湖泊蓝藻水华的精准动态监测, 可为水利及环保部门评价污染水体的防治效果、优化和调整防治政策提供依据。论文以巢湖为研究对象, 利用Landsat TM/OLI、HJ-1B CCD/IRS和NPP-VIIRS三种不同空间分辨率的影像数据, 通过归一化水体指数(Normalized Difference Water Index, NDWI)实现巢湖水域范围提取, 利用归一化植被指数(Normalized Difference Vegetation Index, NDVI)和浮游藻类指数(Floating Algae Index, FAI)提取2010~2014年共22景巢湖蓝藻的爆发区域。进一步的, 对NDVI和FAI两种方法计算的蓝藻爆发区域进行对比分析, 评价Landsat、HJ-1B以及VIIRS三种影像数据对巢湖蓝藻水华空间和时间的监测效果及适用性,进而结合气象因素分析不同气象因子对蓝藻水华爆发的影响。研究结果表明: (1) 相比NDVI指数, FAI指数(Landsat和HJ-1B数据为主, VIIRS数据辅助)能降低薄云对蓝藻水华提取效果的影响, 可提高蓝藻水华爆发区域、程度的识别能力; (2) 气象因子中气温和日照时长加重了蓝藻水华爆发的严重程度, 降水则对蓝藻水华的爆发起到一定的抑制作用。综上所述, 论文引入VIIRS卫星影像研究巢湖蓝藻水华爆发, 利用FAI指数降低薄云对蓝藻水华爆发面积提取精度的影响, 取得的研究结果可为基于多源卫星遥感数据的巢湖蓝藻水华动态监测系统开发提供重要的方法支持, 有利于推进卫星遥感技术在安徽省“河长制”和“湖长制”中发挥重要作用。

Dynamically, accurately monitoring of cyanobacteria blooms in the inland lakes can provide a basis for evaluating the control effects of polluted water bodies, moreover optimize and adjust prevention policies for water conservancy and environmental protection departments. In this paper, Chaohu Lake was chosen as there search object, the satellite imagery data with different spatial resolution such as the Landsat TM/OLI, HJ-1B CCD/IRS and NPP-VIIRS, were used to extract the Chaohu water body by the Normalized Difference Water Index(NDWI). And then the areas of cyanobacterial blooms in the Chaohu Lake were calculated using the Normalized Difference Vegetation Index(NDVI) and the Floating Algae Index (FAI). Further, the extracted cyanobacterial areas using the different methods were compared and analyzed, and the monitoring effects and applicability were evaluated by the spatial and temporal characteristics for Landsat, HJ-1B and VIIRS imagery data. Additionally, the effects of different meteorological factors on the cyanobacterial blooms were also analyzed. The research results displayed that comparing with the NDVI index, the FAI index calculated from the Landsat, HJ-1B and VIIRS imagery data can reduce the effect of thin cloud on the extraction of cyanobacterial blooms, and improve the recognition ability of cyanobacterial blooms and extents. Secondly, the temperature and sunshine duration of meteorological factors aggravate the severity of cyanobacterial blooms, and the rainfall plays a certain role in inhibiting the outbreak of cyanobacterial blooms. In summary, this study introduced the VIIRS imagery data to study the cyanobacterial blooms in Chaohu Lake, and used the FAI index to reduce the influence of thin cloud on the extraction precision of cyanobacterial blooms. These results show that multi-source satellite imagery data can provide the important method support for the development of dynamically monitoring system on cyanobacterial blooms. This is useful to promote the satellite remote sensing technology to improve the "river system" and "lake system" in Anhui Province.