Forest Disturbance and Management

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 497

Special Issue Editors

Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276000, China
Interests: forest disturbance; global climate change; remote sensing; GIS
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China
Interests: forest disturbance; forest management; carbon cycle; land use change; ecosystem modeling; climate change
Special Issues, Collections and Topics in MDPI journals
Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
Interests: spatial ecology; fire ecology; forest ecology
Special Issues, Collections and Topics in MDPI journals
Institute of Loess Plateau, Shanxi University, Taiyuan 030006, China
Interests: climate change; ecosystem service; remote sensing; land use and land cover change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests cover approximately 31% of the global land area and are home to an estimated 80% of the world’s terrestrial biodiversity, playing a crucial role in providing ecosystem services such as carbon sequestration, watershed protection and timber resources. However, forests are increasingly threatened by a variety of disturbances, including wildfires, insect outbreaks, diseases and human activities such as deforestation and land use change.

Climate change is exacerbating these threats, leading to more frequent and severe disturbances in many forested regions. For example, warmer temperatures and changing precipitation patterns are altering the frequency and intensity of wildfires, while also affecting the distribution and abundance of forest pests and diseases. These disturbances can have profound impacts on forest ecosystems, leading to a loss of biodiversity, changes in forest structure and composition and reduced ecosystem resilience.

In response to these challenges, there is a growing need for effective forest management strategies that can help mitigate the impacts of disturbances and ensure the long-term sustainability of forest ecosystems. This Special Issue aims to address this need by bringing together the latest research on forest disturbance and management from around the world. By advancing our understanding of the drivers, impacts and management strategies related to forest disturbances, this Special Issue will contribute to the conservation and sustainable management of forests globally.

This Special Issue aims to bring together cutting-edge research in the field of forest disturbance and management. It will cover a wide range of topics, including, but not limited to:

  • Spatial and temporal patterns of forest disturbance;
  • Drivers and mechanisms of forest disturbances;
  • Impacts of disturbances on forest ecosystems and biodiversity;
  • Innovative approaches for monitoring and assessing forest disturbances;
  • Sustainable forest management practices and their implications;
  • Restoration and rehabilitation of disturbed forest ecosystems;
  • Socio-economic aspects of forest management and conservation.

Dr. Jie Zhao
Dr. Chao Yue
Dr. Zhiwei Wu
Dr. Ziqiang Du
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

  • forest disturbance
  • forest management
  • climate change
  • climate impacts
  • sustainable management
  • vegetation responses
  • biodiversity
  • forest restoration

Published Papers (1 paper)

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Research

17 pages, 6410 KiB  
Article
Comparative Analysis of Machine Learning-Based Predictive Models for Fine Dead Fuel Moisture of Subtropical Forest in China
by Xiang Hou, Zhiwei Wu, Shihao Zhu, Zhengjie Li and Shun Li
Forests 2024, 15(5), 736; https://doi.org/10.3390/f15050736 - 23 Apr 2024
Viewed by 373
Abstract
The moisture content of fine dead surface fuel in forests is a crucial metric for assessing its combustibility and plays a pivotal role in the early warning, occurrence, and spread of forest fires. Accurate prediction of the moisture content of fine dead fuel [...] Read more.
The moisture content of fine dead surface fuel in forests is a crucial metric for assessing its combustibility and plays a pivotal role in the early warning, occurrence, and spread of forest fires. Accurate prediction of the moisture content of fine dead fuel on the forest surface is a critical challenge in forest fire management. Previous research on fine surface fuel moisture content has been mainly focused on coniferous forests in cold temperate zones, but there has been less attention given to understanding the fuel moisture dynamics in subtropical forests, which limits the development of regional forest fire warning models. Here, we consider the coupled influence of multiple meteorological, terrain, forest stand, and other characteristic factors on the fine dead fuel moisture content within the subtropical evergreen broadleaved forest region of southern China. The ability of five machine learning algorithms to predict the moisture content of fine dead fuel on the forest surface is assessed, and the key factors affecting the model accuracy are identified. Results show that when a single meteorological factor is used as a forecasting model, its forecasting accuracy is less than that of the combined model with multiple characteristic factors. However, the prediction accuracy of the model is improved after the addition of forest stand factors and terrain factors. The model prediction ability is the best for the combination of all feature factors including meteorology, forest stand, and terrain. The overall prediction accuracy of the model is ordered as follows: random forest > extreme gradient boosting > support vector machine > stepwise linear regression > k-nearest neighbor. Canopy density in forest stand factors, slope position and altitude in terrain factors, and average relative air humidity and light intensity in the previous 15 days are the key meteorological factors affecting the prediction accuracy of fuel moisture content. Our results provide scientific guidance and support for understanding the variability of forest surface fuel moisture content and improved regional forest fire warnings. Full article
(This article belongs to the Special Issue Forest Disturbance and Management)
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