State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China.; Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China.
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China.
Sci Total Environ. 2020 Oct 20;740:140012. doi: 10.1016/j.scitotenv.2020.140012. Epub 2020 Jun 6.
The widespread occurrence of Cyanobacterial blooms (CABs) in inland waters is a typical and severe challenge for water resources management and environment protection. An accurate and spatially continuous risk assessment of CABs is critical for prediction and preparedness in advance. In this study, a multivariate integrated risk assessment (MIRA) method of CABs in inland waters was proposed. MIRA was simplified with the trophic levels, cyanobacterial and other aquatic plant condition using remote sensing indexes, including the Trophic State Index (TSI), Floating Algae Index (FAI) and Cyanobacteria and Macrophytes Index (CMI). First, the dates of risk assessment were carefully selected based on TSI. Then, we obtained the trophic levels, cyanobacterial, and other aquatic plant condition of water using TSI, CMI and FAI on the selected date, and further scored them pixel by pixel to quantify the risk value. Finally, the risk of CABs in water was accurately assessed based on the pixel risk value. Based on Landsat 8 OLI dataset, MIRA was executed and validated in three different lakes of Wuhan urban agglomeration (WUA) with different trophic states. The results demonstrated that the risk of CABs in Lake LongGan was overall higher than that in Lake LiangZi and Lake FuTou. And the risk of CABs in the east part of Lake LongGan was higher than the other parts. Seasonally, the risk level ranking in Lake LiangZi was the highest in summer, while lowest in winter. However, the seasonal risk ranking was spring, summer, autumn, and winter in Lake LongGan. Based on the comparisons with monthly water quality classification data and results of the existing study, including trophic level, ecology risk, and algal extent, the MIRA method was valuable for accurate and spatially continuous identifying the risk of CABs in inland waters with potential eutrophication trends.
内陆水域蓝藻水华（CAB）的广泛发生是水资源管理和环境保护面临的一个典型而严峻的挑战。对 CAB 进行准确且空间连续的风险评估对于提前预测和防范至关重要。本研究提出了一种内陆水域 CAB 的多元综合风险评估（MIRA）方法。通过使用包括营养状态指数（TSI）、浮游藻类指数（FAI）和蓝藻和大型植物指数（CMI）在内的遥感指标，简化了营养水平、蓝藻和其他水生植物状况的 MIRA。首先，根据 TSI 仔细选择风险评估日期。然后，我们在选定日期使用 TSI、CMI 和 FAI 获取水体的营养水平、蓝藻和其他水生植物状况，并进一步逐像素评分，以量化风险值。最后，根据像素风险值准确评估水体中 CAB 的风险。基于 Landsat 8 OLI 数据集，在武汉城市群（WUA）具有不同营养状态的三个不同湖泊中执行和验证了 MIRA。结果表明，龙感湖的 CAB 风险总体高于梁子湖和斧头湖。而且，龙感湖东部的 CAB 风险高于其他部分。季节性方面，梁子湖的风险水平夏季最高，冬季最低。然而，龙感湖的季节性风险排名是春季、夏季、秋季和冬季。与逐月水质分类数据和现有研究的结果（包括营养水平、生态风险和藻类范围）进行比较，MIRA 方法对于准确且空间连续地识别具有潜在富营养化趋势的内陆水域 CAB 风险具有重要价值。
