Many upland pastures and forest meadows in the western United States contain significant infestations of yellow hawkweed and oxeye daisy. Documentation of infestations is necessary in order to plan and assess control tactics. Previous work with an airborne charge coupled device (CCD) with spectral filters indicated that flowering yellow hawkweed with at least 30% cover was detectable at 1 m resolution. A single image of a large area may not capture all plants in the flowering phase and multiple images are costly. The objective of this paper was to assess the accuracy of images recorded at different phenological stages. We compared three methods of classification: unsupervised classification of a three principal component analysis image, supervised classification of a three principal component analysis image, and supervised classification of a composited image consisting of four bands and normalized difference near infrared (NIR)/red band. Regardless of the classification method, images of yellow hawkweed and oxeye daisy in full bloom had lower classification error than at early bloom or post bloom. The percent error for yellow hawkweed classification was about twice as high at post bloom as at full bloom, but varied slightly depending on the method of classification and cover class. The ability to detect discrete colonies of yellow hawkweed was not affected by phenological stage, but the ability to measure the area of each cluster differed among stages. Less than one-third fo the pixels classified as yellow hawkweed or oxeye daisy in the early bloom image remained in the same class in the full bloom image. About half the pixels in the full bloom image remained in the 90 to 100% cover class at the post bloom image. Seasonal growth of the grasses masked some yellow hawkweed and oxeye daisy plants, and accounted for differences in classification among phenological stages.

Yellow hawkweed infests permanent upland pastures and forest meadows in northern Idaho. Conventional surveys to determine infestations of this weed are not practical. A charge coupled device with spectral filters mounted in an airplane was used to obtain digital images (1 m resolution) of flowering yellow hawkweed. Supervised classification of the digital images predicted more area infested by yellow hawkweed than did unsupervised classification. Where yellow hawkweed was the dominant ground cover species, infestations were detectable with high accuracy from digital images. Moderate yellow hawkweed infestation detection was unreliable, and areas having less than 20% yellow hawkweed cover were not detected.

Ground-based surveys to regularly document the size, shape, and location of weed populations usually are not economically feasible. Digital images of vegetation in semiarid rangeland were obtained from four charge-coupled devices with spectral filters mounted in an airplane. The ability to distinguish yellow starthistle and common St. Johnswort from other rangeland vegetation in images with 0.5, 1, 2, and 4 m spatial resolution was assessed. Detection, positioning, and measurements of size and density of yellow starthistle and common St. Johnswort colonies with densities as low as 30% ground cover were possible at all those resolutions. Images indicated yellow starthistle occupied about one-third of a 180-ha study area. Images taken in June indicated 2 to 10 ha less yellow starthistle ranging from 30 to 100% cover than images taken in July for all resolutions. Images indicated common St. Johnswort occupied less than 4 ha of the 180-ha study area. This procedure provides a method to establish baseline plant community composition and a way to monitor species population changes and dispersal over time.

Global positioning systems (GPS) technology, developed by the Department of Defense, enable accurate documentation of Cartesian coordinates anywhere on the earth's surface. Surveying, mapping, positioning, and subsequent management of weed infestations can be expedited with this technology. Positions and boundaries of infestation may be located with 10-m or better accuracy while the GPS receiver is continuously moving, and with 2-m or better accuracy with brief stops for repeated sampling. GPS data agreed closely with U.S. Geological Survey data. Coordinates for a weed infestation may be relocated for treatment, evaluation or other purposes. Basic geographic information systems (GIS) map features from Digital Line Graph (DLG), Topologically Integrated Encoding and Reference Systems (TIGER) and other sources of information may be used to fully integrate delimiting survey results from GPS readings in order to develop weed management plans.

Russian thistle plant movement and seed dispersal were studied in 1991 and 1992 by placing Russian thistle plants in the center of wheat fields in eastern Washington. Three adjacent site treatments, with 24 plants on each site, were used each year; wheat stubble, summerfallow planted to winter wheat, and a “stationary” site. Plants in the “stationary” site were anchored to the ground to prevent tumbling. Plants in the stubble and summerfallow sites were allowed to tumble naturally. Individual plant movement was monitored and recorded weekly by satellite global positioning systems technology. Average estimated seed number per plant at the beginning of the experiment was 57,400 in 1991 and 66,000 in 1992. The direction plants moved correlated highly with wind direction. Some plants moved a maximum distance of 4069 m in 6 wks, while other plants moved only 60 m because of variable winds and being compressed by snow or frozen into wheat stubble. Average percentage seed loss in 1991 and 1992 for stationary plants was 15 and 26%, and for tumbling plants was 48 and 66%, respectively.

Failure to detect noxious weeds with current survey methods prevents their control and has contributed to their ability to establish and spread in remote range and forest sites. Techniques used in remote sensing can classify plant occurrence on maps, offering a method for surveying invasive species in remote locations and across extensive areas. An imaging hyperspectral spectrometer recorded images on July 19, 1998 in Farragut State Park near Bayview, ID, in the reflected solar region of the electromagnetic spectrum ranging from 440 to 2,543 nm to detect spotted knapweed. The sensor records 128 spectral bands in 12- to 16-nm intervals at a spatial resolution of 5 m. A spectral angle mapper (SAM) algorithm was used to classify the data. Infestations in Idaho with 70 to 100% spotted knapweed cover that were 0.1 ha were detected regardless of the classification angle. However, narrow angles (2 to 8°) did not completely define the extent of the infestation, and the widest angle tested (20°) falsely classified some areas as infested. The overall image error for all classes was lowest (3%) when SAM angles ranged from 10 to 11°. Specific errors for the spotted knapweed class for the 10 to 11° angles showed that omissional and commissional errors were less than 3%. Areas with as little as 1 to 40% spotted knapweed cover were detected with an omissional error of 1% and a commissional error of 6%. Further verification sites were established on August 11, 1998 near Bozeman, MT, using the algorithms developed for Idaho. The omissional error for the Montana sites was 0%, and the commissional error was 10%. The hyperspectral sensor, Probe 1, proved an effective detection tool with the ability to detect spotted knapweed infestations.

Digital imagery from satellites and airborne remote sensing offer an opportunity to accurately detect weed infestations. Image resolution and plant growth stage are critical factors for maximum weed detection with low errors. Data analysis in traditional image assessment has relied on agreement measures, such as Cohen's kappa and asymptotic procedures, that compare what is on the image but not on the ground and what is on the ground but not on the image. Statistical comparisons of multispectral images, however, require some knowledge of the variability of the image classification results to determine significant differences among agreement measures. Bayesian methods were used to develop probability distributions for an agreement measure, conditional kappa, and were then subsequently applied to assess and compare image resolutions and plant growth stages. Results showed that images of a study site known to have yellow starthistle populations could identify the noninfested areas with greater accuracy than infested areas at spatial resolutions of 0.5, 1.0, 2.0, and 4.0 m. The detection accuracy of yellow starthistle in the images taken either prebloom or at flowering with 4.0-m spatial resolution usually was equal to or better than spatial resolutions of 0.5, 1.0, and 2.0 m for the cover classes that were not, moderately (31 to 70%), and highly (71 to 100%) infested. The 0.5-m resolution was better than 4.0-m spatial resolution when detecting the moderate cover class, but both resolutions had high omissional and commissional errors. Contrasting the best detection resolution for finding yellow starthistle colonies across flight times indicated that flying at flowering stage with the 4.0-m spatial resolution provided the best detection of the yellow starthistle cover classes considered. In the cases where different spatial resolutions resulted in equal detection accuracy, the larger spatial resolution was selected because of lower costs of acquiring and processing the data.

Yellow starthistle is an invasive plant species common in the semiarid climate of central Idaho and other western states. Early detection of yellow starthistle and estimation of its infestation potential in semiarid grasslands have important scientific and managerial implications. Weed detection and delineation of infestations are often carried out by using ground survey techniques. However, such methods can be inefficient and expensive in detecting sparse infestations. The distribution of yellow starthistle over a large region may be affected by various landscape variables such as elevation, slope, and aspect. These exogenous variables may be used to develop prediction models to estimate the potential for yellow starthistle invasion into new areas. A nonlinear prediction model has been developed using a polar coordinate transformation of landscape characteristics to predict the likelihood of yellow starthistle occurrence in north-central Idaho. The study region included the lower Snake River and parts of the Salmon and Clearwater basins encompassing various land-use (range, pasture, and forest) categories. The model provided accurate estimates of yellow starthistle incidence within each specified land-use category and performed well in subsequent statistical validations. This prediction model can assist land managers in focusing their efforts by identifying specific areas for survey.

Yellow starthistle is an invasive plant of canyon grasslands in north-central Idaho. The distribution of yellow starthistle is associated with general landscape characteristics that include land use and specific terrain-related features such as elevation, slope, and aspect. Slope and aspect can be considered as indicators of plant community composition and distribution. Hence, these variables may be incorporated into prediction models to estimate the likelihood of yellow starthistle occurrence because plant communities differ in susceptibility to invasion. An empirically derived nonlinear model based on landscape characteristics has previously been developed to predict the likelihood of yellow starthistle occurrence in north-central Idaho. Although the model was used to predict the invasion potential of yellow starthistle into new areas, it could also be used as auxiliary data for classifying this weed species in remotely sensed imagery. To accomplish this, the predicted values from the model are regarded as prior information on the presence of yellow starthistle. A Bayesian image classification algorithm using this prior information is then applied to a corresponding set of remotely sensed data. This results in a map indicating the posterior probabilities of yellow starthistle occurrence given the landscape characteristics. This technique is demonstrated and is shown to reduce omissional error rates by 50% when the landscape characteristics are incorporated into the classification process.

Brazilian pepper is a small evergreen tree that forms dense colonies. It was introduced for horticultural use in the United States in the early 1800s and was widely distributed in Florida in the late 1920s. Previous remote-sensing projects to detect Brazilian pepper achieved moderate success and warranted additional research using a hyperspectral sensor. Detection with remote sensing is desirable because complete access to ground survey crews is not practical. The western half of the Everglades National Park was imaged at a 5-m spatial resolution with a hyperspectral sensor by Earth Search Sciences Inc. of Kalispell, MT, on December 12, 2000, and January 10, 2001. The sensor has 128 channels and spectral resolution between 450 and 2,500 nm. The purpose of this research was to develop spectral reflectance curves for Brazilian pepper and establish the accuracy of classified images. Classified images showed that a hyperspectral sensor could detect a “pure” Brazilian pepper pixel representing the center of an infestation but not “mixed” Brazilian pepper pixels at the sparsely populated edges. To define the sparse populations, images were classified using a spatial buffer (15- to 100-m radius) based on a low–omissional error image. A 25-m buffer reduced the amount of commissional error for Brazilian pepper in mangrove-dominated forest to 8.2% and buttonwood-dominated forest to 0%. Wider buffers did not significantly improve image accuracy when compared with the 25-m buffer distance. Results indicate that removal crews using hyperspectral images will be able to reliably find the colonies of Brazilian pepper but will not be able to use the images to find isolated scattered trees.

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.