dc.contributor.author |
Main, Russell S
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|
dc.contributor.author |
Cho, Moses A
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|
dc.contributor.author |
Mathieu, Renaud SA
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dc.contributor.author |
O'Kennedy, Maretha M
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dc.contributor.author |
Ramoelo, Abel
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dc.contributor.author |
Koch, S
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|
dc.date.accessioned |
2011-10-21T09:28:12Z |
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dc.date.available |
2011-10-21T09:28:12Z |
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dc.date.issued |
2011-11 |
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dc.identifier.citation |
Main, R., Cho, M.A., Mathieu, R.S.A., et al. 2011. An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS Journal of Photogrammetry and Remote Sensing, Vol 66(6), pp 751-761 |
en_US |
dc.identifier.issn |
0924-2716 |
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dc.identifier.uri |
http://hdl.handle.net/10204/5251
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|
dc.identifier.uri |
http://www.sciencedirect.com/science/journal/09242716
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|
dc.description |
Copyright: Elsevier 2011. This is a pre print version of the paper. The definitive version is published in ISPRS Journal of Photogrammetry and Remote Sensing, Vol 66(6), pp 751-761 |
en_US |
dc.description.abstract |
Quantifying photosynthetic activity at the regional scale can provide important information to resource managers, planners and global ecosystem modelling efforts. With increasing availability of both hyperspectral and narrow band multispectral remote sensing data, new users are faced with a plethora of options when choosing an optical index to relate to their chosen or canopy parameter. The literature base regarding optical indices (particularly chlorophyll indices) is wide ranging and extensive, however it is without much consensus regarding robust indices. The wider spectral community could benefit from studies that apply a variety of published indices to differing sets of species data. The consistency and robustness of 73 published chlorophyll spectral indices have been assessed, using leaf level hyperspectral data collected from three crop species and a variety of savanna tree species. Linear regression between total leaf chlorophyll content and bootstrapping were used to determine the leafpredictive capabilities of the various indices. The indices were then ranked based on the prediction error (the average root mean square error (RMSE)) derived from the bootstrapping process involving 1000 iterative resampling with replacement. The results show two red-edge derivative based indices (red-edge position via linear extrapolation index and the modified red-edge inflection point index) as the most consistent and robust, and that the majority of the top performing indices (in spite of species variability) were simple ratio or normalised difference indices that are based on off-chlorophyll absorption centre wavebands (690–730 nm). |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.relation.ispartofseries |
Workflow request;7318 |
|
dc.subject |
Leaf level reflectance |
en_US |
dc.subject |
Leaf chlorophyll |
en_US |
dc.subject |
Vegetation indices |
en_US |
dc.subject |
Photosynthetic activity |
en_US |
dc.subject |
Photogrammetry |
en_US |
dc.subject |
Remote sensing |
en_US |
dc.title |
An investigation into robust spectral indices for leaf chlorophyll estimation |
en_US |
dc.type |
Article |
en_US |
dc.identifier.apacitation |
Main, R. S., Cho, M. A., Mathieu, R. S., O'Kennedy, M. M., Ramoelo, A., & Koch, S. (2011). An investigation into robust spectral indices for leaf chlorophyll estimation. http://hdl.handle.net/10204/5251 |
en_ZA |
dc.identifier.chicagocitation |
Main, Russel S, Moses A Cho, Renaud SA Mathieu, Maretha M O'Kennedy, Abel Ramoelo, and S Koch "An investigation into robust spectral indices for leaf chlorophyll estimation." (2011) http://hdl.handle.net/10204/5251 |
en_ZA |
dc.identifier.vancouvercitation |
Main RS, Cho MA, Mathieu RS, O'Kennedy MM, Ramoelo A, Koch S. An investigation into robust spectral indices for leaf chlorophyll estimation. 2011; http://hdl.handle.net/10204/5251. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Main, Russel S
AU - Cho, Moses A
AU - Mathieu, Renaud SA
AU - O'Kennedy, Maretha M
AU - Ramoelo, Abel
AU - Koch, S
AB - Quantifying photosynthetic activity at the regional scale can provide important information to resource managers, planners and global ecosystem modelling efforts. With increasing availability of both hyperspectral and narrow band multispectral remote sensing data, new users are faced with a plethora of options when choosing an optical index to relate to their chosen or canopy parameter. The literature base regarding optical indices (particularly chlorophyll indices) is wide ranging and extensive, however it is without much consensus regarding robust indices. The wider spectral community could benefit from studies that apply a variety of published indices to differing sets of species data. The consistency and robustness of 73 published chlorophyll spectral indices have been assessed, using leaf level hyperspectral data collected from three crop species and a variety of savanna tree species. Linear regression between total leaf chlorophyll content and bootstrapping were used to determine the leafpredictive capabilities of the various indices. The indices were then ranked based on the prediction error (the average root mean square error (RMSE)) derived from the bootstrapping process involving 1000 iterative resampling with replacement. The results show two red-edge derivative based indices (red-edge position via linear extrapolation index and the modified red-edge inflection point index) as the most consistent and robust, and that the majority of the top performing indices (in spite of species variability) were simple ratio or normalised difference indices that are based on off-chlorophyll absorption centre wavebands (690–730 nm).
DA - 2011-11
DB - ResearchSpace
DP - CSIR
KW - Leaf level reflectance
KW - Leaf chlorophyll
KW - Vegetation indices
KW - Photosynthetic activity
KW - Photogrammetry
KW - Remote sensing
LK - https://researchspace.csir.co.za
PY - 2011
SM - 0924-2716
T1 - An investigation into robust spectral indices for leaf chlorophyll estimation
TI - An investigation into robust spectral indices for leaf chlorophyll estimation
UR - http://hdl.handle.net/10204/5251
ER - |
en_ZA |