Evaluating the Performance of the Resampled nSight-2 Sensor's Spectral Configuration in Discriminating Wetland Plant Species Using Advanced Classifiers and Spectroscopy.

Master Thesis


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Accurate and reliable information about wetland plant species is critical, as it informs improved preservation, conservation and management of wetland ecosystems. Well managed ecosystems guarantee achieving Sustainable Development Goals. Therefore, remote sensing technique has gained prominence in providing such information. However, broadband sensors are affected by effects of soil and water reflectances associated with wetlands hence cannot adequately discern subtle differences among wetland plant species. On the other hand, hyperspectral sensors allow for an in-depth examination of plant leaf and canopy biochemical traits such as lignin, cellulose, nitrogen, chlorophyll, carotenoids, anthocyanin and water content through spectral measurements which is critical for plant species discrimination. This study sought to test the capability of the forthcoming nSight-2 hyperspectral sensor in discriminating among four dominant wetland plant species. To accomplish this, the performance of nSight-2 spectral settings were compared with those of the upcoming EnMap hyperspectral satellite and an already established Worldview-2 multi-spectral sensor that carries strategic wavebands for vegetation studies, i.e. red-edge and near-infrared. The study also evaluated the performances of non-parametric machine learning algorithms in classifying wetland plant species using nSight-2 spectral configuration. The results showed a high discrimination accuracy by nSight-2 spectral settings with an overall accuracy of 84.09%, followed by Worldview-2 i.e. 81.82% while EnMap was the worst i.e. 77.77%. The most important bands for vegetation analysis were within the visible (VIS), Red-edge (RE) and near infrared (NIR) regions of the electromagnetic spectrum. The study also demonstrated that within these spectral bands, the four dominant Verloren Vallei Nature Reserve wetland plant i.e. Crocosmia sp., Grasses, Agapanthus sp. and Cyperus sp. could be differentiated using the spectral settings of these sensors. Furthermore, the results showed a superior performance of Support Vector Machine (SVM) with overall accuracy of 93.18%, compared with the RF and Partial Least Squares-Discriminant Analysis (PLS-DA) that had overall accuracies of 84.09% and 83.63% respectively. In summary, the study demonstrated that the spectral configuration of nSight-2 hyperspectral sensor can discriminate among the wetland plant species with comparable accuracy to that of a stateof-the-art sensor, i.e. Worldview-2 and better than the upcoming EnMap.