Semi-Autonomous Detection: Using Drones for Environmental Assessment

EPA Grant Number: FP917789
Title: Semi-Autonomous Detection: Using Drones for Environmental Assessment
Investigators: Fankhauser, Katie
Institution: Oregon Health & Sciences University
EPA Project Officer: Lee, Sonja
Project Period: September 1, 2015 through August 31, 2018
Project Amount: $88,000
RFA: STAR Graduate Fellowships (2015) RFA Text |  Recipients Lists
Research Category: Academic Fellowships


A public health intervention that distributed 100,000 high-effiency cookstoves in Rwanda has documented health gains, but the corresponding environmental impact of reducing fuelwood use has as of yet been unstudied. Current best practice in assessment of environmental health interventions would involve resource-intensive fieldwork that is susceptible to several methodological limitations. The utility of drone technology to reduce cost, time, and subjectivity in environmental assessment goes unrealized. Thus, this research aims to develop an unmanned aerial system (UAS) to allow program stakeholders to efficiently monitor the forest’s response to a change in cooking behavior.


In order to calibrate the drone and processing algorithms, I will first fly the UAS in Oregon, matching the landscape to that of Rwanda’s as much as possible. With confidence that the data collection and processing procedures perform well, I will take the UAS to Rwanda and fly it over a sample of forests in both intervention and control areas. The images collected will be compared to ground data so that the flight pattern and/or processing can be refined further. Finally, geospatial analysis and illustrations will be prepared to validate the environmental outcomes of the health program. The work process will be clear and well documented with the idea that it will be repeated at regular intervals, elucidating the long-term impact of the program and trends in forest management.

Expected Results:

The high-efficiency cookstove, delivered to 100,000 Rwandan households, is expected to reduce household fuelwood usage by 50%. This translates to a 12% reduction across the distribution region in harvested wood for energy. A change of this magnitude should be appreciable at an aggregate level when comparing tree density, forest extent, and/or vegetation health between the intervention and control areas. Proof of concept will provide Rwanda with a monitoring tool and allow the technology to be applied to other environments and contexts, including environmental management in the United States.

Supplemental Keywords:

drones, UAS, UAV, environmental assessment, forest monitoring, public health impact

Progress and Final Reports:

  • 2016
  • 2017
  • Final