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3D Point Clouds in Forest Remote Sensing III

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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 (29 February 2024) | Viewed by 17865

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Prof. Dr. Sandra Buján

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Guest Editor

Escuela Superior y Técnica de Ingenieros de Minas, University of León, 24401 Ponferrada, Spain
Interests: remote sensing data processing; spatial analysis; development of data processing algorithms; free software; land cover/use classification
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Dr. Andrea Hevia

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Guest Editor

Departamento de Ciencias Agroforestales, Escuela Técnica Superior de Ingeniería, Universidad de Huelva, 21819 Huelva, Spain
Interests: forest management and silviculture; forest inventory and monitoring; forest structure; biomass and carbon; wood properties; forest modelling; forest risks
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Special Issue Information

Dear Colleagues,

This Special Issue is a sequel of a previous Special Issue entitled “3D Point Clouds in Forest Remote Sensing II”.

Three-dimensional point clouds have become a well-established data source for the characterization and monitoring of forest structures. Particularly, the use of such data from active sensors, such as airborne LiDAR, has confirmed its relevance in forest studies, from its early development in the 1970s and 1980s, to the establishment of robust and cost-efficient systems from the 1990s onwards, due to the improvement of global positioning and inertial units (GNSS/IMU). Even though airborne LiDAR has been the most prevalent technology in forest 3D point cloud acquisition, other alternative or complementary technologies have also been used in forest studies at different scales in recent decades, namely airborne/shuttle/satellite radars, terrain laser scanning, or photogrammetry from either photogrammetric or consumer-grade cameras. Regarding the latter, the rapid evolution of the Remotely Piloted Aircraft Systems (RPASs), along with the streamlining of consumer-grade camera data processing using computer vision software, has popularized the use of ultra-high-resolution 3D point clouds at an unprecedented level of cost-efficiency and spatial–temporal flexibility for local-scale studies.

This Special Issue aims to include studies covering different uses of 3D point clouds acquired using different sensors and platforms in forest sciences. Topics may cover anything from the classical estimation of forest variables at a tree or stand level, to more comprehensive aims and scales. Hence, multisource data integration (e.g., multispectral, hyperspectral, and thermal), multiscale approaches, or studies focused on monitoring forest ecosystem services, among other issues, are welcome. Articles may address, but are not limited, to the following topics:

Tree and stand variable inventory;
Forest land cover mapping and pattern analysis;
Forest planning and management;
Forest ecology;
Forest change;
Biodiversity and wildlife;
Forest fuel and fire studies;
Biotic and abiotic forest damage;
Biomass;
Forest plants’ functional traits;
Carbon cycle/sequestration;
Terrain analysis.

Prof. Dr. Sandra Buján
Dr. Andrea Hevia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

forest inventory
forest structure and function
forest dynamics
structure from motion
airborne laser scanning
terrain laser scanning
3D point cloud analysis
spectral and structural data fusion

Related Special Issues

3D Point Clouds in Forest Remote Sensing II in Remote Sensing (15 articles)
3D Point Clouds in Forest Remote Sensing in Remote Sensing (31 articles)

Published Papers (10 papers)

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Research

26 pages, 8393 KiB

Open AccessArticle

Evaluating a Novel Approach to Detect the Vertical Structure of Insect Damage in Trees Using Multispectral and Three-Dimensional Data from Drone Imagery in the Northern Rocky Mountains, USA

by Abhinav Shrestha, Jeffrey A. Hicke, Arjan J. H. Meddens, Jason W. Karl and Amanda T. Stahl

Remote Sens. 2024, 16(8), 1365; https://doi.org/10.3390/rs16081365 - 12 Apr 2024

Viewed by 450

Abstract

Remote sensing is a well-established tool for detecting forest disturbances. The increased availability of uncrewed aerial systems (drones) and advances in computer algorithms have prompted numerous studies of forest insects using drones. To date, most studies have used height information from three-dimensional (3D) point clouds to segment individual trees and two-dimensional multispectral images to identify tree damage. Here, we describe a novel approach to classifying the multispectral reflectances assigned to the 3D point cloud into damaged and healthy classes, retaining the height information for the assessment of the vertical distribution of damage within a tree. Drone images were acquired in a 27-ha study area in the Northern Rocky Mountains that experienced recent damage from insects and then processed to produce a point cloud. Using the multispectral data assigned to the points on the point cloud (based on depth maps from individual multispectral images), a random forest (RF) classification model was developed, which had an overall accuracy (OA) of 98.6%, and when applied across the study area, it classified 77.0% of the points with probabilities greater than 75.0%. Based on the classified points and segmented trees, we developed and evaluated algorithms to separate healthy from damaged trees. For damaged trees, we identified the damage severity of each tree based on the percentages of red and gray points and identified top-kill based on the length of continuous damage from the treetop. Healthy and damaged trees were separated with a high accuracy (OA: 93.5%). The remaining damaged trees were separated into different damage severities with moderate accuracy (OA: 70.1%), consistent with the accuracies reported in similar studies. A subsequent algorithm identified top-kill on damaged trees with a high accuracy (OA: 91.8%). The damage severity algorithm classified most trees in the study area as healthy (78.3%), and most of the damaged trees in the study area exhibited some amount of top-kill (78.9%). Aggregating tree-level damage metrics to 30 m grid cells revealed several hot spots of damage and severe top-kill across the study area, illustrating the potential of this methodology to integrate with data products from space-based remote sensing platforms such as Landsat. Our results demonstrate the utility of drone-collected data for monitoring the vertical structure of tree damage from forest insects and diseases. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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28 pages, 6086 KiB

Open AccessArticle

Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners

by Moonis Ali, Bharat Lohani, Markus Hollaus and Norbert Pfeifer

Remote Sens. 2024, 16(6), 1021; https://doi.org/10.3390/rs16061021 - 13 Mar 2024

Viewed by 1373

Abstract

Terrestrial LiDAR scanning (TLS) has the potential to revolutionize forestry by enabling the precise estimation of aboveground biomass, vital for forest carbon management. This study addresses the lack of comprehensive benchmarking for leaf-filtering algorithms used in TLS data processing and evaluates four widely recognized geometry-based leaf-filtering algorithms (LeWoS, TLSeparation, CANUPO, and a novel random forest model) across openly accessible TLS datasets from diverse global locations. Multiple evaluation dimensions are considered, including pointwise classification accuracy, volume comparisons using a quantitative structure model applied to wood points, computational efficiency, and visual validation. The random forest model outperformed the other algorithms in pointwise classification accuracy (overall accuracy = 0.95 ± 0.04), volume comparison (R-squared = 0.96, slope value of 0.98 compared to destructive volume), and resilience to reduced point cloud density. In contrast, TLSeparation exhibits the lowest pointwise classification accuracy (overall accuracy = 0.81 ± 0.10), while LeWoS struggles with volume comparisons (mean absolute percentage deviation ranging from 32.14 ± 29.45% to 49.14 ± 25.06%) and point cloud density variations. All algorithms show decreased performance as data density decreases. LeWoS is the fastest in terms of processing time. This study provides valuable insights for researchers to choose appropriate leaf-filtering algorithms based on their research objectives and forest conditions. It also hints at future possibilities for improved algorithm design, potentially combining radiometry and geometry to enhance forest parameter estimation accuracy. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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23 pages, 3382 KiB

Open AccessArticle

A Point Cloud Registration Framework with Color Information Integration

by Tianyu Han, Ruijie Zhang, Jiangming Kan, Ruifang Dong, Xixuan Zhao and Shun Yao

Remote Sens. 2024, 16(5), 743; https://doi.org/10.3390/rs16050743 - 20 Feb 2024

Viewed by 703

Abstract

Point cloud registration serves as a critical tool for constructing 3D environmental maps. Both geometric and color information are instrumental in differentiating diverse point features. Specifically, when points appear similar based solely on geometric features, rendering them challenging to distinguish, the color information embedded in the point cloud carries significantly important features. In this study, the colored point cloud is utilized in the FCGCF algorithm, a refined version of the FCGF algorithm, incorporating color information. Moreover, we introduce the PointDSCC method, which amalgamates color consistency from the PointDSC method for outlier removal, thus enhancing registration performance when synergized with other pipeline stages. Comprehensive experiments across diverse datasets reveal that the integration of color information into the registration pipeline markedly surpasses the majority of existing methodologies and demonstrates robust generalizability. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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20 pages, 3964 KiB

Open AccessArticle

Mapping of Rubber Forest Growth Models Based on Point Cloud Data

by Hang Zhou, Gan Zhang, Junxiong Zhang and Chunlong Zhang

Remote Sens. 2023, 15(21), 5083; https://doi.org/10.3390/rs15215083 - 24 Oct 2023

Cited by 1 | Viewed by 823

Abstract

The point cloud-based 3D model of forest helps to understand the growth and distribution pattern of trees, to improve the fine management of forestry resources. This paper describes the process of constructing a fine rubber forest growth model map based on 3D point clouds. Firstly, a multi-scale feature extraction module within the point cloud column is used to enhance the PointPillars learning capability. The Swin Transformer module is employed in the backbone to enrich the contextual semantics and acquire global features with the self-attention mechanism. All of the rubber trees are accurately identified and segmented to facilitate single-trunk localisation and feature extraction. Then, the structural parameters of the trunks calculated by RANSAC and IRTLS cylindrical fitting methods are compared separately. A growth model map of rubber trees is constructed. The experimental results show that the precision and recall of the target detection reach 0.9613 and 0.8754, respectively, better than the original network. The constructed rubber forest information map contains detailed and accurate trunk locations and key structural parameters, which are useful to optimise forestry resource management and guide the enhancement of mechanisation of rubber tapping. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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18 pages, 17063 KiB

Open AccessArticle

Volume Estimation of Stem Segments Based on a Tetrahedron Model Using Terrestrial Laser Scanning Data

by Lei You, Xiaosa Chang, Yian Sun, Yong Pang, Yan Feng and Xinyu Song

Remote Sens. 2023, 15(20), 5060; https://doi.org/10.3390/rs15205060 - 21 Oct 2023

Viewed by 1051

Abstract

Stem volume is a very important parameter in forestry inventory and carbon storage. The stem volume estimated by most existing methods deviates from its true value because the irregularity of the stem is usually overlooked. In this study, we propose a stem segment volume estimation based on the tetrahedron model using TLS data. First, the initial stem segment surface model, including the lower, upper, and outer triangular surface models, was gradually reconstructed. Next, the outer surface model was subdivided based on the edge subdivision. Then, a closed triangular surface model without self-intersection was obtained. Afterward, a tetrahedron model of the stem segment was generated using TetGen software (Version 1.6.0) for the triangular surface model. Finally, the stem segment volume was calculated by summing the volumes of all the tetrahedrons in the tetrahedron model. An experiment with 76 stem segments from different tree species with different parameters showed that the reconstructed stem segment surface model effectively reflected the geometrical features of the stem segment surface. Compared to the volume based on the simulated sectional measurement, the MAPE of the volume based on the tetrahedron model was 2.12%. The results demonstrated the validity of the presented method for stem surface reconstruction and stem volume estimation, and the volume based on the tetrahedron model was closer to the true value than that based on the sectional measurement. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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27 pages, 25124 KiB

Open AccessArticle

Delineating and Reconstructing 3D Forest Fuel Components and Volumes with Terrestrial Laser Scanning

by Zhouxin Xi, Laura Chasmer and Chris Hopkinson

Remote Sens. 2023, 15(19), 4778; https://doi.org/10.3390/rs15194778 - 30 Sep 2023

Cited by 1 | Viewed by 1120

Abstract

Predictive accuracy in wildland fire behavior is contingent on a thorough understanding of the 3D fuel distribution. However, this task is complicated by the complex nature of fuel forms and the associated constraints in sampling and quantification. In this study, twelve terrestrial laser scanning (TLS) plot scans were sampled within the mountain pine beetle-impacted forests of Jasper National Park, Canada. The TLS point clouds were delineated into eight classes, namely individual-tree stems, branches, foliage, downed woody logs, sapling stems, below-canopy branches, grass layer, and ground-surface points using a transformer-based deep learning classifier. The fine-scale 3D architecture of trees and branches was reconstructed using a quantitative structural model (QSM) based on the multi-class components from the previous step, with volume attributes extracted and analyzed at the branch, tree, and plot levels. The classification accuracy was evaluated by partially validating the results through field measurements of tree height, diameter-at-breast height (DBH), and live crown base height (LCBH). The extraction and reconstruction of 3D wood components enable advanced fuel characterization with high heterogeneity. The existence of ladder trees was found to increase the vertical overlap of volumes between tree branches and below-canopy branches from 8.4% to 10.8%. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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20 pages, 7747 KiB

Open AccessArticle

Remote Sensing Parameter Extraction of Artificial Young Forests under the Interference of Undergrowth

by Zefu Tao, Lubei Yi, Zhengyu Wang, Xueting Zheng, Shimei Xiong, Anming Bao and Wenqiang Xu

Remote Sens. 2023, 15(17), 4290; https://doi.org/10.3390/rs15174290 - 31 Aug 2023

Viewed by 830

Abstract

Due to the lower canopy height at the maximum crown width at the bottom of young Picea crassifolia trees, they are mixed with undergrowth. This makes it challenging to accurately determine crown size using CHM data or point cloud data. UAV imagery, on the other hand, incorporates rich color information and, after processing, can effectively distinguish between spruce trees and ground vegetation. In this study, the experimental site was an artificial young forest of Picea crassifolia in Shangshan Village, Qinghai Province, China. UAV images were used to obtain normalized saturation data for the sample plots. A marker-controlled watershed segmentation algorithm was employed to extract tree parameters, and the results were compared with those obtained via point cloud clustering segmentation and the marker-controlled watershed segmentation algorithm based on Canopy Height Model (CHM) images. The research results showed that the single tree recognition capabilities of the three types of data were similar, with F-measures of 0.96, 0.95, and 0.987 for the CHM image, UAV imagery, and point cloud data, respectively. The mean square errors of crown width information extracted from the UAV imagery using the marker-controlled watershed segmentation algorithm were 0.043, 0.125, and 0.046 for the three sample plots, which were better than the values of 0.103, 0.182, and 0.074 obtained from CHM data, as well as the values of 0.36, 0.461, and 0.4 obtained from the point cloud data. The point cloud data exhibited better fitting results for tree height extraction compared to the CHM images. This result indicates that UAV-acquired optical imagery has applicability in extracting individual tree feature parameters and can compensate for the deficiencies of CHM and point cloud data. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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21 pages, 5378 KiB

Open AccessArticle

Point2Tree(P2T)—Framework for Parameter Tuning of Semantic and Instance Segmentation Used with Mobile Laser Scanning Data in Coniferous Forest

by Maciej Wielgosz, Stefano Puliti, Phil Wilkes and Rasmus Astrup

Remote Sens. 2023, 15(15), 3737; https://doi.org/10.3390/rs15153737 - 27 Jul 2023

Cited by 4 | Viewed by 7370

Abstract

Inthis study, we introduce Point2Tree, a modular and versatile framework that employs a three-tiered methodology, inclusive of semantic segmentation, instance segmentation, and hyperparameter optimization analysis, designed to process laser point clouds in forestry. The semantic segmentation stage is built upon the Pointnet++ architecture and is primarily tasked with categorizing each point in the point cloud into meaningful groups or ’segments’, specifically in this context, differentiating between diverse tree parts, i.e., vegetation, stems, and coarse woody debris. The category for the ground is also provided. Semantic segmentation achieved an F1-score of 0.92, showing a high level of accuracy in classifying forest elements. In the instance segmentation stage, we further refine this process by identifying each tree as a unique entity. This process, which uses a graph-based approach, yielded an F1-score of approximately 0.6, signifying reasonable performance in delineating individual trees. The third stage involves a hyperparameter optimization analysis, conducted through a Bayesian strategy, which led to performance improvement of the overall framework by around four percentage points. Point2Tree was tested on two datasets, one from a managed boreal coniferous forest in Våler, Norway, with 16 plots chosen to cover a range of forest conditions. The modular design of the framework allows it to handle diverse pointcloud densities and types of terrestrial laser scanning data. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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30 pages, 17874 KiB

Open AccessArticle

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

by Jie Wang, Chunjing Yao, Hongchao Ma, Junhao Xu and Chen Qian

Remote Sens. 2023, 15(12), 3060; https://doi.org/10.3390/rs15123060 - 11 Jun 2023

Viewed by 1604

Abstract

The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide the precise height and detailed structure of trees, and can estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. First, the tree crowns are segmented using the profile-rotating algorithm, and tree positions are registered based on dominant tree heights. Second, considering the correlation between crown shape and tree species, we use DBN classifier to identify species using features of crowns, which are extracted to 1D array. Third, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass, and stock volume can be extracted from trees, enabling accurate large-scale forest surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R² of tree height, east–west crown width, north–south crown width, diameter at breast height, aboveground biomass, and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based survey is practical and can be widely applied in forest resource monitoring. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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18 pages, 7330 KiB

Open AccessArticle

Study on Single-Tree Extraction Method for Complex RGB Point Cloud Scenes

by Kai Xia, Cheng Li, Yinhui Yang, Susu Deng and Hailin Feng

Remote Sens. 2023, 15(10), 2644; https://doi.org/10.3390/rs15102644 - 19 May 2023

Cited by 1 | Viewed by 1438

Abstract

With the development of sensor technology and point cloud generation techniques, there has been an increasing amount of high-quality forest RGB point cloud data. However, popular clustering-based point cloud segmentation methods are usually only suitable for pure forest scenes and not ideal for scenes with multiple ground features or complex terrain. Therefore, this study proposes a single-tree point cloud extraction method that combines deep semantic segmentation and clustering. This method first uses a deep semantic segmentation network, Improved-RandLA-Net, which is developed based on RandLA-Net, to extract point clouds of specified tree species by adding an attention chain to improve the model’s ability to extract channel and spatial features. Subsequently, clustering is employed to extract single-tree point clouds from the segmented point clouds. The feasibility of the proposed method was verified in the Gingko site, the Lin’an Pecan site, and a Fraxinus excelsior site in a conference center. Finally, semantic segmentation was performed on three sample areas using pre- and postimproved RandLA-Net. The experiments demonstrate that Improved-RandLA-Net had significant improvements in Accuracy, Precision, Recall, and F1 score. At the same time, based on the semantic segmentation results of Improved-RandLA-Net, single-tree point clouds of three sample areas were extracted, and the final single-tree recognition rates for each sample area were 89.80%, 75.00%, and 95.39%, respectively. The results demonstrate that our proposed method can effectively extract single-tree point clouds in complex scenes. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing III)

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