Tree cover discrimination in panchromatic aerial imagery of pinyon-juniper woodlands

Responding to an increasing interest in studying vegetation changes over time, we review current methods of processing black and white digital aerial photographs in order to classify tree cover in pinyon-juniper woodlands. Besides applying commonly used clustering and supervised maximum-likelihood methods, we have developed a new classifier, nearest edge thresholding, which is unsupervised and based on the principals of edge detection and density slicing. Comparison of the three methods' abilities to predict field values at plot scales of 100 m² to 900 m² shows this new method is better or comparable to others at all scales, can be easily applied to digital imagery, and has high correspondence with ground-truthed field values of tree cover.