Module 1

For decades, military UAS missions have attempted to collect information against adversaries using tactics, techniques, and procedures (TTPs) to hinder that collection.  Camouflage, concealment, and deception practices often keep enemy weapons and equipment well hidden.  In a technological struggle, however, the U.S. military has developed sensors that mitigate those practices. In turn, the American public and civilian UAS community has and will continue to reap the benefits of that technology in the form of agricultural advancement.

UAS in agriculture is commonplace and researchers have used UAVs to remotely monitor vegetation (Mitchell et al., 2012), map agricultural areas (Everaerts, 2008), and assess rangeland (Rango et al., 2009).  Companies like CropCam use GPS waypoints, elevations, and other parameters to image agricultural land.  The high resolution imagery produced by CropCam and allows farmers to “check seed coverage, gauge irrigation effectiveness, and spot early signs of insect infestation” (Gantenbein, 2009, p. 1).  Previously, farmers would pay upwards of $6,000 for a survey of 1,500 acres of land by a private company. The benefits are enormous for farmers and are reasons why drones are being used for “precision agriculture” (Montopoli, 2013).  However, a step beyond remote monitoring and standard imagery is spectral imagery.

Based on military needs, Raytheon’s Airborne Cueing and Exploitation System – Hyperspectral (ACES-HY) imagery sensor is capable of detecting disturbed earth, chemicals and gasses, explosives and cave entrances (Cheng, 2014). On today’s battlefield, the ACES-HY sensor rides along an MQ-1 Predator and seeks to detect explosive materials amongst all sorts of concealment methods. 

Some researchers have begun to investigate the uses of this type of UAS spectral imagery in agriculture.  Hyperspectral imagery can be utilized within the agricultural community to analyze soil erosion.  As of 2009, the U.S. Geological Survey already incorporated UAS to survey soil erosion (Hruby, 2012).  With an advanced system capable of determining any disturbed earth, the hyperspectral sensor would allow for incredible accuracy in comparison to current technology.  Turner, Lucieer, and Watson (2011) used multispectral cameras on UAVs to measure plant health.  The sensor analyzed water stress based on the Photochemical Reflectance Index (PRI), which alludes to the overall vigor of grape vines. This type of analysis could change the ways vineyards manage planting, maintenance, and harvesting by pinpointing where extra fertilizer or pesticide might be needed (Reed, 2012).  Furthermore, this type of analysis could change global agriculture altogether.

 Figure 1. Oktokopter fitted with multispectral camera.

References

Cheng, J. (2014). Hyperspectral sensor lets drones see through camouflage, spot explosives. Defense Systems. Retrieved from https://defensesystems.com/articles/2014/02/25/air-force-aces-hy-hyperspectral.aspx?admgarea=DS    

Everaerts, J. (2008). The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. The International Archives of the Photogrammetry: Remote Sensing and Spatial Information Sciences, 37. Retrieved from https://www.google.ae/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiOxa3N1cPOAhUJaRQKHUnMAP4QFggfMAA&url=http%3A%2F%2Fwww.isprs.org%2Fproceedings%2FXXXVII%2Fcongress%2F1_pdf%2F203.pdf&usg=AFQjCNE_ogqstURtMFrzjUccV1WFDygG5g

Gantenbein, D. (2009). Unmanned traffic jam. Air & Space Magazine. Retrieved from http://www.airspacemag.com/flight-today/unmanned-traffic-jam-137094132/?no-ist=&page=2

Hruby, P. (2012). Out of ‘hobby’ class, drones lifting off for personal, commercial use. The Washington Times. Retrieved from http://www.washingtontimes.com/news/2012/mar/14/out-of-hobby-class-drones-lifting-off-for-personal/

Mitchell, J. J., Glenn, N. F., Anderson, M. A., Hruska, R. C., Halford, A., Baun, C., & Nydegger, N. (2012). Unmanned aerial vehicle (UAV) hyperspectral remote sensing for dryland vegetation monitoring. Journal of Applied Remote Sensing, 3(1), 1-10. Retrieved from https://www.google.ae/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjlsJeL1MPOAhXHBBoKHXw3DQ8QFggeMAA&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6874315&usg=AFQjCNEvtCYjqd5kblHkbAw8vt4YoMJpSA

Montopoli, B. (2013). The drone next door. CBS News. Retrieved from http://www.cbsnews.com/news/the-drone-next-door/

Rango, A., Laliberte, A., Herrick, J. E., Winters, C., Havstad, K., Steele, C., & Browning, D. (2009). Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management.  Journal of Applied Remote Sensing, 3(1), 1-15. Retrieved from https://www.google.ae/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiA5pSA1cPOAhXFExoKHWN7DbcQFggdMAA&url=https%3A%2F%2Fwww.ars.usda.gov%2FSP2UserFiles%2FPlace%2F30501000%2FUnmanned.pdf&usg=AFQjCNEX9BL7PgqZIPmcu5XvT5-zCf0z0A

Reed, J. (2012). The skies open up for large civilian drones. BBC News. Retrieved from http://www.bbc.com/news/technology-19397816

Turner, D., Lucieer, A., & Watson, C. (2011). Development of an unmanned aerial vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery. International Symposium on Remote Sensing of Environment  2011. Sydney. https://www.google.ae/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwi495C50sPOAhWEOxQKHadHAg0QFggaMAA&url=http%3A%2F%2Fwww.isprs.org%2Fproceedings%2F2011%2FISRSE-34%2F211104015Final00547.pdf&usg=AFQjCNE_QfmdXuObyp25umDCqCUPNOg2IQ