Over the past decade, the global proliferation of cyanobacterial harmful algal blooms (CyanoHABs) have presented a major risk to the public and wildlife, and ecosystem and economic services provided by inland water resources. As a consequence, water resources, environmental, and healthcare agencies are in need of early information about the development of these blooms to mitigate or minimize their impact. Results from various components of a novel multi-cloud cyber-infrastructure referred to as “CyanoTRACKER” for initial detection and continuous monitoring of spatio-temporal growth of CyanoHABs is highlighted in this study. The novelty of the CyanoTRACKER framework is the collection and integration of combined community reports (social cloud), remote sensing data (sensor cloud) and digital image analytics (computation cloud) to detect and differentiate between regular algal blooms and CyanoHABs. Individual components of CyanoTRACKER include a reporting website, mobile application (App), remotely deployable solar powered automated hyperspectral sensor (CyanoSense), and a cloud-based satellite data processing and integration tool. All components of CyanoTRACKER provided important data related to CyanoHABs assessments for regional and global water bodies. Reports and data received via social cloud including the mobile App, Twitter, Facebook, and CyanoTRACKER website, helped in identifying the geographic locations of CyanoHABs affected water bodies. A significant increase (124.92%) in tweet numbers related to CyanoHABs was observed between 2011 (total relevant tweets = 2925) and 2015 (total relevant tweets = 6579) that reflected an increasing trend of the harmful phenomena across the globe as well as an increased awareness about CyanoHABs among Twitter users. The CyanoHABs affected water bodies extracted via the social cloud were categorized, and smaller water bodies were selected for the deployment of CyanoSense, and satellite data analysis was performed for larger water bodies. CyanoSense was able to differentiate between ordinary algae and CyanoHABs through the use of their characteristic absorption feature at 620 nm. The results and products from this infrastructure can be rapidly disseminated via the CyanoTRACKER website, social media, and direct communication with appropriate management agencies for issuing warnings and alerting lake managers, stakeholders and ordinary citizens to the dangers posed by these environmentally harmful phenomena.

在过去的十年中，蓝藻有害藻华（CyanoHAB）的全球扩散给公众和野生生物以及内陆水资源提供的生态系统和经济服务带来了重大风险。因此，水资源，环境和医疗保健机构需要有关这些水华发展的早期信息，以减轻或减少其影响。在这项研究中，重点介绍了新颖的多云网络基础设施各个组成部分的结果，这些基础设施被称为“ CyanoTRACKER”，用于CyanoHABs的时空生长的初始检测和连续监测。CyanoTRACKER框架的新颖之处在于可以收集和整合社区报告（社交云），遥感数据（传感器云）和数字图像分析（计算云），以检测和区分常规藻华和CyanoHAB。CyanoTRACKER的各个组件包括报告网站，移动应用程序（App），可远程部署的太阳能自动高光谱传感器（CyanoSense）以及基于云的卫星数据处理和集成工具。CyanoTRACKER的所有组件为区域和全球水体提供了有关CyanoHAB评估的重要数据。通过社交云（包括移动应用程序，Twitter，Facebook和CyanoTRACKER网站）接收到的报告和数据，有助于识别受CyanoHAB影响的水体的地理位置。大幅增加（124。在2011年（相关推文总数= 2925）和2015年（相关推文总数= 6579）之间，发现与CyanoHABs相关的推文数量达到92％），这反映出全球有害现象的趋势有所增加，并且人们对CyanoHABs的认识提高Twitter用户。对通过社交云提取的CyanoHAB受影响的水体进行了分类，并选择了较小的水体进行CyanoSense的部署，并对较大的水体进行了卫星数据分析。CyanoSense能够通过使用其在620 nm处的特征吸收特征来区分普通藻类和CyanoHAB。该基础设施的结果和产品可以通过CyanoTRACKER网站，社交媒体，