Detection of two intermixed invasive woody species using color infrared aerial imagery and the support vector machine classifier

Both the evergreen redberry juniper (Juniperus pinchotii Sudw.) and deciduous honey mesquite (Prosopis glandulosa Torr.) are destructive and aggressive invaders that affect range- lands and grasslands of the southern Great Plains of the United States. However, their current spatial extent and future expansion trends are unknown. This study was aimed at: (1) exploring the utility of aerial imagery for detecting and mapping intermixed redberry juniper and honey mesquite while both are in full foliage using the support vector machine classifier at two sites in north central Texas and, (2) assessing and comparing the mapping accuracies between sites. Accuracy assessments revealed that the overall accuracies were 90% with the associated kappa coefficient of 0.86% and 89% with the associated kappa coefficient of 0.85 for sites 1 and 2, respectively. Z-statistics (0.102 < 1.96) used to compare the classification results for both sites indicated an insignificant difference between classifications at 95% probability level. In most instances, juniper and mesquite were identified correctly with <7% being mistaken for the other woody species. These results indicated that assessment of the current infestation extent and severity of these two woody species in a spatial context is possible using aerial remote sens- ing imagery. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.7.073588)