Monitoring Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae) in Eucalyptus plantations with remote sensing / Monitoramento de Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae) em plantios de Eucalyptus com sensoriamento remoto

Isabel Carolina de Lima Santos, Jerffersoney Garcia Costa, Anderson Melo Rosa, Alexandre dos Santos

Abstract


Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae), the eucalyptus bronze bug, is among the many species of insect pests that affect commercial eucalyptus forests in Brazil. T. peregrinus reduces the photosynthetic capacity of trees and, in some cases, can lead to the complete death of plants. The objective of this research was to evaluate the potential of medium spatial resolution images, available for free, in the mapping and prediction of attacks caused by T. peregrinus in eucalyptus plantations in Brazil, using Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA regression model selected three main components, with a cross-validation error rate of 0.245 for the prediction and mapping of stands attacked by T. peregrinus. The important bands were selected from the PLS-DA model, using variable importance in the projection (VIP). The VIP bands predicted healthy and attacked stands with an accuracy of 97.7% in an independent validation dataset. This study demonstrates the potential of medium spatial resolution images as a viable alternative to successfully characterize and map T. peregrinus attacks in planted forests in Brazil.


Keywords


Insects pests; medium resolution images; multivariate analysis; Partial Least Squares Discriminant Analysis.

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Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, n. 6, p. 711-728, 2013. doi: 10.1127/0941-2948/2013/0507

Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecology, v. 26, n. 1, p. 32–46, 2001. doi: 10.1111/j.1442-9993.2001.01070.pp.x

Bivand R, Keitt T, Rowlingson B. rgdal: Bindings for the geospatial data abstraction library. R package version 0.8-16, 2014. http://CRAN.R-project.org/ package=rgdal

Bonneau LR, Shields KS, Civco DL. A technique to identify changes in hemlock forest health over space and time using satellite image data. Biological Invasions, v. 1, n. 2-3, p. 269-279, 1999. doi: 10.1023/A:1010081832761

Chander G, Markham BL, Barsi JA. Revised Landsat-5 Thematic Mapper radiometric calibration. Ieee Geoscience and Remote Sensing Letters, v. 4, n. 3, p. 490-494, 2007. doi: 10.1109/LGRS.2007.898285

Chander G, Markham BL, Helder DL. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, v. 113, n. 5, p. 893-903, 2009. doi: 10.1016/j.rse.2009.01.007

Cohen WB, Goward SN. Landsat's role in ecological applications of remote sensing. BioScience, v. 54, n. 6, p. 535-545. doi: 10.1641/0006-3568(2004)054[0535:LRIEAO] 2.0.CO;2

De Beurs KM, Townsend PA. Estimating the effect of gypsy moth defoliation using MODIS. Remote Sensing of Environment, v. 112, n. 10, 3983–3990, 2008. doi: 10.1016/j.rse.2008. 07.008

Dejean S, Gonzalez I, Cao KAL. mixOmics: Omics data integration project. R package version 5.0-1, 2013. http://CRAN.R-project.org/package=mixOmics

Ennouri, K., & Kallel, A. Remote sensing: An advanced technique for crop condition assessment. Mathematical Problems in Engineering, v. 2019, p. 1–8, 2019. doi:10.1155/2019/9404565

Geladi P, Kowalski BR. Partial least-squares regression: A tutorial. Analytica Chimica Acta, v. 185, n. 1, 1 –17, 1986. doi: 10.1016/0003-2670(86)80028-9

Filella I, Peñuelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, v. 15, n. 7, 1459-1470, 1994. doi: 10.1080/01431169408954177

Fraser RH, Latifovic R. Mapping insect-induced tree defoliation and mortality using coarse spatial resolution imagery. International Journal of Remote Sensing, v. 26, n. 1, p. 193-200, 2005. doi: 10.1080/01431160410001716923

Goslee SC. Analyzing remote sensing data in R: The Landsat package. Journal of Statistical Software, v. 43, n. 4, 1-25, 2011. doi:b10.18637/jss.v043.i04

Pinter Jr. PJ, Hatfield JL, Schepers JS, Barnes EM, Moran MS, Daughtry CST, Upchurch DR. Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, v. 69, n. 6, p. 647–664, 2003.

Hijmans RJ. raster: Geographic data analysis and modeling. R package version 2.2-31, 2014. http://CRAN.R-project.org/package=raster

Horler DNH, Barber J, Barringer AR. Effects of heavy metals on the absorbance and reflectance spectra of plants. International Journal of Remote Sensing, v. 1, n. 2, p. 121-136, 1980. doi: 10.1080/01431168008547550

Mevik BH, Cederkvist HR. Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR). Journal of Chemometrics, v. 18, n. 9, p. 422-429, 2004. doi: 10.1002/cem.887

Mevik BH, Wehrens R. The pls package: Principle component and partial least-squares regression in R. Journal of Statistical Software, v. 18, n. 2, 1-24, 2007. doi: 10.18637/ jss.v018.i02

Nadel RL, Noack AE. Current understanding of the biology of Thaumastocoris peregrinus in the quest for a management strategy. International Journal of Pest Management, v. 58, n. 3, p. 257-266, 2012. doi: 10.1080/09670874.2012.659228

Nadel RL, Slippers B, Scholes MC, Lawson SA, Noack AE, Wilcken CF, Bouvet JP, Wingfield MJ. DNA bar-coding reveals source and patterns of Thaumastocoris peregrinus invasions in South Africa and South America. Biological Invasions, v. 12, n. 5, p. 1067-1077, 2010. doi: 10.1007/s10530-009-9524-2

Peñuelas J, Filella I. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, v. 3, n. 4, 151-156, 1998. doi: 10.1016/S1360 -1385(98)01213-8

Pérez-Enciso M, Tenenhaus M. Prediction of clinical outcome with microarray data: A partial least squares discriminant analysis (PLS-DA) approach. Human Genetics, v. 112, n. 5-6, p. 581-592, 2003. doi: 10.1007/s00439-003-0921-9

Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H. vegan: Community Ecology. R package version 2.3-2, 2015.

Perumal K, Bhaskaran R. Supervised classification performance of multispectral images. Journal of Computing, v. 2, n.2, p. 124-129, 2010.

Oumar Z, Mutanga O. Using WorldView-2 bands and indices to predict bronze bug (Thaumastocoris peregrinus) damage in plantation forests. International Journal of Remote Sensing, v. 34, n. 6, 2236-2249, 2012. doi: 10.1080/01431161.2012.743694

Oumar Z, Mutanga O, Ismail R. Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor. International Journal of Applied Earth Observation and Geoinformation, v. 21, n. 1, p. 113-121, 2013. doi: 10.1016/j.jag.2012.08.006

R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2010.

Radeloff VC, Mladenoff DJ, Boyce MS. Detecting jack pine budworm defoliation using spectral mixture analysis: separating effects from determinants. Remote Sensing of Environment, v. 69, n. 2, 156–169, 1999. doi: 10.1016/S0034-4257(99)00008-5

Rock BN, Vogelmann JE, Williams DL, Vogelmann AF, Hoshizaki T. Remote detection of forest damage. BioScience, v. 36, n. 7, p. 439-445, 1986. doi: 10.2307/1310339

Rodríguez-Cuenca B, García-Cortés S, Ordóñez C, Alonso MC. Automatic detection and classification of pole-like objects in urban point cloud data using an anomaly detection algorithm. Remote Sensing, v. 7, n. 10, p. 12680-12703, 2015. doi: 10.3390/rs71012680

Rowlingson B, Diggle P. splancs: Spatial and space-time point pattern analysis. R package version 2.01-34, 2013. http://CRAN.R-project.org/package=splancs

Santos A, Oumar Z, Arnhold A, Silva N, Silva CO, Zanetti R. Multispectral characterization, prediction and mapping of Thaumastocoris peregrinus (Hemiptera: Thamascoridae) attack in Eucalyptus plantations using remote sensing. Journal of Spatial Science, v. 62, n. 1, p. 127-137, 2017. doi: 10.1080/14498596.2016.1220873

Singh A. Review article digital change detection techniques using remotely sensed data. International Journal of Remote Sensing, v. 10, n. 6, p. 989-1003, 1989. doi: 10.1080/ 01431168908903939

Soliman EP, Wilcken CF, Pereira JM, Dias TKR, Zache B, Dal Pogetto MHFA, Barbosa LR. Biology of Thaumastocoris peregrinus in different eucalyptus species and hybrids. Phytoparasitica v. 40, p. 223-230, 2012. doi: 10.1007/s12600-012-0226-4

Stabler B. shapefiles: Read and write ESRI shapefiles. R package version 0.7, 2013. http://CRAN.R-project.org/package=shapefiles

Teillet PM, Guindon B, Goodenough DG. On the slope-aspect correction of multi-espectral scanner data. Canadian Journal of Remote Sensing, v. 8, n. 2, p. 84-106, 1982. doi: 10.1080/07038992.1982.10855028

Tenenhaus M. La régression PLS: théorie et pratique. Paris: Editions Technip, 1998. 254p.

Townsend PA, Singh A, Foster JR, Rehberg NJ, Kingdon CC, Eshleman KN, Seagle SW. A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sensing of Environment, v. 119, n. 1, p. 55-265, 2012. doi: 10.1016/j.rse.2011.12.023

Transon J, d’Andrimont R, Maugnard A, Defourny P. Survey of hyperspectral earth observation applications from space in the Sentinel-2 context. Remote Sensing, v. 10, n. 2, p. 1-32, 2018. doi: 10.3390/rs10020157.

Vogelmann JE, Rock BN. Use of Thematic Mapper data for the detection of forest damage caused by the pear thrips. Remote Sensing of Environment, v. 30, n. 3, p. 217–225, 1989. doi: 10.1016/0034-4257(89)90063-1

Vygodskaya NN, Gorshkova I, Fadeyeva YV. Theoretical estimates of sensitivity in some vegetation indices to variation in the canopy condition. International Journal of Remote Sensing, v. 10, n. 12, p. 1857-1872, 1989. doi: 10.1080/01431168908904016

Wilcken CF, Soliman EP, Sá LAN, Barbosa LR, Dias TKR, Ferreira Filho PJ, Oliveira RJR. Bronze bug Thaumastocoris peregrinus Carpintero and Dellapé (Hemiptera: Thaumastocoridae) on Eucalyptus in Brazil and its distribution. Journal of Plant Protection Research, v. 50, n.2, p. 201-205, 2010.

Ye X, Sakai K, Sasao A, Asada S. Potential of airborne hyperspectral imagery to estimate fruit yield in citrus. Chemometrics and Intelligent Laboratory Systems, v. 90, n. 2, p. 132-144, 2008. doi: 10.1016/j.chemolab.2007.09.002

Zheng G, Moskal LM. Retrieving leaf area index (LAI) using remote sensing: Theories, methods and sensors. Sensors, v. 9, n. 4, 2719-2745, 2009. doi: 10.3390/s90402719




DOI: https://doi.org/10.34117/bjdv6n4-096

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