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A part of the resulting point cloud after the processing. Each scan line is represented with a different colour.
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Laser scanning of forested areas helps in analyzing and understanding various aspects of forest conditions, including distribution of plants and trees, height distribution of trees, tree density, size and volume of wood, as well as ground surface properties. However, laser scanning of forest areas is also very challenging for many reasons. The best...
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Citations
... For the deciduous forest, two existing data sets were used: i) A TLS point cloud acquired with a Leica blk360 in 2018 in leaf-on condition was used for the stem extraction. ii) The data set for the plant area density combines both TLS scans from a Riegl VZ-1000 and a UAV LiDAR system consisting of a RIEGL VUX-1UAV laser scanner mounted on the industrial Scout B1-100 UAV helicopter measured in 2017 (leaf-on) (for the acquisition and processing of the UAV data see Morsdorf et al. (2017)). The resulting LiDAR point clouds are used to generate the 3D virtual scenes. ...
... Light detection and ranging (LiDAR) sensor has the ability to penetrate the gaps within tree crowns and capture the middle part of stands and even the forest floor [12,13]. Nonetheless, the use of LiDAR technology is costly in current. ...
Aerial images resulting from unmanned aerial vehicle (UAV) are widely used to estimate tree height. The filtering method is required to distinguish between ground and off-ground point clouds to generate a canopy height model. However, the filtering method is not always perfect since UAV data cannot penetrate canopies into the forest floor. The release of iPhone/iPad devices with built-in LiDAR sensors enables the more affordable use of LiDAR for forestry study, including the measurement of local topography below forest stands. This study investigates to what extent iPhone/iPad LiDAR can improve the accuracy of canopy height model from the UAV. The integration of UAV and iPhone/iPad LiDAR data managed to increase the accuracy of tree height model with a mean absolute error (MAE) of 2.188 m, compared to UAV data (MAE = 2.446 m). This preliminary study showed the potential of combining UAV and iPhone/iPad LiDAR data for estimating tree height.
... Unmanned aerial vehicle (UAV) LiDAR, as an active remote sensing technology, enables direct acquisition of precise 3D structural information of forest canopies and terrain at high spatial resolution [33,34]. Compared to traditional remote sensing methods, UAV LiDAR offers higher detection accuracy and is more convenient for data collection in challenging field environments, making it widely utilized for obtaining structural and functional parameters of forest ecosystems [35][36][37][38]. The objective of this study is to accurately estimate AGB of subtropical forests in China by utilizing high-resolution forest canopy and terrain 3D information obtained from UAV LiDAR. ...
Accurately estimating aboveground biomass (AGB) is crucial for assessing carbon storage in forest ecosystems. However, traditional field survey methods are time-consuming, and vegetation indices based on optical remote sensing are prone to saturation effects, potentially underestimating AGB in subtropical forests. To overcome these limitations, we propose an improved approach that combines three-dimensional (3D) forest structure data collected using unmanned aerial vehicle light detection and ranging (UAV LiDAR) technology with ground measurements to apply a binary allometric growth equation for estimating and mapping the spatial distribution of AGB in subtropical forests of China. Additionally, we analyze the influence of terrain factors such as elevation and slope on the distribution of forest biomass. Our results demonstrate a high accuracy in estimating tree height and diameter at breast height (DBH) using LiDAR data, with an R2 of 0.89 for tree height and 0.92 for DBH. In the study area, AGB ranges from 0.22 to 755.19 t/ha, with an average of 121.28 t/ha. High AGB values are mainly distributed in the western and central-southern parts of the study area, while low AGB values are concentrated in the northern and northeastern regions. Furthermore, we observe that AGB in the study area exhibits an increasing trend with altitude, reaching its peak at approximately 1650 m, followed by a gradual decline with further increase in altitude. Forest AGB gradually increases with slope, reaching its peak near 30°. However, AGB decreases within the 30–80° range as the slope increases. This study confirms the effectiveness of using UAV LiDAR for estimating and mapping the spatial distribution of AGB in complex terrains. This method can be widely applied in productivity, carbon sequestration, and biodiversity studies of subtropical forests.
... Over the last years, notable progress has been made in both sensor systems and workflows to derive forest attributes from laser scanning data collected by various platforms Maltamo et al., 2014;Morsdorf et al., 2017;Roussel et al., 2020;Wallace et al., 2014). At the same time, there is still space for improvements, for example, when it comes to (1) understanding and comparing the potential of tree delineation algorithms or (2) deriving certain attributes such as tree species from laser scanning point clouds. ...
Laser scanning from different acquisition platforms enables the collection of 3D point clouds from different perspectives and with varying resolutions. These point clouds allow us to retrieve detailed information on the individual tree and forest structure. We conducted airborne laser scanning (ALS), uncrewed aerial vehicle (UAV)-borne laser scanning (ULS) and terrestrial laser scanning (TLS) in two German mixed forests with species typical of central Europe. We provide the spatially overlapping, georeferenced point clouds for 12 forest plots. As a result of individual tree extraction, we furthermore present a comprehensive database of tree point clouds and corresponding tree metrics. Tree metrics were derived from the point clouds and, for half of the plots, also measured in the field.
Our dataset may be used for the creation of 3D tree models for radiative transfer modeling or lidar simulation studies or to fit allometric equations between point cloud metrics and forest inventory variables. It can further serve as a benchmark dataset for different algorithms and machine learning tasks, in particular automated individual tree segmentation, tree species classification or forest inventory metric prediction.
The dataset and supplementary metadata are available for download, hosted by the PANGAEA data publisher at https://doi.org/10.1594/PANGAEA.942856 (Weiser et al., 2022a).
... Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data is critical because it provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions. Although several previous studies have compared different LiDAR systems [20,24,[26][27][28][29][30][31], this study presents a more comprehensive investigation of data from linear LiDAR (leaf-off), Geiger-mode LiDAR (leaf-on), UAV multi-beam LiDAR (leaf-off and leaf-on), and Backpack multi-beam LiDAR (leaf-off and leaf-on). Qualitative and quantitative evaluations were conducted to determine the point cloud quality and level of information for forest inventory at various scales. ...
LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively.
... Existing solutions for improving the completeness of data can be divided into two groups: the first is to densify the data collection such as to add multiple viewing angles of scanner in order to enhance canopy and stem completeness, e.g., (Roşca et al. 2018;Wu et al. 2020), or to densify trajectories in order to record more trees from more viewing perspectives, e.g., (Morsdorf et al. 2017;Del Perugia et al. 2019;Kuželka et al. 2020); The second is the fusion of datasets from different platforms, especially between terrestrial and aerial platforms, e.g., (Paris et al. 2017;Giannetti et al. 2018;Dai et al. 2019;Pyörälä et al. 2019). ...
Background
Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost.
Results
In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates.
Conclusions
Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.
... In leaf-on conditions, substantial LiDAR signal occlusion from the upper canopy on dense canopy foliage of hardwood trees is an unavoidable constraint of aerial systems [124]. Although the ULS-R system collected up to 7 returns per pulse with an average point density of 353 points/m 2 , most tree stems were missed and trees from the intermediate and lower canopy layers were hardly identifiable ( Figures 3B and 11B). ...
UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R 2 = 0.29 to R 2 = 0.61), and prediction of tree DBH (from R 2 = 0.36 to R 2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R 2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.
... The UAV-LiDAR has been widely used in forestry applications due to its relatively low time-cost, large-scale coverage and lower occlusion compared to the ground-based LiDAR platforms. The main feature of the UAV systems is that they provide the data in global coordinate frame thanks to their onboard GNSS/IMU integration, which continuously measures real-time three-dimensional (3D) positions of the platform during flight with limited accuracy [8][9][10][11][12][13][14][15]. To ensure complete coverage and least occlusion in data acquisition, UAV-LiDAR data are acquired in overlapping strips/scans, which in turn allows for data redundancy that is useful in overcoming the navigation system-related errors. ...
A holistic strategy is established for automated UAV-LiDAR strip adjustment for plantation forests, based on hierarchical density-based clustering analysis of the canopy cover. The method involves three key stages: keypoint extraction, feature similarity and correspondence, and rigid transformation estimation. Initially, the HDBSCAN algorithm is used to cluster the scanned canopy cover, and the keypoints are marked using topological persistence analysis of the individual clusters. Afterward, the feature similarity is calculated by considering the linear and angular relationships between each point and the pointset centroid. The one-to-one feature correspondence is retrieved by solving the assignment problem on the similarity score function using the Kuhn–Munkres algorithm, generating a set of matching pairs. Finally, 3D rigid transformation parameters are determined by permutations over all conceivable pair combinations within the correspondences, whereas the best pair combination is that which yields the maximum count of matched points achieving distance residuals within the specified tolerance. Experimental data covering eighteen subtropical forest plots acquired from the GreenValley and Riegl UAV-LiDAR platforms in two scan modes are used to validate the method. The results are extremely promising for redwood and poplar tree species from both the Velodyne and Riegl UAV-LiDAR datasets. The minimal mean distance residuals of 31 cm and 36 cm are achieved for the coniferous and deciduous plots of the Velodyne data, respectively, whereas their corresponding values are 32 cm and 38 cm for the Riegl plots. Moreover, the method achieves both higher matching percentages and lower mean distance residuals by up to 28% and 14 cm, respectively, compared to the baseline method, except in the case of plots with extremely low tree height. Nevertheless, the mean planimetric distance residual achieved by the proposed method is lower by 13 cm.
... The point density depends on the frequency of the scanner in combination with the forward flying speed, number of flight lines and altitude, which can all be varied during flight planning. Therefore, acquired point densities vary from 50 pts/m 2 (Wallace et al., 2012), to 100-1500 pts/m 2 Jaakkola et al., 2017), to >3000 pts/m 2 Kellner et al., 2019;Morsdorf et al., 2017). Brede et al. (2017) showed that it is essential to include scan angles >30 degrees of nadir to get enough returns on the tree stem (see Figure 2.2). ...
... However, depending on the research question or application, this drawback does not necessarily harm the usefulness or applicability of UAV-SfM or UAV-LS based point data. As found in previous studies Morsdorf et al., 2017;Puliti et al., 2015;Thiel & Schmullius, 2017), a point density of several hundred to thousand points per m² can be achieved with UAV-SfM and UAV-LS techniques. In terms of the delineation of a detailed canopy height model for automatic tree detection the sampling density is more than sufficient, as all required elements (e.g. ...
... The point density depends on the frequency of the scanner in combination with the forward flying speed, number of flight lines and altitude, which can all be varied during flight planning. Therefore, acquired point densities vary from 50 pts/m 2 (Wallace et al., 2012), to 100-1500 pts/m 2 Jaakkola et al., 2017), to >3000 pts/m 2 Kellner et al., 2019;Morsdorf et al., 2017). Brede et al. (2017) showed that it is essential to include scan angles >30 degrees of nadir to get enough returns on the tree stem (see Figure 2.2). ...
... However, depending on the research question or application, this drawback does not necessarily harm the usefulness or applicability of UAV-SfM or UAV-LS based point data. As found in previous studies Morsdorf et al., 2017;Puliti et al., 2015;Thiel & Schmullius, 2017), a point density of several hundred to thousand points per m² can be achieved with UAV-SfM and UAV-LS techniques. In terms of the delineation of a detailed canopy height model for automatic tree detection the sampling density is more than sufficient, as all required elements (e.g. ...
the full text can be found at: https://lpvs.gsfc.nasa.gov/PDF/CEOS_WGCV_LPV_Biomass_Protocol_2021_V1.0.pdf
