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Forests
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Application of Laser Scanning Technology in Forestry
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Application of Laser Scanning Technology in Forestry

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 9757

Special Issue Editors

Dr. Wenxia Dai

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: laser scanning; forest; remote sensing
Dr. Ningning Zhu

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: remote sensing application; forest carbon stock; LiDAR and image fusion; photogrammetry
Dr. Weishu Gong

E-Mail Website
Guest Editor
Department of Geographical Sciences, University of Maryland, 2181 Samuel J. LeFrak Hall, 7251 Preinkert Drive, College Park, MD 20742, USA
Interests: remote sensing; forest disturbance; carbon budget model
Dr. Di Wang

E-Mail Website
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
Prof. Dr. Zhen Dong

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: 3D computer vision; laser scanning; 3D reconstruction; point cloud compression

Special Issue Information

Dear Colleagues,

Accurate and timely measurements of forest spatial configurations and states are increasingly required to understand ecosystem processes and support a wide range of forestry applications. Recent advances in laser scanning technologies provide accurate and direct 3D in situ information on plant community structures. The spatially explicit digitization of forests has revolutionized how we monitor and quantify ecosystem attributes and functions. Early applications of laser scanning in forestry aimed to derive traditional forest inventory attributes (e.g., height, crown size, diameter at breast height (DBH)), then evolved to biophysical variables (e.g., leaf area index (LAI), tree species composition, crown closure and biomass), and currently also included ecosystem processes and modelling (e.g., photosynthesis, carbon and water fluxes, growth and mortality dynamics, and susceptibility to drought/fire/insects). Nonetheless, extensive operational use of laser scanning in forestry applications remains challenging due to the lack of a reliable and automated processing chain of forest point clouds. This Special Issue aims to collect new applications and innovative data processing methods that use laser scanning technologies for forest science and management. The scope of the Special Issue covers but is not limited to the following aspects: 

  • Fusion of forest point clouds from different platforms;
  • Innovative data processing methodologies in satellite, airborne, UAV, mobile and terrestrial laser scanning;
  • Forest inventory using laser scanning;
  • Deriving forest biophysical parameters (e.g., tree species, wood volume, biomass);
  • 3D modeling of forest structures;
  • Forest change detection;
  • Susceptibility to disturbances.

Dr. Wenxia Dai
Dr. Ningning Zhu
Dr. Weishu Gong
Dr. Di Wang
Prof. Dr. Zhen Dong
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. Forests is an international peer-reviewed open access monthly 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 2600 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
  • laser scanning
  • ecosystem structure and function
  • tree species
  • biomass
  • change detection
  • forest disturbance

Published Papers (6 papers)

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Research

19 pages, 11939 KiB  
Article
Supporting Operational Tree Marking Activities through Airborne LiDAR Data in the Frame of Sustainable Forest Management
by Nikos Georgopoulos, Alexandra Stefanidou and Ioannis Z. Gitas
Forests 2023, 14(12), 2311; https://0-doi-org.brum.beds.ac.uk/10.3390/f14122311 - 24 Nov 2023
Viewed by 764
Abstract
Implementing adaptation and mitigation strategies in forest management constitutes a primary tool for climate change mitigation. To the best of our knowledge, very little research so far has examined light detection and ranging (LiDAR) technology as a decision tool for operational cut-tree marking. [...] Read more.
Implementing adaptation and mitigation strategies in forest management constitutes a primary tool for climate change mitigation. To the best of our knowledge, very little research so far has examined light detection and ranging (LiDAR) technology as a decision tool for operational cut-tree marking. This study focused on investigating the potential of airborne LiDAR data in enhancing operational tree marking in a dense, multi-layered forest over complex terrain for actively supporting long-term sustainable forest management. A detailed tree registry and density maps were produced and evaluated for their accuracy using field data. The derived information was subsequently employed to estimate additional tree parameters (e.g., biomass and tree-sequestrated carbon). An integrated methodology was finally proposed using the developed products for supporting the time- and effort-efficient operational cut-tree marking. The results showcased the low detection ability (R2 = 0.15–0.20) of the trees with low DBH (i.e., regeneration and understory trees), while the dominant trees were accurately detected (R2 = 0.61). The stem biomass was accurately estimated, presenting an R2 of 0.67. Overall, despite some products’ low accuracy, their full and efficient exploitability within the aforementioned proposed methodology has been endeavored with the aim of actively contributing to long-term sustainable forest management. Full article
(This article belongs to the Special Issue Application of Laser Scanning Technology in Forestry)
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26 pages, 9936 KiB  
Article
PointDMM: A Deep-Learning-Based Semantic Segmentation Method for Point Clouds in Complex Forest Environments
by Jiang Li, Jinhao Liu and Qingqing Huang
Forests 2023, 14(12), 2276; https://0-doi-org.brum.beds.ac.uk/10.3390/f14122276 - 21 Nov 2023
Viewed by 1143
Abstract
Background. With the advancement of “digital forestry” and “intelligent forestry”, point cloud data have emerged as a powerful tool for accurately capturing three-dimensional forest scenes. It enables the creation and presentation of digital forest systems, facilitates the monitoring of dynamic changes such as [...] Read more.
Background. With the advancement of “digital forestry” and “intelligent forestry”, point cloud data have emerged as a powerful tool for accurately capturing three-dimensional forest scenes. It enables the creation and presentation of digital forest systems, facilitates the monitoring of dynamic changes such as forest growth and logging processes, and facilitates the evaluation of forest resource fluctuations. However, forestry point cloud data are characterized by its large volume and the need for time-consuming and labor-intensive manual processing. Deep learning, with its exceptional learning capabilities, holds tremendous potential for processing forestry environment point cloud data. This potential is attributed to the availability of accurately annotated forestry point cloud data and the development of deep learning models specifically designed for forestry applications. Nonetheless, in practical scenarios, conventional direct annotation methods prove to be inefficient and time-consuming due to the complex terrain, dense foliage occlusion, and uneven sparsity of forestry point clouds. Furthermore, directly applying deep learning frameworks to forestry point clouds results in subpar accuracy and performance due to the large size, occlusion, sparsity, and unstructured nature of these scenes. Therefore, the proposal of accurately annotated forestry point cloud datasets and the establishment of semantic segmentation methods tailored for forestry environments hold paramount importance. Methods. A point cloud data annotation method based on single-tree positioning to enhance annotation efficiency was proposed and challenges such as occlusions and sparse distribution in forestry environments were addressed. This method facilitated the construction of a forestry point cloud semantic segmentation dataset, consisting of 1259 scenes and 214.4 billion points, encompassing four distinct categories. The pointDMM framework was introduced, a semantic segmentation framework specifically designed for forestry point clouds. The proposed method first integrates tree features using the DMM module and constructs key segmentation graphs utilizing energy segmentation functions. Subsequently, the cutpursuit algorithm is employed to solve the graph and achieve the pre-segmentation of semantics. The locally extracted forestry point cloud features from the pre-segmentation are comprehensively inputted into the network. Feature fusion is performed using the MLP method of multi-layer features, and ultimately, the point cloud is segmented using the lightweight PointNet. Result. Remarkable segmentation results are demonstrated on the DMM dataset, achieving an accuracy rate of 93% on a large-scale forest environment point cloud dataset known as DMM-3. Compared to other algorithms, the proposed method improves the accuracy of standing tree recognition by 21%. This method exhibits significant advantages in extracting feature information from artificially planted forest point clouds obtained from TLS. It establishes a solid foundation for the automation, intelligence, and informatization of forestry, thereby possessing substantial scientific significance. Full article
(This article belongs to the Special Issue Application of Laser Scanning Technology in Forestry)
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21 pages, 19484 KiB  
Article
Estimation of Forest Height Using Google Earth Engine Machine Learning Combined with Single-Baseline TerraSAR-X/TanDEM-X and LiDAR
by Junfan Bao, Ningning Zhu, Ruibo Chen, Bin Cui, Wenmei Li and Bisheng Yang
Forests 2023, 14(10), 1953; https://0-doi-org.brum.beds.ac.uk/10.3390/f14101953 - 26 Sep 2023
Viewed by 1647
Abstract
Forest height plays a crucial role in various fields, such as forest ecology, resource management, natural disaster management, and environmental protection. In order to obtain accurate and efficient measurements of forest height over large areas, in this study, Terra Synthetic Aperture Radar-X and [...] Read more.
Forest height plays a crucial role in various fields, such as forest ecology, resource management, natural disaster management, and environmental protection. In order to obtain accurate and efficient measurements of forest height over large areas, in this study, Terra Synthetic Aperture Radar-X and the TerraSAR-X Add-on for Digital Elevation Measurement (TerraSAR-X/TanDEM-X), Sentinel-2A, and Shuttle Radar Topography Mission (SRTM) data were used, and various feature combinations were established in conjunction with measurements from Light Detection and Ranging (LiDAR). Classification and regression tree (CART), gradient-boosting decision tree (GBDT), random forest (RF), and support vector machine (SVM) algorithms were employed to estimate forest height in the study area. Independent validation on the basis of LiDAR forest height samples showed the following results: (1) Regarding feature combinations, the combination of coherence and decorrelation of volume scattering provided by TerraSAR-X/TanDEM-X data outperformed the combination of backscatter coefficient and local incidence angle, as well as the combination of coherence, decorrelation of volume scattering, backscatter coefficient, and local incidence angle. The best results (R2 = 0.67, RMSE = 2.89 m) were achieved with the combination of coherence and decorrelation of volume scattering using the GBDT and RF algorithms. (2) In terms of machine learning methods, the GBDT algorithm proved suitable for estimating forest height. The most effective approach for forest height mapping involved combining the GBDT algorithm with coherence, decorrelation of volume scattering, and a small amount of LiDAR forest height data, used as training data. Full article
(This article belongs to the Special Issue Application of Laser Scanning Technology in Forestry)
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15 pages, 6192 KiB  
Article
ICSF: An Improved Cloth Simulation Filtering Algorithm for Airborne LiDAR Data Based on Morphological Operations
by Shangshu Cai, Sisi Yu, Zhenyang Hui and Zhanzhong Tang
Forests 2023, 14(8), 1520; https://0-doi-org.brum.beds.ac.uk/10.3390/f14081520 - 26 Jul 2023
Cited by 1 | Viewed by 1751
Abstract
Ground filtering is an essential step in airborne light detection and ranging (LiDAR) data processing in various applications. The cloth simulation filtering (CSF) algorithm has gained popularity because of its ease of use advantage. However, CSF has limitations in topographically and environmentally complex [...] Read more.
Ground filtering is an essential step in airborne light detection and ranging (LiDAR) data processing in various applications. The cloth simulation filtering (CSF) algorithm has gained popularity because of its ease of use advantage. However, CSF has limitations in topographically and environmentally complex areas. Therefore, an improved CSF (ICSF) algorithm was developed in this study. ICSF uses morphological closing operations to initialize the cloth, and estimates the cloth rigidness for providing a more accurate reference terrain in various terrain characteristics. Moreover, terrain-adaptive height difference thresholds are developed for better filtering of airborne LiDAR point clouds. The performance of ICSF was assessed using International Society for Photogrammetry and Remote Sensing urban and rural samples and Open Topography forested samples. Results showed that ICSF can improve the filtering accuracy of CSF in the samples with various terrain and non-ground object characteristics, while maintaining the ease of use advantage of CSF. In urban and rural samples, ICSF obtained an average total error of 4.03% and outperformed another eight reference algorithms in terms of accuracy and robustness. In forested samples, ICSF produced more accuracy than the well-known filtering algorithms (including the maximum slope, progressive morphology, and cloth simulation filtering algorithms), and performed better with respect to the preservation of steep slopes and discontinuities and vegetation removal. Thus, the proposed algorithm can be used as an efficient tool for LiDAR data processing. Full article
(This article belongs to the Special Issue Application of Laser Scanning Technology in Forestry)
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19 pages, 4473 KiB  
Article
LWSNet: A Point-Based Segmentation Network for Leaf-Wood Separation of Individual Trees
by Tengping Jiang, Qinyu Zhang, Shan Liu, Chong Liang, Lei Dai, Zequn Zhang, Jian Sun and Yongjun Wang
Forests 2023, 14(7), 1303; https://0-doi-org.brum.beds.ac.uk/10.3390/f14071303 - 25 Jun 2023
Cited by 3 | Viewed by 1482
Abstract
The accurate leaf-wood separation of individual trees from point clouds is an important yet challenging task. Many existing methods rely on manual features that are time-consuming and labor-intensive to distinguish between leaf and wood points. However, due to the complex interlocking structure of [...] Read more.
The accurate leaf-wood separation of individual trees from point clouds is an important yet challenging task. Many existing methods rely on manual features that are time-consuming and labor-intensive to distinguish between leaf and wood points. However, due to the complex interlocking structure of leaves and wood in the canopy, these methods have not yielded satisfactory results. Therefore, this paper proposes an end-to-end LWSNet to separate leaf and wood points within the canopy. First, we consider the linear and scattering distribution characteristics of leaf and wood points and calculate local geometric features with distinguishing properties to enrich the original point cloud information. Then, we fuse the local contextual information for feature enhancement and select more representative features through a rearrangement attention mechanism. Finally, we use a residual connection during the decoding stage to improve the robustness of the model and achieve efficient leaf-wood separation. The proposed LWSNet is tested on eight species of trees with different characteristics and sizes. The average F1 score for leaf-wood separation is as high as 97.29%. The results show that this method outperforms the state-of-the-art leaf-wood separation methods in previous studies, and can accurately and robustly separate leaves and wood in trees of different species, sizes, and structures. This study extends the leaf-wood separation of tree point clouds in an end-to-end manner and demonstrates that the deep-learning segmentation algorithm has a great potential for processing tree and plant point clouds with complex morphological traits. Full article
(This article belongs to the Special Issue Application of Laser Scanning Technology in Forestry)
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16 pages, 9705 KiB  
Article
Feasibility of Low-Cost LiDAR Scanner Implementation in Forest Sampling Techniques
by Michał Brach, Wiktor Tracz, Grzegorz Krok and Jakub Gąsior
Forests 2023, 14(4), 706; https://0-doi-org.brum.beds.ac.uk/10.3390/f14040706 - 30 Mar 2023
Viewed by 1788
Abstract
Despite the growing impact of remote sensing technology in forest inventories globally, there is a continuous need for ground measurements on sample plots. Even though the newest volume assessment methodology requires fewer sample plots, the accuracy of ground-recorded data influences the final accuracy [...] Read more.
Despite the growing impact of remote sensing technology in forest inventories globally, there is a continuous need for ground measurements on sample plots. Even though the newest volume assessment methodology requires fewer sample plots, the accuracy of ground-recorded data influences the final accuracy of forest stand modeling. Therefore, effective and economically justified tools are in the continuous interest of foresters. In the presented research, a consumer-grade light detection and ranging (LiDAR) sensor mounted on iPad was used for forest inventory sample plot data collection—including tree location and diameter breast height. In contrast to other similar research, feasibility and user-friendliness were also documented and emphasized. The study was conducted in 63 real sample plots used for the inventory of Polish forests. In total, 776 trees were scanned in 3 types of forest stands: pine, birch, and oak. The root mean square error was 0.28 m for tree locations and 0.06 m for diameter breast height. Various additional analyses were performed to describe the usage of an iPad in tree inventories. It was contended that low-cost LiDAR scanners might be successfully used in real forest conditions and can be considered a reliable and easy-to-implement tool in forest inventory measurements. Full article
(This article belongs to the Special Issue Application of Laser Scanning Technology in Forestry)
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