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HYPERSPECTRAL REMOTE SENSING, GPS, AND GIS APPLICATIONS IN OPPORTUNISTIC PLANT SPECIES MONITORING OF GREAT LAKES COASTAL WETLANDS
Citation:
Lopez, R D., C M. Edmonds, AND J G. Lyon. HYPERSPECTRAL REMOTE SENSING, GPS, AND GIS APPLICATIONS IN OPPORTUNISTIC PLANT SPECIES MONITORING OF GREAT LAKES COASTAL WETLANDS. Presented at American Society of Agricultural Engineers, Las Vegas, NV, July 27-30, 2003.
Impact/Purpose:
The primary objectives of this research are to:
Develop methodologies so that landscape indicator values generated from different sensors on different dates (but in the same areas) are comparable; differences in metric values result from landscape changes and not differences in the sensors;
Quantify relationships between landscape metrics generated from wall-to-wall spatial data and (1) specific parameters related to water resource conditions in different environmental settings across the US, including but not limited to nutrients, sediment, and benthic communities, and (2) multi-species habitat suitability;
Develop and validate multivariate models based on quantification studies;
Develop GIS/model assessment protocols and tools to characterize risk of nutrient and sediment TMDL exceedence;
Complete an initial draft (potentially web based) of a national landscape condition assessment.
This research directly supports long-term goals established in ORDs multiyear plans related to GPRA Goal 2 (Water) and GPRA Goal 4 (Healthy Communities and Ecosystems), although funding for this task comes from Goal 4. Relative to the GRPA Goal 2 multiyear plan, this research is intended to "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors." Relative to the Goal 4 Multiyear Plan this research is intended to (1) provide states and tribes with an ability to assess the condition of waterbodies in a scientifically defensible and representative way, while allowing for aggregation and assessment of trends at multiple scales, (2) assist Federal, State and Local managers in diagnosing the probable cause and forecasting future conditions in a scientifically defensible manner to protect and restore ecosystems, and (3) provide Federal, State and Local managers with a scientifically defensible way to assess current and future ecological conditions, and probable causes of impairments, and a way to evaluate alternative future management scenarios.
Description:
Coastal wetlands of the Laurentian Great Lakes (LGL) are among the most fragmented and disturbed ecosystems of the world, with a long history of human-induced disturbance. LGL wetlands have undergone losses in the biological diversity that coincides with an increase in the presence and dominance of several opportunistic plant species, including the common reed (Phragmites australis). Typically, P. australis communities form large monospecific "stands" that may predominate in wetland plant communities, supplanting other plant taxa. Compared to other more heterogeneous plant communities, P. australis stands are less suitable as animal habitat and reduce the overall biological diversity of wetlands. From a LGL resource perspective, P. australis is difficult to manage because it is persistent, produces a large amount of biomass, propagates easily, and is very difficult to control with mechanical or chemical techniques. We used a combined field and remote-sensing based approach to develop a semi-automated detection and mapping technique to support P. australis monitoring and assessment. Real-time- corrected GPS locations of field data provided an important measurable link between airborne sensor data and information about the physical structure of these plant communities, including physical structure of individual plants, soil type, soil moisture content, and the characteristics of other associated plant taxa. Ten LGL wetland sites on Lake Erie, Lake St. Clair, and Lake Huron were mapped in 2001, and resampled for mapping accuracy in 2002. User's accuracy of semi-autornated maps for P. australis exceeds 90% at some of the wetland sites. The results of this study demonstrate a technique for combining hyperspectral airborne remote sensing data, precision GPS data, and GIS techniques to map plant species and plant community characteristics under ephemeral wetland conditions. Our results demonstrate how remote sensor technologies may offer effective semi-automated methods for monitoring opportunistic plant species over large geographic regions.