Remote sensing technology is a tool for detecting invasive species affecting forest, rangeland, and pasture environments. This article provides a review of the technology, and algorithms used to process remotely sensed data when detecting weeds and a working example of the detection of spotted knapweed and babysbreath with a hyperspectral sensor. Spotted knapweed and babysbreath frequently invade semiarid rangeland and irrigated pastures of the western United States. Ground surveys to identify the extent of invasive species infestations should be more efficient with the use of classified images from remotely sensed data because dispersal of an invasive plant may have occurred before the discovery or treatment of an infestation. Remote sensing data were classified to determine if infestations of spotted knapweed and babysbreath were detectable in Swan Valley near Idaho Falls, ID. Hyperspectral images at 2-m spatial resolution and 400- to 953-nm spectral resolution with 12-nm increments were used to identify locations of spotted knapweed and babysbreath. Images were classified using the spectral angle mapper (SAM) algorithm at 1, 2, 3, 4, 5, and 10° angles. Ground validation of the classified images established that 57% of known spotted knapweed infestations and 97% of known babysbreath infestations were identified through the use of hyperspectral imagery and the SAM algorithm.

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A localization task required participants to indicate which of 4 locations contained a briefly displayed target. Most displays also contained a distractor that was not equally probable in these locations, affecting performance dramatically. Responses were faster when a display had no distractor and almost as fast when the distractor was in its frequent location. Conversely, responses were slower when targets appeared in frequent-distractor locations, even though targets were equally likely in each location. Negative-priming effects were reliably smaller when targets followed distractors in the frequent-distractor location compared to the rare-distractor location, challenging the episodicretrieval account. Experiment 2 added a 5th location that rarely displayed distractors and never targets, yet responses slowed most when distractors appeared there. The results confirmed that the attentional system is sensitive to first-and higher-order statistical patterns and can make short-and long-term adjustments in preferences based on prior history of inspecting unsuccessful locations.The limited capacity of the human attentional system, combined with the complexity of the environment, necessitates a mechanism for effective selecting and responding to goal-relevant stimuli while disregarding irrelevant ones. The cost of splitting attention or of being distracted can even be seen in simple experimental tasks such as spatial localization. An example of this type of task (Tipper, Brehaut, & Driver, 1990) required participants to press a key corresponding to the spatial location of a target flashed on a computer display. Performance was slower when two stimuli were flashed and one was a distractor. It is not only extraneous information that interferes with the processing of the current display: Research in attention has also documented that when a stimulus (location or object, depending on the task) that should be ignored (i.e., is the distractor) on the prime display becomes the target on the subsequent probe display, performance suffers compared with trials in which this switch does not occur. This type of interference is commonly referred to as negative priming (Tipper, 1985).The fact that irrelevant stimuli interfere with performance implicates a selective attention mechanism that directly allocates processing resources to goal-relevant information (e.g., Neill, 1997). Although there is considerable agreement concerning the necessity of a selective attention mechanism, there is less agreement concerning how this selection occurs, that is, how the process of filtering or ignoring the irrelevant is achieved.

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