Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia
Abstract Effective urban planning and management rely on accurate land cover mapping, which can be achieved through the combination of remote sensing data and machine learning algorithms. This study aimed to explore and demonstrate the potential benefits of integrating Sentinel-1 SAR and Sentinel-2...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-11-01
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| Series: | Environmental Systems Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40068-023-00324-5 |
| _version_ | 1797413843563970560 |
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| author | Shimelis Sishah Dagne Hurgesa Hundera Hirpha Addisu Teshome Tekoye Yeshambel Barko Dessie Adane Addis Endeshaw |
| author_facet | Shimelis Sishah Dagne Hurgesa Hundera Hirpha Addisu Teshome Tekoye Yeshambel Barko Dessie Adane Addis Endeshaw |
| author_sort | Shimelis Sishah Dagne |
| collection | DOAJ |
| description | Abstract Effective urban planning and management rely on accurate land cover mapping, which can be achieved through the combination of remote sensing data and machine learning algorithms. This study aimed to explore and demonstrate the potential benefits of integrating Sentinel-1 SAR and Sentinel-2 MSI satellite imagery for urban land cover classification in Gondar city, Ethiopia. Synthetic Aperture Radar (SAR) data from Sentinel-1A and Multispectral Instrument (MSI) data from Sentinel-2B for the year 2023 were utilized for this research work. Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms were utilized for the classification process. Google Earth Engine (GEE) was used for the processing, classification, and validation of the remote sensing data. The findings of the research provided valuable insights into the performance evaluation of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for image classification using different datasets, namely Sentinel 2B Multispectral Instrument (MSI) and Sentinel 1A Synthetic Aperture Radar (SAR) data. When applied to the Sentinel 2B MSI dataset, both SVM and RF achieved an overall accuracy (OA) of 0.69, with a moderate level of agreement indicated by the Kappa score of 0.357. For the Sentinel 1A SAR data, SVM maintained the same OA of 0.69 but showed an improved Kappa score of 0.67, indicating its suitability for SAR image classification. In contrast, RF achieved a slightly lower OA of 0.66 with Sentinel 1A SAR data. However, when the datasets of Sentinel 2B MSI and Sentinel 1A SAR were combined, SVM achieved an impressive OA of 0.91 with a high Kappa score of 0.80, while RF achieved an OA of 0.81 with a Kappa score of 0.809. These findings highlight the potential of fusing satellite data from multiple sources to enhance the accuracy and effectiveness of image classification algorithms, making them valuable tools for various applications, including land use mapping and environmental monitoring. |
| first_indexed | 2024-03-09T05:23:54Z |
| format | Article |
| id | doaj.art-019b374fd4af4ddc92f5bab833349ee0 |
| institution | Directory Open Access Journal |
| issn | 2193-2697 |
| language | English |
| last_indexed | 2024-03-09T05:23:54Z |
| publishDate | 2023-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Environmental Systems Research |
| spelling | doaj.art-019b374fd4af4ddc92f5bab833349ee02023-12-03T12:37:59ZengSpringerOpenEnvironmental Systems Research2193-26972023-11-0112111610.1186/s40068-023-00324-5Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, EthiopiaShimelis Sishah Dagne0Hurgesa Hundera Hirpha1Addisu Teshome Tekoye2Yeshambel Barko Dessie3Adane Addis Endeshaw4College of Social Science and Humanities, Department of Geography and Environmental Studies, Arsi UniversityCollege of Social Science and Humanities, Department of Geography and Environmental Studies, Arsi UniversityCollege of Social Science and Humanities, Department of Geography and Environmental Studies, University of GondarEthiopian Wildlife Conservation Authority, Kafta Sheraro National ParkCollege of Social Science and Humanities, Department of Geography and Environmental Studies, University of GondarAbstract Effective urban planning and management rely on accurate land cover mapping, which can be achieved through the combination of remote sensing data and machine learning algorithms. This study aimed to explore and demonstrate the potential benefits of integrating Sentinel-1 SAR and Sentinel-2 MSI satellite imagery for urban land cover classification in Gondar city, Ethiopia. Synthetic Aperture Radar (SAR) data from Sentinel-1A and Multispectral Instrument (MSI) data from Sentinel-2B for the year 2023 were utilized for this research work. Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms were utilized for the classification process. Google Earth Engine (GEE) was used for the processing, classification, and validation of the remote sensing data. The findings of the research provided valuable insights into the performance evaluation of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for image classification using different datasets, namely Sentinel 2B Multispectral Instrument (MSI) and Sentinel 1A Synthetic Aperture Radar (SAR) data. When applied to the Sentinel 2B MSI dataset, both SVM and RF achieved an overall accuracy (OA) of 0.69, with a moderate level of agreement indicated by the Kappa score of 0.357. For the Sentinel 1A SAR data, SVM maintained the same OA of 0.69 but showed an improved Kappa score of 0.67, indicating its suitability for SAR image classification. In contrast, RF achieved a slightly lower OA of 0.66 with Sentinel 1A SAR data. However, when the datasets of Sentinel 2B MSI and Sentinel 1A SAR were combined, SVM achieved an impressive OA of 0.91 with a high Kappa score of 0.80, while RF achieved an OA of 0.81 with a Kappa score of 0.809. These findings highlight the potential of fusing satellite data from multiple sources to enhance the accuracy and effectiveness of image classification algorithms, making them valuable tools for various applications, including land use mapping and environmental monitoring.https://doi.org/10.1186/s40068-023-00324-5Sentinel 2B MSISentinel 1A SARRandom forestSupport vector machine |
| spellingShingle | Shimelis Sishah Dagne Hurgesa Hundera Hirpha Addisu Teshome Tekoye Yeshambel Barko Dessie Adane Addis Endeshaw Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia Environmental Systems Research Sentinel 2B MSI Sentinel 1A SAR Random forest Support vector machine |
| title | Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia |
| title_full | Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia |
| title_fullStr | Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia |
| title_full_unstemmed | Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia |
| title_short | Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia |
| title_sort | fusion of sentinel 1 sar and sentinel 2 msi data for accurate urban land use land cover classification in gondar city ethiopia |
| topic | Sentinel 2B MSI Sentinel 1A SAR Random forest Support vector machine |
| url | https://doi.org/10.1186/s40068-023-00324-5 |
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