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Changes in water quality in rivers and lakes caused by water pollution are generally investigated by extracting water samples directly from several locations. However, this method is not appropriate for understanding water quality conditions over a large area. On the other hand, remote sensing techniques are especially suitable for understanding water quality conditions over a large area. In our previous studies, we applied fuzzy c-means (FCM) clustering for analysis of water quality parameters in Lake Hachiroko. The results indicated that FCM was effective in estimating the level of pollution in Lake Hachiroko. Moreover, we considered the setting of the best initial values in FCM. The result showed that estimation maps by FCM showed detailed water quality conditions in Lake Hachiroko. However, some estimation maps were classified at the same level in the whole region because the slice level range preset was too wide. The estimation maps created by FCM were in agreement with the water quality conditions, while identifying the water pollution area in the lake was difficult. In this study, we proposed stepwise level slice processing to create water quality estimation maps by FCM. As a result, FCM with stepwise level slice processing (i) creates water quality estimation maps that identify the pollution area and (ii) is useful for water quality estimation of Lake Hachiroko.

Fuzzy C-Means with Stepwise Level Slice Processing for Water Quality Analysis in Lake Hachiroko