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Forest/Urban Forest Systems under Climate Change: Carbon Dynamics, Ecological Functions,...
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Forest/Urban Forest Systems under Climate Change: Carbon Dynamics, Ecological Functions, and Sustainable Management

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2121

Special Issue Editors

Dr. Junyuan Guo

E-Mail Website
Guest Editor
College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China
Interests: ecological restoration; wastewater treatment; soil remediation
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Huang

E-Mail Website
Guest Editor
College of Environmental Science and Technology, Central South University of Forestry and Technology, Changsha, China
Interests: heavy metals; microbiology; ecosystem restoration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the impact of climate change (like global warming, changes in precipitation regime, and an extreme global climate) on plant communities is a major potential threat to global forest plant biodiversity and plant ecological functions. However, the distribution and survival of species and ecological functions of forest/urban forest ecosystems under climate change have not been closely studied. Under climate change, forest plants can survive, adapt, or die, and the response of plant communities to climate change may be the comprehensive result of the interaction of various components of the forest ecosystem, and the study of this response can help predict and manage the continuous evolution of forest ecosystems. Global climate change makes forests more important than ever before. Restoring forest ecosystems and building new plantations can mitigate climate change by slowing the accumulation of carbon dioxide in the atmosphere. Forest systems can absorb atmospheric carbon dioxide, producing large carbon sinks and having low costs to maintain. Accurate estimation of forest biomass/carbon storage and monitoring of carbon dynamics are essential to simulate the global carbon cycle, quantify carbon flux, and achieve carbon neutrality goals. Advanced artificial intelligence (machine learning, deep learning, transfer learning) and large amounts of remote sensing data provide powerful tools for accurately estimating forest biomass/carbon stocks and monitoring carbon dynamics. The fixation, transport, distribution, stabilization, and storage of carbon in forest/urban forest ecosystems has received much attention due to climate change. A deeper understanding of these relationships with environmental factors will help to understand the complex responses of forest ecosystems to projected succession changes caused by climate fluctuations. Therefore, it is extremely necessary to study forest ecosystems (growth models), develop forest plant growth models to adapt to climate change, and provide ecosystem service analysis. Economic and policy analysis is essential for forest management, coordinated economic growth, and environmental protection. Innovative approaches, policy tools, and innovative digital approaches that explore the ecosystem services and economic value provided by forests are highly effective in achieving the sustainable development of forest ecosystems and can influence forest growth trajectories, promoting resilience and diversity.

Submissions may cover, but should not be limited to: the responses of forest plant species and plant communities to climate change; carbon fixation, transport, and storage in forest ecosystems and the economic value of forest carbon sinks; the application of remote sensing and related technologies in assessing forest biomass and monitoring carbon dynamics; the development of biomass carbon assessment methods and modelling, and the use of all remote sensing platforms to analyze or monitor changes in forest ecosystem types; and the interaction between economics and policy in sustainable forest management. In the meantime, contributions to this Special Issue are encouraged using any software or application, including traditional and advanced machine learning methods.

Dr. Junyuan Guo
Dr. Chao Huang
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 ecosystem
  • ecosystem services
  • sustainable forest management
  • climate change
  • green policies
  • multi-source remote sensing
  • artificial intelligence
  • carbon dynamic
  • carbon allocation
  • carbon transport
  • carbon storage
  • carbon sinks
  • GIS

Published Papers (3 papers)

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20 pages, 3894 KiB  
Article
Detection of the Pine Wilt Disease Using a Joint Deep Object Detection Model Based on Drone Remote Sensing Data
by Youping Wu, Honglei Yang and Yunlei Mao
Forests 2024, 15(5), 869; https://doi.org/10.3390/f15050869 - 16 May 2024
Viewed by 459
Abstract
Disease and detection is crucial for the protection of forest growth, reproduction, and biodiversity. Traditional detection methods face challenges such as limited coverage, excessive time and resource consumption, and poor accuracy, diminishing the effectiveness of forest disease prevention and control. By addressing these [...] Read more.
Disease and detection is crucial for the protection of forest growth, reproduction, and biodiversity. Traditional detection methods face challenges such as limited coverage, excessive time and resource consumption, and poor accuracy, diminishing the effectiveness of forest disease prevention and control. By addressing these challenges, this study leverages drone remote sensing data combined with deep object detection models, specifically employing the YOLO-v3 algorithm based on loss function optimization, for the efficient and accurate detection of tree diseases and pests. Utilizing drone-mounted cameras, the study captures insect pest image information in pine forest areas, followed by segmentation, merging, and feature extraction processing. The computing system of airborne embedded devices is designed to ensure detection efficiency and accuracy. The improved YOLO-v3 algorithm combined with the CIoU loss function was used to detect forest pests and diseases. Compared to the traditional IoU loss function, CIoU takes into account the overlap area, the distance between the center of the predicted frame and the actual frame, and the consistency of the aspect ratio. The experimental results demonstrate the proposed model’s capability to process pest and disease images at a slightly faster speed, with an average processing time of less than 0.5 s per image, while achieving an accuracy surpassing 95%. The model’s effectiveness in identifying tree pests and diseases with high accuracy and comprehensiveness offers significant potential for developing forest inspection protection and prevention plans. However, limitations exist in the model’s performance in complex forest environments, necessitating further research to improve model universality and adaptability across diverse forest regions. Future directions include exploring advanced deep object detection models to minimize computing resource demands and enhance practical application support for forest protection and pest control. Full article
(This article belongs to the Special Issue Forest/Urban Forest Systems under Climate Change: Carbon Dynamics, Ecological Functions, and Sustainable Management)
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16 pages, 4639 KiB  
Article
Prediction of the Potential Distribution of Teinopalpus aureus Mell, 1923 (Lepidoptera, Papilionidae) in China Using Habitat Suitability Models
by Yinghan Liu, Xuemei Zhang and Shixiang Zong
Forests 2024, 15(5), 828; https://doi.org/10.3390/f15050828 - 8 May 2024
Viewed by 540
Abstract
The Golden Kaiser-I-Hind (Teinopalpus aureus Mell, 1923) is the only butterfly among Class I national protected animals in China and is known as the national butterfly. In this study, by accurately predicting the suitable habitat in China under current and future climate [...] Read more.
The Golden Kaiser-I-Hind (Teinopalpus aureus Mell, 1923) is the only butterfly among Class I national protected animals in China and is known as the national butterfly. In this study, by accurately predicting the suitable habitat in China under current and future climate scenarios, the potential distribution area of T. aureus was defined, providing a theoretical basis for conservation and management. Based on species distribution records, we utilized the Biomod2 platform to combine climate data from the BCC-CSM2-MR climate model, future shared socio-economic pathways, and altitude data. The potential distribution areas of T. aureus in the current (1970–2000s) and future SSP1_2.6 and SSP5_8.5 climate scenarios in China in 2041–2060 (2050s), 2061–2080 (2070s), and 2081–2100 (2090s) were predicted. The AUC and TSS values of the combined model based on five algorithms were greater than those of the single models, and the AUC value of the receiver operating characteristic curve was 0.990, indicating that the model had high reliability and accuracy. The screening of environmental variables showed that the habitat area of T. aureus in China was mainly affected by annual precipitation, precipitation in the driest month, the lowest temperature in the coldest month, temperature seasonality, elevation, and other factors. Under the current circumstances, the habitat area of T. aureus was mainly located in southern China, including Fujian, Guangdong, Guangxi, Hainan, Zhejiang, Yunnan, Guizhou, Hunan, Taiwan, and other provinces. The suitable area is approximately 138.95 × 104 km2; among them, the highly suitable area of 34.43 × 104 km2 is a priority area in urgent need of protection. Under both SSP1_2.6 and SSP5_8.5, the population centroid tended to shift southward in the 2050s and 2070s, and began to migrate northeast in the 2090s. Temperature, rainfall, and altitude influenced the distribution of T. aureus. In the two climate scenarios, the habitat area of T. aureus declined to different degrees, and the reduction was most obvious in the SSP5_8.5 scenario; climate was the most likely environmental variable to cause a change in the geographical distribution. Climate change will significantly affect the evolution and potential distribution of T. aureus in China and will increase the risk of extinction. Accordingly, it is necessary to strengthen protection and to implement active and effective measures to reduce the negative impact of climate change on T. aureus. Full article
(This article belongs to the Special Issue Forest/Urban Forest Systems under Climate Change: Carbon Dynamics, Ecological Functions, and Sustainable Management)
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19 pages, 5290 KiB  
Article
The Application of Geographic Information System in Urban Forest Ecological Compensation and Sustainable Development Evaluation
by Liwei An, Guifeng Liu and Meiling Hou
Forests 2024, 15(2), 285; https://doi.org/10.3390/f15020285 - 2 Feb 2024
Cited by 1 | Viewed by 817
Abstract
Urban forests can alleviate the urban heat island effect, improve air quality, and improve residents’ mental health. By studying urban forests, these resources can be better used and managed to create more livable urban environments. Therefore, the urban forest in the Taishan region [...] Read more.
Urban forests can alleviate the urban heat island effect, improve air quality, and improve residents’ mental health. By studying urban forests, these resources can be better used and managed to create more livable urban environments. Therefore, the urban forest in the Taishan region is taken as the research object, and the ecological compensation and sustainable development of urban forest in Tai’an City are deeply analyzed by GIS. It divided the area into forest land, water bodies, wetlands, grasslands, and shrubs as the basic ecosystem types. And through secondary interpretation and combination, a complete urban forest information database was established. To evaluate the comprehensive benefits of urban forests, the analytic hierarchy process was utilized to establish a corresponding evaluation index system. Based on the assessment outcomes of the comprehensive benefits of urban forests in the area, a standard accounting method for urban forest ecological compensation was proposed. The results showed that each index of the comprehensive benefits of urban forests and the random consistency ratio were both less than 0.1. This indicated that the matrix calculation results of various indicators of urban forest comprehensive benefits had good consistency. At the target level, the comprehensive evaluation score of urban forests in the study area was 7.69. At the factor level, the weight value of the urban forest landscape structure was 0.675, and the comprehensive score was 7.62. The weight value of urban forest comprehensive benefits was 0.325, and the comprehensive score was 7.82. The quantitative weight value of urban forest greening in the study area was 0.6138, with a comprehensive score of 7.57. Based on the analysis of the issues in urban forests and ecological compensation in the research area of Tai’an City, corresponding ecological compensation strategies have been proposed. It is of great value to study the urban forest of Tai’an city, which can help to formulate more effective urban planning and sustainable development strategies. The research results can also provide a valuable reference and inspiration for the improvement of urban forest ecological environment and biodiversity protection in other areas. Full article
(This article belongs to the Special Issue Forest/Urban Forest Systems under Climate Change: Carbon Dynamics, Ecological Functions, and Sustainable Management)
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