Classification ofTypha-dominated wetlands using airborne hyperspectral imagery along Lake Ontario, USA

Abstract

Shoreline wetlands along Lake Ontario are valuable, multi-functional resources that have historically provided large numbers of important ecosystem goods and services. However, alterations to the lake’s natural hydrologic regime have impacted traditional meadow marsh in the wetlands, resulting in competition and colonization by dense and aggressive Typha angustifolia and Typha x glauca (Cattails). The shift to a Typha-dominated landscape resulted in an array of negative impacts, including increased Typha density, substantial decreases in plant species richness and diversity, and altered habitat and changes in associated ecosystem services. Successful long-term adaptive management of these wetland resources requires timely and accurate monitoring. Historically, wetland landscapes have been surveyed and mapped using field-based surveys and/or photointerpretation. However, given their resource- and cost-intensive nature, these methods are often prohibitively time- and labor-consuming or geographically limited. Other remote sensing applications can provide more rapid and efficient assessments when evaluating wetland change trajectories or analyzing direct and indirect impacts across larger spatial and temporal scales. The primary goal of this study was to develop and describe methodology using U.S. Army Corps of Engineers National Coastal Mapping Program hyperspectral imagery, light detection and ranging data, and high-spatial resolution true-color imagery to provide updated wetland classifications for Lake Ontario coastal wetlands. This study used existing field-collected vegetation survey data (Great Lakes Coastal Wetland Monitoring Program), ancillary imagery, and existing classification information as training data for a supervised classification approach. These data were used along with a generalized wetland schema (classes based on physical and biological gradients: elevation, Typha, meadow marsh, mixed emergent, upland vegetation) to generate wetland classification data with Kappa values near 0.85. Ultimately, these data and methods provide helpful knowledge elements that will allow for more efficient inventorying and monitoring of Great Lake resources, forecasting of resource condition and stability, and adaptive management strategies.

Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance

“…Remote sensing is well equipped to monitor and quantify vegetation properties and project performance because the vegetation is typically easily observed with remote sensors. Remote monitoring of vegetation measures includes but are not limited to assessing changes in vegetation growth dynamics (i.e., shoot density, height) (Suir and Sasser 2019b;Jiang et al 2020), detecting and monitoring plant diversity and switching (e.g., invasive plants, species abundance, and competition) (Royimani et al 2019;Suir et al 2021), and plant health (e.g., dieback), resiliency, and recovery (Suir et al 2020).…”