Low-altitude drone flight can produce observations at scales clearly aligned with biological processes, like metabolism, natural selection, and resource allocation within and among individual plants. The quantitative improvement represented by this technology is significant, but the most important advance is conceptual. New measurements from low-altitude drones open the door to characterizing processes that have been beyond our grasp, and properties related to organismal condition, like leaf chemistry, canopy temperature and solar induced fluorescence.

 

The Kellner lab developed the Brown Platform for Autonomous Remote Sensing (BPAR) as a sensor package carried by a heavy-lift helicopter drone. The aircraft was designed and and is operated by Aeroscout GmbH of Hochdorf, Switzerland. The sensor package includes up to five remote sensing technologies: (i) a visible and near infrared (VNIR) imaging spectrometer, (ii) a shortwave infrared (SWIR) imaging spectrometer, (iii) a chlorophyll fluorescence imaging spectrometer that observes light within bands of 0.05 nm, (iv) a wide-angle scanning lidar sensor, and (v) a high-resolution digital camera. The BPAR has completed successful flight operations in Central America, Switzerland, and the Czech Republic. These efforts support fundamental research in plant biology and the carbon cycle, conservation, and the calibration and validation activities of the NASA Global Ecosystem Dynamics Investigation.

 

Funding sources: NASA, NSF, Brown University

Publications:

Impact of leaf phenology on estimates of aboveground biomass density in deciduous broadleaf forest from simulated GEDI lidar.
Cushman, K. C., Armston, J., Dubayah, R. O., Duncanson, L., Hancock, S., Janik, D., Král, K., Krůček, M., Minor, D. M., Tang, H., Kellner, J. R. Environmental Research Letters. 18: 065009. [ Full Text ]

Impact of a tropical forest blowdown on aboveground carbon balance..
Cushman, K. C., Burley, J. T., Imbach, B., Saatchi, S. S., Silva, C. E., Vargas, O., Zgraggen, C., Kellner, J. R. Scientific Reports. 11: 11279.. [ Full Text ]

Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees.
Krůček, M., Král, K., Cushman, KC, Missarov, A., Kellner, J.R. Remote Sensing. 2020 12(19): 3260. [ Full Text ]

The case for remote sensing of individual plants.
Kellner, J. R. Albert, L. P., Burley, J. T., and Cushman, K. C. American Journal of Botany. 2019. [ Full Text ]

New opportunities for forest remote sensing through ultra-high-density drone lidar. Surveys in Geophysics.
Kellner, J. R., Armston, J. D., Birrer, M., Cushman, K. C., Duncanson, L. I., Eck, C., Falleger, C., Imbach, B., Král, K., Krůček, M., Trochta, J., Vrška, T., Zgraggen, C. Surveys in Geophysics.. 2019. [ Full Text ]