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Forests
Special Issues
Advances in Precision Forestry
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Advances in Precision Forestry

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: closed (30 April 2018)

Special Issue Editors

Dr. Johannes Breidenbach

E-Mail Website
Guest Editor
National Forest Inventory, Division for Forestry and Forest Resources, Høgskoleveien 8, 1430 Ås, Norway
Interests: national forest inventory; small area estimation; carbon reporting
Dr. Rasmus Astrup

E-Mail Website
Guest Editor
National Forest Inventory, Division for Forestry and Forest Resources, Høgskoleveien 8, 1430 Ås, Norway
Interests: national forest inventory; small area estimation; carbon reporting

Special Issue Information

Dear Colleagues,

For optimizing the forest value chain, the right forest products need to be matched to further processing in a timely manner. Technological developments improve information on forest resources, which facilitates optimized forest operations in the sense of costs, sustainability, and product quality. Precision forestry therefore plays a key role in assuring a competitive forest sector. This Special Issue collects studies that describe advances in the field of precision forestry that focus on two main aspects: 1) Improvement of resource assessments by using new sensors and methods; and 2) Use of modern resource assessments in optimized forest operations.

Dr. Johannes Breidenbach
Dr. Rasmus Astrup
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

  • Machine-Based Measurements with New Sensors
  • Drone-Based Measurements
  • Robotics
  • Inventory Improvement
  • Sustainability Indicators of Harvests
  • Improved Resource Assessments
  • Data Assimilation
  • Computer Vision
  • LiDAR

Published Papers (2 papers)

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Research

14 pages, 2232 KiB  
Article
Automatic Assessment of Crown Projection Area on Single Trees and Stand-Level, Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning
by Tim Ritter and Arne Nothdurft
Forests 2018, 9(5), 237; https://doi.org/10.3390/f9050237 - 1 May 2018
Cited by 21 | Viewed by 5495
Abstract
Crown projection area (CPA) is a critical parameter in assessing inter-tree competition and estimating biomass volume. A multi-layer seeded region growing-based approach to the fully automated assessment of CPA based on 3D-point-clouds derived from terrestrial laser scanning (TLS) is presented. Independently repeated manual [...] Read more.
Crown projection area (CPA) is a critical parameter in assessing inter-tree competition and estimating biomass volume. A multi-layer seeded region growing-based approach to the fully automated assessment of CPA based on 3D-point-clouds derived from terrestrial laser scanning (TLS) is presented. Independently repeated manual CPA-measurements in a subset of the stand serve as the reference and enable quantification of the inter-observer bias. Allometric models are used to predict CPA for the whole stand and are compared to the TLS-based estimates on the single tree- and stand-level. It is shown that for single trees, the deviation between CPA measurements derived from TLS data and manual measurements is on par with the deviations between manual measurements by different observers. The inter-observer bias propagates into the allometric models, resulting in a high uncertainty of the derived estimates at tree-level. Comparing the allometric models to the TLS measurements at stand-level reveals the high influence of crown morphology, which only can be taken into account by the TLS measurements and not by the allometric models. Full article
(This article belongs to the Special Issue Advances in Precision Forestry)
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14 pages, 5673 KiB  
Article
Tree-Stump Detection, Segmentation, Classification, and Measurement Using Unmanned Aerial Vehicle (UAV) Imagery
by Stefano Puliti, Bruce Talbot and Rasmus Astrup
Forests 2018, 9(3), 102; https://doi.org/10.3390/f9030102 - 27 Feb 2018
Cited by 52 | Viewed by 9281
Abstract
Unmanned aerial vehicles (UAVs) are increasingly used as tools to perform a detailed assessment of post-harvest sites. One of the potential use of UAV photogrammetric data is to obtain tree-stump information that can then be used to support more precise decisions. This study [...] Read more.
Unmanned aerial vehicles (UAVs) are increasingly used as tools to perform a detailed assessment of post-harvest sites. One of the potential use of UAV photogrammetric data is to obtain tree-stump information that can then be used to support more precise decisions. This study developed and tested a methodology to automatically detect, segment, classify, and measure tree-stumps. Among the potential applications for single stump data, this study assessed the possibility (1) to detect and map root- and butt-rot on the stumps using a machine learning approach, and (2) directly measure or model tree stump diameter from the UAV data. The results revealed that the tree-stumps were detected with an overall accuracy of 68–80%, and once the stump was detected, the presence of root- and butt-rot was detected with an accuracy of 82.1%. Furthermore, the root mean square error of the UAV-derived measurements or model predictions for the stump diameter was 7.5 cm and 6.4 cm, respectively, and with the former systematically under predicting the diameter by 3.3 cm. The results of this study are promising and can lead to the development of more cost-effective and comprehensive UAV post-harvest surveys. Full article
(This article belongs to the Special Issue Advances in Precision Forestry)
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