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High Resolution Imagery-Based Statistical Analysis for Urban Rivers Extraction and Quality Classification—Case Study in Beijing District of China

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Proceedings of the 8th China High Resolution Earth Observation Conference (CHREOC 2022) (CHREOC 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 969))

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Abstract

The previous work on monitoring the urban water quality using remote sensing focused mainly on the specific spectral/band math and the regression relationship between the spectral signature and water surface’s Chlorophyll-a content, while conducting urban wastewater monitoring on the basis of indirect interpretation indicators is still a gap to be bridged. Here we report a novel hybrid method to extract and classify the urban wastewaters (caused mainly by eutrophication) from 4-band high resolution imagery using a multi-step statistical approach. First, the MTSUWI (Modified Two-Step Urban Water Index) algorithm is presented for extracting the urban water automatically, and the Kappa Coefficient comes up to 0.92. Second, using the Minimum Message Length Criterion-Expectation Maximization algorithm (MML-EM), the histogram of the water-masked 1st principal component image was screened into two subpopulations, which are mainly a re-flection of the existing background of the waters, rather than the pollution. Third, the water floating matters (most of them are green algae) is selected as an indirect interpretation key of the polluted wastewaters, and by gradually reducing the interpretation target area, pixels containing green alga were enhanced and then extracted in the brightness image. Fourth, any river reach containing at least one patch of green alga is labeled as “polluted”, otherwise they are labeled as “clean/fresh”, and finally, eight black-and-odorous wastewaters in the study area were selected out, which are consistent with literatures and observations from the field. This research is one of the first to apply indirect interpretation indicators to 4-band high resolution imagery for the classification of urban polluted rivers.

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Acknowledgments

Great thanks go to Dr. Wang Yuxiang, who is the president of Beijing PIESAT Information Technology Co., Ltd., for his help in providing the relevant remote sensing imagery. This work was financially supported by Beijing Water Authority’s scientific research project: high resolution imagery-based urban black-odorous-water mapping and Fundamental Re-search Funds for the Central Universities of China (set for developing innovative R & D teams) (Project No. 30010226940). Helpful review comments on the manuscript were provided by several anonymous reviewers.

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Authors and Affiliations

  1. School of Geology Engineering and Geomatics, Chang’an University, Xi’an, China

    Dongsheng Liu & Ling Han

  2. PIESAT Information Technology Co., Ltd, Beijing, China

    Dongsheng Liu

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  1. Dongsheng Liu
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  2. Ling Han
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Correspondence to Ling Han .

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Editors and Affiliations

  1. China Aerospace Science and Technology, Beijing, China

    Liheng Wang

  2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Yirong Wu

  3. Wuhan University, Wuhan, China

    Jianya Gong

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Liu, D., Han, L. (2023). High Resolution Imagery-Based Statistical Analysis for Urban Rivers Extraction and Quality Classification—Case Study in Beijing District of China. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 8th China High Resolution Earth Observation Conference (CHREOC 2022). CHREOC 2022. Lecture Notes in Electrical Engineering, vol 969. Springer, Singapore. https://doi.org/10.1007/978-981-19-8202-6_22

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  • DOI: https://doi.org/10.1007/978-981-19-8202-6_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8201-9

  • Online ISBN: 978-981-19-8202-6

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