Characterizing wetland dynamics using dense Landsat time series data

Authors: Jennifer Rover*, United States Geological Survey, Qiang Zhou, ASRC Federal InuTeq, contractor to USGS EROS, Alisa Gallant, United States Geological Survey
Topics: Remote Sensing, Water Resources and Hydrology, Land Use and Land Cover Change
Keywords: Remote sensing, Landsat, lakes, wetlands, hydroperiod, time series, temporal analysis, cluster analysis, climate
Session Type: Paper
Day: 4/11/2018
Start / End Time: 3:20 PM / 5:00 PM
Room: Iberville, Marriott, River Tower Elevators, 4th Floor
Presentation File: No File Uploaded

Wetlands play a critical role in the global water cycle, are among the most productive ecosystems in the world, and provide valuable ecosystem services. Geophysical settings (landform, substrate, position within a watershed, etc.) interacting with climate dynamics affect the water-holding capacity, hydroperiod, and water chemistry of wetlands, which, in turn, influence the biota and ecosystem services that can be supported. These factors contribute to highly dynamic spatial and temporal wetland functions that are vital to track to meet wetland resource management, program, and policy needs. Furthermore, existing land-cover products do not characterize these wetland dynamics, yet this information is fundamental to determining wetland functional types and detecting changes in functional roles driven by climate and land use. To meet the needs of the wetland science community, we developed a landscape-level approach that places wetlands into a spatial and temporal functional classification. This classification is based on all clear Landsat observations from 1984 to 2014 for a portion of the Prairie Pothole Region, North Dakota, USA. The long-term spectral trends from all Landsat bands were used to cluster pixels into groups with similar trends. Within each cluster group, we evaluated the brightness, greenness, and wetness from a tasseled-cap transformation and then tested correlation of each to the Palmer Hydrological Drought Index. The results describe how the 20 cluster groups responded to climate shifts that occurred during the decades between 1984 and 2014. We also investigated how a Landsat-derived functional classification of wetlands could complement existing federal wetland mapping and monitoring products.

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