Formulation and assessment of narrow-band vegetation indices from EO-1 hyperion imagery for discriminating sugarcane disease
Paper
Paper/Presentation Title | Formulation and assessment of narrow-band vegetation indices from EO-1 hyperion imagery for discriminating sugarcane disease |
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Presentation Type | Paper |
Authors | Apan, Armando (Author), Held, Alex (Author), Phinn, Stuart (Author) and Markley, John (Author) |
Journal or Proceedings Title | Proceedings of the Spatial Sciences Institute Biennial Conference (SSC 2003): Spatial Knowledge Without Boundaries |
Number of Pages | 13 |
Year | 2003 |
Place of Publication | Canberra, Australia |
Conference/Event | 2003 Spatial Sciences Institute Biennial Conference: Spatial Knowledge Without Boundaries (SSC2003) |
Event Details | 2003 Spatial Sciences Institute Biennial Conference: Spatial Knowledge Without Boundaries (SSC2003) Event Date 22 to end of 27 Sep 2003 Event Location Canberra, Australia |
Abstract | The increasing commercial availability of hyperspectral image data promotes growing interests in the development of application-specific narrow-band spectral vegetation indices (SVIs). However, the selection of the optimum SVIs for a particular purpose is not straightforward, due to the wide choice of band combinations and transformations, combined with specific application purposes and conditions. Thus, the aim of this study was to develop an approach for formulating and assessing narrow-band vegetation indices, particularly those from EO-1 Hyperion imagery. The focus of SVI development was for discriminating sugarcane areas affected by 'orange rust' (Puccinia kuehnii) disease in Mackay, Queensland, Australia. After a series of pre-processing and post-atmospheric correction techniques, an empirical-statistical approach to SVI development was designed and implemented. This included the following components: a) selection of sample pixels of diseased and nondiseased areas, b) visual examination of spectral plots to identify bands of maximum spectral separability, c)generation of SVIs, d) use of multiple discriminant function analysis, and e) result interpretation and validation. |
Keywords | hyperspectral remote sensing; spectral vegetation indices; sugarcane disease; Hyperion |
ANZSRC Field of Research 2020 | 300206. Agricultural spatial analysis and modelling |
401304. Photogrammetry and remote sensing | |
300409. Crop and pasture protection (incl. pests, diseases and weeds) | |
Public Notes | No evidence of copyright restrictions. |
Byline Affiliations | Faculty of Engineering and Surveying |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
University of Queensland | |
Mackay Sugar, Australia |
https://research.usq.edu.au/item/9zxwy/formulation-and-assessment-of-narrow-band-vegetation-indices-from-eo-1-hyperion-imagery-for-discriminating-sugarcane-disease
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