Theses and Dissertations at Montana State University (MSU)
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Item Remote sensing for wetland restoration analysis: Napa-Sonoma Marsh as case study(Montana State University - Bozeman, College of Agriculture, 2019) Bryne, Charles; Chairperson, Graduate Committee: Scott PowellHuman-caused ecosystem change and habitat loss is a major worldwide concern. Wetland loss has been remarkable worldwide and in the US. In the San Francisco Bay system, the largest estuary on the eastern rim of the Pacific Ocean and a biodiversity hotspot, more than 90 percent of the wetlands have been lost to urban development, salt production and agriculture, a loss that primarily occurred in the century following 1850. Restoration is our primary mechanism for confronting this challenge. While wetland restoration design has advanced dramatically since the early designs of the 1980s, restoration analysis and evaluation remain challenges that until now have wholly or primarily required on-site sampling. This is a major challenge for larger restoration projects, such as the Napa- Sonoma Salt Marsh restoration in California. Previous studies have indicated that the Normalized Difference Vegetation Index (NDVI) has been used in some restoration analyses with apparent success, but data is limited. To better understand its potential, this study examines issues in restoration analysis in the context of wetland restorations. By comparing pre- and post-restoration remote sensing data, I found that two sites in the Napa-Sonoma Marsh restoration demonstrated mixed NDVI results and that changes depended on subarea and whether median or maximum NDVI was analyzed. The mixed results are explained by several factors: the inherent limitations of NDVI; the large restoration size; the fact that wetlands, less vegetated, present special challenges for analysis; and the fact that it is early in the post-restoration period. The case study supports the use of remote sensing and GIS for restoration analysis and evaluation, but also emphasizes their current limitations. Many of these limitations, which hinge on the complexity of the potential data involved, are likely to be addressed in the next generation as the relevant technology continues to develop.