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Forests
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Airborne and Terrestrial Laser Scanning in Forests
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Airborne and Terrestrial Laser Scanning in Forests

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: 30 June 2024 | Viewed by 1697

Special Issue Editors

Dr. Juepeng Zheng

E-Mail Website
Guest Editor
School of Artificial Intelligence, Sun Yat-Sen University, Guangdong 510275, China
Interests: remote sensing image understanding; deep learning; high-performance computing
Dr. Zhouxin Xi

E-Mail Website
Guest Editor
Natural Resources Canada, Canadian Forest Service, Ottawa, ON K1A 0E4, Canada
Interests: artificial intelligence in forest ecosystem monitoring; multi-scale sensor fusion for natural resource mapping wildland fuel and fire connectivity; tree morphology assessment and simulation
Dr. Zhen Ye

E-Mail Website
Guest Editor
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Interests: satellite; satellite image processing; geospatial science; image matching
Dr. Shangshu Cai

E-Mail Website
Guest Editor
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: mapping and remote sensing; forest

Special Issue Information

Dear Colleagues,

Nowadays, airborne and terrestrial laser scanning are widely utilized to collect point cloud data for hectare- or larger-scale forest resource surveys, aiding in the estimation of tree and stand attributes, detection and delineation of tree crowns, analysis of forest borders, changes and its ecosystem and biodiversity, etc.

In this Special Issue, we welcome new research progress and contributions for forest-related research using airborne and terrestrial laser scanning (or combining other earth observation data). We look forward to novel datasets, new algorithm design or broader application domains in forest resource surveys.

Traditional pipelines for extracting different forest structural attributes from point cloud data include pulse-based or voxel-based gap probability methods (used for the estimation of plant or leaf area indexes of forest stands) and geometrical modeling (used for explicit reconstruction of individual tree structure). In addition, radiative transfer models (RTMs) also improve the accuracy of the retrieval of forest biophysical properties when coupled with earth observation data.

Over the recent years, artificial intelligence, especially the machine learning and deep learning model, has had a significant effect on processing point cloud data and shown great potential in forest resource surveys. Moreover, fusing other multi-modal data (such as optical remote sensing data or GIS data) with point cloud data (collected from UAV, airborne and spaceborne) has become a popular way to improve the performance in regional and large-scale forest resource surveys.

We are looking for papers that focus on forest-related surveys using point cloud data from airborne and terrestrial laser scanning (or combining other kinds of remote sensing data or GIS data), including forest parameter estimation (such as diameter at breast height, tree height, aboveground biomass, leaf area index, etc.), tree detection (such as location, delineation, counting, species classification, etc.) and large-scale forest surveys (such as forest borders, change detection, forest ecosystem, biodiversity, etc.)

Dr. Juepeng Zheng
Dr. Zhouxin Xi
Dr. Zhen Ye
Dr. Shangshu Cai
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

  • airborne and terrestrial laser scanning
  • forest parameter estimation
  • individual tree detection and delineation
  • point cloud data processing
  • data fusing
  • radiative transfer models
  • machine learning
  • deep learning

Published Papers (2 papers)

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Research

20 pages, 7124 KiB  
Article
An Improved RANSAC-ICP Method for Registration of SLAM and UAV-LiDAR Point Cloud at Plot Scale
by Shuting Zhang, Hongtao Wang, Cheng Wang, Yingchen Wang, Shaohui Wang and Zhenqi Yang
Forests 2024, 15(6), 893; https://doi.org/10.3390/f15060893 - 21 May 2024
Viewed by 407
Abstract
Simultaneous Localization and Mapping (SLAM) using LiDAR technology can acquire the point cloud below the tree canopy efficiently in real time, and the Unmanned Aerial Vehicle LiDAR (UAV-LiDAR) can derive the point cloud of the tree canopy. By registering them, the complete 3D [...] Read more.
Simultaneous Localization and Mapping (SLAM) using LiDAR technology can acquire the point cloud below the tree canopy efficiently in real time, and the Unmanned Aerial Vehicle LiDAR (UAV-LiDAR) can derive the point cloud of the tree canopy. By registering them, the complete 3D structural information of the trees can be obtained for the forest inventory. To this end, an improved RANSAC-ICP algorithm for registration of SLAM and UAV-LiDAR point cloud at plot scale is proposed in this study. Firstly, the point cloud features are extracted and transformed into 33-dimensional feature vectors by using the feature descriptor FPFH, and the corresponding point pairs are determined by bidirectional feature matching. Then, the RANSAC algorithm is employed to compute the transformation matrix based on the reduced set of feature points for coarse registration of the point cloud. Finally, the iterative closest point algorithm is used to iterate the transformation matrix to achieve precise registration of the SLAM and UAV-LiDAR point cloud. The proposed algorithm is validated on both coniferous and broadleaf forest datasets, with an average mean absolute distance (MAD) of 11.332 cm for the broadleaf forest dataset and 6.150 cm for the coniferous forest dataset. The experimental results show that the proposed method in this study can be effectively applied to the forest plot scale for the precise alignment of multi-platform point clouds. Full article
(This article belongs to the Special Issue Airborne and Terrestrial Laser Scanning in Forests)
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14 pages, 2636 KiB  
Article
Tree Diameter at Breast Height (DBH) Estimation Using an iPad Pro LiDAR Scanner: A Case Study in Boreal Forests, Ontario, Canada
by Matthew Guenther, Muditha K. Heenkenda, Dave Morris and Brigitte Leblon
Forests 2024, 15(1), 214; https://doi.org/10.3390/f15010214 - 21 Jan 2024
Cited by 1 | Viewed by 1059
Abstract
The aim of this study was to determine whether the iPad Pro 12th generation LiDAR sensor is useful to measure tree diameter at breast height (DBH) in natural boreal forests. This is a follow-up to a previous study that was conducted in a [...] Read more.
The aim of this study was to determine whether the iPad Pro 12th generation LiDAR sensor is useful to measure tree diameter at breast height (DBH) in natural boreal forests. This is a follow-up to a previous study that was conducted in a research forest and identified the optimal method for (DBH) estimation as a circular scanning and fitting ellipses to 4 cm stem cross-sections at breast height. The iPad Pro LiDAR scanner was used to acquire point clouds for 15 sites representing a range of natural boreal forest conditions in Ontario, Canada, and estimate DBH. The secondary objective was to determine if tested stand (species composition, age, density, understory) or tree (species, DBH) factors affected the accuracy of estimated DBH. Overall, estimated DBH values were within 1 cm of actual DBH values for 78 of 133 measured trees (59%). An RMSE of 1.5 cm (8.6%) was achieved. Stand age had a large effect (>0.15) on the accuracy of estimated DBH values, while density, understory, and DBH had moderate effects (0.05–0.14). No trend was identified between accuracy and stand age. Accuracy improved as understory density decreased and as tree DBH increased. Inertial measurement unit (IMU) and positional accuracy errors with the iPad Pro scanner limit the feasibility of using this device for forest inventories. Full article
(This article belongs to the Special Issue Airborne and Terrestrial Laser Scanning in Forests)
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