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Samuel Emard

 Time Series of a Forest Canopy: Detecting Changes using the Visible Atmospherically Resistant Index and a Low-Cost Drone ©2021

The drone industry has expanded as the technology has become more affordable in the last few years. The use of drone images in remote-sensing research became an increasingly attractive alternative to other methods such as satellite imagery as it allows for a faster, more efficient method to capture spatial phenomenon. Unfortunately, drones are often costly and require additional sensors and lenses to capture multispectral data, making the technology difficult to access without financial support. However, modern drones designed for more casual flights are now affordable and equipped with high-quality cameras. This major research paper aims to find out whether the combination of a low-cost (< $1000 CAD), lightweight (< 249 grams) drone such as the DJI Mavic Mini is an adequate tool to monitor and detect subtle changes in forest canopy. The Visible Atmospherically Resistant Index is utilized to assess vegetation changes and monitor vegetation growth while minimizing research costs. After capturing a forest canopy for three months, the results show that the DJI Mavic Mini is an adequate tool for research purposes, given that the study area is relatively small and has temperate weather. In addition, the Visible Atmospherically Resistant Index showed mixed results when detecting fine changes in the canopy of the study area. It showed inconsistencies and significant variances in terms of the acquired images. For the index to detect subtle changes in the canopy accurately, the study area needs to be under the same weather conditions and similar sunlight at the time of capture, which is not a realistic expectation.