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The present study was conducted using secondary database, remote sensing, geographical information system (GIS) and multivariate analysis tools in order to develop Multiple Linear Regression (MLR) models that could be able to predict level of water quality variables using compositional and spatial attributes of land cover in the river basins. The study encompasses 21 river basins with 32 000 Km² area, located in the Chugoku district in West Japan. Biological Oxygen Demand (BOD), pH, Dissolved Oxygen (DO), Suspended Solid (SS), Total Nitrogen (TN), and Total Phosphorus (TP) were considered as water quality variables of the stream. Satellite data was used to generate the land cover map of the study area. MLR models were developed using the compositional (%) and spatial attributes (landscape metrics) of the land cover at watershed and class levels for representing the land cover-stream water quality linkage. The results of the MLR modeling using the land cover data at the class level revealed that 92%, 74% and 62% of the total variations in concentration of DO, pH and TP were explained by changes in the measure of the spatial attributes of the land cover at the class level in the study area. These models can help local and regional land managers to understand the relationships between the compositional attribute (%) and the spatial features of the land cover and river water quality and would be applied in formulating plan for watershed-level management.
