Abstract
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.
Highlights
Many human-dominated landscapes are invaded by non-native species, causing adverse impacts for native flora and fauna (Wilcove et al, 1998; Ehrenfeld, 2003), public health (Mack et al, 2000; Laaidi et al, 2003; Jones et al, 2009), and on ecosystem services including agricultural production, water filtration, recreation and tourism, flood mitigation, and cultural services (Pimentel et al, 2000)
The aim of this study is to analyze the use of a combination of light detection and ranging (LiDAR) data and hyperspectral imagery to map the distributions of two invasive plant species, Himalayan blackberry (Rubus armeniacus) and English ivy (Hedera helix), in the urban area of Surrey, British Columbia, Canada
Himalayan blackberry in open areas was detected best, followed by English ivy in open areas, English ivy in areas with closed canopies, and Himalayan blackberry in areas with closed canopies (Table 3)
Summary
Many human-dominated landscapes are invaded by non-native species, causing adverse impacts for native flora and fauna (Wilcove et al, 1998; Ehrenfeld, 2003), public health (Mack et al, 2000; Laaidi et al, 2003; Jones et al, 2009), and on ecosystem services including agricultural production, water filtration, recreation and tourism, flood mitigation, and cultural services (Pimentel et al, 2000). The decreased resilience and compromised integrity of these ecosystem services caused by invasive plant species costs at least US$34 billion annually in the United States alone (Pimentel et al, 2005; Pejchar and Mooney, 2009). Managers in urban areas are interested in controlling invasive plants (Pysek, 1998; Pimentel et al, 2000; Pysek and Hulme, 2005), as novel habitats and increased habitat disturbance associated with urban environments provide areas where certain invasive species can thrive (Gundale et al, 2008; Lampinen et al, 2015; Hawthorne et al, 2015). It is critical that the integrity of these urban natural areas is maintained, partially by controlling the spread of invasive plant species
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