Liu S, Glamore W, Tamburic B, Morrow A, Johnson F
Water Research Centre, University of New South Wales, Sydney, NSW 2052, Australia.
Water Research Laboratory, University of New South Wales, Sydney, NSW 2093, Australia.
Sci Total Environ. 2022 Dec 10;851(Pt 1):158096. doi: 10.1016/j.scitotenv.2022.158096. Epub 2022 Aug 18.
Harmful algal blooms (HABs) are an issue of concern for water management worldwide. As such, effective monitoring strategies of HAB spatio-temporal variability in waterbodies are needed. Remote sensing has become an increasingly important tool for HAB detection and monitoring in large lakes. However, accurate HAB detection in small-medium waterbodies via satellite data remains a challenge. Current barriers include the waterbody size, the limited freely available high resolution satellite data, and the lack of field calibration data. To test the applicability of remote sensing for detecting HABs in small-medium waterbodies, three satellites (Planetscope, Sentinel-2 and Landsat-8) were used to understand how spatial resolution, the availability of spectral bands, and the waterbody size itself effect HAB detection skill. Different algorithms and a non-parametric method, Self-Organizing Map (SOM), were tested. Curvature Around Red and NIR minus Red had the best HAB detection skill of the 20 existing algorithms that were tested. Landsat 8 and Sentinel 2 were the best satellites for HAB detection in small to medium waterbodies. The most critical attribute for detecting HABs were the available satellite bands, which determine the detection algorithms that can be used. Importantly, algorithm performance was mostly unrelated to waterbody size. However, there remain some barriers in utilizing satellite data for HAB detection, including algae dynamics, macrophyte cover within the waterbody, weather effects, and the correction models for satellite data. Moreover, it is important to consider the match time between satellite overpass and sampling activities for calibration. Given these challenges, integrating regular sampling activities and remote sensing is recommended for monitoring and managing small-medium waterbodies.
有害藻华（HABs）是全球水资源管理中备受关注的问题。因此，需要有效的水体有害藻华时空变化监测策略。遥感已成为大型湖泊有害藻华检测与监测中日益重要的工具。然而，利用卫星数据在中小水体中准确检测有害藻华仍然是一项挑战。目前的障碍包括水体大小、免费高分辨率卫星数据有限以及缺乏现场校准数据。为了测试遥感在中小水体中检测有害藻华的适用性，使用了三颗卫星（行星scope卫星、哨兵 - 2卫星和陆地卫星8号）来了解空间分辨率、光谱波段可用性以及水体大小本身如何影响有害藻华检测技能。测试了不同的算法和一种非参数方法——自组织映射（SOM）。在测试的20种现有算法中，红边曲率和近红外减红算法具有最佳的有害藻华检测技能。陆地卫星8号和哨兵2号卫星是中小水体有害藻华检测的最佳卫星。检测有害藻华的最关键属性是可用的卫星波段，这决定了可以使用的检测算法。重要的是，算法性能大多与水体大小无关。然而，在利用卫星数据进行有害藻华检测方面仍然存在一些障碍，包括藻类动态、水体内的大型植物覆盖、天气影响以及卫星数据的校正模型。此外，在校准过程中考虑卫星过境与采样活动之间的匹配时间很重要。鉴于这些挑战，建议将定期采样活动与遥感相结合，以监测和管理中小水体。
