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Atmosphere
Special Issues
Forest Ecosystems in a Changing Climate
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Forest Ecosystems in a Changing Climate

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 1535

Special Issue Editors

Prof. Dr. Chuixiang Yi

E-Mail Website
Guest Editor
School of Earth and Environmental Sciences Queens College, City University of New York, 65-30 Kissena Blvd, Flushing, NY 11367, USA
Interests: biosphere–atmosphere interaction; boundary layer meteorology; eddy flux measurements and modelling from globally synthetic data analysis to site-specific analysis; tree mortality; forest resilience and tipping point; ecosystem responses to extreme weather/climate
Special Issues, Collections and Topics in MDPI journals
Dr. Eric Kutter

E-Mail Website
Guest Editor
Barry Commoner Center for Health and the Environment, Queens College, City University of New York, 65-30 Kissena Blvd, Flushing, NY 11367, USA
Interests: biosphere-atmosphere interaction; eddy covariance; urban air quality
Dr. Zhenkun Tian

E-Mail Website
Guest Editor
School of Computer Science, China University of Labor Relations, Beijing 100048, China
Interests: global change ecology; atmosphere–biosphere interactions; ecosystem modeling; carbon cycle; climate change; geosciences; remote sensing; machine learning

Special Issue Information

Dear Colleagues,

Forests, the main body of the terrestrial ecosystem and where most carbon sequestration occurs on land, are the main non-oceanic force to slow the carbon dioxide (CO2) accumulation rate in the atmosphere. The volume, age, health, growth, and spatial and temporal variance impact the capacity of forests for carbon sequestration and climate warming mitigation. This Special Issue aims to better understand the interactions between forest ecosystems and the atmosphere, as well as the forests’ response to a changing climate, through quantitative analysis of the exchange of energy, water, and CO2 between forest and atmosphere, based on observations from the platform of land, airplanes, and satellites, and the technology of Remote Sensing, GIS, Machine Learning, and so on. We welcome any submission of original research articles and reviews on the interactions between forest and atmosphere, including but not limited to forest production, evapotranspiration, forest fires, forest cover change, tree mortality, forest ecology, etc., as well as their variations across space and time.

We look forward to receiving your contributions to this Special Issue on forest ecosystem response to climate.

Prof. Dr. Chuixiang Yi
Dr. Eric Kutter
Dr. Zhenkun Tian
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. Atmosphere 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 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

  • forest gross/net primary production
  • forest volume and area change
  • forest evapotranspiration
  • forest eddy covariance measurements and modeling
  • afforestations and deforestations
  • tree mortality
  • forest fire
  • droughts
  • heatwaves
  • pathogens
  • bark beetles
  • remote sensing
  • geographic information system

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Published Papers (2 papers)

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16 pages, 17622 KiB  
Article
Knowledge Map-Based Analysis of Carbon Sequestration Research Dynamics in Forest and Grass Systems: A Bibliometric Analysis
by Quanlin Ma, Xinyou Wang, Baoru Mo, Zaiguo Liu, Yangjun Zhang, Wenzheng Zong and Meiting Bai
Atmosphere 2025, 16(4), 474; https://doi.org/10.3390/atmos16040474 - 18 Apr 2025
Viewed by 162
Abstract
Forest and grass systems are globally significant carbon-sequestering ecosystems, crucial for mitigating climate change and optimizing ecological management. To clarify the research history, major contributing groups, and research hotspots related to carbon sequestration in global forest and grass systems, this study utilizes the [...] Read more.
Forest and grass systems are globally significant carbon-sequestering ecosystems, crucial for mitigating climate change and optimizing ecological management. To clarify the research history, major contributing groups, and research hotspots related to carbon sequestration in global forest and grass systems, this study utilizes the core ensemble of the Web of Science database as its data source. Employing bibliometric methodology and software, such as VOSviewer 1.6.20 and CiteSpace 5.7.R1, we analyzed the development of 594 relevant publications from 2010 to 2024, focusing on their developmental lineage, research groups, current research status, and visualizing and analyzing research hotspots and frontiers. The results indicate that the volume of the literature on carbon sequestration in forest and grass systems generally follows the pattern of a logistic growth curve, demonstrating an upward trend from 2010 to 2024. The primary contributors consist of 400 researchers, including Nath, Arun Jyoti, and Ajit, as well as 378 research organizations across 42 countries, including China, the USA, and India. China’s contribution to this field is rapidly increasing, accounting for over 20% of the total articles, with ‘Chinese Acad Sci’ and ‘Univ Chinese Acad Sci’ being the most prominent contributors, together representing 10.45% of the total publications in this field. The 179 journals, including Agroforestry Systems and Forests, serve as a significant platform for academic exchange in the development of this field. The predominant research directions are found in the areas of ‘Environmental Sciences & Ecology’ and ‘Agriculture’, which collectively account for over 50% of the publications. Additionally, research focused on ‘Sequestration’ is increasingly examining the relationship between carbon sequestration in forest and grassland systems and factors such as climate change, ecosystem productivity, and biodiversity. The keyword clusters ‘#0 ferralsol’ and ‘#4 forest ecosystem’ have consistently represented important research directions throughout this period. A total of 21 keywords were identified, with ‘land use change’ exhibiting the highest intensity at 4.4524. Future research should not only prioritize the integration of the impacts of global climate change but also enhance collaboration among authors and institutions. Furthermore, it is essential to promote multidisciplinary and cross-regional collaborative innovations by leveraging emerging technologies such as AI and genetic engineering. Full article
(This article belongs to the Special Issue Forest Ecosystems in a Changing Climate)
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18 pages, 2383 KiB  
Article
An AutoML-Powered Analysis Framework for Forest Fire Forecasting: Adapting to Climate Change Dynamics
by Shuo Zhang and Mengya Pan
Atmosphere 2024, 15(12), 1481; https://doi.org/10.3390/atmos15121481 - 11 Dec 2024
Viewed by 1000
Abstract
Wildfires pose a serious threat to ecosystems and human safety, and with the backdrop of global climate change, the prediction of forest fires has become increasingly important. Traditional machine learning methods face challenges in forest fire prediction, such as difficulty identifying feature parameters, [...] Read more.
Wildfires pose a serious threat to ecosystems and human safety, and with the backdrop of global climate change, the prediction of forest fires has become increasingly important. Traditional machine learning methods face challenges in forest fire prediction, such as difficulty identifying feature parameters, manual intervention in model selection, and hyperparameter tuning, which affect prediction accuracy and efficiency. This study proposes an analytical framework for forest fire prediction based on Automated Machine Learning (AutoML) technology to address the challenges traditional machine learning methods face in forest fire prediction. We collected meteorological, topographical, and vegetation data from Guangxi Province, with meteorological data covering 1994 to 2023, providing comprehensive background information for our prediction model. Using the prediction model, which was constructed with the AutoGluon framework, the experimental results indicate that models under the AutoGluon framework (e.g., KNeighborsDist classifier) significantly outperform traditional machine learning models in terms of accuracy, precision, recall, and F1-Score, with the highest accuracy rate reaching 0.960. Model error analysis shows that models under the AutoGluon framework perform better in error control. This study provides an efficient and accurate method for forest fire prediction, which is of great significance for decision-making in forest fire management and for protecting forest resources and ecological security. Full article
(This article belongs to the Special Issue Forest Ecosystems in a Changing Climate)
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