Abstract
Invasive species pose one of the greatest threats to global biodiversity. Early detection of invasive species is critical in order to prevent or manage their spread before they exceed the ability of land management groups to control them. Optical remote sensing has been established as a useful technology for the early detection and mapping of invasive vegetation populations. Through the use of airborne hyperspectral imagery (HSI), this study establishes a target detection methodology used to identify and map the invasive reed Phragmites australis subsp. australis within the entire extent of Îles-de-Boucherville National Park (Quebec, ON, Canada). We applied the Spectral Angle Mapper (SAM) target detection algorithm trained with a high accuracy GNSS ground truth data set to produce a park-wide map illustrating the extent of detected Phragmites. The total coverage of detected Phragmites was 26.74 ha (0.267 km2), which represents 3.28% of the total park area of 814 ha (8.14 km2). The inherent spatial uncertainty of the airborne HSI (∼2.25 m) was accounted for with uncertainty buffers, which, when included in the measurement of detected Phragmites, lead to a total area of 59.17 ha (0.591 km2), or 7.26% of the park. The overall accuracy of the Phragmites map was 84.28%, with a sensitivity of 76.32% and a specificity of 91.57%. Additionally, visual interpretation of the validation ground truth dataset was performed by 10 individuals, in order to compare their performance to that of the target detection algorithm. The overall accuracy of the visual interpretation was lower than the target detection (i.e., 69.18%, with a sensitivity of 59.21% and a specificity of 78.31%). Overall, this study is one of the first to utilize airborne HSI and target detection to map the extent of Phragmites over a moderately large extent. The uses and limitations of such an approach are established, and the methodology described here in detail could be adapted for future remote sensing studies of Phragmites or other vegetation species, native or invasive, at study sites around the world.
Highlights
Second only to habitat loss, invasive species are one of the most significant threats to global biodiversity (Early et al, 2016; IUCN, 2017; Mack et al, 2000; U.S Congress Office of Technology Assessment, 1993), and can adversely affect the structure and function of the ecosystems to which they are introduced (Mack et al, 2000)
Investigation of the input spectra used to train the target detection algorithm revealed that the brown Phragmites seed heads present within the stands were spectrally distinct from the leafy green portion of the plant (Figure 7)
In order to include this part of the plant in the target detection results, which could be the dominant material present in some Phragmites pixels, additional target spectra were collected from the brown seed heads and used to train the target detection algorithm on this separate class
Summary
Second only to habitat loss, invasive species are one of the most significant threats to global biodiversity (Early et al, 2016; IUCN, 2017; Mack et al, 2000; U.S Congress Office of Technology Assessment, 1993), and can adversely affect the structure and function of the ecosystems to which they are introduced (Mack et al, 2000). Given the detrimental impact of invasive species observed in various ecosystems (He et al, 2011), concerns surrounding invasive species have spurred an increase in research that covers a broad range of topics, including understanding mechanisms of invasion and determining the proper information needed to create management plans for specific invasive species (Hastings et al, 2006; Belzile et al, 2010). Geographic Information Systems (GIS) and airborne and satellite based remote sensing can produce critical information to help land managers establish appropriate management plans (Shaw, 2005), react rapidly to detect and respond to an early invasive before a species becomes established (Westbrooks, 2004), or forecast the potential spread of invasive species to susceptible areas (Rocchini et al, 2015). Remote sensing provides a powerful way to identify and map invasive species over different spatial and temporal scales while simultaneously building a strong understanding of key physical characteristics of invasive species (C.-y. Huang and Asner, 2009; Underwood et al, 2007)
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