Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta

Details

• Journal Title:
  Remote Sensing of Environment
• Personal Author:
  Wang, Chao ; Pavelsky, Tamlin M. ; Kyzivat, Ethan D. ; Garcia-Tigreros, Fenix ; Podest, Erika ; Yao, Fangfang ; Yang, Xiao ; Zhang, Shuai ; Song, Conghe ; Langhorst, Theodore ; Dolan, Wayana ; Kurek, Martin R. ; Harlan, Merritt E. ; Smith, Laurence C. ; Butman, David E. ; Spencer, Robert G.M. ; Gleason, Colin J. ; Wickland, Kimberly P. ; Striegl, Robert G. ; Peters, Daniel L.
• NOAA Program & Office:
  CIRES (Cooperative Institute for Research in Environmental Sciences)
• Description:
  Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification 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.
• Source:
  Remote Sensing of Environment, 294, 113646
• DOI:
  https://doi.org/10.1016/j.rse.2023.113646
• ISSN:
  0034-4257
• Format:
  pdf
• Publisher:
  Elsevier BV
• Document Type:
  Journal Article-Accepted Manuscript
• License:
  https://creativecommons.org/licenses/by-nc-nd/4.0/
• Rights Information:
  CC BY-NC-ND
• Rights Statement:
  The NOAA IR provides access to this content under the authority of the government's retained license to distribute publications and data resulting from federal funding. While users may legally access this content, the copyright owners retain rights that govern the reproduction, redistribution, and re-use of this work. The user is solely responsible for complying with applicable copyright law.
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:b27bc1b8a6ba966f611ab8713828ef265831f3020438a441fb59f4d9093de4290f272da51f36bf354be5f4515c456e835717d4ded9babe48471839dda0a18958
• Download URL:
  https://repository.library.noaa.gov/view/noaa/68435/noaa_68435_DS1.pdf
• File Type:
  [PDF - 4.26 MB ]