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Special Issue "Forest Ground Observations through Terrestrial Point Clouds"

A special issue of Forests (ISSN 1999-4907).

Deadline for manuscript submissions: closed (30 June 2016)

Special Issue Editors

Guest Editor
Prof. Juha Hyyppä

Finish Geospatial Research Institute, Masala, Finland
Website | E-Mail
Interests: laser scanning (airborne, mobile and terrestrial); 3D remote sensing; individual tree detection; virtual forests
Guest Editor
Dr. Xinlian Liang

Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, Geodeetinrinne 2, P.O.Box 15, FI-02430 Masala, Finland
Website | E-Mail
Interests: laser scanning (airborne, mobile, and terrestrial); 3D remote sensing; Forest ecosystems
Co-Guest Editor
Dr. Eetu Puttonen

Remote Sensing and Photogrammetry Finnish Geodet Inst, Masala 02430, Finland
E-Mail

Special Issue Information

Dear Colleagues,

Ground observations of the forest environment collect tree-related information that are used as fundamental inputs in various applications, such as forest monitoring, inventories, conservation, to name only a few. Forest ground measurements are conventionally carried out though destructive and/or non-destructive manual measurements, which are widely recognized as expensive and labor-intensive. Consequently, the amount of ground observations made in a particular application is typically very limited, often much less than what is needed.

More recently, terrestrial point clouds, which consist of millions/billions of three-dimensional points reflected from objects collected by, e.g., laser scanning, have become available for collecting forest ground data. By applying sophisticated algorithms, many tree attributes can be extracted at both individual-tree- and plot-levels, such as diameter-at-breast-height (DBH), leaf area index (LAI), stem volume, and biomass. The last decade has witnessed great changes in the research and acceptance of terrestrial point clouds as a forest ground observation tool, where innovative algorithms, applications, and hardware have played key roles in these rapid developments.

In this context, this Special Issue tries to document state-of-the-art developments in forest ground observations through terrestrial point clouds in the form of innovative papers concerning research, development, and operational use. Prospective authors are invited to contribute to this Special Issue of Forests by submitting manuscripts of their latest research on related topics. Practice-orientated papers are preferred. Reviews are also welcomed. Topics may be from, but not limited to:

•           New sources of terrestrial point clouds in forest environments

•           New methods in information extraction in forest environments, using terrestrial point clouds, e.g., automated feature extraction and object recognition from terrestrial and mobile point clouds

•           New applications and concepts of using terrestrial point clouds in areas, e.g., of forest inventory, forest ecology, and biodiversity.

•           Techniques for the fusion of terrestrial point cloud data with data from other sensors, e.g., airborne sensors and satellite data

•           Accuracy and performance evaluations

Submitted manuscripts must be original contributions, not ones previously published or submitted to other journals. Papers published or submitted for publication in conference proceedings may be considered, provided that they are considerably extended and improved. Papers must follow the instructions for authors at http://www.mdpi.com/journal/forests/instructions.

Prof. Dr. Juha Hyyppä
Dr. Xinlian Liang
Guest Editor

Dr.Eetu Puttonen
Co-Guest Editor

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 papers will be 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 1200 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

  • point cloud
  • terrestrial laser scanning/ground-based LiDAR
  • mobile laser scanning
  • airborne laser scanning
  • volume/biomass
  • leaf area index
  • canopy structure
  • modelling forest stand structure
  • modelling of forests

Published Papers (13 papers)

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Research

Open AccessArticle Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna
Forests 2016, 7(12), 294; doi:10.3390/f7120294
Received: 8 August 2016 / Revised: 3 November 2016 / Accepted: 18 November 2016 / Published: 25 November 2016
PDF Full-text (7028 KB) | HTML Full-text | XML Full-text
Abstract
The use of optical remote sensing data for savanna vegetation structure mapping is hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud cover and unpredictable weather
[...] Read more.
The use of optical remote sensing data for savanna vegetation structure mapping is hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud cover and unpredictable weather conditions. Longwave microwave data, with its low sensitivity to clouds addresses some of these problems, but many space borne studies are still limited by low quality structural reference data. Terrestrial laser scanning (TLS) derived canopy cover and height metrics can improve aboveground biomass (AGB) prediction at both plot and landscape level. To date, few studies have explored the strength of TLS for vegetation structural mapping, and particularly few focusing on savannas. In this study, we evaluate the potential of high resolution TLS-derived canopy cover and height metrics to estimate plot-level aboveground biomass, and to extrapolate to a landscape-wide biomass estimation using multi-temporal L-band Synthetic Aperture Radar (SAR) within a 9 km2 area savanna in Kruger National Park (KNP). We inventoried 42 field plots in the wet season and computed AGB for each plot using site-specific allometry. Canopy cover, canopy height, and their product were regressed with plot-level AGB over the TLS-footprint, while SAR backscatter was used to model dry season biomass for the years 2007, 2008, 2009, and 2010 for the study area. The results from model validation showed a significant linear relationship between TLS-derived predictors with field biomass, p < 0.05 and adjusted R2 ranging between 0.56 for SAR to 0.93 for the TLS-derived canopy cover and height. Log-transformed AGB yielded lower errors with TLS metrics compared with non-transformed AGB. An assessment of the backscatter based on root mean square error (RMSE) showed better AGB prediction with cross-polarized (RMSE = 6.6 t/ha) as opposed to co-polarized data (RMSE = 6.7 t/ha), attributed to volume scattering of woody vegetation along river valleys and streams. The AGB change analysis showed 32 ha (3.5%) of the 900 ha experienced AGB loses above an average of 5 t/ha per annum, which can mainly be attributed to the falling of trees by mega herbivores such as elephants. The study concludes that SAR data, especially L-band SAR, can be used in the detection of small changes in savanna vegetation over time. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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Open AccessArticle Automating Plot-Level Stem Analysis from Terrestrial Laser Scanning
Forests 2016, 7(11), 252; doi:10.3390/f7110252
Received: 25 August 2016 / Revised: 8 October 2016 / Accepted: 24 October 2016 / Published: 28 October 2016
PDF Full-text (5759 KB) | HTML Full-text | XML Full-text
Abstract
Terrestrial laser scanning (TLS) provides an accurate means of analyzing individual tree attributes, and can be extended to plots using multiple TLS scans. However, multiple TLS scans may reduce the effectiveness of individual tree structure quantification, often due to understory occupation, mutual tree
[...] Read more.
Terrestrial laser scanning (TLS) provides an accurate means of analyzing individual tree attributes, and can be extended to plots using multiple TLS scans. However, multiple TLS scans may reduce the effectiveness of individual tree structure quantification, often due to understory occupation, mutual tree occlusion, and other influences. The procedure to delineate accurate tree attributes from plot scans involves onerous steps and automated integration is challenging in the literature. This study proposes a fully automatic approach composed of ground filtering, stem detection, and stem form extraction algorithms, with emphasis on accuracy and feasibility. The delineated attributes can be useful to analyze terrain, tree biomass and fiber quality. The automation was experimented on a mature pine plot in Finland with both single scan (SS) and multiple scans (MS) datasets. With mensuration as reference, digital terrain models (DTM), stem locations, diameters at breast height (DBHs), stem heights, and stem forms of the whole plot were extracted and validated. Results of this study were best using the multiple scans (MS) dataset, where 76% of stems were detected (n = 49). Height extraction accuracy was 0.68 (r2) and 1.7 m (RMSE), and DBH extraction accuracy was 0.97 (r2) and 0.90 cm (RMSE). Height-wise stem diameter extraction accuracy was 0.76 (r2) and 2.4 cm (RMSE). Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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Open AccessArticle Tree Stem Diameter Estimation from Mobile Laser Scanning Using Line-Wise Intensity-Based Clustering
Forests 2016, 7(9), 206; doi:10.3390/f7090206
Received: 29 June 2016 / Revised: 25 August 2016 / Accepted: 12 September 2016 / Published: 16 September 2016
Cited by 1 | PDF Full-text (2103 KB) | HTML Full-text | XML Full-text
Abstract
Diameter at breast height has been estimated from mobile laser scanning using a new set of methods. A 2D laser scanner was mounted facing forward, tilted nine degrees downwards, on a car. The trajectory was recorded using inertial navigation and visual SLAM (simultaneous
[...] Read more.
Diameter at breast height has been estimated from mobile laser scanning using a new set of methods. A 2D laser scanner was mounted facing forward, tilted nine degrees downwards, on a car. The trajectory was recorded using inertial navigation and visual SLAM (simultaneous localization and mapping). The laser scanner data, the trajectory and the orientation were used to calculate a 3D point cloud. Clusters representing trees were extracted line-wise to reduce the effects of uncertainty in the positioning system. The intensity of the laser echoes was used to filter out unreliable echoes only grazing a stem. The movement was used to obtain measurements from a larger part of the stem, and multiple lines from different views were used for the circle fit. Two trigonometric methods and two circle fit methods were tested. The best results with bias 2.3% (6 mm) and root mean squared error 14% (37 mm) were acquired with the circle fit on multiple 2D projected clusters. The method was evaluated compared to field data at five test areas with approximately 300 caliper-measured trees within a 10-m working range. The results show that this method is viable for stem measurements from a moving vehicle, for example a forest harvester. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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Open AccessArticle Single Tree Stem Profile Detection Using Terrestrial Laser Scanner Data, Flatness Saliency Features and Curvature Properties
Forests 2016, 7(9), 207; doi:10.3390/f7090207
Received: 30 June 2016 / Revised: 23 August 2016 / Accepted: 9 September 2016 / Published: 16 September 2016
PDF Full-text (2951 KB) | HTML Full-text | XML Full-text
Abstract
A method for automatic stem detection and stem profile estimation based on terrestrial laser scanning (TLS) was validated. The root-mean-square error was approximately 1 cm for stem diameter estimations. The method contains a new way of extracting the flatness saliency feature using the
[...] Read more.
A method for automatic stem detection and stem profile estimation based on terrestrial laser scanning (TLS) was validated. The root-mean-square error was approximately 1 cm for stem diameter estimations. The method contains a new way of extracting the flatness saliency feature using the centroid of a subset of a point cloud within a voxel cell that approximates the point by point calculations. The loss of accuracy is outweighed by a much higher computational speed, making it possible to cover large datasets. The algorithm introduces a new way to connect surface patches belonging to a stem and investigates if they belong to curved surfaces. Thereby, cylindrical objects, like stems, are found in the pre-filtering stage. The algorithm uses a new cylinder fitting method that estimates the axis direction by transforming the TLS points into a radial-angular coordinate system and evaluates the deviations by a moving window convex hull algorithm. Once the axis direction is found, the cylinder center is chosen as the position with the smallest radial deviations. The cylinder fitting method works on a point cloud in both the single-scan setup, as well as a multiple scan setup of a TLS system. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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Open AccessArticle Forest Stand Inventory Based on Combined Aerial and Terrestrial Close-Range Photogrammetry
Forests 2016, 7(8), 165; doi:10.3390/f7080165
Received: 29 June 2016 / Revised: 27 July 2016 / Accepted: 27 July 2016 / Published: 30 July 2016
Cited by 4 | PDF Full-text (9653 KB) | HTML Full-text | XML Full-text
Abstract
In this article we introduce a new method for forest management inventories especially suitable for highly-valued timber where the precise estimation of stem parameters (diameter, form, and tapper) plays the key role for market purposes. The unmanned aerial system (UAS)-based photogrammetry is combined
[...] Read more.
In this article we introduce a new method for forest management inventories especially suitable for highly-valued timber where the precise estimation of stem parameters (diameter, form, and tapper) plays the key role for market purposes. The unmanned aerial system (UAS)-based photogrammetry is combined with terrestrial photogrammetry executed by walking inside the stand and the individual tree parameters are estimated. We compare two automatic methods for processing of the point clouds and the delineation of stem circumference at breast height. The error of the diameter estimation was observed to be under 1 cm root mean square error (RMSE) and the height estimation error was 1 m. Apart from the mentioned accuracy, the main advantage of the proposed work is shorter time demand for field measurement; we could complete both inventories of 1 hectare forest stand in less than 2 h of field work. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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Open AccessArticle Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning
Forests 2016, 7(6), 127; doi:10.3390/f7060127
Received: 16 April 2016 / Revised: 1 June 2016 / Accepted: 6 June 2016 / Published: 21 June 2016
Cited by 4 | PDF Full-text (3608 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The application of static terrestrial laser scanning (TLS) in forest inventories is becoming more effective. Nevertheless, the occlusion effect is still limiting the processing efficiency to extract forest attributes. The use of a mobile laser scanner (MLS) would reduce this occlusion. In this
[...] Read more.
The application of static terrestrial laser scanning (TLS) in forest inventories is becoming more effective. Nevertheless, the occlusion effect is still limiting the processing efficiency to extract forest attributes. The use of a mobile laser scanner (MLS) would reduce this occlusion. In this study, we assessed and compared a hand-held mobile laser scanner (HMLS) with two TLS approaches (single scan: SS, and multi scan: MS) for the estimation of several forest parameters in a wide range of forest types and structures. We found that SS is competitive to extract the ground surface of forest plots, while MS gives the best result to describe the upper part of the canopy. The whole cross-section at 1.3 m height is scanned for 91% of the trees (DBH > 10 cm) with the HMLS leading to the best results for DBH estimates (bias of −0.08 cm and RMSE of 1.11 cm), compared to no fully-scanned trees for SS and 42% fully-scanned trees for MS. Irregularities, such as bark roughness and non-circular cross-section may explain the negative bias encountered for all of the scanning approaches. The success of using MLS in forests will allow for 3D structure acquisition on a larger scale and in a time-efficient manner. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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Open AccessArticle Deriving Merchantable Volume in Poplar through a Localized Tapering Function from Non-Destructive Terrestrial Laser Scanning
Forests 2016, 7(4), 87; doi:10.3390/f7040087
Received: 19 October 2015 / Revised: 24 March 2016 / Accepted: 9 April 2016 / Published: 20 April 2016
Cited by 2 | PDF Full-text (1329 KB) | HTML Full-text | XML Full-text
Abstract
Timber volume is an important ecological component in forested landscapes. The application of terrestrial laser scanning (TLS) to volume estimation has been widely accepted though few species have well-calibrated taper functions. This research uses TLS technology in poplar (Populus × canadensis Moench
[...] Read more.
Timber volume is an important ecological component in forested landscapes. The application of terrestrial laser scanning (TLS) to volume estimation has been widely accepted though few species have well-calibrated taper functions. This research uses TLS technology in poplar (Populus × canadensis Moench cv. ‘I-72/58’) to extract stem diameter at different tree heights and establish the relationship between point cloud data and stem curve, which constitutes the basis for volume estimation of single trees and the stand. Eight plots were established and scanned by TLS. Stem curve functions were then fitted after extraction of diameters at different height, and tree heights from the point cloud data. Lastly, six functions were evaluated by R2 and RMSE. A modified Schumacher equation was the most suitable taper function. Volume estimates from the TLS-derived taper function were better than those derived using the stem-analysis data. Finally, regression analysis showed that predictions of stem size were similar when data were based on TLS versus stem analysis. Its high accuracy and efficiency indicates that TLS technology can play an important role in forest inventory assessment. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
Open AccessArticle Estimation of Tree Stem Attributes Using Terrestrial Photogrammetry with a Camera Rig
Forests 2016, 7(3), 61; doi:10.3390/f7030061
Received: 16 September 2015 / Revised: 24 February 2016 / Accepted: 26 February 2016 / Published: 8 March 2016
Cited by 3 | PDF Full-text (16837 KB) | HTML Full-text | XML Full-text
Abstract
We propose a novel photogrammetric method for field plot inventory, designed for simplicity and time efficiency on-site. A prototype multi-camera rig was used to acquire images from field plot centers in multiple directions. The acquisition time on-site was less than two minutes. From
[...] Read more.
We propose a novel photogrammetric method for field plot inventory, designed for simplicity and time efficiency on-site. A prototype multi-camera rig was used to acquire images from field plot centers in multiple directions. The acquisition time on-site was less than two minutes. From each view, a point cloud was generated using a novel, rig-based matching of detected SIFT keypoints. Stems were detected in the merged point cloud, and their positions and diameters were estimated. The method was evaluated on 25 hemi-boreal forest plots of a 10-m radius. Due to difficult lighting conditions and faulty hardware, imagery from only six field plots was processed. The method performed best on three plots with clearly visible stems with a 76% detection rate and 0% commission. Diameters could be estimated for 40% of the stems with an RMSE of 2.8–9.5 cm. The results are comparable to other camera-based methods evaluated in a similar manner. The results are inferior to TLS-based methods. However, our method is easily extended to multiple station image schemas, something that could significantly improve the results while retaining low commission errors and time on-site. Furthermore, with smaller hardware, we believe this could be a useful technique for measuring stem attributes in the forest. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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Open AccessArticle Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds
Forests 2016, 7(3), 62; doi:10.3390/f7030062
Received: 12 January 2016 / Revised: 29 February 2016 / Accepted: 3 March 2016 / Published: 7 March 2016
Cited by 22 | PDF Full-text (10047 KB) | HTML Full-text | XML Full-text
Abstract
This study investigates the potential of unmanned aerial vehicles (UAVs) to measure and monitor structural properties of forests. Two remote sensing techniques, airborne laser scanning (ALS) and structure from motion (SfM) were tested to capture three-dimensional structural information from a small multi-rotor UAV
[...] Read more.
This study investigates the potential of unmanned aerial vehicles (UAVs) to measure and monitor structural properties of forests. Two remote sensing techniques, airborne laser scanning (ALS) and structure from motion (SfM) were tested to capture three-dimensional structural information from a small multi-rotor UAV platform. A case study is presented through the analysis of data collected from a 30 × 50 m plot in a dry sclerophyll eucalypt forest with a spatially varying canopy cover. The study provides an insight into the capabilities of both technologies for assessing absolute terrain height, the horizontal and vertical distribution of forest canopy elements, and information related to individual trees. Results indicate that both techniques are capable of providing information that can be used to describe the terrain surface and canopy properties in areas of relatively low canopy closure. However, the SfM photogrammetric technique underperformed ALS in capturing the terrain surface under increasingly denser canopy cover, resulting in point density of less than 1 ground point per m2 and mean difference from ALS terrain surface of 0.12 m. This shortcoming caused errors that were propagated into the estimation of canopy properties, including the individual tree height (root mean square error of 0.92 m for ALS and 1.30 m for SfM). Differences were also seen in the estimates of canopy cover derived from the SfM (50%) and ALS (63%) pointclouds. Although ALS is capable of providing more accurate estimates of the vertical structure of forests across the larger range of canopy densities found in this study, SfM was still found to be an adequate low-cost alternative for surveying of forest stands. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
Open AccessArticle SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds
Forests 2015, 6(11), 4245-4294; doi:10.3390/f6114245
Received: 23 July 2015 / Revised: 9 November 2015 / Accepted: 10 November 2015 / Published: 23 November 2015
Cited by 10 | PDF Full-text (8048 KB) | HTML Full-text | XML Full-text
Abstract
An open source tool named SimpleTree, capable of modelling highly accurate cylindrical tree models from terrestrial laser scan point clouds, is presented and evaluated. All important functionalities, accessible in the software via buttons and dialogues, are described including the explanation of all necessary
[...] Read more.
An open source tool named SimpleTree, capable of modelling highly accurate cylindrical tree models from terrestrial laser scan point clouds, is presented and evaluated. All important functionalities, accessible in the software via buttons and dialogues, are described including the explanation of all necessary input parameters. The method is validated utilizing 101 point clouds of six different tree species, in the main evergreen and coniferous trees. All scanned trees have been destructively harvested to get accurate estimates of above ground biomass with which we assess the accuracy of the SimpleTree-reconstructed cylinder models. The trees were grouped into four data sets and for each one a Concordance Correlation Coefficient of at least 0.92 (0.92, 0.97, 0.92, 0.94) and an total relative error at most ~8 % (2.42%, 3.59%, –4.59%, 8.27%) was achieved in the comparison of the model results to the ground truth data. A global statistical improvement of derived cylinder radii is presented as well as an efficient optimization approach to automatically improve user given input parameters. An additional check of the SimpleTree results is presented via comparison to the results of trees reconstructed using an alternative, published method. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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Open AccessArticle Does Tree Architectural Complexity Influence the Accuracy of Wood Volume Estimates of Single Young Trees by Terrestrial Laser Scanning?
Forests 2015, 6(11), 3847-3867; doi:10.3390/f6113847
Received: 31 July 2015 / Revised: 20 October 2015 / Accepted: 27 October 2015 / Published: 30 October 2015
Cited by 7 | PDF Full-text (906 KB) | HTML Full-text | XML Full-text
Abstract
Accurate estimates of the wood volume or biomass of individual trees have gained considerable importance in recent years. The accuracy of wood volume estimation by terrestrial laser scanning (TLS) point cloud data may differ between individual trees due to species-specific differences in tree
[...] Read more.
Accurate estimates of the wood volume or biomass of individual trees have gained considerable importance in recent years. The accuracy of wood volume estimation by terrestrial laser scanning (TLS) point cloud data may differ between individual trees due to species-specific differences in tree architecture. We selected three common and ecologically important central European deciduous tree species, which differ considerably in tree architectural complexity in early ontogenetic stages: Acer pseudoplatanus (simple), Sorbus aucuparia (intermediate) and Betula pendula (complex). We scanned six single young trees for each species (18 trees in total) under optimal scan conditions (single tree stand, leafless state, four scanning positions, high resolution). TLS-based volume estimates were derived for the total tree as well as for the two compartments; trunk and branches, using a voxel-based bounding box method. These estimates were compared with highly accurate xyolmetric (water displacement) volume measurements. Coefficients of determination between xylometric measurements and bounding box estimates were very high for total trees (R2adj = 0.99), trunks (R2adj = 0.99), and high for branches (R2adj = 0.78). The accuracy of estimations for total tree and trunk volume was highly similar among the three tree species. In contrast, significant differences were found for branch volume estimates: the accuracy was very high for Sorbus aucuparia, intermediate for Betula pendula, and low for Acer pseudoplatanus. A stepwise multiple regression showed that the accuracy of branch volume estimates was negatively related to the number of the first-order branches within diameter sizes of D ≤ 5 mm and crown surface area (R2adj = 0.61). We conclude that the accuracy in total tree and trunk volume estimates was not affected by the studied types of tree architectural complexity. The impact of the structural variability of branches and occlusion by branches was, thus, not as high as expected. In contrast, the accuracy of branch volume estimates was strongly influenced by tree architectural complexity, though not in a simple way. Because underestimations originated from different sources, the accuracy of branch volume estimates cannot be directly derived from the degree of architectural complexity. These results imply that the voxel-based bounding box method provides highly accurate total tree and trunk volume estimates, whereas further research is needed to improve branch volume estimation for young trees of different architectural types. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
Open AccessArticle Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS
Forests 2015, 6(11), 3923-3945; doi:10.3390/f6113923
Received: 31 August 2015 / Revised: 31 August 2015 / Accepted: 28 October 2015 / Published: 30 October 2015
Cited by 3 | PDF Full-text (1637 KB) | HTML Full-text | XML Full-text
Abstract
Stem characteristics of plants are of great importance to both ecology study and forest management. Terrestrial laser scanning (TLS) may provide an effective way to characterize the fine-scale structures of vegetation. However, clumping plants, dense foliage and thin structure could intensify the shadowing
[...] Read more.
Stem characteristics of plants are of great importance to both ecology study and forest management. Terrestrial laser scanning (TLS) may provide an effective way to characterize the fine-scale structures of vegetation. However, clumping plants, dense foliage and thin structure could intensify the shadowing effect and pose a series of problems in identifying stems, distinguishing neighboring stems, and merging disconnected stem parts in point clouds. This paper presents a new method to automatically detect stems in dense and homogeneous forest using single-scan TLS data. Stem points are first identified with a two-scale classification method. Then a clustering approach is used to group the candidate stem points. Finally, a direction-growing algorithm based on a simple stem curve model is applied to merge stem points. Field experiments were carried out in two different bamboo plots with a stem density of about 7500 stems/ha. Overall accuracy of the stem detection is 88% and the quality of detected stems is mainly affected by the shadowing effect. Results indicate that the proposed method is feasible and effective in detection of bamboo stems using TLS data, and can be applied to other species of single-stem plants in dense forests. Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
Open AccessArticle Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density
Forests 2015, 6(4), 1274-1300; doi:10.3390/f6041274
Received: 1 December 2014 / Revised: 26 March 2015 / Accepted: 1 April 2015 / Published: 21 April 2015
Cited by 15 | PDF Full-text (16917 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a method for predicting the above ground leafless biomass of trees in a non destructive way. We utilize terrestrial laserscan data to predict the volume of the trees. Combining volume estimates with density measurements leads to biomass predictions. Thirty-six trees
[...] Read more.
This paper presents a method for predicting the above ground leafless biomass of trees in a non destructive way. We utilize terrestrial laserscan data to predict the volume of the trees. Combining volume estimates with density measurements leads to biomass predictions. Thirty-six trees of three different species are analyzed: evergreen coniferous Pinus massoniana, evergreen broadleaved Erythrophleum fordii and leafless deciduous Quercus petraea. All scans include a large number of noise points; denoising procedures are presented in detail. Density values are considered to be a minor source of error in the method if applied to stem segments, as comparison to ground truth data reveals that prediction errors for the tree volumes are in accordance with biomass prediction errors. While tree compartments with a diameter larger than 10 cm can be modeled accurately, smaller ones, especially twigs with a diameter smaller than 4 cm, are often largely overestimated. Better prediction results could be achieved by applying a biomass expansion factor to the biomass of compartments with a diameter larger than 10 cm. With this second method the average prediction error for Q. petraea could be reduced from 33.84% overestimation to 3.56%. E. fordii results could also be improved reducing the average prediction error from Full article
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
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