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Forests
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Spatial Information for Forest Biomass and Carbon Stock Estimation: New...
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Spatial Information for Forest Biomass and Carbon Stock Estimation: New Technologies and Approaches

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: 20 January 2026 | Viewed by 305

Special Issue Editors

Dr. Raheleh Farzanmanesh

E-Mail Website
Guest Editor
Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia
Interests: blue carbon; GIS and remote sensing; environmental planning; climate change
Dr. Christopher Weston

E-Mail Website
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. Liubov Volkova

E-Mail Website
Guest Editor
School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Creswick, VIC 3363, Australia
Interests: forest carbon cycling; temperate and tropical forests; forest and peat fires and greenhouse gas emissions; biogeochemical cycles; physiological plant ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are critical components of the global carbon cycle, functioning as both carbon sinks and sources depending on their health, structure and management. Accurately estimating forest biomass and carbon stock is essential therefore for climate change mitigation, sustainable forests management, and assessing ecosystem services. In recent years, remote sensing has emerged as a powerful tool for the monitoring of forest dynamics across various spatial and temporal scales, offering cost-effective and scalable solutions.

This Special Issue focuses on advancing research related to forest biomass and carbon stock estimation through remote sensing, and we invite original contributions that apply or evaluate diverse remote sensing technologies—such as optical imagery, LiDAR, synthetic aperture radar (SAR), and hyperspectral data—to quantify forest structure and carbon content. We are particularly interested in studies that employ novel methodologies, including machine learning, time series analysis, data fusion techniques, and spatial modelling. We also welcome submissions that examine the accuracy, uncertainty, and limitations of current approaches, as well as those that demonstrate practical applications in various forest ecosystems. This Special Issue aims to foster interdisciplinary collaboration and innovation in forest carbon monitoring.

Dr. Raheleh Farzanmanesh
Dr. Christopher Weston
Dr. Liubov Volkova
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 biomass
  • carbon stock
  • remote sensing
  • biodiversity
  • climate change
  • sustainable forest management
  • carbon monitoring
  • REDD+

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Published Papers (1 paper)

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Research

35 pages, 7115 KB  
Article
Age-Based Biomass Carbon Estimation and Soil Carbon Assessment in Rubber Plantations Integrating Geospatial Technologies and IPCC Tier 1–2 Guidelines
by Supet Jirakajohnkool, Sangdao Wongsai, Manatsawee Sanpayao and Noppachai Wongsai
Forests 2025, 16(11), 1652; https://doi.org/10.3390/f16111652 - 30 Oct 2025
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Abstract
This study presents an integrated framework for spatiotemporal mapping of carbon stocks in rubber plantations in Rayong Province, Eastern Thailand—an area undergoing rapid agricultural transformation and rubber expansion. Unlike most existing assessments that rely on Tier 1 IPCC defaults or coarse plantation age [...] Read more.
This study presents an integrated framework for spatiotemporal mapping of carbon stocks in rubber plantations in Rayong Province, Eastern Thailand—an area undergoing rapid agricultural transformation and rubber expansion. Unlike most existing assessments that rely on Tier 1 IPCC defaults or coarse plantation age classes, our framework combines annual plantation age derived from Landsat time series, age-specific allometric growth models, and Tier 2 soil organic carbon (SOC) accounting. This enables fine-scale, age- and site-sensitive estimation of both tree and soil carbon. Results show that tree biomass dominates the carbon pool, with mean tree carbon stocks of 66.94 ± 13.1% t C ha−1, broadly consistent with national field studies. SOC stocks averaged 45.20 ± 0.043% t C ha−1, but were overwhelmingly inherited from pre-conversion land use (43.7 ± 0.042% t C ha−1). Modeled SOC changes (ΔSOC) were modest, with small gains (2.06 t C ha−1) and localized losses (−9.96 t C ha−1), producing a net mean increase of only 1.44 t C ha−1. These values are substantially lower than field-based estimates (5–15 t C ha−1), reflecting structural limitations of the global empirical ΔSOC model and reliance on generalized default parameters. Uncertainties also arise from allometric assumptions, generalized soil factors, and Landsat resolution constraints in smallholder landscapes. Beyond carbon, ecological trade-offs of rubber expansion—including biodiversity loss, soil fertility decline, and hydrological impacts—must be considered. By integrating methodological innovation with explicit acknowledgment of uncertainties, this framework provides a conservative but policy-relevant basis for carbon accounting, subnational GHG reporting, and sustainable land-use planning in tropical agroecosystems. Full article
(This article belongs to the Special Issue Spatial Information for Forest Biomass and Carbon Stock Estimation: New Technologies and Approaches)
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