Flood Inundation Mapping with Supervised Classifiers: 2021 Gediz Plain Flood
Öz
Generation of flood inundation maps is beneficial in flood risk assessment and evaluation. Flood inundation mapping can be achieved by many remote sensing techniques like change detection (CD) with thresholding and machine learning-based (ML) methods. Optical and synthetic aperture radar (SAR) imagery are widely used, provided by different satellite systems. This study used Sentinel-1 SAR and Sentinel-2 MSI satellite data in Google Earth Engine (GEE) with supervised ML algorithms. Gediz Plain, Turkey was selected as the study area, which is an agricultural area covered mostly by croplands. A flood event that occurred on February 2, 2021, was examined and flood inundation map for the study area was composed. Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) ML algorithms were selected and models were trained with manually created labelled data in GEE. Also, CD was applied on after and before event SAR images in a traditional approach. RF classifier performs best in Sentinel-2 MSI imagery with 94% overall classification accuracy where KNN classifier gives 93.3% accuracy value for Sentinel-1 SAR dataset, indicating the robustness of SAR imagery for all-weather conditions.
Anahtar Kelimeler
Kaynakça
- Afify, H. A. (2011). Evaluation of change detection techniques for monitoring land-cover changes: A case study in new Burg El-Arab area. Alexandria Engineering Journal, 50(2), 187-195.
- Amani, M., Brisco, B., Afshar, M., Mirmazloumi, S. M., Mahdavi, S., Mirzadeh, S. M. J., & Granger, J. (2019). A generalized supervised classification scheme to produce provincial wetland inventory maps: An application of Google Earth Engine for big geo data processing. Big Earth Data, 3(4), 378-394.
- Arslan, D., Çiçek, K., Döndüren, Ö., & Ernoul, L. (2021). Threat ranking to improve conservation planning: an example from the Gediz Delta, Turkey. Land, 10(12), 1381. doi: 10.3390/land10121381.
- Baghi, A., & Karami, A. (2017). SAR image segmentation using region growing and spectral cluster. In 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), 2017. Proceedings. (pp. 229-232). IEEE.
- Benoudjit, A., & Guida, R. (2019). A novel fully automated mapping of the flood extent on SAR images using a supervised classifier. Remote Sensing, 11(7), 779. doi: 10.3390/rs11070779.
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
- Brown, K. M., Hambidge, C. H., & Brownett, J. M. (2016). Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data. Progress in Physical Geography, 40(2), 196-214.
- Cao, H., Zhang, H., Wang, C., & Zhang, B. (2019). Operational flood detection using Sentinel-1 SAR data over large areas. Water, 11(4), 786. doi: 10.3390/w11040786.
- Carincotte, C., Derrode, S., & Bourennane, S. (2006). Unsupervised change detection on SAR images using fuzzy hidden Markov chains. IEEE Transactions on Geoscience and Remote Sensing, 44(2), 432-441.
- Chang, Y. L., Anagaw, A., Chang, L., Wang, Y. C., Hsiao, C. Y., & Lee, W. H. (2019). Ship detection based on YOLOv2 for SAR imagery. Remote Sensing, 11(7), 786. doi: 10.3390/rs11070786.
