Version 1
: Received: 1 July 2022 / Approved: 5 July 2022 / Online: 5 July 2022 (10:07:01 CEST)
How to cite:
Javed, A.; Shao, Z.; Ara, I.; Huq, M. E.; Ali, M. Y.; Saleem, N.; Ahmad, M. N. Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping. Preprints2022, 2022070071. https://doi.org/10.20944/preprints202207.0071.v1
Javed, A.; Shao, Z.; Ara, I.; Huq, M. E.; Ali, M. Y.; Saleem, N.; Ahmad, M. N. Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping. Preprints 2022, 2022070071. https://doi.org/10.20944/preprints202207.0071.v1
Javed, A.; Shao, Z.; Ara, I.; Huq, M. E.; Ali, M. Y.; Saleem, N.; Ahmad, M. N. Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping. Preprints2022, 2022070071. https://doi.org/10.20944/preprints202207.0071.v1
APA Style
Javed, A., Shao, Z., Ara, I., Huq, M. E., Ali, M. Y., Saleem, N., & Ahmad, M. N. (2022). Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping. Preprints. https://doi.org/10.20944/preprints202207.0071.v1
Chicago/Turabian Style
Javed, A., Nayyer Saleem and Muhammad Nasar Ahmad. 2022 "Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping" Preprints. https://doi.org/10.20944/preprints202207.0071.v1
Abstract
Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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