Detection Thresholds for Rare, Spectrally Unique Targets within Semiarid Rangelands

Abstract

Many factors influence classification accuracy, and this study assessed detection thresholds for various sub-pixel targets using QuickBird multispectral imagery. Six iterations of maximum-likelihood classification were used to determine classification accuracy for 100 spectrally unique targets randomly placed over a semiarid rangeland site. Error matrices were calculated using independent validation sites and producer’s, user’s, and overall accuracy, Kappa Index of Agreement, and transformed divergence were analyzed to compare the performance of each classification and determine detection thresholds. Results indicate a strong relationship between target size and classification accuracy (R 2 0.94) as well as an increasingly prominent role played by training site selection as target size decreased. Strong spectral separability and good classification accuracies were achieved for targets 25 percent cover. Sub-pixel targets 25 percent in size were not detectable. This study highlights the effect of target size upon classification accuracy and has direct implications for invasive plant research and rare target detection.