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Special Issue "Terrestrial Laser Scanning of Forest Structure"

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 11192

Special Issue Editors

Dr. Markus Eichhorn
E-Mail Website
Guest Editor
School of Biological, Earth and Environmental Sciences, University College Cork, Distillery Fields, North Mall, Cork T23 N73K, Ireland
Interests: forest structure; terrestrial laser scanning; spatial patterning; plant growth; agroforestry

Special Issue Information

Dear Colleagues,

Terrestrial laser scanning (TLS) has the potential to revolutionise forest surveying. By allowing forests to be described in three dimensions, and at high resolution, it opens up the possibility for increasing both the accuracy of existing measurements and developing novel insights. While the foundations of this work have been laid through proof-of-concept studies, we are now in a position to make transformative advances.

Nevertheless, there remain a number of challenges. These include practical issues such as evaluating new and upgraded platforms for data collection, with many new devices in development; designing and assessing survey protocols; comparing TLS with traditional approaches; and integrating TLS data with complementary methods. Once scans have been collected, a new set of problems emerge, particularly in how to convert point cloud data into robust and replicable metrics of forest structure. Finally, while proof-of-principle work is still necessary, there have to date been relatively few direct applications of TLS to applied problems.

In this issue, we welcome all studies which deploy TLS approaches in forest ecosystems, whether natural or designed. We intend to cover all aspects from field methods, data processing, statistical analyses, and applications. The issue will therefore be a comprehensive account of the state of the field, along with providing inspiration and direction to this rapidly advancing area of research.

Specific topics include, but are not limited to:

  • Demonstration of methodologies for field data collection
  • Evaluation of novel TLS technologies (static or mobile)
  • Comparison between TLS and other survey approaches
  • Integration of airborne and terrestrial laser scanning
  • Software approaches to data visualisation
  • Extraction of forest physical parameters from point clouds
  • Statistical approaches to inference from point clouds
  • Applications of TLS data in forestry, forest ecology or conservation
  • Calibration and validation of forest growth models using TLS data

Dr. Markus Eichhorn
Dr. Ting Yun
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 2500 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 clouds
  • terrestrial laser scanning
  • computer graphics
  • forest mensuration
  • forest structure
  • forestry
  • forest ecology

Published Papers (12 papers)

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Research

Article
Refined Aboveground Biomass Estimation of Moso Bamboo Forest Using Culm Lengths Extracted from TLS Point Cloud
Remote Sens. 2022, 14(21), 5537; https://doi.org/10.3390/rs14215537 - 03 Nov 2022
Viewed by 316
Abstract
Bamboo forest is a special forest type, and its aboveground biomass (AGB) is a key indicator of its carbon sequestration capacity and ecosystem productivity. Due to its complex canopy structure and particular growth pattern, the AGBs of individual bamboos that were estimated using [...] Read more.
Bamboo forest is a special forest type, and its aboveground biomass (AGB) is a key indicator of its carbon sequestration capacity and ecosystem productivity. Due to its complex canopy structure and particular growth pattern, the AGBs of individual bamboos that were estimated using traditional remotely sensed data are of relatively low accuracy. In recent years, the point cloud data scanned by terrestrial laser scanners (TLS) offer the possibility for more accurate estimations of bamboo AGB. However, bamboo culms tend to have various bending degrees during the growth process, which causes the AGB estimated on culm height (H) to be generally less than the true value. In this paper, taking one sample plot of the Moso bamboo forest in Hutou Village, Chongqing, China as the study site, we employed a TLS to acquire the point cloud data. The layer-wise distance discrimination method was first developed to accurately segment individual bamboos from the dense stand. Next, the diameter at breast height (DBH) and culm length (L) of an individual bamboo were precisely extracted by fitting the cross-section circle and constructing the longitudinal axis of the bamboo culm, respectively. Lastly, the AGBs of the Moso bamboos in the study site were separately calculated using the allometric equations with the DBH and L as predictor variables. As results, the precision of the complete bamboo segmentation was 90.4%; the absolute error (AE) of the extracted DBHs ranged from −1.22 cm to 0.88 cm (R2 = 0.93, RMSE = 0.40 cm); the AE of the extracted Hs varied from –0.77 m to 1.02 m (R2 = 0.91, RMSE = 0.45 m); and the AE of the extracted Ls varied from −1.08 m to 0.77 m (R2 = 0.95, RMSE = 0.23 m). The total estimated AGB of the Moso bamboos in the sample plot increased by 2.85%, from 680.40 kg on H to 696.36 kg on L. These measurements demonstrated the unique benefits of the TLS-acquired point cloud in characterizing the structural parameters of Moso bamboos and estimating their AGBs with high accuracy. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Terrestrial Laser Scanning in Assessing the Effect of Different Thinning Treatments on the Competition of Scots Pine (Pinus sylvestris L.) Forests
Remote Sens. 2022, 14(20), 5196; https://doi.org/10.3390/rs14205196 - 17 Oct 2022
Viewed by 544
Abstract
Thinning is a forest management activity that regulates the competition between the trees within a forest. However, the effect of different thinning treatments on competition is largely unexplored, especially because of the difficulty in measuring crown characteristics. This study aimed to investigate how [...] Read more.
Thinning is a forest management activity that regulates the competition between the trees within a forest. However, the effect of different thinning treatments on competition is largely unexplored, especially because of the difficulty in measuring crown characteristics. This study aimed to investigate how different type and intensity thinning treatments affect the stem- and crown-based competition of trees based on terrestrial laser scanning (TLS) point clouds. The research was conducted in three study sites in southern Finland where the Scots pine (Pinus sylvestris L.) is the dominant tree species. Nine rectangular sample plots of varying sizes (1000 m2 to 1200 m2) were established within each study site, resulting in 27 sample plots in total. The experimental design of each study site included two levels of thinning intensities and three thinning types, resulting in six different thinning treatments. To assess the competition between the trees, six distance-dependent competition indices were computed for each tree. The indices were based on diameter at breast height (DBH) (CIDBH), height (CIH), maximum crown diameter (CIMCD), crown projection area (CICA), crown volume (CICV), and crown surface area (CICS). The results showed that for both moderate and intensive intensities, the competition decrease was 45.5–82.5% for thinning from below, 15.6–73.6% for thinning from above, and 12.8–66.8% for systematic thinning when compared with control plots. In most cases, the crown- and stem-based metrics were affected by thinning treatments significantly when compared with control plots at a 95% confidence interval. Moreover, moderate from-below and from-above thinning showed no statistical difference with each other in both crown- and stem-based competition indices except for CIDBH (p-value ≤ 0.05). Our results confirm the great potential of TLS point clouds in quantifying stem- and crown-based competition between trees, which could be beneficial for enhancing ecological knowledge on how trees grow in response to competition. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Mobile Laser Scanning for Estimating Tree Structural Attributes in a Temperate Hardwood Forest
Remote Sens. 2022, 14(18), 4522; https://doi.org/10.3390/rs14184522 - 10 Sep 2022
Viewed by 748
Abstract
The emergence of mobile laser scanning (MLS) systems that use simultaneous localization and mapping (SLAM) technology to map their environment opens up new opportunities for characterizing forest structure. The speed and accuracy of data acquisition makes them particularly adapted to operational inventories. MLS [...] Read more.
The emergence of mobile laser scanning (MLS) systems that use simultaneous localization and mapping (SLAM) technology to map their environment opens up new opportunities for characterizing forest structure. The speed and accuracy of data acquisition makes them particularly adapted to operational inventories. MLS also shows great potential for estimating inventory attributes that are difficult to measure in the field, such as wood volume or crown dimensions. Hardwood species represent a significant challenge for wood volume estimation compared to softwoods because a substantial portion of the volume is included in the crown, making them more prone to allometric bias and more complex to model. This study assessed the potential of MLS data to estimate tree structural attributes in a temperate hardwood stand: height, crown dimensions, diameter at breast height (DBH), and merchantable wood volume. Merchantable wood volume estimates were evaluated to the third branching order using the quantitative structural modeling (QSM) approach. Destructive field measurements and terrestrial laser scanning (TLS) data of 26 hardwood trees were used as reference to quantify errors on wood volume and inventory attribute estimations from MLS data. Results reveal that SLAM-based MLS systems provided accurate estimates of tree height (RMSE = 0.42 m (1.78%), R2 = 0.93), crown projected area (RMSE = 3.23 m2 (5.75%), R2 = 0.99), crown volume (RMSE = 71.4 m3 (23.38%), R2 = 0.99), DBH (RMSE = 1.21 cm (3.07%), R2 = 0.99), and merchantable wood volume (RMSE = 0.39 m3 (18.57%), R2 = 0.95), when compared to TLS. They also estimated operational merchantable volume with good accuracy (RMSE = 0.42 m3 (21.82%), R2 = 0.94) compared to destructive measurements. Finally, the merchantable stem volume derived from MLS data was estimated with high accuracy compared to TLS (RMSE = 0.11 m3 (8.32%), R2 = 0.96) and regional stem taper models (RMSE = 0.16 m3 (14.7%), R2 = 0.93). We expect our results would provide a better understanding of the potential of SLAM-based MLS systems to support in-situ forest inventory. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Extraction of Liana Stems Using Geometric Features from Terrestrial Laser Scanning Point Clouds
Remote Sens. 2022, 14(16), 4039; https://doi.org/10.3390/rs14164039 - 18 Aug 2022
Viewed by 502
Abstract
Lianas are self-supporting systems that are increasing their dominance in tropical forests due to climate change. As lianas increase tree mortality and reduce tree growth, one key challenge in ecological remote sensing is the separation of a liana and its host tree using [...] Read more.
Lianas are self-supporting systems that are increasing their dominance in tropical forests due to climate change. As lianas increase tree mortality and reduce tree growth, one key challenge in ecological remote sensing is the separation of a liana and its host tree using remote sensing techniques. This separation can provide essential insights into how tropical forests respond, from the point of view of ecosystem structure to climate and environmental change. Here, we propose a new machine learning method, derived from Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting) algorithms, to separate lianas and trees using Terrestrial Laser Scanning (TLS) point clouds. We test our method on five tropical dry forest trees with different levels of liana infestation. First, we use a multiple radius search method to define the optimal radius of six geometric features. Second, we compare the performance of RF and XGBoosting algorithms on the classification of lianas and trees. Finally, we evaluate our model against independent data collected by other projects. Our results show that the XGBoosting algorithm achieves an overall accuracy of 0.88 (recall of 0.66), and the RF algorithm has an accuracy of 0.85 (recall of 0.56). Our results also show that the optimal radius method is as accurate as the multiple radius method, with F1 scores of 0.49 and 0.48, respectively. The RF algorithm shows the highest recall of 0.88 on the independent data. Our method provides a new flexible approach to extracting lianas from 3D point clouds, facilitating TLS to support new studies aimed to evaluate the impact of lianas on tree and forest structures using point clouds. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
A Deep Learning-Based Method for Extracting Standing Wood Feature Parameters from Terrestrial Laser Scanning Point Clouds of Artificially Planted Forest
Remote Sens. 2022, 14(15), 3842; https://doi.org/10.3390/rs14153842 - 08 Aug 2022
Cited by 1 | Viewed by 737
Abstract
The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and [...] Read more.
The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and ranging (LiDAR) scanning, the stand information of trees in large areas and complex terrain can be obtained more efficiently. However, due to the diversity of the forest floor, the morphological diversity of the trees, and the fact that forestry is often planted as large-scale plantations, efficiently segmenting the point cloud of artificially planted forests and extracting standing wood feature parameters remains a considerable challenge. An effective method based on energy segmentation and PointCNN is proposed in this work to address this issue. The network is enhanced for learning point cloud features by geometric feature balance model (GFBM), enabling the efficient segmentation of tree point clouds from forestry point cloud data collected by terrestrial laser scanning (TLS) in outdoor environments. The 3D Forest software is then used to obtain single wood point cloud after semantic segmentation, and the extracted single wood point cloud is finally employed to extract standing wood feature parameters using TreeQSM. The point cloud semantic segmentation method is the most important part of our research. According to our findings, this method can segment datasets of two different artificially planted woodland point clouds with an overall accuracy of 0.95 and a tree segmentation accuracy of 0.93. When compared with the manual measurements, the root-mean-square error (RMSE) for tree height in the two datasets are 0.30272 and 0.21015 m, and the RMSEs for the diameter at breast height are 0.01436 and 0.01222 m, respectively. Our method is a robust framework based on deep learning that is applicable to forestry for extracting the feature parameters of artificially planted trees. It solves the problem of segmenting tree point clouds in artificially planted trees and provides a reliable data processing method for tree information extraction, trunk shape analysis, etc. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping
Remote Sens. 2022, 14(14), 3344; https://doi.org/10.3390/rs14143344 - 11 Jul 2022
Cited by 1 | Viewed by 1026
Abstract
Phenotyping has been a reality for aiding the selection of optimal crops for specific environments for decades in various horticultural industries. However, until recently, phenotyping was less accessible to tree breeders due to the size of the crop, the length of the rotation [...] Read more.
Phenotyping has been a reality for aiding the selection of optimal crops for specific environments for decades in various horticultural industries. However, until recently, phenotyping was less accessible to tree breeders due to the size of the crop, the length of the rotation and the difficulty in acquiring detailed measurements. With the advent of affordable and non-destructive technologies, such as mobile laser scanners (MLS), phenotyping of mature forests is now becoming practical. Despite the potential of MLS technology, few studies included detailed assessments of its accuracy in mature plantations. In this study, we assessed a novel, high-density MLS operated below canopy for its ability to derive phenotypic measurements from mature Pinus radiata. MLS data were co-registered with above-canopy UAV laser scanner (ULS) data and imported to a pipeline that segments individual trees from the point cloud before extracting tree-level metrics. The metrics studied include tree height, diameter at breast height (DBH), stem volume and whorl characteristics. MLS-derived tree metrics were compared to field measurements and metrics derived from ULS alone. Our pipeline was able to segment individual trees with a success rate of 90.3%. We also observed strong agreement between field measurements and MLS-derived DBH (R2 = 0.99, RMSE = 5.4%) and stem volume (R2 = 0.99, RMSE = 10.16%). Additionally, we proposed a new variable height method for deriving DBH to avoid swelling, with an overall accuracy of 52% for identifying the correct method for where to take the diameter measurement. A key finding of this study was that MLS data acquired from below the canopy was able to derive canopy heights with a level of accuracy comparable to a high-end ULS scanner (R2 = 0.94, RMSE = 3.02%), negating the need for capturing above-canopy data to obtain accurate canopy height models. Overall, the findings of this study demonstrate that even in mature forests, MLS technology holds strong potential for advancing forest phenotyping and tree measurement. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
LiDAR Voxel-Size Optimization for Canopy Gap Estimation
Remote Sens. 2022, 14(5), 1054; https://doi.org/10.3390/rs14051054 - 22 Feb 2022
Cited by 1 | Viewed by 728
Abstract
Terrestrial laser scanning of forest structure is used increasingly in place of traditional technologies; however, deriving physical parameters from point clouds remains challenging because LiDAR returns do not have defined areas or volumes. While voxelization methods overcome this challenge, estimation of canopy gaps [...] Read more.
Terrestrial laser scanning of forest structure is used increasingly in place of traditional technologies; however, deriving physical parameters from point clouds remains challenging because LiDAR returns do not have defined areas or volumes. While voxelization methods overcome this challenge, estimation of canopy gaps and other structural attributes are often performed by reducing the point cloud to two-dimensions, thus decreasing the fidelity of the data. Furthermore, relatively few studies have evaluated voxel-size effects on estimation accuracy. Here, we show that voxelized laser-scanning data can be used for canopy-gap estimation without performing dimensionality reduction to the point cloud. Both airborne and terrestrial LiDAR were used to estimate canopy gaps along six vertical transects and four height intervals. Voxel-based estimates were evaluated against hemispherical photography and a sensitivity analysis was performed to identify an optimal voxel size. While the results indicate that our approach can be used with both airborne and terrestrial LiDAR, voxel size has a considerable influence on canopy-gap estimation. Results from our sensitivity analysis indicate that TLS estimation performs best when using 10 cm voxels, yielding canopy gaps ranging from 32–78%. The optimal voxel size for ALS estimation was obtained with 25 cm voxels, yielding estimates ranging from 25–68%. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Quantifying Crown Morphology of Mixed Pine-Oak Forests Using Terrestrial Laser Scanning
Remote Sens. 2021, 13(23), 4955; https://doi.org/10.3390/rs13234955 - 06 Dec 2021
Cited by 5 | Viewed by 1518
Abstract
Mixed forests make up the majority of natural forests, and they are conducive to improving the resilience and resistance of forest ecosystems. Moreover, it is in the crown of the trees where the effect of inter- and intra-specific interaction between them is evident. [...] Read more.
Mixed forests make up the majority of natural forests, and they are conducive to improving the resilience and resistance of forest ecosystems. Moreover, it is in the crown of the trees where the effect of inter- and intra-specific interaction between them is evident. However, our knowledge of changes in crown morphology caused by density, competition, and mixture of specific species is still limited. Here, we provide insight on stand structural complexity based on the study of four response crown variables (Maximum Crown Width Height, MCWH; Crown Base Height, CBH; Crown Volume, CV; and Crown Projection Area, CPA) derived from multiple terrestrial laser scans. Data were obtained from six permanent plots in Northern Spain comprising of two widespread species across Europe; Scots pine (Pinus sylvestris L.) and sessile oak (Quercus petraea (Matt.) Liebl.). A total of 193 pines and 256 oaks were extracted from the point cloud. Correlation test were conducted (ρ ≥ 0.9) and finally eleven independent variables for each target tree were calculated and categorized into size, density, competition and mixture, which was included as a continuous variable. Linear and non-linear multiple regressions were used to fit models to the four crown variables and the best models were selected according to the lowest AIC Index and biological sense. Our results provide evidence for species plasticity to diverse neighborhoods and show complementarity between pines and oaks in mixtures, where pines have higher MCWH and CBH than oaks but lower CV and CPA, contrary to oaks. The species complementarity in crown variables confirm that mixtures can be used to increase above ground structural diversity. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Reliable Estimates of Merchantable Timber Volume from Terrestrial Laser Scanning
Remote Sens. 2021, 13(18), 3610; https://doi.org/10.3390/rs13183610 - 10 Sep 2021
Cited by 3 | Viewed by 693
Abstract
Simple and accurate determination of merchantable tree height is needed for accurate estimations of merchantable volume. Conventional field methods of forest inventory can lead to biased estimates of tree height and diameter, especially in complex forest structures. Terrestrial laser scanner (TLS) data can [...] Read more.
Simple and accurate determination of merchantable tree height is needed for accurate estimations of merchantable volume. Conventional field methods of forest inventory can lead to biased estimates of tree height and diameter, especially in complex forest structures. Terrestrial laser scanner (TLS) data can be used to determine merchantable height and diameter at different heights with high accuracy and detail. This study focuses on the use of the random sampling consensus method (RANSAC) for generating the length and diameter of logs to estimate merchantable volume at the tree level using Huber’s formula. For this study, we used two plots; plot A contained deciduous trees and plot B consisted of conifers. Our results demonstrated that the TLS-based outputs for stem modelling using the RANSAC method performed very well with low bias (0.02 for deciduous and 0.01 for conifers) and a high degree of accuracy (97.73% for deciduous and 96.14% for conifers). We also found a high correlation between the proposed method and log length (−0.814 for plot A and −0.698 for plot B), which is an important finding because this information can be used to determine the optimum log properties required for analyzing stem curvature changes at different heights. Furthermore, the results of this study provide insight into the applicability and ergonomics during data collection from forest inventories solely from terrestrial laser scanning, thus reducing the need for field reference data. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis
Remote Sens. 2021, 13(17), 3444; https://doi.org/10.3390/rs13173444 - 30 Aug 2021
Cited by 2 | Viewed by 750
Abstract
Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) [...] Read more.
Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Design of a Generic Virtual Measurement Workflow for Processing Archived Point Cloud of Trees and Its Implementation of Light Condition Measurements on Stems
Remote Sens. 2021, 13(14), 2801; https://doi.org/10.3390/rs13142801 - 16 Jul 2021
Viewed by 1085
Abstract
Virtual measurement workflow (VMW) was a generic data mining method developed in this study. It was used to extract tree information from archived point clouds under limited conditions by applying virtual measurements in virtual reality. As an example of how to use VMW [...] Read more.
Virtual measurement workflow (VMW) was a generic data mining method developed in this study. It was used to extract tree information from archived point clouds under limited conditions by applying virtual measurements in virtual reality. As an example of how to use VMW for a specific topic, the VMW implementation of light condition measurement was further developed. This implementation could measure the temporal and spatial distribution of sunlight on virtual trees (stems). The output was expected as a new type of raw measurement data for tree morphology and phycological studies. At a single tree scale, it facilitated the quantitative interpretation of the growth strategy of branches. By measuring a single tree, it was found that only 4.34% of the stem surface could be illuminated throughout the day (8 h). Meanwhile, 35.87% of the stem surfaces were exposed to sunlight for less than one hour a day. A further mathematical processing of the output, i.e., γ (a ratio between relative area of triangles and relative quantities of triangles in each exposure duration group) improved the sensitivity of identifying differences in lighting conditions. Furthermore, we measured virtual trees of four species from an additional data source using a standardized setting. These include the sessile oak, gemu tree, Masson’s pine, and cherry tree. It was found that the shape of the crown was also significant for the distribution of solar energy on stems. For instance, the gemu tree had a cylindrical tree crown with narrow tree skeleton. A percentage of 10.38 of the surface on the gemu tree was illuminated throughout the day (8 h). The Masson’s pine had similar height and DBH with the gemu tree. However, the elliptical tree crown of the Masson’s pines prevented more lights. The area on the stem that was exposed to sunlight (8 h) dropped from 10.38% to 5.71%. This good differentiation of different crown structures might help this VMW implementation to continue to develop as a tool for identifying the effect of various crown shapes on radiosity for different tree species. The successful development of this VMW implementation had several practical applications for tree studies. Meanwhile, it demonstrated the overall feasibility of VMW and provided a paradigm for further development of other VMW implementations. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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Article
Quantifying Understory Complexity in Unmanaged Forests Using TLS and Identifying Some of Its Major Drivers
Remote Sens. 2021, 13(8), 1513; https://doi.org/10.3390/rs13081513 - 14 Apr 2021
Cited by 5 | Viewed by 1179
Abstract
The structural complexity of the understory layer of forests or shrub layer vegetation in open shrublands affects many ecosystem functions and services provided by these ecosystems. We investigated how the basal area of the overstory layer, annual and seasonal precipitation, annual mean temperature, [...] Read more.
The structural complexity of the understory layer of forests or shrub layer vegetation in open shrublands affects many ecosystem functions and services provided by these ecosystems. We investigated how the basal area of the overstory layer, annual and seasonal precipitation, annual mean temperature, as well as light availability affect the structural complexity of the understory layer along a gradient from closed forests to open shrubland with only scattered trees. Using terrestrial laser scanning data and the understory complexity index (UCI), we measured the structural complexity of sites across a wide range of precipitation and temperature, also covering a gradient in light availability and basal area. We found significant relationships between the UCI and tree basal area as well as canopy openness. Structural equation models (SEMs) confirmed significant direct effects of seasonal precipitation on the UCI without mediation through basal area or canopy openness. However, annual precipitation and temperature effects on the UCI are mediated through canopy openness and basal area, respectively. Understory complexity is, despite clear dependencies on the available light and overall stand density, significantly and directly driven by climatic parameters, particularly the amount of precipitation during the driest month. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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