Assessing invasive plant infestation in freshwater wetlands

Recent shifts in wetland ecosystem management goals have directed efforts toward measuring ecological integrity, rather than only using physical and chemical measures of ecosystems as health indicators. Invasive species pose one of the largest threats to wetlands integrity. Resource managers can benefit from improved methods for identifying invasive plant species, assessing infestation, and monitoring control measures. The utilization of advanced remote sensing tools for species-level mapping has been increasing and techniques need to be explored for identifying species of interest and characterizing infestation.; The overarching goal of this research was to develop monitoring technologies to map invasive plants and quantify wetland infestation. The first field-level objective was to characterize absorption and reflectance features and assess processing techniques for separating wetland species. The second field-level objective was to evaluate the abilities of a shape filter to identify wetland invasive plant species. The first landscape-level objective was to classify hyperspectral imagery in order to identify invasives of interest. The second landscape-level objective was to quantify infestation within the study area.; Field-level hyperspectral data (350-2500nm) were collected for twenty-two wetland plant species in a wetland located in the lower Muskegon River watershed in Michigan, USA. The Jeffries-Matusita distance measure, continuum removal, and a shape-filter were applied to hyperspectral species reflectance data to characterize spectral features. Generally, continuum removal decreased separation distance for the invasive species of interest. Using the shape-filter, Lythrum salicaria, Phragmites australis, and Typha latifolia possessed maximum separation (distinguished from other species) at the near-infrared edge (700nm) and water absorption region (1350nm), the near-infrared down slope (1000 and 1100nm), and the visible/chlorophyll absorption region (500nm) and near-infrared edge (650nm), respectively.; Airborne hyperspectral imagery was classified using a two-step approach in order to obtain an optimal map (overall accuracy ∼ 70%). Information in the near-infrared enabled relatively accurate classification for Phragmites australis using the Spectral Angle Mapper algorithm and image-derived training, while Typha latifolia signatures possessed high spectral overlap and required ISODATA clustering techniques. Landscape pattern metrics relate infestation to disturbances and hydrological controls. The highest levels of infestation and infestation patterns coincide with the most substantial levels of hydrological modifications indicating human disturbances are correlated with Typha and Phragmites percentages in the landscape.; Overall the approach was successful and increased the level of information ultimately desired by decision makers. The rapidly advancing field of wetland remote sensing science can obtain more meaningful information from hyperspectral imagery; however, the data are challenging to work with and only the most precisely calibrated datasets will provide utility. Combining these data with traditional wetland assessment techniques can substantially advanced goals of preserving and restoring wetland ecosystem integrity.