Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology

Abstract

The estimation of single tree and complete stand information is one of the central tasks of forest inventory. In recent years, automatic algorithms have been successfully developed for the detection and measurement of trees with laser scanning technology. Nevertheless, most of the forest inventories are nowadays carried out with manual tree measurements using traditional instruments. This is due to the high investment costs for modern laser scanner equipment and, in particular, the time-consuming and incomplete nature of data acquisition with stationary terrestrial laser scanners. Traditionally, forest inventory data are collected through manual surveys with calipers or tapes. Practically, this is both labor and time-consuming. In 2020, Apple implemented a Light Detection and Ranging (LiDAR) sensor in the new Apple iPad Pro (4th Gen) and iPhone Pro 12. Since then, access to LiDAR-generated 3D point clouds has become possible with consumer-level devices. In this study, an Apple iPad Pro was tested to produce 3D point clouds, and its performance was compared with a personal laser scanning (PLS) approach to estimate individual tree parameters in different forest types and structures. Reference data were obtained by traditional measurements on 21 circular forest inventory sample plots with a 7 m radius. The tree mapping with the iPad showed a detection rate of 97.3% compared to 99.5% with the PLS scans for trees with a lower diameter at a breast height (dbh) threshold of 10 cm. The root mean square error (RMSE) of the best dbh measurement out of five different dbh modeling approaches was 3.13 cm with the iPad and 1.59 cm with PLS. The data acquisition time with the iPad was approximately 7.51 min per sample plot; this is twice as long as that with PLS but 2.5 times shorter than that with traditional forest inventory equipment. In conclusion, the proposed forest inventory with the iPad is generally feasible and achieves accurate and precise stem counts and dbh measurements with efficient labor effort compared to traditional approaches. Along with future technological developments, it is expected that other consumer-level handheld devices with integrated laser scanners will also be developed beyond the iPad, which will serve as an accurate and cost-efficient alternative solution to the approved but relatively expensive TLS and PLS systems. Such a development would be mandatory to broadly establish digital technology and fully automated routines in forest inventory practice. Finally, high-level progress is generally expected for the broader scientific community in forest ecosystem monitoring, as the collection of highly precise 3D point cloud data is no longer hindered by financial burdens.

Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

“…A LiDAR scanner ubiquitously available on consumer-grade devices was presented with the introduction of the iPad Pro 2020 11-inch and 12.9-inch display (hereafter iPad) on March 25, 2020 and the iPhone 12 Pro and iPhone 12 Pro Max (hereafter iPhone) on October 23, 2020 by Apple Inc.. Comparisons between the iPad LiDAR sensor, a hand-held personal laser scanning approach and traditional forest inventory equipment already demonstrated a high detection rate of tree stems above a threshold of 10 cm diameter 23 , 24 .…”

The Potential of Low-Cost 3D Imaging Technologies for Forestry Applications: Setting a Research Agenda for Low-Cost Remote Sensing Inventory Tasks

“…They derived the best DBH estimates using a direct least squares ellipsoid fitting algorithm on resultant point clouds. Gollob et al [68] found that DBH was underestimated for stems with DBH < 15 cm and overestimated with stems >30 cm. However, the DBH threshold where stems were over-or underestimated depended on the software used to pre-process the RGB-D information.…”

“…Of the identified literature, seven publications explored the application of RGB-D devices. Two of these studies were conducted at the individual stem scale [42,47] and four at the plot scale [43,55,68,69]. Only Fan et al [44] utilised an RGB-D sensor at both the individual stem and plot capture scales within the one study in order to best target different inventory measurements.…”

“…Recently, publications have explored the application of the CW ToF RGB-D sensor integrated into the 2020-generation iPad Pro and iPhone 13 Pro (Apple Inc., Cupertino, CA, USA) [55,68]. Both Gollob et al [68] and Mokroš et al [55] investigated the performance of the integrated RGB-D sensor, alongside a selection of software native to the device that is used to capture and pre-process the data within a forestry setting. Gollob et al [68] found that stem location estimates had an RMSE between 10.9 cm and 21.8 cm when comparing each stem's location to that of neighbouring trees.…”

“…Both Gollob et al [68] and Mokroš et al [55] investigated the performance of the integrated RGB-D sensor, alongside a selection of software native to the device that is used to capture and pre-process the data within a forestry setting. Gollob et al [68] found that stem location estimates had an RMSE between 10.9 cm and 21.8 cm when comparing each stem's location to that of neighbouring trees. When predicting DBH, RMSE ranged between 3.13 cm (10.5%) and 4.51 cm (15.1%), depending on the preprocessing software used to capture the data.…”