Subpixel abundance estimates in mixture-tuned matched filtering classifications of leafy spurge (Euphorbia esula L.)

Abstract

Two demonstration sites in southeast Idaho, USA were used to extend remote sensing of leafy spurge research to fine-scale detection for abundance mapping using matched filtering (MF) scores. Linear regression analysis was used to quantify the relationship between MF estimates and calibrated ground estimates of leafy spurge abundance. The two sites had r 2 values of 0.46 and 0.64. Both the slope of the regressions and the scaling behaviour of MF scores indicate that the technique consistently underestimated true abundance (defined here as percentage canopy cover) by roughly one-third. This underestimation may be influenced by field estimation bias and algorithm confusion between target and background signal. Further results indicate that MF exhibits linear scaling behaviour in six locations containing dense, uniform infestations. At these locations, where canopy cover was held relatively constant, high spatial resolution (3 m) estimates were not significantly different from coarser spatial resolution estimates (up to 16 m). Given the mathematically unconstrained nature of the estimation technique, MF is not a straightforward method for estimating leafy spurge canopy cover.