Lake water body mapping with multiresolution based image analysis from medium‐resolution satellite imagery

Abstract

Shoreline mapping and shoreline change detection are critical for safe navigation, coastal resource management, coastal environmental protection and sustainable coastal development and planning. The main difficulty of traditional shoreline mapping from remote sensing classification is the lack of adequate tools to characterize and combine texture and spectral information effectively. This paper introduces a method for unsupervised lake shoreline delineation through combination of scene texture and spectral characteristics. The framework is based on multiresolution image segmentation via multispectral anisotropic diffusion neural network, in combination with texture derived from 2D‐wavelet transform algorithm. We illustrate the application of this algorithm by extracting water body pixels from Landsat ETM+, TM and MSS for case study of Lake Nakuru in Kenya. The results are very superior compared to the conventional methods (NDWI) and indicate that the lake has reduced by 18.8% between 1976–2001.