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Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

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Abstract

The application of remote sensing technology and precision agriculture in the oil palm industry is in development. This study investigated the potential of high resolution QuickBird satellite imagery, which has a synoptic overview, for detecting oil palms infected by basal stem rot disease and for mapping the disease. Basal stem rot disease poses a major threat to the oil palm industry, especially in Indonesia. It is caused by Ganoderma boninense and the symptoms can be seen on the leaf and basal stem. At present there is no effective control for this disease and early detection of the infection is essential. A detailed, accurate and rapid method of monitoring the disease is needed urgently. This study used QuickBird imagery to detect the disease and its spatial pattern. Initially, oil palm and non oil palm object segmentation based on the red band was used to map the spatial pattern of the disease. Secondly, six vegetation indices derived from visible and near infrared bands (NIR) were used for to identify palms infected by the disease. Finally, ground truth from field sampling in four fields with different ages of plant and degrees of infection was used to assess the accuracy of the remote sensing approach. The results show that image segmentation effectively delineated areas infected by the disease with a mapping accuracy of 84%. The resulting maps showed two patterns of the disease; a sporadic pattern in fields with older palms and a dendritic pattern in younger palms with medium to low infection. Ground truth data showed that oil palms infected by basal stem rot had a higher reflectance in the visible bands and a lower reflectance in the near infrared band. Different vegetation indices performed differently in each field. The atmospheric resistant vegetation index and green blue normalized difference vegetation index identified the disease with an accuracy of 67% in a field with 21 year old palms and high infection rates. In the field of 10 year old palms with medium rates of infection, the simple ratio (NIR/red) was effective with an accuracy of 62% for identifying the disease. The green blue normalized difference vegetation index was effective in the field of 10 years old palms with low infection rates with an accuracy of 59%. In the field of 15 and 18 years old palms with low infection rates, all the indices showed low levels of accuracy for identifying the disease. This study suggests that high resolution QuickBird imagery offers a quick, detailed and accurate way of estimating the location and extent of basal stem rot disease infections in oil palm plantations.

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Notes

  1. PT is Peseroran Terbatas, or company limited.

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Acknowledgements

The researchers would like to acknowledge the Director of Indonesian Oil Palm Research Institute (IOPRI) in providing the grant for this research. Mr. Agus Susanto, A. Fadillah Lubis, Tumin and Sanip who provided great help in the field and Mr. Michael Short who corrected the paper. The authors thank Dr. Margaret Oliver and two anonymous reviewers for their useful comments.

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Authors and Affiliations

  1. Indonesian Oil Palm Research Institute (IOPRI), Jalan Brigjen. Katamso 51, Kampung Baru, 20158, Medan, North Sumatera, Indonesia

    Heri Santoso & Witjaksana Darmosarkoro

  2. Faculty of Geography, Gadjah Mada University, Yogyakarta, Indonesia

    Totok Gunawan & Retnadi Heru Jatmiko

  3. Australian Centre for Precision Agriculture, The University of Sydney, Sydney, NSW, 2006, Australia

    Budiman Minasny

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  1. Heri Santoso
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  2. Totok Gunawan
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  4. Witjaksana Darmosarkoro
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  5. Budiman Minasny
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Correspondence to Heri Santoso.

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Santoso, H., Gunawan, T., Jatmiko, R.H. et al. Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery. Precision Agric 12, 233–248 (2011). https://doi.org/10.1007/s11119-010-9172-7

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