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Classification of Typha-dominated wetlands using airborne hyperspectral imagery along Lake Ontario, USA
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Aquatic Ecosystem Health & Management 24(2): 140–155
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2021
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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.