Testing the utility of the resampled nSight-2 spectral configurations in discriminating wetland plant species using Random Forest classifier

Abstract

Hyperspectral sensors have the potential of discerning subtle differences among wetland plant species. Assessment of wetland plant species is critical for the effective management of wetland ecosystems. We evaluated the utility of the forthcoming nSight-2 hyperspectral sensor in wetland plant species differentiation, by testing the utility of its spectral settings and compared its performance to EnMap and WorldView-2 sensors in classifying four dominant wetland plant species in Verloren Vallei Nature Reserve, South Africa. For this purpose, we used the Random Forest classifier and field spectrometer data of various wetland plant species. The results showed that the spectral settings of nSight-2, EnMap, and WorldView-2 yielded high overall accuracies of 84.09%, 81.82% and 77.77%, respectively. The best accuracies were achieved with spectral bands sampled across the visible, red-edge and near-infrared spectral regions. Overall, the findings demonstrate the potential of the upcoming hyperspectral satellite missions for wetland ecosystems monitoring and management.

Keywords:

• nSight 2 satellite
• hyperspectral remote sensing
• wetland plant species
• WorldView-2
• EnMap

Acknowledgements

We gratefully acknowledge the support material, financial and time from Space Advisory Company (SAC), South African National Space Agency Earth observation directorate (SANSA-EO), Mpumalanga Tourism Board Agency. On the same strength, special thanks also go to Shirley Sibiya, the Manager at Verloren Vallei Nature Reserve and her team for their help and support during data collection.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data supporting this study's findings are available from the corresponding author [GM], upon reasonable request.