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Digitalisation in Forest Inventory – New Pathways to Fulfill Information...
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Digitalisation in Forest Inventory – New Pathways to Fulfill Information Needs for Sustainable Forest Management and Conservation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Forestry".

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 1165

Special Issue Editor

Dr. Tim Ritter

E-Mail Website
Guest Editor
Department of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, Austria
Interests: forest monitoring; forest mensuration; forest inventory; forest biometrics; forest growth; forest yield; sampling techniques; LiDAR; TLS

Special Issue Information

Dear Colleagues,

Traditional forest inventories were mainly designed to provide precise information on the timber growing stock and relied on simple mechanical and optical instruments (e.g., tape-measures, callipers and relascopes) that have been unaltered and used for decades.

Nowadays, precision forestry applications, a broader view of sustainability and redefined goals of sustainable forest management raised additional information needs. The integration of digital age technology into multi-purpose forest inventories can help to fulfil forest managers’ and conservationists’ increased information needs and will revolutionise forest inventories: Different digital sensor technologies (e.g., LiDAR, photogrammetry) on different sensor platforms (e.g., satellites, UAVs, persons) allow for a fast collection of big data from forests that can be combined over different scales; artificial intelligence, in particular, machine learning techniques allow for automated extraction of meaningful forest metrics from these complex data.

This Special Issue aims at gathering contributions on the application of digital technology in the forest inventory context that help to fulfil information needs for sustainable forest management or conservation. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Remote Sensing;
  • Airborne Laser Scanning;
  • Terrestrial Laser Scanning;
  • Mobile Laser Scanning;
  • Photogrammetry;
  • Low-Cost Sensors;
  • Artificial Intelligence;
  • Machine Learning.

I look forward to receiving your contributions.

Dr. Tim Ritter
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. Sustainability 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 2400 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

  • forest inventory
  • digitalisation
  • LiDAR
  • TLS
  • ALS
  • photogrammetry
  • artificial intelligence
  • machine learning

Published Papers (1 paper)

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Research

16 pages, 1636 KiB  
Article
The Extension and Improvement of the Forest Land Net Present Value Model and Its Application in the Asset Evaluation of Cunninghamia lanceolata Forest Land
by Weiping Hua, Tian Qiu, Xin Pan, Chengzhen Wu, Chongyang Zhuang, Shangping Chi, Xidian Jiang and Jianwei Wu
Sustainability 2023, 15(11), 9096; https://doi.org/10.3390/su15119096 - 05 Jun 2023
Cited by 1 | Viewed by 837
Abstract
In order to solve the problem in that the classical forest land expectation value method ignores the actual forest stock volume when assessing the income at the end of the current production cycle in the forest, and fill the research gap in this [...] Read more.
In order to solve the problem in that the classical forest land expectation value method ignores the actual forest stock volume when assessing the income at the end of the current production cycle in the forest, and fill the research gap in this area, the technical system of the forest land asset evaluation was enriched. The forest land returns were divided into two parts, i.e., the segmented forest land return price from the growth of the actual forest stand to the end of the growth cycle (Bu1), and the segmented forest land return price for an infinite number of growth cycles after the growth of the actual forest stand to the end of the growth cycle (Bu2). Through structure, the forest land gain price expansion model was obtained, and the stand quality including the average diameter at breast height, average height, stock volume, and outturn of stand as dummy variables were used to construct the growth harvest model related to asset evaluation. Taking Cunninghamia lanceolata forest land as an example, the traditional asset evaluation methods were comparatively analyzed. The residual standard deviation (RSD) of the growth model was less than 10%, the total relative error (TRE) and mean system error (MSE) were within ±10%, the mean prediction errors (MPE) were less than 5%, and the mean percentage standard errors (MPSE) were less than 10%, respectively. Combining the forest land net present value expansion model with the traditional evaluation method, the evaluation value of the forest land assets was subsequently calculated, and accordingly, the forest land asset evaluation prime stand factors were predicated. It was found that the valuation results of the forest land net present value expansion model were consistent with the actual situation. The forest land net present value expansion model can therefore be used for asset evaluation of tree forest land (including natural uneven-aged forest land), bamboo forest land, shrub forest land, and economic forest land, and provide new technical support for forest land asset evaluation. Full article
(This article belongs to the Special Issue Digitalisation in Forest Inventory – New Pathways to Fulfill Information Needs for Sustainable Forest Management and Conservation)
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