A Comparative Evaluation of ISODATA and Spectral Angle Mapping for the Detection of Saltcedar Using Airborne Hyperspectral Imagery

Abstract

Abstract Nonnative plant species often cause adverse ecological and environmental impacts on the indigenous species of an area. Remote sensing methods have had mixed successes in providing spatial information on the distribution characteristics of specific vegetation species. Such research has been limited to broad‐band satellite based sensor systems whose spatial and spectral capabilities may not be adequate. Our research focuses on using hyperspectral data and innovative image processing techniques for mapping specific invasive species based on their spectral characteristics. Using the Airborne Imaging Spectroradiometer for Applications (AISA) hyperspectral imager (from Visible to Near Infrared (VNIR)). This research evaluated two methods of processing hyperspectral imagery including the Iterative Self‐Organizing Data (ISODATA) algorithm and Spectral Angle Mapping (SAM) for detecting saltcedar (Tamarix sp.) in Lake Meredith Recreational Area, Texas. A Minimum Noise Fraction (MNF) algorithm was used to remove the inherent noise and redundancy within the dataset during the SAM classification. Validation procedures revealed higher accuracies for the SAM method (83%) when compared to ISODATA (76%) in identifying saItcedar. The immediate benefit of this research has been to provide improved information on the spatial extent and density of saltcedar to land managers for the effective implementation of management programs to control this invasive plant.