基于多源遥感数据的巢湖水华长时序时空变化(20092018年)分析与发生概率预测

湖泊富营养化导致的水华问题严重影响了淡水资源的利用和保护,快速、全面、准确的监测水华信息对于湖泊水环境的治理具有十分重要的意义.本文以巢湖为研究区域,利用多源光学遥感影像和时空融合技术,采用波段融合的方式将NDVI指数波段加入到遥感影像当中,并通过监督分类解译水华信息,以此揭示20092018年10年间巢湖水华的时空变...

Long-term spatiotemporal variation analysis and probability prediction of algal blooms in Lake Chaohu (2009-2018) based on multi-source remote sensing data

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.