Coastal zones are the epicenter of economic activities in most developing countries in the world hence are bound to change. With advancement in technology, monitoring degradation of coastal zones using remote sensing (RS) and geographic information system (GIS) has become a critical subject for scientific research. RS refers to the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft) while GIS is a system that creates, manages, analyzes, and maps all types of data. This paper reports an evolving Kenyan coastline over the past three decades using RS and GIS. Literature on chronological evolution and its impacts along this coastline is scanty and it is therefore envisaged that this work will instigate more enquiry into factors that were involved in landscape formation along the Kenyan coast. Prior reports indicate that research work done along this coastline were by field survey. We report a prograding beach on Mto Tamamba delta as displayed by multispectral Landsat imagery. The marine ecosystems, riparian communities and tourism industry are facing a major threat. The finding establishes hydrodynamic effects and human influence as key contributors of this menace. This study is aimed at detecting the coastline change using multispectral Landsat images from the years 1990 to 2021. For this purpose, coastlines belonging to these years were drawn numerically with the aid of GIS and RS on ENVI 5.3 software. We utilized maximum likelihood algorithm to categorize the images, and employed a time series methodology on classified endmembers to infer the presence of erosion and accretion. Using the image subset technique, areas prone to erosion and accretion within the coastal datum were identified and cut. The patterns of spatiotemporal trend of these endmembers were effectively employed to corroborate these findings. Change in distribution of endmembers over the 31 years’ period was assessed using thematic change technique; an approach not previously applied in this study area. From the classified Mto Tamamba image, the pixel coverage area for each endmember was extracted. Validation using field campaign data gave an overall accuracy of 84% and kappa coefficient of 0.799. Taking into account that, the traditional methods of monitoring land degradation over a large area are time consuming and expensive; remote sensing data used in this study has offered alternatively cheap, consistent, reliable and retrievable data.

Monitoring Evolution of Coastline along Mto Tamamba Delta in Kenya North Coast Using Satellite Images

G. Laneve
Methodology
2022

Abstract

Coastal zones are the epicenter of economic activities in most developing countries in the world hence are bound to change. With advancement in technology, monitoring degradation of coastal zones using remote sensing (RS) and geographic information system (GIS) has become a critical subject for scientific research. RS refers to the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft) while GIS is a system that creates, manages, analyzes, and maps all types of data. This paper reports an evolving Kenyan coastline over the past three decades using RS and GIS. Literature on chronological evolution and its impacts along this coastline is scanty and it is therefore envisaged that this work will instigate more enquiry into factors that were involved in landscape formation along the Kenyan coast. Prior reports indicate that research work done along this coastline were by field survey. We report a prograding beach on Mto Tamamba delta as displayed by multispectral Landsat imagery. The marine ecosystems, riparian communities and tourism industry are facing a major threat. The finding establishes hydrodynamic effects and human influence as key contributors of this menace. This study is aimed at detecting the coastline change using multispectral Landsat images from the years 1990 to 2021. For this purpose, coastlines belonging to these years were drawn numerically with the aid of GIS and RS on ENVI 5.3 software. We utilized maximum likelihood algorithm to categorize the images, and employed a time series methodology on classified endmembers to infer the presence of erosion and accretion. Using the image subset technique, areas prone to erosion and accretion within the coastal datum were identified and cut. The patterns of spatiotemporal trend of these endmembers were effectively employed to corroborate these findings. Change in distribution of endmembers over the 31 years’ period was assessed using thematic change technique; an approach not previously applied in this study area. From the classified Mto Tamamba image, the pixel coverage area for each endmember was extracted. Validation using field campaign data gave an overall accuracy of 84% and kappa coefficient of 0.799. Taking into account that, the traditional methods of monitoring land degradation over a large area are time consuming and expensive; remote sensing data used in this study has offered alternatively cheap, consistent, reliable and retrievable data.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1657285
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