|Qi Guohua,Ma Xiaoshuang,He Shiyu,Wu Penghai.Long-term spatiotemporal variation analysis and probability prediction of algal blooms in Lake Chaohu (2009-2018) based on multi-source remote sensing data. J. Lake Sci.2021,33(2):414-427. DOI:10.18307/2021.0204
|关键词: 巢湖 水华 遥感监测 监督分类 时空变化 气象数据 Logistic模型
|Long-term spatiotemporal variation analysis and probability prediction of algal blooms in Lake Chaohu (2009-2018) based on multi-source remote sensing data
Qi Guohua1,2, Ma Xiaoshuang1,2, He Shiyu1, Wu Penghai1,2
1.School of Resources and Environmental Engineering, Anhui University, Hefei 230601, P. R. China;2.Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, P. R. China
|The algal blooms problem caused by eutrophication of lakes has seriously affected the utilization and protection of freshwater resources, hence the rapid, comprehensive and accurate monitoring of algal blooms information is of great significance for the governance of water environment. Taking Lake Chaohu as the research area, this article uses multi-source optical remote sensing images and spatio-temporal fusion technology to reveal the spatio-temporal change trends of algal blooms from the year of 2009 to 2018, by employing band fusion method and supervised classification-based blooms extraction method. The results show that:The algal blooms in Lake Chaohu are mainly sporadic and localized. In 2012, regional cyanobacteria blooms appeared for the first time. Algal blooms in Lake Chaohu has a strong seasonal variation, and there is a big difference between summer and winter which is most significant in 2018; in the last five years, the occurrence frequency of algal blooms was significantly higher than that of the previous five years. The west half lake blooms were more serious than the east half lake blooms, and the northwest part of Lake Chaohu had a high incidence of algal blooms. In 2011, the algal blooms began to spread to the coast; In 2014, the algal blooms first appeared in the southwest lake area; In 2016, the eastern and central parts of Lake Chaohu became the high-occurrence area. Besides, based on the relevant meteorological data of Lake Chaohu, this paper constructs a Logistic forecast model of algal meteorological risk probability with an average prediction accuracy rate of 87.52%. The study of the spatio-temporal variation of cyanobacteria blooms in Lake Chaohu can help us to grasp its dynamic trend from a macro perspective, providing a theoretical basis for subsequent lake area governance and ecological environment construction. Besides, the algal blooms meteorological risk probability prediction model can provide decision support for early warning and prevention of Lake Chaohu's algal blooms.
|Key words: Lake Chaohu algal blooms remote sensing monitoring supervised classification spatiotemporal changes meteorological data Logistic model