Method for estimating the concentration of total suspended matter in lakes based on goci images using a classification system

Total suspended matter( TSM) is an important water quality indicator that can directly affect the propagation of light in water and influence the aquatic ecological environment,and ultimately determines the primary productivity of a lake. Empirical TSM concentration estimation models are often built...

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Veröffentlicht in:Sheng tai xue bao 2015-01, Vol.35 (16)
Hauptverfasser: Zhao, Lina, Wang, Yannan, Jin, Qi, Feng, Chi, Pan, Hongzhou, Zhang, Jie, Lu, Heng, Li, Yunmei
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container_issue 16
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container_title Sheng tai xue bao
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creator Zhao, Lina
Wang, Yannan
Jin, Qi
Feng, Chi
Pan, Hongzhou
Zhang, Jie
Lu, Heng
Li, Yunmei
description Total suspended matter( TSM) is an important water quality indicator that can directly affect the propagation of light in water and influence the aquatic ecological environment,and ultimately determines the primary productivity of a lake. Empirical TSM concentration estimation models are often built for specific study areas,ignoring variation in the optical properties of water among diverse areas. In addition,common satellite sensors cannot be successfully used to monitor inland lakes owing to their temporal and spatial resolution. Taihu Lake,Chaohu Lake,Dianchi Lake,and Dongting Lake were selected as our study lakes,and an automatic two-step cluster method was applied for water classification based on simulated geostationary ocean color imager( GOCI) reflectance spectra. The results showed that the water samples could be classified into three types. The optical features of Water Type 1 were influenced by the TSM,the optical characteristics of Water Type 2 were influenced by both TSM and chlorophyll-a( Chl-a),and the optical properties of Water Type 3 weremainly determined by Chl-a. Estimation models were then developed for each water type using a band ratio of B7 / B4 for Water Type 1 and B7 /( B8 + B4) for Water Types 2 and 3 to retrieve the concentration of suspended solids. The root mean-squared errors( RMSEs) and minimum absolute percentage errors( MAPEs) of Water Type 1 were 9. 19 mg / L and3%,and those of Water Type 3 were 5. 63 mg / L and 13. 97%,respectively,which were significantly lower than those estimated using methods that do not consider this classification. The RMSE and MAPE of Water Type 2 were slightly higher than those estimated with the general algorithm. The diurnal variation of the TSM concentration in Taihu Lake was studied based on the GOCI data acquired on May 13,2013 using this classification method,and the results showed that the concentration of TSM was higher in the southwest than in the northeast. In addition,the area of higher TSM concentration in the southern region of the lake was reduced from 9: 00 to 15: 00( Beijing Local Time).
doi_str_mv 10.5846/stxb201411152264
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Empirical TSM concentration estimation models are often built for specific study areas,ignoring variation in the optical properties of water among diverse areas. In addition,common satellite sensors cannot be successfully used to monitor inland lakes owing to their temporal and spatial resolution. Taihu Lake,Chaohu Lake,Dianchi Lake,and Dongting Lake were selected as our study lakes,and an automatic two-step cluster method was applied for water classification based on simulated geostationary ocean color imager( GOCI) reflectance spectra. The results showed that the water samples could be classified into three types. The optical features of Water Type 1 were influenced by the TSM,the optical characteristics of Water Type 2 were influenced by both TSM and chlorophyll-a( Chl-a),and the optical properties of Water Type 3 weremainly determined by Chl-a. Estimation models were then developed for each water type using a band ratio of B7 / B4 for Water Type 1 and B7 /( B8 + B4) for Water Types 2 and 3 to retrieve the concentration of suspended solids. The root mean-squared errors( RMSEs) and minimum absolute percentage errors( MAPEs) of Water Type 1 were 9. 19 mg / L and3%,and those of Water Type 3 were 5. 63 mg / L and 13. 97%,respectively,which were significantly lower than those estimated using methods that do not consider this classification. The RMSE and MAPE of Water Type 2 were slightly higher than those estimated with the general algorithm. The diurnal variation of the TSM concentration in Taihu Lake was studied based on the GOCI data acquired on May 13,2013 using this classification method,and the results showed that the concentration of TSM was higher in the southwest than in the northeast. 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Estimation models were then developed for each water type using a band ratio of B7 / B4 for Water Type 1 and B7 /( B8 + B4) for Water Types 2 and 3 to retrieve the concentration of suspended solids. The root mean-squared errors( RMSEs) and minimum absolute percentage errors( MAPEs) of Water Type 1 were 9. 19 mg / L and3%,and those of Water Type 3 were 5. 63 mg / L and 13. 97%,respectively,which were significantly lower than those estimated using methods that do not consider this classification. The RMSE and MAPE of Water Type 2 were slightly higher than those estimated with the general algorithm. The diurnal variation of the TSM concentration in Taihu Lake was studied based on the GOCI data acquired on May 13,2013 using this classification method,and the results showed that the concentration of TSM was higher in the southwest than in the northeast. 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title Method for estimating the concentration of total suspended matter in lakes based on goci images using a classification system
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