Abstract
The invasive poplar leaf miner Leucoptera sinuella (Lepidoptera: Lyonetiidae), has spread throughout poplar plantations in central Chile. We developed and validated models based on two methodologies of foliar damage estimation, and from different bands and indexes obtained from Sentinel-2 satellite images. We estimated foliar damage with field visual application of an ordinal severity scale, or with a laboratory estimation of leaf mined area with an image software (ImageJ) from a sample of leaves using an ordinal severity scale. We developed four models for the visual estimation on the field using red band and three spectral indexes, while we developed four models for the laboratory image software estimation using near infrared (NIR) band and the same three spectral indexes. Models developed from field visual estimation with red band (R2 = 0.88) and Normalized Difference Vegetation Index (NDVI) (R2 = 0.89) produced better results, as well as from image software estimation with NIR band (R2 = 0.86) and NDVI (R2 = 0.83). The field visual estimation and red band model got the best validation results, with R2 of 0.90, mean square error of 0.73, mean absolute error of 0.59, and a slope of 0.91. This model could predict the severity of foliar damage by L. sinuella in poplar plantations, representing a potentially useful monitoring tool for decision-making in the management of the poplar leaf miner in large areas of poplar plantations in central Chile.
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References
Barros-Parada W, Bergmann J, Curkovic T, Espinosa C, Fuentes-Contreras E, Guajardo J, Herrera H, Morales S, Queiroz AFO, Vidal A (2020) 3,7-Dimethylpentadecane: a Novel sex pheromone component from Leucoptera sinuella (Lepidoptera: Lyonetiidae). J Chem Ecol 46:820–829. https://doi.org/10.1007/s10886-020-01208-z
Bell GE, Howell BM, Johnson GV, Raun WR, Solie JB, Stone ML (2004) Optical sensing of turfgrass chlorophyll content and tissue nitrogen. HortSci 39(5):1130–1132. https://doi.org/10.21273/HORTSCI.39.5.1130
Boyd MA, Berner LT, Foster AC, Goetz SJ, Rogers BM, Walker XJ, Mack MC (2021) Historic declines in growth portend trembling aspen death during a contemporary leaf miner outbreak in Alaska. Ecosphere 12(6):e03569. https://doi.org/10.1002/ecs2.3569
Brockerhoff EG, Liebhold AM (2017) Ecology of forest insect invasions. Biol Invasions 19:3141–3159. https://doi.org/10.1007/s10530-017-1514-1
Charles JG, Nef L, Allegro G, Collins CM, Delplanque A, Gimenez R, Höglund S, Jiafu H, Larsson S, Luo Y, Parra P, Singh AP, Volney WJA, Augustin S (2014) Insect and other pests of poplars and willows. In: Isebrands JG, Richardson J (eds) Poplars and willows trees for society and the environment. CAB International and FAO, Wallingford, pp 459–526
Choi W-I, Kim E-S, Yun S-J, Lim J-H, Kim Y-E (2021) Quantification of one-year gypsy moth defoliation extent in Wonju, Korea, using Landsat satellite images. Forests 12:545. https://doi.org/10.3390/f12050545
Cotrozzi L (2022) Spectroscopic detection of forest diseases: a review (1970–2020). J for Res 33:21–38. https://doi.org/10.1007/s11676-021-01378-w
de Beurs KM, Townsend PA (2008) Estimating the effect of gypsy moth defoliation using MODIS. Remote Sens Environ 112(10):3983–3990. https://doi.org/10.1016/j.rse.2008.07.008
Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25–36. https://doi.org/10.1016/j.rse.2011.11.026
Franklin S, Fan H, Guo X (2007) Relationship between landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation. Remote Sens Lett 29(4):1215–1220. https://doi.org/10.1080/01431160701730136
Fuentes-Contreras E, Yánez-Segovia S, Guajardo G (2022) Avances en El manejo integrado de la polilla del álamo en Chile. Rev Frutic 44(2):32–38
Hall R, Castilla G, White J, Cooke B, Skakun R (2016) Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective. Can Entomol 148(S1):S296–S356. https://doi.org/10.4039/tce.2016.11
Kirichenko N, Augustin S, Kenis M (2019) Invasive leafminers on woody plants: a global review of pathways, impact, and management. J Pest Sci 92:93–106. https://doi.org/10.1007/s10340-018-1009-6
Lee HS, Lee KS (2019) Multi-temporal analysis of high-resolution satellite images for detecting and monitoring canopy decline by pine pitch canker. Korean J Remote Sens 35(4):545–560. https://doi.org/10.7780/KJRS.2019.35.4.5
Louis J, Debaecker V, Pflug B, Main-Knorn M, Bieniarz J, Mueller-Wilm U, Cadau E, Gascon F (2016) Sentinel-2 SEN2COR: L2A processor for users. SP-, vol 740. European Space Agency (Special Publication) ESA SP, pp 9–13. August
Mayer DG, Butler DG (1993) Statistical validation. Ecol Model 68(1–2):21–32. https://doi.org/10.1016/0304-3800(93)90105-2
Pangga IB, Hanan J, Chakraborty S (2013) Climate change impacts on plant canopy architecture: implications for pest and pathogen management. Eur J Plant Pathol 135(3):595–610. https://doi.org/10.1007/s10658-012-0118-y
Pasquarella VJ, Elkinton JS, Bradley BA (2018) Extensive gypsy moth defoliation in Southern New England characterized using Landsat satellite observations. Biol Invasions 20:3047–3053. https://doi.org/10.1007/s10530-018-1778-0
Rahimzadeh-Bajgiran P, Weiskittel AR, Kneeshaw D, MacLean DA (2018) Detection of annual spruce budworm defoliation and severity classification using landsat imagery. Forests 9(6):357. https://doi.org/10.3390/f9060357
Rullan-Silva C, Olthoff AE, de la Delgado JA, Pajares-Alonso JA (2013) Remote monitoring of forest insect defoliation – a review. For Syst 22:377–391. https://doi.org/10.5424/fs/2013223-04417
San Blas G, Quiroga V, Holgado M (2022) Detección de la polilla del álamo, Leucoptera sinuella (Lepidoptera: Lyonetiidae), en Argentina. Rev Soc Entomol Argent 81(1): 79–82. https://doi.org/1025085/rsea.810108
Sandoval A, Ide S, Rothmann S, Zuñiga E, Bosch P, Peragallo M (2019) Detección de Leucoptera sinuella (Reutti) (Lepidoptera: Lyonetiidae) en Chile, con la identificación de algunos parasitoides asociados. Rev Chil Entomol 45(1): 65–77. https://www.biotaxa.org/rce/article/view/46595 Accessed 26 December 2022
Sangüesa-Barreda G, Camarero JJ, García-Martín A, Hernández R, De la Riva J (2014) Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. For Ecol Manag 320:171–181. https://doi.org/10.1016/j.foreco.2014.03.008
Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675. https://doi.org/10.1038/nmeth.2089
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. https://doi.org/10.1016/j.jag.2017.04.004
Simler-Williamson AB, Rizzo DM, Cobb RC (2019) Interacting effects of global change on forest pest and pathogen dynamics. Annu Rev Ecol Evol Syst 50(1):381–403. https://doi.org/10.1146/annurev-ecolsys-110218-024934
Simović I, Šikoparija B, Panić M, Radulović M, Lugonja P (2022) Remote sensing of poplar phenophase and leaf miner attack in urban forests. Remote Sens 14:6331. https://doi.org/10.3390/rs14246331
Spruce J, Sader S, Ryan R, Smoot J, Kuper P, Ross K, Prados D, Russell J, Gasser G, McKellip R, Hargrove W (2011) Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens Environ 115(2):427–437. https://doi.org/10.1016/j.rse.2010.09.013
Thomas S, Deschamps A, Landry R, van der Sanden JJ, Hall RJ (2007) Mapping insect defoliation using multi-temporal Landsat data. Proceedings: Our Common Borders–Safety, Security, and the Environment through Remote Sensing. CRSS/ASPRS 2007. https://d1ied5g1xfgpx8.cloudfront.net/pdfs/27754.pdf. Accessed 26 Dec 2022
Thomas S, Kuska MT, Bohnenkamp D, Brugger A, Alisaac E, Wahabzada M, Behmann J, Mahlein AK (2018) Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J Plant Dis Prot 125(1):5–20. https://doi.org/10.1007/s41348-017-0124-6
Townsend PA, Singh A, Foster JR, Rehberg NJ, Kingdon CC, Eshleman KN, Seagle SW (2012) A general landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sens Environ 119:255–265. https://doi.org/10.1016/j.rse.2011.12.023
Vilela EF, Ferreira WPM, Castro GDMD, Faria ALRD, Leite DH, Lima IA, Matos CDSMD, Silva RA, Venzon M (2023) New spectral index and machine learning models for detecting coffee leaf miner infestation using Sentinel-2 multispectral imagery. Agriculture 13:388. https://doi.org/10.3390/agriculture13020388
Vogelmann J, Tolk B, Zhu Z (2009) Monitoring forest changes in the southwestern United States using multitemporal landsat data. Remote Sens Environ 113:1739–1748. https://doi.org/10.1016/j.rse.2009.04.014
Yánez-Segovia S, Ramírez CC, Lindroth RL, Fuentes-Contreras E (2023) Resistance against Leucoptera sinuella (Lepidoptera: Lyonetiidae), among hybrid clones of Populus spp. in central Chile. J Econ Entomol 116:16621670. https://doi.org/10.1093/jee/toad129
Zhang J, Huang Y, Pu R, Gonzalez-Moreno P, Yuan L, Wu K, Huang W (2019) Monitoring plant diseases and pests through remote sensing technology: a review. Comput Electron Agric 165:104943. https://doi.org/10.1016/j.compag.2019.104943
Acknowledgements
This project was funded by Fondo de Innovación para la Competitividad de la Región de O´Higgins (FIC-O’Higgins grant IDI 40008896-0) and the National Research and Development Agency in Chile (ANID) project number 7819I320003. This research could be carried out thanks to the support of the Compañía Agrícola y Forestal el Álamo (CAF), who provided everything necessary to correctly carry out the measurements.
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P.C: Conceived the ideas and designed methodology, Did the model development and validation, Writing of the manuscript. D.F: Review and editing. S.Y: Collected the data, Review and editing. J. G: Collected the data, Review and editing. J.V: Conceptualization, Funding acquisition, Review and editing. F.Z: Conceptualization, Funding acquisition, Review and editing. C.E: Funding acquisition, Review and editing. J.U: Collected the data, Review and editing. E.F: Conceptualization, Writing of the manuscript, Funding acquisition.
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Cañete-Salinas, P., de la Fuente-Sáiz, D., Yánez-Segovia, S. et al. Use of satellite images to monitor Leucoptera sinuella leaf damage in poplar plantations in central Chile. New Forests (2024). https://doi.org/10.1007/s11056-024-10029-x
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DOI: https://doi.org/10.1007/s11056-024-10029-x