Inland waterbody mapping: towards improving discrimination and extraction of inland surface water features

Surface waterbodies in arid and semi-arid environments are threatened by both natural and anthropogenic pressures. Mapping the distribution of surface waterbodies is crucial for managing their dwindling quantities and quality. In this study, a fast and reliable method of water extraction has been introduced. A remote-sensing index called the simple water index (SWI) was formulated to differentiate waterbodies from vegetation class automatically, and to differentiate waterbodies from shadows or built-up areas (water-like features). Its performance was compared with the automated water extraction index (AWEI) and the modified normalized difference water index (MNDWI) on Landsat 8 Operational Land Imager (OLI) image of South Africa. The robustness of the algorithm was tested on images in Madagascar and the Democratic Republic of Congo (DRC) with different biomes. The overall accuracies and kappa coefficient (.) were used to compare the performance of each index. The McNemar test was performed to assess the significance of the output map and the validation data set. The SWI showed the highest overall accuracy of 91.9% (kappa = 0.83), whereas the AWEI and MNDWI yielded overall accuracies of 83.8% (kappa = 0.65) and 78.4% (kappa = 0.53), respectively. The McNemar test showed that there was no significant difference between the SWI map (p = 0.248), whereas both AWEI and MNDWI maps were significantly different from the validation data set at p = 0.041 and p = 0.013, respectively. The SWI approach reduces the thresholding problem by 50% over the conventional MNDWI and AWEI. It is expected that the SWI will also be useful for the accurate quantification of waterbodies for large areas.