Hyperspectral LiDAR (HSL) is a promising active detection technique for underwater positioning and remote sensing, enabling the simultaneous acquisition of three-dimensional topographic and spectral information of underwater targets. This study presents an advanced underwater hyperspectral LiDAR (UDHSL) system with a spectral range of 450–700 nm, adjustable spectral bandwidth of 10–300 nm, and tunable repetition frequency of 50 kHz to 1 MHz. The system achieves high precision with a laser divergence angle of ≤1 mrad, pulse width of 7 ns, laser energy of 7.5 µJ, ranging resolution of 1.13 cm and ranging accuracy of 1.02 m@distance of 27 m. Hyperspectral point clouds spanning 11 bands (450–650 nm) are generated during 3D pool experiments. The distance-colored point clouds precisely align with the geometric characteristics of targets, the normalized intensity-colored point clouds across spectral bands exhibit discriminative capabilities for target identification, and the color-composite point clouds approximate the true colors of targets, collectively validating the system’s ability to concurrently acquire spectral and topographic data. These results underscore the potential of this technology for underwater exploration and positioning applications.

The analysis of greenhouse gas emission characteristics on urban roads is of great significance for understanding the sources and sinks of urban greenhouse gases and their changing patterns. Based on tunable diode laser derivative absorption spectroscopy technology, which features high resolution, high sensitivity, and fast response, a vehicle-mounted system capable of simultaneously detecting CO₂ and CH₄ has been developed. The system has a response time of 0.38 s, with detection sensitivities of 5 ppb for CH₄ and 0.2 ppm for CO₂, power consumption of approximately 4.8 W, a weight of less than 3 kg, and dimensions of 255 mm × 275 mm × 85 mm. Using this system, monitoring campaigns were conducted on the same road in Beijing, running north–south, during different time periods in April and June 2023. The results show that there is little correlation between changes in CO₂ and CH₄ concentrations on the road, and these gas concentrations exhibit different influencing factors and spatiotemporal characteristics. The CO₂ concentration on the road is primarily related to the degree of traffic congestion and does not exhibit significant seasonal variations. The average CO₂ concentration measured on the road is much higher than the global average CO₂ concentration during the same period. On the other hand, the CH₄ concentration on the road is not strongly correlated with traffic congestion but is closely related to the leakage of methane from specific emission wells or covers. The CH₄ concentration is higher in the morning, gradually decreases as the sun rises, and then increases again after sunset. The CH₄ concentration measured at night in June is significantly lower than that in April, reflecting some seasonal variation. The CH₄ concentration on the Beijing urban road is slightly higher than the global average CH₄ concentration during the same period. The vehicle-mounted experiments verified the feasibility of using this self-developed system for vehicle-mounted detection of greenhouse gas concentrations on urban roads. The research results can provide data for analyzing the spatial pattern of regional carbon sources and sinks.

The highway is a crucial component of modern transportation systems, and its efficient management is essential for ensuring safety and facilitating communication. The automatic understanding and reconstruction of highway environments are therefore pivotal for advanced traffic management and intelligent transportation systems. This work introduces a methodology for the fine-grained semantic segmentation and reconstruction of highway environments using dense 3D point cloud data acquired via mobile laser scanning. First, a multi-scale, object-based data augmentation and down-sampling method is introduced to address the issue of training sample imbalance. Subsequently, a deep learning approach utilizing the KPConv convolutional network is proposed to achieve fine-grained semantic segmentation. The segmentation results are then used to reconstruct a 3D model of the highway environment. The methodology is validated on a 32 km stretch of highway, achieving semantic segmentation across 27 categories of environmental features. When evaluated against a manually annotated ground truth, the results exhibit a mean Intersection over Union (mIoU) of 87.27%. These findings demonstrate that the proposed methodology is effective for fine-grained semantic segmentation and instance-level reconstruction of highways in practical scenarios.

Multispectral LiDAR can simultaneously obtain distance and spectral information and shows great potential for underwater detection. However, absorption and scattering caused by suspended particles in water lead to energy attenuation and multiple scattering, which affect echo intensity and ranging accuracy, while the propagation characteristics under multi-wavelength conditions remain insufficiently studied. In this study, a simplified underwater propagation simulation model for multispectral LiDAR is established based on the equivalent spherical-particle assumption, combining Mie scattering theory with a semi-analytical Monte Carlo method. The effects of particle size on echo intensity and ranging error are analyzed under fixed concentration conditions. Based on this model, a detection-threshold-constrained optimal wavelength selection criterion is formulated. Multi-distance analysis (3, 5, 8, and 15 m) confirms that the preferred wavelength is primarily governed by particle size and remains stable across depths. The results show that the optimal detection wavelength shifts with particle size, being about 560 nm for fine particles and gradually moving toward the 400–480 nm blue–green band for larger particles. Experimental validation shows that the simulation-based ranging correction reduces RMSE by 9.4–25.9% (average 18.1%) and MAE by 11.8–29.7% (average 22.0%) across five experimental distances. The results provide a preliminary reference for wavelength selection in multispectral LiDAR systems under simplified conditions.

Targeting the long-term goal of synchronous acquisition of underwater terrain and material composition information, this study establishes a radiative transfer model for underwater hyperspectral LiDAR (UDHSL) and systematically verifies the effects of target reflectance, detection distance, and laser wavelength on backscattering echo intensity through controlled laboratory experiments. A wavelength-dependent water attenuation correction term incorporating absorption and scattering was introduced into the conventional LiDAR equation to derive a hyperspectral LiDAR radiative transfer equation applicable to underwater environments, and a normalized echo intensity processing method using window glass reflection as a reference was proposed. This study uses a custom-built UDHSL system (wavelength range: 450; detection range approximately 5–6 m). The echo intensity exhibits pronounced wavelength selectivity, peaking at 450–550 nm in clear water and shifting to 530–570 nm in turbid water. These experimental results are consistent with theoretical predictions of the radiative transfer model, validating its fundamental correctness and providing an experimental basis for radiometric calibration and underwater target reflectance retrieval of UDHSL systems.