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Proceedings Paper

Estimation of high density wetland biomass: combining regression model with vegetation index developed from Worldview-2 imagery
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Paper Abstract

The saturation problem associated with the use of NDVI for biomass estimation in high canopy density vegetation is a well-known phenomenon. Recent field spectroscopy experiments have shown that narrow band vegetation indices computed from the red edge and the NIR shoulder can improve the estimation of biomass in such situations. However, the wide scale unavailability of high spectral resolution satellite sensors with red edge bands has not seen the up-scaling of these techniques to spaceborne remote sensing of high density biomass. This paper explored the possibility of estimating biomass in a densely vegetated wetland area using indices computed from Worldview-2 imagery, which contains a red edge band centred at 725 nm. Indices derived from the red edge band and the NIR shoulder yielded higher accuracies (R2 = 0.71) for biomass estimation as compared to indices computed from other portions of the electromagnetic spectrum. Predicting biomass on an independent test data set using the Random forest algorithm and 3 NDVIs computed from the red edge and NIR bands yielded a root mean square error of prediction (RMSEP) of 441g/m2 ( 13 % of observed mean biomass) as compared to the traditional spectral bands. The results demonstrate the utility of Worldview-2 imagery in estimating and ultimately mapping vegetation biomass at high density - a previously challenging task with broad band satellite sensors.

Paper Details

Date Published: 19 October 2012
PDF: 9 pages
Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 85310V (19 October 2012); doi: 10.1117/12.970469
Show Author Affiliations
Elhadi M. I. Adam, Univ. of KwaZulu-Natal (South Africa)
Onisimo Mutanga, Univ. of KwaZulu-Natal (South Africa)


Published in SPIE Proceedings Vol. 8531:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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