## 3 Bias Impact Analysis
### Calibration Strategy for UAV-Based LiDAR System
The lever arm \(\Delta Z\) for the LiDAR unit cannot be estimated in the calibration procedure since any change in \(\Delta Z\) will not introduce discrepancies among the different versions of the same feature captured from different flight lines. It would only result in a shift of the point cloud in the vertical direction as a whole. It is either manually measured or determined using vertical control (such as, horizontal planar patches with known elevation) and fixed during the calibration procedure. Finally, the mounting parameters of each sensor are derived by minimizing the discrepancies between non-conjugate point pairings among conjugate features in overlapping flight lines. In case of a planar feature, the discrepancy arising from each non-conjugate point pair would consist of a non-random component lying along the planar surface. Hence, a modified weight is applied to the point pair so as to retain only the component of the discrepancy lying along the direction normal to the planar surface. Similarly, in case of a linear/cylindrical feature, a modified weight is applied so as to nullify the component of discrepancy along the direction of the feature. The modified weight is determined according to the direction of the planar/linear feature as obtained from the feature parameters derived by a plane/line fitting conducted on the points from the flight line that captures the most number of points belonging to the corresponding feature. A more detailed discussion regarding the theoretical basis for weight modification while using non-conjugate points along corresponding features can be found in [PERSON] et al. (2011) [PERSON] et al. (2011). Finally, the mounting parameters for the laser unit can be derived by minimizing the discrepancies among the conjugate features arising from the above-mentioned pairs. However, when the initial estimate of the mounting parameters is inaccurate, the estimated modified weight matrix would be imprecise which would affect the accuracy of the derived mounting parameters. Hence, this research proposes an iterative calibration procedure. Firstly, the discrepancy among extracted features is minimized to derive mounting parameters through the weight modification process. Then, the points along the extracted features are re-generated using the newly estimated mounting parameters and the discrepancy among conjugate features is minimized again using a newly defined modified weight matrix. The above steps are repeated until the change in the estimates of the mounting parameters is below a predefined threshold. In this study, these stopping criteria are enforced by monitoring the difference between the a-posteriori variance factors after two consecutive iterations of LSA and when the difference is less than \(10^{-8}\), the adjustment is deemed complete. Since the a-posteriori variance factor indicates the average accuracy of calibration by quantifying the normal distance of points from best-fitting plane/line for all the extracted features, the threshold of \(10^{-8}\) indicates a change of less than 0.01 \(\upmu\)m in the overall normal distance, which can be definitely regarded as insignificant. Figure 8: Intensity data from a point cloud that includes highly reflective boards highlighted by the zoomed-in areas.