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Emergent Aquatic Vegetation Mapping
Swan Lake, Nicollet County

For this study, we used an IKONOS image acquired on September 1, 2001 of the Swan Lake area. Due to the size of Swan Lake and the abundance of aquatic plants in the lake, the collection of reference data would be very difficult without the aid of modern technology. We used Global Positioning System (GPS) technology on a field pen computer with software capable of displaying the satellite data. While in the field, we identified different types of aquatic vegetation and located them directly on the IKONOS imagery using the field computer.

Being able to accurately identify specific locations on the image while in the field was especially useful on this large wetland. Having the image available quickly after its acquisition for use in reference data collection was also a significant advantage in field sampling because we could identify unique areas with different spectral-radiometric responses on the image and target them for field identification. We targeted emergent vegetation for the evaluation, but also noted the location of submerged vegetation appearing at the surface.

The first step for the Swan Lake IKONOS image was to separate wetland features from terrestrial features by digitizing the aquatic terrestrial boundary around the entire wetland and all islands. We identified this boundary using spectral-radiometric differences and spatial patterns visible on the image. We then subset the image with the wetland polygon to mask out all terrestrial features and create a wetland-only image.

Click on map to mask out land surrounding Swan Lake.
Click again to return to non-mask image.

Swan Lake has a maximum depth of two meters, clear water throughout, and an abundance of aquatic vegetation. Consequently, we assumed that aquatic vegetation was present throughout the wetland. An aquatic vegetation survey conducted by researchers at Minnesota State University, in which the presence or absence of 27 species of aquatic plants at 118 evenly distributed sample points on the lake was recorded, verified this assumption.

The next step was to stratify the wetland into emergent and submergent vegetation by performing an unsupervised classification. Using the field reference data, we identified five different emergent classes on the emergent vegetation images and recoded the images to create an emergent vegetation map. We repeated this procedure for the thick submerged vegetation image and identified two submerged classes. Finally, we created an Aquatic Plant Classification Map by overlaying the submerged aquatic plant map and the emergent aquatic plant map over the panchromatic image.

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