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Remote Sensing
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Laser Scanning for Quantifying Sustainable Forest and Agriculture Management
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Laser Scanning for Quantifying Sustainable Forest and Agriculture Management

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

Deadline for manuscript submissions: 26 May 2024 | Viewed by 3673

Special Issue Editors

Prof. Dr. Chi-Kuei Wang

E-Mail Website
Guest Editor
Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
Interests: airborne laser scanning; mobile laser scanning; UAV laser scanning; DEM; LiDAR radiometric modeling; LiDAR simulation
Prof. Dr. Chinsu Lin

E-Mail Website
Guest Editor
Department of Forestry and Natural Resources, National Chiayi University, Chiayi 600355, Taiwan
Interests: forest management; forest ecology; forest conservation; biodiversity; remote sensing; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juha Hyyppä

E-Mail Website
Guest Editor
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02430 Masala, Finland
Interests: laser scanning (airborne, mobile, and terrestrial); 3D remote sensing; individual tree detection; virtual forests
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological advancements of laser, scanning electromechanics, GPS/IMU, and vehicle platforms have realized a wide variety of laser scanning systems, ranging from satellite, airborne, unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs) to single-person backpack, hand-held, and even pads. Point clouds and waveforms collected from some of these systems have been utilized and proven their value for forestry and agricultural management, while other systems require further study to unleash their full potential. Novel algorithms are also sought to expedite information extraction from the ever-growing data volume.

This Special Issue aims at studies covering different uses of laser scanning systems in agricultural and forest sciences. Topics include but are not limited to an investigation of the characteristics of various laser scanning systems, such as point density, penetration, intensity values, case studies to derive forest structures or single tree parameters and to estimate crop yield, and algorithm development to handle large volumes of data based on artificial intelligence or machine learning techniques.

Prof. Dr. Chi-Kuei Wang
Prof. Dr. Chinsu Lin
Prof. Dr. Juha Hyyppä
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

  • agricultural and forestry management
  • forest structure
  • agricultural monitoring
  • laser scanning
  • mobile systems
  • point clouds and waveform processing
  • tree extraction/segmentation
  • biomass
  • crop yield

Published Papers (2 papers)

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Research

20 pages, 11985 KiB  
Article
Simultaneous Vehicle Localization and Roadside Tree Inventory Using Integrated LiDAR-Inertial-GNSS System
by Xianghua Fan, Zhiwei Chen, Peilin Liu and Wenbo Pan
Remote Sens. 2023, 15(20), 5057; https://doi.org/10.3390/rs15205057 - 21 Oct 2023
Cited by 2 | Viewed by 1136
Abstract
Autonomous driving systems rely on a comprehensive understanding of the surrounding environment, and trees, as important roadside features, have a significant impact on vehicle positioning and safety analysis. Existing methods use mobile LiDAR systems (MLS) to collect environmental information and automatically generate tree [...] Read more.
Autonomous driving systems rely on a comprehensive understanding of the surrounding environment, and trees, as important roadside features, have a significant impact on vehicle positioning and safety analysis. Existing methods use mobile LiDAR systems (MLS) to collect environmental information and automatically generate tree inventories based on dense point clouds, providing accurate geometric parameters. However, the use of MLS systems requires expensive survey-grade laser scanners and high-precision GNSS/IMU systems, which limits their large-scale deployment and results in poor real-time performance. Although LiDAR-based simultaneous localization and mapping (SLAM) techniques have been widely applied in the navigation field, to the best of my knowledge, there has been no research conducted on simultaneous real-time localization and roadside tree inventory. This paper proposes an innovative approach that uses LiDAR technology to achieve vehicle positioning and a roadside tree inventory. Firstly, a front-end odometry based on an error-state Kalman filter (ESKF) and a back-end optimization framework based on factor graphs are employed. The updated poses from the back-end are used for establishing point-to-plane residual constraints for the front-end in the local map. Secondly, a two-stage approach is adopted to minimize global mapping errors, refining accumulated mapping errors through GNSS-assisted registration to enhance system robustness. Additionally, a method is proposed for creating a tree inventory that extracts line features from real-time LiDAR point cloud data and projects them onto a global map, providing an initial estimation of possible tree locations for further tree detection. This method uses shared feature extraction results and data pre-processing results from SLAM to reduce the computational load of simultaneous vehicle positioning and roadside tree inventory. Compared to methods that directly search for trees in the global map, this approach benefits from fast perception of the initial tree position, meeting real-time requirements. Finally, our system is extensively evaluated on real datasets covering various road scenarios, including urban and suburban areas. The evaluation metrics are divided into two parts: the positioning accuracy of the vehicle during operation and the detection accuracy of trees. The results demonstrate centimeter-level positioning accuracy and real-time automatic creation of a roadside tree inventory. Full article
(This article belongs to the Special Issue Laser Scanning for Quantifying Sustainable Forest and Agriculture Management)
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20 pages, 11858 KiB  
Article
Trunk-Constrained and Tree Structure Analysis Method for Individual Tree Extraction from Scanned Outdoor Scenes
by Xiaojuan Ning, Yishu Ma, Yuanyuan Hou, Zhiyong Lv, Haiyan Jin, Zengbo Wang and Yinghui Wang
Remote Sens. 2023, 15(6), 1567; https://doi.org/10.3390/rs15061567 - 13 Mar 2023
Cited by 4 | Viewed by 1636
Abstract
The automatic extraction of individual tree from mobile laser scanning (MLS) scenes has important applications in tree growth monitoring, tree parameter calculation and tree modeling. However, trees often grow in rows and tree crowns overlap with varying shapes, and there is also incompleteness [...] Read more.
The automatic extraction of individual tree from mobile laser scanning (MLS) scenes has important applications in tree growth monitoring, tree parameter calculation and tree modeling. However, trees often grow in rows and tree crowns overlap with varying shapes, and there is also incompleteness caused by occlusion, which makes individual tree extraction a challenging problem. In this paper, we propose a trunk-constrained and tree structure analysis method to extract trees from scanned urban scenes. Firstly, multi-feature enhancement is performed via PointNet to segment the tree points from raw urban scene point clouds. Next, the candidate local tree trunk clusters are obtained by clustering based on the intercepted local tree trunk layer, and the real local tree trunk is obtained by removing noise data. Then, the trunk is located and extracted by combining circle fitting and region growing, so as to obtain the center of the tree crown. Further, the points near the tree’s crown (core points) are segmented through distance difference, and the tree crown boundary (boundary points) is distinguished by analyzing the density and centroid deflection angle. Therefore, the core and boundary points are deleted to obtain the remaining points (intermediate points). Finally, the core, intermediate and boundary points, as well as the tree trunks, are combined to extract individual tree. The performance of the proposed method was evaluated on the Pairs-Lille-3D dataset, which is a benchmark for point cloud classification, and data were produced using a mobile laser system (MLS) applied to two different cities in France (Paris and Lille). Overall, the precision, recall, and F1-score of instance segmentation were 90.00%, 98.22%, and 99.08%, respectively. The experimental results demonstrate that our method can effectively extract trees with multiple rows of occlusion and improve the accuracy of tree extraction. Full article
(This article belongs to the Special Issue Laser Scanning for Quantifying Sustainable Forest and Agriculture Management)
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