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.

Acknowledgements

We thank the anonymous reviewers who helped to strengthen the merit of this paper. This research was funded by USDA Natural Resources Conservation Service Conservation Innovation Grant No. 68-0211-6-124, Pacific Northwest Regional Collaboratory, as part of a Pacific Northwest National Laboratory project funded by NASA through Grant No. AGRNNX06AD43G, and NOAA OAR ESRL/Physical Sciences Division (PSD) Grant No. NA04OAR4600161. Field data collection was made possible through the generous advice and assistance of Jeffrey Pettingill and staff at Bonneville County Weed and Pest Control, Shane Jacobson (US Forest Service, Dubois, Idaho), Keith Bramwell (Continental Divide Cooperative Weed Management Area), and Tom Stohlgren (USGS Fort Collins Science Center).