Tamlin M. Pavelsky Ethan D. Kyzivat Fenix Garcia-Tigreros Erika Podest Fangfang Yao Xiao Yang Shuai Zhang Conghe Song Theodore Langhorst Wayana Dolan Martin R. Kurek Merritt E. Harlan Laurence C. Smith David Butman Robert G.M. Spencer Colin J. Gleason Kimberly Wickland Robert G. Striegl Daniel L. Peters Chao Wang 2023 <div id="abstracts" class="Abstracts u-font-gulliver text-s"><div id="ab0005" class="abstract author" lang="en"><div id="as0005"><p id="sp0060"><span>Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing&nbsp;hydrological changes&nbsp;including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and&nbsp;biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and&nbsp;spectral resolution&nbsp;satellite imagery lack community-level&nbsp;</span>vegetation classification<span>&nbsp;</span>and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.</p></div></div></div> application/pdf 10.1016/j.rse.2023.113646 en Elsevier Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta article