| Summary: | Ash (Fraxinus spp.) species are under serious threat by an invasive insect named the Emerald Ash Borer (EAB), already spreading across 35 states in the USA. To track the dramatic progression for effective implementation of ash conservation plans, information on the spatial distribution of host resources is essential. This study aimed at delineating ash trees at the crown level in a mature bottomland hardwood forest under a remote sensing mapping framework. We employed five-band Worldview-3 imagery at 31 cm spatial resolution acquired in the spring and conducted the mapping activity using geographic object-based image analysis (GEOBIA). Because the training samples and features play important roles in classification, we tested the effects of training sample size and the textural features on the mapping performance. A set of segments that were optimized through a series of test-and-trials in the multiresolution segmentation were used as the basic mapping unit. By testing against field samples, the largest average overall accuracy 82%, was achieved through the random forest classifier. Our findings suggest that the classification accuracy is improved with increased training sample size. The combination of spectral and textural features improved the classification accuracy significantly (p-value <0.05) for two large sample sizes (i.e. n = 200 and n = 230). The GEOBIA framework we demonstrated may guide the future ash tree mapping endeavors regarding the selection of segmentation parameters, sample sizes, and classification features.
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