Conference Agenda

Terrestrial laser scanning technology can efficiently acquire high-precision three-dimensional spatial information in complex forest environments, making it an important technical means for detailed analysis of forest structure and resource monitoring. However, traditional terrestrial laser scanners planning methods are prone to coverage gaps and data redundancy due to factors such as tree obstructions, terrain undulations, and canopy overlap, making it difficult to simultaneously balance observation completeness and scanner station deployment cost. To address this, this paper proposes an intelligent survey station planning for terrestrial laser scanners in complex forest environments. The method first uses airborne LiDAR data to build a prior forest model, which is then used to quantitatively evaluate forest visibility features by calculating the cumulative visible central angle through visibility analysis. Finally, an integer linear programming model is further introduced to achieve global optimization of the station set based on an initial feasible coverage solution obtained using a greedy algorithm. To test the performance of the proposed method, this paper applies the proposed method to the forest plot located in Lushan city, Jiangxi province, China. Experimental results indicate that the proposed method achieves an overall coverage rate of 94.55% with only seven stations, reducing the number of stations by approximately 30% and 22% compared with the greedy algorithm and genetic algorithm, respectively. The results demonstrate the effectiveness and superiority of this method for station planning in complex forest areas and provide efficient and precise technical support for forest structure monitoring and spatial information acquisition.

Reflectance is a widely used feature for all types laser scanning data. Thus, the accuracy and improvement of the reflectance parameter is a persistent topic of research. For short laser pulses with medium-sized footprints, previous work has investigated the effects of inclined targets on the recorded waveform of full-waveform LiDAR systems. In this work, a new methods to extract incidence angle from only a single waveform can be leveraged to improve reflectance values through recalculation based on the laser-radar equation and correcting for angle of incidence artifacts. The results of the proposed method are evaluated with two datasets based on two different topo-bathymtric laser scanners. For both systems, we calculated the relative biconical reflectance and relative averaged bidirectional reflectance distribution function (rBRDF) and evaluated them on homogeneous roof faces. The two reflectance measures are then compared to the initial reflectance values of the laser scanners used in the study. Both measures showed improvements compared to the standard values. The biconical reflectance shows the best overall mean score for all surveyed roofs with an MAD improvement of 0.80 dB to 62 dB for Sensor I and 0.61 dB to 0.56 dB for Sensor II, in addition the rBRDF also displays an improvement with varying results depending on the deployed system. These results highlight the advantages of the proposed reflectance measures and the potential improvement of the widely used LiDAR attribute.

Accurate classification of topo-bathymetric LiDAR data remains challenging due to the heterogeneous nature of land-water transitional environments, where terrestrial, water surface, and submerged features must be distinguished simultaneously. This study presents a multi-branch deep learning architecture for classifying bathymetric LiDAR data into different classes: soil ground, trees and vegetation, water surface, seabed, aquatic plants and other underwater objects (dead wood, coral reef). The proposed framework employs three parallel feature extraction branches, while the first branch captures spatial structure by focusing on three-dimensional geometric coordinates (XYZ), the other two branches use two independent 1D U-Net architectures to extract signal-based features from RGB spectral reflectance and waveform-derived attributes (intensity, return number, number of returns). The discrete LiDAR attributes, though represented as point-wise numerical values, preserve signal characteristics derived from full-waveform analysis. The encoder-decoder of 1D U-Net architecture with skip connections effectively captures sequential patterns and multi-return patterns in different classes especially in vegetation canopies. The three feature streams are fused through fully-connected layers before final classification. Evaluation using different metrics demonstrates the capability of the framework to simultaneously classify diverse coastal zone and inland waters contexts spanning terrestrial and submerged domains within a unified processing pipeline, eliminating the need for separate terrestrial and bathymetric classification workflows.

Permanent laser scanning (PLS) is an effective tool for near-continuous monitoring of topographical changes in beach-dune systems. While PLS systems were traditionally costly, the emergence of affordable LiDAR sensors enables larger-scale setups with multiple scanners or sites. However, the different characteristics compared to high-end devices, create challenges for one-on-one replacement. To assess how low-cost sensors can replace high-end sensors, we compare the performance of a setup with several low-cost Livox AVIA sensors to a single high-end RIEGL VZ-2000i sensor in its ability to capture an embryonic dune field with large variation in topography. This is evaluated using HELIOS++ virtual laser scanning (VLS). To also assess the representativeness of the simulations, we further compare the VLS to real-world measurements with the Livox AVIA. Based on a VLS setup with six AVIAs mounted on tripods at 2 m above ground, a coverage of 52% can be obtained, which is similar to the coverage of a single RIEGL VZ-2000i on a tower 8 m high. The real-world experiments confirm the VLS results with a slightly lower point cloud coverage of 42%. Furthermore, the effective range of the Livox AVIA in a beach-dune system lies around 100-150 m. At larger ranges, only pulses at high incidence angles (angle between surface and incoming beam, >20°) are registered at the scanner. The variations in coverage between the VLS and real-world scans highlight the need for careful consideration of the occlusion potential of different representations of the topography, beam divergence shapes, and the moisture conditions.

The aim of this abstract is to assess the accuracy of the trajectory of the handheld laser scanner CHCNAV RS10. The trajectory data of this PLS device is compared with a simultaneously measured total station measurement.

Green-wavelength LiDAR systems enable high-resolution 3D sensing in underwater environments, but the geometric evaluation of measurements across the waterline remains difficult. A main challenge is that traceable reference instruments usually operate only in air, while refraction at the air-water interface systematically affects both the reconstructed 3D point cloud and the geometry of partially submerged objects. To address this problem, this study presents a controlled experimental framework for evaluating waterline-induced effects in an Underwater LiDAR (ULi) system, using the Z+F IMAGER 5016A as an in-air reference. A rigid reference frame (RRF) spanning the waterline was deployed in a swimming pool. The RRF was first scanned by the IMAGER in air to establish the reference geometry and was then measured by the ULi system under waterline conditions. The analysis considered the above-water, cross-waterline, and underwater parts of the RRF. The evaluation was based not only on overall geometric deviations but also on rigid-body-invariant internal quantities, especially pairwise distances that are independent of the pose of the RRF. In addition, the sensitivity of the reconstructed geometry to the refractive-index setting used in processing was assessed by perturbing the refractive index and quantifying the resulting changes. The proposed workflow provides a practical and traceable basis for isolating and evaluating waterline-related refraction effects in controlled ULi experiments.