Fuzzy estimation of trophic state using satellite data

Eutrophication is one of the major water quality problems in developing and developed countries. Generally, trophic state of water sources is under a strict watch and is determined by in situ water sampling for some important water resources. However, a limited number of water samples provide insufficient statistical confidence with an overall eutrophic status by using point-basis water sampling data which is usually executed under a physical and financial limitation. Also, traditional trophic state indices, such as Carlson index and OECD index, employ a crisp determination that often causes debated argument. This research applies fuzzy set theory on satellite data to describe the trophic state for reservoir water. A multi-spectral SPOT satellite image was converted into water quality variables, such as phosphorus, Secchi depth, and chlorophyll that are the major affecting factors of eutrophication. Fuzzy synthetic evaluation for the trophic state was developed by using satellite-derived two-dimensional water variables. Feitsui Reservoir, which is the most important water supply for over five million people in the great Taipei area, Taiwan, was the study site for demonstrating the trophic state determination through the fuzzy evaluation method.

[1]  Ming-Der Yang,et al.  Estimation of algal biological parameters using water quality modeling and SPOT satellite data , 2000 .

[2]  Ming-Der Yang,et al.  WATER QUALITY MODELING FOR THE FEITSUI RESERVOIR IN NORTHERN TAIWAN 1 , 2003 .

[3]  Ke-Sheng Cheng,et al.  RESERVOIR TROPHIC STATE EVALUATION USING LANISAT TM IMAGES 1 , 2001 .

[4]  Ming-Der Yang,et al.  Application of fuzzy theory to satellite data for determining eutrophic status , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Hermann Kaufmann,et al.  Lake water quality monitoring using hyperspectral airborne data—a semiempirical multisensor and multitemporal approach for the Mecklenburg Lake District, Germany , 2002 .

[6]  Olga Kosheleva,et al.  IEEE International Conference on Fuzzy Systems , 1996 .

[7]  T. Stein International Geoscience And Remote Sensing Symposium , 1992, [Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium.

[8]  B. Yegnanarayana,et al.  Rough-fuzzy membership functions , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[9]  J. Campbell Introduction to remote sensing , 1987 .

[10]  L. Chen,et al.  A study of applying genetic programming to reservoir trophic state evaluation using remote sensor data , 2003 .

[11]  S. Ekstrand Landsat TM based quantification of chlorophyll-a during algae blooms in coastal waters , 1992 .