Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/7382
Title: Leaf level experiments to discriminate between eucalyptus species using high spectral resolution reflectance data: use of derivatives, ratios and vegetation indices
Contributor(s): Kumar, Lalit  (author)orcid ; Skidmore, Andrew K (author); Mutanga, Onisimo (author)
Publication Date: 2010
DOI: 10.1080/10106040903505996
Handle Link: https://hdl.handle.net/1959.11/7382
Abstract: The purpose of this study was to investigate the potential of imaging spectroscopy for the discrimination between eucalyptus species. High spectral reflectance signatures of 11 eucalyptus species were measured in the laboratory and significant differences at a number of wavelength positions were detected. There were differences in terms of absolute reflectance, depths of absorption features and the relative position of change in terms of the wavelength. The differences between species were more noticeable in the first derivative spectra when compared with the raw spectra. This was attributed to the ability of derivatives to remove the noise from raw reflectance spectra. The results also indicate the possibility of utilizing the common vegetation indices and ratios used in remote sensing for discriminating species and highlight the need to select spectral channels at pertinent positions where the differences are the greatest. This study has identified many of these positions in relation to some eucalyptus species. However, the study also shows that there is no single wavelength which will discriminate between all species and it also shows that even with hyperspectral data, issues with detailed level of mapping will still exist.
Publication Type: Journal Article
Source of Publication: Geocarto International, 25(4), p. 327-344
Publisher: Taylor & Francis Ltd
Place of Publication: United Kingdom
ISSN: 1010-6049
1752-0762
Field of Research (FOR): 050206 Environmental Monitoring
Socio-Economic Objective (SEO): 960501 Ecosystem Assessment and Management at Regional or Larger Scales
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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