Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations

Liu, Kun; Shen, Xin; Cao, Lin; Wang, Guibin; Cao, Fuliang

doi:10.1016/j.isprsjprs.2018.11.001

Cited by 148 publications

(110 citation statements)

References 82 publications

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“…Researchers [40] verified a better performance in estimates for six forest parameters using UAV-lidar than those obtained in [54], which used airborne lidar. The authors attributed this better performance to the flight characteristics, such as lower flight height (60 m) and slower flight speed (4.8 m·s −1 ) than the ALS system, which reflected at an average UAV-lidar point density of approximately 160 pts per meter squared, which exceeds the typical point density of the lidar ALS data (usually <25 pts per meter squared) [55].…”

Section: Discussionmentioning

confidence: 85%

“…TLS has advantages for accurate forest inventory. Previous research [40] explored the methods of using TLS to obtain point cloud data and estimate the height and diameter of individual trees at breast height (dbh), and found a mean RMSE for dbh of 1.28 cm and a mean RMSE for tree height of 0.95 m. In [39], researchers reported RMSE ranging from 1.06 to 3.21 cm for Scots pine (Pinus sylvestris) and 1.19 to 3.58 cm for Norway spruce (Picea abies) using a small set of trees, performing scans specifically designed for single trees, making field measurements from various heights and testing many different modeling methods. Researchers [37] obtained a mean error of less than 1 cm in the lower parts of the loblolly pines (Pinus taeda), which were planted in rows with regular spacing.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

et al. 2020

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Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

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Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

et al. 2020

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“…Among the multitude of surveying methodologies by which it is possible to adopt in coastal environments [4], the most important are certainly (i) the direct survey with GNSS receivers, (ii) Terrestrial Laser Scanning (TLS) [5], (iii) aerial survey by planes and drones with Light Detection And Ranging (LiDAR) [6], and (iv) the use of aerial photogrammetry [7]. However, direct methods are very time-consuming, and this makes them hard to apply to the mapping of large coastal section extents.…”

Section: Introductionmentioning

confidence: 99%

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Taddia

Stecchi²,

Pellegrinelli

2020

Drones

125

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Topographic and geomorphological surveys of coastal areas usually require the aerial mapping of long and narrow sections of littoral. The georeferencing of photogrammetric models is generally based on the signalization and survey of Ground Control Points (GCPs), which are very time-consuming tasks. Direct georeferencing with high camera location accuracy due to on-board multi-frequency GNSS receivers can limit the need for GCPs. Recently, DJI has made available the Phantom 4 Real-Time Kinematic (RTK) (DJI-P4RTK), which combines the versatility and the ease of use of previous DJI Phantom models with the advantages of a multi-frequency on-board GNSS receiver. In this paper, we investigated the accuracy of both photogrammetric models and Digital Terrain Models (DTMs) generated in Agisoft Metashape from two different image datasets (nadiral and oblique) acquired by a DJI-P4RTK. Camera locations were computed with the Post-Processing Kinematic (PPK) of the Receiver Independent Exchange Format (RINEX) file recorded by the aircraft during flight missions. A Continuously Operating Reference Station (CORS) located at a 15 km distance from the site was used for this task. The results highlighted that the oblique dataset produced very similar results, with GCPs (3D RMSE = 0.025 m) and without (3D RMSE = 0.028 m), while the nadiral dataset was affected more by the position and number of the GCPs (3D RMSE from 0.034 to 0.075 m). The introduction of a few oblique images into the nadiral dataset without any GCP improved the vertical accuracy of the model (Up RMSE from 0.052 to 0.025 m) and can represent a solution to speed up the image acquisition of nadiral datasets for PPK with the DJI-P4RTK and no GCPs. Moreover, the results of this research are compared to those obtained in RTK mode for the same datasets. The novelty of this research is the combination of a multitude of aspects regarding the DJI Phantom 4 RTK aircraft and the subsequent data processing strategies for assessing the quality of photogrammetric models, DTMs, and cross-section profiles.

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“…Besides, a Novatel global navigation satellite system ground base station was used to ensure GPS accuracies. The real-time UAV LiDAR data were transferred to the ground data terminals through a long-range Wi-Fi system connected to the UAV [35]. The parameters of the UAV platform are listed in Table 1.…”

Section: Test Uav Lidar Datamentioning

confidence: 99%

An Automated Hierarchical Approach for Three-Dimensional Segmentation of Single Trees Using UAV LiDAR Data

et al. 2018

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Forests play a key role in terrestrial ecosystems, and the variables extracted from single trees can be used in various fields and applications for evaluating forest production and assessing forest ecosystem services. In this study, we developed an automated hierarchical single-tree segmentation approach based on the high density three-dimensional (3D) Unmanned Aerial Vehicle (UAV) point clouds. First, this approach obtains normalized non-ground UAV points in data preprocessing; then, a voxel-based mean shift algorithm is used to roughly classify the non-ground UAV points into well-detected and under-segmentation clusters. Moreover, potential tree apices for each under-segmentation cluster are obtained with regard to profile shape curves and finally input to the normalized cut segmentation (NCut) algorithm to segment iteratively the under-segmentation cluster into single trees. We evaluated the proposed method using datasets acquired by a Velodyne 16E LiDAR system mounted on a multi-rotor UAV. The results showed that the proposed method achieves the average correctness, completeness, and overall accuracy of 0.90, 0.88, and 0.89, respectively, in delineating single trees. Comparative analysis demonstrated that our method provided a promising solution to reliable and robust segmentation of single trees from UAV LiDAR data with high point cloud density.

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Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations

Cited by 148 publications

References 82 publications

Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

An Automated Hierarchical Approach for Three-Dimensional Segmentation of Single Trees Using UAV LiDAR Data

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