Remote Sensing (May 2024)
Inner Niger Delta Inundation Extent (2010–2022) Based on Landsat Imagery and the Google Earth Engine
Abstract
The Inner Niger Delta (IND), one of the largest floodplain systems in Africa, sustains the livelihoods of more than three million people and is a driver of the rural economy of Mali as far as agriculture, fish production, and livestock are concerned. Because the IND ecosystem and economy are flood-dependent, it is important to monitor seasonal flooding variations. Many attempts to accomplish this task have relied on detailed datasets, such as daily discharge, daily rainfall, and evapotranspiration, which are not easily accessible for data-sparse areas. Additionally, because the area is large, this remains a challenging task. In this study, the interannual variability of seasonal inundation in the IND was investigated by leveraging the computing power of the Google Earth Engine and its large catalogue of open datasets. The main objective was to analyse the temporal and spatial distributions of the inundation extent during the last 13 years. A collection of Landsat 5, 7, 8, and 9 images were composited and different bands were used with various water and vegetation indices in a pixel-based supervised classification to detect the flood extent between 2010 and 2022. A significant improvement in classification accuracy was observed thanks to the different indices. The results suggest a general increasing trend in the maximum annual inundation extent. Throughout the study period, the maximum inundated area varied between 15,209 km2 in autumn 2011 and 21,536 km2 in autumn 2022. The upstream water intake led to a decrease of about 6–10% of the inundated area. Similar fluctuations in the inundated area, precipitation, and river discharge were observed. The proposed approach demonstrates a great potential for monitoring annual inundation, especially for large areas such as the IND, where in situ measurements are sparse.
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