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Sustainable Forestry Management and Technologies
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Sustainable Forestry Management and Technologies

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 8085

Special Issue Editors

Prof. Dr. Francisco Rodríguez-Puerta

E-Mail Website
Guest Editor
Department of Vegetal Production and Forestry Resources, University of Valladolid, 47002 Valladolid, Spain
Interests: big data; remote sensing; forest decision support systems; computing; artificial intelligence; forest sustainability; forest inventory; forest monitoring; wood science; climate change; forest management; forest ecology; forest products; lidar remote sensing; forest modeling; forest biometrics
Dr. Fernando Pérez-Rodríguez

E-Mail Website
Guest Editor
Föra Forest Technologies S.L.L., Universidad de Valladolid, Campus de Soria, 42004 Soria, Spain
Interests: big data; remote sensing; forest decision support systems; computing; artificial intelligence; forest sustainability; forest inventory; forest monitoring
Dr. Juan Picos

E-Mail Website
Guest Editor
Forestry Engineering School, University of Vigo, University Campus A Xunqueira s/n, 36005 Pontevedra, Spain
Interests: environmental impact assessment; wildlife management; silviculture; planning; climate change; agroforestry; landscape ecology; sustainable forest management; forest industry; fire; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Esteban Gómez-García

E-Mail Website
Guest Editor
Centro de Investigación Forestal de Lourizán, Xunta de Galicia, 36080 Pontevedra, Spain
Interests: sustainable forest management; forest inventory; forest growth and yield modeling; tree biomass and carbon; non-wood forest products

Special Issue Information

Dear Colleagues,

The growth of technology in recent decades has ushered in a new era of sustainable forest management. This is due to the significant number of tools that have been created, new methodologies that have emerged, and the wealth of information and data available for incorporation into decision-making processes. This growth has, for example, contributed to the adoption of a new paradigm for forest inventory using active sensors such as ALS or TLS, marking the onset of the big data era in forest management.

Furthermore, the increasing availability of diverse data sources, like satellite data, has made it easier to monitor forests and has opened new possibilities for obtaining spatial and temporal information. However, this framework implies that forest managers must require more knowledge in handling big data to achieve the results they seek. In this context, the development of tools facilitates the exchange of knowledge among users, regardless of their level of technological expertise.

In this context, artificial intelligence is not merely a concept for the future, it is already part of the present. Several tools and methods offer robust assessments of big data, leading to extraordinary results in areas such as species recognition, land use cover changes, and pest prediction, among many others. However, the applications extend beyond land monitoring. For instance, we are witnessing the emergence of future applications like unmanned forest machinery, which could be a game-changer in forest management and harvesting.

The present and the future hold great promise, and this Special Issue aims to compile recent research results and studies in this field. It will explore how these advances contribute to our understanding of forest sustainability and their practical applications. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Artificial intelligence applications with sustainable forest management purposes;
  • Forest monitoring tools based on active and passive sensor data;
  • Forest tools and decisions support systems developments for sustainable forest management;
  • Parametric and non-parametric modeling to describe and predict forest characteristics;
  • Hardware improvements of passive and active sensors and their applications in forest;
  • New forest measures technology and software.

We look forward to receiving your contributions.

Prof. Dr. Francisco Rodríguez-Puerta
Dr. Fernando Pérez-Rodríguez
Dr. Juan Picos
Dr. Esteban Gómez-García
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. 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

  • big data
  • remote sensing
  • forest decision support systems
  • computing
  • artificial intelligence
  • forest sustainability
  • forest inventory
  • forest monitoring

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Published Papers (5 papers)

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23 pages, 3686 KiB  
Article
A Whole-Stand Model for Estimating the Productivity of Uneven-Aged Temperate Pine-Oak Forests in Mexico
by María Guadalupe Nava-Miranda, Juan Gabriel Álvarez-González, José Javier Corral-Rivas, Daniel José Vega-Nieva, Jaime Briseño-Reyes, Jesús Aguirre-Gutiérrez and Klaus von Gadow
Sustainability 2025, 17(8), 3393; https://doi.org/10.3390/su17083393 - 10 Apr 2025
Viewed by 612
Abstract
This study presents a model for estimating forest productivity based on a sample of 2048 permanent field plots covering a wide range of growing sites in Mexico. Our state-space approach assumes that the growth behavior of any stand over time can be estimated [...] Read more.
This study presents a model for estimating forest productivity based on a sample of 2048 permanent field plots covering a wide range of growing sites in Mexico. Our state-space approach assumes that the growth behavior of any stand over time can be estimated on the basis of its current state, defined by the dominant height (H), number of trees per hectare (N), and stand basal area (BA). We used transition functions to estimate the change in states as a function of the current state. We also present transition functions for the change in stand volume (V) and total above-ground biomass (AGB). The first transition function relates dominant height to dominant diameter by using the guide-curve method to estimate site form. The transition function for N consists of two models, one for estimating natural mortality and the other for estimating recruitment. These models were developed in two steps: in the first step, the logistic regression and maximum likelihood approach were used to estimate the probability of the occurrence of mortality or recruitment, and in the second step, the rate of change associated with each event was modeled when mortality or recruitment was assumed to have occurred as a result of the first step. The remaining three transition functions (BA, V, and AGB) were fitted simultaneously to account for possible correlations between errors. The model estimating total above-ground biomass (AGB), which can be considered a state variable that summarizes the performance of the whole model, explained more than 97% of the observed variability, with a root mean square error value of 10.57 Mg/ha. Full article
(This article belongs to the Special Issue Sustainable Forestry Management and Technologies)
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27 pages, 45791 KiB  
Article
Application of Remote Sensing for the Evaluation of the Forest Ecosystem Functions and Tourism Services
by Monika Kozłowska-Adamczak, Aleksandra Jezierska-Thöle and Patrycja Essing-Jelonkiewicz
Sustainability 2025, 17(5), 2060; https://doi.org/10.3390/su17052060 - 27 Feb 2025
Viewed by 1139
Abstract
Assessing the functions of forest ecosystems is important for a proper understanding of their role in the natural environment and society. Ecotourism emphasizes minimizing negative impacts on the environment and supports environmental education. Modern information and communication technologies, including forest apps, are helping [...] Read more.
Assessing the functions of forest ecosystems is important for a proper understanding of their role in the natural environment and society. Ecotourism emphasizes minimizing negative impacts on the environment and supports environmental education. Modern information and communication technologies, including forest apps, are helping in this regard. Precision forestry uses GIS technologies and remote sensing to obtain spatial data, identify the components of the natural environment, and evaluate the changes that they are subject to. A tool enabling the evaluation of synergy between ecosystem functions and tourism, in addition to traditional field research and surveys, is remote sensing. This paper aims to show the feasibility of evaluating the synergy of ecosystem and tourism services in forests using remote sensing as an alternative to traditional terrestrial measurements. This study’s temporal scope is from 2019 (i.e., the introduction of the pilot program on making forests available for bushcraft and survival activities in Poland) until the beginning of 2024. Thus, it covers the time when the State Forests program called “Stay Overnight in the Forest” related to dispersed camping in forests was in force. Additionally, online surveys were conducted using the Microsoft Forms platform among representatives of all forest districts participating in implementing the “Stay Overnight in the Forest” program from 1 May 2021. This program is a crucial element of the contemporary tourist and recreational offer of the State Forests in Poland and influences the course of the ecosystem and tourist services in the forests. From the recorded digital images, it is possible to obtain information about threats in forest ecosystems caused by natural disasters, such as windstorms and fires. The precise provision of information about degraded forest areas can contribute to the more efficient management of forest reclamation works and the restoration of damaged stands. On the other hand, the rehabilitated forest can be a destination point for educational trails in forests. Full article
(This article belongs to the Special Issue Sustainable Forestry Management and Technologies)
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15 pages, 1799 KiB  
Article
Assessment of Mycological Possibility Using Machine Learning Models for Effective Inclusion in Sustainable Forest Management
by Raquel Martínez-Rodrigo, Beatriz Águeda, Teresa Ágreda, José Miguel Altelarrea, Luz Marina Fernández-Toirán and Francisco Rodríguez-Puerta
Sustainability 2024, 16(13), 5656; https://doi.org/10.3390/su16135656 - 2 Jul 2024
Cited by 2 | Viewed by 2189
Abstract
The integral role of wild fungi in ecosystems, including provisioning, regulating, cultural, and supporting services, is well recognized. However, quantifying and predicting wild mushroom yields is challenging due to spatial and temporal variability. In Mediterranean forests, climate-change-induced droughts further impact mushroom production. Fungal [...] Read more.
The integral role of wild fungi in ecosystems, including provisioning, regulating, cultural, and supporting services, is well recognized. However, quantifying and predicting wild mushroom yields is challenging due to spatial and temporal variability. In Mediterranean forests, climate-change-induced droughts further impact mushroom production. Fungal fruiting is influenced by factors such as climate, soil, topography, and forest structure. This study aims to quantify and predict the mycological potential of Lactarius deliciosus in sustainably managed Mediterranean pine forests using machine learning models. We utilize a long-term dataset of Lactarius deliciosus yields from 17 Pinus pinaster plots in Soria, Spain, integrating forest-derived structural data, NASA Landsat mission vegetation indices, and climatic data. The resulting multisource database facilitates the creation of a two-stage ‘mycological exploitability’ index, crucial for incorporating anticipated mycological production into sustainable forest management, in line with what is usually done for other uses such as timber or game. Various Machine Learning (ML) techniques, such as classification trees, random forest, linear and radial support vector machine, and neural networks, were employed to construct models for classification and prediction. The sample was always divided into training and validation sets (70-30%), while the differences were found in terms of Overall Accuracy (OA). Neural networks, incorporating critical variables like climatic data (precipitation in January and humidity in November), remote sensing indices (Enhanced Vegetation Index, Green Normalization Difference Vegetation Index), and structural forest variables (mean height, site index and basal area), produced the most accurate and unbiased models (OAtraining = 0.8398; OAvalidation = 0.7190). This research emphasizes the importance of considering a diverse array of ecosystem variables for quantifying wild mushroom yields and underscores the pivotal role of Artificial Intelligence (AI) tools and remotely sensed observations in modeling non-wood forest products. Integrating such models into sustainable forest management plans is crucial for recognizing the ecosystem services provided by them. Full article
(This article belongs to the Special Issue Sustainable Forestry Management and Technologies)
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18 pages, 6583 KiB  
Article
Landscape Restoration Using Individual Tree Harvest Strategies
by Robert Schriver, John Sessions and Bogdan M. Strimbu
Sustainability 2024, 16(12), 5124; https://doi.org/10.3390/su16125124 - 16 Jun 2024
Viewed by 1432
Abstract
Western juniper (Juniperus occidentalis Hook.) is a native species west of the Rocky Mountains that has become noxious as its area increased ten times in the last 140 years. Restoration of the landscapes affected by the spread of juniper through harvesting poses [...] Read more.
Western juniper (Juniperus occidentalis Hook.) is a native species west of the Rocky Mountains that has become noxious as its area increased ten times in the last 140 years. Restoration of the landscapes affected by the spread of juniper through harvesting poses several challenges related to the sparse spatial distribution (trees per hectare) of the resource. Therefore, the objective of the present study is to develop a harvest scheduling strategy that converts the western juniper from a noxious species to a timber resource. We propose a procedure that aggregates individual trees into elementary harvest units by considering the location of each tree. Using the coordinates of each harvest unit and its corresponding landing, we developed a spatially explicit algorithm that aims at the maximization of net revenue from juniper harvest. We applied the proposed landscape restoration approach to two areas of similar size and geomorphology. We implemented the restoration algorithm using two heuristics: simulated annealing and record-to-record travel. To account for the closeness to the mill, we considered two prices at the landing for the juniper: 45 USD/ton and 65 USD/ton. Our results suggest that restoration is possible at higher prices, but it is economically infeasible when prices are low. Simulated annealing outperformed record-to-record travel in both study areas and for both prices. Our approach and formulation to the restoration of landscapes invaded by western juniper could be applied to similar instances where complex stand structures preclude the use of traditional forest stand-level harvest scheduling and require a more granular approach. Full article
(This article belongs to the Special Issue Sustainable Forestry Management and Technologies)
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15 pages, 77254 KiB  
Technical Note
Accuracy Assessment of Advanced Laser Scanner Technologies for Forest Survey Based on Three-Dimensional Point Cloud Data
by Jin-Soo Kim, Sang-Min Sung, Ki-Suk Back and Yong-Su Lee
Sustainability 2024, 16(23), 10636; https://doi.org/10.3390/su162310636 - 4 Dec 2024
Cited by 4 | Viewed by 1490
Abstract
Forests play a crucial role in carbon sequestration and climate change mitigation, offering ecosystem services, biodiversity conservation, and water resource management. As global efforts to reduce greenhouse gas emissions intensify, the demand for accurate spatial information to monitor forest conditions and assess carbon [...] Read more.
Forests play a crucial role in carbon sequestration and climate change mitigation, offering ecosystem services, biodiversity conservation, and water resource management. As global efforts to reduce greenhouse gas emissions intensify, the demand for accurate spatial information to monitor forest conditions and assess carbon absorption capacity has grown. LiDAR (Light Detection and Ranging) has emerged as a transformative tool, providing high-resolution 3D spatial data for detailed analysis of forest attributes, including tree height, canopy structure, and biomass distribution. Unlike traditional manpower-intensive forest surveys, which are time-consuming and often limited in accuracy, LiDAR offers a more efficient and reliable solution. This study evaluates the accuracy and applicability of advanced LiDAR technologies—drone-mounted, terrestrial, and mobile scanners—for generating 3D forest spatial data. The results show that the terrestrial LiDAR achieved the highest precision for diameter at breast height (DBH) and tree height measurements, with RMSE values of 0.66 cm and 0.91 m, respectively. Drone-mounted LiDAR demonstrated excellent efficiency for large-scale surveys, while mobile LiDAR offered portability and speed but required further improvement in accuracy (e.g., RMSE: DBH 0.76 cm, tree height 1.83 m). By comparing these technologies, this study identifies their strengths, limitations, and optimal application scenarios, contributing to more accurate forest management practices and carbon absorption assessments. Full article
(This article belongs to the Special Issue Sustainable Forestry Management and Technologies)
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