Jonathan Dandois

Ph.D., Department of Geography and Environmental Systems, University of Maryland, Baltimore County, 2013
B.S., University of Maryland, Baltimore County, 2003

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My research focuses on the development and use of a low-cost remote sensing system comprised of off-the-shelf digital cameras, hobbyist remote controlled aircraft and computer vision software, Ecosynth, for measuring tree and forest canopy 3D structure and composition at fine spatial scale.  Broadly, I am interested in using remote sensing  for studying patterns of forest structure and composition across different types of natural and anthropogenic landscapes.  For my dissertation research, I am focused on understanding how forest canopies are measured by a computer vision structure from motion system and also in understanding how we can use this new combination of existing technologies to improve understanding of forest ecosystems.  For example, a key research interest that could be advanced by Ecosynth would be in improving understanding of the spatial and temporal patterns of canopy structure and spectral traits at fine-spatial scale throughout the growing season.  My research is primarily based on the UMBC campus in Baltimore MD, but I also study the forest at the Smithsonian Environmental Research Center (SERC) in Edgewater MD.

Alumni: 2008-2013; GES, IGERT

Related Publications

Dandois, Jonathan P.. 2014. Remote sensing of vegetation structure using computer vision. Doctoral dissertation, University of Maryland Baltimore County
Dandois, Jonathan P.; Ellis, Erle C.. 2013. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sensing of Environment 136: 259-276. [Download PDF]
Dandois, Jonathan P.; Ellis, Erle C.. 2010. Remote sensing of vegetation structure using computer vision. Remote Sensing 2: 1157-1176. [Download PDF]
Dandois, Jonathan P.; Baker, Matthew; Olano, Marc; Parker, Geoffrey G.; Ellis, Erle C.. 2017. What is the point? Evaluating the structure, color, and semantic traits of computer vision point clouds of vegetation. Remote Sensing 9(4): 355. [Download PDF]
Zahawi, Rakan; Dandois, Jonathan P.; Holl, Karen D.; Nadwodny, Dana; Reid, Leighton J.; Ellis, Erle C.. 2015. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation 186: 287–295. [Download PDF]
Pirokka, Michalis; Ellis, Erle C.; Tredici, Peter Del. 2015. Personal Remote Sensing: Computer Vision Landscapes. New Geographies #7: Geographies of Information: 178-187. [Download PDF]
Dandois, Jonathan; Olano, Marc; Ellis, Erle C.. 2015. Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure. Remote Sensing 7(10): 13895-13920. [Download PDF]
Dandois, Jonathan P.; Nadwodny, Dana; Anderson, Erik; Bofto, Andrew; Baker, Matthew; Ellis, Erle C.. 2015. Forest census and map data for two temperate deciduous forest edge woodlot patches in Baltimore, Maryland, USA. Ecology 96(6): 1734-1734. [Download PDF]