A Unifying Approach to Classifying Wetlands in the Ontonagon River Basin, Michigan, Using Multi-temporal Landsat-8 OLI Imagery

AbstractAccurate spatial information is critical to the assessment and protection of wetlands in the context of human intervention and global climate change. However, it is challenging to map and monitor wetland vegetation classes with satisfactory results because of their highly seasonal dynamics, spatial heterogeneity, and spectral similarity. This paper examines the effectiveness of various classification approaches commonly employed in wetland mapping, including the support vector machine (SVM) algorithm, maximum likelihood classifier (MLC), classification and regression tree (CART) and other remote sensing indices, by using multi-temporal Landsat-8 Operational Land Imager (OLI) spectral data and end-member fraction data as well as terrain data. These different mapping approaches were compared in the Ontonagon River drainage basin in upper Michigan, USA, where easily-confused wetland types such as forested wetland, palustrine scrub/shrub wetland, and palustrine emergent wetland are extensively distrib...