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Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management

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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.

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Data availability

The datasets used during the current study are available from the Copernicus Open Access Hub (https://scihub.copernicus.eu) and the US Geological Survey (https://earthexplorer.usgs.gov). The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgments

The authors are grateful to the United States Geological Survey (USGS) and the European Space Agency (ESA) for providing satellite data. We express our sincere thanks to Dr. Kaan Kalkan for his useful suggestions.

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Authors and Affiliations

  1. Department of Remote Sensing and Geographical Information Systems, Earth and Space Sciences Institute, Eskisehir Technical University, 26555, Eskisehir, Turkey

    Sayed Ishaq Deliry

  2. Department of Environmental Engineering, Eskisehir Technical University, 26555, Eskisehir, Turkey

    Zehra Yiğit Avdan

  3. Earth and Space Sciences Institute, Eskisehir Technical University, 26555, Eskisehir, Turkey

    Uğur Avdan

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  1. Sayed Ishaq Deliry
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  3. Uğur Avdan
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Contributions

SID conceptualized, collected the data, and wrote the original draft of the manuscript. ZYA and UA participated in the analysis and interpretation of data. SID, ZYA, and UA reviewed and edited the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sayed Ishaq Deliry.

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Deliry, S.I., Avdan, Z.Y. & Avdan, U. Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management. Environ Sci Pollut Res 28, 6572–6586 (2021). https://doi.org/10.1007/s11356-020-11007-4

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  • DOI: https://doi.org/10.1007/s11356-020-11007-4

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