In North America, the Great Lakes coastal wetlands are valuable ecosystems that are threatened by invasive plants. In fact, aquatic invasive-plant species are estimated to cause hundreds of millions of dollars in environmental damage and associated control costs. A primary species of concern is Phragmites australis (hereafter Phragmites: see Figure 1), a densely rooted perennial grass that adversely impacts plant diversity, and ecosystem functions and services. Recently, nonnative strains underwent rapid expansion in the Great Lakes region.1,2
Figure 1. Phragmites australis in Saginaw Bay.
Some of the latest advances in sensor technology and remote-sensing science include the development of hyperspectral data analysis for mapping wetlands at the species level. Advanced spectroscopic systems can capture data at narrow spectral bandwidths—on the order of three to ten nanometers wide—while contiguously covering large portions of the spectrum (e.g., 350–1500nm). This allows for the recording of small variations in plant and substrate absorptance and reflectance. Incorporating such relatively high spectral detail makes it possible to detect individual species.3,4 In the Great Lakes coastal wetlands, our team is developing tools to assess infestation and floristic quality using hyperspectral remote-sensing tools.
Recently, our research focused on the Muskegon River Watershed as a larger ecosystem study, simultaneously incorporating a thorough field campaign and airborne image collection. The field campaign collected georeferenced data for Phragmites and other plant communities and species. A Garmin Trimble Pro XR®global-positioning system (GPS), a portable spectroradiometer (FieldSpec Pro FR®, Analytical Spectral Devices), and digital cameras, all mounted on an airboat, were used to record stratified ground polygons (see Figure 2).
Figure 2. Data collection in the Muskegon River Complex.
In addition, we equipped a light aircraft with a push-broom airborne imaging spectroradiometer for applications (AISA) and a GPS differential navigation system. The 16-bit AISA sensor was a linear array sensor that used a system of semiconductive elements (e.g., a CCD array) to record each line of an image simultaneously. A fiber-optic downwelling irradiance system was configured to match the upwelling radiance measurements to calculate the apparent reflectance at the sensor. Twelve overlapping flight lines covering the visible to near-IR domains were flown at an altitude of approximately 1000m, and 1m pixels were obtained for twenty bands.
Figure 3. Classified hyperspectral image data (top) depicting water (blue) and Phragmites (red) across the Muskegon River coastal-wetland area (~2×16km2, graphic not to scale). Indicator semivariograms were constructed to characterize landscape structure and assist in the transect (n=40) design. Generally, as the shape complexity of the water class increased, the Phragmites mean patch size (R2=0.6), and their percentage of the landscape (PLAND) decreased (R2=0.5). Using shape complexity as a proxy for hydrological disturbance/modification, we found that Phragmites infestations were moderately associated with disturbance gradients.
To classify the imagery, we used the spectral-angle mapper algorithm,5 which relies on spectral shape and angles formed between a reference spectrum and an unclassified pixel in n-dimensional space, where n represents the number of bands (see Figure 3). The classified imagery provided a distribution map of Phragmites across the wetlands. Generally, areas with greater human disturbance were associated with higher intensities of Phragmites infestation. This has implications for managing the invasive plants as well as assessing potential consequences of lake-level or other hydrological changes.
The rapidly advancing field of wetlands remote-sensing science can provide meaningful information that is widely applicable to comprehensive assessment. Our near-term work focuses on fusing optical (hyperspectral), radar, and light detection and ranging (lidar) measurements for improved wetland management and monitoring. The information products generated will be used to assess how hydrologic and climate change as well as other human activities shape wetland environments.
Department of Geography
Central Michigan University
Mount Pleasant, MI