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Forests
Special Issues
Modelling and Estimation of Forest Biomass
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Modelling and Estimation of Forest Biomass

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 April 2026 | Viewed by 1264

Special Issue Editors

Dr. Pradip Saud

E-Mail Website
Guest Editor
College of Forestry, Agriculture and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71656, USA
Interests: forest growth and yield modeling; survival analysis; taper equation modeling; forest biomass and carbon; stand competition; climate impact; treatment impact on growth and yield
Special Issues, Collections and Topics in MDPI journals
Dr. Mukti Ram Subedi

E-Mail Website
Guest Editor
Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA
Interests: remote sensing; biomass and bioenergy; disturbance and restoration ecology; ecological modeling
Prof. Dr. Daniel J. Vega-Nieva

E-Mail Website
Guest Editor
Faculty of Forestry and Environmental Sciences, Juarez University of the State of Durango, Durango 34120, Mexico
Interests: forestry and environmental sciences; analysis of information on forest growth; LiDAR; biomass; forest fires; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites original research articles dedicated to the modeling and estimation of forest biomass. We also welcome comprehensive review articles that provide a detailed account of the latest methodologies for assessing forest biomass using field inventories, repeated measurements, and advanced remote sensing technologies, such as optical imagery and point clouds (LiDAR, UAV, Sentinel, etc.). Accurate quantification of forest biomass is crucial for estimating carbon sequestration, which plays a significant role in climate change mitigation and engaging landowners and communities in potential carbon credit markets.

Forest biomass estimation can vary significantly according to forest types, tree species, and management objectives, integrating field-based and remote sensing approaches. This will provide valuable guidelines and information to forest managers and practitioners in selecting the appropriate analytical methods. Potential study topics include the following.

  • Comparison of empirical and process-based modeling techniques for estimating forest biomass.
  • Identifying key factors at the individual tree, stand, and environmental levels that influence allometric relationships in biomass estimation.
  • Modeling forest biomasses using machine learning and deep learning approaches.
  • Assessing long-term changes in forest biomass dynamics.
  • Modeling biomass adjustments following silvicultural treatments.
  • Analyzing the impacts of drought, insect pests, and diseases on forest biomass estimation.
  • Evaluating changes in forest biomass and structure under environmental stress using remote sensing technologies (e.g., LiDAR and UAV).
  • Assessing the effects of wind, hurricanes, and ice damage on tree biomass estimation.
  • Investigating how changes in forest structure and composition affect forest biomass estimation.

We encourage authors interested in these topics to discuss their ideas with the Editors prior to submission.

Dr. Pradip Saud
Dr. Mukti Ram Subedi
Prof. Dr. Daniel J. Vega-Nieva
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 biomass
  • carbon sequestration
  • modeling
  • estimation
  • remote sensing
  • machine learning

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Published Papers (3 papers)

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Research

23 pages, 7222 KB  
Article
A Multi-Model Framework to Quantify the Carbon Sink Potential of Larix olgensis Plantations in Northeast China
by Yaqi Zhao, Haoran Li, Xuanzhu Hou, Qilong Wang, Jie Ouyang, Lirong Zhang and Weifang Wang
Forests 2026, 17(4), 423; https://doi.org/10.3390/f17040423 - 27 Mar 2026
Abstract
Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. [...] Read more.
Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R2 = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha−1), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R2 = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha−1), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R2 = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha−1, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha−1); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article
(This article belongs to the Special Issue Modelling and Estimation of Forest Biomass)
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35 pages, 15596 KB  
Article
Biomass Estimation of Picea schrenkiana Forests in the Western Tianshan Mountains Using Integrated ICESat-2 and GF-6 Data
by Yan Tang, Donghua Chen, Xinguo Li, Juluduzi Shashan and Pinghao Xu
Forests 2026, 17(4), 421; https://doi.org/10.3390/f17040421 - 27 Mar 2026
Abstract
Forest biomass reflects the carbon storage capacity of forest ecosystems. Although remote sensing-based biomass estimation techniques have become increasingly mature, the issue of signal saturation in optical remote sensing still requires further investigation. This study was conducted in the Picea schrenkiana forest of [...] Read more.
Forest biomass reflects the carbon storage capacity of forest ecosystems. Although remote sensing-based biomass estimation techniques have become increasingly mature, the issue of signal saturation in optical remote sensing still requires further investigation. This study was conducted in the Picea schrenkiana forest of the Ili River Valley in the western Tianshan Mountains. By integrating multimodal data from ICESat-2 LiDAR and GF-6 optical imagery, we developed machine learning and deep learning models to achieve high-precision biomass estimation. Based on forest management inventory data, we extracted spectral and textural features from GF-6, along with canopy structure attributes derived from the four acquisition modes (day/night, strong/weak beams) of ICESat-2. After correlation-based feature selection, LightGBM, CatBoost, and TabNet models were trained and compared. The results showed that models integrating multi-source data significantly outperformed those based on a single data source. The TabNet model not only achieved high estimation accuracy but also provided clear feature importance rankings, with ICESat-2-derived canopy height percentiles and GF-6 red-edge vegetation indices contributing most significantly to the biomass estimation of Picea schrenkiana. These findings demonstrate the feasibility of synergistically utilizing domestic high-resolution satellites and multi-mode spaceborne LiDAR for forest biomass estimation in arid regions, providing an effective technical reference for accurate carbon sink monitoring of specific tree species in forest areas. Full article
(This article belongs to the Special Issue Modelling and Estimation of Forest Biomass)
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29 pages, 28659 KB  
Article
Assessing Anthropogenic Impacts on the Carbon Sink Dynamics in Tropical Lowland Rainforest Using Multiple Remote Sensing Data: A Case Study of Jianfengling, China
by Shijie Mao, Mingjiang Mao, Wenfeng Gong, Yuxin Chen, Yixi Ma, Renhao Chen, Miao Wang, Xiaoxiao Zhang, Jinming Xu, Junting Jia and Lingbing Wu
Forests 2025, 16(10), 1611; https://doi.org/10.3390/f16101611 - 20 Oct 2025
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Abstract
Aboveground biomass (AGB) is a key indicator of forest structure and carbon sequestration, yet its dynamics under concurrent anthropogenic disturbances remain poorly understood. This study investigates the spatiotemporal dynamics and driving mechanisms of AGB in the Jianfengling tropical lowland rainforest (JFLTLR) within Hainan [...] Read more.
Aboveground biomass (AGB) is a key indicator of forest structure and carbon sequestration, yet its dynamics under concurrent anthropogenic disturbances remain poorly understood. This study investigates the spatiotemporal dynamics and driving mechanisms of AGB in the Jianfengling tropical lowland rainforest (JFLTLR) within Hainan Tropical Rainforest National Park (NRHTR) from 2015 to 2023. Six machine learning models—Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF)—were evaluated, with RF achieving the highest accuracy (R2 = 0.83). Therefore, RF was employed to generate high-resolution annual AGB maps based on Sentinel-1/2 data fusion, field surveys, socio-economic indicators, and topographic variables. Human pressure was quantified using the Human Influence Index (HII). Threshold analysis revealed a critical breakpoint at ΔHII ≈ 0.1712: below this level, AGB remained relatively stable, whereas beyond it, biomass declined sharply (≈−2.65 mg·ha−1 per 0.01 ΔHII). Partial least squares structural equation modeling (PLS-SEM) identified plantation forests as the dominant negative driver, while GDP (−0.91) and road (−1.04) exerted strong indirect effects through HII, peaking in 2019 before weakening under ecological restoration policies. Spatially, biomass remained resilient within central core zones but declined in peripheral regions associated with road expansion. Temporally, AGB exhibited a trajectory of decline, partial recovery, and renewed loss, resulting in a net reduction of ≈ 0.0393 × 106 mg. These findings underscore the urgent need for a “core stabilization–peripheral containment” strategy integrating disturbance early-warning systems, transportation planning that minimizes impacts on high-AGB corridors, and the strengthening of ecological corridors to maintain carbon-sink capacity and guide differentiated rainforest conservation. Full article
(This article belongs to the Special Issue Modelling and Estimation of Forest Biomass)
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