The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B3-2021
29 Jun 2021
 | 29 Jun 2021


D. Halder and R. D. Garg

Keywords: Normalized Classification, Regression Modelling, SVM Classification, Impervious surface mapping

Abstract. The cities where the future happens first, they are open, creative, cosmopolitan and sexy and the perfect antidote to reactionary nationalism but the urbanization in unplanned manner is becoming an environmental-social-economical threat to accommodate the huge number of population which is literally boosting the present situation of climate change due to global warming. Extracting, measuring and treating the urban area which compiles of dense built-up and complex road network, is very essential to decrease the negative impact on environment. If most of the impervious surfaces can be replaced with permeable or semi-permeable materials or solar panel then the habitation will be saved from natural disastrous events like heat wave and flash flood. Urbanization can be categorized mainly into two: a) Static (urban open space + built space) and b) Dynamic (transportation). The static and dynamic urbanizations largely consist of impermeable or impervious materials. Impervious surfaces are alluded as the anthropogenic elements through that water can't infiltrate into the soil, such as streets, driveways, parking areas, houses, structures etc. An urban area is a densely populated human settlement, facilitated with multiple infrastructures including built and un-built. These areas or settlements are categorized as towns, suburbs, cities by urban morphology. Through balancing the ratio between the un-built (urban space) and built (building & roads), urban disastrous events can be minimized. This research mainly focused on the extraction of impervious areas using regression modelling approach which is used to generate an impervious surface map from Sentinel-2A dataset of Delhi. Utilising multiple normalised indices can provide better classification results. This study shows that in urban areas imperviousness is becoming one of the prominent computational parameter and monitoring impervious areas could help us understand a lot of urban phenomena which are built-up induced and its rapid change in urban environment is giving rise to unhealthy living conditions.