Visual analytics of high-frequency lake monitoring data: A case study of multiple stressors on a large inland lake system

Abstract

In recognizing the cumulative effects of multiple stressors on altering aquatic ecosystem function, scientists have become increasingly interested in capturing high-frequency response variables using a variety of sensors. This practice has led to a demand for novel ways to visualize and analyze the wealth of data in order to meet policy and management goals. Time series data collected as part of these monitoring activities are not easily analyzed with traditional methods. In this paper, a visual analytics system is described that leverages humans’ innate capability for pattern recognition and feature detection. High-frequency monitoring of weather and water conditions in Lake Nipissing, a large, shallow, inland lake in northeastern Ontario, Canada, is used as a case study. These visualizations are presented as Web-based tools to facilitate community-based participatory research among scientists, government agencies, and community stakeholders. These analytics techniques contribute to collaborative research endeavors and to the understanding of the response of lake conditions to environmental change.