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|Title:||Satellite-derived land use changes along the Xinan River watershed for supporting water quality investigation for potential fishing grounds in Qiandao Lake, China||Authors:||Agapiou, Athos
Hadjimitsis, Diofantos G.
|Keywords:||Un-mixing;Land use change;Classification;Optical remote sensing;Trophic State Index;Radar||Category:||Civil Engineering;Civil Engineering||Field:||Engineering and Technology||Issue Date:||1-Jan-2014||Source:||2nd International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2014, Paphos, Cyprus, 7 April 2014 through 10 April 2014||Link:||http://www.cyprusremotesensing.com/rscy2014/||DOI:||10.1117/12.2066314||Project:||Dragon 3 Cooperation Project: Monitoring water quality features and prediction of potential fishing grounds by using multi-source and multi-scale remote sensing imageries||Abstract:||It is estimated that more than 20,000 natural lakes are found across China. Most of these lakes are undergoing eutrophication or other severe environmental nuisances owing to natural and/or anthropogenic processes. In order to prevent or to minimize such damaging impacts, and to ascertain a proper quality management of the lake water and the associated fish resources, it is required to have access to up-to-date, accurate, and relevant data and information on the aquatic ecosystem in a timely manner. The "Dragon 3"? project, supported by the European Space Agency, is focusing on Xinan river watershed and investigates the impact of water quality and land cover/use change on the spatiooral distribution of the fishing grounds in Qiandao Lake. In this paper, the land use changes derived from satellite images is presented. Initially, Landsat 5 TM and Landsat 8 LDCM have been analyzed for the last 20 years in the vicinity of the Xinan river watershed. Following the radiometric calibration of the images, several pixel-base classification algorithms have been evaluated including Spectral Angle Mapper (SAM), Support Vector Machine (SVM) as well Neural Network (NN). As it was found using the multioral satellite imagery, the SVM algorithm was able to give high kappa accuracy estimated at around 0.90. In addition EO-Hyperion images over the western part of the Xinan River were evaluated using hyperspectral vegetation indices as well using linear spectral un-mixing techniques. In addition, ENVISAT radar images have been evaluated in terms of land use change. The final outcomes indicate a significant urban expansion in the surrounding area of the Xinan River which impacts the water quality investigation. Finally, a Landsat image was processed in order to estimate the Trophic State Index (TSI) values over the water bodies and the highest values were observed over the Xinan river watershed and more specifically for the urban sites. © 2014 SPIE.||URI:||http://ktisis.cut.ac.cy/handle/10488/9430||ISBN:||978-162841276-5||Rights:||SPIE||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια /Conference papers - poster -presentation|
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