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Journal of Applied Remote Sensing

Exploring the potential of in situ hyperspectral data and multivariate techniques in discriminating different fertilizer treatments in grasslands
Author(s): Mbulisi Sibanda; Onisimo Mutanga; Mathieu Rouget; John Odindi
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Paper Abstract

Optimizing the productivity of native rangelands has received considerable attention in range management. Rangeland fertilizer application has emerged as a popular intervention for improving rangeland quality. To achieve optimal range quality from such intervention, there is a need for quick and accurate methods of assessing the effects of different fertilizer combinations. The utility of <italic<in situ</italic< hyperspectral data and multivariate techniques in distinguishing 12 complex ammonium nitrate, ammonium sulfate, lime, and phosphorus fertilizer combinations on a grassland is assessed. Partial least squares regression discriminant analysis (PLS-DA) and discriminant analysis (DA) classification results derived using hyperspectral grass reflectance that were (1) fertilized using 11 combinations of ammonium sulfate, ammonium nitrate, phosphorus, and lime and (2) unfertilized experimental plots were compared. Results illustrate the strength of in situ hyperspectral data and multivariate techniques in detecting and discriminating grasses with different fertilizer treatments. Specifically, four bands within the red edge (731 and 737 nm) and the shortwave infrared (1310 and 1777 nm) regions of the electromagnetic spectrum demonstrated a high potential for discriminating the effects of fertilizer treatments on grasslands. DA outperformed PLS-DA in discriminating complex combinations of ammonium nitrate, ammonium sulfate combined with lime and phosphorus, as well as unfertilized grasses. Overall, spectroscopy and DA offer great potential for discriminating complex fertilizer combinations.

Paper Details

Date Published: 2 July 2015
PDF: 19 pages
J. Appl. Remote Sens. 9(1) 096033 doi: 10.1117/1.JRS.9.096033
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Mbulisi Sibanda, Univ. of KwaZulu-Natal (South Africa)
Onisimo Mutanga, Univ. of KwaZulu-Natal (South Africa)
Mathieu Rouget, Univ. of KwaZulu-Natal (South Africa)
John Odindi, Univ. of KwaZulu-Natal (South Africa)

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