Characterizing Wetland Inundation and Vegetation Dynamics in the Arctic Coastal Plain Using Recent Satellite Data and Field Photos ...

• Main Authors: Zou, Zhenhua, DeVries, Ben, Huang, Chengquan, Lang, Megan W., Thielke, Sydney, McCarty, Greg W., Robertson, Andrew G., Knopf, Jeff, Wells, Aaron F., Macander, Matthew J., Du, Ling
• Format: Text
• Language: English
• Published: MDPI 2021
• Subjects: wetland; inundation; vegetation; sentinel; arctic; ANWR; NWI; Arctic; Climate change; Alaska
• Online Access: https://dx.doi.org/10.13016/dspace/d69a-3yxw; https://drum.lib.umd.edu/handle/1903/31253

Arctic wetlands play a critical role in the global carbon cycle and are experiencing disproportionate impacts from climate change. Even though Alaska hosts 65% of U.S. wetlands, less than half of the wetlands in Alaska have been mapped by the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) or other high-resolution wetlands protocols. The availability of time series satellite data and the development of machine learning algorithms have enabled the characterization of Arctic wetland inundation dynamics and vegetation types with limited ground data input. In this study, we built a semi-automatic process to generate sub-pixel water fraction (SWF) maps across the Coastal Plain of the Arctic National Wildlife Refuge (ANWR) in Alaska using random forest regression and 139 Sentinel-2 images taken in ice-free seasons from 2016 to 2019. With this, we characterized the seasonal dynamics of wetland inundation and explored their potential usage in determining NWI water regimes. The highest levels of ...