Geospatial scientist with over 10 years experience across academic, commercial, environmental/non-profit sectors, with emphasis on combining field measurements with GIS and remote sensing data for map production, spatial analysis and research. Seven years of experience programming automated image processing, feature extraction and analysis tasks in Python. I want to work with multidisciplinary teams of ecologists and computer scientists in the lab, at the computer, and in the field to tackle difficult problems related to measuring and observing ecosystem patterns and processes.
Research Interests: forest ecology, vegetation structure, computer vision, remote sensing, phenology, 3D, forest dynamics, Arducopter, UAS (Unmanned Aerial Systems), multirotors, Ecosynth, drones
My current professional resume here.
Dandois, J.P., Olano, M., and Ellis, E.C. (2015). Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure. Remote Sensing, 7(1), 13895-13920. (available here)
Dandois, J.P., Nadwodny, D., Anderson, E., Bofto, A., Baker, M., and Erle C. Ellis. (2015). Forest census and map data for two Temperate Deciduous forest edge woodlot patches in Baltimore MD, USA. Ecology, 96:6, 1734. (available here)
Zahawi, R., Dandois, J.P., Holl, K., Nadwodny, D., Reid, J.L., Ellis, E. (2015). Using lightweight unmanned aerial vehicles to monitor tropical forest canopy recovery. Biological Conservation, 186, 287-295. (available here)
Dandois, J.P. and Erle C. Ellis (2013). High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sensing of Environment. 136, 259-276. DOI: 10.1016/j.rse.2013.04.005 (available here)
Dandois, J.P. and E.C. Ellis (2010). Remote Sensing of Vegetation Structure and Using Computer Vision. Remote Sensing, 2(4): 1157-1176. DOI: 10.3390/rs2041157 (available here)
Dissertation Advisor: Dr. Erle C. Ellis, Associate Professor