Underwater laser signal attenuation challenges conventional detection, while single-photon LiDAR (SPL) with high sensitivity shows promise. Existing underwater SPL studies primarily focus on isolated parameters, while the coupled effects of environmental and system parameters remain insufficiently investigated. In this work, a 532 nm underwater SPL system was developed to systematically explore multi-parameter coupling mechanisms in laboratory water tanks, including air and three turbidity levels, three detection distances, four laser energy levels, three integration times, and seven targets. This provides quantitative guidance for optimizing SPL systems in complex underwater environments. The results show that the SPL system maintained sub-nanosecond ranging precision, with the standard deviation (SD) of the ranging measurement at 50 cm being 0.0117 ns under low turbidity (0.11 m⁻¹) with 50% laser energy, while under high turbidity (4.2 m⁻¹) conditions, it increased to 0.0338 ns. At 100 cm, the SD was 0.0187 ns in low turbidity and rose to 0.0877 ns in high turbidity. Furthermore, the inversion error of the highly reflectivity minerals was kept within 3%, and the inversion value of reflectivity decreased exponentially with the increase of turbidity. Moreover, there is an important discovery for the phenomenon of the forward shift of photon flight time detected for highly reflectivity targets. Longer integration times effectively enhanced the signal-to-noise ratio (SNR) under severe attenuation, whereas excessive laser energy risked detector saturation. These findings provide a systematic characterization of how multifactor coupling governs SPL signal dynamics. The results validate the feasibility of SPL for complex underwater detection and offer theoretical insights and technical guidance for future marine applications in resource exploration, environmental monitoring, and national security.

The spectral selectivity of underwater multiwavelength single-photon LiDAR offers a promising pathway to discriminate target materials beyond conventional geometric imaging. However, the complex interactions among wavelength-dependent water attenuation, target reflectance, and scattering-induced waveform distortion remain poorly quantified. This study establishes a comprehensive theoretical and experimental framework linking these factors, validated through controlled experiments across two water turbidity levels (attenuation coefficients of 0.1 m⁻¹ and 2.0 m⁻¹), six wavelengths (490–570 nm), and diverse target types. We demonstrate that target ranging bias exhibits a wavelength-dependent linear trend (8.3 ps/nm) in turbid waters. This phenomenon is fundamentally attributable to forward-scattering-induced centroid shifts rather than true spatial displacements, a mechanism we quantify through comparative peak-detection and Gaussian fitting analyses. Contrary to intuitive expectations, we reveal that spectral discrimination efficacy decouples from received photon counts. Principal component analysis confirms that a multidimensional spectral feature space enables accurate target clustering independent of absolute intensity, with specific bands (e.g., 510 nm and 550 nm) exhibiting heightened sensitivity to material signatures. These findings establish that underwater target recognition is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than solely depending on received photon counts, providing a robust physical basis for next-generation underwater LiDAR optimization.

Current methods for the high-precision real-time monitoring and parameter inversion of industrial methane point source leakage are insufficient. This research introduces a novel laser-based methane leakage monitoring approach for deployment on an unmanned aerial vehicle platform. An enhanced two-dimensional integral Gaussian diffusion model paired with a point sampling technique is employed to simultaneously determine the leakage rate and source location, integrating a genetic algorithm and an interior point penalty function algorithm for optimization. Simulations incorporating observational error sources are performed to quantitatively assess the accuracy of leakage parameter inversion under diverse errors, demonstrating the scheme’s viability. The accuracy of leakage parameter inversion achieved by the algorithm across various point sampling methods, gas plume characteristics, and wind speeds was examined, validating the assessment under multivariable influences in real observations. The proposed methodology was compared with two other leakage inversion optimization techniques, demonstrating its efficiency in addressing wind speed and directional effects. This study offers a practical method with significant implications for monitoring and quantifying industrial methane point source leakages.