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Satellite Remote Sensing for Monitoring Forest Carbon and Supporting Nature-Based Carbon Crediting Mechanisms

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 459

Special Issue Editors


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Guest Editor
Department of Marine Environment and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
Interests: remote sensing; forest carbon monitoring; carbon credits and carbon markets; offshore wind power
College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL, USA
Interests: remote sensing; GIS; urban ecology; climate change; greenhouse gas emissions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are a cornerstone of global climate mitigation strategies due to their ability to sequester and store carbon. However, accurately monitoring forest carbon dynamics and linking them to carbon crediting mechanisms requires advanced tools and methodologies. Satellite remote sensing has become a critical enabler in this field, offering precise, large-scale, and temporal insights into forest carbon stocks, fluxes, and land use changes. These capabilities are essential for implementing nature-based solutions (NbSs) and supporting transparent, efficient carbon crediting mechanisms that encourage sustainable forest management. As the global focus on achieving net-zero emissions intensifies, the integration of satellite remote sensing with carbon credit systems provides a robust framework for addressing climate challenges.

This Special Issue aims to highlight the latest advancements in satellite-based monitoring and their applications in supporting nature-based solutions and carbon credit markets. The issue seeks to bridge scientific innovation with practical implementation, aligning with the journal's mission to advance remote sensing technologies for environmental and climate-related applications. It invites studies that demonstrate the potential of remote sensing to enhance the accuracy, accountability, and scalability of carbon crediting processes, ensuring the effectiveness of forest conservation and restoration efforts.

We welcome submissions of original research, reviews, and case studies. Potential topics include the following:

  • Advanced remote sensing techniques for mapping forest biomass and carbon stocks;
  • Applications of satellite data in verifying and enhancing carbon credit mechanisms;
  • Integration of ground-based and satellite observations for forest carbon monitoring;
  • Remote sensing approaches to detect deforestation, degradation, and land use changes;
  • Case studies on the use of remote sensing for implementing nature-based solutions;
  • Innovations in modeling forest carbon fluxes using remote sensing technologies;
  • Uncertainty assessment in satellite-based carbon monitoring.

Authors are encouraged to present multidisciplinary research that advances the science and implementation of satellite remote sensing in forest carbon management and carbon credit systems.

Dr. You-Ren Wang
Dr. Zutao Yang
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • satellite remote sensing
  • forest carbon monitoring
  • carbon flux analysis
  • nature-based solutions (NbSs)
  • carbon crediting mechanisms
  • carbon MRV
  • blue carbon

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Published Papers (1 paper)

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Research

27 pages, 6579 KiB  
Article
Spatiotemporal Dynamics of Forest Carbon Sinks in China’s Qinba Mountains: Insights from Sun-Induced Chlorophyll Fluorescence Remote Sensing
by Yuhang Lian, Yi He, Li Wang, Yaoting Wu, Yujie Wang, Zixuan Xu, Xinwen Xu and Lei Wang
Remote Sens. 2025, 17(8), 1418; https://doi.org/10.3390/rs17081418 - 16 Apr 2025
Viewed by 187
Abstract
Forest carbon sinks are crucial in mitigating climate change as integral components of the global carbon cycle. Accurately estimating forest carbon sinks using traditional remote sensing indices, such as Normalized Difference Vegetation Index(NDVI), presents significant challenges, particularly in complex terrains and regions with [...] Read more.
Forest carbon sinks are crucial in mitigating climate change as integral components of the global carbon cycle. Accurately estimating forest carbon sinks using traditional remote sensing indices, such as Normalized Difference Vegetation Index(NDVI), presents significant challenges, particularly in complex terrains and regions with variable climates. These limitations hinder the effective capture of photosynthetic dynamics. To address this gap, this study leverages Sun-Induced Chlorophyll Fluorescence (SIF) remote sensing, highlighting its superiority over traditional indices in capturing photosynthetic processes and offering a more precise approach to estimating carbon sinks in climate-sensitive mountainous areas. Using SIF data from GOSIF, alongside models for light-use efficiency and ecosystem respiration, this study estimates forest carbon sinks in the Qinba Mountains of China during the growing season (June to September) from 2011 to 2018. The results are further validated and analyzed in terms of forest age and type. Key findings include: (1) The average annual forest carbon sinks during the growing season was approximately 24.51 TgC; (2) Spatially, higher carbon sinks values (average 36.79 gC·m⁻2·month⁻1) were concentrated in the western and central Qinba areas, while southeastern and central-northern regions exhibited lower values (average 7.75 gC·m⁻2·month⁻1); (3) Temporally, minimal interannual variation was observed in the northwest, whereas the southeast showed fluctuating trends, with an initial decline followed by an increase; (4) Forest carbon sinks was significantly influenced by forest age, type, and altitude. Our findings demonstrate that plantation forests aged 10 to 30 years exhibit superior carbon sequestration capacity compared to natural forests, while natural forests aged 70 to 90 years also show significant carbon sinks potential. These results underscore the crucial influence of forest characteristics on carbon sequestration dynamics. By examining these spatiotemporal patterns in the Qinba Mountains, our study offers valuable insights for advancing China’s ‘dual carbon’ goals, emphasizing the importance of strategic forest management in mitigating climate change. Full article
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