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Forests
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LiDAR Remote Sensing for Forestry
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LiDAR Remote Sensing for 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 August 2025 | Viewed by 10622

Special Issue Editors

Dr. Zhenyang Hui

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Guest Editor
School of Surveying and Geoinformation Engineering, East China University of Technology (ECUT), Nanchang 330013, China
Interests: LiDAR remote sensing; 3D point cloud analysis; forest inventory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Penggen Cheng

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Guest Editor
School of Surveying and Geoinformation Engineering, East China University of Technology (ECUT), Nanchang 330013, China
Interests: LiDAR remote sensing; geographical analysis; spatial analysis
Prof. Dr. Bo Liu

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Guest Editor
School of Surveying and Geoinformation Engineering, East China University of Technology (ECUT), Nanchang 330013, China
Interests: fuzzy topology; spatial database; 3D data processing
Dr. Mark Vanderwel

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Guest Editor
Department of Biology, University of Regina, 3737 Wascana Pkwy, Regina, SK S4S 0A2, Canada
Interests: tree demography; stand dynamics; forest simulation modelling; global change; forest distributions; carbon sequestration; LiDAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are essential to maintaining ecological function, biodiversity and the health of the planet. To better investigate forest resources and promote the study of tree growth mechanisms, it is urgent to obtain more accurate and timely forest inventory information. In recent decades, with continuous improvements made to the measurement accuracy and sampling rates of laser scanners, LiDAR has been widely employed for calculating tree metrics (e.g., height, diameter at breast height (DBH), crown width), estimating above-ground biomass (AGB), and identifying tree species remotely. Nonetheless, existing studies continue to encounter the challenges of low accuracy or low robustness across different forest environments. Thus, this Special Issue focuses on the latest studies addressing forest inventory using LiDAR technology. We hope this Special Issue will elevate research interest in LiDAR technology and unleash its geometric and topological potential within the forest and plant sciences. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Multi-platform point cloud fusion;
  • Filtering for forest environment;
  • Individual tree detection;
  • Biomass estimation;
  • Tree species identification;
  • Quantitative structure modeling for trees;
  • Forest parameters estimation;
  • Forest ecology;
  • Carbon cycle analysis;
  • Forest planning and management.

Dr. Zhenyang Hui
Prof. Dr. Penggen Cheng
Prof. Dr. Bo Liu
Dr. Mark Vanderwel
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

  • LiDAR
  • point cloud
  • forest inventory
  • filtering
  • biomass
  • quantitative structure model
  • forest dynamics
  • forest structure analysis
  • carbon cycle/sequestration
  • forest management

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Published Papers (7 papers)

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Research

18 pages, 4336 KiB  
Article
Estimation of Forest Canopy Height from Spaceborne Full-Waveform LiDAR Data Using a Bisection Approximation Decomposition Method
by Song Chen, Ming Gong, Hua Sun, Ming Chen and Binbin Wang
Forests 2025, 16(1), 145; https://doi.org/10.3390/f16010145 - 14 Jan 2025
Viewed by 705
Abstract
Forest canopy height (FCH) is a vital indicator for assessing forest health and ecosystem service capacity. Over the past two decades, full-waveform (FW) LiDAR has been widely employed for estimating forest biophysical variables due to its high precision in measuring vertical forest structures. [...] Read more.
Forest canopy height (FCH) is a vital indicator for assessing forest health and ecosystem service capacity. Over the past two decades, full-waveform (FW) LiDAR has been widely employed for estimating forest biophysical variables due to its high precision in measuring vertical forest structures. However, the impact of terrain undulations on forest parameter estimation remains challenging. To address this issue, this study proposes a bisection approximation decomposition (BAD) method for processing GEDI L1B data and FCH estimation. The BAD method analyzes the energy composition of simplified echo signals and determines the fitting parameters by integrating overall signal energy, the differences in unresolved signals, and the similarity of inter-forest signal characteristics. FCH is subsequently estimated based on waveform peak positions. By dynamically adjusting segmentation points and Gaussian fitting parameters, the BAD method achieved precise separation of mixed canopy and ground signals, substantially enhancing the physical realism and applicability of decomposition results. The effectiveness and robustness of the BAD method for FCH estimation were evaluated using 2049 footprints across varying slope conditions in the Harvard Forest region of Petersham, Massachusetts. The results demonstrated that digital terrain models (DTMs) extracted using the GEDI data and the BAD method exhibited high consistency with the DTMs derived using airborne laser scanning (ALS) data (coefficient of determination R2 > 0.99). Compared with traditional Gaussian decomposition (GD), wavelet decomposition (WD), and deconvolution decomposition (DD) methods, the BAD method showed significant advantages in FCH estimation, achieved the smallest relative root mean square error (rRMSE) of 17.19% and greatest mean estimation accuracy of 84.57%, and reduced the rRMSE by 10.74%, 21.49%, and 28.93% compared to GD, WD, and DD methods, respectively. Moreover, the BAD method exhibited a significantly stronger correlation with ALS-derived canopy height mode data than the relative height metrics from GEDI L2A products (r = 0.84, p < 0.01). The robustness and adaptability of the BAD method to complex terrain conditions provide great potential for forest parameters using GEDI data. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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23 pages, 5173 KiB  
Article
Multi-Criteria Filtration and Extraction Strategy for Understory Elevation Control Points Using ICESat-2 ATL08 Product
by Jiapeng Huang, Yunqiu Wang and Yang Yu
Forests 2024, 15(12), 2064; https://doi.org/10.3390/f15122064 - 22 Nov 2024
Viewed by 777
Abstract
Understory terrain plays a multi-faceted role in ecosystems, biodiversity, and productivity in forests by influencing different major factors, such as hydrological processes, soils, climate, and light conditions. Strong illuminants (e.g., sunlight) from ground surfaces and atmosphere can introduce additional photons into the ATLAS [...] Read more.
Understory terrain plays a multi-faceted role in ecosystems, biodiversity, and productivity in forests by influencing different major factors, such as hydrological processes, soils, climate, and light conditions. Strong illuminants (e.g., sunlight) from ground surfaces and atmosphere can introduce additional photons into the ATLAS system. These photons can, consequently, be mistakenly identified as laser photons reflected from ground surfaces. The presence of such ambient light, particularly under low-photon-count conditions, can significantly increase elevation measurement errors. In this context, this study aims to propose a method for extracting reliable understory elevation control points under varying forest conditions, based on the parameter attributes of ICESat-2/ATLAS data. The overall filtered data resulted in a coefficient of determination (R2), root mean square error (RMSE), and standard deviation (STD) of 0.99, 2.77 m, and 2.42 m, respectively. The greatest accuracy improvement was found in the Puerto Rico study area, showing decreases in the RMSE and STD values by 2.68 and 2.67 m, respectively. On the other hand, canopy heights and slopes exhibited relatively large impacts on noise interferences. In addition, there were decreases in the RMSE and STD values by 4.57 and 4.64 m, respectively, under the very tall canopy category, whereas under steep slope conditions, the RMSE and STD values of the filtering results decreased by 4.59 and 4.34 m, respectively. The proposed method can enhance the overall accuracy of elevation data, allowing for the significant extraction of understory elevation control points, ultimately optimizing forest management practices and improving ecological assessments. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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23 pages, 39653 KiB  
Article
Registration of TLS and ULS Point Cloud Data in Natural Forest Based on Similar Distance Search
by Yuncheng Deng, Jinliang Wang, Pinliang Dong, Qianwei Liu, Weifeng Ma, Jianpeng Zhang, Guankun Su and Jie Li
Forests 2024, 15(9), 1569; https://doi.org/10.3390/f15091569 - 6 Sep 2024
Cited by 4 | Viewed by 1179
Abstract
Multiplatform fusion point clouds can effectively compensate for the disadvantages of individual platform point clouds in forest parameter extraction, maximizing the potential of LiDAR technology. However, existing registration algorithms often suffer from insufficient feature extraction and limited registration accuracy. To address these issues, [...] Read more.
Multiplatform fusion point clouds can effectively compensate for the disadvantages of individual platform point clouds in forest parameter extraction, maximizing the potential of LiDAR technology. However, existing registration algorithms often suffer from insufficient feature extraction and limited registration accuracy. To address these issues, we propose a ULS (Unmanned Aerial Vehicle Laser Scanning)-TLS (Terrestrial Laser Scanning) point cloud data registration method based on Similar Distance Search (SDS). This method enhances coarse registration by accurately retrieving points with similar features, leading to high overlap in the rough registration stage and further improving fine registration precision. (1) The proposed method was tested on four natural forest plots, including Pinus densata Mast., Pinus yunnanensis Franch., Pices asperata Mast., Abies fabri (Mast.) Craib, and demonstrated high registration accuracy. Both coarse and fine registration achieved superior results, significantly outperforming existing algorithms, with notable improvements over the TR algorithm. (2) In addition, the study evaluated the accuracy of individual tree parameter extraction from fusion point clouds versus single-platform point clouds. While ULS point clouds performed slightly better in some metrics, the fused point clouds offered more consistent and reliable results across varying conditions. Overall, the proposed SDS method and the resulting fusion point clouds provide strong technical support for efficient and accurate forest resource management, with significant scientific implications. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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17 pages, 13656 KiB  
Article
A Reliable DBH Estimation Method Using Terrestrial LiDAR Points through Polar Coordinate Transformation and Progressive Outlier Removal
by Zhenyang Hui, Lei Lin, Shuanggen Jin, Yuanping Xia and Yao Yevenyo Ziggah
Forests 2024, 15(6), 1031; https://doi.org/10.3390/f15061031 - 13 Jun 2024
Cited by 3 | Viewed by 1524
Abstract
Diameter at breast height (DBH) is a crucial parameter for forest inventory. However, accurately estimating DBH remains challenging due to the noisy and incomplete cross-sectional points. To address this, this paper proposed a reliable DBH estimation method using terrestrial LiDAR points through polar [...] Read more.
Diameter at breast height (DBH) is a crucial parameter for forest inventory. However, accurately estimating DBH remains challenging due to the noisy and incomplete cross-sectional points. To address this, this paper proposed a reliable DBH estimation method using terrestrial LiDAR points through polar coordinate transformation and progressive outlier removal. In this paper, the initial center was initially detected by rasterizing the convex hull, and then the Cartesian coordinates were transformed into polar coordinates. In the polar coordinate system, the outliers were classified as low and high outliers according to the distribution of polar radius difference. Both types of outliers were then removed using adaptive thresholds and the moving least squares algorithm. Finally, DBH was estimated by calculating the definite integral of arc length in the polar coordinate system. Twenty publicly available individual trees were adopted for the test. Experimental results indicated that the proposed method performs better than the other four classical DBH estimation methods. Furthermore, several extreme cases scanned using terrestrial LiDAR in practice, such as cross-sectional points with lots of outliers or larger data gaps, were also tested. Experimental results demonstrate that the proposed method accurately calculates DBH even in these challenging cases. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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21 pages, 3934 KiB  
Article
Rethinking Productivity Evaluation in Precision Forestry through Dominant Height and Site Index Measurements Using Aerial Laser Scanning LiDAR Data
by Iván Raigosa-García, Leah C. Rathbun, Rachel L. Cook, Justin S. Baker, Mark V. Corrao and Matthew J. Sumnall
Forests 2024, 15(6), 1002; https://doi.org/10.3390/f15061002 - 7 Jun 2024
Cited by 1 | Viewed by 1609
Abstract
Optimizing forest plantation management has become imperative due to increasing forest product demand, higher fertilization and management costs, declining land availability, increased competition for land use, and the growing demands for carbon sequestration. Precision forestry refers to the ability to use data acquired [...] Read more.
Optimizing forest plantation management has become imperative due to increasing forest product demand, higher fertilization and management costs, declining land availability, increased competition for land use, and the growing demands for carbon sequestration. Precision forestry refers to the ability to use data acquired with technology to support the forest management decision-making process. LiDAR can be used to assess forest metrics such as tree height, topographical position, soil surface attributes, and their combined effects on individual tree growth. LiDAR opens the door to precision silviculture applied at the tree level and can inform precise treatments such as fertilization, thinning, and herbicide application for individual trees. This study uses ALS LiDAR and other ancillary data to assess the effect of scale (i.e., stand, soil type, and microtopography) on dominant height and site index measures within loblolly pine plantations across the southeastern United States. This study shows differences in dominant height and site index across soil types, with even greater differences observed when the interactions of microtopography were considered. These results highlight how precision forestry may provide a unique opportunity for assessing soil and microtopographic information to optimize resource allocation and forest management at an individual tree scale in a scarce higher-priced fertilizer scenario. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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16 pages, 8621 KiB  
Article
Extracting the DBH of Moso Bamboo Forests Using LiDAR: Parameter Optimization and Accuracy Evaluation
by Longwei Li, Linjia Wei, Nan Li, Shijun Zhang, Zhicheng Wu, Miaofei Dong and Yuyun Chen
Forests 2024, 15(5), 804; https://doi.org/10.3390/f15050804 - 2 May 2024
Cited by 2 | Viewed by 1840
Abstract
The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and [...] Read more.
The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and enhanced the accuracy of diameter fitting by optimizing denoising parameters. Specifically, we fine-tuned two denoising parameters, neighborhood point number and standard deviation multiplier, across five gradient levels for denoising. Subsequently, DBH fitting was conducted on data processed with varying denoising parameters, followed by a precision evaluation to investigate the key factors influencing the accuracy of Moso bamboo DBH fitting. The research results indicate that a handheld laser was used to scan six plots, from which 132 single Moso bamboo trees were selected. Out of these, 122 single trees were successfully segmented and identified, achieving an accuracy rate of 92.4% in identifying single Moso bamboo trees, with an average accuracy of 95.64% in extracting DBH for individual plants; the mean error was ±1.8 cm. Notably, setting the minimum neighborhood point to 10 resulted in the highest fitting accuracy for DBH. Moreover, the optimal standard deviation multiplier threshold was found to be 1 in high-density forest plots and 2 in low-density forest plots. Forest condition and slope were identified as the primary factors impacting the accuracy of Moso bamboo DBH fitting. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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16 pages, 10176 KiB  
Article
Evaluation of Accuracy in Estimating Diameter at Breast Height Based on the Scanning Conditions of Terrestrial Laser Scanning and Circular Fitting Algorithm
by Yongkyu Lee and Jungsoo Lee
Forests 2024, 15(2), 313; https://doi.org/10.3390/f15020313 - 7 Feb 2024
Cited by 2 | Viewed by 1691
Abstract
A growing societal interest exists in the application of lidar technology to monitor forest resource information and forestry management activities. This study examined the possibility of estimating the diameter at breast height (DBH) of two tree species, Pinus koraiensis (PK) and [...] Read more.
A growing societal interest exists in the application of lidar technology to monitor forest resource information and forestry management activities. This study examined the possibility of estimating the diameter at breast height (DBH) of two tree species, Pinus koraiensis (PK) and Larix kaempferi (LK), by varying the number of terrestrial laser scanning (TLS) scans (1, 3, 5, 7, and 9) and DBH estimation methods (circle fitting [CF], ellipse fitting [EF], circle fitting with RANSAC [RCF], and ellipse fitting with RANSAC [REF]). This study evaluates the combination that yields the highest estimation accuracy. The results showed that for PK, the lowest RMSE of 0.97 was achieved when REF was applied to the data from nine scans after noise removal. For LK, the lowest RMSE of 1.03 was observed when applying CF to the data from seven scans after noise removal. Furthermore, ANOVA revealed no significant difference in the estimated DBH from nine scans when more than three scans were used for CF and RCF and more than five for EF and REF. These results are expected to be useful in establishing efficient and accurate DBH estimation plans using TLS for forest resource monitoring. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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