Using Remote Sensing to Map Invasive Species

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Invasive species can have devastating impact on economies and the environment. Species that rapidly takeover new ecosystems can alter the presence of native wildlife while transforming the overall ecology.

This can have long-term negative repercussions that threaten endangered species and can cost tax payers a lot of money through altered landscapes.

Scientists and researchers are increasingly using remote sensing methods to track and estimate where invasive species might go next as a way to limit their impact. 

Using NDVI to Map Invasive Species

Increasingly, researchers are finding many different platforms suitable for tracking invasive species. Multispectral satellites such as Landsat 7 ETM offers one possibility. Invasive species such as tamarix (Tamarix spp.) can cause a lot of damage because it is difficult to remove from riparian regions in the western United States.

Using normalized difference vegetation index (NDVI) to take advantage of the fact that tamarix sprouts its leaves early in the spring and sheds its leaves late in autumn has allowed researchers to track how this plant has moved along rivers and regions in Colorado.[1]  

Map of detected tamarisk in southeastern Colorado, along the Arkansas River and irrigation ditches. Tamarisk infestations are shown from moderate (orange) to high (red). Map: Evangelista et al., 2009.
Map of detected tamarisk in southeastern Colorado, along the Arkansas River and irrigation ditches. Tamarisk infestations are shown from moderate (orange) to high (red). Map: Evangelista et al., 2009.

Even for terrestrial mammals that are invasive, NDVI can be used to measure where these animals have gone and where they will likely go based on vegetation changes and types of vegetation these animals eat.

Introduced spotted deer and elephants in the Andaman islands off of India can be detected by the fact that vegetation growth significantly drops where these animals go as they migrate across the regions. Using NDVI from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data can determine areas of vegetation vulnerability by measuring the vigor of vegetation growth in a region.[2] 

More recent methods have tried to forecast where invasive species would go in order to treat regions before they are threatened by invasive species.

Cheatgrass (Bromus tectorum).  Photo David Pyke. USGS Public domain.
Cheatgrass (Bromus tectorum). Photo David Pyke. USGS Public domain.

This was the case in the western United States, in Wyoming’s Medicine Bow National Forest, where cheatgrass (Bromus tectorum) has been a problem invasive species as it grows throughout the region. Using Landsat 8 Operational Land Image (OLI) and Sentinel-2’s multispectral instruments, researchers forecast where the cheatgrass would grow. They then applied a general linear model to show that treated areas, where active campaigns to fight the cheatgrass spread was applied, reduced cheatgrass by 36%, while untreated areas increased cheatgrass by 6%.[3]

Using UAVs to Map Invasive Species

Researchers are increasingly using unmanned aerieal vehicles (UAVs) to now monitor invasive species as such instruments can give data much faster and allow higher resolution in specific areas.

One study used continual monitoring from UAVs to assess the spread of water chestnut (Trapa natan), which is invasive in the regions of the Erie canal in the United States. Through a combination of imagery assessment, active removal campaigns, and differentiation of regions where the water chestnut grew, it became clear that open, non-urban areas where the most successful in removing this invasive species, while urban regions were more difficult.

An invasive species of water chestnut covers the surface of a lake in Fairfax County, VA.  Photo: Nancy Rybicki, Hydro-Eco Interactions Branch. Public domain. USGS
An invasive species of water chestnut covers the surface of a lake in Fairfax County, VA. Photo: Nancy Rybicki, Hydro-Eco Interactions Branch. Public domain. USGS

The application of UAVs also helps land managers to better assess the likely areas where the invasive species could grow, allowing preemptive measures and periodic checking to take place.[4] 

Interestingly, new efforts at crowdsourcing data, including remote sensing data, from the public, with ever increasing presence of cameras in the sky and elsewhere, may mean that detection of invasive species becomes easier as large data efforts to upload images could limit the need for only scientists and research teams to be actively searching for invasive species.

Such efforts could help more rapid detection of invasive species before they become a significant problem warranting scientific inquiry.[5]

While invasive species have been recognized to be major economic and environmental problems, researchers are increasingly using available remote sensing tools to mitigate their impact on different regions.

Large, regional monitoring using platforms such as MODIS, Landsat, and Sentinel-2 are possible, but increasingly UAVs and other smaller, local platforms could also be effective at fine-scale monitoring.

Crowdsourcing could be an effective tool for also mass monitoring efforts that enable more rapid reaction to the presence of invasive species. 

References

[1]    For more on how tamarix can be mapped using NDVI, see:  Evangelista, P., Stohlgren, T., Morisette, J., Kumar, S., 2009. Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data. Remote Sensing 1, 519–533. https://doi.org/10.3390/rs1030519.

[2]    For more on how invasive animals were detected using NDVI, see:  Ali, R., Pelkey, N., 2013. Satellite images indicate vegetation degradation due to invasive herbivores in the Andaman Islands. Current Science 105, 209-214. https://www.jstor.org/stable/24092640

[3]    For more on this NASA supported effort, see:  https://develop.larc.nasa.gov/2019/fall/MedicineBowDisasters.html.

[4]    For more on how UAVs have been used to monitor water chestnut growth, see:  Tang, T., Jiang, C., Perrelli, M., 2020. Data Collection and Analyses Applying Unmanned Helicopter (UAV) Remote Sensing to Survey Water Chestnut Invasive Species: International Journal of Data Analytics 1, 38–51. https://doi.org/10.4018/IJDA.2020010103 

[5]    For more on crowdsourcing efforts to monitor invasive species using different data platforms, see:  Larson, E.R., Graham, B.M., Achury, R., Coon, J.J., Daniels, M.K., Gambrell, D.K., Jonasen, K.L., King, G.D., LaRacuente, N., Perrin‐Stowe, T.I., Reed, E.M., Rice, C.J., Ruzi, S.A., Thairu, M.W., Wilson, J.C., Suarez, A.V., 2020. From eDNA to citizen science: emerging tools for the early detection of invasive species. Front Ecol Environ 18, 194–202. https://doi.org/10.1002/fee.2162.

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