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Light detection and ranging (LiDAR) is a widely used technology for the acquisition of three-dimensional (3D) information about a wide variety of physical objects and environments. However, before conducting a campaign, a test is typically conducted to assess the potential of the utilized algorithm for information retrieval. It might not be a real campaign but rather a simulation to save time and costs. Here, a multi-platform LiDAR simulation model considering the location, direction, and wavelength of each emitted laser pulse was developed based on the large-scale remote sensing (RS) data and image simulation framework (LESS) model, which is a 3D radiative transfer model for simulating passive optical remote sensing signals using the ray tracing algorithm. The LESS LiDAR simulator took footprint size, returned energy, multiple scattering, and multispectrum LiDAR into account. The waveform and point similarity were assessed with the LiDAR module of the discrete anisotropic radiative transfer (DART) model. Abstract and realistic scenes were designed to assess the simulated LiDAR waveforms and point clouds. A waveform comparison in the abstract scene with the DART LiDAR module showed that the relative error was lower than 1%. In the realistic scene, airborne and terrestrial laser scanning were simulated by LESS and DART LiDAR modules. Their coefficients of determination ranged from 0.9108 to 0.9984 . Their mean was 0.9698 . The number of discrete returns fitted well and the coefficient of determination was 0.9986 . A terrestrial point cloud comparison in the realistic scene showed that the coefficient of determination between the two sets of data could reach 0.9849 . The performance of the LESS LiDAR simulator was also compared with the DART LiDAR module and HELIOS++. The results showed that the LESS LiDAR simulator is over three times faster than the DART LiDAR module and HELIOS++ when simulating terrestrial point clouds in a realistic scene. The proposed LiDAR simulator offers two modes for simulating point clouds: single-ray and multi-ray modes. The findings demonstrate that utilizing a single-ray simulation approach can significantly reduce the simulation time, by over 28 times, without substantially affecting the overall point number or ground pointswhen compared to employing multiple rays for simulations. This new LESS model integrating a LiDAR simulator has great potential in terms of simultaneously simulating LiDAR data and optical images based on the same 3D scene and parameters. As a proof of concept, the normalized difference vegetation index (NDVI) results from multispectral images and the vertical profiles from multispectral LiDAR waveforms were simulated and analyzed. The results showed that the proposed LESS LiDAR simulator can fulfill its design goals. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) of tree height estimation is up to.94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping. [ABSTRACT FROM AUTHOR]

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for ground- and aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloud-based methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and non-photosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists. [ABSTRACT FROM AUTHOR]

Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands. [ABSTRACT FROM AUTHOR]

The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology. [ABSTRACT FROM AUTHOR]