Project Team: Great Lakes Climate Team
Team Location: NASA Langley Research Center, Hampton, Virginia
Georgian Bay off Lake Huron in Ontario, Canada, shown with 10 km of surrounding land and outset boxes displaying two examples of land cover classifications. Image Credit: Great Lakes Climate Team
Authors:
Janice Maldonado Jaime
Emily Adams
Lydia Cuker
Kathy Currie
Lacey Freese
Miriam Harris
Pamela King
Daniel Marx
Mentors/Advisors:
Dr. Kenton Ross (NASA DEVELOP National Science Advisor)
Abstract:
Wetlands are considered to be some of the most productive and diverse ecosystems on Earth. The Laurentian Great Lakes region of North America includes diverse types of coastal wetlands (e.g., swamps and marshes) that support numerous flora and fauna species. Studies of this ecosystem are very important for ecological communities and economic industries, which benefit from fisheries and tourism. The effects of climate change, such as variations in temperature, precipitation, and evaporation, could impact the water level of the Great Lakes, and therefore, the development and survival of coastal wetlands in this area. An updated land cover classification was developed, using a Random Forest classification method, to evaluate and monitor changes in the wetlands around Georgian Bay. NASA Earth Observing System (EOS) data from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) were used to classify land cover from 1987 and 2013. To improve the accuracy of the maps, resultant land cover classifications were validated with ground truth data for 2013. Additionally, TOPEX/Poseidon, Jason-1 and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeters and in situ water gauges data served as a resource for tracking water levels over time to compare these fluctuations with wetlands.åÊ The methodology to create land cover classification maps showing the changes in coastal wetland health and extent offers a more cost-effective approach to monitoring wetlands in the region. Results presented are preliminary and will undergo further refinement in the future.