Estimating estuarine suspended sediment concentration through spectral indices and band ratios derived from Sentinel-2 data: a case of Umzimvubu Estuary, South Africa
- Authors: Tshazi, Zamavuso
- Date: 2022-11
- Subjects: Sediments (Geology) , Suspended sediments , Remote sensing
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/27743 , vital:69406
- Description: The current study was aimed at evaluating the reliability and efficacy of selected remote sensing band ratios and indices in accurately estimating the spatial patterns of suspended sediment concentration level in Umzimvubu Estuary, Eastern Cape, South Africa. Sentinel-2 imagery was acquired on the 29th of March 2022. Band reflectance values were extracted from Sentinel -2 imagery, and laboratory measurements of suspended sediment concentration were obtained from samples collected from fifty (50) sampling points in the estuary on the 29th of March 2022. Sentinel-2 imagery was then validated with the field data in estimating and mapping the suspended sediment concentration. Several remote sensing band ratios Red/(Green plus Near-Infrared), Near-Infrared/Green, Red plus Near-Infrared/Green, Blue(Green plus Red)/Blue and Green plus Near-Infrared)/Blue and indices, that is the Normalised Difference Turbidity Index (NDTI), Normalized Difference Suspended Sediment Index (NDSSI) and Normalized Suspended Material Index (NSMI)) were then used to predict the suspended sediment concentration from Sentinel-2 imagery. The accuracy of band ratios and indices was evaluated by correlating the prediction against the observed suspended sediment concentration from Sentinel-2 imagery. A total of 50 points were randomly surveyed in the Umzimvubu estuary for analyzing suspended sediment concentration. Results indicate that the Blue (Green plus Red)/Blue, the Green plus Near-Infrared)/Blue and NMSI performed well based on their R-squared. The Blue (Green plus Red)/Blue and Green + Near-Infrared)/Blue band ratios had 0.86 and 0, 94, respectively. While NSMI yielded an R-squared of 0,76 and RMSE of 19,2 mg/L. The results in the current study indicate that Sentinel-2 imagery can reliably estimate the concentration of suspended sediment level in the Umzimvubu Estuary using band ratios and indices. , Thesis (MSc) -- Faculty of Science and Agriculture, 2022
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- Date Issued: 2022-11
- Authors: Tshazi, Zamavuso
- Date: 2022-11
- Subjects: Sediments (Geology) , Suspended sediments , Remote sensing
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/27743 , vital:69406
- Description: The current study was aimed at evaluating the reliability and efficacy of selected remote sensing band ratios and indices in accurately estimating the spatial patterns of suspended sediment concentration level in Umzimvubu Estuary, Eastern Cape, South Africa. Sentinel-2 imagery was acquired on the 29th of March 2022. Band reflectance values were extracted from Sentinel -2 imagery, and laboratory measurements of suspended sediment concentration were obtained from samples collected from fifty (50) sampling points in the estuary on the 29th of March 2022. Sentinel-2 imagery was then validated with the field data in estimating and mapping the suspended sediment concentration. Several remote sensing band ratios Red/(Green plus Near-Infrared), Near-Infrared/Green, Red plus Near-Infrared/Green, Blue(Green plus Red)/Blue and Green plus Near-Infrared)/Blue and indices, that is the Normalised Difference Turbidity Index (NDTI), Normalized Difference Suspended Sediment Index (NDSSI) and Normalized Suspended Material Index (NSMI)) were then used to predict the suspended sediment concentration from Sentinel-2 imagery. The accuracy of band ratios and indices was evaluated by correlating the prediction against the observed suspended sediment concentration from Sentinel-2 imagery. A total of 50 points were randomly surveyed in the Umzimvubu estuary for analyzing suspended sediment concentration. Results indicate that the Blue (Green plus Red)/Blue, the Green plus Near-Infrared)/Blue and NMSI performed well based on their R-squared. The Blue (Green plus Red)/Blue and Green + Near-Infrared)/Blue band ratios had 0.86 and 0, 94, respectively. While NSMI yielded an R-squared of 0,76 and RMSE of 19,2 mg/L. The results in the current study indicate that Sentinel-2 imagery can reliably estimate the concentration of suspended sediment level in the Umzimvubu Estuary using band ratios and indices. , Thesis (MSc) -- Faculty of Science and Agriculture, 2022
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- Date Issued: 2022-11
Assessing Drought Conditions using NDVI, Land Surface Temperature and Precipitation in Amathole District Municipality, Eastern Cape, Province, South Africa
- Authors: Dyosi, Masonwabe
- Date: 2021-05
- Subjects: Remote sensing , Earth sciences--Remote sensing
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/20793 , vital:46570
- Description: The world is faced with unprecedented environmental changes, which can be linked to population growth, and economic development. Several studies have indicated that these changes are likely to accelerate in the future and cause adverse impact on the environment. To this end, the Eastern Cape Province and in particular the Amathole District Municipality (ADM) has recorded high number of climate change related disasters such as prolonged drought conditions witnessed during the winter season of 2008, 2009, 2014 and 2015 among others. To this end, this study aimed to use remote sensing imagery to assess and document drought occurrences in the ADM from 2007 to 2017. To accomplish the aim, the Normalized Difference Vegetation Index, Land Surface Temperature and Precipitation were explored to assess drought spatiotemporal occurrences. To assess the relationship between abovementioned variables, the Pearson’s correlation was used. For the analysis a total of 396 satellite imagery (MODIS NDVI and Land Surface Temperature as well as TRMM precipitation) were used. The study results revealed that different correlations exist between the three variables. The strength of correlations differed by season. Furthermore, it was revealed that the drought conditions in the district differed in the spatial distribution. The study accurately identified the drought episodes which occurred in the ADM in the years 2008, 2009, 2014, 2015 and 2016. The chosen methodology and variables proved to be suitable for analysing drought conditions offering space and temporal variation dimension, which is vital in monitoring disasters such as drought. , Thesis (MSc) (Geography) -- University of Fort Hare, 2021
- Full Text:
- Date Issued: 2021-05
- Authors: Dyosi, Masonwabe
- Date: 2021-05
- Subjects: Remote sensing , Earth sciences--Remote sensing
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/20793 , vital:46570
- Description: The world is faced with unprecedented environmental changes, which can be linked to population growth, and economic development. Several studies have indicated that these changes are likely to accelerate in the future and cause adverse impact on the environment. To this end, the Eastern Cape Province and in particular the Amathole District Municipality (ADM) has recorded high number of climate change related disasters such as prolonged drought conditions witnessed during the winter season of 2008, 2009, 2014 and 2015 among others. To this end, this study aimed to use remote sensing imagery to assess and document drought occurrences in the ADM from 2007 to 2017. To accomplish the aim, the Normalized Difference Vegetation Index, Land Surface Temperature and Precipitation were explored to assess drought spatiotemporal occurrences. To assess the relationship between abovementioned variables, the Pearson’s correlation was used. For the analysis a total of 396 satellite imagery (MODIS NDVI and Land Surface Temperature as well as TRMM precipitation) were used. The study results revealed that different correlations exist between the three variables. The strength of correlations differed by season. Furthermore, it was revealed that the drought conditions in the district differed in the spatial distribution. The study accurately identified the drought episodes which occurred in the ADM in the years 2008, 2009, 2014, 2015 and 2016. The chosen methodology and variables proved to be suitable for analysing drought conditions offering space and temporal variation dimension, which is vital in monitoring disasters such as drought. , Thesis (MSc) (Geography) -- University of Fort Hare, 2021
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- Date Issued: 2021-05
Monitoring the impact of deforestation on an aquatic ecosystem using remote sensing: a case study of the Mngazana mangrove forest in the eastern cape province.
- Authors: Madasa, Akhona
- Date: 2020-12
- Subjects: Remote sensing , Mangrove forests , Climatic changes
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/20815 , vital:46598
- Description: Coastal mangrove vegetation at Mngazana continues to be threatened and reduced periodically due to unmonitored harvesting. Covering an area of 148ha, the Mngazana mangrove forest remains unreserved, thus, research on the Mngazana mangroves is essential in order to monitor their state and sustainable management. Since in-situ monitoring of mangrove areas is both challenging and time-consuming, remote sensing technologies have been used to monitor these ecosystems. This study was carried out to monitor the impact of deforestation using ASTER satellite images over ten years: from 2008 - 2018. Validation was carried out by comparing classification results with the ground-referenced data, which yielded satisfactory agreement, with an overall accuracy of 94.64 percent and Kappa coefficient of 0.93 for 2008; and in 2009, the overall accuracy was 88.62 percent and a Kappa coefficient of 0.85. While the overall accuracy of 95.08 percent and a Kappa coefficient of 0.92 for 2016 and 2018 were observed, the overall accuracy of 93.58 percent and a Kappa coefficient of 0.91 was yielded. NDVI and SAVI indices were used as monitoring indicators. The results obtained in the study indicated that the canopy density of the mangrove forest remained unchanged in the years under investigation. However, insignificant changes in canopy density were identified between 2009 and 2016. , Thesis (MSc) (Applied Remote Sensing & GIS) -- University of Fort Hare, 2021
- Full Text:
- Date Issued: 2020-12
- Authors: Madasa, Akhona
- Date: 2020-12
- Subjects: Remote sensing , Mangrove forests , Climatic changes
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/20815 , vital:46598
- Description: Coastal mangrove vegetation at Mngazana continues to be threatened and reduced periodically due to unmonitored harvesting. Covering an area of 148ha, the Mngazana mangrove forest remains unreserved, thus, research on the Mngazana mangroves is essential in order to monitor their state and sustainable management. Since in-situ monitoring of mangrove areas is both challenging and time-consuming, remote sensing technologies have been used to monitor these ecosystems. This study was carried out to monitor the impact of deforestation using ASTER satellite images over ten years: from 2008 - 2018. Validation was carried out by comparing classification results with the ground-referenced data, which yielded satisfactory agreement, with an overall accuracy of 94.64 percent and Kappa coefficient of 0.93 for 2008; and in 2009, the overall accuracy was 88.62 percent and a Kappa coefficient of 0.85. While the overall accuracy of 95.08 percent and a Kappa coefficient of 0.92 for 2016 and 2018 were observed, the overall accuracy of 93.58 percent and a Kappa coefficient of 0.91 was yielded. NDVI and SAVI indices were used as monitoring indicators. The results obtained in the study indicated that the canopy density of the mangrove forest remained unchanged in the years under investigation. However, insignificant changes in canopy density were identified between 2009 and 2016. , Thesis (MSc) (Applied Remote Sensing & GIS) -- University of Fort Hare, 2021
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- Date Issued: 2020-12
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