Integrating Kinect Depth Data with a Stochastic Object Classification Framework for Forestry Robots

Pordel, Mostafa; Hellström, Thomas; Ostovar, Ahmad

doi:10.5220/0004045203140320

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…Under optimal conditions, Kinect is able to capture several million data points per second [52]. The maximum measurement range is 4 m. Besides offering the gaming sector an augmented reality context, Kinetic was successfully connected with other applications, such as indoor mapping or forest measurements (e.g., [52,[55][56][57]). A benefit of Kinect is that it efficiently brings 3D data capturing to a standard personal computer (PC), thus allowing application development [52].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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