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 Xin’an river watershed and investigates the impact of water quality and land cover/use change on the spatio-temporal 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 Xin’an 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 multi-temporal 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 Xin’an 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 Xin’an 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 Xin’an river watershed and more specifically for the urban sites.

Date Published: 12 August 2014
PDF: 10 pages
Proc. SPIE 9229, Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014), 92290M (12 August 2014); doi: 10.1117/12.2066314

Published in SPIE Proceedings Vol. 9229:
Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014)
Diofantos G. Hadjimitsis; Kyriacos Themistocleous; Silas Michaelides; Giorgos Papadavid, Editor(s)