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Catchment Characterization Using Remote Sensing Satellite Image to Support Catchment Monitoring and Site Verification
Implementation of advanced processing using satellite remote sensing technology has contributed to catchment characterization to aid in catchment monitoring and site verification. This paper aims to assess the capability of free source satellite images such as Landsat 7 and Landsat 8 OLI in catchment management applications through the generation of Land Use Land Cover (LULC) information, water bodies extraction, and suspended sediment estimation. The land Use Land Cover (LULC) map of Cameron Highlands, Malaysia, has been executed by using Landsat 7, 8, and SPOT-6 satellite images and a machine learning classifier of Support Vector Machine (SVM). Results indicate that satellite images are able to provide information on distribution of the land use land cover classes and land use change trends. It is also found that the spatial resolution of satellite imagery plays an important role in land use class detection. This paper also estimates Total Suspended Sediment (TSS) by using a simple band ratio technique. The result shows that Landsat-driven TSS has the potential to estimate suspended sediment of a reservoir and could be used as input for developing regional algorithm development for TSS monitoring. This paper has demonstrated the practical advantage of exploiting remotely sensed data in catchment monitoring and could be used to assist in the catchment management and decision-making process.
Catchment Characterization Using Remote Sensing Satellite Image to Support Catchment Monitoring and Site Verification
Implementation of advanced processing using satellite remote sensing technology has contributed to catchment characterization to aid in catchment monitoring and site verification. This paper aims to assess the capability of free source satellite images such as Landsat 7 and Landsat 8 OLI in catchment management applications through the generation of Land Use Land Cover (LULC) information, water bodies extraction, and suspended sediment estimation. The land Use Land Cover (LULC) map of Cameron Highlands, Malaysia, has been executed by using Landsat 7, 8, and SPOT-6 satellite images and a machine learning classifier of Support Vector Machine (SVM). Results indicate that satellite images are able to provide information on distribution of the land use land cover classes and land use change trends. It is also found that the spatial resolution of satellite imagery plays an important role in land use class detection. This paper also estimates Total Suspended Sediment (TSS) by using a simple band ratio technique. The result shows that Landsat-driven TSS has the potential to estimate suspended sediment of a reservoir and could be used as input for developing regional algorithm development for TSS monitoring. This paper has demonstrated the practical advantage of exploiting remotely sensed data in catchment monitoring and could be used to assist in the catchment management and decision-making process.
Catchment Characterization Using Remote Sensing Satellite Image to Support Catchment Monitoring and Site Verification
Water Res.Develop.Managem.
Mohd Sidek, Lariyah (editor) / Salih, Gasim Hayder Ahmed (editor) / Ahmed, Ali Najah (editor) / Escuder-Bueno, Ignacio (editor) / Basri, Hidayah (editor) / Samsudin, Sarah Hanim (author) / Nor, Tuan Nur Atikah Tuan Mohd (author) / Razad, Azwin Zailti Abdul (author) / Ismail, Mohd Nadzari (author) / Yusop, Hanafi (author)
International Conference on Dam Safety Management and Engineering ; 2023 ; Kuala Lumpur, Malaysia
Proceedings of the 2nd International Conference on Dam Safety Management and Engineering ; Chapter: 9 ; 131-143
2024-02-05
13 pages
Article/Chapter (Book)
Electronic Resource
English
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