AI SearchBuilding and validating a general chlorophyll-a prediction algorithm for inland/coastal waters in the Mediterranean Sea using low resolution satellite imagery
Call Number: AIT Thesis no.RS-19-03 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. RS-19-03Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2019Description: 64 leaves : illSubject(s): Dissertation note: Thesis (M. Sc.) - Asian Institute of Technology, 2019 Summary: Mediterranean Sea waters is an oligotrophic basin, water quality deterioration and eutrophication are well-known phenomena along its coastal and estuarine regions. The phytoplankton is one of the worst factors that lives on the surface of water and absorbs the sunlight and nutrient by chlorophyll pigments for the process of photosynthesis. This affects many problems in environment such as adding air pollution, reducing water quality, and reducing productivity agriculture. Remote sensing is an art of technology which can monitor Chl-a concentration in inland or coastal waters (case II) through the special function with MODIS (Terra satellite), MERIS (Envisat satellite) because these images have an acceptable spectral and radiometric resolution. On the other hand, case II water includes many components: phytoplankton, Chromophoric Dissolved Organic Matter or colored dissolved organic matter (CDOM), and Total Suspended Materials (TSM) as a challenge. However, Chl-a is a part of pigment in phytoplankton that can be estimated by the spectral reflectance of remote sensing technique at NIR and Red regions and time series. ChI predictive algorithm implemented in Cabras and Santa Giusta lagoons, and performances of those different sensors evaluated. The empirical algorithm has been developed and improved which based on the linear regression. The results can be applied even in other case II waters with improved performance.
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Remote Sensing and Geographic Information System
Thesis (M. Sc.) - Asian Institute of Technology, 2019
Mediterranean Sea waters is an oligotrophic basin, water quality deterioration and eutrophication are well-known phenomena along its coastal and estuarine regions. The phytoplankton is one of the worst factors that lives on the surface of water and absorbs the sunlight and nutrient by chlorophyll pigments for the process of photosynthesis. This affects many problems in environment such as adding air pollution, reducing water quality, and reducing productivity agriculture. Remote sensing is an art of technology which can monitor Chl-a concentration in inland or coastal waters (case II) through the special function with MODIS (Terra satellite), MERIS (Envisat satellite) because these images have an acceptable spectral and radiometric resolution. On the other hand, case II water includes many components: phytoplankton, Chromophoric Dissolved Organic Matter or colored dissolved organic matter (CDOM), and Total Suspended Materials (TSM) as a challenge. However, Chl-a is a part of pigment in phytoplankton that can be estimated by the spectral reflectance of remote sensing technique at NIR and Red regions and time series. ChI predictive algorithm implemented in Cabras and Santa Giusta lagoons, and performances of those different sensors evaluated. The empirical algorithm has been developed and improved which based on the linear regression. The results can be applied even in other case II waters with improved performance.
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