Segmentation of Landsat Thematic Mapper imagery improves buffelgrass (Pennisetum ciliare) pasture mapping in the Sonoran Desert of Mexico

Jacob C. Brenner, Ithaca College
Zachary Christman, Middlebury College
John Rogan, Clark University


The invasive exotic buffelgrass (Pennisetum ciliare) is a principal threat to the Sonoran Desert of southwestern North America. Buffelgrass is cultivated in cattle pastures throughout Mexico's northwestern state of Sonora, where it poses an invasion threat to surrounding desert lands. Automated remote sensing methods to detect buffelgrass pasture conversion at a regional scale have shown limited success, due in part to variable land-cover conditions, and in part to intrinsic heterogeneity in desertscrub land-cover. This paper discusses a novel technique for delineating and mapping buffelgrass pastures based on vector-based satellite image segmentation followed by pixel-based classification using ancillary spatial environmental data. Based on quantitative accuracy metrics and visual inspection of known pasture sites, we report that segmentation considerably improved the mapping process, in particular the detection and delineation of pastures. Comparisons of paired classifications with segmented and nonsegmented imagery revealed higher overall map accuracies and higher buffelgrass class accuracies, as well as lower errors of commission and omission for buffelgrass in segmented maps. This new application of object-based image analysis has promising implications for ongoing efforts to map and monitor buffelgrass expansion region-wide and other similar changes in land-cover type and condition across human-modified landscapes.