[Effects of Green, Blue, and Blue-green Roofs on Runoff Quality].

Roofs occupy a great proportion of urban impervious surfaces, and the implementation of eco-roof construction in urban areas is beneficial to alleviate the ecological and environmental problems caused by rapid urbanization. In this study, different eco-roofs (i.e., 68.6%-90.7%, and 39.8%-54.5%, respectively. However, all the eco-roofs were sources of NO-3-N, DCr, DFe, and DNi. The blue roof was a sink of DCu (with a pollutant load reduction rate of 21.9%) and did not affect the cumulative load of PO3-4-P in runoff. However, the green roof and blue-green roof were the sources of PO3-4-P and DCu. The RQI value of the blue roof was the highest, followed by that of the blue-green roof and green roof. The RQI value of the green roof was significantly lower than that of the blue and blue-green roofs (P<0.05). These results indicated that the runoff quality of the blue roof was the best, whereas that of the green roof was the worst. Adding a storage layer to the green roofs could significantly improve the runoff quality. The results of this study provide scientific references for the selection and design of eco-roof facilities.

Extraction of blue roofs using BRSAM and the newly created spectral index derived from WorldView-2/3 imagery.

To extract blue roofs (BRs) from remote sensing images quickly, first, this study used WorldView-2 and WorldView-3 as the data sources and created a new BR spectral area model (BRSAM) using the polygon area difference between the spectral curves of BRs and other image categories in bands 2-8. Then the extraction effect of BRSAM with those of the blue ground object spectral index (BGOSI), blue object spectrum index (BOSI) and maximum likelihood classification was compared; the results showed that BRSAM overcomes the shortcomings of BGOSI and BOSI, i.e. erroneously extracted shadow and white and yellow ground objects as BRs. However, BRSAM has the disadvantage of erroneously extracting some vegetation and green plastic playground as BRs. Considering that the disadvantage of one of BRSAM, BGOSI, and BOSI in extracting BRs can be compensated by the two other spectral models/indices, we combined the three spectral models/indices and used the synthetic spectral model to extract BRs. Notably, the synthetic spectral model overcomes the shortcomings of the three spectral models in BR extraction, and its effect is better than any one of them separately used. Meanwhile, the spectral model/index method used in BR extraction is better than the classification method. The spectral model/index method is a convenient and effective method for BR extraction, which could be used as a reference in the classification of other data.