AUTHOR=Windle Anna E. , Staver Lorie W. , Elmore Andrew J. , Scherer Stephanie , Keller Seth , Malmgren Ben , Silsbe Greg M. TITLE=Multi-temporal high-resolution marsh vegetation mapping using unoccupied aircraft system remote sensing and machine learning JOURNAL=Frontiers in Remote Sensing VOLUME=4 YEAR=2023 URL=https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2023.1140999 DOI=10.3389/frsen.2023.1140999 ISSN=2673-6187 ABSTRACT=

Coastal wetlands are among the most productive ecosystems in the world and provide important ecosystem services related to improved water quality, carbon sequestration, and biodiversity. In many locations, wetlands are threatened by coastal development and rising sea levels, prompting an era of tidal wetland restoration. The creation and restoration of tidal marshes necessitate the need for ecosystem monitoring. While satellite remote sensing is a valuable monitoring tool; the spatial and temporal resolution of imagery often places operational constraints, especially in small or spatially complex environments. Unoccupied aircraft systems (UAS) are an emerging remote sensing platform that collects data with flexible on-demand capabilities at much greater spatial resolution than sensors on aircraft and satellites, and resultant imagery can be readily rendered in three dimensions through Structure from Motion (SfM) photogrammetric processing. In this study, UAS data at 5 cm resolution was collected at an engineered wetland at Poplar Island, located in Chesapeake Bay, MD United States five times throughout 2019 to 2022. The wetland is dominated by two vegetation species: Spartina alterniflora and Spartina patens that were originally planted in 2005 in low and high marsh elevation zones respectively. During each survey, UAS multispectral reflectance, canopy elevation, and texture were derived and used as input into supervised random forest classification models to classify species-specific marsh vegetation. Overall accuracy ranged from 97% to 99%, with texture and canopy elevation variables being the most important across all datasets. Random forest classifications were also applied to down-sampled UAS data which resulted in a decline in classification accuracy as spatial resolution decreased (pixels became larger), indicating the benefit of using ultra-high resolution imagery to accurately and precisely distinguish between wetland vegetation. High resolution vegetation classification maps were compared to the 2005 as-built planting plans, demonstrating significant changes in vegetation and potential instances of marsh migration. The amount of vegetation change in the high marsh zone positively correlated with interannual variations in local sea level, suggesting a feedback between vegetation and tidal inundation. This study demonstrates that UAS remote sensing has great potential to assist in large-scale estimates of vegetation changes and can improve restoration monitoring success.