volume: 46, issue: 2
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.
volume: issue, issue:
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.
