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Remote Sens. 2016, 8(2), 144; doi:10.3390/rs8020144

Examining the Spectral Separability of Prosopis glandulosa from Co-Existent Species Using Field Spectral Measurement and Guided Regularized Random Forest

1
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa
2
Hitachi America Big Data Laboratory, Santa Clara, CA 95054, USA
3
Department of Geography, Environmental Management & Energy Studies, Kingsway Campus, University of Johannesburg, Johannesburg 2092, South Africa
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Eyal Ben-Dor, Magaly Koch and Prasad S. Thenkabail
Received: 10 December 2015 / Revised: 23 January 2016 / Accepted: 4 February 2016 / Published: 15 February 2016
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Abstract

The invasive taxa of Prosopis is rated the world’s top 100 unwanted species, and a lack of spatial data about the invasion dynamics has made the current control and monitoring methods unsuccessful. This study thus tests the use of in situ spectroscopy data with a newly-developed algorithm, guided regularized random forest (GRRF), to spectrally discriminate Prosopis from coexistent acacia species (Acacia karroo, Acacia mellifera and Ziziphus mucronata) in the arid environment of South Africa. Results show that GRRF was able to reduce the high dimensionality of the spectroscopy data and select key wavelengths (n = 11) for discriminating amongst the species. These wavelengths are located at 356.3 nm, 468.5 nm, 531.1 nm, 665.2 nm, 1262.3 nm, 1354.1 nm, 1361.7 nm, 1376.9 nm, 1407.1 nm, 1410.9 nm and 1414.6 nm. The use of these selected wavelengths increases the overall classification accuracy from 79.19% and a Kappa value of 0.7201 when using all wavelengths to 88.59% and a Kappa of 0.8524 when the selected wavelengths were used. Based on our relatively high accuracies and ease of use, it is worth considering the GRRF method for reducing the high dimensionality of spectroscopy data. However, this assertion should receive considerable additional testing and comparison before it is accepted as a substitute for reliable high dimensionality reduction. View Full-Text
Keywords: Prosopis glandulosa; spectroscopy; guided regularized random forest; field spectroscopy; variable selection Prosopis glandulosa; spectroscopy; guided regularized random forest; field spectroscopy; variable selection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mureriwa, N.; Adam, E.; Sahu, A.; Tesfamichael, S. Examining the Spectral Separability of Prosopis glandulosa from Co-Existent Species Using Field Spectral Measurement and Guided Regularized Random Forest. Remote Sens. 2016, 8, 144.

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