Remote Sensing for Insect Outbreak Detection and Assessment in Latin America

  • Roberto O. ChávezEmail author
  • Ronald Rocco


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.


Open-access Satellite imagery Early alerts Technological challenges Pest management opportunities 



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.


  1. ABRAF (2012) Anuário estatístico da ABRAF 2012: ano base 2011. ABRAF, Associação Brasileira de Produtores de Florestas, BrasiliaGoogle Scholar
  2. 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 3329–3332Google Scholar
  3. 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):1–56Google Scholar
  4. 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):637–646CrossRefGoogle Scholar
  5. Barbosa P, Letourneau D, Agrawal A (2012) Insect outbreaks revisited. Wiley, ChichesterCrossRefGoogle Scholar
  6. 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):869–880CrossRefGoogle Scholar
  7. Chávez RO, Estay SA, Riquelme G (2017) npphen: an R package for estimating annual phenological cycle. UACH, PUCV, ChileGoogle Scholar
  8. 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):204CrossRefGoogle Scholar
  9. Clark RN, Roush TL (1984) Reflectance spectroscopy—quantitative analysis techniques for remote sensing applications. J Geophys Res 89:6329–6340CrossRefGoogle Scholar
  10. 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):127–137Google Scholar
  11. Estay SA, Chávez RO (2018) npphen: an R-package for non-parametric reconstruction of vegetation phenology and anomaly detection using remote sensing. BioRxiv 301143Google Scholar
  12. 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):787–796CrossRefGoogle Scholar
  13. 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 140:533–548CrossRefGoogle Scholar
  14. Fuchs R, Brown C, Cossar F et al (2019) US-China trade war imperils Amazon rainforest. Nature 567:451CrossRefGoogle Scholar
  15. Gara RI (1990) Defoliation of an Ecuadorian mangrove forest by the bagworm, Oiketicus kirbyi Guilding (Lepidoptera: Psychidae). J Trop For Sci 3(2):181–186Google Scholar
  16. Garreaud R, Lopez P, Minvielle M et al (2013) Large-scale control on the Patagonian climate. J Clim 26(1):215–230CrossRefGoogle Scholar
  17. 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–S356CrossRefGoogle Scholar
  18. Jamali S, Jönsson P, Eklundh L et al (2015) Detecting changes in vegetation trends using time series segmentation. Remote Sens Environ 156:182–195CrossRefGoogle Scholar
  19. Kröger M (2014) The political economy of global tree plantation expansion: a review. J Peasant Stud 41(2):235–261CrossRefGoogle Scholar
  20. 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 308:76–89CrossRefGoogle Scholar
  21. Paritsis J, Veblen TT, Smith JM et al (2011) Spatial prediction of caterpillar (Ormiscodes) defoliation in Patagonian Nothofagus forests. Landsc Ecol 26(6):791–803CrossRefGoogle Scholar
  22. 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):377–391Google Scholar
  23. Sakamoto T, Gitelson AA, Arkebauer TJ (2014) Near real-time prediction of US corn yields based on time-series MODIS data. Remote Sens Environ 147:219–231CrossRefGoogle Scholar
  24. Senf C, Seidl R, Hostert P (2017) Remote sensing of forest insect disturbances: current state and future directions. Int J Appl Earth Obs Geoinf 60:49–60CrossRefGoogle Scholar
  25. 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 187:202–217CrossRefGoogle Scholar
  26. Slaton MR, Hunt ER Jr, Smith WK (2001) Estimating near-infrared leaf reflectance from leaf structural characteristics. Am J Bot 88(2):278–284CrossRefGoogle Scholar
  27. SM (2019) Inventario nacional de plantaciones forestales por superficie. Secretaría de Modernización (SM): Presidencia de la Nación, ArgentinaGoogle Scholar
  28. 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):364–376CrossRefGoogle Scholar
  29. 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):427–437CrossRefGoogle Scholar
  30. 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):75–92Google Scholar
  31. 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 224:202–218CrossRefGoogle Scholar
  32. Thomas JR, Gausman HW (1977) Leaf reflectance vs leaf chlorophyll and carotenoid concentrations for eight crops. Agron J 69(5):799–802CrossRefGoogle Scholar
  33. Townsend PA, Singh A, Foster JR et al (2012) A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sens Environ 119:255–265CrossRefGoogle Scholar
  34. 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):1220–1234CrossRefGoogle Scholar
  35. Verbesselt J, Hyndman R, Newnham G et al (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 114(1):106–115CrossRefGoogle Scholar
  36. 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):201–205CrossRefGoogle Scholar
  37. 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 135:234–247CrossRefGoogle Scholar
  38. 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 345:10–20CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Laboratorio de Geo-Información y Percepción RemotaInstituto de Geografía, Pontificia Universidad Católica de ValparaísoValparaísoChile

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