The spread of invasive weeds continues to be a serious economic and environmental threat. Weed prevention has the potential to stop weeds before they become well established in an area conserving time, energy, and resources. Unfortunately, weed prevention is often overlooked. Weed Prevention Areas (WPAs) are a relatively new tool developed to help improve the application of weed prevention. They are cooperatively managed areas that focus on implementing prevention and early detection strategies at a community level. The purpose of this research was to establish baseline data that will be used to evaluate the overall effectiveness of the WPA concept, as well as to produce materials to assist individuals with the development of a WPA. Two rural communities, with new WPAs, were each paired with a non-WPA community for data collection purposes. On-the-ground GPS vegetation inventories were conducted to determine the initial abundance and distribution of selected invasive weed species. This information was then used to demonstrate how to prioritize species and sites within a WPA in order to utilize limited resources more efficiently. A mail-back survey was also conducted to evaluate landowners' current opinions and activities in regards to prevention and control. In general, weed prevention was viewed as an important part of integrated weed management, but fewer than half of all respondents applied prevention strategies on their property. The failure to implement prevention strategies was attributed to limited funding and resources, a lack of weed prevention knowledge, and a high level of perceived risk. The knowledge gained from this research was then used to create Step-By-Step Guidelines for Establishing a Weed Prevention Area. These Guidelines provide interested individuals the resources necessary to successfully organize a WPA in their community. In addition, four wildland weed mapping methods were evaluated to identify their strengths and weaknesses. There was no difference in the estimation of patch size or location between the buffered point, screen-drawn, and perimeter walked methods. In most situations, time and distance factors favor the selection of either the buffered point or screen-drawn method. If patch shape is an important consideration, the perimeter-walked or buffered point method should be selected.

A portable dual-camera video system was used to evaluate the potential for using total projected green cover as an indirect measure of weed infestations in a wheat crop during early growth stages. The video system would have applications in mapping weed infestations to assist precision farming operations. <p>The two cameras provided a real-time composite image of reflected light measured in red (640 nm), and near-infrared (860 nm) wavelengths. A simple ratio of reflected light intensity in each wavelength was used to isolate the growing plants from the background. Software was developed to automatically adjust for varying ambient light conditions and calculate the percentage of the image occupied by growing plants. Total green cover was measured at randomly selected sites prior to direct seeding wheat and at four growth stages following wheat emergence. The portion of green cover observed was compared to crop and weed dry matter at each location. Weed infestations at each location were estimated by measuring the total green cover and subtracting the projected green cover due to the crop alone. A minimum weed dry matter of 20 g/m2 and 30 g/m2 could be detected by the video system at the 3-leaf and 5-leaf growth stages, respectively. Weed dry matter less than 20 g/m2 could not be detected reliably due to the variability of the wheat crop. Detection of weeds within the crop beyond the 5-leaf stage using this method was difficult due to crop canopy closure.

A portable dual-camera video system was used to evaluate the potential for using total projected green cover as an indirect measure of weed infestations in a wheat crop during early growth stages. The video system would have applications in mapping weed infestations to assist precision farming operations. <p>The two cameras provided a real-time composite image of reflected light measured in red (640 nm), and near-infrared (860 nm) wavelengths. A simple ratio of reflected light intensity in each wavelength was used to isolate the growing plants from the background. Software was developed to automatically adjust for varying ambient light conditions and calculate the percentage of the image occupied by growing plants. Total green cover was measured at randomly selected sites prior to direct seeding wheat and at four growth stages following wheat emergence. The portion of green cover observed was compared to crop and weed dry matter at each location. Weed infestations at each location were estimated by measuring the total green cover and subtracting the projected green cover due to the crop alone. A minimum weed dry matter of 20 g/m2 and 30 g/m2 could be detected by the video system at the 3-leaf and 5-leaf growth stages, respectively. Weed dry matter less than 20 g/m2 could not be detected reliably due to the variability of the wheat crop. Detection of weeds within the crop beyond the 5-leaf stage using this method was difficult due to crop canopy closure.

Mapping their location and extent is a critical step in noxious weed management. One of the most common methods of mapping noxious weeds is to walk the perimeter of each patch with a handheld GPS receiver. This is the method used at Camp Williams, a National Guard Bureau training facility in Utah where this study was conducted. It was proposed that a low-cost Unmanned Aerial Vehicle (UAV) that made use of a hobbyist remote control airplane equipped with a Global Positioning System (GPS) receiver and digital camera could be used along with automated post-processing techniques to reduce the cost of weed mapping compared to the on foot method. Two noxious weeds were studied: musk thistle (Carduus nutans) and dalmation toadflax (Linaria dalmatica). The musk thistle was visually identifiable in the imagery but the dalmation toadflax was confused with yellow sweet clover (Melilotus officinalis). It was found that after the automated post-processing the photos were not positioned well enough to produce a consistent and accurate weed perimeter. A supervised classification was attempted with imagery of the musk thistle, however, the accuracy of the classification was too low to be able to identify the weed perimeter from the classification. To achieve accurate results the photos had to be registered to a base image and the perimeter of each patch hand digitized. The time it took to do so increased the costs well above the on foot method. A number of improvements to the UAV could make the image registration step unnecessary. There are other applications for which this low cost UAV could be used.