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Forests
Special Issues
Forest Inventory: The Monitoring of Biomass and Carbon Stocks—2nd Edition
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Forest Inventory: The Monitoring of Biomass and Carbon Stocks—2nd Edition

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 531

Special Issue Editors

Prof. Dr. Tianxiang Yue

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Guest Editor
Institute of Geographic Science and Natural Resources Research, University of Chinese Academy of Sciences, Beijing 100101, China
Interests: spatial analysis; land cover; digital terrain modeling; ecological and environmental models; system simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Zhe Xu

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Guest Editor
School of Life Sciences and Environmental Resources, Yichun University, Yichun 336000, China
Interests: remote sensing; environment modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Zong Wang

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Guest Editor
The College of Forestry, Beijing Forestry University, Beijing 100107, China
Interests: forest ecosystems; carbon stock simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests act as carbon sinks, absorbing carbon dioxide from the atmosphere and storing it in biomass and soil. Therefore, the monitoring of biomass and carbon stocks is crucial for our understanding of the capacity of forests to mitigate climate change, our ability to assess the health of forests and their ability to sustain biodiversity, as well as being crucial in terms of helping governments and international organizations to formulate policies for forest conservation, sustainable land use, and climate change mitigation. This Special Issue aims to explore the critical role of forest inventory in assessing and managing biomass and carbon stocks. Meanwhile, it also seeks to foster interdisciplinary dialog and promote advancements in forest inventory methodologies for the effective management of biomass and carbon stocks. Research articles, review papers, and case studies on the following potential topics are all welcome. 

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

  • Modeling approaches for forest carbon stock estimation;
  • The integration of field and remote sensing data in monitoring biomass and carbon stocks;
  • Predictions of forest carbon stocks and climate change mitigation potential;
  • Methodologies and standards for accounting for forest carbon stocks and changes;
  • Temporal and spatial dynamic variation characteristics and research methods of forest ecosystem carbon storage;
  • Future trends of forest carbon storage under different scenarios;
  • Identification of challenges and gaps in current monitoring and assessment practices.

Prof. Dr. Tianxiang Yue
Dr. Zhe Xu
Dr. Zong Wang
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-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 inventory
  • biomass assessment
  • carbon stocks
  • monitoring techniques
  • remote sensing
  • data fusion
  • climate change mitigation
  • forest carbon dynamics
  • biodiversity conservation
  • policy implications

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Published Papers (1 paper)

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Research

23 pages, 10924 KB  
Article
Spatial Imbalance Patterns of Forest Carbon Density and Their Driving Mechanisms in the Xiuhe River Basin
by Dongping Zha, Meng Zhang, Ligang Xu, Zhan Shen, Junwei Wu, Weiwei Deng, Meng Yuan, Nan Wu and Renhao Ouyang
Forests 2026, 17(3), 312; https://doi.org/10.3390/f17030312 - 28 Feb 2026
Viewed by 289
Abstract
Forest carbon sinks are central to climate change mitigation, and prior work has established a solid basis for assessing carbon sinks at regional scales. At the basin scale, however, forest carbon density (vegetation biomass carbon density, i.e., aboveground + belowground biomass carbon; t [...] Read more.
Forest carbon sinks are central to climate change mitigation, and prior work has established a solid basis for assessing carbon sinks at regional scales. At the basin scale, however, forest carbon density (vegetation biomass carbon density, i.e., aboveground + belowground biomass carbon; t C ha−1) often shows pronounced spatial clustering and inequality, while its temporal evolution and underlying mechanisms remain poorly quantified and interpreted for management-relevant units such as townships. Using the Xiuhe River Basin as a case study and townships as the basic analytical units, this study identifies the clustered spatial structure and inequality characteristics of forest carbon density and clarifies the joint effects of natural constraints and human disturbances, including potential threshold responses. We first assessed global spatial autocorrelation within a spatial weights framework using Global Moran’s I with permutation tests, and delineated local clustering by classifying local indicators of spatial association (LISA) types based on Local Moran’s I. We then measured the magnitude and stage-wise evolution of inter-township disparities using the Gini coefficient and the Theil T index. Finally, we applied GeoDetector factor, interaction, and risk detection to identify dominant drivers, interaction enhancement, and class-based contrasts. The results show significant and persistent positive spatial autocorrelation in forest carbon density from 2002 to 2024, with Moran’s I ranging from 0.68786 to 0.73849 (p < 0.01). Significant LISA units account for 40.74%–45.37% of townships, and the pattern is dominated by high–high (HH) and low–low (LL) clusters. Inequality follows a stage-wise trajectory: it expanded slightly during 2002–2019, converged markedly during 2019–2021, and rebounded modestly by 2024, while remaining below the levels observed in 2002 and 2019. Strong type-based differentiation is evident in 2024: mean carbon density is 46.06 t C ha−1 in HH areas versus 17.64 t C ha−1 in LL areas; HH areas contribute 38.44% of total carbon stock, whereas LL areas contribute only 5.08%. In terms of drivers, natural and human factors jointly shape the spatial pattern and commonly exhibit interaction enhancement. Elevation (q = 0.7832), slope (q = 0.7133), and NPP (q = 0.6373) are the leading natural constraints, while population density (q = 0.6054) and the built-up land ratio (q = 0.5374) are key indicators of human disturbance. Risk detection further indicates a stable negative gradient for the built-up land ratio and nonlinear class differences for population density, implying that once disturbance intensity reaches higher levels, low-value clustering is more likely to persist. By linking clustered spatial structure, stage-wise inequality, and disturbance-related threshold signals, our results support basin-scale zoning and differentiated management at the township level. Specifically, HH clusters should be prioritized for conservation and connectivity maintenance, whereas LL clusters warrant stricter control of built-up expansion and fragmentation to reduce the risk of persistent low-carbon locking under high disturbance. By linking spatial structure, inequality dynamics, and threshold responses, this study provides a quantitative basis for basin-scale zoning to enhance carbon sinks and for implementing differentiated spatial controls. Full article
(This article belongs to the Special Issue Forest Inventory: The Monitoring of Biomass and Carbon Stocks—2nd Edition)
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