Object-based image analysis of optical and radar variables for wetland evaluation

Optical and radar imagery has been shown to be useful for classifying wetland types and surrounding non-wetland classes such as forest and agriculture. Throughout the literature, recommendations have been made that optical and radar image variables together should improve overall and individual class accuracies. object-based image analysis (OBIA) uses multiple data types to segment objects representing land cover entities that are subsequently classified. There are few studies that have utilized optical and polarimetric radar variables together in OBIA to map wetland classes. This research investigated the potential to combine WorldView-2 optical image variables with fully polarimetric Radarsat-2 image variables in OBIA classification of wetland type. With the addition of radar polarimetric variables, classification accuracy improved for the wetland classes of fen, bog, and swamp over the use of optical imagery alone; specifically the addition of Cloude–Pottier (CP) variables of entropy, anisotropy, and alpha angle improved the classification of fen, and the addition of horizontal transmit and horizontal receive (HH) and horizontal transmit and vertical receive (HV) backscatter intensity improved the classification of swamp.