Improved Urban Impervious Surface Mapping Using Multi-Sensor Feature Fusion: A Cross-City Analysis

Muhammad Nasar Ahmad; Fahad Almutlaq; Md. Enamul Huq; George D. Bathrellos; Akib Javed; Fakhrul Islam; Hariklia D. Skilodimou

doi:10.20944/preprints202409.1033.v1

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preprints.org > environmental and earth sciences > remote sensing > doi: 10.20944/preprints202409.1033.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Improved Urban Impervious Surface Mapping Using Multi-Sensor Feature Fusion: A Cross-City Analysis

^* ,

Hariklia D. Skilodimou

Version 1 : Received: 12 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (15:48:22 CEST)

How to cite: Ahmad, M. N.; Almutlaq, F.; Huq, M. E.; Bathrellos, G. D.; Javed, A.; Islam, F.; Skilodimou, H. D. Improved Urban Impervious Surface Mapping Using Multi-Sensor Feature Fusion: A Cross-City Analysis. Preprints 2024, 2024091033. https://doi.org/10.20944/preprints202409.1033.v1 Ahmad, M. N.; Almutlaq, F.; Huq, M. E.; Bathrellos, G. D.; Javed, A.; Islam, F.; Skilodimou, H. D. Improved Urban Impervious Surface Mapping Using Multi-Sensor Feature Fusion: A Cross-City Analysis. Preprints 2024, 2024091033. https://doi.org/10.20944/preprints202409.1033.v1

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MDPI and ACS Style

Ahmad, M. N.; Almutlaq, F.; Huq, M. E.; Bathrellos, G. D.; Javed, A.; Islam, F.; Skilodimou, H. D. Improved Urban Impervious Surface Mapping Using Multi-Sensor Feature Fusion: A Cross-City Analysis. Preprints 2024, 2024091033. https://doi.org/10.20944/preprints202409.1033.v1

APA Style

Ahmad, M. N., Almutlaq, F., Huq, M. E., Bathrellos, G. D., Javed, A., Islam, F., & Skilodimou, H. D. (2024). Improved Urban Impervious Surface Mapping Using Multi-Sensor Feature Fusion: A Cross-City Analysis. Preprints. https://doi.org/10.20944/preprints202409.1033.v1

Chicago/Turabian Style

Ahmad, M. N., Fakhrul Islam and Hariklia D. Skilodimou. 2024 "Improved Urban Impervious Surface Mapping Using Multi-Sensor Feature Fusion: A Cross-City Analysis" Preprints. https://doi.org/10.20944/preprints202409.1033.v1

Abstract

The study put forward a data fusion approach for urban remote sensing that combines SAR (Synthetic Aperture Radar) and optical satellite data. By integrating datasets from different sensors and spatial-temporal scales, the technique aims to extract more accurate information. The fusion approach utilizes two methods: feature-based fusion, where relevant features are extracted and fused, and simple layer stacking (SLS), where the original datasets are directly stacked as multiple layers. This study extracted features using SAR textures (using Sentinel-1) and modified indices (using Landsat-8), and then classified these features using an XGBoost algorithm implemented in Python and Google Earth Engine. Researchers examined five cities, each representing a distinct climatic zone and urban dynamic: Cape Town, Guangzhou, Los Angeles, Mumbai, and Osaka. An accuracy assessment was conducted using random validation points, achieving an overall accuracy of 89.5% using the proposed MSFI method. A comparison was also performed with three well-known global products. The proposed approach, outperformed all three global products achived 89% accuracy while ESA (84%), ESRI (81%) and Dynamic World (82%). Additionally, Land surface temperature analysis was accomplished to investigate the relationship between extracted UIS and Land Surface Temperature (LST) across selected cities to show the practical use of proposed MSFI method. Los Angeles, a warm temperate city, showed the highest LST among all five cities. The datasets, along with the GEE and Python codes, are available at https://github.com/mnasarahmad/sls.

Keywords

Data Fusion; Impervious surface; Landsat 8; LST; Sentinel-1

Subject

Environmental and Earth Sciences, Remote Sensing

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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