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Application of LiDAR Point Cloud in Forest Structure

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 29332

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Special Issue Editors

Dr. Sheng Nie

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Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: LiDAR remote sensing; point cloud processing; forest mapping and monitoring; LiDAR applications in forestry and ecology
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Dr. Chenglu Wen

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Guest Editor

Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen 361005, China
Interests: 3D vision; point cloud processing; mobile mapping; artificial intelligence
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Dr. Di Wang

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Guest Editor

Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, South Taibai Road 2, Xi'an 710071, China
Interests: LiDAR remote sensing; point cloud processing; 3D reconstruction; tree modeling; vegetation structure analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Xuebo Yang

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: LiDAR remote sensing; point cloud imaging mechanism; radiative transfer model; forest structural parameter inversions; LiDAR sensor design.

Dr. Shaobo Xia

E-Mail Website
Guest Editor

Department of Geomatics, Changsha University of Science and Technology, Changsha 410004, China
Interests: point cloud processing; multi-modal data processing; 3D vision; remote sensing and its applications in mapping
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Special Issue Information

Dear Colleagues,

As a vital natural resource, forests are of extreme importance for all living beings on our planet. The forest structure plays a significant role in ecosystem function and diversity. Therefore, an accurate measurement of forest structure can help understand the function and diversity of a forest ecosystem. The advent of LiDAR enables the acquisition of 3D point clouds in forests and a detailed 3D analysis of forest structures. LiDAR point clouds have become a well-stablished data source for characterizing and monitoring forest structure. This Special Issue aims to present the state-of-the-art of point cloud processing in forests and to highlight new methods for forest structure retrieval from LiDAR point clouds. Both review papers and research contributions will be accepted. The scope of topics to be discussed includes but is not limited to the following:

LiDAR point cloud processing in forests;
New methods for the retrieval of forest structure parameters at various scales from LiDAR point cloud;
New methods and concepts for the quantitative assessment of forest biomass based on LiDAR point cloud;
Artificial intelligence-based methods for forest information retrieval from LiDAR point cloud;
Comparison and benchmarking studies using various LiDAR sensors and/or LiDAR processing methods for forest structure retrieval;
Feasibility studies on the forestry application of new LiDAR sensors;
New approaches for forest change monitoring with multi-temporal LiDAR;
Multi-platform LiDAR data fusion for tree modeling and 3D reconstruction;
Multi-sensor (LiDAR, radar, and optical sensors) data fusion to define novel approaches, concepts, and applications for forest structure mapping and monitoring.

Dr. Sheng Nie
Dr. Chenglu Wen
Dr. Di Wang
Dr. Xuebo Yang
Dr. Shaobo Xia
Guest Editors

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Keywords

LiDAR remote sensing
point clouds
forest structure
biomass
artificial intelligence
data fusion
forestry

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Published Papers (9 papers)

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25 pages, 1885 KiB

Open AccessArticle

LESS LiDAR: A Full-Waveform and Discrete-Return Multispectral LiDAR Simulator Based on Ray Tracing Algorithm

by Yaotao Luo, Donghui Xie, Jianbo Qi, Kun Zhou, Guangjian Yan and Xihan Mu

Remote Sens. 2023, 15(18), 4529; https://doi.org/10.3390/rs15184529 - 14 Sep 2023

Cited by 4 | Viewed by 1937

Abstract

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

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. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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21 pages, 2073 KiB

Open AccessArticle

Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection

by Jiahao Fang, Yongliang Shi, Jianhua Cao, Yao Sun and Weimin Zhang

Remote Sens. 2023, 15(15), 3717; https://doi.org/10.3390/rs15153717 - 25 Jul 2023

Cited by 2 | Viewed by 1668

Abstract

To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose tracking, which introduces normal distribution transform (NDT) measurement factors, inertial measurement unit (IMU) pre-integration factors, and prior factors generated by sliding window marginalization. To actively pursue goals in navigation, a distance-adaptive Euclidean clustering method is utilized in conjunction with cylinder fitting and composite criteria screening to identify tree trunks. Additionally, a hybrid map navigation approach involving 3D point cloud map localization and 2D grid map planning is proposed to apply these methods to the robot. Experiments show that our pose-tracking approach obtains generally better performance in accuracy and robustness compared to existing methods. The precision of our trunk detection method is 93% and the recall is 87%. A practical validation is completed in robot rubber-tapping tasks of a real rubber plantation. The proposed method can guide the rubber-tapping robot in complex forest environments and improve efficiency. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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20 pages, 13886 KiB

Open AccessArticle

Accuracy Assessment and Impact Factor Analysis of GEDI Leaf Area Index Product in Temperate Forest

by Cangjiao Wang, Duo Jia, Shaogang Lei, Izaya Numata and Luo Tian

Remote Sens. 2023, 15(6), 1535; https://doi.org/10.3390/rs15061535 - 10 Mar 2023

Cited by 11 | Viewed by 3373

Abstract

The leaf area index (LAI) is a vital parameter for quantifying the material and energy exchange between terrestrial ecosystems and the atmosphere. The Global Ecosystem Dynamics Investigation (GEDI), with its mission to produce a near-global map of forest structure, provides a product of the effective leaf area index (referred to as GEDI LAIe). However, it is unclear about the performance of GEDI LAIe across different temperate forest types and the degree of factors influencing GEDI LAIe performance. This study assessed the accuracy of GEDI LAIe in temperate forests and quantifies the effects of various factors, such as the difference of gap fraction (DGF) between GEDI and discrete point cloud Lidar of the National Ecological Observatory Network (NEON), sensor system parameters, and characteristics of the canopy, topography, and soil. The reference data for the LAIe assessment were derived from the NEON discrete point cloud Lidar, referred to as NEON Lidar LAIe, covering 12 forest types across 22 sites in the Continental United States (the CONUS). Results showed that GEDI underestimated LAIe (Bias: −0.56 m²/m²), with values of the mean absolute error (MAE), root mean square error (RMSE), percent bias (%Bias), and percent RMSE (%RMSE) of 0.70 m²/m², 0.89 m²/m², −0.20, and 0.31, respectively. Among forest types, the underestimation of GEDI LAIe in broadleaf forests and mixed forests was generally greater than that in coniferous forests, which showed a moderate error (%RMSE: 0.33~0.52). Factor analysis indicated that multiple factors explained 52% variance of the GEDI LAIe error, among which the DGF contributed the most with a relative importance of 49.82%, followed by characteristics of canopy and soil with a relative importance of 23.20% and 16.18%, respectively. The DGF was a key pivot for GEDI LAIe error; that is, other factors indirectly influence the GEDI LAIe error by affecting the DGF first. Our findings demonstrated that the GEDI LAIe product has good performance, and the factor analysis is expected to shed some light on further improvements in GEDI LAIe estimation. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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23 pages, 7285 KiB

Open AccessArticle

A Comparison of Modeling Methods for Predicting Forest Attributes Using Lidar Metrics

by Angel Adhikari, Cristian R. Montes and Alicia Peduzzi

Remote Sens. 2023, 15(5), 1284; https://doi.org/10.3390/rs15051284 - 25 Feb 2023

Cited by 13 | Viewed by 3786

Abstract

Recent advancements in laser scanning technology have demonstrated great potential for the precise characterization of forests. However, a major challenge in utilizing metrics derived from lidar data for the forest attribute prediction is the high degree of correlation between these metrics, leading to multicollinearity issues when developing multivariate linear regression models. To address this challenge, this study compared the performance of four different modeling methods for predicting various forest attributes using aerial lidar data: (1) Least Squares Regression (LSR), (2) Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO), (3) Random Forest (RF), and (4) Generalized Additive Modeling Selection (GAMSEL). The study used three primary plot-level forest attributes (volume, basal area, and dominant height) as response variables and thirty-nine plot-level lidar metrics as explanatory variables. A k-fold cross-validation approach was used, with consistent folds to assess the performance of each method. Our results revealed that no single method demonstrated a significant advantage over the others. Nonetheless, the highest R² values of 0.88, 0.83, and 0.87 for volume, basal area, and dominant height, respectively, were achieved using the ALASSO method. This method was also found to be less biased, followed by GAMSEL and LSR. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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21 pages, 4613 KiB

Open AccessArticle

Crown-Level Structure and Fuel Load Characterization from Airborne and Terrestrial Laser Scanning in a Longleaf Pine (Pinus palustris Mill.) Forest Ecosystem

by Kleydson Diego Rocha, Carlos Alberto Silva, Diogo N. Cosenza, Midhun Mohan, Carine Klauberg, Monique Bohora Schlickmann, Jinyi Xia, Rodrigo V. Leite, Danilo Roberti Alves de Almeida, Jeff W. Atkins, Adrian Cardil, Eric Rowell, Russ Parsons, Nuria Sánchez-López, Susan J. Prichard and Andrew T. Hudak

Remote Sens. 2023, 15(4), 1002; https://doi.org/10.3390/rs15041002 - 11 Feb 2023

Cited by 14 | Viewed by 5789

Abstract

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m² each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R² between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R² using ALS data was achieved predicting CBH (R² = 0.98), while for TLS and ALS + TLS, the highest R² was observed predicting HT, CW, and CBD (R² = 0.94) and HT (R² = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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29 pages, 3574 KiB

Open AccessArticle

Effects of Viewing Geometry on Multispectral Lidar-Based Needle-Leaved Tree Species Identification

by Brindusa Cristina Budei, Benoît St-Onge, Richard A. Fournier and Daniel Kneeshaw

Remote Sens. 2022, 14(24), 6217; https://doi.org/10.3390/rs14246217 - 8 Dec 2022

Viewed by 2533

Abstract

Identifying tree species with remote sensing techniques, such as lidar, can improve forest management decision-making, but differences in scan angle may influence classification accuracy. The multispectral Titan lidar (Teledyne Optech Inc., Vaughan, ON, Canada) has three integrated lasers with different wavelengths (1550, 1064 and 532 nm), and with different scan angle planes (respectively tilted at 3.5°, 0° and 7° relative to a vertical plane). The use of multispectral lidar improved tree species separation, compared to mono-spectral lidar, by providing classification features that were computed from intensities in each channel, or from pairs of channels as ratios and normalized indices (NDVIs). The objective of the present study was to evaluate whether scan angle (up to 20°) influences 3D and intensity feature values and if this influence affected species classification accuracy. In Ontario (Canada), six needle-leaf species were sampled to train classifiers with different feature selection. We found the correlation between feature values and scan angle to be poor (mainly below |±0.2|), which led to changes in tree species classification accuracy of 1% (all features) and 8% (3D features only). Intensity normalization for range improved accuracies by 8% for classifications using only single-channel intensities, and 2–4% when features that were unaffected by normalization were added, such as 3D features or NDVIs. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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17 pages, 3457 KiB

Open AccessArticle

Terrain-Net: A Highly-Efficient, Parameter-Free, and Easy-to-Use Deep Neural Network for Ground Filtering of UAV LiDAR Data in Forested Environments

by Bowen Li, Hao Lu, Han Wang, Jianbo Qi, Gang Yang, Yong Pang, Haolin Dong and Yining Lian

Remote Sens. 2022, 14(22), 5798; https://doi.org/10.3390/rs14225798 - 16 Nov 2022

Cited by 8 | Viewed by 2899

Abstract

In recent years, a rise in interest in using Unmanned Aerial Vehicles (UAV) with LiDAR (Light Detection and Ranging) to capture the 3D structure of forests for forestry and ecosystem monitoring applications has been witnessed. Since the terrain is an essential basis for the vertical structure modeling of a forest, the point cloud filtering delivering a highly accurate Digital Terrain Model (DTM) contributes significantly to forest studies. Conventional point cloud filtering algorithms require users to select suitable parameters according to the knowledge of the algorithm and the characteristics of scanned scenes, which are normally empirical and time-consuming. Deep learning offers a novel method in classifying and segmenting LiDAR point cloud, while there are only few studies reported on utilizing deep learning to filter non-ground LiDAR points of forested environments. In this study, we proposed an end-to-end and highly-efficient network named Terrain-net which combines the 3D point convolution operator and self-attention mechanism to capture local and global features for UAV point cloud ground filtering. The network was trained with over 15 million labeled points of 70 forest sites and was evaluated at 17 sites covering various forested environments. Terrain-net was compared with four classical filtering algorithms and one of the most well-recognized point convolution-based deep learning methods (KP-FCNN). Results indicated that Terrain-net achieved the best performance in respect of the Kappa coefficient (0.93), MIoU (0.933) and overall accuracy (98.0%). Terrain-net also performed well in transferring to an additional third-party open dataset for ground filtering in large-scale scenes and other vegetated environments. No parameters need to be tuned in transferring predictions. Terrain-net will hopefully be widely applied as a new highly-efficient, parameter-free, and easy-to-use tool for LiDAR data ground filtering in varying forest environments. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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21 pages, 6171 KiB

Open AccessArticle

Assessment of Permeability Windbreak Forests with Different Porosities Based on Laser Scanning and Computational Fluid Dynamics

by Likun An, Jia Wang, Nina Xiong, Yutang Wang, Jiashuo You and Hao Li

Remote Sens. 2022, 14(14), 3331; https://doi.org/10.3390/rs14143331 - 11 Jul 2022

Cited by 8 | Viewed by 2270

Abstract

Accurate modeling of windbreaks is essential for the precise assessment of wind protection performance. However, in most windbreak studies, the models used the approximate shape of the simulated trees, resulting in significant differences between the simulated results and the actual situation. In this study, terrestrial laser scanning (TLS) was used to extract tree parameters, which were used in a quantitative structural model (AdQSM) to recreate the tree structure and restore the wind field environment using the computational fluid dynamics software PHOENICS. In addition, we compared the bias, precision, and accuracy of porosity of Ginkgo biloba (with elliptical crown) and Populus alba (with conical crown), which have been commonly used in previous windbreak studies. The results showed that AdQSM has a high reduction rate and ability to reproduce the field conditions of the study area. After wind field simulation, the wind speed root mean square errors of the point cloud model at three heights (3, 6, and 9 m) were 0.272, 0.377, and 0.437 m/s, respectively, and the wind speed correlation coefficients r were 0.967, 0.965, and 0.937, respectively, which were significantly more accurate than those of the remaining two structures. Finally, the porosity of the windbreak forest obtained using the modeled sample plot showed a higher correlation with the wind permeability coefficient than that obtained using the existing approach. Windbreak models with three different porosities under the same conditions had different effects on the wind environment, particularly the location of the maximum wind speed reduction, variation of wind speed with porosity, and recovery rate of leeward wind speed. TLS can accurately extract windbreak factors and calculate the porosity, thus greatly improving the reliability of windbreak effect research in windbreak forests. This study provides a promising direction for future research related to the simulation of windbreak effects in windbreak forests. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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16 pages, 4103 KiB

Open AccessTechnical Note

Forest Emissions Reduction Assessment Using Optical Satellite Imagery and Space LiDAR Fusion for Carbon Stock Estimation

by Yue Jiao, Dacheng Wang, Xiaojing Yao, Shudong Wang, Tianhe Chi and Yu Meng

Remote Sens. 2023, 15(5), 1410; https://doi.org/10.3390/rs15051410 - 2 Mar 2023

Cited by 10 | Viewed by 3160

Abstract

Forests offer significant climate mitigation benefits, but existing emissions reduction assessment methodologies in forest-based mitigation activities are not scalable, which limits the development of carbon offset markets. In this study, we propose a measurement method using optical satellite imagery and space LiDAR data fusion to assess forest emissions reduction. Compared with the ALS-based carbon stock density estimation method, our approach presented a strong scalability for mapping 10 m-resolution carbon stock at a large scale. It was observed that dense canopy top height estimated by combining GEDI and Sentinel-2 could accurately predict forest carbon stock measurements estimated by the ALS-based method (

R^{2}

= 0.72). By conducting an on-site experiment of an ongoing forest carbon project in China, we found the consistency between the emissions reduction assessed by the data fusion measurement method (589,169

{tCO}_{2} e

) and the official ex post-monitored emissions reduction in the monitoring report (598,442

{tCO}_{2} e

). Our results demonstrated that forest carton stock estimation using optical satellite imagery and space LiDAR data fusion is efficient and economical for forest emissions reduction assessment. The acquisition of the data was more efficient over large areas with high frequencies using space-based technology. We further discussed the challenge of building a near-real-time monitoring system for forest-based mitigation activities by utilizing optical satellite imagery and space LiDAR data and pointed out that a quality control framework should be established to help us understand the sources of uncertainty in LiDAR-based models and improve carbon stock estimation from individual trees to forest carbon projects to meet the requirements of carbon standards better. Full article

(This article belongs to the Special Issue Application of LiDAR Point Cloud in Forest Structure)

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