Multiple scattering of laser ray leads to distance calculation error and accumulated intensity error in LiDAR waveform. This study quantitatively assesses multiple scattering in LiDAR canopy waveform and explores its relationship with sensor configurations, forest structure, and leaf biochemistry. Airborne LiDAR measurements were conducted to verify presence of multiple scattering in spaceborne LiDAR waveforms. A Monte Carlo-based radiative transfer model, DART, was applied to simulate laser multiple scattering. The simulated results were compared with the actual measurements and further revealed how forest characteristics and sensor parameters influence multiple scattering. Results show that: 1) multiple scattering contributes significantly (∼30% of canopy subwaveform and ∼18% of total waveform) when there are sufficient scatterers in large LiDAR footprint, and the scatterers possess a reasonable spatial distribution and a strong albedo at the laser wavelength; 2) contribution of multiple scattering is typically less than 5% for small-footprint LiDAR; 3) multiple scattering of large-footprint LiDAR increases with forest leaf area index and fractional coverage, while also varying with laser pointing angle, crown shape, and leaf angle distribution; 4) leaf biochemical contents affect multiple scattering only at their sensitive wavelength. These findings help to quantitatively explain errors in the simulation, validation, and interpretation of LiDAR signal.

Flight line planning is the first step in an aerial photography campaign. Its two key parameters, ground sampling distance (GSD), and overlap should be designed carefully as they will impact subsequent flying workload and even the final mapping accuracy. For a flat terrain region, flight line planning is easy using a traditional method with fixed baseline, flight spacing, and flight altitude. But for a steep terrain region, photo overlap and GSD will change greatly with topography, and thus a flight line planning using the traditional method is not sufficient. This article presents a novel adaptable flight line planning method for frame camera with four application modes. The flight line planning procedure is implemented through rigorous imaging geometry model using digital elevation data, where actual overlap and GSD can be calculated precisely. Using the desired overlap and GSD as constraint conditions, the flight spacing, baseline, and flight altitude can be adjusted adaptively according to ground topography. To assess the effect, a hypothetical test in a rugged terrain region was conducted to compare the project workload, GSD, and overlap of the four modes. The results show that Mode 2, Mode 3, and Mode 4 can realize better overlap with lower workload than the traditional method Mode 1. Mode 3 and Mode 4 have better GSD distribution than Mode 1 and Mode 2. The Mode 3 method was also validated in an actual project, showing a favorable performance in final reality 3D model result.

Three-dimensional (3D) point cloud registration is a prerequisite for scene reconstruction and 3D object recognition in computer vision and remote sensing. Numerous previous studies have presented a series of point cloud registration algorithms with diverse efficiencies and accuracies. However, registering point clouds with repetitive structures is still challenging. In this study, we propose the Global Planar Structural Constraint Optimal-based algorithm (GPSCO), which is specifically designed to handle registration of repetitive structures. Its novelty lies in establishing the geometric constraint of multiple planes and registering based on the global optimal geometric constraint. The specific algorithm involves clustering the parallel planes into plane groups, estimating matching scores between plane groups, and selecting three corresponding pairs of nonparallel plane groups to form the plane structural constraint. The transformation matrix determined in the case of the optimal structural constraint is taken as the final result. The two terrestrial LiDAR datasets (HS1 and HS2) of real scenes with repetitive structures were collected to evaluate the GPSCO algorithm. Additionally, the GPSCO algorithm is validated on four public benchmark datasets, such as Whu-Park, Whu-Campus, ETH-Hauptgebaude, and ETH-Stairs. The registration results demonstrate that the GPSCO algorithm achieves 95.65%, 86.36%, 100%, 100%, 100%, and 88.89% successful registration rates on the six datasets, respectively, and significantly outperforms the existing methods on HS1 and HS2 with repetitive structures. The corresponding datasets and code are available at [https://github.com/fog223/GPSCO].

Accurate and spatially explicit forest canopy height (FCH) estimates are essential for biomass estimation, carbon accounting, and ecosystem assessment at global scales. Nevertheless, generating a coherent high-resolution global FCH product remains challenging due to cross-sensor inconsistencies among space-borne LiDAR missions, pronounced ecological heterogeneity, and highly uneven sampling density. This study presents a global 30 m FCH map for 2020 derived from harmonized GEDI and ICESat-2 observations integrated with Sentinel-1 SAR, Sentinel-2 optical imagery, and ancillary environmental datasets. A regression-based cross-sensor calibration model was developed to align ICESat-2 canopy height metrics with the GEDI reference scale, producing a unified LiDAR training dataset and extending structurally consistent information into high-latitude regions beyond GEDI coverage. Experimental results indicate that harmonized multi-mission integration improves accuracy relative to single-sensor or uncalibrated fusion approaches. To account for spatial heterogeneity and sampling imbalance, we implemented an adaptive quad-tree regionalization scheme driven by LiDAR sample density. This approach enhances model stability in data-rich regions while avoiding unnecessary subdivision in data-sparse areas. Regional FCH estimation was performed using a hybrid Random Forest-Ordinary Kriging (RFOK) framework that integrates nonlinear regression with spatially structured residual interpolation. Compared with regression-only models, RFOK increased R2 from 0.65 to 0.72 and reduced RMSE by 11.39% under space-borne validation, with an additional 9.16% reduction in RMSE against independent airborne LiDAR data. Map accuracy validation using independent space-borne and airborne LiDAR datasets demonstrates strong agreement, yielding R2 = 0.72 (RMSE = 4.98 m; bias = 0.34 m) and R2 = 0.70 (RMSE = 5.06 m; bias = −0.06 m), respectively. Comparative analyses further show that the proposed product outperforms existing global FCH datasets, particularly in high-latitude regions beyond GEDI coverage. The resulting 30 m global FCH map provides a spatially coherent and sensor-consistent representation of forest vertical structure, offering a robust data foundation for biomass estimation, carbon cycle assessment, and ecosystem monitoring.