An accuracy assessment of tree detection algorithms in juniper woodlands

This research provides a comprehensive accuracy assessment of five methods for classifying western juniper (Juniperus occidentalis) canopy cover from 1 m, 4-band National Agriculture Imagery Program (NAIP) imagery. Two object-oriented classification approaches (image segmentation and spatial wavelet analysis, (SWA)) are compared to three pixel based classification approaches (random forests, Iterative Self-Organizing Data Analysis (ISODATA), and maximum likelihood). Methods are applied to approximately 250 km² in the intermountain western USA. A robust suite of statistical approaches, which offer an alternative to traditional kappa-based methods, are utilized to determine equivalence between methods and overall effectiveness. Object-oriented approaches have the highest overall accuracy among the assessed methods. Each of the methods varied considerably in cover class accuracy. SWA has the highest class accuracy when juniper canopy cover is low (0 to 40 percent cover), ISODATA performs best at moderate cover (60 to 80 percent) and maximum likelihood performs best at higher cover (60 to 100 percent cover).