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Leaf area index (LAI) is an important vegetation parameter. Active light detection and ranging (LiDAR) technology has been widely used to estimate vegetation LAI. In this study, LiDAR technology, LAI retrieval and validation methods, and impact factors are reviewed. First, the paper introduces types of LiDAR systems and LiDAR data preprocessing methods. After introducing the application of different LiDAR systems, LAI retrieval methods are described. Subsequently, the review discusses various LiDAR LAI validation schemes and limitations in LiDAR LAI validation. Finally, factors affecting LAI estimation are analyzed. The review presents that LAI is mainly estimated from LiDAR data by means of the correlation with the gap fraction and contact frequency, and also from the regression of forest biophysical parameters derived from LiDAR. Terrestrial laser scanning (TLS) can be used to effectively estimate the LAI and vertical foliage profile (VFP) within plots, but this method is affected by clumping, occlusion, voxel size, and woody material. Airborne laser scanning (ALS) covers relatively large areas in a spatially contiguous manner. However, the capability of describing the within-canopy structure is limited, and the accuracy of LAI estimation with ALS is affected by the height threshold and sampling size, and types of return. Spaceborne laser scanning (SLS) provides the global LAI and VFP, and the accuracy of estimation is affected by the footprint size and topography. The use of LiDAR instruments for the retrieval of the LAI and VFP has increased; however, current LiDAR LAI validation studies are mostly performed at local scales. Future research should explore new methods to invert LAI and VFP from LiDAR and enhance the quantitative analysis and large-scale validation of the parameters.

Canopy gaps and the processes that generate them play an integral role in shaping the structure and dynamics of forests. However, it is only with recent advances in remote sensing technologies such as airborne laser scanning that studying canopy gaps at scale has become a reality. Consequently, we still lack an understanding of how the size distribution and spatial organization of canopy gaps varies among forests ecosystems, nor have we determined whether these emergent properties can be reconciled with existing theories of forest dynamics. Here, I outline a roadmap for integrating remote sensing with field data and individual-based models to build a comprehensive picture of how environmental constraints and disturbance regimes shape the three-dimensional structure of the world’s forests.

Forests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. An important tool for forest structure assessments are canopy height models (CHMs): high resolution maps of canopy height obtained using airborne laser scanning (ALS). CHMs are widely used for monitoring canopy dynamics, mapping forest biomass and calibrating satellite products, but surprisingly little is known about how differences between CHM algorithms impact ecological analyses. Here, we used high-quality ALS data from nine sites in Australia, ranging from semi-arid shrublands to 90-m tall Mountain Ash canopies, to comprehensively assess CHM algorithms. This included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure. We found that CHM algorithms varied widely both in their height predictions (differences up to 10 m, or 60% of canopy height) and in their sensitivity to point cloud characteristics (biases of up to 5 m, or 40% of canopy height). Impacts of point cloud properties on CHM-derived metrics varied, from robust inference for height percentiles, to considerable errors in above-ground biomass estimates (~50 Mg ha−1, or 10% of total) and high volatility in metrics that quantify spatial associations in canopies (e.g. gaps). However, we also found that two CHM algorithms—a variation on a ‘spikefree’ algorithm that adapts to local pulse densities and a simple Delaunay triangulation of first returns—allowed for robust canopy characterisation and should thus create a secure foundation for ecological comparisons in space and time. We show that CHM choice has a strong impact on forest structural characterisation that has previously been largely overlooked. To address this, we provide a sample workflow to create robust CHMs and best-practice guidelines to minimise biases and uncertainty in downstream analyses. In doing so, our study paves the way for more rigorous large-scale assessments of forest structure and dynamics from airborne laser scanning.

The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. The framework comprises five steps: (1) determination of the initial dominant segments using a region growing algorithm, (2) identification of segments to be redefined based on the 2D hull convex area of each segment, (3) establishment and selection of profiles based on the tree structures, (4) determination of the number of trees using the correlation coefficient of residuals between Gaussian fitting and the tree canopy shape described in each profile, and (5) k-means segmentation to obtain the point cloud of a single tree. The accuracy was evaluated in terms of correct matching, recall, precision, and F-score in eight plots with different stem densities. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.

Tropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. Airborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. Therefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in French Guiana. We measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. We also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. The different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. Segmentation methods based on the point cloud (AMS3D and Graph-Cut) globally outperformed methods based on the Canopy Height Models, especially for small crowns; the AMS3D method outperformed the other methods tested for the overlap analysis, and AMS3D and Graph-Cut performed the best for the automatic matching validation. Nevertheless, other methods based on the Canopy Height Model performed better for very large emergent crowns. The dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method.

Aim Vegetation structure is a key determinant of animal diversity and species distributions. The introduction of Light Detection and Ranging (LiDAR) has enabled the collection of massive amounts of point cloud data for quantifying habitat structure at fine resolution. Here, we review the current use of LiDAR‐derived vegetation metrics in diversity and distribution research of birds, a key group for understanding animal–habitat relationships. Location Global. Methods We review 50 relevant papers and quantify where, in which habitats, at which spatial scales and with what kind of LiDAR data current studies make use of LiDAR metrics. We also harmonize and categorize LiDAR metrics and quantify their current use and effectiveness. Results Most studies have been conducted at local extents in temperate forests of North America and Europe. Rasterization is currently the main method to derive LiDAR metrics, usually from airborne laser scanning data with low point densities (<10 points/m2) and small footprints (<1 m diameter). Our metric harmonization suggests that 40% of the currently used metric names are redundant. A categorization scheme allowed to group all metric names into 18 out of 24 theoretically possible classes, defined by vegetation part (total vegetation, single trees, canopy, understorey, and other single layers as well as multi‐layer) and structural type (cover, height, horizontal variability and vertical variability). Metrics related to canopy cover, canopy height and canopy vertical variability are currently most often used, but not always effective. Main conclusions Light Detection and Ranging metrics play an important role in understanding animal space use. Our review and the developed categorization scheme may facilitate future studies in the selection, prioritization and ecological interpretation of LiDAR metrics. The increasing availability of airborne and spaceborne LiDAR data and the development of voxel‐based and object‐based approaches will further allow novel ecological applications, also for open habitats and other vertebrate and invertebrate taxa.

This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%.

Digital aerial photogrammetric (DAP) techniques applied to unmanned aerial system (UAS) acquired imagery have the potential to offer timely and affordable data for monitoring and updating forest inventories. Development of methods for individual tree crown detection (ITCD) and delineation enables the development of individual tree-based, rather than stand based inventories, which are important for harvesting operations, biomass and carbon stock estimations, forest damage assessment, and forest monitoring in mixed species stands. To achieve these inventory goals, consistent and robust DAP estimates are required over time. Currently, the influence of seasonal changes in deciduous tree structure on the consistency of DAP point clouds, from which tree-based inventories can be derived, is unknown. In this study, we investigate the influence of the timing of DAP acquisition on ITCD accuracies and estimation of tree attributes for a deciduous-dominated forest stand in New Brunswick, Canada. UAS imagery was acquired five times between June and September 2017 over the same stand and consistently processed into DAP point clouds. Airborne laser scanning (ALS) data, acquired the same year, was used to reconstruct a digital terrain model (DTM) and served as a reference for UAS-DAP-based ITCD. Marker-controlled watershed segmentation (MCWS) was used to delineate individual tree crowns. Accuracy index percentages between 55% (July 25) and 77.1% (September 22) were achieved. Omission errors were found to be relatively high for the first three DAP acquisitions (June 7, July 5, and July 25) and decreased gradually thereafter. The commission error was relatively high on July 25. Point cloud metrics were found to be predominantly consistent over the 4-month period, however, estimated tree heights gradually decreased over time, suggesting a trade-off between ITCD accuracies and measured tree heights. Our findings provide insight into the potential influence of seasonality on DAP-ITCD approaches to derive individual tree inventories.

Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth. Gao Fen-7 (GF-7) Satellite is China's first sub-meter-level three-dimensional (3D) mapping satellite for civilian use, which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously. Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land, but few have in-depth studied its ability to measure forest terrain elevation and canopy height. The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement. The Airborne Laser Scanning (ALS) data were utilized to generate reference terrain and forest vertical information. The validation test was conducted in Pu'er City, Yunnan Province of China, and encouraging results have obtained. The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification; meanwhile, when it was used to calculate the forest height, R 2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification. The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m × 20 m compared with ALS data (R 2  = 0.88, RMSE = 2.98 m). When the results were verified at the forest sub-compartment scale that taking into account the forest types, further higher accuracy (R 2  = 0.96, RMSE = 1.23 m) was obtained. These results show that GF-7 has considerable application potential in forest resources monitoring.

The objective of this study was to assess the use of unmanned aerial vehicle (UAV) data for modelling tree density and canopy height in young boreal forests stands. The use of UAV data for such tasks can be beneficial thanks to the high resolution and reduction of the time spent in the field. This study included 29 forest stands, within which 580 clustered plots were measured in the field. An area-based approach was adopted to which random forest models were fitted using the plot data and the corresponding UAV data and then applied and validated at plot and stand level. The results were compared to those of models based on airborne laser scanning (ALS) data and those from a traditional field-assessment. The models based on UAV data showed the smallest stand-level RMSE values for mean height (0.56 m) and tree density (1175 trees ha−1 ). The RMSE of the tree density using UAV data was 50% smaller than what was obtained using ALS data (2355 trees ha−1 ). Overall, this study highlighted that the use of UAVs for the inventory of forest stands under regeneration can be beneficial both because of the high accuracy of the derived data analytics and the time saving compared to traditional field assessments.