Analysis of Water Quality Conditions of Lake Hachiroko, Japan, with Fuzzy C-means Using Terra ASTER Data

Abstract

Lake Hachiroko, Japan, has many water quality issues, evident from phenomena such as green algae blooms. Understanding the details of the surface water quality of the lake and the effect of seasons on its quality is necessary. In our previous studies, we conducted fuzzy regression analysis of remote sensing data and direct measurements of water quality. The results showed that estimation maps of water quality were well created, using only five data points of water quality parameters. To obtain maps that are in good agreement with experimental data, remote sensing data and water quality values should be acquired simultaneously. However, performing such simultaneous observations can affect the preparation of the water quality estimation maps. We overcame this obstacle by using fuzzy c-means clustering (FCM). With this method, we considered the effects of specific disturbances and uncertainties on the remote sensing data. The results indicated that FCM was particularly effective in determining suspended solids during water quality analysis. However, FCM has the issue that the initial values given to the classes used in FCM affect the FCM results. Thus, it is necessary to study the setting of the best initial values so as to obtain good estimation results. In this study, we evaluated the setting of the best initial values of FCM. First, we defined the classes of FCM based on the water quality data acquired at measurements sites in the study area. Then, we set the initial values of the classes from information acquired at water quality measurements sites in the defined classes. We performed classification by FCM using these initial values of the classes. To evaluate the usefulness of FCM, we compared the estimation maps with the water quality data and the result of the fuzzy regression analysis.