Monitoring temporal change of river islands in the Yangtze River by remotely sensed data

Abstract

The spatial extent and area of river islands are always changing due to the impact of hydrodynamic conditions, sediment supply and human activities. A catastrophic flood disaster was driven by sustained and heavy rainfall around the middle and lower Yangtze River in 18 June to 21 July 2016. The flood resulted in the most serious social-economic loss since 1954 and caused a larger-scale inundation for a short time. It is essential to continuously monitor the dynamics changes of river islands because this can avoid frequent field measurements in river islands before and after flood disasters, which are helpful for flood warning. This paper focuses on the temporal change of three river islands called Fenghuangzhou, Changshazhou, and one uninhabited island in the Yangtze River in 2016. In this study, GF-1 (GaoFen-1) WFV (wide field view) data was used for our study owing to its fine spatial and temporal resolution. A simple NDWI (Normalized Difference Water Index) method was used for the river island mapping. Human checking was then performed to ensure mapping accuracy. We estimated the relationship between the area of river islands and measured water levels using four models. Furthermore, we mapped the spatial pattern of inundation risk of river islands. The results indicate a good ability of the GF-1 WFV data with a 16-m spatial resolution to characterize the variation of river islands and to study the association between flood disaster and river islands. A significantly negative but nonlinear relationship between the water level and the area of the river island was observed. We also found that the cubic function fits best among three models (R2 > 0.8, P < 0.001). The maximum of the inundated area at the river island appeared in the rainy season on 8 July 2016 and the minimum occurred in the dry season on 28 December 2016, which is consistent with the water level measured by the hydrological station. Our results derived from GF-1 data can provide a useful reference for decision-making of flood warning, disaster assessment, and post-disaster reconstruction.