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Forests
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Natural Resources Management in Tropical, Temperate and Boreal Forests
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Natural Resources Management in Tropical, Temperate and Boreal Forests

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

Deadline for manuscript submissions: closed (1 August 2021) | Viewed by 8082

Special Issue Editor

Prof. Dr. Pete Bettinger

E-Mail Website
Guest Editor
School of Forestry and Natural Resources, University of Georgia, 180 East Green Street, Athens, GA 30602, USA
Interests: forest management; forest planning; forest operations; GIS; GPS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are currently faced with many important social and environmental challenges when it comes to forest management. We are, however, fortunate that forest resources can support the many economic, environmental, and social needs of society. Managing forests for diverse, sustainable goals can be made complicated by the volume and complexity of information that one should consider, and the broad array of scenarios that one should examine. As forest managers continue to adjust to societal demands for production, it is evident that cultural, regulating, and supportive ecosystem services, as well as new and innovative approaches, should be considered and assessed, to promote knowledge development. This Special Issue welcomes original research studies that describe advances in decision-making processes and analyses aimed at the active management of forests. Applications of well-described decision-making processes to new management problems involving the scheduling and placement of management activities are also welcome. Papers should have a practical or place-based issue at their core. Moreover, advances in theories (silvicultural or otherwise) should be accompanied by practical, quantitative applications to real landscapes.

Prof. Dr. Pete Bettinger
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

  • Forest Management
  • Forest Planning
  • Forest Economics
  • Decision-Making Sciences

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Published Papers (2 papers)

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Research

15 pages, 2532 KiB  
Article
Machine Learning Modeling of Forest Road Construction Costs
by Abolfazl Jaafari, Iman Pazhouhan and Pete Bettinger
Forests 2021, 12(9), 1169; https://doi.org/10.3390/f12091169 - 28 Aug 2021
Cited by 20 | Viewed by 3764
Abstract
The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting [...] Read more.
The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting in the early stages of a project would therefore be of great help for timely costing of alternatives and more economical solutions. This study describes the development and application of such cost estimation models. First, the main cost elements and variables affecting total construction costs were determined for which the real-world data were derived from the project bids and an analysis of 300 segments of a three kilometer road constructed in the Hyrcanian Forests of Iran. Then, five state-of-the-art machine learning methods, i.e., linear regression (LR), K-Star, multilayer perceptron neural network (MLP), support vector machine (SVM), and Instance-based learning (IBL) were applied to develop models that would estimate construction costs from the real-world data. The performance of the models was measured using the correlation coefficient (R), root mean square error (RMSE), and percent of relative error index (PREI). The results showed that the IBL model had the highest training performance (R = 0.998, RMSE = 1.4%), whereas the SVM model had the highest estimation capability (R = 0.993, RMSE = 2.44%). PREI indicated that all models but IBL (mean PREI = 0.0021%) slightly underestimated the construction costs. Despite these few differences, the results demonstrated that the cost estimations developed here were consistent with the project bids, and our models thus can serve as a guideline for better allocating financial resources in the early stages of the bidding process. Full article
(This article belongs to the Special Issue Natural Resources Management in Tropical, Temperate and Boreal Forests)
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14 pages, 5412 KiB  
Article
Heuristic Optimization of Thinning Individual Douglas-Fir
by Todd West, John Sessions and Bogdan M. Strimbu
Forests 2021, 12(3), 280; https://doi.org/10.3390/f12030280 - 28 Feb 2021
Cited by 4 | Viewed by 2911
Abstract
Research Highlights: (1) Optimizing mid-rotation thinning increased modeled land expectation values by as much as 5.1–10.1% over a representative reference prescription on plots planted at 2.7 and 3.7 m square spacings. (2) Eight heuristics, five of which were newly applied to selecting individual [...] Read more.
Research Highlights: (1) Optimizing mid-rotation thinning increased modeled land expectation values by as much as 5.1–10.1% over a representative reference prescription on plots planted at 2.7 and 3.7 m square spacings. (2) Eight heuristics, five of which were newly applied to selecting individual trees for thinning, produced thinning prescriptions of near identical quality. (3) Based on heuristic sampling properties, we introduced a variant of the hero heuristic with a 5.3–20% greater computational efficiency. Background and Objectives: Thinning, which is arguably the most subjective human intervention in the life of a stand, is commonly executed with limited decision support in tree selection. This study evaluated heuristics’ ability to support tree selection in a factorial experiment that considered the thinning method, tree density, thinning age, and rotation length. Materials and Methods: The Organon growth model was used for the financial optimization of even age Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) harvest rotations consisting of a single thinning followed by clearcutting on a high-productivity site. We evaluated two versions of the hero heuristic, four Monte Carlo heuristics (simulated annealing, record-to-record travel, threshold accepting, and great deluge), a genetic algorithm, and tabu search for their efficiency in maximizing land expectation value. Results: With 50–75 years rotations and a 4% discount rate, heuristic tree selection always increased land expectation values over other thinning methods. The two hero heuristics were the most computationally efficient methods. The four Monte Carlo heuristics required 2.8–3.4 times more computation than hero. The genetic algorithm and the tabu search required 4.2–8.4 and 21–52 times, respectively, more computation than hero. Conclusions: The accuracy of the resulting thinning prescriptions was limited by the quality of stand measurement, and the accuracy of the growth and yield models was linked to the heuristics rather than to the choice of heuristic. However, heuristic performance may be sensitive to the chosen models. Full article
(This article belongs to the Special Issue Natural Resources Management in Tropical, Temperate and Boreal Forests)
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