Invasive plants present significant challenges for ecosystem integrity, biodiversity preservation, and agricultural production. Continuous surveillance efforts are necessary to detect and effectively respond to emerging infestations. However, monitoring methods currently available each have their own limits, whether due to cost, time, or sampling bias. Computer vision applied to roadside imagery is a previously undeveloped methodological synergy that can support existing monitoring efforts. Further, because roadsides are a vector of spread, they are an ideal pathway to monitor. In this study, we present research and management applications of a dataset generated by a computer vision model for Phragmites (Common reed) and knotweed complex species across roadsides in New York State. To better understand spread and inform risk assessment, we examined plant presence in relation to road size, site, and culvert characteristics. Results indicated that Phragmites presence decreased with increasing distance from a culvert and was less likely near forested areas and on roadsides with low traffic volume. Results also suggest that the risk of invasion relative to road size is species specific: Phragmites was found to occur more frequently on highway ramps and primary roads, while the knotweed complex was found to occur more often on secondary roads. Additionally, we developed a framework in partnership with stakeholders that enables managers and community scientists to verify, interpret and act upon the very large datasets resulting from computer vision models. Two ArcGIS Dashboards, several GIS layers, and web-based forms were created to this end. Through this work, we identified that culverts and highway ramps should be monitored given their role as Phragmites hotspots. We also generated a new framework that distills a large amount of new data into a form usable by both professionals and the public, expanding upon the capacity of existing monitoring workflows. Supplemental tables S1 and S2 detail data sources, filters, and product dependencies.
Environmental Science (MS)
Department, Program, or Center
Thomas H. Gosnell School of Life Sciences (COS)
Anna Christina Tyler
Megraw, Liam, "Invasive Species Identification and Monitoring using Computer Vision, Street View Imagery, and Community Science" (2022). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus
Available for download on Thursday, June 22, 2023