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Forests
Special Issues
Long-Term Monitoring and Driving Forces of Forest Cover
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Long-Term Monitoring and Driving Forces of Forest Cover

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

Deadline for manuscript submissions: 17 September 2026 | Viewed by 4482

Editors

Dr. Jianwan Ji

E-Mail Website
Guest Editor
School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Interests: eco-environment monitoring and assessment; coordination analysis; spatial analysis; sustainable development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bing Guo

E-Mail Website
Guest Editor
School of Civil and Architectural Engineering, Shandong University of Technology, Zibo, China
Interests: climate change; land use; remote sensing; land degradation; natural hazards
Special Issues, Collections and Topics in MDPI journals
Dr. Eshetu Shifaw

E-Mail
Guest Editor
Department of Geography and Environmental Studies, Wollo University, Dessie, Ethiopia
Interests: remote sensing; ecosystem services; ecological security; landscape; soil
Dr. Rui Zhang

E-Mail Website
Guest Editor
Northwest Institute of Eco-Environment and Resources, Lanzhou, China
Interests: disaster monitoring; disaster reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are the largest terrestrial ecosystem on Earth, which play a vital role in conserving water resources, regulating the climate, purifying the air, preventing wind erosion and sand drift, and protecting biodiversity. However, in recent years, the rapid decline in global forest cover has attracted widespread attention. Therefore, quickly and accurately determining the area and coverage of forests is of great significance for better realizing their ecological, social, and economic value.

With the continuous increase in remote sensing satellites and the gradual enrichment of satellite data, the long-term monitoring of forest cover has become available in recent years. However, as a type of big data, wide and long-term monitoring of forest cover still remains a challenge. Different from traditional remote sensing classification methods, machine learning has advantages in terms of big data processing and its calculation capabilities, which have been widely used in numerous fields. Hence, these recent advanced monitoring methods and analysis methods are our motivation to organize this Special Issue to welcome colleagues to share their work on forest cover monitoring and driving force analyses with advanced methods.

This Special Issue is dedicated to advances in long-term forest cover monitoring and driving force analysis. Articles should include aspects of forest cover monitoring, driving forces of forest change, and forest cover change. Techniques and methods from machine learning are welcome. Generally, manuscripts demonstrating novel applications of these techniques to forest cover would be appropriate for this Special Issue.

Potential topics include, but are not limited to, the following:

  • Forest cover monitoring;
  • Driving forces of forest change;
  • Forest cover change;
  • Machine learning;

Dr. Jianwan Ji
Prof. Dr. Bing Guo
Dr. Eshetu Shifaw
Dr. Rui Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 cover monitoring
  • driving forces of forest change
  • forest cover change
  • long-term monitoring
  • remote sensing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

29 pages, 62630 KB  
Article
Spatiotemporal Variation in Forest Cover and Its Driving Factors Revealed by eXtreme Gradient Boosting–SHapley Additive exPlanations Model: A Case Study of a Typical Karst Mountain Area in China
by Lei Yin, Jianwan Ji, Yuchao Hu, Xiaoxiao Zhu, Haixia Chen, Lei Zhang and Yinpeng Zhou
Forests 2026, 17(5), 544; https://doi.org/10.3390/f17050544 - 29 Apr 2026
Cited by 1 | Viewed by 668
Abstract
Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor [...] Read more.
Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor driver analysis, with insufficient in-depth exploration of the interactions among multiple factors and their associated nonlinear mechanisms. To address this gap, this study focuses on the Wumeng Mountain area, a typical ecologically fragile karst region in Southwest China. By comprehensively employing methods such as Theil–Sen Median trend analysis, land use transfer matrix, standard deviation ellipse, and spatial autocorrelation analysis, this study systematically reveals the spatiotemporal evolution characteristics of forest cover from 1985 to 2024. On this basis, an integrated eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) model is introduced to construct an indicator system comprising 16 driving variables, including elevation, slope, aspect, temperature, precipitation, soil type, soil pH, soil thickness, soil organic matter, soil moisture content, GDP, population, distance from water, distance from railway, distance from grade highway, and distance from government. This model quantifies the influence intensity of each driving factor on forest change. The main findings are as follows: (1) From 1985 to 2024, the forest cover rate in the Wumeng Mountain area significantly increased from 54.7% to 60.2%, exhibiting a “high-low-high” heterogeneous spatial distribution pattern along the northeast-southwest axis; (2) Forest increase primarily originated from the conversion of cropland and grassland, with contribution rates reaching 93.58% and 5.9%, respectively, indicating an overall trend of “increase in low-value areas and decrease in high-value areas”; (3) Forest cover change is driven by both natural and anthropogenic factors, with dominant driving factors exhibiting phased replacement over time. Overall, this is manifested as long-term stable constraints exerted by natural background factors, alongside strong disturbances from anthropogenic factors such as social-economic, and transportation-related activities. Natural factors remain the primary driving force behind changes in forest cover. The core findings of this study elucidate the complex driving factors of forest change in karst mountainous areas, thereby providing scientific support for the precise management of regional forest resources, the planning of ecological restoration projects, and the implementation of sustainable development strategies. Full article
(This article belongs to the Special Issue Long-Term Monitoring and Driving Forces of Forest Cover)
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21 pages, 6090 KB  
Article
Interactive Visualizations of Integrated Long-Term Monitoring Data for Forest and Fuels Management on Public Lands
by Kate Jones and Jelena Vukomanovic
Forests 2025, 16(11), 1706; https://doi.org/10.3390/f16111706 - 9 Nov 2025
Cited by 2 | Viewed by 1586
Abstract
Adaptive forest and fire management in parks and protected areas is becoming increasingly complex as climate change alters the frequency and intensity of disturbances (wildfires, pest and disease outbreaks, etc.), while park visitation and the number of people living adjacent to publicly managed [...] Read more.
Adaptive forest and fire management in parks and protected areas is becoming increasingly complex as climate change alters the frequency and intensity of disturbances (wildfires, pest and disease outbreaks, etc.), while park visitation and the number of people living adjacent to publicly managed lands continues to increase. Evidence-based, climate-adaptive forest and fire management practices are critical for the responsible stewardship of public resources and require the continued availability of long-term ecological monitoring data. The US National Park Service has been collecting long-term fire monitoring plot data since 1998, and has continued to add monitoring plots, but these data are housed in databases with limited access and minimal analytic capabilities. To improve the availability and decision support capabilities of this monitoring dataset, we created the Trends in Forest Fuels Dashboard (TFFD), which provides an implementation framework from data collection to web visualization. This easy-to-use and updatable tool incorporates data from multiple years, plot types, and locations. We demonstrate our approach at Rocky Mountain National Park using the ArcGIS Online (AGOL) software platform, which hosts TFFD and allows for efficient data visualizations and analyses customized for the end user. Adopting interactive, web-hosted tools such as TFFD allows the National Park Service to more readily leverage insights from long-term forest monitoring data to support decision making and resource allocation in the context of environmental change. Our approach translates to other data-to-decision workflows where customized visualizations are often the final steps in a pipeline designed to increase the utility and value of collected data and allow easier integration into reporting and decision making. This work provides a template for similar efforts by offering a roadmap for addressing data availability, cleaning, storage, and interactivity that may be adapted or scaled to meet a variety of organizational and management use cases. Full article
(This article belongs to the Special Issue Long-Term Monitoring and Driving Forces of Forest Cover)
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19 pages, 14441 KB  
Article
Study on Forest Extraction and Ecological Network Construction of Remote Sensing Images Combined with Dynamic Large Kernel Convolution
by Feiyue Wang, Fan Yang, Xinyue Chang and Yang Ye
Forests 2025, 16(8), 1342; https://doi.org/10.3390/f16081342 - 18 Aug 2025
Cited by 1 | Viewed by 1243
Abstract
As an important input parameter of the ecological network, the accuracy and detail with which forest cover is extracted directly constrain the accuracy of forest ecological network construction. The development of medium- and high-resolution remote sensing technology has provided an opportunity to obtain [...] Read more.
As an important input parameter of the ecological network, the accuracy and detail with which forest cover is extracted directly constrain the accuracy of forest ecological network construction. The development of medium- and high-resolution remote sensing technology has provided an opportunity to obtain accurate and high-resolution forest coverage data. As forests have diverse contours and complex scenes on remote sensing images, a model of them will be disturbed by the natural distribution characteristics of complex forests, which in turn will affect the extraction accuracy. In this study, we first constructed a rather large, complex, diverse, and scene-rich forest extraction dataset based on Sentinel-2 multispectral images, comprising 20,962 labeled images with a spatial resolution of 10 m, in a manually and accurately labeled manner. At the same time, this paper proposes the Dynamic Large Kernel Segformer and conducts forest extraction experiments in Liaoning Province, China. We then used forest coverage as an input parameter and classified the forest landscape patterns in the study area using a landscape spatial pattern characterization method, based on which a forest ecological network was constructed. The results show that the Dynamic Large Kernel Segformer obtains 80.58% IoU, 89.29% precision, 88.63% recall, and a 88.96% F1 Score in extraction accuracy, which is 4.02% higher than that of the Segformer network, and achieves large-scale forest extraction in the study area. The forest area in Liaoning Province increased during the 5-year period from 2019 to 2023. With respect to the overall spatial pattern change, the Core area of Liaoning Province saw an increase in 2019–2023, and the overall quality of the forest landscape improved. Finally, we constructed the forest ecological network for Liaoning Province in 2023, which consists of ecological sources, ecological nodes, and ecological corridors based on circuit theory. This method can be used to extract large areas of forest based on remote sensing images, which is helpful for constructing forest ecological networks and achieving coordinated regional, ecological, and economic development. Full article
(This article belongs to the Special Issue Long-Term Monitoring and Driving Forces of Forest Cover)
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