An open-source approach to characterizing Chihuahuan Desert vegetation communities using object-based image analysis

Abstract

Methodsfor quantifying vegetative cover across landscapes have, until recently, been limited to ground-based surveys or remote sensing via satellites or aircraft, both of which can limit the spatial scale of resulting data. Unmanned Aircraft Systems (UAS) can efficiently collect high-resolution sub-decimeter imagery of landscapes; geographic, object-based image analysis (GEOBIA) of the collected imagery can then be used to estimate vegetation cover. To date, few researchers have utilized open-source programs for GEOBIA. We developed GEOBIA methods in the open-source Program R to analyze visible spectrum UAS imagery from four sites in the Chihuahuan Desert of North America. These desert grasslands are difficult to quantify due to the patchiness of ground cover at small scales (e.g. <1 m) and the rarity of shrubs on the landscape. We used site-specific training data and multiple segmentation parameters to create vegetative and shrub cover data layers at a 15 cm resolution. We report overall accuracies of 77.2%–88.8% for vegetation classification and 95.7%–99.2% for shrub classification. Our work is some of the first to use open-source GEOBIA in grasslands and provides objective, reproducible data layers of desert vegetation, particularly shrubs, at the spatial scale necessary to inform management and conservation of Chihuahuan Desert grassland communities.