Cao, M.; Qing, S.; Du, Y.; Yuan, R.; Shun, B.; Hao, Y., and Zhao, W., 2020. Remote sensing classification of aquatic vegetation in Ulansuhai Lake based on discrete particle swarm optimization algorithm. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 176-186. Coconut Creek (Florida), ISSN 0749-0208.
As the largest natural wetland in the same latitude on the earth, water ecological environment of Ulansuhai Lake, China is seriously threatened, and there is various aquatic vegetation spread over the lake. Remote sensing is considered to be an effective way to map the distribution of aquatic vegetation. Different from the method used in most of the previous studies, discrete particle swarm optimization (DPSO) algorithm was used to identify and classify emergent vegetation (EV), yellow algae (YA), submerged aquatic vegetation (SAV) and water in Ulansuhai Lake based on Landsat-8 Operational Land Imager (OLI) in this research. The classification results were validated by 284 investigation sites data and visual interpretation of Gao Fen 2 (GF-2) image. The results indicated that determination coefficient (R2) for EV, YA, SAV and water were greater than 0.91, root mean square error (RMSE) were less than 0.025 km2. Besides, the overall accuracy (95.4 %) and Kappa coefficient (0.93) of DPSO algorithm are superior to spectral index, unsupervised classification methods and supervised classification methods. In addition, DPSO algorithm to other regions (the Yellow Sea) and sensors (Sentinel-2) have been successfully applied, which further proves the applicability of DPSO algorithm. The research provides a new tool to assist people in locating and quantifying aquatic vegetation, so that purposeful actions can be taken to control the eutrophication of lake water and improve the water ecological environment.
