CLEAR research assistant Anna Chabaeva conducted research to develop an impervious surface estimation model for use with National Land Cover Data (NLCD) and Census Tract population density data. High-resolution planimetric data for buildings and transportation features provided the footprints for impervious features from towns in Connecticut, Massachusetts, and New York and were used to calibrate the model. Regression analyses were done to investigate the relationship of population density, impervious cover and remotely-sensed land cover data. A regression model was developed from this study and applied to the entire state, with the data averaged by local watershed (map, below). Testing for accuracy using 236 watersheds for which CLEAR has detailed impervious coverage data showed a very strong correlation between actual percent imperviousness and that predicted by the model (R2 = 0.89). Please see our reference paper for the complete study.

Application of regression model to local watersheds in Connecticut.

Testing the model showed a strong correlation between actual percent imperviousness and predicted.