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.
The applicability of high spatial resolution hyperspectral data and small-footprint Light Detection and Ranging (lidar) data to map and describe sagebrush in a semi-arid shrub steppe rangeland is demonstrated. Hyperspectral processing utilized a spectral subset (605 nm to 984 nm) of the reflectance data to classify sagebrush presence to an overall accuracy of 74 percent. With the inclusion of co-registered lidar data, this accuracy increased to 89 percent. Furthermore, lidar data were utilized to generate stand specific descriptive information in areas of sagebrush presence and sagebrush absence. The methods and results of this study lay the framework for utilizing co-registered hyperspectral and lidar data to describe semi-arid shrubs in greater detail than would be feasible using either dataset independently or by most ground based surveys.
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