Wetland managers, citizens and government leaders are observing rapid changes in coastal wetlands and associated habitats around the Great Lakes Basin due to human activity and climate variability. SAR and optical satellite sensors offer cost effective management tools that can be used to monitor wetlands over time, covering large areas like the Great Lakes and providing information to those making management and policy decisions. In this paper we describe ongoing efforts to monitor dynamic changes in wetland vegetation, surface water extent, and water level change. Included are assessments of simulated Radarsat Constellation Mission data to determine feasibility of continued monitoring into the future. Results show that integration of data from multiple sensors is most effective for monitoring coastal wetlands in the Great Lakes region. While products developed using methods described in this article provide valuable management tools, more effort is needed to reach the goal of establishing a dynamic, near-real-time, remote sensing-based monitoring program for the basin.
Bibliographical noteFunding Information:
Michigan Technological University, University of Minnesota, and SharedGeo were funded by the U.S. Department of Interior, U.S. Fish and Wildlife Service with funding from the Great Lakes Restoration Initiative. Other contributors received no external funding. This large, binational project requires acknowledgement of many other unseen contributors: Robert Krska, U.S. Fish & Wildlife for instigating this project; Kevin O?Donnell, EPA for supporting the project, Colin Brooks, Michigan Tech Research Institute for mapping into the water without losing a UAS; Bob Ryerson, Kim Geomatics for his assessment of Great Lakes remote sensing research; Robb Macleod, Ducks Unlimited for stakeholder input; Steve Kloiber, MNDNR for updating the Minnesota NWI; Dan Heines, University of Minnesota for the Lake Superior UAS imagery, and the entire crews at SharedGeo and the Polar Geospatial Center for exposing the collaboration to petalscale computing. Finally, we also need to acknowledge the loss of Chuck Olson, whose research was instrumental in helping us observe the Great Lakes from near and far.
Funding: Michigan Technological University, University of Minnesota, and SharedGeo were funded by the U.S. Department of Interior, U.S. Fish and Wildlife Service with funding from the Great Lakes Restoration Initiative. Other contributors received no external funding.
The amount of computation required to produce the surface models needed for this project was massive. It takes roughly 12 h on average for SETSM to produce a 2 m DSM for a single stamp pair. As we had tens to hundreds of thousands of stamp pairs to process, we needed a massive amount computer time, certainly more than SharedGeo could assemble locally or in the cloud, and more than what was reasonable to run at the Minnesota Supercomputing Institute at the University of Minnesota. The project team was awarded a grant with the help of the Minnesota Department of Natural Resources of 540,000 node hours of computer time on the NSF funded Blue Waters petascale supercomputer through the Great Lakes Consortium for Petascale Computation [72,73]. The massive amount of data ingested required an additional 495,000 node hours after the first phase. By using various charge rate breaks such as running interruptible jobs and taking advantage of times when Blue Waters was underutilized, we were able to process 107,000 stamp pairs in 812,000 charged node hours on Blue Waters.
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- Change detection
- Land cover
- Surface water extent