Using Geospatial Technologies to Fight Locust Swarms

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Locusts plaguing crops have been an ancient problem for societies since the beginnings of agriculture in different parts of the world. In Africa, it can mean the difference between having enough food and famine. Now, locusts are once again threatening crops in East Africa, but this time distinctly modern geospatial technologies can help farmers prevent major disaster.

Geospatial Tools, Satellite Data, and Locusts

Countries such as Kenya, South Sudan, and Somalia are facing major catastrophe in 2020 from locusts. The combined effects of COVID-19 and locust swarms can be particularly devastating to vulnerable populations dependent on locally produced food.  Tools created by the Food and Agriculture Organization (FAO) of the United Nations are helping to monitor and predict where locusts might travel to next before they can harm crops.

This includes eLocust3, which is an mobile phone application that allows georeferenced chat data to be transmitted to users as a form of group monitoring. Data from compatible tools include eLocust3g, a GPS device that provides reports on locusts and the stage of development or treatment in specific areas. These tools not only allow the sharing of data about locusts in given regions but allow a more detailed map and assessment to be made based on the wider spatial distribution of locust, including if they are migrating and if they are in a stage where they are likely to attack crops.

Other technologies used for surveillance include fixed wing and rotary UAVs. Satellite imagery is also used, which include systems supported by NASA  and the European Space Agency (ESA) such as the Sentinel-2 satellite. In fact, NASA’s SERVIR team, which helps create environmental policy using satellite imagery, will join efforts in helping countries in East Africa to develop agricultural policy in light of the evolving situation. Data that could be determined from satellite systems include vegetation and soil moisture information, as the presence of vegetation growth and moist soils lead to more rapid and robust locust growth.[1][2] Additionally, the FAO updates their risk map for locusts periodically to reflect the changing situation and new data they receive in large part from satellite and other remote sensing sources.[3]

Researchers are using remote sensing observations of soil moisture and vegetation to map out environmental conditions conducive to promoting locust swarms.  Map of soil conditions in Eastern Africa (locusts lay eggs in moist soil): NASA, 2020.
Researchers are using remote sensing observations of soil moisture and vegetation to map out environmental conditions conducive to promoting locust swarms. Map of soil conditions in Eastern Africa (locusts lay eggs in moist soil): NASA, 2020.

Using Drones to Track and Fight Locust Swarms

New techniques that have been advocated in pest management of locust outbreaks include using UAVs not only to identify the location of locusts but also respond by dispersing anti-pest measures, including natural or chemical sprays, over targeted areas. With UAVs, the potential is they can be used in targeted or specific spots in anticipation of an outbreak, with sprays and agents deployed from the UAVs as they fly over given areas, whereas traditional crop spraying generally covers a wide area, increasing costs and potentially leading to health hazards particularly when chemical agents are used.[4] Additionally, UAVs have demonstrated to be effective in providing relatively accurate vegetation loss data using multi-spectral data from sensors used, which helps warn about additional damage that could be coming to other areas not yet affected by locust swarms. In comparing accuracy of data from UAV imagery, Normalized Difference Vegetation Index (NDVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Green Normalized Difference Vegetation Index (GNDVI) metrics have all proven to be sensitive to dry weight loss from vegetation. These metrics derived from multi-spectral imagery help measure damage by locusts by providing more accurate estimates relative to other metrics, while the rapid pace of getting these data from UAV imagery has proven to be better able to warn farmers of impending damage from locusts than satellite data that is often slower to obtain.[5]  

A locust swarm in  Andranomena, Toliara, Madagascar in May 2016.  Photo: Laika ac, CC BY-SA 2.0
A locust swarm in Andranomena, Toliara, Madagascar in May 2016. Photo: Laika ac, CC BY-SA 2.0

Locusts can be devastating for crops, particularly in Western Asia, North Africa, and East Africa. Using a combination of satellite and UAV monitoring, the impact of locusts can be minimized by anticipating where swarms are likely to form next. Monitoring tools include hand-held devices that provide ground-level data, which can update into a large network of information that can be combined with the latest satellite and UAV data. While locusts swarms have not been eliminated as a threat, managing their impact should be improved with spatial technologies that could better forecast their presence. 

References

[1]    For more on the locusts and monitoring of them in East Africa using spatial tools, see:  http://www.fao.org/fao-stories/article/en/c/1270472/.


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[2]    Information on satellite data that is being used by NASA to help fight locusts in East Africa are indicated in this article:  https://www.ibtimes.com/nasa-using-satellites-stop-locust-swarms-invading-africa-2951044

[3]    Map data from the FAO can be seen here:  http://www.fao.org/ag/locusts/en/archives/1340/2517/2518/index.html.

[4]    For more on using UAVs to fight locusts, see:  Iost Filho FH, Heldens WB, Kong Z, et al. (2020) Drones: Innovative Technology for Use in Precision Pest Management. Rondon S (ed.) Journal of Economic Entomology 113(1): 1–25. DOI: 10.1093/jee/toz268.

[5]    For more on using multi-spectral data and metrics to measure crop and vegetation loss, see:  Song P, Zheng X, Li Y, et al. (2020) Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data. Science of The Total Environment 719: 137519. DOI: 10.1016/j.scitotenv.2020.137519.

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