Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.

Most conventional Leaf Area Index (LAI) retrieval methods using Terrestrial Laser Scanning (TLS) data are based on Beer’s law and are severely affected by the effects of leaf occlusion and aggregation. Moreover, the correction of LAI using the Clumping Index (CI) relies on assumptions and is generally not robust. This paper exploits the high spatial resolution and penetration capability of TLS to explore the physical meaning of point cloud data sampling and then model the leaf cluster envelope by the Alpha-shape algorithm. Subsequently, canopy LAI is obtained by counting the surface area of the envelope of each leaf cluster within the canopy and combining it with the projected area of the canopy. The entire process is physically based and introduces a new LAI inversion approach based on TLS. We tested the approach by simulating the TLS data of 25 synthetic trees with different leaf areas and morphologies to evaluate its robustness. Four strategies were adopted for parameter selection in the envelope modeling step to automate the process of finding the optimal envelope radius and improve the inversion accuracy of LAI. In comparison with the traditional LAI retrieval method based on Beer’s law (RMSE% is 47.3%), we found that the method proposed in this letter has a higher inversion accuracy with a minimum RMSE% of 27.7%. Our method also is significantly more robust for high LAI scenes and performs well in scenes with high occlusion and aggregation.

Canopy gaps affect the spatial distribution of radiation in the canopy. The estimation of gap fraction (GF) is important for the study of leaf area index (LAI). Terrestrial laser scanning (TLS) has been widely used for retrieving canopy structure parameters through massive high-resolution spatial samples in the form of 3-D point cloud data sets. Monte Carlo simulations can be used to obtain approximate solutions to quantitative problems through a large number of random sampling while avoiding complicated mathematical calculations. Monte Carlo simulations are suitable for analyzing TLS data and could help overcome resolution reductions inherent to current point cloud processing approaches. However, few studies have applied the Monte Carlo method to capture the canopy structure features implicit in high-resolution point clouds. This letter proposes a method for estimating the GF based on Monte Carlo simulations. A large number of randomly simulated laser beams were emitted to identify the canopy and gaps according to the discrimination distance, and the results were compared with the GFs derived from digital hemispherical photography (DHP). In general, the proposed TLS method estimated smaller GFs than the DHP method since DHP was vulnerable to exposure conditions and complex canopy structure. The GF estimated by the two methods is consistent (R² = 0.7522), indicating the effectiveness of the Monte Carlo method.

Forest canopy height is a fundamental control on aerodynamic conductance and surface energy partitioning, but its influence on evapotranspiration (ET) at a global scale remains under-investigated. We combined biophysical theory with Community Land Model version 5 (CLM5) simulations to quantify how canopy height affects the magnitude and partitioning of global forest ET. By uniformly scaling canopy height by up to ±20%, we first establish the intrinsic sensitivity of ET to canopy structure: a 20% increase in canopy height reduces global transpiration by 4.15% while increasing canopy evaporation by 2.94%, as enhanced aerodynamic conductance shifts energy partitioning between latent and sensible heat fluxes. We then prescribe canopy height fields derived from GEDI (circa 2020) and ICESat-1 GLAS (2003-2009) to assess how differences in data source, observation epoch, and spatial aggregation methods (maximum, mean, median) propagate into simulated ET. The ET response to canopy height is strongly biome dependent: tropical broadleaf evergreen forests show modest relative responses but the largest absolute flux perturbations; temperate broadleaf forests show the strongest relative responses, propagating directly to total ET; and boreal broadleaf deciduous forests display opposite-sign changes in canopy evaporation and transpiration that nearly cancel in total ET, hiding the height bias in flux partitioning. Differences among canopy height datasets and aggregation methods produced up to 1.76% divergence in global ET and up to 4.93% in individual flux components such as transpiration, with substantially larger differences at regional and biome scales. Our results highlight the need for updated canopy height constraints and standardized aggregation protocols to reduce structural uncertainty in the representation of vegetation-atmosphere coupling in Earth system models.