Application of knowledge-based classification techniques and geographic information systems (GIS) on satellite imagery for stormwater management

Stormwater management is concerned with runoff control and water quality optimization. A stormwater model is a tool applied to reach this goal. Hydrologic variables required to run this model are usually obtained from field surveys and aerial photo-interpretation. However, these procedures are slow and difficult. An alternative is the automated processing of satellite imagery. We examined various studies that utilized satellite data to provide inputs to stormwater models. The overall results of the modeling effort are acceptable even if the outputs of satellite data processing are used instead of those obtained from standard techniques. One important model input parameter is land use because it is associated with the amounts of runoff and pollutants generated in a parcel of land. Hence, we also explored new ways that land use can be identified from satellite imagery.; Next, we demonstrated how the combined technologies of satellite remote sensing, knowledge-based systems, and geographic information systems (GIS) are used to delineate impervious surfaces from a Landsat ETM+ data. Imperviousness is a critical model input parameter because it is proportional to runoff rates and volumes. We found that raw satellite image, normalized difference vegetation image, and ancillary data can provide rules to distinguish impervious surfaces satisfactorily. We also identified different levels of pollutant loadings (high, medium, low) from the same satellite imagery using similar techniques. It is useful to identify areas with high stormwater pollutant emissions so that they can be prioritized for the implementation of best management practices. The contaminants studied were total suspended solids, biochemical oxygen demand, total phosphorus, total Kjeldahl nitrogen, copper, and oil and grease. We observed that raw data, tasseled cap transformed images, and ancillary data can be utilized to make rules for mapping pollution levels. Finally, we devised a method to compute weights associated with the severity of misclassification errors. We proposed the use of the weighted equivalents of the overall accuracy and kappa coefficient to evaluate the quality of classifications for pollutant loadings estimation. Overall, we conclude that the automated classification of satellite imagery can provide valuable information that can be used in stormwater management.