Measuring Tree Diameter with Photogrammetry Using Mobile Phone Cameras

Abstract

Tree inventories are a cornerstone of forest science and management. Inventories are essential for quantifying forest growth rates, determining biomass and carbon stock variation, assessing species diversity, and evaluating the impacts of both forest management and climate change. Recent advances in digital sensing technologies on mobile phones have the potential to improve traditional forest inventories through increased efficiency in measurement and transcription and potentially through increasing participation in data collection by non-experts. However, the degree to which digital sensing tools (e.g., camera-enabled smartphone applications) can accurately determine the tree parameters measured during forest inventories remains unclear. In this study, we assess the ability of a smartphone application to perform a user-assisted tree inventory and compare digital estimates of tree diameter to measurements made using traditional forestry field sampling approaches. The results suggest that digital sensing tools on mobile phones can accurately measure tree diameter (R2 = 0.95; RMSE = 2.71 cm compared to manual measurements) while saving time during both the data-collection stage and data-entry stage of field sampling. Importantly, we compare measurements of the same tree across users of the phone application in order to determine the per-user, per-tree, and per-species uncertainty associated with each form of measurement. Strong agreement between manual and digital measurements suggests that digital sensing technologies have the potential to facilitate the efficient collection of high-quality and auditable data collected by non-experts but with some important limitations compared to traditional tree measurement approaches. Most people in the world own a smartphone. Enabling accurate tree inventory data collection through mobile phones at scale can improve our understanding of tree growth and biomass accumulation and the key factors (e.g., climate change or management practices) that affect these processes, ultimately advancing forest science and management.

A Deformable Shape Model for Automatic and Real-Time Dendrometry

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