Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental m
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RESEARCH ARTICLE
Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management Sayed Ishaq Deliry 1
&
Zehra Yiğit Avdan 2
&
Uğur Avdan 3
Received: 5 February 2020 / Accepted: 25 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Impervious surface is mainly defined as any surface which water cannot infiltrate the soil. Due to the impact of urban impervious surfaces (UIS) on environmental issues, the amount of impervious surfaces has been recognized as the most significant index of environmental quality. Detection and analysis of impervious surfaces within a watershed is one of the developing areas of scientific interest. This study evaluates and compares the accuracy and performance of five classification algorithms— supervised object-based nearest neighbour (NN) classifier, supervised pixel-based maximum likelihood classifier (MLC), supervised pixel-based spectral angle mapper (SAM), band ratioing normalized difference built-up index (NDBI), and normalized difference impervious index (NDII)—in extracting urban impervious surfaces. Our first aim was to identify the most effective method for mapping UIS using Sentinel-2A and Landsat-8 satellite data. The second aim was to compare and reveal the efficiency of the spatial and spectral resolution of Sentinel-2A and Landsat-8 data in extracting UIS. The results revealed that the supervised object-based NN approach using the visible and near-infrared bands of both satellite imagery produced the most homogenous and accurate map among the other methods. The object-based NN algorithm achieved an overall classification accuracy of 90.91% and 88.64%, and Kappa coefficient of 0.82 and 0.77 for Sentinel-2 and Landsat-8 images, respectively. The study also showed that the Sentinel-2 image yielded better results than the Landsat-8 pan-sharpened image in extracting detail and classification accuracy. Comparing these methods in the selected challenging study area can provide insight into the selection of the classification method for rapid and reliable extraction of UIS. Keywords MLC . NDBI . NDII . Supervised object-based NN classification . Supervised pixel-based classification . SAM . Urban impervious surfaces
Introduction Responsible Editor: Philippe Garrigues * Sayed Ishaq Deliry [email protected] Zehra Yiğit Avdan [email protected] Uğur Avdan [email protected] 1
Department of Remote Sensing and Geographical Information Systems, Earth and Space Sciences Institute, Eskisehir Technical University, 26555 Eskisehir, Turkey
2
Department of Environmental Engineering, Eskisehir Technical University, 26555 Eskisehir, Turkey
3
Earth and Space Sciences Institute, Eskisehir Technical University, 26555 Eskisehir, Turkey
Impervious surface is mainly defined as any surface which water cannot infiltrate the soil (Arnold Jr and Gibbons 1996; Slonecker et al. 2001; Bauer et al. 2005). Due to the impact of urban impervious surfaces on environmental issues such as
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