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Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing

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Abstract

Defoliation by leaf-cutting ants alters the physiological processes of plants, and this defoliation can be inferred from satellite imagery used to identify plant injuries. The aim of this study was to evaluate the spectral pattern of defoliation by leaf-cutting ants in eucalyptus plants on a pixel level using unsupervised machine learning techniques applied to remote sensing by satellites. The study was carried out in a eucalyptus plantation in the municipality of Telêmaco Borba, Paraná state, Brazil. The nests of leaf-cutting ants were located and georeferenced. Multispectral images were obtained from the Sentinel-2 (S-2) and planet scope (PS) satellites. The response variables were the RGB-NIR bands and four vegetation indices (VIs). The data obtained from these bands and vegetation indices was separated in an unsupervised method by the k-medoids clustering algorithm and input into a Random Forest (RF) model. The significance of the models was tested with permutational multivariate analysis of variance (PERMANOVA). The k-medoids algorithm classified the spectral response of the RGB-NIR and VIs bands into two main factors of variation in the tree canopy. The models selected were 1200 trees and 6 variables for the S2 satellite (accuracy = 97.74 ± 0.040%) and 900 trees and 5 variables for the PS (accuracy = 97.42 ± 0.026%). The unsupervised machine learning technique, applied to remote sensing, was effective to map defoliation caused by leaf-cutting ants, and this approach can be used in precision agriculture for pest management purposes.

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References

  • Adam, E., Mutanga, O., Odindi, J., & Abdel-Rahman, E. M. (2014). Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, 35, 3440–3458. doi:https://doi.org/10.1080/01431161.2014.903435

    Article  Google Scholar 

  • Almeida, K. E. C., Silva, J. G. S., Silva, I. M. A., Costa, A. L., & Laia, M. L. (2018). Ecophysiological analysis of Eucalyptus camaldulensis (Dehnh) submitted to attack from Thaumastocoris peregrinus. Revista Árvore, 42, e420120. https://doi.org/10.1590/1806-90882018000100020. Carpintero & Dellape

    Article  Google Scholar 

  • Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. M., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22, 711–728. https://doi.org/10.1127/0941-2948/2013/0507

    Article  Google Scholar 

  • Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26, 32–46. https://doi.org/10.1111/j.1442-9993.200101070.pp.x

    Article  Google Scholar 

  • Baloloy, A. B., Blanco, A. C., Candido, C. G., Argamosa, R. J. L., Dumalag, J. B. L. C., Dimapilis, L. L. C., & Paringit, E. C. (2018). Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: Rapideye, Planetscope and Sentinel-2. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3, 29–36. https://doi.org/10.5194/isprs-annals-IV-3-29-2018

  • Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

    Article  Google Scholar 

  • Bolyn, C., Michez, A., Gaucher, P., Lejeune, P., & Bonnet, S. (2018). Forest mapping and species composition using supervised per pixel classification of Sentinel-2 imagery. Biotechnology, Agronomy and Society and Environment, 22, 172–187. https://doi.org/10.25518/1780-4507. 16524

    Article  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A. : 1010933404324

    Article  Google Scholar 

  • Chemura, A., Mutanga, O., & Dube, T. (2017). Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions. Precision Agriculture, 18, 859–881. https://doi.org/10.1007/s11119-016-9495-0

    Article  Google Scholar 

  • Clevers, J. G. P. W., Kooistra, L., & Van den Brande, M. M. M. (2017). Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sensing, 9, 405. https://doi.org/10.3390/rs9050405

    Article  Google Scholar 

  • Costa, A. N., Vasconcelos, H. L., Vieira-Neto, E. H. M., & Bruna, E. M. (2008). Do herbivores exert top-down effects in Neotropical savannas? Estimates of biomass consumption by leaf-cutter ants. Journal of International Association of Vegetation Science, 19, 849–854. https://doi.org/10.3170/2008-8-18 461

    Article  Google Scholar 

  • Della Lucia, T. M. C., Gandra, L. C., & Guedes, R. N. C. (2014). Managing leaf-cutting ants: peculiarities, trends and challenges. Pest Management Science, 70, 14–23. https://doi.org/10. 1002/ps.3660

    Article  PubMed  CAS  Google Scholar 

  • Dietz, E. J. (1983). Permutation tests for association between two distance matrices. Systematic Zoology, 32, 21–26. https://doi.org/10.3170/10.2307/241321

    Article  Google Scholar 

  • Du, Y., Zhang, Y., Feng, L., Wang, Q., Wenbo, L., & Xiaodong, L. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water indices at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8, 354. https://doi.org/10.3390/rs8040354

    Article  Google Scholar 

  • ESA (European Space Agency). (2020). Copernicus Scientific Data Hub. https://scihubcopernicus.eu/dhus

  • FSC (Forest Stewardship Council) (2020). List of approved derogations for use of ‘highly hazardous’ pesticides. FSC-PRO-30-001a EN. https://ic.fsc.org/file-download.list-of-approved-derogations-for-use-of-highly-hazardous-pesticides.a-2651.pdf

  • Grabska, E., Hostert, P., Pflugmacher, D., & Ostapowicz, K. (2019). Forest stand species mapping using the Sentinel-2 time series. Remote Sensing, 11, 1197. https://doi.org/10.3390/rs11101197

    Article  Google Scholar 

  • Han, H., Guo, X., & Yu, H. (2016). Variable selection using mean decrease accuracy and mean decrease gini based on random forest. In 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), 219–224, Beijing. https://doi.org/10.1109/ICSESS.2016.7883053

  • Hawryło, P., Bednarz, B., Wężyk, P., & Szostak, M. (2018). Estimating defoliation of scots pine stands using machine learning methods and vegetation indices of Sentinel-2. European Journal of Remote Sensing, 51, 194–204. https://doi.org/10.1080/22797254.2017.1417745

    Article  Google Scholar 

  • Huang, Z., & Zhang, Y. (2016). Remote sensing of spruce budworm defoliation using EO-1mHyperion hyperspectral data: an example in Quebec, Canada. IOP Conference Series: Earth and Environmental Science, 34, e012017. https://doi.org/10.1088/1755-1315/34/1/012017

  • IBÁ (Indústria Brasileira de Árvores) (2019). Relatório 2019. https://iba.org/datafiles/publicacoes/relatórios/iba-relatorioanual2019.pdf

  • Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 8, 166–192. https://doi.org/10.1590/S1982-56762010000200004

    Article  Google Scholar 

  • Isering, R., & Neumeister, L. (2010). Recommendations regarding derogations to use alpha-cypermetrin, deltametrin, fenitrothion, fipronil and sulfluramid in FSC Certified forests in Brazil. Insecticides for Control of Pest Insects in FSC Certified Forests in Brazil: Recommendations by Technical Advisors. FSC Pesticides Committee

  • Johnson, B. R., McGlinchy, J., Cattau, M. E., Joseph, M. B., & Scholl, V. (2018). Harnessing commercial satellite technologies to monitor our forests. In Proceedings Volume 10767, Remote Sensing and Modelling of Ecosystems for Sustainability XV, 1076702. San Diego, California, USA. https://doi.org/10.1117/12. 2321648

  • Jones, H. G., & Schofield, P. N. (2008). Thermal and other remote sensing on plant stress. General and Applied Plant Physiology, 34, 19–32

    Google Scholar 

  • Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. Wiley

  • Khatami, R., Mountrakis, G., & Stehman, S. V. (2017). Mapping per-pixel predicted accuracy of classified remote sensing images. Remote Sensing of Environment, 191, 156–167. https://doi.org/10.1016/j.rse.2017.01.025

    Article  Google Scholar 

  • Keshavarzi, A., Sarmadian, F., Omran, E. S. E., & Iqbal, M. (2015). A neural network model for estimating soil phosphorus using terrain analysis. The Egyptian Journal of Remote Sensing and Space Science, 18, 127–135. https://doi.org/10.1016/j.ejrs. 2015.06.004

    Article  Google Scholar 

  • Langford, Z., Kumar, J., Hoffman, F., Breen, A., & Iversen, C. (2019). Arctic vegetation mapping using unsupervised training datasets and convolutional neural networks. Remote Sensing, 11, 69. https://doi.org/10.3390/rs11010069

    Article  Google Scholar 

  • Lasaponara, R., Tucci, B., & Ghermandi, L. (2018). On the use of satellite Sentinel 2 data for automatic mapping of burnt areas and burn severity. Sustainability, 10, 3889. https://doi.org/10.3390/su10113889

    Article  Google Scholar 

  • Lastovicka, J., Svec, P., Paluba, D., Kobliuk, N., Svoboda, J., Hladky, H., & Stych, P. (2020). Sentinel-2 data in an evaluation of the impact of the disturbances on forest vegetation. Remote Sensing, 12, 1914. https://doi.org/10.3390/rs12121914

  • Matrangolo, C. A. R., Castro, R. V. O., Della Lucia, T. M. C., Della Lucia, R. M., Mendes, A. F. N., Costa, J. M. F. N., & Leite, H. G. (2010). Crescimento de eucalipto sob efeito de desfolhamento artificial. Pesquisa Agropecuária Brasileira, 45, 952–957. https://doi.org/10.1590/S0100-204x2010000900003

    Article  Google Scholar 

  • Martinez, A. P. (2020). pairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4. https://github.com/pmartinezarbizu/pairwiseAdonis

  • Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., & Ramon, H. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 11, 75–83. https://doi.org/10.1016/j.rti.2005.03.003

    Article  Google Scholar 

  • Mudereri, B. T., Dube, T., Adel-Rahman, E. M., Niassy, S., Kimathi, E., Khan, Z., & Landmann, T. (2019). A comparative analysis of Planetscope and Sentinel Sentinel-2 space-borne sensors in mapping striga weed using guided regularised random forest classification ensemble. International Archives of the Photogrammetry, -2/W13, 701–708. https://doi.org/10.5194/isprs-archives-XLII-2-W13-701-2019. Remote Sensing and Spatial Information Sciences XLII

    Article  Google Scholar 

  • Mutanga, O., Dube, T., & Galal, O. (2017). Remote sensing of crop health for food security in Africa: Potentials and constraints. Remote Sensing Applications: Society and Environment, 8, 231–239. https://doi.org/10.1016/j.rsase.2017.10.004

    Article  Google Scholar 

  • Nomura, K., & Mitchard, E. (2018). More than meets the eye: using Sentinel-2 to map small plantations in complex forest landscapes. Remote Sensing, 10, 1693. https://doi.org/10.3390/rs10111693

    Article  Google Scholar 

  • Oliveira, M. A., Della Lucia, T. M. C., Della Lucia, R. M., Anjos, N., Araujo, M. S., & Leite, B. S. (2014). The simulated effect of in the growth of the Eucalyptus grandis. Chemical Engineering Transactions, 39, 1543–1548. https://doi.org/10.3303/CET1439258

    Article  Google Scholar 

  • Park, H. H., & Jun, C. H. (2009). A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications, 36, 3336–3341. https://doi.org/10.1016/j.eswa.2008.01.039

    Article  Google Scholar 

  • Perumal, K., & Bhaskaran, R. (2010). Supervised classification performance of multispectral images. Journal of Computing, 2, 124–129

    Google Scholar 

  • Planet (Planet Labs Inc.) (2016). Planet imagery product specification: Planetscope & Rapideye. https://www.planet.com/products/satellite-imagery/files/1610.06_Spec%20Sheet_Combined_Imagery_Product_Letter_ENGv1.pdf

  • Radoux, J., Chomé, G., Jacques, D. C., Waldner, F., Bellemans, N., Matton, N. … Defourny, P. (2016). Sentinel-2’s potential for sub-pixel landscape feature detection. Remote Sensing, 8, 488. https://doi.org/10.3390/rs8060488

    Article  Google Scholar 

  • R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • Ranghetti, L., Boschetti, M., Nutini, F., & Busetto, L. (2020). sen2r: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data. Computers & Geosciences, 139, 104473. https://doi.org/10.1016/j.cageo.2020.104473

    Article  Google Scholar 

  • Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Earth Resources Technology Satellite Symposium, NASA, 309–317. Washington

  • Sangüesa-Barreda, G., Camarero, J. J., García-Martin, A., Hernandez, R., & De La Riva, R. (2014). Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. Forest Ecology and Management, 320, 171–181. https://doi.org/10.1016/j.foreco.2014.03.008

    Article  Google Scholar 

  • Santos, A., Santos, I. C. L., Silva, N., Zanetti, R., Oumar, Z., Guimarães, L. F. R. … Zanuncio, J. C. (2020). Mapping defoliation by leaf-cutting ants Atta species in Eucalyptus plantations using the Sentinel-2 sensor. International Journal of Remote Sensing, 41, 1542–1554. https://doi.org/10.1016/10.1080/01431161.2019.1672903

    Article  Google Scholar 

  • Santos, I. C. L., Santos, A., Oumar, Z., Soares, M. A., Silva, J. C. C., Zanetti, R., & Zanuncio, J. C. (2019). Remote sensing to detect nests of the leaf-cutting ant Atta sexdens (Hymenoptera: Formicidae) in teak plantations. Remote Sensing, 11, 1641. https://doi.org/10.3390/rs11141641

    Article  Google Scholar 

  • Souza, A., Zanetti, R., & Calegario, N. (2011). Economic damage level for leaf-cutting ants in function of the productivity indices of eucalyptus plantations in an Atlantic forest region. Neotropical Entomology, 40, 483–488. https://doi.org/10.1590/S1519-566x2011000400012

    Article  PubMed  CAS  Google Scholar 

  • Shi, Y., Huang, W., Ye, H., Ruan, C., Xing, N., Geng, Y. … Peng, D. (2018). Partial least square discriminant analysis based on normalized two-stage vegetation indices for mapping damage from rice diseases using PlanetScope datasets. Sensors, 18, 1901. https://doi.org/10. 3390/s18061901

  • Song, X., Yang, C., Wu, M., Zhao, C., Yang, G., Hoffmann, W. C., & Huang, W. (2017). Evaluation of Sentinel-2A satellite imagery for mapping cotton root rot. Remote Sensing, 9, 906–923. https://doi.org/10.3390/rs9090906

    Article  Google Scholar 

  • Stephan, J. G., Wirth, R., Leal, I. R., & Meyer, S. T. (2015). Spatially heterogeneous nest-clearing behavior coincides with rain event in the leaf-cutting ant Atta cephalotes (L.) (Hymenoptera: Formicidae). Neotropical Entomology, 44, 123–128. https://doi.org/10.1007/s13744-014-0267-0

    Article  PubMed  CAS  Google Scholar 

  • Transon, J., D’Andrimont, R., Maugnard, A., & Defourny, P. (2018). Hyperspectral Earth observation applications from space in the Sentinel-2 context. Remote Sensing, 10, 157–189. https://doi.org/10.3390/rs10020157

    Article  Google Scholar 

  • Vasconcelos, H. L. (1990). Foraging activity of two species of leaf-cutting ants (Atta) in a primary forest of the Central Amazon. Insectes Sociaux, 37, 131–145. https://doi.org/10. 1007/BF02224026

    Article  Google Scholar 

  • Wang, B., Jia, K., Liang, S., Xie, X., Wei, X., Zhao, X. … Zhang, X. (2018). Assessment of Sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover. Remote Sensing, 10, 1927. https://doi.org/10.3390/rs10121927

  • Wang, F., Franco-Penya, H. H., Kelleher, J. D., Pugh, J., & Ross, R. (2017). An analysis of the application of simplified silhouette to the evaluation of k-means clustering validity. In Perner, P. (Ed.), Machine learning and data mining in pattern recognition, (1st. ed., pp. 291–305). Springer. https://doi.org/10.1007/978-3-319-62416-7_21

  • Wang, L., Huang, H., & Luo, Y. (2010). Remote sensing of insect pests in larch forest based on physical model. In IEEE International Geoscience and Remote Sensing Symposium, 3299–3302. Honolulu: Hawaii (USA). https://doi.org/10.1109/IGARSS.2010.5649528

  • 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. Computers and Electronics in Agriculture, 165, e104943. https://doi.org/10.1016/j.compag.2019.104943

    Article  Google Scholar 

  • Zanetti, R., Zanuncio, J. C., Santos, J. C., Silva, W. L. P., Ribeiro, G. T., & Lemes, P. G. (2014). An overview of integrated management of leaf-cutting ants (Hymenoptera: Formicidae) in Brazilian forest plantations. Forests, 5, 439–454. https://doi.org/10.3390/f5030439

    Article  Google Scholar 

  • Zanuncio, J. C., Lemes, P. G., Antunes, L. R., Maia, J. L. S., Mendes, J. E. P., Tanganelli, K. M. … Serrão, J. E. (2016). The impact of the Forest Stewardship Council (FSC) pesticide policy on the management of leaf-cutting ants and termites in certified forests in Brazil. Annals of Forest Science, 73, 205–214. https://doi.org/10.1007/s13595-016-0548-3

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Hornero, A., Hernández-Clemente, R., & Beck, P. S. A. (2018). Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 134–148. https://doi.org/10.1016/j.isprsjprs.2018.01.017

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zheng, H., Du, P., Chen, J., Xia, J., Li, E., Xu, Z. … Yokoya, N. (2017). Performance evaluation of downscaling Sentinel-2 imagery for land use and land cover classification by spectral-spatial features. Remote Sensing, 9, 1274. https://doi.org/10.3390/rs9121274

    Article  Google Scholar 

  • Zheng, Q., Huang, W., Cui, X., Shi, Y., & Liu, L. (2018). New spectral indices for detecting wheat yellow rust using Sentinel-2. multispectral imagery. Sensors, 18, 868–887. https://doi.org/10.3390/s1803

    Article  Google Scholar 

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Acknowledgements

This research was financially supported by the Pró-reitoria de Pesquisa (PROPES/IFMT), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), and the Programa Cooperativo sobre Proteção Florestal (PROTEF) do Instituto de Pesquisas e Estudos Florestais (IPEF)”.

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  1. Laboratório de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, 78201-380, Cáceres, Mato Grosso, Brazil

    Alexandre dos Santos, Isabel Carolina de Lima Santos & Jeffersoney Garcia Costa

  2. School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa

    Zakariyyaa Oumar

  3. Departamento de Pesquisa Florestal, KLABIN S/A, Avenida Brasil 26, 84275-000, Telêmaco Borba, Paraná, Brazil

    Mariane Camargo Bueno

  4. Departamento de Produção Vegetal, Faculdade de Ciências Agronômicas, UNESP, Caixa Postal 237, 18603-970, Botucatu, São Paulo, Brazil

    Tarcísio Marcos Macedo Mota Filho

  5. Departamento de Entomologia, Universidade Federal de Lavras, 37200-900, Lavras, Minas Gerais, Brazil

    Ronald Zanetti

  6. Departamento de Entomologia/BIOAGRO, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil

    José Cola Zanuncio

  7. Laboratório de Fitossanidade (FitLab), Instituto Federal de Mato Grosso, IFMT, P.O. Box 244, 78200- 000, Zip Code, Cáceres, Mato Grosso, Brasil

    Alexandre dos Santos

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Santos, A.d., de Lima Santos, I.C., Costa, J.G. et al. Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing. Precision Agric 23, 2253–2269 (2022). https://doi.org/10.1007/s11119-022-09919-x

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