Sentinel-1 and Sentinel-2 data fusion to distinguish building damage level of the 2018 Lombok Earthquake

Abstract

Following an earthquake, Building Damage Assessment (BDA) is crucial in detecting areas that need immediate rescue actions and planning for the best evacuation strategy. Remote sensing has been widely used for BDA. The availability of Sentinel-1 and Sentinel-2 images that are freely accessible could enhance remote sensing applications for building damage classification caused by natural disasters. The accuracy of using the two satellite images for BDA mapping in tropical regions is still uncertain. This research aims to assess Sentinel-1 and Sentinel-2 data fusion performance for BDA mapping following the catastrophic 2018 Lombok Earthquake. Pre- and post-earthquake images of Sentinel-1 and Sentinel-2 were selected, preprocessed, and integrated using the random forest classifier. Three data scenarios were used in this study, i.e., Sentinel-1, Sentinel-2, and the fusion of both datasets. The results demonstrated that all models showed excellent performance in classifying destroyed buildings compared to severely and moderately damaged buildings. This is due to the medium spatial resolution of the images could not identify the damage level in detail. Sentinel-1 and Sentinel-2 data fusion provided the highest overall accuracy value (62.4%) compared to other single dataset models. According to the feature importance analysis result, GLCM Mean, GLCM Variance, and NIR Band played crucial roles in the damage classification. In conclusion, the fusion of Sentinel-1 and Sentinel-2 can provide the best model for destroyed building mapping. However, severely and moderately building damaged mapping can only be accurately mapped using higher spatial resolution images.