Robust Single-Image Tree Diameter Estimation with Mobile Phones

Abstract

Ground-based forest inventories are reliable methods for forest carbon monitoring, reporting, and verification schemes and the cornerstone of forest ecology research. Recent work using LiDAR-equipped mobile phones to automate parts of the forest inventory process assumes that tree trunks are well-spaced and visually unoccluded, or else require manual intervention or offline processing to identify and measure tree trunks. In this paper, we designed an algorithm that exploits a low-cost smartphone LiDAR sensor to estimate the trunk diameter automatically from a single image in complex and realistic field conditions. We implemented our design and built it into an app on a Huawei P30 Pro smartphone, demonstrating that the algorithm has low enough computational costs to run on this commodity platform in near real-time. We evaluated our app in 3 different forests across 3 seasons and found that in a corpus of 97 sample tree images, our app estimated the trunk diameter with a RMSE of 3.7 cm (R2 = 0.97; 8.0% mean absolute error) compared to manual DBH measurement. It achieved a 100% tree detection rate while reducing the surveyor time by up to a factor of 4.6. Our work contributes to the search for a low-cost, low-expertise alternative to terrestrial laser scanning that is nonetheless robust and efficient enough to compete with manual methods. We highlight the challenges that low-end mobile depth scanners face in occluded conditions and offer a lightweight, fully automatic approach for segmenting depth images and estimating the trunk diameter despite these challenges. Our approach lowers the barriers to in situ forest measurements outside of an urban or plantation context, maintaining a tree detection and accuracy rate comparable to previous mobile phone methods even in complex forest conditions.

Measuring Tree Diameter with Photogrammetry Using Mobile Phone Cameras

“…The greatest underprediction (−2.45 cm) occurred with Black Walnut trees. The overall RMSE (2.71 cm) was comparable to previous studies: Holcomb et al (2023) [14] reported an overall RMSE of 3.7 cm and a RMSE of 2.7 cm for trunks under 100 cm in diameter (all trunks in this study). Tatsumi et al (2022) [13] reported an RMSE of 2.27 cm using a LiDAR-enabled mobile phone and iPad, while Fan et al (2019) [10] reported an RMSE of 1.26 cm with an offline post-processing approach.…”

“…In general, diameter-estimation approaches can be performed in real-time (as the sampler is making the measurement) or via post-processing applied to images, point clouds, or other sensor data. Both hardware (e.g., [12]) and software (e.g., [13]) have been developed for this purpose, and various studies have sought to measure diameter in realtime (i.e., the user can view the measurement while they are still at the location of the tree, as in [14]), or via offline postprocessing algorithms to extract the diameter from images or other data after the user has left the location (e.g., [11]). Proudman et al (2022) [12] developed a handheld Light Detection and Ranging (LiDAR) unit to estimate tree diameters by modeling each tree trunk as a cylinder through a least-squares optimization within a Random Sample Consensus (RANSAC) loop, producing inventory results that can be viewed in the field, in ~1/4 the time of a traditional inventory.…”

“…Although promising, this approach requires an infrared light source, which is not typically included with mobile phone camera units. Holcomb et al (2023) [14] developed an algorithm that uses LiDAR and camera data in order to estimate tree diameter via segmentation, filtering, depth estimation, and trunk boundary identification. The approach produced an RMSE of 3.7 cm and an R 2 of 0.97 when evaluated against manually measured diameters and has the advantage of adequate performance, even in occluded conditions.…”

“…Each of the studies described in this section has either relied on Light Detection and Ranging (LiDAR) sensors (e.g., [12][13][14]), infrared sensors [10], or offline post-processing of images or point clouds [11] in order to determine tree diameter. Because most smartphones currently being used are not equipped with LiDAR sensors, these approaches cannot be readily applied, effectively limiting field applications in most areas of the world.…”

“…Recent advances in sensing technologies present an opportunity to improve tree inventories by facilitating the simple, rapid, and auditable collection of digital measurements of tree diameter. Existing camera sensors on most mobile phones can be leveraged to cheaply acquire these measurements, as well as other relevant information such as images and geolocation (e.g., [10][11][12][13][14]). If inventory data acquired from mobile phones are accurate enough to replace manual measurements, mobile-enabled data collection could significantly simplify the process of collecting the data required for a tree inventory, permitting the acquisition of high-quality tree measurements at a fraction of the time and cost of current approaches.…”