Analysis of airborne hyperspectral image using vegetation indices red edge position and continuum removal for detection of ganoderma disease in oil palm

Idris A. S., . and Nisfariza M. N., . and Steven M. D., . and Izzuddin M. A., . and Ezzati B., . and Boyd D., . Analysis of airborne hyperspectral image using vegetation indices red edge position and continuum removal for detection of ganoderma disease in oil palm. pp. 416-428. ISSN 1511-2780

The basal stem rot (BSR) of oil palm caused by Ganoderma has brought huge losses to the oil palm industry in Malaysia. Airborne hyperspectral remote sensing technology may provide assistance to detect and classify different categories of Ganoderma disease severity index (DSI) in oil palm. In this study five common vegetation indices (VI) four red edge position (REP) and four continuum removal (CR) were applied to categorise oil palm into T1 (healthy) T2 (mild) and T3 (severe) infection of Ganoderma disease in oil palm. The accuracy of the VI REP and CR were assessed using confusion matrix and t-test. The results revealed that two VI i.e. Simple Ratio Index (SRI) and Enhanced Vegetation Index (EVI) have moderate capability for the detection of Ganoderma disease in oil palm. SRI showed moderate classification accuracy (44.4) compared to EVI with 40.7 accuracy; while the other three VI had poor accuracy (40). The analysis of REP using t-test showed that none of the REP could differentiate between T1 vs. T2 significantly but differences between T1 vs. T3 and T2 vs. T3 are statistically obvious. Meanwhile analysis using CR gave promising results when there are statistical significant differences between T1 vs. T2 in the 500 nanometer (nm) absorption region of Band Depth Normalised to Area (BDNA). In conclusion the common VI and REP generated from airborne hyperspectral image had low to moderate accuracy for detection of Ganoderma disease. Meanwhile CR gave promising results for early detection of the disease. Further analysis must be conducted to validate and ensure the robustness of the results and also should look towards generating specific spectral indices and bi-directional reflectance (BRDF) model for detection of Ganoderma disease in oil palm.

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Abstract

The basal stem rot (BSR) of oil palm caused by Ganoderma has brought huge losses to the oil palm industry in Malaysia. Airborne hyperspectral remote sensing technology may provide assistance to detect and classify different categories of Ganoderma disease severity index (DSI) in oil palm. In this study five common vegetation indices (VI) four red edge position (REP) and four continuum removal (CR) were applied to categorise oil palm into T1 (healthy) T2 (mild) and T3 (severe) infection of Ganoderma disease in oil palm. The accuracy of the VI REP and CR were assessed using confusion matrix and t-test. The results revealed that two VI i.e. Simple Ratio Index (SRI) and Enhanced Vegetation Index (EVI) have moderate capability for the detection of Ganoderma disease in oil palm. SRI showed moderate classification accuracy (44.4) compared to EVI with 40.7 accuracy; while the other three VI had poor accuracy (40). The analysis of REP using t-test showed that none of the REP could differentiate between T1 vs. T2 significantly but differences between T1 vs. T3 and T2 vs. T3 are statistically obvious. Meanwhile analysis using CR gave promising results when there are statistical significant differences between T1 vs. T2 in the 500 nanometer (nm) absorption region of Band Depth Normalised to Area (BDNA). In conclusion the common VI and REP generated from airborne hyperspectral image had low to moderate accuracy for detection of Ganoderma disease. Meanwhile CR gave promising results for early detection of the disease. Further analysis must be conducted to validate and ensure the robustness of the results and also should look towards generating specific spectral indices and bi-directional reflectance (BRDF) model for detection of Ganoderma disease in oil palm.

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Item Type:	Article
AGROVOC Term:	Oil palm
AGROVOC Term:	Stems
AGROVOC Term:	Ganoderma
AGROVOC Term:	Plant diseases
AGROVOC Term:	Remote sensing
AGROVOC Term:	Vegetation index
AGROVOC Term:	Data collection
AGROVOC Term:	Data analysis
AGROVOC Term:	Detectors
AGROVOC Term:	Infectious diseases
Depositing User:	Ms. Suzila Mohamad Kasim
Last Modified:	24 Apr 2025 06:29
URI:	http://webagris.upm.edu.my/id/eprint/24708

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