Integrated environmental health policies, including scalable solutions, must be part of any successful public health strategy in the face of climate change impacts. The literature on algal blooms suggests that they can pose problems for human health, including digestive and respiratory difficulties. A holistic approach involving the systematic collection and analysis of data from hospital records, public health databases and population health surveys can be used to link environmental events such as algal blooms to disease, identify trends early and proactively take countermeasures. This paper presents a framework that combines remote sensing-based environmental monitoring systems with human health monitoring to combat eutrophication and dangerous algal blooms in urban streams, using environmental (NDCI) and morbidity indicators for public health. To demonstrate its utility, we developed an interactive web application using Python’s Dash library that allows users to visualize and explore spatiotemporal correlations between NDCI-derived bloom intensity and rates of health incidents rates (synthetic data) in the area of Gazanos and Almyros, Crete.