CyberSEES: Type 1: Meghdoot: A Multi-Cloud Infrastructure for Enhancing Sustainability via Effective Monitoring of Inland Waters and Coastal Wetlands

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The overall goal of the project is the design and prototype implementation of a multi-cloud computing framework that seamlessly integrates community observations, remote sensing measurements, and advanced multimedia analytics for effective environmental monitoring. This infrastructure, called Meghdoot, will be employed for early detection of salt marsh stress in coastal areas and cyanobacterial harmful algal blooms in inland waters. This framework will harness multiple clouds including community clouds, sensor clouds, and computational clouds to develop efficient and timely event detection strategies. In addition, efficacious mechanisms for motivating and enabling community members to contribute high-quality content for enhanced environmental monitoring will be investigated. The first research thrust of the proposed project is to invent mechanisms to effectively leverage community clouds for monitoring lakes and salt marshes. In this community-as-sensors paradigm, data produced by individuals of the community (i.e., blogs, images, tweets) in online social media platforms (Facebook, Twitter, Flickr, etc.) will be used to generate trustworthy, actionable information. This, in turn, will act as the initial trigger for activating the traditional sensing infrastructure. The data from sensor clouds comprised of high resolution cameras and hyperspectral radiometers will be processed through the computational cloud using specialized techniques such as segmentation, feature extraction, registration, indexing, and spectral models for producing estimates of the cyanobacteria concentration and marsh biophysical characteristics from the study sites. The final research thrust of this project will focus on the optimization of the deployment and operation of the sensor cloud based on the information from the community cloud and the results from the computational cloud.This project addresses two environmental issues important to coastal states in southeastern United States, namely, harmful algal blooms and marsh browning events. Over the last decade the frequency and magnitude of these environmentally detrimental events have gone up primarily because of drought as well as sea level rise. Therefore, accurate, cost-effective, and targeted monitoring of these events is indispensable to sustainable management of the environment. The proposed cyber-infrastructure-based warning system will enable early detection and timely implementation of preemptive measures to reduce the frequency and severity of future events while ensuring environmental conservation and sustainability. The project plans to engage students, community leaders, resource managers, and the general public via training, workshops, and social media in various aspects of the research starting from crowd sourcing to environmental sensor deployments, data acquisition, processing, and interpretation. The success of the proposed project will pave the way for a state-wide automated early detection, warning and rapid response system that can be adopted by state-level environmental agencies to alert restoration officials and lay citizens of impending risks associated with these events.