Coconut Tree Stress Detection as an Indicator of Red Palm Weevil (RPW) Attack Using Sentinel Data

Authors

  • Faradina Marzukhi School of Civil Engineering, Universiti Sains Malaysia (Engineering Campus), 14300, Nibong Tebal, Pulau Pinang, Malaysia
  • Md Azlin Md Said School of Civil Engineering, Universiti Sains Malaysia (Engineering Campus), 14300, Nibong Tebal, Pulau Pinang, Malaysia
  • Amirul Audi Ahmad Department of Surveying Science and Geomatics, Universiti Teknologi MARA Perlis Branch, 02600, Arau, Perlis, Malaysia

DOI:

https://doi.org/10.11113/ijbes.v7.n3.459

Keywords:

coconut palm, tree stress, red palm weevil, vegetation indices, Sentinel 2A

Abstract

The red palm weevil (RPW) is one of the worst destructive pests of palms in the world. This study focuses for the first time on the coconut tree stress detection and discrimination among different stages of red palm weevil (RPW) stress-attacks using vegetation indices (VI) and the percentage of accuracy assessed. Different spectral indices were assessed using Sentinel 2A data of year 2018. Based on field identification, four classes of coconut tree were considered and evaluated using visual maps of VI: severe, moderate, early and healthy coconut trees. Results showed that the vegetation indices Normalized Differenced Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), SQRT (IR/R), Difference Vegetation Index (DVI) and Green Vegetation Index (GVI) are sensitive to coconut trees caused by RPW attacks. They discriminated among the considered classes with more than 50% accurate from census data of field observation compared with remote sensing data of Sentinel 2A image.  Nevertheless, they express the healthiness of tree stress between 0.308 – 0.673 range with 55% to 91% accurate. According to these results, it was concluded that remote sensing technique using Sentinel 2A data is a promising alternative for RPW detection based on VI.

Author Biographies

Faradina Marzukhi, School of Civil Engineering, Universiti Sains Malaysia (Engineering Campus), 14300, Nibong Tebal, Pulau Pinang, Malaysia

Institute of Postgraduate Studies, School of Civil Engineering, Engineering Campus

Md Azlin Md Said, School of Civil Engineering, Universiti Sains Malaysia (Engineering Campus), 14300, Nibong Tebal, Pulau Pinang, Malaysia

School of Civil Engineering, Engineering Campus

Amirul Audi Ahmad, Department of Surveying Science and Geomatics, Universiti Teknologi MARA Perlis Branch, 02600, Arau, Perlis, Malaysia

Department of Surveying Science and Geomatics, Faculty of Architecture, Planning and Surveying, UiTM Perlis Branch

References

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Bannari, A., Mohamed, A. M. A., & Peddle, D. R. (2016). Biophysiological spectral indices retrieval and statistical analysis for red palm weevil stressattack prediction using Worldview-3 data. International Geoscience and Remote Sensing Symposium (IGARSS), 3512–3515.

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Congalton, R. G. (1991). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, 46(October 1990), 35–46.

Conte, A., Goffredo, M., Ippoliti, C., & Meiswinkel, R. (2007). Influence of biotic and abiotic factors on the distribution and abundance of Culicoides imicola and the Obsoletus Complex in Italy. Veterinary Parasitology, 150(4): 333–344.

Das, B., Sahoo, R. N., Pargal, S., Krishna, G., Verma, R., Chinnusamy, V., … Gupta, V. K. (2019). Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands. Geocarto International, 0(0): 1–19.

Dembilio, Ó., & Jacas, J. A. (2010). Basic bio-ecological parameters of the invasive Red Palm Weevil, Rhynchophorus ferrugineus (Coleoptera: Curculionidae), in phoenix canariensis under mediterranean climate. Bulletin of Entomological Research, 101(2): 153–163.

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Lichtenthaler, H. K., Lang, M., Sowinska, M., Heisel, F., & Miehé, J. A. (1996). Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology, 148(5): 599–612.

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Mankin, R. W. (2012). Recent Developments in the use of Acoustic Sensors and Signal Processing Tools to Target Early Infestations of Red Palm Weevil in Agricultural Environments 1 . Florida Entomologist, 94(4): 761–765.

Nakash, J., Osem, Y., & Kehat, M. (2000). A suggestion to use dogs for detecting red palm weevil (Rhynchophorus ferrugineus) infestation in date palms in Israel. Phytoparasitica, 28(2): 153–155.

Panda, S. S., Ames, D. P., & Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing, 2(3): 673–696.

Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12): 1541–1552.

Roujean, J., & Breon, F. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3): 375–384.

Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1: 309–317.

Salama, H. S., Zaki, F. N., & Abdel-Razek, A. S. (2009). Ecological and biological studies on the red palm weevil Rhynchophorus ferrugineus (Olivier). Archives of Phytopathology and Plant Protection, 42(4): 392–399.

Sellers, P. J. (1987). Canopy reflectance, photosynthesis, and transpiration, II. The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment, 21(2), 143–183.

Siriwardena, K. A. P., Fernando, L. C. P., Nanayakkara, N., Perera, K. F. G., Kumara, A. D. N. T., & Nanayakkara, T. (2010). Portable acoustic device for detection of coconut palms infested by Rynchophorus ferrugineus (Coleoptera: Curculionidae). Crop Protection, 29(1): 25–29.

Sonobe, R., Yamaya, Y., Tani, H., Wang, X., Kobayashi, N., & Mochizuki, K. (2018). Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. Journal of Applied Remote Sensing, 12(02): 1.

Soroker, V., A. La Pergula, Y. Cohen, V. Alchanatis, O Golomb., E. Goldshtein, M. Brandstetter. (2013). Early Detection and Monitoring of Red Palm Weevil: Approaches and Challenges. In Association Française de Protection des Plantes (AFPP)- Palm Pest Mediterranean Conference Nice.

Soroker, V., Suma, P., La Pergola, A., Llopis, V. N., Vacas, S., Cohen, Y., Hetzroni, A. (2016). Surveillance Techniques and DetectionMethods for Rhynchophorus ferrugineus and Paysandisia archon. Handbook of Major Palm Pests: Biology and Management, 209–232.

SUHET. (2013). SENTINEL-2 User Handbook. European Space Agency.

Taylor, S., Kumar, L., & Reid, N. (2011). Accuracy comparison of Quickbird, Landsat TM and SPOT 5 imagery for Lantana camara mapping. Journal of Spatial Science, 56(2): 241–252.

Ávalos, J. A., Martí-Campoy, A., & Soto, A. (2014). Study of the flying ability of Rhynchophorus ferrugineus (Coleoptera: Dryophthoridae) adults using a computer-monitored flight mill . Bulletin of Entomological Research, 104(4), 462–470.

Bannari, A., Mohamed, A. M. A., & Peddle, D. R. (2016). Biophysiological spectral indices retrieval and statistical analysis for red palm weevil stressattack prediction using Worldview-3 data. International Geoscience and Remote Sensing Symposium (IGARSS), 3512–3515.

Barati, S., Rayegani, B., Saati, M., Sharifi, A., & Nasri, M. (2011). Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. Egyptian Journal of Remote Sensing and Space Science, 14(1), 49–56.

Centre for Agriculture and Bioscience International CABI. (2017). "Red palm weevil datasheet. In: Invasive Species Compendium." Retrieved March, 10, 2017, from http://www.cabi.org/isc/datasheet/47472

Colwell, J. E. (1974). Vegetation canopy reflectance. Remote Sensing of Environment, 3(3), 175–183.

Congalton, R. G. (1991). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, 46(October 1990), 35–46.

Conte, A., Goffredo, M., Ippoliti, C., & Meiswinkel, R. (2007). Influence of biotic and abiotic factors on the distribution and abundance of Culicoides imicola and the Obsoletus Complex in Italy. Veterinary Parasitology, 150(4), 333–344.

Das, B., Sahoo, R. N., Pargal, S., Krishna, G., Verma, R., Chinnusamy, V., … Gupta, V. K. (2019). Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands. Geocarto International, 0(0), 1–19.

Dembilio, Ó., & Jacas, J. A. (2010). Basic bio-ecological parameters of the invasive Red Palm Weevil, Rhynchophorus ferrugineus (Coleoptera: Curculionidae), in phoenix canariensis under mediterranean climate. Bulletin of Entomological Research, 101(2), 153–163.

DOA. (2017). Prosedur Operasi Standard (SOP): Kawalan Perosak Kumbang Merah Palma (RPW). Jabatan Pertanian Malaysia.

El-Shafie, H. A. ., Faleiro, J. R., & Aleid, S. M. (2013). Full Length Research Paper A meridic diet for laboratory rearing of Red Palm Weevil, Rhynchophorus ferrugineus ( Coleoptera : Curculionidae ), 8(39), 1924–1932.

Faghih, A. A. (1996). The biology of red palm weevil, Rhynchophorus ferrugineus Oliv. (Coleoptera, Curculionidae) in Saravan region (Sistan & Balouchistan province, Iran). Applied Entomology and Phytopathology,.63, 16-18.

Fiaboe, K. K. M., Peterson, A. T., Kairo, M. T. K., & Roda, A. L. (2013). Predicting the Potential Worldwide Distribution of the Red Palm Weevil Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) using Ecological Niche Modeling. Florida Entomologist, 95(3), 659–673.

Food and Agriculture Organization of the United (FAO) & International Center for Advanced Mediterranean Agronomic Studies (CIHEAM). (2017). "The Scientific Consultation and High-Level Meeting on Red Palm Weevil Management." Retrieved March, 13, 2017, from http://www.fao.org/3/a-bu018e.pdf

Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289–298.

Golomb, O., Alchanatis, V., Cohen, Y., Levin, N., Cohen, Y., & Soroker, V. (2015). Detection of red palm weevil infected trees using thermal imaging. Precision Agriculture ’15, 643–650.

Jensen, J. R., & Lulla, D. K. (1987). Introductory digital image processing: A remote sensing perspective. Geocarto International, 2(1), 65.

Ju, R. T., Wang, F., Wan, F. H., & Li, B. (2011). Effect of host plants on development and reproduction of Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae). Journal of Pest Science, 84(1), 33–39.

Kalshoven, L. G. E., Laan, P. A. van der, & Rothschild, G. H. L. (1981). Pests of crops in Indonesia. Van Hoeve, Jakarta : P. T. Ichtiar Baru.

Lichtenthaler, H. K., Lang, M., Sowinska, M., Heisel, F., & Miehé, J. A. (1996). Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology, 148(5), 599–612.

Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote Sensing and Image Interpretation (5th ed.). Wiley.

Mankin, R. W. (2012). Recent Developments in the use of Acoustic Sensors and Signal Processing Tools to Target Early Infestations of Red Palm Weevil in Agricultural Environments 1. Florida Entomologist, 94(4), 761–765.

Nakash, J., Osem, Y., & Kehat, M. (2000). A suggestion to use dogs for detecting red palm weevil (Rhynchophorus ferrugineus) infestation in date palms in Israel. Phytoparasitica, 28(2), 153–155.

Panda, S. S., Ames, D. P., & Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing, 2(3), 673–696.

Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541–1552.

Roujean, J., & Breon, F. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375–384.

Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1, 309–317.

Salama, H. S., Zaki, F. N., & Abdel-Razek, A. S. (2009). Ecological and biological studies on the red palm weevil Rhynchophorus ferrugineus (Olivier). Archives of Phytopathology and Plant Protection, 42(4), 392–399.

Sellers, P. J. (1987). Canopy reflectance, photosynthesis, and transpiration, II. The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment, 21(2), 143–183.

Siriwardena, K. A. P., Fernando, L. C. P., Nanayakkara, N., Perera, K. F. G., Kumara, A. D. N. T., & Nanayakkara, T. (2010). Portable acoustic device for detection of coconut palms infested by Rynchophorus ferrugineus (Coleoptera: Curculionidae). Crop Protection, 29(1), 25–29.

Sonobe, R., Yamaya, Y., Tani, H., Wang, X., Kobayashi, N., & Mochizuki, K. (2018). Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. Journal of Applied Remote Sensing, 12(02), 1.

Soroker, V., A. La Pergula, Y. Cohen, V. Alchanatis, O Golomb., E. Goldshtein, M. Brandstetter. (2013). Early Detection and Monitoring of Red Palm Weevil: Approaches and Challenges. In Association Française de Protection des Plantes (AFPP)- Palm Pest Mediterranean Conference Nice.

Soroker, V., Suma, P., La Pergola, A., Llopis, V. N., Vacas, S., Cohen, Y., Hetzroni, A. (2016). Surveillance Techniques and DetectionMethods for Rhynchophorus ferrugineus and Paysandisia archon. Handbook of Major Palm Pests: Biology and Management, 209–232.

SUHET. (2013). SENTINEL-2 User Handbook. European Space Agency.

Taylor, S., Kumar, L., & Reid, N. (2011). Accuracy comparison of Quickbird, Landsat TM and SPOT 5 imagery for Lantana camara mapping. Journal of Spatial Science, 56(2), 241–252.

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.

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Published

2020-08-27

How to Cite

Marzukhi, F., Md Said, M. A., & Ahmad, A. A. (2020). Coconut Tree Stress Detection as an Indicator of Red Palm Weevil (RPW) Attack Using Sentinel Data. International Journal of Built Environment and Sustainability, 7(3), 1–9. https://doi.org/10.11113/ijbes.v7.n3.459

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Vol. 7 No. 3 (2020): International Journal of Built Environment and Sustainability Volume 7, Issue 3, 2020

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