Search Results (108)

In airborne LiDAR measurements of shallow water bathymetry, conventional data processing often overlooks the radiative losses associated with multiple scattering events, affecting detection accuracy. This study presents a Monte Carlo-based approach to construct a mathematical model that accurately characterizes the multiple returns in airborne laser bathymetric systems. The model enables rapid simulation of laser propagation through water, accounting for multiple scattering events. Based on the Beer–Lambert law and incorporating the parameters of typical Jerlov 1 clear coastal water, the proposed model achieves a seamless integration of the H-G phase function with a Monte Carlo random process, enabling accurate simulation and validation of pulse temporal broadening in waters with varying optical transparency. Unlike most existing studies, which primarily focus on modeling the laser emission process, this work introduces a novel perspective by analyzing the probability of light reception in LiDAR return signals, offering a more comprehensive understanding of signal attenuation and detection performance in underwater environments. The results demonstrate that, for detecting underwater targets at depths of 10 m, considering three or more scattering events improves the accuracy by ~7%. For detecting underwater targets at depths of 50 m, considering three or more scattering events improves the accuracy by 15~33%. These findings can help enhance the detection accuracy and efficiency of experimental systems. Full article

This study aims to mitigate slope-collapse hazards that threaten life and property at the Lujiawan resettlement site in Wanbi Town, Dayao County, Yunnan Province, within the Guanyinyan hydropower reservoir. It integrates centimeter-level point-cloud data collected by a DJI Matrice 350 RTK equipped with a Zenmuse L2 airborne LiDAR (Light Detection And Ranging) sensor with detailed structural-joint survey data. First, qualitative structural interpretation is conducted with stereographic projection. Next, safety factors are quantified using the limit-equilibrium method, establishing a dual qualitative–quantitative diagnostic framework. This framework delineates six hazardous rock zones (WY1–WY6), dominated by toppling and free-fall failure modes, and evaluates their stability under combined rainfall infiltration, seismic loading, and ambient conditions. Subsequently, six-degree-of-freedom Monte Carlo simulations incorporating realistic three-dimensional terrain and block geometry are performed in RAMMS::ROCKFALL (Rapid Mass Movements Simulation—Rockfall). The resulting spatial patterns of rockfall velocity, kinetic energy, and rebound height elucidate their evolution coupled with slope height, surface morphology, and block shape. Results show peak velocities ranging from 20 to 42 m s⁻¹ and maximum kinetic energies between 0.16 and 1.4 MJ. Most rockfall trajectories terminate within 0–80 m of the cliff base. All six identified hazardous rock masses pose varying levels of threat to residential structures at the slope foot, highlighting substantial spatial variability in hazard distribution. Drawing on the preceding diagnostic results and dynamic simulations, we recommend a three-tier “zonal defense with in situ energy dissipation” scheme: (i) install 500–2000 kJ flexible barriers along the crest and upper slope to rapidly attenuate rockfall energy; (ii) place guiding or deflection structures at mid-slope to steer blocks and dissipate momentum; and (iii) deploy high-capacity flexible nets combined with a catchment basin at the slope foot to intercept residual blocks. This staged arrangement maximizes energy attenuation and overall risk reduction. This study shows that integrating high-resolution 3D point clouds with rigid-body contact dynamics overcomes the spatial discontinuities of conventional surveys. The approach substantially improves the accuracy and efficiency of hazardous rock stability assessments and rockfall trajectory predictions, offering a quantifiable, reproducible mitigation framework for long slopes, large rock volumes, and densely fractured cliff faces. Full article

Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km² in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less). Full article

In this paper, a wind retrieval method based on genetic algorithm-particle swarm optimization (GA-PSO) for the coherent Doppler wind lidar (CDWL) is proposed. The algorithm incorporates an advanced optimization framework that considers wind field spatial continuity, simultaneously enhancing retrieval accuracy and computational efficiency. Comprehensive validations of the GA-PSO algorithm are conducted using a 1.5 μm all-fiber CDWL through ground-based and airborne experiments. In ground-based experiments, the GA-PSO algorithm extends the detection range by 20%~30% compared with traditional methods. The validation against meteorological tower data demonstrates excellent agreement, with mean deviations better than 0.27 m/s for horizontal wind speed and 3.07° for horizontal wind direction and corresponding RMSE values better than 0.36 m/s and 6.04°, respectively. During high-altitude airborne experiments at 5.5 km, the GA-PSO algorithm recovers up to 31% more horizontal wind speed and direction information compared with traditional algorithms, demonstrating exceptional performance in low signal-to-noise ratio (SNR) conditions. Both simulation analysis and field experiments demonstrate that the GA-PSO algorithm achieves processing speeds comparable to traditional real-time methods, establishing its suitability for real-time, three-dimensional wind retrieval applications. Full article

Traditional remote sensing techniques face notable limitations in accurately estimating forest canopy height. Optical data often suffer from vegetation occlusion, while radar systems, though capable of penetrating foliage, show reduced accuracy in complex terrains. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne LiDAR mission, offers high-resolution measurements that address these challenges. However, the complexity of waveform processing and the influence of geolocation uncertainty demand rigorous assessment. This study employs GEDI Version 2.0 data, which demonstrates substantial improvement in geolocation accuracy compared to Version 1.0, and integrates airborne laser scanning (ALS) data from the Changbai Mountain forest region to simulate GEDI waveforms. A Monte Carlo-based approach was used to quantify and correct geolocation offsets, resulting in a reduction in the average relative error (defined as the mean of the absolute differences between estimated and reference canopy heights divided by the reference values) in canopy height estimates from 11.92% to 8.55%. Compared to traditional correction strategies, this method demonstrates stronger robustness in heterogeneous forest conditions. The findings emphasize the effectiveness of simulation-based optimization in enhancing the geolocation accuracy and canopy height retrieval reliability of GEDI data, especially in complex terrain environments. This contributes to more precise global forest structure assessments and provides a methodological foundation for future improvements in spaceborne LiDAR applications. Full article

Airborne hyperspectral LiDAR (AHSL) is a technology that integrates the spectral content collected using hyperspectral imaging and the precise 3D descriptions of observed objects obtained using LiDAR (light detection and ranging). AHSL detects the spectral and three-dimensional (3D) information on an object simply using laser measurements. Nevertheless, the advantageous richness of spectral properties also introduces novel issues into the scan unit, the mechanical–optical trade-off. Specifically, the abundant spectral information requires a larger optical aperture, limiting the acceptance of the mechanic load by the scan unit at a demanding rotation speed and flight height. Via the simulation and analysis of scan models, it is exhibited that Palmer scans fit the large optical aperture required by AHSL best. Furthermore, based on the simulation of the Palmer scan model, 45.23% is explored as the optimized ratio of overlap (ROP) for minimizing the diversity of the point density, with a reduction in the coefficient of variation (CV) from 0.47 to 0.19. The other issue is that it is intricate to calibrate the scanning geometry using outside devices due to the complex optical path. A self-calibration strategy is proposed for tackling this problem, which integrates indoor laser vector retrieval and airborne orientation correction. The strategy is composed of the following three improvements: (1) A self-determined laser vector retrieval strategy that utilizes the self-ranging feature of AHSL itself is proposed for retrieving the initial scanning laser vectors with a precision of 0.874 mrad. (2) A linear residual estimated interpolation method (LREI) is proposed for enhancing the precision of the interpolation, reducing the RMSE from 1.517 mrad to 0.977 mrad. Compared to the linear interpolation method, LREI maintains the geometric features of Palmer scanning traces. (3) A least-deviated flatness restricted optimization (LDFO) algorithm is used to calibrate the angle offset in aerial scanning point cloud data, which reduces the standard deviation in the flatness of the scanning plane from 1.389 m to 0.241 m and reduces the distortion of the scanning strip. This study provides a practical scanning method and a corresponding calibration strategy for AHSL. Full article

Maximizing the energy output of photovoltaic (PV) systems is becoming increasingly important. Consequently, numerous approaches have been developed over the past few years that utilize remote sensing data to predict or map solar potential. However, they primarily address hypothetical scenarios, and few focus on improving existing installations. This paper presents a novel method for optimizing the tilt angles of existing PV arrays by integrating Very High Resolution (VHR) satellite imagery and airborne Light Detection and Ranging (LiDAR) data. At first, semantic segmentation of VHR imagery using a deep learning model is performed in order to detect PV modules. The segmentation is refined using a Fine Optimization Module (FOM). LiDAR data are used to construct a 2.5D grid to estimate the modules’ tilt (inclination) and aspect (orientation) angles. The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. The method was validated using PV systems in Maribor, Slovenia, achieving a 0.952 F1-score for module detection (using FT-UnetFormer with SwinTransformer backbone) and an estimated electricity production error of below 6.7%. Optimization results showed potential energy gains of up to 4.9%. Full article

Field observations provide valuable information for rockfall assessments, but estimating physical and statistical quantities related to rockfall propagation directly is challenging. Simulations are commonly used to infer these quantities, but their subjectivity can result in varying hazard land use zonation extents for different projects. This paper focuses on the application of simulated trajectories for rockfall hazard assessments, with an emphasis on reducing subjectivity. A quantitative guiding rockfall hazard methodology based on earlier concepts is presented and put in the context of legislated requirements. It details how the temporal hazard component, related to the likelihood of failure, can be distributed spatially using simulated trajectories. The method can be applied with results from any process-based software and combined with various prediction methods of the temporal aspect, although this aspect is not the primary focus. Applied examples for static objects and moving objects, such as houses and vehicles, are shown to illustrate the important effect of the object size. For that purpose, the methodology was applied at an indicative level over Norway utilizing its 1 m detailed digital terrain model (DTM) acquired from airborne LiDAR. Potential rockfall sources were distributed in 3D where slopes are steeper than 50°, as most rockfall events in the national landslide database (NSDB) occurred in such areas. This threshold considerably shifts toward gentler slopes when repeating the analysis with coarser DTMs. Simulated trajectories were produced with an adapted version of the simulation model stnParabel. Comparing the number of trajectories reaching the road network to the numerous related registered rockfall events of the NSDB, an indicative averaged yearly frequency of released rock fragments of 1/25 per 10,000 m² of cliff was obtained for Norway. This average frequency can serve as a starting point for hazard assessments and should be adjusted to better match local conditions. Full article

Accurately monitoring aboveground biomass (AGB) and tree mortality is crucial for understanding forest health and carbon dynamics. LiDAR (Light Detection and Ranging) has emerged as a powerful tool for capturing forest structure across different spatial scales. However, the effectiveness of LiDAR for predicting AGB and tree mortality depends on the type of instrument, platform, and the resolution of the point cloud data. We evaluated the effectiveness of three distinct LiDAR-based approaches for predicting AGB and tree mortality in a 25.6 ha North American temperate forest. Specifically, we evaluated the following: GEDI-simulated waveforms from airborne laser scanning (ALS), grid-based structural metrics derived from unmanned aerial vehicle (UAV)-borne lidar data, and individual tree detection (ITD) from ALS data. Our results demonstrate varying levels of performance in the approaches, with ITD emerging as the most accurate for AGB modeling with a median R² value of 0.52, followed by UAV (0.38) and GEDI (0.11). Our findings underscore the strengths of the ITD approach for fine-scale analysis, while grid-based forest metrics used to analyze the GEDI and UAV LiDAR showed promise for broader-scale monitoring, if more uncertainty is acceptable. Moreover, the complementary strengths across scales of each LiDAR method may offer valuable insights for forest management and conservation efforts, particularly in monitoring forest dynamics and informing strategic interventions aimed at preserving forest health and mitigating climate change impacts. Full article

Riverine environmental information includes important data to collect, and the data collection still requires personnel’s field surveys. These on-site tasks still face significant limitations (i.e., hard or danger to entry). In recent years, as one of the efficient approaches for data collection, air-vehicle-based Light Detection and Ranging technologies have already been applied in global environmental research, i.e., land cover classification (LCC) or environmental monitoring. For this study, the authors specifically focused on seven types of LCC (i.e., bamboo, tree, grass, bare ground, water, road, and clutter) that can be parameterized for flood simulation. A validated airborne LiDAR bathymetry system (ALB) and a UAV-borne green LiDAR System (GLS) were applied in this study for cross-platform analysis of LCC. Furthermore, LiDAR data were visualized using high-contrast color scales to improve the accuracy of land cover classification methods through image fusion techniques. If high-resolution aerial imagery is available, then it must be downscaled to match the resolution of low-resolution point clouds. Cross-platform data interchangeability was assessed by comparing the interchangeability, which measures the absolute difference in overall accuracy (OA) or macro-F1 by comparing the cross-platform interchangeability. It is noteworthy that relying solely on aerial photographs is inadequate for achieving precise labeling, particularly under limited sunlight conditions that can lead to misclassification. In such cases, LiDAR plays a crucial role in facilitating target recognition. All the approaches (i.e., low-resolution digital imagery, LiDAR-derived imagery and image fusion) present results of over 0.65 OA and of around 0.6 macro-F1. The authors found that the vegetation (bamboo, tree, grass) and road species have comparatively better performance compared with clutter and bare ground species. Given the stated conditions, differences in the species derived from different years (ALB from year 2017 and GLS from year 2020) are the main reason. Because the identification of clutter species includes all the items except for the relative species in this research, RGB-based features of the clutter species cannot be substituted easily because of the 3-year gap compared with other species. Derived from on-site reconstruction, the bare ground species also has a further color change between ALB and GLS that leads to decreased interchangeability. In the case of individual species, without considering seasons and platforms, image fusion can classify bamboo and trees with higher F1 scores compared to low-resolution digital imagery and LiDAR-derived imagery, which has especially proved the cross-platform interchangeability in the high vegetation types. In recent years, high-resolution photography (UAV), high-precision LiDAR measurement (ALB, GLS), and satellite imagery have been used. LiDAR measurement equipment is expensive, and measurement opportunities are limited. Based on this, it would be desirable if ALB and GLS could be continuously classified by Artificial Intelligence, and in this study, the authors investigated such data interchangeability. A unique and crucial aspect of this study is exploring the interchangeability of land cover classification models across different LiDAR platforms. Full article

Accurately quantifying fine-scale forest canopy-absorbed photosynthetically active radiation (APAR) is essential for monitoring forest growth and understanding ecological processes. The development of 3D radiative transfer models (3D RTMs) enables the precise simulation of canopy–light interactions, facilitating better quantification of forest canopy radiation dynamics. However, the complex parameters of 3D RTMs, particularly detailed 3D scene structures, pose challenges to the simulation of radiative information. While high-resolution LiDAR offers precise 3D structural data, the effectiveness of different tree crown reconstruction methods for APAR quantification using airborne laser scanning (ALS) data has not been fully investigated. In this study, we employed three ALS-based tree crown reconstruction methods: alphashape, ellipsoid, and voxel-based combined with the 3D RTM LESS to assess their effectiveness in simulating and quantifying 3D APAR distribution. Specifically, we used two distinct 3D forest scenes from the RAMI-V dataset to simulate ALS data, reconstruct virtual forest scenes, and compare their simulated 3D APAR distributions with the benchmark reference scenes using the 3D RTM LESS. Furthermore, we simulated branchless scenes to evaluate the impact of branches on APAR distribution across different reconstruction methods. Our findings indicate that the alphashape-based tree crown reconstruction method depicts 3D APAR distributions that closely align with those of the benchmark scenes. Specifically, in scenarios with sparse (HET09) and dense (HET51) canopy distributions, the APAR values from scenes reconstructed using this method exhibit the smallest discrepancies when compared to the benchmark scenes. For HET09, the branched scenario yields RMSE, MAE, and MAPE values of 33.58 kW, 33.18 kW, and 40.19%, respectively, while for HET51, these metrics are 12.74 kW, 12.97 kW, and 10.27%. In the branchless scenario, HET09′s metrics are 10.65 kW, 10.22 kW, and 9.79%, and for HET51, they are 2.99 kW, 2.65 kW, and 2.11%. However, differences remain between the branched and branchless scenarios, with the extent of these differences being dependent on the canopy structure. Our conclusion demonstrated that among the three tree crown reconstruction methods tested, the alphashape-based method has the potential for simulating and quantifying fine-scale APAR at a regional scale. It provides a convenient technical support for obtaining fine-scale 3D APAR distributions in complex forest environments at a regional scale. However, the impact of branches in quantifying APAR using ALS-reconstructed scenes also needs to be further considered. Full article

Airborne LiDAR (ALS) and terrestrial LiDAR (TLS) data integration provides complementary perspectives for acquiring detailed 3D forest information. However, challenges in registration arise due to feature instability, low overlap, and differences in cross-platform point cloud density. To address these issues, this study proposes an automatic point cloud registration method based on the consistency of the single-tree position distribution in multi-species and complex forest scenes. In this method, single-tree positions are extracted as feature points using the Stepwise Multi-Form Fitting (SMF) technique. A novel feature point matching method is proposed by constructing a polar coordinate system, which achieves fast horizontal registration. Then, the Z-axis translation is determined through the integration of Cloth Simulation Filtering (CSF) and grid-based methods. Finally, the Iterative Closest Point (ICP) algorithm is employed to perform fine registration. The experimental results demonstrate that the method achieves high registration accuracy across four forest plots of varying complexity, with root-mean-square errors of 0.0423 m, 0.0348 m, 0.0313 m, and 0.0531 m. The registration accuracy is significantly improved compared to existing methods, and the time efficiency is enhanced by an average of 90%. This method offers robust and accurate registration performance in complex and diverse forest environments. Full article

Characterizing Management Units (MUs) with tree-level data is instrumental for a comprehensive understanding of forest structure and for providing information needed to support forest management decision-making. Airborne Laser Scanning (ALS) data may enhance this characterization. While some studies rely on Individual Tree Detection (ITD) methods using ALS data to estimate tree diameters within stands, these methods often face challenges when the goal is to characterize MUs in dense forests. This study proposes a methodology that simulates diameter distributions from LiDAR data using an Area-Based Approach (ABA) to overcome these limitations. Focusing on maritime pine (Pinus pinaster Ait.) MUs within a forest intervention zone in northern Portugal, the research initially assesses the suitability of two highly flexible Probability Density Functions (PDFs), Johnson’s SB and Weibull, for simulating diameter distribution in maritime pine stands in Portugal using the PINASTER database. The selected PDF is then used in conjunction with ABA to derive the variables needed for parameter recovery, enabling the simulation of diameter distributions within each MU. Monte Carlo Simulation (MCS) is applied to generate a sample list of tree diameters from the simulated distributions. The results indicate that this methodology is appropriate to estimate diameter distributions within maritime pine MUs by using ABA combined with Johnson’s SB and Weibull PDFs. Full article

Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when extending to transmission line scenes. This paper proposes a filter-assisted airborne point cloud semantic segmentation for transmission lines. First, a large number of ground point clouds is identified by introducing the well-developed cloth simulation filter to alleviate the impact of the imbalance of the target object proportion on the classifier’s performance. The multi-dimensional features are then defined, and the classification model is trained to achieve the multi-element semantic segmentation of the transmission line scene. The experimental results and analysis indicate that the proposed filter-assisted algorithm can significantly improve the semantic segmentation performance of the transmission line point cloud, enhancing both the point cloud segmentation efficiency and accuracy by more than 25.46% and 3.15%, respectively. The filter-assisted point cloud semantic segmentation method reduces the volume of sample data, the number of sample classes, and the sample imbalance index in power line scenarios to a certain extent, thereby improving the classification accuracy of classifiers and reducing time consumption. This research holds significant theoretical reference value and engineering application potential for scene reconstruction and intelligent understanding of airborne laser point cloud transmission lines. Full article

Global Ecosystem Dynamics Investigation (GEDI) is a relatively new technology for global forest research, acquiring LiDAR measurements of vertical vegetation structure across Earth’s tropical, sub-tropical, and temperate forests. Previous GEDI validation efforts have largely focused on top of canopy accuracy, and findings vary by geographic region and forest type. Despite this, many applications utilize measurements of vertical vegetation distribution from the lower canopy, with a wide diversity of uses for GEDI data appearing in the literature. Given the variability in data requirements across research applications and ecosystems, and the regional variability in GEDI data quality, it is imperative to understand GEDI error to draw strong inferences. Here, we quantify the accuracy of GEDI relative height metrics through canopy layers for the Brazilian Amazon. To assess the accuracy of on-orbit GEDI L2A relative height metrics, we utilize the GEDI waveform simulator to compare detailed airborne laser scanning (ALS) data from the Sustainable Landscapes Brazil project to GEDI data collected by the International Space Station. We also assess the impacts of data filtering based on biophysical and GEDI sensor conditions and geolocation correction on GEDI error metrics (RMSE, MAE, and Bias) through canopy levels. GEDI data accuracy attenuates through the lower percentiles in the relative height (RH) curve. While top of canopy (RH₉₈) measurements have relatively high accuracy (R² = 0.76, RMSE = 5.33 m), the accuracy of data decreases lower in the canopy (RH₅₀: R² = 0.54, RMSE = 5.59 m). While simulated geolocation correction yielded marginal improvements, this decrease in accuracy remained constant despite all error reduction measures. Some error rates for the Amazon are double those reported in studies from other regions. These findings have broad implications for the application of GEDI data, especially in studies where forest understory measurements are particularly challenging to acquire (e.g., dense tropical forests) and where understory accuracy is highly important. Full article