2012 年 4 月 兩岸力學科技論壇論文摘要集 臺北、臺南
Multi-platforms of remote sensing for mapping the water
quality
LIU Cheng-Chien
1,21 Department of Earth Science, National Cheng-Kung University, No 1, Ta-Hsueh Road, Tainan 701 Taiwan ROC 2 Global Earth Observation and Data Analysis Centre, National Cheng-Kung University, No 1, Ta-Hsueh Road, Tainan 701
Taiwan ROC
Abstract: Monitoring the inland and coastal water quality receives a lot research interests in Taiwan. Because of the geology, topography, climate, and human activities in the river basin, both physical and chemical weathering rates in Taiwan are very high. Together with the triggering of heavy rainfall during the raining and typhoon season, the sediment loading to the inland and coastal systems is usually concentrated in a small region within a short period of time. To gain a better understanding of the source and sink of these sediments, we need an innovative way to map their temporal and spatial variation. Remote sensing technique is widely used to provide the two dimensional synoptic observation of the sea surface, such as the space-borne sensors MODIS, SeaWiFS and MERIS, etc. The recent progress of integrating the semi-analytical algorithm and the genetic algorithm has demonstrated that the water constituents can be routinely derived from ocean color remote sensing. To catch the dynamic changes of sediment loading in the coastal systems near Taiwan, however, none of the single platform is able to provide an observation with high-spatial-, temporal-, and spectral resolutions. This motivates us to develop the technique of multi-stage observations of water quality for the coastal and inland waters in Taiwan. This presentation includes (1) Genetic And Semi-Analytical algorithms (GA-SA) to retrieve the constituents of water bodies from remote sensing of ocean color, (2) Using MODIS imagery to categorize the dispersal patterns of river-borne substances in the Gaoping River, Shelf and Canyon System, (3) Monitoring reservoir water quality with Formosat-2 high spatiotemporal imagery, (4) Export Landslide and Shaded Area Delineation System (ELSADS) to prepare the landslide inventory of Taiwan, (5) Application of ISIS-1 hyperspectral optical remote sensing imagery on monitoring the reservoir water quality, (6) Rapid mapping of slope hazards with photos acquired from a low-cost unmanned aerial vehicle using the Automatic Mission Planning And Image Processing System (AMPIPS), and (7) Retrieving benthic coral reef distribution and water quality using the genetic analytical and shallow water semi-analytical model to process the underwater hyperspectral reflectance. Some discussions and our research strategy for the future works would be given, and hopefully, more collaboration could be initiated after this workshop.
Key words: remote sensing, water quality, multi-platform, hyperspectral, unmanned aerial vehicle.
Project: National Science Council of Taiwan
Corresponding author: LIU Cheng-Chien, Professor, IEEE member. Research interest: Remote sensing, ocean optics, geospatial information science, unmanned aerial vehicle. Email: ccliu88@mail.ncku.edu.tw.
