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Forests
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Terrestrial and Mobile Laser Scanning in Forestry
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Terrestrial and Mobile Laser Scanning in 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: closed (24 March 2018)

Special Issue Editor

Prof. Dr. Dominik Seidel

E-Mail Website
Guest Editor
Department for Spatial Structures and Digitization of Forests, University of Goettingen, 37077 Goettingen, Germany
Interests: forest structure; tree architecture; structural complexity; LiDAR; structure from motion; structure-function-relationships
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, tripod-based light detection and ranging (LiDAR)—usually referring to terrestrial laser scanning or ground-based LiDAR—has become a powerful tool in forest research. More recently, this was accompanied by mobile LiDAR approaches that utilize instruments mounted on moving platforms, such as cars, ATV’s or transported manually. Both approaches can produce quantitative high-resolution spatial information on our forests. The current major scientific challenge is to derive useful information from the created data (point clouds) in order to generate new insights and benefits for forest research and forest management. In this Special Issue, we encourage studies from all forest ecosystems, including experimental studies, case studies, monitoring and modelling approaches that utilize three-dimensional point clouds from ground-based laser scanning. Articles covering single-scan based approaches, multi-scan based as well as mobile approaches from any kind of ground-based platform will be considered. We seek studies that are not solely technical, but contribute to any field of forest research, such as forest inventory, forest protection, or silviculture.

Dr. Dominik Seidel
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 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

  • Terrestrial LiDAR
  • Mobile Laser Scanning
  • Hand-Held Laser Scanning
  • Point Cloud Processing
  • Supporting Forest Research
  • Forest Structure
  • Tree Structure and Architecture
  • Spatial Information
  • Three-Dimensional Forest Inventory
  • New Measures
  • New Sampling Techniques

Published Papers (3 papers)

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Research

23 pages, 4777 KiB  
Article
Analyzing the Vertical Distribution of Crown Material in Mixed Stand Composed of Two Temperate Tree Species
by Olivier Martin-Ducup, Robert Schneider and Richard A. Fournier
Forests 2018, 9(11), 673; https://doi.org/10.3390/f9110673 - 26 Oct 2018
Cited by 16 | Viewed by 3936
Abstract
The material distribution inside tree crowns is difficult to quantify even though it is an important variable in forest management and ecology. The vertical distribution of a relative density index (i.e., vertical profile) of the total, woody, and leafy material at the crown [...] Read more.
The material distribution inside tree crowns is difficult to quantify even though it is an important variable in forest management and ecology. The vertical distribution of a relative density index (i.e., vertical profile) of the total, woody, and leafy material at the crown scale were estimated from terrestrial laser scanner (TLS) data on two species, sugar maple (Acer saccharum Marsh.) and balsam fir (Abies Balsamea Mill.). An algorithm based on a geometrical approach readily available in the Computree open source platform was used. Beta distributions were then fitted to the vertical profiles and compared to each other. Total and leafy profiles had similar shapes, while woody profiles were different. Thus, the total vertical distribution could be a good proxy for the leaf distribution in the crown. Sugar maple and balsam fir had top heavy and bottom heavy distributions respectively, which can be explained by their respective architectural development. Moreover, the foliage distribution of sugar maples shifted towards the crown base when it was found in mixed stands, when compared to pure stands. The opposite behavior was observed for balsam firs, but less pronounced. According to the shape of the foliage distribution, sugar maple takes advantages from mixture contrarily to balsam fir. From a methodological point of view, we proposed an original approach to separate wood from leaf returns in TLS data while taking into account occlusion. Wood and leaf separation and occlusion problems are two challenging issues for most TLS-based studies in forest ecology. Full article
(This article belongs to the Special Issue Terrestrial and Mobile Laser Scanning in Forestry)
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25 pages, 62806 KiB  
Article
Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
by Anne Bienert, Louis Georgi, Matthias Kunz, Hans-Gerd Maas and Goddert Von Oheimb
Forests 2018, 9(7), 395; https://doi.org/10.3390/f9070395 - 04 Jul 2018
Cited by 66 | Viewed by 6970
Abstract
Terrestrial laser scanning (TLS) has been successfully used for three-dimensional (3D) data capture in forests for almost two decades. Beyond the plot-based data capturing capabilities of TLS, vehicle-based mobile laser scanning (MLS) systems have the clear advantage of fast and precise corridor-like 3D [...] Read more.
Terrestrial laser scanning (TLS) has been successfully used for three-dimensional (3D) data capture in forests for almost two decades. Beyond the plot-based data capturing capabilities of TLS, vehicle-based mobile laser scanning (MLS) systems have the clear advantage of fast and precise corridor-like 3D data capture, thus providing a much larger coverage within shorter acquisition time. This paper compares and discusses advantages and disadvantages of multi-temporal MLS data acquisition compared to established TLS data recording schemes. In this pilot study on integrated TLS and MLS data processing in a forest, it could be shown that existing TLS data evaluation routines can be used for MLS data processing. Methods of automatic laser scanner data processing for forest inventory parameter determination and quantitative structure model (QSM) generation were tested in two sample plots using data from both scanning methods and from different seasons. TLS in a multi-scan configuration delivers very high-density 3D point clouds, which form a valuable basis for generating high-quality QSMs. The pilot study shows that MLS is able to provide high-quality data for an equivalent determination of relevant forest inventory parameters compared to TLS. Parameters such as tree position, diameter at breast height (DBH) or tree height can be determined from MLS data with an accuracy similar to the accuracy of the parameter derived from TLS data. Results for instance in DBH determination by cylinder fitting yielded a standard deviation of 1.1 cm for trees in TLS data and 3.7 cm in MLS data. However, the resolution of MLS scans was found insufficient for successful QSM generation. The registration of MLS data in forests furthermore requires additional effort in considering effects caused by poor GNSS signal. Full article
(This article belongs to the Special Issue Terrestrial and Mobile Laser Scanning in Forestry)
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23 pages, 13470 KiB  
Article
Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging
by Di Wang, Jasmin Brunner, Zhenyu Ma, Hao Lu, Markus Hollaus, Yong Pang and Norbert Pfeifer
Forests 2018, 9(5), 252; https://doi.org/10.3390/f9050252 - 05 May 2018
Cited by 31 | Viewed by 5774
Abstract
Many biophysical forest properties such as wood volume and leaf area index (LAI) require prior knowledge on either photosynthetic or non-photosynthetic components. Laser scanning appears to be a helpful technique in nondestructively quantifying forest structures, as it can acquire an accurate three-dimensional point [...] Read more.
Many biophysical forest properties such as wood volume and leaf area index (LAI) require prior knowledge on either photosynthetic or non-photosynthetic components. Laser scanning appears to be a helpful technique in nondestructively quantifying forest structures, as it can acquire an accurate three-dimensional point cloud of objects. In this study, we propose an unsupervised geometry-based method named Dynamic Segment Merging (DSM) to identify non-photosynthetic components of trees by semantically segmenting tree point clouds, and examining the linear shape prior of each resulting segment. We tested our method using one single tree dataset and four plot-level datasets, and compared our results to a supervised machine learning method. We further demonstrated that by using an optimal neighborhood selection method that involves multi-scale analysis, the results were improved. Our results showed that the overall accuracy ranged from 81.8% to 92.0% with an average value of 87.7%. The supervised machine learning method had an average overall accuracy of 86.4% for all datasets, on account of a collection of manually delineated representative training data. Our study indicates that separating tree photosynthetic and non-photosynthetic components from laser scanning data can be achieved in a fully unsupervised manner without the need of training data and user intervention. Full article
(This article belongs to the Special Issue Terrestrial and Mobile Laser Scanning in Forestry)
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