Two double-blind sampling campaigns took place at Virginia Tech research farms to evaluate measurement accuracy of our mobile application. Digital diameter measurements were collected through our mobile application while manual measurements were obtained using a tape measure. While mobile-based measurements varied more than manual measurements, the relatively low errors (mean error = 1.9 cm) and high correlation (R² = 0.95) between mobile and manual estimates of diameter suggest this approach can be highly effective.

Many widely cited allometric equations rely exclusively on the use of diameter measurements to estimate standing biomass. Strong agreement between the phone application and traditional measures (R² > 0.95; p < 0.01; RMSE = 2.71 cm) indicate our technology can provide reasonable, rapid, and conservative estimates of woody biomass in orchard systems.

Through the use of computer vision, above canopy tree counts validate below canopy ground truth data. Extrapolating app-based data across forests enables ground truth carbon quantification at scale. Higher accuracy than one-dimensional remote sensing practices. Cheaper than drones. Faster than manual processes.

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.

All collected data and code used to generate the figures and statistics reported in the Measuring Tree Diameter with Photogrammetry Using Mobile Phone Cameras paper can be found on Github.

Every tree is tracked in a Working Trees project. See how we embed science, transparency, and trust into every carbon market transaction.