Quantifying Forest Structure, Biomass, and Dynamics Using Inventory and Remote Sensing Data

Topic Information

Dear Colleagues,

Forests are essential for supporting life on Earth. As major carbon sinks, they play a key role in reducing climate change, promoting global carbon neutrality, and providing a wide range of ecosystem services. However, forest ecosystems face increasing threats from human activities and environmental disturbances, including fires, droughts, floods, deforestation, pests, and diseases. Therefore, the accurate monitoring of forest structure, biomass, and dynamics is crucial for sustainable forest management, biodiversity conservation, and climate adaptation efforts.

The topic “Quantifying Forest Structure, Biomass, and Dynamics Using Inventory and Remote Sensing Data” invites original research and reviews that combine forest inventory data with remote sensing. Traditional methods remain essential and are strongly encouraged. At the same time, we welcome contributions that expand these approaches through advanced modeling, including machine learning, deep learning, hybrid strategies, and physically based models supported by artificial intelligence.

We especially encourage studies that combine data from multiple sensors and platforms (e.g., optical, radar, LiDAR, UAVs, airborne systems, and satellites) and exploit multitemporal or time-series data to track forest changes over time. This integration enables more precise, scalable assessments of forest attributes at local, national, and transnational levels.

The scope of this topic is global and encompasses all major forest ecosystems (tropical, temperate, boreal, Mediterranean, and alpine), each with its own structural and environmental features. All forest management scenarios are included, from unmanaged primary forests to highly managed plantations. Large-scale applications are especially encouraged, along with innovative methods with potential for broader use. Small-scale or precision forestry studies are also welcome when supported by solid field data and clear methodological advancements.

Key methodological challenges include sensor and platform heterogeneity, the harmonization of national inventories, uncertainty propagation across different scales, limited ground truth in remote regions, and model transferability across ecosystems and biogeographical gradients. We invite contributions that address these challenges through new algorithms, data harmonization strategies, open-source tools, or reproducible workflows.

Topics of interest include, but are not limited to, the following:

• Forest structure and biomass estimation at tree, stand, and landscape levels;
• Tree- and stand-level inventorying and mapping using field and remote sensing data;
• Forest change detection, disturbance monitoring, and recovery dynamics;
• Multisource and multiscale data integration and data fusion strategies;
• Innovative modeling approaches for forest attribute prediction, uncertainty quantification, and upscaling;
• Use of multitemporal and time-series data to investigate forest growth, degradation, and carbon stock changes.

By contributing to this topic, authors will help advance the scientific understanding of forest ecosystem monitoring and strengthen the data-driven foundation for sustainable and climate-resilient forest management worldwide.

Dr. Giovanni D'Amico
Dr. Davide Travaglini
Dr. Walter Mattioli
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Ecologies ecologies	2.9	3.6	2020	23 Days	CHF 1200	Submit
Forests forests	3.1	5.4	2010	16.8 Days	CHF 2600	Submit
Land land	3.5	6.4	2012	17.5 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.3	9.4	2009	24.3 Days	CHF 2700	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Sustainability sustainability	4.1	8.9	2009	17.9 Days	CHF 2400	Submit

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Published Papers (2 papers)

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All Sensors Sustainability Remote Sensing Land Forests Ecologies

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22 pages, 4799 KB  

Open AccessArticle

Forest Disturbance Classification Under Imbalanced and Small-Sample Conditions Based on Collaborative Semi-Supervised Learning and Sample Generation

by Yudan Liu, Yuxin Zhao, Yan Yan, Yan Shao, Xinqi Qu and Ling Wu

Remote Sens. 2026, 18(10), 1579; https://doi.org/10.3390/rs18101579 - 14 May 2026

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Abstract

Accurate and timely information on forest disturbance drivers is important for sustainable forest management, global carbon cycle accounting, and climate change response. However, forest disturbance classification is difficult due to two major challenges: limited labeled samples and highly imbalanced disturbance class distribution. In [...] Read more.

Accurate and timely information on forest disturbance drivers is important for sustainable forest management, global carbon cycle accounting, and climate change response. However, forest disturbance classification is difficult due to two major challenges: limited labeled samples and highly imbalanced disturbance class distribution. In this article, a new framework for multi-type forest disturbance classification based on collaborative semi-supervised learning and sample generation was proposed. First, forest disturbance is detected using long-term remote sensing time series data and disturbance detection algorithms. Spatiotemporal, spectral and terrain features of different disturbance types are extracted. On this basis, to address the problem of imbalanced and small-sample conditions, a collaborative classification strategy is developed. Based on a small number of labeled samples, Support Vector Machine (SVM) and Random Forest (RF) are used to build dual base classifiers. A confident learning (CL) framework is applied to select high-confidence pseudo-labeled samples from unlabeled data. Then, a latent diffusion model (LDM) is introduced to generate high-fidelity pseudo-samples. This increases the sample size and balances the class distribution. Based on the augmented dataset, the dual classifiers are iteratively optimized using a co-training strategy, which improves model generalization under complex conditions. The results show that the proposed framework could generate high-quality pseudo-samples and effectively reduce class imbalance. The overall accuracy (OA) of the proposed framework reaches 93.2%, which is 5.7% and 4.4% higher than single classifier baselines, respectively. After introducing the LDM-based balancing mechanism, performance is further improved by 1.8% compared with the pure semi-supervised framework. This study provides an efficient and reliable solution for large-scale forest ecosystem monitoring. Full article

(This article belongs to the Topic Quantifying Forest Structure, Biomass, and Dynamics Using Inventory and Remote Sensing Data)

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36 pages, 19872 KB  

Open AccessArticle

Measurement-Driven Estimates of Above-Ground Biomass Change in the Eastern Canadian Boreal Forests from Permanent Sample Plots and Landsat Time Series

by Hadi Mahmoudi Meimand, Jiaxin Chen, Daniel Kneeshaw and Changhui Peng

Forests 2026, 17(5), 575; https://doi.org/10.3390/f17050575 - 8 May 2026

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Abstract

Monitoring boreal above-ground biomass (AGB) change requires approaches that are both measurement-based and spatially explicit. We integrated permanent sample plots from Quebec and Ontario with Landsat-7 spectral trajectories (1999–2023) to quantify non-fire-related AGB change after excluding wildfire-affected intervals and to evaluate whether annualized [...] Read more.

Monitoring boreal above-ground biomass (AGB) change requires approaches that are both measurement-based and spatially explicit. We integrated permanent sample plots from Quebec and Ontario with Landsat-7 spectral trajectories (1999–2023) to quantify non-fire-related AGB change after excluding wildfire-affected intervals and to evaluate whether annualized AGB change can be predicted from spectral change at the plot-interval scale. Tree height was estimated using a multilayer perceptron model (R² = 0.83) and combined with species-specific allometry to derive plot-level AGB and interval ΔAGB. These estimates were aggregated to ecodistricts using effective sample sizes and confidence intervals. Across well-sampled ecodistricts, mean annualized ΔAGB ranged from −0.82 to +3.54 t ha⁻¹ yr⁻¹, with lower or negative changes mainly occurring in eastern regions. Spectral indices derived from NIR–SWIR bands showed relatively stronger associations with ΔAGB than greenness-based indices, consistent with the sensitivity of moisture- and disturbance-related metrics to canopy stress, including defoliation. An XGBoost ensemble correctly predicted the direction of change in 77% of intervals. These results provide a measurement-constrained and scalable framework for monitoring non-fire-related biomass change and supporting greenhouse-gas reporting across boreal forest landscapes. Full article

(This article belongs to the Topic Quantifying Forest Structure, Biomass, and Dynamics Using Inventory and Remote Sensing Data)

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