Leafy spurge is a deep-rooted perennial weed that displaces native rangeland vegetation and replaces forage for cattle and other forages used by vertebrate herbivores. Strategic planning to control this weed requires monitoring its distribution and spread. Classical monitoring techniques, which often involve extensive ground survey efforts, can be aided by the synoptic nature of remotely sensed imagery. This research addresses the use of Space Imaging's 4-m multispectral Ikonos imagery for the survey and detection of leafy spurge infestations. Survey data were collected at a site in western North Dakota and used to produce supervised classifications of leafy surge infestations with Ikonos imagery. Multiple image dates per year were combined with each other to assess the added accuracy afforded by multitemporal imagery. Finally, individual patches of leafy spurge were analyzed to determine the minimum patch size and percent cover that were detectable with supervised classification of Ikonos imagery. Under some circumstances, the imagery was effective at detecting leafy spurge, but in areas with a higher forb component, the classification was not as effective. Multidate imagery provided increased accuracy, but improvements were not consistently significant. Leafy spurge infestations of <30% cover and 200 m2 were not reliably detected.

Satellite surveys in Syria have made use of imagery recorded some 30 years apart. By comparing the earlier pictures (Corona) with the later (Ikonos), sites captured on the former can be accurately located by the latter. The comparison also reveals the stark implications for archaeology as large parts of west Asian landscape change from a state of ‘benign neglect’ to active redevelopment. Based on their experience in the Homs survey, the authors have important advice to offer in the design and costing of surveys using satellite imagery.