Evaluation of the Impact of Image Fusion of Landsat 8 and Sentinel 2 Satellites on Flood Zone Estimation

Document Type : Research Paper


1 Water Resources Engineering and Management, Faculty of Civil Engineering, Shahrood university of technology, Shahrood, Iran.

2 Faculty of Civil Engineering, Shahrood university of technology, Shahrood, Iran.



Accurate monitoring of surface water is one of the important and necessary applications in the use of remote sensing systems. Meeting the needs raised in the use of remote sensing data collected from the earth's surface in many applications, using only one product and classification algorithm is not sufficient and possible, and for a more accurate understanding, data fusion can be a better option. In this system, various approaches such as water extraction indices or classification algorithms are used to identify water areas. In this research, an fusion approach of Landsat-8 and Sentinel-2 optical sensor images was used. Firstly, the spatial resolution of these sensors was enhanced from 30 to 10 meters by Pansharpening them and preserving spectral information. Then, water extraction indices such as NDWI, MNDWI, AWEI_sh, AWEI_nsh, and WI were applied to the integrated images. Subsequently, using classification algorithms such as SVM, Maximum Likelihood, Minimum Distance, Neural Network, and Random Forest, the study area was classified into two categories of water and non-water areas. Finally, the results obtained from all classification algorithms for pre and post-flood images of Mazandaran province in the 2019 flood event were merged using the majority voting method, which is considered an integration approach at the decision-making level. Random forest classification algorithm with overall accuracy of 97.76 and 94.12 and Kappa coefficient 94.49 and 91.41 for images before and after flood had the best classification performance among the algorithms used in this research. The fusion of classification algorithms showed an improvement in the separation performance of water and non-water areas with an increase in the overall accuracy of separation to 98.41 and 95.24 and Kappa coefficient 96.12 and 92.81 for the images before and after the flood.


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