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引用本文:祁国华,马晓双,何诗瑜,吴鹏海.基于多源遥感数据的巢湖水华长时序时空变化(2009—2018年)分析与发生概率预测.湖泊科学,2021,33(2):414-427. DOI:
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:
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基于多源遥感数据的巢湖水华长时序时空变化(2009—2018年)分析与发生概率预测
祁国华1,2, 马晓双1,2, 何诗瑜1, 吴鹏海1,2
1.安徽大学资源与环境工程学院, 合肥 230601;2.安徽大学湿地生态保护与修复安徽省重点实验室, 合肥 230601
摘要:
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2009-2018年10年间巢湖水华的时空变化规律.结果表明:巢湖发生的水华以零星和局部水华为主,2012年巢湖水体首次出现区域蓝藻水华;巢湖水华的季节变化性强,夏、冬半年水华变化差异大,其中2018年季节间的差异最为显著;巢湖水华后五年发生频率明显高于前五年,西半湖水华较东半湖严重,且巢湖西北部是水华的高发区域,2011年水华开始向沿岸蔓延,2014年水华首次出现在西南湖区,2016年巢湖水华高发区域新增巢湖东部和中部地区.另外,本文根据巢湖的相关气象数据,构建了Logistic水华气象风险概率预测模型,模型平均预测准确率高达87.52%.探究巢湖水华长时序的时空变化规律有助于从宏观上把握其动态趋势,为后续湖区治理以及周边生态环境建设提供理论依据,水华气象风险概率预测模型可为巢湖水华的预警和防控提供决策支持.
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
DOI:
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基金项目:国家自然科学基金项目(41701390)和安徽省科技重大专项(201903a07020014)联合资助.
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
Abstract:
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
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