Analysis of water spectral features of petroleum pollution and estimate models from remote sensing data

Miao-fen Huang; Wu-yi Yu; Yi-min Zhang; Jin-li Shen; Xiao-ping Qi

doi:10.1117/12.816200

24 November 2008 Analysis of water spectral features of petroleum pollution and estimate models from remote sensing data

Miao-fen Huang, Wu-yi Yu, Yi-min Zhang, Jin-li Shen, Xiao-ping Qi

Author Affiliations +

Proceedings Volume 7123, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China; 712312 (2008) https://doi.org/10.1117/12.816200
Event: Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, 2007, Beijing, China

Abstract

Petroleum pollution is a key indicator to monitor and assess water environment in petroleum fields. Five sessions of field work were made in Liaohe River in Panjin city, Liaoning province of China in 2006 and 2007. Field water spectra and concurrent water samples for laboratory measurements of chlorophyll, petroleum pollution, and suspended material were collected. An important feature of water spectra influenced by petroleum pollution was found to show that there are three peaks and two troughs in spectral curves. The peaks are at 570-590, 680-710, and 810-830nm, while troughs are at 650-680 and 740-760nm. The field spectra were used as to correspond to Landsat TM bands to establish estimate models of petroleum pollution concentration. The models were applied to the Landsat/ TM image on 11th Oct 2006 to obtain the distribution image of petroleum pollution. The accuracy is up to 80% for petroleum pollution estimation with the validation of reserved samples. The result shows that the estimate models from remotely sensing data provide an effective means to obtain rapidly and low-cost the distribution of petroleum pollution concentration in the study area.

Citation Download Citation

Miao-fen Huang, Wu-yi Yu, Yi-min Zhang, Jin-li Shen, and Xiao-ping Qi "Analysis of water spectral features of petroleum pollution and estimate models from remote sensing data", Proc. SPIE 7123, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, 712312 (24 November 2008); https://doi.org/10.1117/12.816200

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