In-situ measurements from mobile platforms: An emerging approach to address the old challenges associated with forest inventories

Accurate assessments of forest resources rely on ground truth data that are collected via in-situ measurements, which are fundamental for all other statistical- and/or remote-sensing-based deductions on quantified forest attributes. The major bottleneck of the current in-situ observation system is that the data collection is time consuming, and, thus, limited in extent, which potentially biases any further inferences made. Consequently, conventional field-data-collection approaches can hardly keep pace with the coverage, scale and frequency required for contemporary and future forest inventories. In-situ measurements from mobile platforms seem to be a promising technique to solve this problem and are estimated at least 10 times faster than static techniques (e.g., terrestrial laser scanning, TLS) at the plot level. However, the mobile platforms are still at the very early stages of development, and it is unclear which three-dimensional (3D) forest measurements the mobile systems can provide and at what accuracy. This study presents a quantitative evaluation of the performance of mobile platforms in a variety of forest conditions and through a comparison with state-of-the-art static in-situ observations. Two mobile platforms were used to collect field data, where the same laser-scanning system was both mounted on top of a vehicle and wore by an operator. The static in-situ observation from TLS is used as a baseline for the evaluation. All point clouds involved were processed through the same processing chain and compared to conventional manual measurement. The evaluation results indicate that the mobile platforms can assess homogeneous forests as well as static observations, but they cannot yet assess heterogeneous forest as required by practical applications. The major challenge is twofold: mobile-data coverage and accuracy. Future research should focus on the robust registration techniques between strips, especially in complex forest conditions, since errors of data registration results in significant impacts on tree attributes estimation accuracy. In cases that the spatial inconstancy cannot be eliminated, attributes estimation in single strips, i.e., the multi-single-scan approach, is an alternative. Meanwhile, operator training deserves attention since the data quality from mobile platforms is partly determined by the operators' selection of trajectory in the field.