Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery

Apan, Armando and Held, Alex and Phinn, Stuart and Markley, John (2004) Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing, 25 (2). pp. 489-498. ISSN 0143-1161

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Abstract

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by 'orange rust' (Puccinia kuehnii) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660 nm) yielded increased separability of rust-affected areas. The newly formulated 'Disease–Water Stress Indices' (DWSI-1~R800/R1660; DSWI-2~R1660/R550; DWSI-5~(R800zR550)/ (R1660zR680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.

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Item Type:	Article (Commonwealth Reporting Category C)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	Author's version deposited in accordance with the copyright policy of the publisher (Taylor and Francis).
Faculty / Department / School:	Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information
Date Deposited:	13 Apr 2008 12:48
Last Modified:	02 Jul 2013 22:48
Uncontrolled Keywords:	hyperspectral remote sensing, Hyperion, sugarcane, disease
Fields of Research :	09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing 07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070308 Crop and Pasture Protection (Pests, Diseases and Weeds)
Identification Number or DOI:	doi: 10.1080/01431160310001618031
URI:	http://eprints.usq.edu.au/id/eprint/2899

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