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

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 p...

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Main Author: Gasela, Mchasisi
Other Authors: De Jager, Gerhard
Format: Thesis
Language:English
Published: Department of Electrical Engineering 2023
Subjects:
Space Studies
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access_status_str Open Access
author Gasela, Mchasisi
author2 De Jager, Gerhard
author_browse De Jager, Gerhard
Gasela, Mchasisi
author_facet De Jager, Gerhard
Gasela, Mchasisi
author_sort Gasela, Mchasisi
collection Thesis
description 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.
format Thesis
id oai:open.uct.ac.za:11427/37176
institution University of Cape Town (South Africa)
language eng
last_indexed 2026-06-10T12:32:12.136Z
license_str Not specified — see source repository
provenance_str_mv Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository
publishDate 2023
publishDateRange 2023
publishDateSort 2023
publisher Department of Electrical Engineering
publisherStr Department of Electrical Engineering
record_format dspace
source_str UCTD — University of Cape Town Open Access Repository
spelling oai:open.uct.ac.za:11427/37176 Evaluating the Performance of the Resampled nSight-2 Sensor's Spectral Configuration in Discriminating Wetland Plant Species Using Advanced Classifiers and Spectroscopy. Gasela, Mchasisi De Jager, Gerhard Kganyago, Mahlatse L Space Studies 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. 2023-03-03T09:16:19Z 2023-03-03T09:16:19Z 2022 2023-02-20T12:46:59Z Master Thesis Masters MPhil http://hdl.handle.net/11427/37176 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment
spellingShingle Space Studies
Gasela, Mchasisi
Evaluating the Performance of the Resampled nSight-2 Sensor's Spectral Configuration in Discriminating Wetland Plant Species Using Advanced Classifiers and Spectroscopy.
thesis_degree_str Master's
title Evaluating the Performance of the Resampled nSight-2 Sensor's Spectral Configuration in Discriminating Wetland Plant Species Using Advanced Classifiers and Spectroscopy.
title_full Evaluating the Performance of the Resampled nSight-2 Sensor's Spectral Configuration in Discriminating Wetland Plant Species Using Advanced Classifiers and Spectroscopy.
title_fullStr Evaluating the Performance of the Resampled nSight-2 Sensor's Spectral Configuration in Discriminating Wetland Plant Species Using Advanced Classifiers and Spectroscopy.
title_full_unstemmed Evaluating the Performance of the Resampled nSight-2 Sensor's Spectral Configuration in Discriminating Wetland Plant Species Using Advanced Classifiers and Spectroscopy.
title_short Evaluating the Performance of the Resampled nSight-2 Sensor's Spectral Configuration in Discriminating Wetland Plant Species Using Advanced Classifiers and Spectroscopy.
title_sort evaluating the performance of the resampled nsight 2 sensor s spectral configuration in discriminating wetland plant species using advanced classifiers and spectroscopy
topic Space Studies
url http://hdl.handle.net/11427/37176
work_keys_str_mv AT gaselamchasisi evaluatingtheperformanceoftheresamplednsight2sensorsspectralconfigurationindiscriminatingwetlandplantspeciesusingadvancedclassifiersandspectroscopy

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