Multitemporal image encoding for monitoring spatiotemporal variations of water bodies using Landsat 8 Operational Land Imager (OLI) data

Remote sensing enables multitemporal information of the Earth’s surface and the dynamic processes that affect the environment. Given the considerable data availability, methods to summarize multitemporal datasets are needed to support the analysis. Our study introduces and compares methods to monitor temporal variations of water bodies based on multitemporal image composition. For this purpose, the presence of water at different dates is mapped applying the normalized difference water index using two encoding methods. The first one is based on the cumulative analysis of water in the pixel along time, and the second one uses the principle of binary encoding. The cumulative analysis helps to visualize more humid and dry areas, while binary encoding indicates the monthly variations of the lake surface, storing information about the dynamics of the phenomenon. The methods are compared using Landsat time series of Lake Poopo obtained between 2013 and 2019. The results showed that binary encoding allows detecting when and where severe droughts affect the water body and its recovery. In addition, it was possible to monitor the severe drought that affected the lake in 2016 and it was also noticed that its surface is still below the level registered before the drought in 2013.