Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

Ultra-high-resolution digital aerial imagery has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment This research investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern USA arid rangelands with 4 cm resolution digital aerial imagery We obtained high r-square values for the regressions relating ground- to image-based measures of percent covet (r-square values 0 82-0 92) OBIA enabled us to automate the classification process and demonstrated potential for quantifying fine-scale land covet attributes with ultra-high-resolution imagery This approach exhibits promise for nationwide application for monitoring grazing lands