:1219–1227

    Google Scholar 

  • Carter GA (1993) Responses of leaf spectral reflectance to plant stress. Am J Bot 80:239–243

    Article  Google Scholar 

  • Carter GA, Miller RL (1994) Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sens Environ 50:295–302

    Article  Google Scholar 

  • Cibula WG, Carter GA (1992) Identification of a far-red reflectance response to ectomycorrhizae in slash pine. Int J Remote Sens 13:925–932

    Article  Google Scholar 

  • Corwin DL, Lesch SM (2003) Application of soil electrical conductivity to precision agriculture: theory, principles, and guidelines. Agron J 95:455–471

    Article  Google Scholar 

  • D’Agostino RB, Stephens MA (1986) Goodness-of-fit techniques. Marcel Dekker, New York

    Google Scholar 

  • de Smith MJ, Goodchild MF, Longley PA (2007) Geospatial analysis. A comprehensive guide to principles, techniques and software tools. Matador, Leicester

    Google Scholar 

  • Dupont JK, Campanella R, Seal MR et al (2000) Spatially variable insecticide applications through remote sensing. In Duggar P, Richter D (eds) Proceedings of the Beltwide Cotton Conferences, vol 2. National Cotton Council, Memphis

    Google Scholar 

  • Gotway CA, Bullock DG, Pierce FJ et al (1997) Experimental design issues and statistical evaluation techniques for site-specific management. In Pierce FJ, Sadler EJ (eds) The state of site-specific management for agriculture. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison

    Google Scholar 

  • Hoque E, Hutzler PJS, Hiendl H (1992) Reflectance, colour, and histological features as parameters for the early assessment of forest damages. Can J Remote Sens 18:104–110

    Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley-Interscience, Wiley, New York

    Book  Google Scholar 

  • Jackson RD, Huete AR (1991) Interpreting vegetation indices. Prev Vet Med 11:185–200

    Article  Google Scholar 

  • Jähne B (1997) Image processing for scientific applications. CRC Press, Boca Raton

    Google Scholar 

  • Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Jensen JR (2007) Remote sensing of the environment: an earth resource perspective, 2nd edn. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Kasischke ES, Goetz S, Hansen MC et al (2004) Temperate and boreal forests in remote sensing for natural resource management and environmental monitoring. In Ustin SL (ed) Manual of remote sensing, 3rd edn. Wiley, Hoboken

    Google Scholar 

  • Kerr JT, Southwood TRE, Cihlar J (2001) Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada. Proc Natl Acad Sci 98:11365–11370

    Article  PubMed  CAS  Google Scholar 

  • Liebhold AM, Rossi RE, Kemp WP (1993) Geostatistics and geographic information systems in insect ecology. Annu Rev Entomol 38:303–327

    Article  Google Scholar 

  • Littell RC, Milliken GA, Stroup WW et al (2006) SAS® system for mixed models, 2nd edn. SAS Institute Inc., Cary

    Google Scholar 

  • Long JS (1997) Regression models for categorical and limited dependent variables. Sage Publications, Thousand Oaks

    Google Scholar 

  • Lu D (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27:1297–1328

    Article  Google Scholar 

  • McCarter KS, Burris E, Milliken GA et al (2007) Specifications of a prototype software system for developing variable-rate treatment prescriptions for use in precision agriculture. In: Boyer JE (ed) Proceedings of the 19th annual Kansas State University conference on applied statistics in agriculture, Manhattan, Kansas, 27 April–1 May 1 2007

    Google Scholar 

  • McKinion JM, Jenkins JN, Willers JL, Zusmanis A (2009) Spatially variable insecticide applications for early season control of cotton insect pests. Comput Electron Agric 67:71–79

    Article  Google Scholar 

  • Milliken GA, Johnson DE (2002) Analysis of messy data. Analysis of covariance, vol. 3. Chapman & Hall/CRC Press, New York

    Google Scholar 

  • Moran MS, Inoue Y, Barnes EM (1997) Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens Environ 61:319–346

    Article  Google Scholar 

  • Parresol BR (1999) Assessing tree and stand biomass: a review with examples and critical comparisons. For Sci 45:573–593

    Google Scholar 

  • Pinter PJ, Hatfield JL, Scheppers JS, Barnes EM, Moran MS, Daughtry CST (2003) Remote sensing for crop management. Photogramm Eng Remote Sens 69:647–664

    Google Scholar 

  • Pontius J, Hallett R, Martin M (2005a) Assessing hemlock decline using visible and near-infrared spectroscopy: indices comparison and algorithm development. Appl Spectrosc 59:836–843

    Article  PubMed  CAS  Google Scholar 

  • Pontius J, Hallett R, Martin M (2005b) Using AVIRIS to assess hemlock abundance and early decline in the Catskills, New York. Remote Sens Environ 97:163–173

    Article  Google Scholar 

  • Pouncey R, Swanson K, Hart K (eds) (1999) ERDAS field guide, 5th edn. ERDAS, Atlanta

    Google Scholar 

  • Richards JA, Jia X (1999) Remote sensing digital image analysis. An introduction, 3rd edn. Springer, Berlin

    Book  Google Scholar 

  • Riggins JJ, Tullis JA, Stephen FM (2009) Per-segment aboveground forest biomass estimation using LIDAR-derived height percentile statistics. GIScience Remote Sens 46:232–248

    Article  Google Scholar 

  • Riley JR (1989) Remote sensing in entomology. Ann Rev Entomol 34:247–271

    Article  Google Scholar 

  • Sampson PH, Zarco-Tejada PJ, Mohammed GH, Miller JR, Noland TL (2003) Hyperspectral remote sensing of forest condition: estimating chlorophyll content in tolerant hardwoods. For Sci 49:381–391

    Google Scholar 

  • SAS Institute Inc (2008) SAS/ETS® 9.2 User’s guide. Chapter 10. The COUNTREG Procedure. SAS Institute Inc, Cary

    Google Scholar 

  • Seelan SK, Laguette S, Sasady GM, Seielstad GA (2003) Remote sensing applications for precision agriculture: a learning community approach. Remote Sens Environ 88:157–169

    Article  Google Scholar 

  • Shaw DR, Willers JL (2006) Improving pest management with remote sensing. Outlooks Pest Manag 17:197–201

    Article  Google Scholar 

  • Smith HA, McSorley R (2000) Intercropping and pest management: a review of major concepts. Am Entomol 46:154–161

    Google Scholar 

  • Stefansson V (1947) Great adventures and explorations. The Dial Press, New York

    Google Scholar 

  • Stern VM, Smith RF, van den Bosch R, Hagen KS (1959) The integrated control concept. Hilgardia 9:81–101

    Google Scholar 

  • Strahler A (1980) The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sens Environ 10:135–163

    Article  Google Scholar 

  • Thompson SK (1992) Sampling. Wiley-Interscience, New York

    Google Scholar 

  • White JC, Coops NC, Hilker T et al (2007) Detecting mountain pine beetle red attack damage with EO-1 HYPERION moisture indices. Int J Remote Sens 28:2111–2121

    Article  Google Scholar 

  • Willers JL, Jenkins JN, Ladner WL et al (2005) Site-specific approaches to cotton insect control. Sampling and remote sensing analysis techniques. Prec Agric 6:431–452

    Article  Google Scholar 

  • Willers JL, Jenkins JN, McKinion JM et al (2009a) Methods of analysis for georeferenced sample counts of tarnished plant bugs in cotton. Prec Agric 10:189–212

    Article  Google Scholar 

  • Willers JL, McKinion JM, Jenkins JN (2006) Remote sensing, sampling and simulation applications in analyses of insect dispersion and abundance in cotton. In Aguirre-Bravo C, Pellicane PJ, Burns DP, Draggan S (eds) Monitoring science and technology symposium: unifying knowledge for sustainability in the western hemisphere. USDA Forest Service Proceedings RMRS-P-42CD. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins. http://www.fs.fed.us/rm/pubs/rmrs_p042.html. Accessed 9 Dec 2009

    Google Scholar 

  • Willers JL, Milliken GA, Jenkins JN et al (2008) Defining the experimental unit for the design and analysis of site-specific experiments in commercial cotton fields. Agric Syst 96:237–249

    Article  Google Scholar 

  • Willers JL, Seal MR, Luttrell RG (1999) Remote sensing, line-intercept sampling for tarnished plant bugs (Heteroptera: Miridae) in Mid-South cotton. J Cotton Sci 3:160–170

    Google Scholar 

  • Willers JL, Wu J, Jenkins JN (2009b) Categorical likelihood method for combining NDVI and elevation information for cotton precision agricultural applications. Proceedings of the 5th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Groton, 28–30 July 2009

    Google Scholar 

Download references

Acknowledgements

Appreciation is expressed to Mr. Ronald E. Britton for assistance and discussion on the preparation of the manuscript. Thanks are also expressed to Mr. Kenneth Hood, Perthshire Farms, Gunnison, MS, and Paul Good and Dale Weaver, Longview Farms, Macon, MS. The efforts of the editors and anonymous reviewers are also acknowledged. Approved for publication as Journal Article No. BC-11735 of the Mississippi Agricultural and Forestry Experiment Station, Mississippi State University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeffrey L. Willers .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Willers, J.L., Riggins, J.J. (2010). Geographical Approaches for Integrated Pest Management of Arthropods in Forestry and Row Crops. In: Oerke, EC., Gerhards, R., Menz, G., Sikora, R. (eds) Precision Crop Protection - the Challenge and Use of Heterogeneity. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9277-9_12

Download citation

Publish with us

Policies and ethics