A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red Sea
Natural crises manifested in floods and droughts are counted as the most severe impacts of climate change on the world. In this regard, flash floods are the most common cause of economic and human losses worldwide. However, the present study focuses on the flash flood-affected area between Zafaarana...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
|
| Series: | Egyptian Journal of Remote Sensing and Space Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110982323000698 |
| _version_ | 1797642557076799488 |
|---|---|
| author | Rasha Elstohy Eman M. Ali |
| author_facet | Rasha Elstohy Eman M. Ali |
| author_sort | Rasha Elstohy |
| collection | DOAJ |
| description | Natural crises manifested in floods and droughts are counted as the most severe impacts of climate change on the world. In this regard, flash floods are the most common cause of economic and human losses worldwide. However, the present study focuses on the flash flood-affected area between Zafaarana and Ras Ghareb coastal roads. Sentinel-2 satellite images of recent years before and after the flash flood have been utilized to detect flooded areas and investigate their environmental conditions.Initially, the captured images were pre-processed to compare the environmental conditions before and after flooding. Consequently, the Normalized Difference Water Index (NDWI) was utilized to classify water bodies in different bands. Finally, an image difference feature (IDF) model with computation of per-pixel features, merging image disparities, and calculation of the characteristic value phases was constructed to extract various image differences after photo processing, that's to identify flooded pixels in the images and assess their performance in the proposed model. The proposed IDF model was compared by rating each model on the same test set, while changing the training set. In conclusion, the proposed algorithm shows an accuracy of 98.9%, which is a better flood image processing technique than other methods. The insights from this research will help decision makers in structuring their rescue strategies and evacuation maps during and before the environmental crisis. |
| first_indexed | 2024-03-11T14:01:55Z |
| format | Article |
| id | doaj.art-298e8be4b3134c318de7593d7176af16 |
| institution | Directory Open Access Journal |
| issn | 1110-9823 |
| language | English |
| last_indexed | 2024-03-11T14:01:55Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Journal of Remote Sensing and Space Sciences |
| spelling | doaj.art-298e8be4b3134c318de7593d7176af162023-11-02T04:13:15ZengElsevierEgyptian Journal of Remote Sensing and Space Sciences1110-98232023-12-01263807814A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red SeaRasha Elstohy0Eman M. Ali1Information System Department, Obour Institutes, Egypt; ICT Department, New Cairo Technological University, Cairo, EgyptScientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, EgyptNatural crises manifested in floods and droughts are counted as the most severe impacts of climate change on the world. In this regard, flash floods are the most common cause of economic and human losses worldwide. However, the present study focuses on the flash flood-affected area between Zafaarana and Ras Ghareb coastal roads. Sentinel-2 satellite images of recent years before and after the flash flood have been utilized to detect flooded areas and investigate their environmental conditions.Initially, the captured images were pre-processed to compare the environmental conditions before and after flooding. Consequently, the Normalized Difference Water Index (NDWI) was utilized to classify water bodies in different bands. Finally, an image difference feature (IDF) model with computation of per-pixel features, merging image disparities, and calculation of the characteristic value phases was constructed to extract various image differences after photo processing, that's to identify flooded pixels in the images and assess their performance in the proposed model. The proposed IDF model was compared by rating each model on the same test set, while changing the training set. In conclusion, the proposed algorithm shows an accuracy of 98.9%, which is a better flood image processing technique than other methods. The insights from this research will help decision makers in structuring their rescue strategies and evacuation maps during and before the environmental crisis.http://www.sciencedirect.com/science/article/pii/S1110982323000698Flood detectionImage processingNDWIIDFCrisis management |
| spellingShingle | Rasha Elstohy Eman M. Ali A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red Sea Egyptian Journal of Remote Sensing and Space Sciences Flood detection Image processing NDWI IDF Crisis management |
| title | A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red Sea |
| title_full | A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red Sea |
| title_fullStr | A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red Sea |
| title_full_unstemmed | A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red Sea |
| title_short | A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red Sea |
| title_sort | flash flood detected area using classification based image processing for sentinel 2 satellites data a case study of zafaraana road at red sea |
| topic | Flood detection Image processing NDWI IDF Crisis management |
| url | http://www.sciencedirect.com/science/article/pii/S1110982323000698 |
| work_keys_str_mv | AT rashaelstohy aflashflooddetectedareausingclassificationbasedimageprocessingforsentinel2satellitesdataacasestudyofzafaraanaroadatredsea AT emanmali aflashflooddetectedareausingclassificationbasedimageprocessingforsentinel2satellitesdataacasestudyofzafaraanaroadatredsea AT rashaelstohy flashflooddetectedareausingclassificationbasedimageprocessingforsentinel2satellitesdataacasestudyofzafaraanaroadatredsea AT emanmali flashflooddetectedareausingclassificationbasedimageprocessingforsentinel2satellitesdataacasestudyofzafaraanaroadatredsea |