Applications of Remote Sensing to Alien Invasive Plant Studies

Abstract

Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions.

Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy

“…Because MESMA is a classification method that accounts for spectral variability of endmembers on a per-pixel basis when solving spectrally unmixing problems [17], this method has the potential to also be tested in future studies using our methodological framework. Previous studies have suggested that the efficacy of spectral unmixing methods may be species-and site-specific [28], may depend on seasonality [19] or on the source of remote sensing data [9,10]. Therefore, we suggest that our methodological approach (i.e., vertically stratified field sampling, MTMF-threshold approach or BSVM-volumetric approach) be further evaluated in other classification conditions.…”

“…Remote sensing approaches to map invasive plant species have been assessed for different species, vegetation types, and sources of remotely-sensed data (reviewed in [9,10]). Most previous plant species mapping studies have naturally focused on the upper canopy stratum, as this is the portion of the forest most accessible to remote sensing mapping approaches [11].…”

Open-Source Processing and Analysis of Aerial Imagery Acquired with a Low-Cost Unmanned Aerial System to Support Invasive Plant Management

“…Remotely-sensed vegetation indices (VI) have been extensively used for monitoring and detecting invasive tree species (Huang and Asner, 2009;Huang et al, 2010;He et al, 2011;Bradley, 2014). VI related to physical leaf features like greenness, chlorophyll or water/ moisture content, richness in lignin, nitrogen, and cellulose have especially proved to be highly effective in differentiating Acacia species from surrounding vegetation (Larsson, 1993;Oldeland et al, 2010;Große-Stoltenberg et al, 2016).…”

“…Several studies addressing the management of invasive plant species demonstrate the great potential of remote sensing techniques for detecting, mapping and predicting the spatial spread of plant invaders, in particular when using multispectral or hyperspectral sensors (Huang and Asner, 2009;He et al, 2011;Bradley, 2014). Early detection and monitoring of invading plant species using satellite or airborne imagery can provide a valuable basis for control efforts at both the local and landscape level (Bradley and Mustard, 2006;Vilà and Ibáñez, 2011).…”

Unmanned Aerial Vehicles for Alien Plant Species Detection and Monitoring

“…This approach is applicable only in case the data provide enough spectral and/or spatial detail, the species is distinct from the background, forms dense and uniform stands, and/or is large enough to be detected (Müllerová et al, 2005;Bradley and Mustard, 2006;Jones et al, 2011). Due to these difficulties, hyperspectral data are often used to compensate for low differentiation of some invasive species in visible spectrum (for reviews see Huang and Asner, 2009;He et al, 2011). New possibilities of automated or semi-automated classification of invasive species arose with the development of the object-based image analysis (OBIA; Jones et al, 2011;Müllerová et al, 2013).…”