Abstract

The growth patterns of emergent vegetation can be a useful indicator for factors affecting lake health. However, field data to characterize emergent vegetation at many reservoirs may not be available or may be limited to small, isolated areas. We present a case study using remotely sensed data from the Landsat satellite to generate data to represent emergent vegetation in the near-shoreline and tributary delta areas of Malheur Lake, Oregon. We selected late June images for this study as vegetation is relatively mature in late June and visible, but has not completely grown-in providing a better indication of vegetation coverage in satellite images. We investigated the correlation of vegetation coverage (an indicator of emergent vegetation) with lake area on the day of the satellite collection, average daily maximum temperatures for April, May, June, and July, and average daily precipitation in June, all parameters that could affect vegetation. To estimate historic emergent vegetation extent, we computed the Normalized Difference Vegetation Index (NDVI) for 30 years of Landsat satellite images from 1984 to 2013. Around Malheur Lake we identified eight regions-of-interest (ROI): three inlet areas, three wet-shore areas (swampy areas), and two dry-shore areas (less swampy areas). For each ROI we generated time-series data to quantify the emergent vegetation as determined by the percent of area covered by pixels with NDVI values greater than 0.2. We measured lake area by computing the Modified Normalized Difference Water Index (MNDWI) and computing the area by summing the pixels that indicated water. We compared NDVI time-series values with the time series for lake area, June precipitation, and maximum daily temperatures for April, May, June, and July to determine if these parameters were correlated. Correlation would imply that emergent vegetation was influenced by the parameter. We found that correlations of vegetative extent in any of the eight ROIs with the selected parameters were minimal, indicating that there are other factors besides the ones chosen that drive emergent vegetation levels in Malheur Lake. This study demonstrates that Landsat data have sufficient spatial and temporal detail for quantification and description of ecosystem changes and thus offer a good source of information to understand historic trends in reservoir health. We expect that future work will explore other potential drivers for emergent vegetation extent, such as carp populations in Malheur Lake which are known to affect emergent vegetation. Carp were not considered in this study as we did not have access to data that reflect carp numbers over this 30 year period.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2015-03-01

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd7647

Keywords

remote sensing, emergent vegetation, Landsat, NDVI time series, MNDWI

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