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Proactive management of invasive species in urban areas is critical to restricting their overall distribution. The objective of this work is to determine whether advanced remote sensing technologies can help to detect invasions effectively and efficiently in complex urban ecosystems such as parks. In Surrey, BC, Canada, Himalayan blackberry (Rubus armeniacus) and English ivy (Hedera helix) are two invasive shrub species that can negatively affect native ecosystems in cities and managed urban parks. Random forest (RF) models were created to detect these two species using a combination of hyperspectral imagery, and light detection and ranging (LiDAR) data. LiDAR-derived predictor variables included irradiance models, canopy structural characteristics, and orographic variables. RF detection accuracy ranged from 77.8 to 87.8% for Himalayan blackberry and 81.9 to 82.1% for English ivy, with open areas classified more accurately than areas under canopy cover. English ivy was predicted to occur across a greater area than Himalayan blackberry both within parks and across the entire city. Both Himalayan blackberry and English ivy were mostly located in clusters according to a Local Moran’s I analysis. The occurrence of both species decreased as the distance from roads increased. This study shows the feasibility of producing highly accurate detection maps of plant invasions in urban environments using a fusion of remotely sensed data, as well as the ability to use these products to guide management decisions.

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The research in this paper addresses human — environment interactions in Canadian cities by examining the spatial distribution of vegetation in relation to various socioeconomic indicators. Specifically, intercity and intracity comparisons are evaluated using correlation analysis and geographically weighted regression (GWR). Vegetation abundance estimates derived from spectral mixture analysis of Landsat imagery are compared with Canadian census data for the cities of Montreal, Toronto, and Vancouver to quantify vegetation-related environmental equity in Canada's largest urban centres. Results exhibit strong and consistent correlations between median family income and vegetation fraction for Montreal (r = 0.473), Toronto (r = 0.467), and Vancouver (r = 0.456). Furthermore, examining the GWR results suggests that employing an adaptive bandwidth kernel technique with a manual selection of ten neighbours for each observation provides a greater range and higher median values for local regression estimates (Montreal: 0.69; Toronto: 0.74; Vancouver: 0.73) as compared with the Akaike information criterion-selection method. Finally, we discuss the potential application of the presented analysis techniques for urban planning and community-development initiatives, specifically associated with managing vegetation-related environmental equity at various scales. Possible applications of these techniques for urban planning purposes are discussed, and key methodological considerations for performing such an analysis are highlighted.