Urban stormwater runoff has been identified as the major source of many pollutants. Estimating stormwater mass emissions is inherently difficult due to the large area, many emission points, and the difficulty associated with sampling episodic storm events. Modeling urban stormwater runoff often requires land use information to estimate emissions.

Conventional collection of land use information, i.e. ground surveys, is time-consuming and expensive. Alternatively, land use data can be estimated from satellite imagery, and pollutants can be correlated using previously developed relationships. An alternate but less often used approach is to estimate stormwater pollutant loadings directly from satellite imagery, which is the objective of this research.

We conducted Bayesian network classification with Landsat TM image of Marina del Rey area in Santa Monica Bay Watershed. The results suggest an improved classification system for stormwater modeling, using open land use as low pollutant loading areas and transportation land use as high pollutant loading areas. Commercial and industrial land uses were medium and high loading areas, depending on the pollutant type. This indicates that management strategies should generally address transportation areas first. Classification systems should be developed for each water quality parameter. These results are useful in developing management practices for stormwater runoff.