Skip to main content

Remote Sensing for Insect Outbreak Detection and Assessment in Latin America

  • Chapter
  • First Online:
Forest Pest and Disease Management in Latin America

Abstract

Compared to the Northern Hemisphere, literature concerning remote sensing applications for insect outbreak detection and assessment is scarce in the Southern Hemisphere in general and in Latin America in particular. After a thorough literature review, we found few studies describing insect outbreaks in this part of the world, from which the case of the native moth Ormiscodes amphimone outbreaks in the Argentinian and Chilean Patagonia seems to be most relevant in Latin America. Only in Chile Ormiscodes amphimone disruptions have caused complete defoliation over 164,000 ha between 2000 and 2015 with the largest single continuous event (one growing season) accurately measured with remote sensing of about 25,000 ha. There are indications of other relevant outbreaks in Latin American countries, like the case of Thaumastocoris peregrinus attacks in Eucalyptus plantations in Brazil, but remote sensing assessments still need to be done. Potential causes of this scientific literature shortage could be that (1) there would be ongoing remote sensing applications for detecting and mapping forest pests in commercial plantations, but they would not be publicly available due to restrictions from timber companies; (2) main national and international remote sensing efforts are focused on assessing deforestation and degradation of Latin American forests (a threat especially relevant for tropical forest in the Amazon), while insect outbreaks may not be a main threat; and (3) there may be a lack of remote sensing specialists or existing specialists are not interested in insect outbreaks. We believe there is a research gap on insect outbreak detection and mapping using remote sensing in Latin America and that we have a great opportunity to fill this gap considering the large amount of open access satellite data and software.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

  • ABRAF (2012) Anuário estatístico da ABRAF 2012: ano base 2011. ABRAF, Associação Brasileira de Produtores de Florestas, Brasilia

    Google Scholar 

  • Anees A, Olivier JC, O’Rielly M et al (2013) Detecting beetle infestations in pine forests using MODIS NDVI time-series data. In: International geoscience and remote sensing symposium (IGARSS), pp 3329–3332

    Google Scholar 

  • Anjos N, Santos GP, Zanuncio JC (1987) The eucalyptus defoliator Thyrinteina arnobia Stoll 1782 (Lepidoptera: Geometridae). Boletim Tecnico, Empresa de Pesquisa Agropecuaria de Minas Gerais 25(56):1–56

    Google Scholar 

  • Babst F, Esper J, Parlow E (2010) Landsat TM/ETM+ and tree-ring based assessment of spatiotemporal patterns of the autumnal moth (Epirrita autumnata) in northernmost Fennoscandia. Remote Sens Environ 114(3):637–646

    Article  Google Scholar 

  • Barbosa P, Letourneau D, Agrawal A (2012) Insect outbreaks revisited. Wiley, Chichester

    Book  Google Scholar 

  • Baret F, Houlès V, Guérif M (2007) Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. J Exp Bot 58(4):869–880

    Article  CAS  Google Scholar 

  • Chávez RO, Estay SA, Riquelme G (2017) npphen: an R package for estimating annual phenological cycle. UACH, PUCV, Chile

    Google Scholar 

  • Chávez OR, Rocco R, Gutiérrez GÁ et al (2019) A self-calibrated non-parametric time series analysis approach for assessing insect defoliation of broad-leaved deciduous Nothofagus pumilio forests. Remote Sens 11(2):204

    Article  Google Scholar 

  • Clark RN, Roush TL (1984) Reflectance spectroscopy—quantitative analysis techniques for remote sensing applications. J Geophys Res 89:6329–6340

    Article  CAS  Google Scholar 

  • dos Santos A, Oumar Z, Arnhold A et al (2017) Multispectral characterization, prediction and mapping of Thaumastocoris peregrinus (Hemiptera: Thamascoridae) attack in Eucalyptus plantations using remote sensing. J Spat Sci 62(1):127–137

    Google Scholar 

  • Estay SA, Chávez RO (2018) npphen: an R-package for non-parametric reconstruction of vegetation phenology and anomaly detection using remote sensing. BioRxiv 301143

    Google Scholar 

  • Estay SA, Chávez RO, Rocco R et al (2019) Quantifying massive outbreaks of the defoliator moth Ormiscodes amphimone in deciduous Nothofagus-dominated southern forests using remote sensing time series analysis. J Appl Entomol 143(7):787–796

    Article  Google Scholar 

  • Fassnacht FE, Latifi H, Ghosh A et al (2014) Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sens Environ 140:533–548

    Article  Google Scholar 

  • Fuchs R, Brown C, Cossar F et al (2019) US-China trade war imperils Amazon rainforest. Nature 567:451

    Article  CAS  Google Scholar 

  • Gara RI (1990) Defoliation of an Ecuadorian mangrove forest by the bagworm, Oiketicus kirbyi Guilding (Lepidoptera: Psychidae). J Trop For Sci 3(2):181–186

    Google Scholar 

  • Garreaud R, Lopez P, Minvielle M et al (2013) Large-scale control on the Patagonian climate. J Clim 26(1):215–230

    Article  Google Scholar 

  • Hall RJ, Castilla G, White JC et al (2016) Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective. Can Entomol 148(S1):S296–S356

    Article  Google Scholar 

  • Jamali S, Jönsson P, Eklundh L et al (2015) Detecting changes in vegetation trends using time series segmentation. Remote Sens Environ 156:182–195

    Article  Google Scholar 

  • Kröger M (2014) The political economy of global tree plantation expansion: a review. J Peasant Stud 41(2):235–261

    Article  Google Scholar 

  • Lausch A, Heurich M, Gordalla D et al (2013) Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales. For Ecol Manag 308:76–89

    Article  Google Scholar 

  • Paritsis J, Veblen TT, Smith JM et al (2011) Spatial prediction of caterpillar (Ormiscodes) defoliation in Patagonian Nothofagus forests. Landsc Ecol 26(6):791–803

    Article  Google Scholar 

  • Rullan-Silva CD, Olthoff AE, Delgado de la Mata JA et al (2013) Remote monitoring of forest insect defoliation: a review. For Syst 22(3):377–391

    Google Scholar 

  • Sakamoto T, Gitelson AA, Arkebauer TJ (2014) Near real-time prediction of US corn yields based on time-series MODIS data. Remote Sens Environ 147:219–231

    Article  Google Scholar 

  • Senf C, Seidl R, Hostert P (2017) Remote sensing of forest insect disturbances: current state and future directions. Int J Appl Earth Obs Geoinf 60:49–60

    Article  Google Scholar 

  • Shendryk I, Broich M, Tulbure MG et al (2016) Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: a case study for a floodplain Eucalyptus forest. Remote Sens Environ 187:202–217

    Article  Google Scholar 

  • Slaton MR, Hunt ER Jr, Smith WK (2001) Estimating near-infrared leaf reflectance from leaf structural characteristics. Am J Bot 88(2):278–284

    Article  CAS  Google Scholar 

  • SM (2019) Inventario nacional de plantaciones forestales por superficie. Secretaría de Modernización (SM): Presidencia de la Nación, Argentina

    Google Scholar 

  • Solberg S, Næsset E, Hanssen KH et al (2006) Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sens Environ 102(3–4):364–376

    Article  Google Scholar 

  • Spruce JP, Sader S, Ryan RE et al (2011) Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens Environ 115(2):427–437

    Article  Google Scholar 

  • Stone C, Mohammed C (2017) Application of remote sensing technologies for assessing planted forests damaged by insect pests and fungal pathogens: a review. Curr For Rep 3(2):75–92

    Google Scholar 

  • Tang X, Bullock EL, Olofsson P et al (2019) Near real-time monitoring of tropical forest disturbance: new algorithms and assessment framework. Remote Sens Environ 224:202–218

    Article  Google Scholar 

  • Thomas JR, Gausman HW (1977) Leaf reflectance vs leaf chlorophyll and carotenoid concentrations for eight crops. Agron J 69(5):799–802

    Article  CAS  Google Scholar 

  • Townsend PA, Singh A, Foster JR et al (2012) A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sens Environ 119:255–265

    Article  Google Scholar 

  • Vastaranta M, Kantola T, Lyytikäinen-Saarenmaa P et al (2013) Area-based mapping of defoliation of scots pine stands using airborne scanning LiDAR. Remote Sens 5(3):1220–1234

    Article  Google Scholar 

  • Verbesselt J, Hyndman R, Newnham G et al (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 114(1):106–115

    Article  Google Scholar 

  • Wilcken C, Soliman E, De Sá L et al (2010) Bronze bug Thaumastocoris peregrinus Carpintero and Dellapé (Hemiptera: Thaumastocoridae) on Eucalyptus in Brazil and its distribution. J Plant Protect Res 50(2):201–205

    Article  Google Scholar 

  • Xin Q, Olofsson P, Zhu Z et al (2013) Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sens Environ 135:234–247

    Article  Google Scholar 

  • Zamorano-Elgueta C, Rey Benayas JM, Cayuela L et al (2015) Native forest replacement by exotic plantations in southern Chile (1985–2011) and partial compensation by natural regeneration. For Ecol Manag 345:10–20

    Article  Google Scholar 

Download references

Acknowledgments

This research was funded by Fondo Nacional de Desarrollo Científico y Tecnológico of Chile, Grant Number: 1160370; CONICYT PAI Number: 82140001; Fondecyt Iniciación Grant Number: 11171046. The authors also want to thank Matías Olea for making Fig. 4.2.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roberto O. Chávez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chávez, R.O., Rocco, R. (2020). Remote Sensing for Insect Outbreak Detection and Assessment in Latin America. In: Estay, S. (eds) Forest Pest and Disease Management in Latin America. Springer, Cham. https://doi.org/10.1007/978-3-030-35143-4_4

Download citation

Publish with us

Policies and ethics