Some non-natives or introduced plants species can thrive in their new environments without disrupting the existing ecosystem. However, an invasive plant is defined as being any plant species introduced by humans that harm its environment.
Japanese knotweed is an invasive species that is difficult to control. Image Credit: gabriel12/Shutterstock.com
Invasive plants can wreak havoc on existing biodiversity and cause major changes to the local ecosystem. This can be because there are no natural predators or diseases so the population of the invasive species can explode and remain uncontrolled. It can also be due to better adaptation to their new local environment than existing plant species.
Some researchers consider that invasive species may pose an even bigger threat to biodiversity than climate change.1 Climate change and invasive species are an unfortunate combination as shifting climates often make it easier for invasive species to spread and further destabilize already struggling local ecosystems.
The problem with invasive plants is so severe that many countries have strict biosecurity laws to try and avoid this issue. This includes restrictions on which kinds of plant material can be brought into a country and screening international passengers for any potential soil contaminants that could accidentally lead to the introduction of new diseases or invasive species.
A famous example of the impact of invasive species includes the prevalence of Japanese knotweed. Japanese knotweed is an incredibly fast-growing invasive species whose growth rates have led to the crowding out of many native species. It can also cause extensive damage to buildings and negatively impact local aquatic life by disrupting local nutrient systems as well as the availability of light.
As a result of all the issues caused by Japanese knotweed growth, numerous methods have been devised to try and control the Japanese knotweed populations.2 However, all of these methods require that the growing invasive plant population is identified, preferably as early as possible before extensive root populations are established, and the plant is even more problematic to remove and kill.
Machine Vision and Identification
Many invasive species may take hold in wild areas of grasslands or open spaces that may not be routinely monitored. Automated detection methods can be a very powerful tool for environmental monitoring. One such approach is the use of machine vision applications to identify plant species that can then be flagged for further investigation.
Machine vision and imaging spectroscopy approaches have been successfully used for the identification of Japanese knotweed and can discriminate between different plant species in the same area.2 Other species, such as serica, can also be identified using such methodologies and instrumentation attached to airborne vehicles that can either be piloted remotely or run in autonomous modes.3
The advantage of using imaging spectroscopy, or hyperspectral imaging approaches is that the plant species can be identified not just by its physical appearance and factors such as leaf shapes and typical colors, but by its chemical composition.3 This gives a much greater degree of confidence in the plant identification, particularly in complex natural environments where there may be large numbers of species clustered together or environmental conditions such as poor light levels contributing to poor signal levels in the image.
In a hyperspectral imaging experiment, a full spectrum is recorded at each point in the image. This leads to large three-dimensional datasets where spatial information can be associated with the spectroscopic information on the molecular content of the object. Infrared spectroscopy is commonly used in hyperspectral imaging methods as this spectroscopic technique is sensitive to the vibrational modes in molecules.
As the energies of the vibrational modes in a molecular species are dependent on the types of atoms bonded together and their local chemical environments, these energies and associated line shapes can provide an excellent tool for the identification of chemical species or families of chemical compounds containing a particular motif.
For the identification of serica plants in grassland regions, the use of infrared hyperspectral imaging with remote sensing capabilities made it possible to identify the plant species and recognize factors that made the plants particularly successful in a given region.3 This is essentially the ability to ‘see into’ the leaves of the plant and examine the nutrient content and concentrations of species such as Mg and K that correlated with larger canopy growth and coverage.
Researchers used remote vehicles to identify geographical areas particularly at risk from overgrowth so removal efforts could be targeted.
The amount of chlorophyll was also an important indicator for plants that were likely to be highly successful.
With invasive plant species estimated to cost the US economy over $1 trillion,3 techniques such as imaging spectroscopy that allow for the rapid identification and understanding of invasive plants under true environmental conditions could be a powerful tool for developing new interventions.
Understanding plants' mechanisms for success could provide researchers with new aspects of plant physiology to target when developing removal techniques.
References and Further Reading
- Mainka, S. A., & Howard, G. W. (2010). Climate change and invasive species: Double jeopardy. Integrative Zoology, 5(2), 102–111. https://doi.org/10.1111/j.1749-4877.2010.00193.x
- Jones, D., Pike, S., Thomas, M., & Murphy, D. (2011). Object-based image analysis for detection of Japanese Knotweed s.l. taxa (polygonaceae) in Wales (UK). Remote Sensing, 3(2), 319–342. https://doi.org/10.3390/rs3020319
- Gholizadeh, H., Friedman, M. S., McMillan, N. A., Hammond, W. M., Hassani, K., Sams, A. V., Charles, M. D., Garrett, D. A. R., Joshi, O., Hamilton, R. G., Fuhlendorf, S. D., Trowbridge, A. M., & Adams, H. D. (2022). Mapping invasive alien species in grassland ecosystems using airborne imaging spectroscopy and remotely observable vegetation functional traits. Remote Sensing of Environment, 271, 112887. https://doi.org/10.1016/j.rse.2022.112887