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Application of LiDAR Remote Sensing and Mapping
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Application of LiDAR Remote Sensing and Mapping

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 21 February 2026 | Viewed by 4376

Special Issue Editors

Dr. Sheng Nie

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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoxiao Zhu

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Guest Editor
State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: data analysis statistical analysis multiple linear regression lidar remote sensing geology
Dr. Cheng Wang

E-Mail Website
Guest Editor
Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Interests: lidar remote sensing; vegetation structure; ICESat; forest mapping; biomass estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

LiDAR (Light detection and ranging) technology has revolutionized the field of remote sensing and mapping by providing high-resolution, three-dimensional data of the Earth's surface. Its applications span various domains, including forestry, agriculture, urban planning, geology, and environmental monitoring. This Special Issue aims to gather cutting-edge research and advancements in the application of LiDAR remote sensing and mapping. Researchers and practitioners are invited to submit original research articles, comprehensive reviews, and detailed case studies that highlight the transformative impact of LiDAR technology. 

Dr. Sheng Nie
Dr. Xiaoxiao Zhu
Dr. Cheng Wang
Guest Editors

Manuscript Submission Information

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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

  • LiDAR remote sensing
  • point cloud processing
  • LiDAR applications in forestry and ecology
  • forest height and mapping
  • 3D scene reconstruction
  • space-borne LiDAR
  • building height retrieval and mapping

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Published Papers (6 papers)

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Research

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18 pages, 4807 KiB  
Article
The Aboveground Biomass Estimation of the Grain for Green Program Stands Using UAV-LiDAR and Sentinel-2 Data
by Gaoke Yueliang, Gentana Ge, Xiaosong Li, Cuicui Ji, Tiancan Wang, Tong Shen, Yubo Zhi, Chaochao Chen and Licheng Zhao
Sensors 2025, 25(9), 2707; https://doi.org/10.3390/s25092707 - 24 Apr 2025
Viewed by 92
Abstract
Aboveground biomass (AGB) serves as a crucial indicator of the effectiveness of the Grain for Green Program (GGP), and its accurate estimation is essential for evaluating forest health and carbon sink capacity. However, due to the dominance of sparse forests in GGP stands, [...] Read more.
Aboveground biomass (AGB) serves as a crucial indicator of the effectiveness of the Grain for Green Program (GGP), and its accurate estimation is essential for evaluating forest health and carbon sink capacity. However, due to the dominance of sparse forests in GGP stands, research in this area remains significantly limited. In this study, we developed the optimal tree height-diameter at breast height (DBH) growth models for major tree species and constructed a high-quality AGB sample dataset by integrating airborne LiDAR data and tree species information. Then, the AGB of the GGP stands was estimated using the Sentinel-2 data and the gradient boosting decision tree (GBDT) algorithm. The results showed that the AGB sample dataset constructed using the proposed approach exhibited strong consistency with field-measured data (R2 = 0.89). The GBDT-based AGB estimation model shows high accuracy, with an R2 of 0.96 and a root mean square error (RMSE) of 560 g/m2. Key variables such as tasseled cap greenness (TCG), red-edge normalized difference vegetation index (RENDVI), and visible-band difference vegetation index (VDVI) were identified as highly important. This highlights that vegetation indices and tasseled cap transformation index information are key factors in estimating AGB. The AGB of major tree species in the new round of the GGP stands in Inner Mongolia ranged from 120 to 9253 g/m2, with mean values of 978 g/m2 for poplar, 622 g/m2 for Mongolian Scots pine, and 313 g/m2 for Chinese red pine species. This study offers a practical method for AGB estimation in GGP stands, contributing significantly to sustainable forest management and ecological conservation efforts. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
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12 pages, 4763 KiB  
Article
Gain-Switched Ho:YAP Laser with a 6.7 ns Pulse Duration at 2117 nm
by David Goldfisher, Rotem Nahear, Neria Suliman, Yechiel Bach and Salman Noach
Sensors 2025, 25(3), 878; https://doi.org/10.3390/s25030878 - 31 Jan 2025
Viewed by 498
Abstract
This study demonstrates, for the first time, an innovative gain-switched Ho:YAP laser designed for laser distance measurement and remote sensing applications. The laser operates at a wavelength of 2117 nm, well positioned within the short-wavelength infrared (SWIR) atmospheric transmission window of between 2.1 [...] Read more.
This study demonstrates, for the first time, an innovative gain-switched Ho:YAP laser designed for laser distance measurement and remote sensing applications. The laser operates at a wavelength of 2117 nm, well positioned within the short-wavelength infrared (SWIR) atmospheric transmission window of between 2.1 and 2.5 µm. The laser delivers short pulse durations of 6.7 ns, and pulse energies of 0.645 mJ, allowing for an enhanced range and improved resolution. The Ho:YAP laser is pumped by a passively Q-switched Tm:YLF laser, resulting in a compact and energy-efficient system suitable for various sensing applications in precise distance measurement and environmental gas detection. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
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23 pages, 9203 KiB  
Article
Improved Cylinder-Based Tree Trunk Detection in LiDAR Point Clouds for Forestry Applications
by Shaobo Ma, Yongkang Chen, Zhefan Li, Junlin Chen and Xiaolan Zhong
Sensors 2025, 25(3), 714; https://doi.org/10.3390/s25030714 - 24 Jan 2025
Viewed by 879
Abstract
The application of LiDAR technology in extracting individual trees and stand parameters plays a crucial role in forest surveys. Accurate identification of individual tree trunks is a critical foundation for subsequent parameter extraction. For LiDAR-acquired forest point cloud data, existing two-dimensional (2D) plane-based [...] Read more.
The application of LiDAR technology in extracting individual trees and stand parameters plays a crucial role in forest surveys. Accurate identification of individual tree trunks is a critical foundation for subsequent parameter extraction. For LiDAR-acquired forest point cloud data, existing two-dimensional (2D) plane-based algorithms for tree trunk detection often suffer from spatial information loss, resulting in reduced accuracy, particularly for tilted trees. While cylinder fitting algorithms provide a three-dimensional (3D) solution for trunk detection, their performance in complex forest environments remains limited due to sensitivity to parameters like distance thresholds. To address these challenges, this study proposes an improved individual tree trunk detection algorithm, Random Sample Consensus Cylinder Fitting (RANSAC-CyF), specifically optimized for detecting cylindrical tree trunks. Validated in three forest plots with varying complexities in Tianhe District, Guangzhou, the algorithm demonstrated significant advantages in the inlier rate, detection success rate, and robustness for tilted trees. The study showed the following results: (1) The average difference between the inlier rates of tree trunks and non-tree points for the three sample plots using RANSAC-CyF were 0.59, 0.63, and 0.52, respectively, which were significantly higher than those using the Least Squares Circle Fitting (LSCF) algorithm and the Random Sample Consensus Circle Fitting (RANSAC-CF) algorithm (p < 0.05). (2) RANSAC-CyF required only 2 and 8 clusters to achieve a 100% detection success rate in Plot 1 and Plot 2, while the other algorithms needed 26 and 40 clusters. (3) The effective distance threshold range of RANSAC-CyF was more than twice that of the comparison algorithms, maintaining stable inlier rates above 0.9 across all tilt angles. (4) The RANSAC-CyF algorithm still achieved good detection performance in the challenging Plot 3. Together, the other two algorithms failed to detect. The findings highlight the RANSAC-CyF algorithm’s superior accuracy, robustness, and adaptability in complex forest environments, significantly improving the efficiency and precision of individual tree trunk detection for forestry surveys and ecological research. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
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16 pages, 18891 KiB  
Article
Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR
by Tsung-Chiang Wu, Neng-Gang Kuan and Wei-Cheng Lu
Sensors 2025, 25(2), 461; https://doi.org/10.3390/s25020461 - 15 Jan 2025
Viewed by 599
Abstract
Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud [...] Read more.
Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management. Commercial software primarily relies on manual classification, which is time-intensive. This study addresses these challenges by using the laser reflectance intensity (I-value) for automated classification. Boxplot theory is applied to calibrate the data, remove noise, and establish polynomial regression equations correlating intensity with scanning distances. These equations serve as attribute functions for classifying datasets. Focusing on materials in traditional Minnan architecture on Kinmen Island, controlled indoor experiments and outdoor case studies validate the approach. The results show classification accuracies of 74% for wood, 98% for stone, and 93% for brick, demonstrating this method’s effectiveness in enhancing point cloud data applications and management. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
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15 pages, 2064 KiB  
Article
Research on the Depth Image Reconstruction Algorithm Using the Two-Dimensional Kaniadakis Entropy Threshold
by Xianhui Yang, Jianfeng Sun, Le Ma, Xin Zhou, Wei Lu and Sining Li
Sensors 2024, 24(18), 5950; https://doi.org/10.3390/s24185950 - 13 Sep 2024
Viewed by 865
Abstract
The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In [...] Read more.
The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In order to solve this problem, a depth image estimation method based on a two-dimensional (2D) Kaniadakis entropy thresholding method is proposed which transforms a weak signal extraction problem into a denoising problem for point cloud data. The characteristics of signal peak aggregation in the data and the spatio-temporal correlation features between target image elements in the point cloud-intensity data are exploited. Through adequate simulations and outdoor target-imaging experiments under different signal-to-background ratios (SBRs), the effectiveness of the method under low signal-to-background ratio conditions is demonstrated. When the SBR is 0.025, the proposed method reaches a target recovery rate of 91.7%, which is better than the existing typical methods, such as the Peak-picking method, Cross-Correlation method, and the sparse Poisson intensity reconstruction algorithm (SPIRAL), which achieve a target recovery rate of 15.7%, 7.0%, and 18.4%, respectively. Additionally, comparing with the SPIRAL, the reconstruction recovery ratio is improved by 73.3%. The proposed method greatly improves the integrity of the target under high-background-noise environments and finally provides a basis for feature extraction and target recognition. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
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Review

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45 pages, 1218 KiB  
Review
Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey
by Emil Dumic and Luís A. da Silva Cruz
Sensors 2025, 25(6), 1660; https://doi.org/10.3390/s25061660 - 7 Mar 2025
Viewed by 1084
Abstract
This meta-survey provides a comprehensive review of 3D point cloud (PC) applications in remote sensing (RS), essential datasets available for research and development purposes, and state-of-the-art point cloud compression methods. It offers a comprehensive exploration of the diverse applications of point clouds in [...] Read more.
This meta-survey provides a comprehensive review of 3D point cloud (PC) applications in remote sensing (RS), essential datasets available for research and development purposes, and state-of-the-art point cloud compression methods. It offers a comprehensive exploration of the diverse applications of point clouds in remote sensing, including specialized tasks within the field, precision agriculture-focused applications, and broader general uses. Furthermore, datasets that are commonly used in remote-sensing-related research and development tasks are surveyed, including urban, outdoor, and indoor environment datasets; vehicle-related datasets; object datasets; agriculture-related datasets; and other more specialized datasets. Due to their importance in practical applications, this article also surveys point cloud compression technologies from widely used tree- and projection-based methods to more recent deep learning (DL)-based technologies. This study synthesizes insights from previous reviews and original research to identify emerging trends, challenges, and opportunities, serving as a valuable resource for advancing the use of point clouds in remote sensing. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
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