Efficient co-registration of UAV and ground LiDAR forest point clouds based on canopy shapes

Shao, Jie; Yao, Wei; Wan, Peng; Luo, Lei; Wang, Puzuo; Yang, Lingbo; Lyu, Jiaxin; Zhang, Wuming

doi:10.1016/j.jag.2022.103067

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…The ULS point clouds were obtained by processing DJI L1 raw data using the DJI Terra software application, and then strip adjustments between consecutive flight paths were performed to obtain seamless consistent ULS point clouds [25,54,67]. As shown in Figure 3b, TLS point clouds obtained using the Livox sensor were first registered with ULS point clouds obtained on the same date, i.e., 27 September 2022, using control points found in both datasets (see reference scale in Figure 2d) using CloudCompare (https://www.cloudcompare.org/, accessed on 30 November 2022)-an open-source software application [68,69]. For alignment, we used at least four solid targets located at different locations in the study area, e.g., light poles and reference scale bar (see Figure 2d).…”

Section: Methodology 31 Data Processingmentioning

confidence: 99%

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Fareed,

Das,

Flores

et al. 2024

Remote Sensing

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Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

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Section: Methodology 31 Data Processingmentioning

confidence: 99%

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Fareed,

Das,

Flores

et al. 2024

Remote Sensing

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“…• Point cloud registration: If the LiDAR data is collected from multiple flight passes or from different sensors, aligning and registering the point clouds into a single coordinate system is necessary [51]. Iterative closest point (ICP) algorithms or feature-based registration methods can be used for point cloud registration.…”

Section: Uas Lidar-based 3d Point Clouds Enhancementmentioning

confidence: 99%

Unmanned Aerial Systems (UAS)-derived 3D models for Digital Twin Construction applications

G. Martinez,

A. Alarcon,

Wandahl

2024

Point Cloud Generation and Its Applications [Working Title]

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The advent of Construction 4.0 has marked a paradigm shift in industrial development, integrating advanced technologies such as cyber-physical systems (CPS), sensors, unmanned aerial systems (UAS), building information modeling (BIM), and robotics. Notably, UASs have emerged as invaluable tools seamlessly embedded in construction processes, facilitating the comprehensive monitoring and digitization of construction projects from the early design phase through construction to the post-construction phases. Equipped with various sensors, such as imaging sensors, light detection and rangers (LiDAR), and thermal sensors, UASs play an important role in data collection processes, especially for 3D point cloud generation. Presently, UASs are recognized as one of the most effective means of generating a Digital Twin (DT) of construction projects, surpassing traditional methods in terms of speed and accuracy. This chapter provides a comprehensive overview of the applications of UAS-derived 3D models in DT, outlining their advantages and barriers and offering recommendations to augment their quality and accuracy.

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“…This study constructs a 2D local descriptor on the structured tree positions. In 2022, Olofsson [45] and Holmgren proposed a method for co-referencing static (TLS) point clouds and dynamic (ALS) point clouds. They presented a stem diameter weighted linking algorithm and a threshold as quality criteria of co-registration.…”

Section: A Related Workmentioning

confidence: 99%

A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations

Ghorbani,

Chen,

Hollaus

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The utilization of terrestrial laser scanning (TLS) and airborne laser scanning (ALS ) point cloud data in forest inventory studies has significantly increased. Fusing of TLS and ALS point cloud data has been recognized as an effective approach in forest studies. In this regard, co-registration of point clouds is considered one of the crucial steps in the integration process. Co-registering point clouds in forest environments faces various challenges, including unstable features, extensive occlusions, different viewpoints, and differences in point cloud densities. To address these intricate challenges, this study introduces an automated and robust method for co-registering TLS and ALS point clouds based on the correspondence of individual tree locations in forest environments. Recently, tree location-based methodologies have been advanced to grapple with these complexities in forest environments. However, many of these methods are highly sensitive to the accuracy of tree positioning. The proposed approach aims to reduce sensitivity to individual tree positioning accuracy. Initially, the positions of individual trees in both TLS and ALS data are extracted. Then, a filtering approach is applied to eliminate positions with low potential for corresponding matches in the TLS and ALS dataset. S ince larger trees in the TLS data have a higher potential for corresponding matches in the ALS data, an iterative process is applied to identify correspondences between trees in both datasets. After estimating transformation parameters, the coregistration process is executed. The proposed method is applied on six datasets with varying forest complexities. The results demonstrate a high success rate up to 100% if the starting position of the TLS plots are located within ~4 hectares (~2000 trees). Additionally, the potential of the proposed method for co-registering TLS data with ALS data across different search areas and varying number of trees is evaluated in detail. The outcomes indicate that successful co-registration of TLS plot with 50 m diameter to ALS data is successful in the best case within a search radius of approximately 113 hectares (~60,000 tree locations) and in the worst case for around 20 hectares (~10,000 tree locations) depending on the forest complexity.T his paragraph of the first footnote will contain the date on which you submitted your paper for review, which is populated by IEEE. It is IEEE style to display support information, including sponsor and financial support acknowledgment, here and not in an acknowledgment section at the end of the article. For example, "This work was supported in part by the U.S. Department of Commerce under Grant BS123456." The name of the corresponding author appears after the financial information, e.g.

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Efficient co-registration of UAV and ground LiDAR forest point clouds based on canopy shapes

Cited by 14 publications

References 24 publications

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Unmanned Aerial Systems (UAS)-derived 3D models for Digital Twin Construction applications

A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations

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