Precisely estimating the position, diameter at breast height (DBH), and height of trees is essential in forest resource inventory. Augmented reality (AR)-based devices help overcome the issue of inconsistent global point cloud data under thick forest canopies with insufficient Global Navigation Satellite System (GNSS) coverage. Although monocular simultaneous localization and mapping (SLAM) is one of the current mainstream systems, there is still no monocular SLAM solution for forest resource inventories, particularly for the precise measurement of inclined trees. We developed a forest plot survey system based on monocular SLAM that utilizes array cameras and Inertial Measurement Unit (IMU) sensors provided by smartphones, combined with augmented reality technology, to achieve a real-time estimation of the position, DBH, and height of trees within forest plots. Our results from the tested plots showed that the tree position estimation is unbiased, with an RMSE of 0.12 m and 0.11 m in the x-axis and y-axis directions, respectively; the DBH estimation bias is −0.17 cm (−0.65%), with an RMSE of 0.83 cm (3.59%), while the height estimation bias is −0.1 m (−0.95%), with an RMSE of 0.99 m (5.38%). This study will be useful in designing an algorithm to estimate the DBH and position of inclined trees using point clouds constrained by sectional planes at the breast height of the trunk, developing an algorithm to estimate the height of inclined trees utilizing the relationship between rays and plane positions, and providing observers with visual measurement results using augmented reality technology, allowing them to judge the accuracy of the estimates intuitively. Clearly, this system has significant potential applications in forest resource management and ecological research.

In current forestry investigation studies, the research hotspots have tended to concentrate on ascertaining the precision of certain tree parameters. This has resulted in an augmented intricacy of the technique in terms of algorithms and observation instruments. The complexity of the technology and the cost of the equipment make it impossible to use for large-scale forest surveys, for example, a national forest inventory (NFI). The aim of our study was to design a new type of low-cost measuring method that could be utilized in a NFI and in developing countries. Meanwhile, the newly designed method was expected to be able to output certain forest measurement factors without necessitating data processing by NFI field investigators. Based on these objectives, we developed a measuring method that included hardware comprised of two tools. The first tool was an electronic measuring tape that contained a microcontroller unit (MCU) and could automatically record and collaborate with other equipment via wireless protocols. The second tool was a tree stem position mapper that utilized our own designed mechanisms. The results showed that the tree DBH measurements exhibited a 0.05 cm (0.20%) bias and a 0.36 cm (1.45%) root mean square error (RMSE), and the biases on the x-axis and the y-axis of the tree position estimations were −15.92–9.92 cm and −25.90–10.88 cm, respectively, accompanied by corresponding RMSEs of 15.27–29.40 cm and 14.49–34.68 cm. Moreover, an efficiency test determined that the average measurement time per tree was 20.34 s, thus, demonstrating a marked improvement in speed by nearly one-fold compared to the conventional method. Meanwhile, this measurement kit costs less than 150 Euros and is economically suitable for large-scale applications. We posit that our method has the potential to serve as a standard tool in a Chinese NFI and in developing countries in the future.