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Forests
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Laser Scanning of Forest Dynamics
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Laser Scanning of Forest Dynamics

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: closed (1 March 2022) | Viewed by 14263

Special Issue Editors

Prof. Dr. Qin Ma

E-Mail Website
Guest Editor
Department of Forestry, Mississippi State University, Mississippi State, MS 39762, USA
Interests: forestry; ecology; LiDAR; remote sensing
Dr. Shichao Jin

E-Mail Website
Guest Editor
Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Nanjing Agriculture University, Nanjing, China
Interests: plant phenomics; LiDAR; deep learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests ecosystems provide a range of economic benefits and ecosystem services including wildlife habitation, timber supply, and carbon stock, which is an important natural solution to climate change. In the paste decades, however, forests worldwide are experiencing disturbances and stresses from natural disasters and climate change, severely threatening their ecosystem functions and services.  Numerous efforts have been made to improve our understanding of forest ecosystems, and their responses to climate change and human activities. Accurate and efficient mapping and monitoring of forest structural and biophysical attributes over space and time are essential for assessing forest ecosystem functions. Light Detection and Ranging (LiDAR) can provide 3D forest structural information from individual leaf, branch, tree to forest stand. Increasing LiDAR data have been collected from multiple platforms, such as terrestrial, airborne to spaceborne LiDAR. These LiDAR datasets have significantly extended our capabilities to quantify forest structural changes at multiple spatial and temporal scales. Time series optical imagery contains abundant spectral and texture information for mapping forest species and biophysical changes. The integration of LiDAR and optical imagery, therefore, provides greater potential for monitoring forest structural, compositional, and functional changes.

This Special Issue of Forests emphasize mapping, monitoring, and modeling forest vegetation changes through LiDAR and optical imagery, to understanding how forest ecosystems respond to changes in climate, environment, and management strategies. Research articles may focus on, but are not limited to, topics including new approaches for forest vegetation change detection, tree growth modeling, mortality mapping, disturbance and recovery evaluation, and restoration assessment, based on LiDAR and/or optical data. Applicational studies regarding forest biodiversity, wildlife habitat management, restoration, conservation, and plant phenotyping with the help of LiDAR data are also welcome.

Prof. Dr. Qin Ma
Dr. Shichao Jin
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
  • forest ecosystem
  • change detection
  • structural mapping
  • data fusion

Published Papers (6 papers)

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Research

13 pages, 20787 KiB  
Article
Assessing the Traffic Noise Reduction Effect of Roadside Green Space Using LiDAR Point Cloud Data in Shenzhen, China
by Chao Xu, Baolong Han, Fei Lu and Tong Wu
Forests 2022, 13(5), 765; https://doi.org/10.3390/f13050765 - 16 May 2022
Cited by 2 | Viewed by 2039
Abstract
The characteristics of vegetation in urban road side green spaces affect their noise reduction capacity. How to objectively, extensively, and accurately evaluate the noise reduction effect of these complex structures is challenging. In this study, we take urban roadside green space quadrats as [...] Read more.
The characteristics of vegetation in urban road side green spaces affect their noise reduction capacity. How to objectively, extensively, and accurately evaluate the noise reduction effect of these complex structures is challenging. In this study, we take urban roadside green space quadrats as the research object, use knapsack LiDAR to collect point cloud data of vegetation in the quadrats, and then construct and extract factor indices that can reflect the different vegetation characteristics based on LiDAR point cloud data with LiDAR360 software. We then combine the actual collected and calculate attenuation of traffic noise using correlation analysis and ordinary least square regression analysis to clarify the characteristic factors and correlation of noise attenuation in order to explore the influence of vegetation characteristics on the effect of noise reduction. The results show that a variety of factors affect the noise reduction effect of complex vegetation structures, and the importance degree is the following: horizontal occlusion degree > width > percentage of point cloud grid > leaf area index > coverage degree. By comparing the vegetation characteristic factors at different heights, we found that coverage degree, leaf area index, horizontal occlusion degree, and the percentage of the point cloud grid have the most significant positive correlation with the actual attenuation at a height of 5 m, but the coverage degree and leaf area index at absolute height have no correlation with the actual attenuation. The amount of vegetation near the road has a greater effect on noise reduction than that on the far side. The actual noise attenuation and the vegetation characteristic factors of green space have a non-linear relationship, and the interaction has a comprehensive influence on the noise reduction effect. These findings can provide a scientific basis for the reduction of traffic noise through the structural optimization of urban green space. Full article
(This article belongs to the Special Issue Laser Scanning of Forest Dynamics)
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18 pages, 8732 KiB  
Article
Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
by Hongping Fu, Hao Li, Yanqi Dong, Fu Xu and Feixiang Chen
Forests 2022, 13(4), 566; https://doi.org/10.3390/f13040566 - 02 Apr 2022
Cited by 20 | Viewed by 3200
Abstract
Terrestrial laser scanning (TLS) can provide accurate and detailed three-dimensional (3D) structure information of the forest understory. Segmenting individual trees from disordered, discrete, and high-density TLS point clouds is the premise for obtaining accurate individual tree structure parameters of forest understory, pest control [...] Read more.
Terrestrial laser scanning (TLS) can provide accurate and detailed three-dimensional (3D) structure information of the forest understory. Segmenting individual trees from disordered, discrete, and high-density TLS point clouds is the premise for obtaining accurate individual tree structure parameters of forest understory, pest control and fine modeling. In this study, we propose a bottom-up method to segment individual trees from TLS forest data based on density-based spatial clustering of applications with noise (DBSCAN). In addition, we also improve the DBSCAN based on the distance distribution matrix (DDM) to automatically and adaptively determine the optimal cluster number and the corresponding input parameters. Firstly, the proposed method is based on the improved DBSCAN to detect the trunks and obtain the initial clustering results. Then, the Hough circle fitting method is used to modify the trunk detection results. Finally, individual tree segmentation is realized based on regional growth layer-by-layer clustering. In this paper, we use TLS multi-station scanning data from Chinese artificial forest and German mixed forest, and then evaluate the efficiency of the method from three aspects: overall segmentation, trunk detection and small tree segmentation. Furthermore, the proposed method is compared with three existing individual tree segmentation methods. The results show that the total recall, precision, and F1-score of the proposed method are 90.84%, 95.38% and 0.93, respectively. Compared with traditional DBSCAN, recall, accuracy and F1-score are increased by 6.96%, 4.14% and 0.06, respectively. The individual tree segmentation result of the proposed method is comparable to those of the existing methods, and the optimal parameters can be automatically extracted and the small trees under tall trees can be accurately segmented. Full article
(This article belongs to the Special Issue Laser Scanning of Forest Dynamics)
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19 pages, 12822 KiB  
Article
LiDAR as a Tool for Assessing Change in Vertical Fuel Continuity Following Restoration
by Julia H. Olszewski and John D. Bailey
Forests 2022, 13(4), 503; https://doi.org/10.3390/f13040503 - 24 Mar 2022
Cited by 3 | Viewed by 2199
Abstract
The need for fuel reduction treatments and the restoration of ecosystem resilience has become widespread in forest management given fuel accumulation across many forested landscapes and a growing risk of high-intensity wildfire. However, there has been little research on methods of assessing the [...] Read more.
The need for fuel reduction treatments and the restoration of ecosystem resilience has become widespread in forest management given fuel accumulation across many forested landscapes and a growing risk of high-intensity wildfire. However, there has been little research on methods of assessing the effectiveness of those treatments at landscape scales. Most research has involved small-scale opportunistic case studies focused on incidents where wildland fires encountered recent restoration projects. It is important to assess whether restoration practices are successful at a landscape scale so improvements may be made as treatments are expanded and their individual effectiveness ages. This study used LiDAR acquisitions taken before and after a large-scale forest restoration project in the Malheur National Forest in eastern Oregon to broadly assess changes in fuel structure. The results showed some areas where treatments appeared effective, and other areas where treatments appeared less effective. While some aspects could be modified to improve accuracy, the methods investigated in this study offer forest managers a new option for evaluating the effectiveness of fuel reduction treatments in reducing potential damage due to wildland fire. Full article
(This article belongs to the Special Issue Laser Scanning of Forest Dynamics)
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16 pages, 2620 KiB  
Article
Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster
by Covadonga Prendes, Elena Canga, Celestino Ordoñez, Juan Majada, Mauricio Acuna and Carlos Cabo
Forests 2022, 13(3), 431; https://doi.org/10.3390/f13030431 - 09 Mar 2022
Cited by 2 | Viewed by 2290
Abstract
Tree morphological characteristics, particularly straightness and lean, significantly influence the value of the commercial products that can be obtained. Despite this, they are not usually evaluated in timber field inventories because traditional techniques are labor-intensive and largely subjective, hence the use of these [...] Read more.
Tree morphological characteristics, particularly straightness and lean, significantly influence the value of the commercial products that can be obtained. Despite this, they are not usually evaluated in timber field inventories because traditional techniques are labor-intensive and largely subjective, hence the use of these parameters is limited to research and genetic breeding programs. Here, a non-destructive, fully automated methodology is presented that estimates the parameters for describing straightness and lean using terrestrial laser scanning (TLS) data. It is based on splitting stems into evenly spaced sections and estimating their centers, which are then used to automatically calculate the maximum sagitta, sinuosity, and lean of each tree. The methodology was applied in a breeding trial plot of Pinus pinaster, and the results obtained were compared with field measurements of straightness and lean based on visual classification. The methodology is robust to errors in the estimation of section centers, the basis for calculating shape parameters. Besides, its accuracy compares favorably with traditional field techniques, which often involve problems of misclassification. The new methodology is easy to use, less expensive, and overcomes the drawbacks of traditional field techniques for obtaining straightness and lean measurements. It can be modified to apply to any species and stand typology. Full article
(This article belongs to the Special Issue Laser Scanning of Forest Dynamics)
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23 pages, 4190 KiB  
Article
Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density
by Olga Viedma
Forests 2022, 13(3), 380; https://doi.org/10.3390/f13030380 - 25 Feb 2022
Cited by 3 | Viewed by 1642
Abstract
A key aspect of vegetation monitoring from LiDAR is concerned with the use of comparable data acquired from multitemporal surveys and from different sensors. Accurate digital elevation models (DEMs) to derive vegetation products, are required to make comparisons among repeated LiDAR data. Here, [...] Read more.
A key aspect of vegetation monitoring from LiDAR is concerned with the use of comparable data acquired from multitemporal surveys and from different sensors. Accurate digital elevation models (DEMs) to derive vegetation products, are required to make comparisons among repeated LiDAR data. Here, we aimed to apply an improved empirical method based on DEMs of difference, that adjust the ground elevation of a low-density LiDAR dataset to that of a high-density LiDAR one for ensuring credible vegetation changes. The study areas are a collection of six sites over the Sierra de Gredos in Central Spain. The methodology consisted of producing “the best DEM of difference” between low- and high-density LiDAR data (using the classification filter, the interpolation method and the spatial resolution with the lowest vertical error) to generate a local “pseudo-geoid” (i.e., continuous surfaces of elevation differences) that was used to correct raw low-density LiDAR ground points. The vertical error of DEMs was estimated by the 50th percentile (P50), the normalized median absolute deviation (NMAD) and the root mean square error (RMSE) of elevation differences. In addition, we analyzed the effects of site-properties (elevation, slope, vegetation height and distance to the nearest geoid point) on DEMs accuracy. Finally, we assessed if vegetation height changes were related to the ground elevation differences between low- and high-density LiDAR datasets. Before correction and aggregating by sites, the vertical error of DEMs ranged from 0.02 to −2.09 m (P50), from 0.39 to 0.85 m (NMDA) and from 0.54 to 2.5 m (RMSE). The segmented-based filter algorithm (CSF) showed the highest error, but there were not significant differences among interpolation methods or spatial resolutions. After correction and aggregating by sites, the vertical error of DEMs dropped significantly: from −0.004 to −0.016 m (P50), from 0.10 to 0.06 m (NMDA) and from 0.28 to 0.46 m (RMSE); and the CSF filter algorithm continued showing the greatest vertical error. The terrain slope and the distance to the nearest geoid point were the most important variables for explaining vertical accuracy. After corrections, changes in vegetation height were decoupled from vertical errors of DEMs. This work showed that using continuous surfaces with the lowest elevation differences (i.e., the best DEM of difference) the raw elevation of low-density LiDAR was better adjusted to that of a benchmark for being adapted to site-specific conditions. This method improved the vertical accuracy of low-density LiDAR elevation data, minimizing the random nature of vertical errors and decoupling vegetation changes from those errors. Full article
(This article belongs to the Special Issue Laser Scanning of Forest Dynamics)
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Graphical abstract

10 pages, 2700 KiB  
Article
Planning of Commercial Thinnings Using Machine Learning and Airborne Lidar Data
by Tauri Arumäe, Mait Lang, Allan Sims and Diana Laarmann
Forests 2022, 13(2), 206; https://doi.org/10.3390/f13020206 - 29 Jan 2022
Cited by 1 | Viewed by 2071
Abstract
The goal of this study was to predict the need for commercial thinning using airborne lidar data (ALS) with random forest (RF) machine learning algorithm. Two test sites (with areas of 14,750 km2 and 12,630 km2) were used with a [...] Read more.
The goal of this study was to predict the need for commercial thinning using airborne lidar data (ALS) with random forest (RF) machine learning algorithm. Two test sites (with areas of 14,750 km2 and 12,630 km2) were used with a total of 1053 forest stands from southwestern Estonia and 951 forest stands from southeastern Estonia. The thinnings were predicted based on the ALS measurements in 2019 and 2017. The two most important ALS metrics for predicting the need for thinning were the 95th height percentile and the canopy cover. The prediction accuracy based on validation stands was 93.5% for southwestern Estonia and 85.7% for southeastern Estonia. For comparison, the general linear model prediction accuracy was less for both test sites—92.1% for southwest and 81.8% for southeast. The selected important predictive ALS metrics differed from those used in the RF algorithm. The cross-validation of the thinning necessity models of southeastern and southwestern Estonia showed a dependence on geographic regions. Full article
(This article belongs to the Special Issue Laser Scanning of Forest Dynamics)
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