Missouri Coteau prairie glacial wetlands are subject to numerous anthropogenic disturbances, such as cultivation, construction, and chemical inputs from upland land-use practices. High wetland density and temporal variability among these ecosystems necessitate synoptic tools for watershed-scale wetlands assessment and comprehensive monitoring. We developed a Geographic Information Systems (GIS) classification model for Missouri Coteau prairie glacial wetlands in North Dakota, USA and derived two indices that provide data salient to landscape-scale wetland assessment and monitoring. One, the Basin Buffer Index (BBI), delineates areas of natural vegetation that buffer waters from anthropogenic disturbance. The second, the Hydric Vegetation Index (HVI), delineates areas of hydric vegetation communities essential to prairie glacial wetland function. The model was developed by spectrally characterizing structural attributes common to glacial basins, such as hydroperiod, canopy architecture, and plant life-form and evaluating the potential for spectral detection of the natural vegetation buffers that surround these wetlands. Spectral separation among community zones in the green, red, and mid-infrared regions were evident in hyperspectral data and were convolved to fit multi-spectral satellite sensors. Model application using Landsat ETM and SPOT 5 satellite data over the central North Dakota study area produced classifications for buffer delineation and hydric community detection with 89 and 85% accuracy, respectively. By integrating remote sensing technology with those structural factors fundamental to wetland quality (i.e., land-use and natural vegetation buffers surrounding water bodies), we illustrate a method for evaluating wetland condition at a landscape scale.
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Vol. 25 • No. 2