Mobile Phone Depth Sensors for Forest Carbon Measurement
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The monitoring, reporting, and verification (MRV) of forest plots, especially their tree-trunk diameters, is critical to achieving both forest protection and reforestation goals. Today’s MRV processes are mostly manual, error-prone, and costly to carry out. In this thesis, we design and implement an app running on a smartphone equipped with a time-of-flight sensor that allows efficient, low-cost, and accurate measurement of tree trunk diameters. The core focus is on designing an algorithm to identify, segment, and compute the diameter of a target tree trunk in a depth image of a forest scene, even in the face of natural leaf and branch occlusion. The algorithm runs in real-time on the phone, allowing user interaction to improve the quality of the results. We evaluate the app in realistic settings and find that in a corpus of 55 sample tree images, it estimates trunk diameter with mean error of 7.8%. We also explore a newly released alternative to the time-of-flight sensor, Google's ARCore Depth API, which uses a depth-from-motion algorithm based on a monocular phone camera and accelerometer sensors. We conclude that this API is currently inadequate for the proposed application and offer suggestions for its improvement.
Cite this version of the work
Amelia Holcomb (2021). Mobile Phone Depth Sensors for Forest Carbon Measurement. UWSpace. http://hdl.handle.net/10012/17043