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Forests
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Fire Ecology and Management in Forest—2nd Edition
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Fire Ecology and Management in Forest—2nd Edition

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 4559

Special Issue Editors

Prof. Dr. Long Sun

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Guest Editor
School of Forestry, Northeast Forestry University, Harbin, China
Interests: fire ecology; forest fire behavior; fire monitoring; fuel control and management
Special Issues, Collections and Topics in MDPI journals
Dr. Futao Guo

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Guest Editor
College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China
Interests: wildfire prediction; wildfire ecology; fire smoke
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiwei Wu

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Guest Editor
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
Dr. Christopher Weston

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Guest Editor
International School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: forest ecology; biogeochemistry; carbon and nutrient cycling; bushfire fuel dynamics; soil science
Special Issues, Collections and Topics in MDPI journals
Dr. Tongxin Hu

E-Mail Website
Guest Editor
School of Forestry, Northeast Forestry University, Harbin, China
Interests: forest fire
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The overall impact of fires on forest ecosystems is complex, ranging from a reduction in or elimination of above-ground biomass, to the changes in below-ground biomasses and the soil’s physical, chemical and biological properties. The severity of a fire depends on multiple conditions such as combustion intensity, fire duration, fuel load, fire occurrence time and fire weather. With changes in climate (warmer temperatures, changes in precipitation patterns, etc.), fire seasons are expected to lengthen, and with that, forest resistance to fires is undermined. In the conditions of the changing climate, extensive forest fire research is needed, in order to study the changes in fire dynamics and to resolve research questions dealing with current and future forest fire ecology and management issues. At the same time, it is important to quantify the impact of fire disturbance on forest ecosystems and to understand the necessity of new fire prevention and control technologies in forest fire management. Ultimately, our goal should be to provide a scientific basis for developing and clarifying fire management policies.

Therefore, this Special Issue focuses on fire ecology, fire management and their interactions in the context of global climate change—how fire regimes change in the context of the global climate, what the different effects of fire are on forest ecosystems, what the possible effects of fuel management measures are on fires and forest ecosystems, and what the new fire prediction applications and firefighting techniques are in forest fire management.

Prof. Dr. Long Sun
Dr. Futao Guo
Dr. Zhiwei Wu
Dr. Christopher Weston
Dr. Tongxin Hu
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

  • wildfire
  • prescribed burning
  • fire ecology
  • fire regime
  • fire behavior
  • fuel characteristics and management
  • fire prediction and fighting techniques

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Published Papers (8 papers)

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Research

21 pages, 7692 KiB  
Article
The Hidden Carbon Cost of Forest Fire Management: Quantifying Greenhouse Gas Emissions from Both Vegetation Burning and Social Rescue Activities in Yajiang County, China
by Zilin Ye, Yanjun Wang, Xijin Zhao, Yugang Wang, Jing Liao, Jian Min, Xun Gong, Dongmei Wang and Zhengjun Gong
Forests 2025, 16(5), 803; https://doi.org/10.3390/f16050803 - 11 May 2025
Viewed by 198
Abstract
Quantifying greenhouse gas (GHG) emissions from forest fires is essential for climate change mitigation strategies, yet current methodologies predominantly focus on vegetation combustion, neglecting emissions from firefighting operations. This study presents a comprehensive assessment of GHG emissions from a forest fire in Yajiang [...] Read more.
Quantifying greenhouse gas (GHG) emissions from forest fires is essential for climate change mitigation strategies, yet current methodologies predominantly focus on vegetation combustion, neglecting emissions from firefighting operations. This study presents a comprehensive assessment of GHG emissions from a forest fire in Yajiang County, China, by integrating remote sensing data with ground-based measurements to quantify emissions from both vegetation combustion and emergency response activities. Analysis revealed that the fire, which affected 20,688.67 hectares, generated 63,764.59 tons of GHGs—with vegetation combustion accounting for 83.5% (53,266.29 tons) and emergency response activities contributing 16.5% (10,498.30 tons). Moderate-severity fires in evergreen forests yielded the highest emissions, while aerial operations constituted the primary source of emergency-response-related emissions. Significantly, NOx emissions from emergency response activities exceeded those from vegetation combustion. This research advances forest fire management by establishing a holistic accounting framework that incorporates previously unquantified emission sources, thereby providing foundational data for developing environmentally optimized fire social rescue activity protocols. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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19 pages, 3703 KiB  
Article
Carbon Emission Prediction Following Pinus koraiensis Plantation Surface Fuel Combustion Based on Carbon Consumption Analysis
by Daotong Geng, Jibin Ning, Guang Yang, Shangjiong Ma, Lixuan Wang and Hongzhou Yu
Forests 2025, 16(5), 726; https://doi.org/10.3390/f16050726 - 24 Apr 2025
Viewed by 262
Abstract
The accurate measurement of surface fire carbon emissions is critical for assessing their impact on carbon sinks and role in climate change. This study aims to investigate the relationships between surface fire behaviour characteristics and carbon consumption for Pinus koraiensis plantation forests and [...] Read more.
The accurate measurement of surface fire carbon emissions is critical for assessing their impact on carbon sinks and role in climate change. This study aims to investigate the relationships between surface fire behaviour characteristics and carbon consumption for Pinus koraiensis plantation forests and construct a carbon consumption prediction model. A total of 288 combustion experiments were conducted on a laboratory burning bed using varying fuel loads, moisture contents, and slope conditions, with measurements taken of surface fire behaviour characteristics and the carbon content of combustion ash. The Byram fireline intensity model was reintegrated to build a predictive model for fuel combustion carbon consumption, and the model parameters were adjusted based on the results of the combustion experiments. The direct use of the Byram fireline intensity model parameters predicted surface fire carbon consumption in Pinus koraiensis plantation forests with significant errors (R2 = 0.75; MAE = 0.197 kg m−2; MRE = 66.76%). After the parameters were modified using the combustion experiment data, the new model yielded R2 = 0.75, MAE = 0.087 kg m−2, and MRE = 28.28%. This study significantly improved the accuracy of the new model in predicting the carbon consumption of surface fuel combustion in Pinus koraiensis plantation forests. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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23 pages, 25076 KiB  
Article
Integrating DEM and Deep Learning for Forested Terrain Analysis: Enhancing Fire Risk Assessment Through Mountain Peak and Water System Extraction in Chongli District
by Yihui Wu, Xueying Sun, Liang Qi, Jiang Xu, Demin Gao and Zhengli Zhu
Forests 2025, 16(4), 692; https://doi.org/10.3390/f16040692 - 16 Apr 2025
Viewed by 384
Abstract
Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance fire risk assessment in Chongli District. Our framework [...] Read more.
Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance fire risk assessment in Chongli District. Our framework innovatively combines DEM data with Faster Regions with Convolutional Neural Networks (Faster R-CNN) and CNN-based methods, breaking through the limitations of traditional approaches that rely on manual feature extraction. It is capable of automatically identifying critical terrain features, such as mountain peaks and water systems, with higher accuracy and efficiency. DEMs provide high-resolution topographical information, which deep learning models leverage to accurately identify and delineate key geographical features. Our results show that the integration of DEMs and deep learning significantly improves the accuracy of fire risk assessment by offering detailed and precise terrain analysis, thereby providing more reliable inputs for fire behavior prediction. The extracted mountain peaks and water systems, as fundamental inputs for fire behavior prediction, enable more accurate predictions of fire spread and potential impact areas. This study not only highlights the great potential of combining geospatial data with advanced machine learning techniques but also offers a scalable and efficient solution for forest fire risk management in mountainous regions. Future work will focus on expanding the dataset to include more environmental variables and validating the model in different geographical areas to further enhance its robustness and applicability. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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18 pages, 8730 KiB  
Article
How Prescribed Burning Affects Surface Fine Fuel and Potential Fire Behavior in Pinus yunnanensis in China
by Xilong Zhu, Shiying Xu, Ruicheng Hong, Hao Yang, Hongsheng Wang, Xiangyang Fang, Xiangxiang Yan, Xiaona Li, Weili Kou, Leiguang Wang and Qiuhua Wang
Forests 2025, 16(3), 548; https://doi.org/10.3390/f16030548 - 20 Mar 2025
Viewed by 305
Abstract
Forest fine fuels are a crucial component of surface fuels and play a key role in igniting forest fires. However, despite nearly 20 years of long-term prescribed burning management on Zhaobi Mountain in Xinping County, Yunnan Province, China, there remains a lack of [...] Read more.
Forest fine fuels are a crucial component of surface fuels and play a key role in igniting forest fires. However, despite nearly 20 years of long-term prescribed burning management on Zhaobi Mountain in Xinping County, Yunnan Province, China, there remains a lack of specific quantification regarding the effectiveness of fine fuel management in Pinus yunnanensis forests. In this study, 10 m × 10 m sample plots were established on Zhaobi Mountain following one year of growth after prescribed burning. The plots were placed in a prescribed burning (PB) area and an unburned control (UB) area. We utilized indicators such as forest stand characteristics, fine fuel physicochemical properties, and potential fire behavior parameters for evaluation. The results indicate that prescribed burning at one-year intervals significantly affects stand characteristics, particularly in metrics such as crown base height, diameter breast height, and fuel load (p < 0.05). However, the physical and chemical properties of fine fuels did not show significant differences. Notably, the mean range of spread (RS) of PB fuels downhill was 43.3% lower than that of UB fuels, and the mean flaming height (FH) was 35.2% lower. The fire line intensity was <750 kW/m, categorizing it as a low-intensity fire. These findings provide data on the composition of fine fuels and the variables of fire behavior affected by prescribed burning, demonstrating that low-intensity prescribed burns can regulate fine fuels in the understory and maintain a stable regional fire risk level. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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16 pages, 4069 KiB  
Article
Flame Image Classification Based on Deep Learning and Three-Way Decision-Making
by Xuguang Zhang, Deting Miao and Linping Guo
Forests 2025, 16(3), 544; https://doi.org/10.3390/f16030544 - 19 Mar 2025
Viewed by 302
Abstract
The classification and recognition of flame images play an important role in avoiding forest fires. Deep learning technology has shown good performance in flame image recognition tasks. In order to further improve the accuracy of classification, this paper combines deep learning technology with [...] Read more.
The classification and recognition of flame images play an important role in avoiding forest fires. Deep learning technology has shown good performance in flame image recognition tasks. In order to further improve the accuracy of classification, this paper combines deep learning technology with the idea of three-way decision-making. First, a ResNet34 network is used for initial classification. The probability value calculated by the SoftMax function is used as the decision evaluation criterion for initial classification. Using the idea of three-way decision-making, the flame image is divided into positive domain, negative domain, and boundary domain based on decision evaluation indicators. Furthermore, we perform secondary classification on images divided into boundary domains. In the secondary classification, a DualArchClassNet structure was constructed to extract new features and combine them with the features of the initial classification. The integrated features are optimized and used to reclassify images in uncertain domains to improve overall classification accuracy. The experimental results show that the proposed method improves the accuracy of flame image recognition compared to using a single ResNet34 network. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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21 pages, 19780 KiB  
Article
Post-Fire Forest Ecological Quality Recovery Driven by Topographic Variation in Complex Plateau Regions: A 2006–2020 Landsat RSEI Time-Series Analysis
by Jiayue Gao, Yue Chen, Bo Xu, Wei Li, Jiangxia Ye, Weili Kou and Weiheng Xu
Forests 2025, 16(3), 502; https://doi.org/10.3390/f16030502 - 12 Mar 2025
Viewed by 499
Abstract
Forest fires are an important disturbance that affects ecosystem stability and pose a serious threat to the ecosystem. However, the recovery process of forest ecological quality (EQ) after a fire in plateau mountain areas is not well understood. This study utilizes the Google [...] Read more.
Forest fires are an important disturbance that affects ecosystem stability and pose a serious threat to the ecosystem. However, the recovery process of forest ecological quality (EQ) after a fire in plateau mountain areas is not well understood. This study utilizes the Google Earth Engine (GEE) and Landsat data to generate difference indices, including NDVI, NBR, EVI, NDMI, NDWI, SAVI, and BSI. After segmentation using the Simple Non-Iterative Clustering (SNIC) method, the data were input into a random forest (RF) model to accurately extract the burned area. A 2005–2020 remote sensing ecological index (RSEI) time series was constructed, and the recovery of post-fire forest EQ was evaluated through Theil–Sen slope estimation, Mann–Kendall (MK) trend test, stability analysis, and integration with topographic information systems. The study shows that (1) from 2006 to 2020, the post-fire forest EQ improved year by year, with an average annual increase rate of 0.014/a. The recovery process exhibited an overall trend of “decline initially-fluctuating increase-stabilization”, indicating that RSEI can be used to evaluate the post-fire forest EQ in complex plateau mountainous regions. (2) Between 2006 and 2020, the EQ of forests exhibited a significant increasing trend spatially, with 84.32% of the areas showing notable growth in RSEI, while 1.80% of the regions experienced a declining trend. (3) The coefficient of variation (CV) of RSEI in the study area was 0.16 during the period 2006–2020, indicating good overall stability in the process of post-fire forest EQ recovery. (4) Fire has a significant impact on the EQ of forests in low-altitude areas, steep slopes, and sun-facing slopes, and recovery is slow. This study offers scientific evidence for monitoring and assessing the recovery of post-fire forest EQ in plateau mountainous regions and can also inform ecological restoration and management efforts in similar areas. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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18 pages, 5755 KiB  
Article
Wildfire Impacts Pinus tabulaeformis Forests on Soil Properties, Actinobacteriota, and Enzyme Activity in Northern China: Direct Effects or Mutual Interactions?
by Guanhong Liu, Bingyi Li, Jia Li, Ze Gu and Xiaodong Liu
Forests 2025, 16(2), 344; https://doi.org/10.3390/f16020344 - 14 Feb 2025
Viewed by 582
Abstract
Wildfires are significant disturbances that reshape soil ecosystems, impacting soil properties, microbial communities, and enzyme activities. In Pinus tabulaeformis forests in northern China, the effects of wildfire on soil health, particularly on Actinobacteriota and enzymatic functions, remain poorly understood. This study investigates both [...] Read more.
Wildfires are significant disturbances that reshape soil ecosystems, impacting soil properties, microbial communities, and enzyme activities. In Pinus tabulaeformis forests in northern China, the effects of wildfire on soil health, particularly on Actinobacteriota and enzymatic functions, remain poorly understood. This study investigates both the direct and indirect effects of fire severity on these factors and examines how fire-induced changes in soil properties mediate microbial and enzymatic responses. Our findings show that wildfire significantly alters soil chemical properties, including an increase in soil pH and a reduction in organic carbon and water content, particularly under high fire severities. These changes directly impact microbial communities, with Actinobacteriota showing resilience under light and moderate fire intensities but declining under high severity, especially in subsoil layers. Soil enzymes, such as urease and protease, played a crucial role in mitigating the negative impacts of fire on nutrient cycling. Their activity promoted nutrient availability, aiding ecosystem recovery, even as fire intensity reduced overall soil fertility. Structural Equation Modeling (SEM) further revealed that the relationships between fire severity, soil properties, Actinobacteriota, and enzyme activity are shaped by both direct thermal effects and complex indirect interactions mediated by changes in soil moisture and nutrient levels. This study underscores the importance of considering both direct fire effects and the mutual interactions between soil properties, microbial communities, and enzymatic activities in post-fire recovery. The findings highlight that while high-severity fires disrupt soil health and microbial dynamics, soil enzymes can help regulate these impacts by enhancing nutrient cycling and supporting ecosystem stability. These insights contribute to a better understanding of wildfire-induced soil degradation and provide actionable strategies for enhancing post-fire soil restoration and microbial management in fire-prone ecosystems. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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21 pages, 8374 KiB  
Article
Response of Fuel Characteristics, Potential Fire Behavior, and Understory Vegetation Diversity to Thinning in Platycladus orientalis Forest in Beijing, China
by Min Gao, Sifan Chen, Aoli Suo, Feng Chen and Xiaodong Liu
Forests 2024, 15(9), 1667; https://doi.org/10.3390/f15091667 - 22 Sep 2024
Viewed by 997
Abstract
Objective: Active fuel management operations, such as thinning, can minimize extreme wildfire conditions while preserving ecosystem services, including maintaining understory vegetation diversity. However, the appropriate thinning intensity for balancing the above two objectives has not been sufficiently studied. Methods: This study was conducted [...] Read more.
Objective: Active fuel management operations, such as thinning, can minimize extreme wildfire conditions while preserving ecosystem services, including maintaining understory vegetation diversity. However, the appropriate thinning intensity for balancing the above two objectives has not been sufficiently studied. Methods: This study was conducted to assess the impact of various thinning intensities (light thinning, LT, 15%; moderate thinning, MT, 35%; heavy thinning, HT, 50%; and control treatment, CK) on fuel characteristics, potential fire behavior, and understory vegetation biodiversity in Platycladus orientalis forest in Beijing using a combination of field measurements and fire behavior simulations (BehavePlus 6.0.0). Results: A significant reduction in surface and canopy fuel loads with increasing thinning intensity, notably reducing CBD to below 0.1 kg/m3 under moderate thinning, effectively prevented the occurrence of active crown fires, even under extreme weather conditions. Additionally, moderate thinning enhanced understory species diversity, yielding the highest species diversity index compared to other treatments. Conclusions: These findings suggest that moderate thinning (35%) offers an optimal balance, substantially reducing the occurrence of active crown fires while promoting biodiversity. Therefore, it is recommended to carry out moderate thinning in the study area. Forest managers can leverage this information to devise technical strategies that simultaneously meet fire prevention objectives and enhance understory vegetation species diversity in areas suitable for thinning-only treatments. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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