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Croatian Journal of Forest Engineering : Journal for Theory and Application of Forestry Engineering, Vol. 46 No. 2, 2025.

Original scientific paper

https://doi.org/10.5552/crojfe.2025.3246

Detecting Severity and Extent of Soil Disturbance in Forest Operations Using Mobile LiDAR Technology

Gabriel Osei Forkuo orcid id orcid.org/0000-0001-8478-8066 ; Transilvania University of Braşov Faculty of Silviculture and Forest Engineering Department of Forest Engineering Forest Management Planning and Terrestrial Measurements Şirul Beethoven No. 1 500 123 Braşov ROMANIA
Andrea Rosario Proto orcid id orcid.org/0000-0003-4630-8986 ; University Mediterranea of Reggio Calabria Department of Agraria Feo di Vito 89122 Reggio Calabria ITALY
Stelian Alexandru Borz orcid id orcid.org/0000-0003-4571-7235 ; Transilvania University of Braşov Faculty of Silviculture and Forest Engineering Department of Forest Engineering Forest Management Planning and Terrestrial Measurements Şirul Beethoven No. 1 500 123 Braşov ROMANIA *

* Corresponding author.

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Abstract

The evaluation of soil impact of forest operations has been done using professional platforms and time-consuming traditional methods. However, today low-cost LiDAR technology may achieve a potentially effective 3D mapping of soil impact. This work aimed at evaluating the accuracy of smartphone and GeoSLAM Zeb-Revo LiDAR platforms, by comparing the scanned data to a manual reference. Manual measurements using a tape were taken on four sample plots to obtain reference data, followed by scanning with LiDAR platforms to obtain data in the form of point clouds. CloudCompare was then used to process the LiDAR data, and the Bland and Altman’s method was used to check the agreement between the manually taken and scanned data. The results showed that the low-cost LiDAR technology of iPhone has the potential for mapping and estimating soil impact with a high accuracy. The Mean Absolute Error was estimated at 0.64 cm for the iPhone measurements with SiteScape App, while the figure ranged from 0.68 to 0.91 cm for the iPhone measurements done with 3D Scanner App. Zeb-Revo measurements, however, had an estimated MAE of 0.61 cm. The Root Mean Squared Error was estimated at 0.95 cm for the iPhone measurements with SiteScape, whereas the iPhone with 3D Scanner App and Zeb-Revo measurements produced RMSEs of 0.99–1.51 cm and 1.11 cm, respectively. These findings might provide the basis for further studies on the applicability of low-cost LiDAR technology to larger sample sizes and different operating conditions.

Keywords

3D mapping, proximal remote sensing, environment, skid trails, comparison

Hrčak ID:

333425

URI

https://hrcak.srce.hr/333425

Publication date:

18.7.2025.

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