Research Papers

Optimizing spectral resolutions for the classification of C3 and C4 grass species, using wavelengths of known absorption features

[+] Author Affiliations
Clement Adjorlolo, Onisimo Mutanga, Riyad Ismail

University of KwaZulu-Natal, School of Agriculture, Earth and Environmental Sciences, P. Bag X01, Scottsville 3209, Pietermaritzburg, South Africa

Moses A. Cho

University of KwaZulu-Natal, School of Agriculture, Earth and Environmental Sciences, P. Bag X01, Scottsville 3209, Pietermaritzburg, South Africa

Natural Resources and the Environment, Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa

J. Appl. Remote Sens. 6(1), 063560 (Sep 04, 2012). doi:10.1117/1.JRS.6.063560
History: Received January 6, 2012; Revised June 19, 2012; Accepted July 2, 2012
Text Size: A A A

Abstract.  Hyperspectral remote-sensing approaches are suitable for detection of the differences in 3-carbon (C3) and four carbon (C4) grass species phenology and composition. However, the application of hyperspectral sensors to vegetation has been hampered by high-dimensionality, spectral redundancy, and multicollinearity problems. In this experiment, resampling of hyperspectral data to wider wavelength intervals, around a few band-centers, sensitive to the biophysical and biochemical properties of C3 or C4 grass species is proposed. The approach accounts for an inherent property of vegetation spectral response: the asymmetrical nature of the inter-band correlations between a waveband and its shorter- and longer-wavelength neighbors. It involves constructing a curve of weighting threshold of correlation (Pearson’s r) between a chosen band-center and its neighbors, as a function of wavelength. In addition, data were resampled to some multispectral sensors—ASTER, GeoEye-1, IKONOS, QuickBird, RapidEye, SPOT 5, and WorldView-2 satellites—for comparative purposes, with the proposed method. The resulting datasets were analyzed, using the random forest algorithm. The proposed resampling method achieved improved classification accuracy (κ=0.82), compared to the resampled multispectral datasets (κ=0.78, 0.65, 0.62, 0.59, 0.65, 0.62, 0.76, respectively). Overall, results from this study demonstrated that spectral resolutions for C3 and C4 grasses can be optimized and controlled for high dimensionality and multicollinearity problems, yet yielding high classification accuracies. The findings also provide a sound basis for programming wavebands for future sensors.

Figures in this Article
© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Clement Adjorlolo ; Moses A. Cho ; Onisimo Mutanga and Riyad Ismail
"Optimizing spectral resolutions for the classification of C3 and C4 grass species, using wavelengths of known absorption features", J. Appl. Remote Sens. 6(1), 063560 (Sep 04, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063560


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.