Advances in Estimation and Monitoring of Forest Biomass and Fuel Load Components

Forests play a pivotal role in global carbon sequestration, biodiversity conservation, and climate change mitigation. Accurately quantifying forests’ biomass and fuel load components is essential for sustainable forest management and carbon accounting. This Special Issue of Forests, titled “Estimation and Monitoring of Forest Biomass and Fuel Load Components,” includes 13 research papers addressing methodological advancements, regional case studies, and novel applications in biomass modeling, carbon storage, stand structure optimization, and regeneration dynamics. In general, these contributions underscore the importance of integrating multi-scale data, advanced statistical methods, and ecological considerations to estimate and monitor forests’ biomass and carbon storage.

• Methodological Innovations in Biomass and Carbon Stock Modeling

A recurring theme across multiple studies is the development of robust models to estimate biomass and carbon storage. Zeng et al. established a three-level model system for China’s diverse range of forests, integrating weighted regression, dummy variables, and simultaneous equations. Their hierarchical framework—spanning forest categories (Level I), types (Level II), and sub-types (Level III)—achieved remarkable precision, with determination coefficients (R²) exceeding 0.95 at Level III. This system addresses the limitations of earlier models; for example, three major forest types exhibited considerable uncertainties (total relative error (TRE): −20% to 74%) [1]. Similarly, Yin et al. devised additive biomass models for poplar plantations using seemingly unrelated regression (SUR), demonstrating the significance of tree height as a predictor and revealing carbon allocation shifts from competitive growth to self-stabilization as trees mature [2].

Soil variables and structural diversity, which are often overlooked in traditional models, were focused on by Guo et al. and He et al. [3,4]. Guo’s soil-sensitive Weibull distribution model for Larix principis-rupprechtii plantations incorporated available potassium and nitrogen, improving the parameter accuracy (the root mean squared error (RMSE) was reduced by 10.4%) [3]. He et al. quantified carbon sink dynamics in mixed forests by integrating stand structural diversity and carbon growth grades, projecting a 51% increase in Jilin Province’s carbon stocks by 2060 compared with age-only models. These studies highlight the necessity of multidimensional approaches for precise carbon accounting [4].

• Harnessing Technology and Machine Learning

Remote sensing and machine learning are revolutionizing forest monitoring. Xu et al. contrasted the Random Forest (RF) and nonlinear mixed-effects (NLME) models for predicting tree height–diameter relationships in mixed coniferous–broadleaf forests. The RF model outperformed NLME, achieving an R² of 0.970, underscoring its adaptability for complex, high-dimensional data [5]. Yang et al. employed LiDAR-derived variables and Gaussian process regression to predict the height-to-crown-base (HCB) in Picea crassifolia forests, identifying spatial competition indices as critical drivers of HCB variability [6]. These methodologies enable rapid, non-destructive assessments that are critical for large-scale inventories.

Climate resilience modeling has also benefited from technological integration. Meng et al. developed a climate-sensitive transition matrix model for natural forests in Chongqing, predicting forest growth under three RCP (representative concentration pathways) scenarios [7]. Their findings showed minimal divergence in carbon stocks across emission pathways but highlighted successional shifts from pioneer to climax species, stressing the need for adaptive management policies.

• Optimizing Stand Structure and Management Practices

Several studies explored silvicultural interventions to enhance the productivity and resilience of forests. Zhou et al. demonstrated that restorative thinning in Quercus variabilis plantations increased the spatial heterogeneity, reducing DBH and density autocorrelation distances while amplifying tree height gradients [8]. Such interventions mimic natural disturbance regimes, promoting structural complexity. Similarly, Sheng et al. classified mixed broadleaf forests into developmental stages (establishment, competition, and quality selection) and optimized their spatial structure using a harvest intensity of 10%, quadrupling the structural quality index (Q(x)) [9]. These findings demonstrate the need for stage-specific management to maximize ecological and economic outcomes.

Seedling regeneration dynamics, which are crucial for forest sustainability, were analyzed by Hu et al. [10]. Their study of Quercus forests in Beijing revealed species-specific regeneration capacities (Q. aliena and Q. variabilis outperformed others) and identified soil factors (e.g., exchangeable calcium) and stand diversity as key determinants of later-stage regeneration. This underscores the interplay between biotic and abiotic factors in sustaining forest health.

• Regional Applications and Model Validation

Region-specific insights emerged as a cornerstone of this Special Issue. Huang et al. incorporated the stand age into allometric equations for eucalypt hybrids, revealing age-driven shifts in biomass allocation (e.g., the stem biomass increased from 40% to 60% with age) [11]. Wang et al. addressed bamboo biomass estimation, constructing models for Dendrocalamus brandisii that balance simplicity and accuracy—a critical step for managing economically vital bamboo plantations [12]. Meanwhile, Yao et al. proposed a crown-width-based algorithm to locate tree pith positions, reducing estimation errors to 7.6%, which enhances dendrochronological and biomass studies [13].

• Conclusions and Future Directions

The studies in this Special Issue collectively advance the precision and scope of forest biomass and fuel load monitoring. Key innovations include the integration of soil variables, structural diversity, and climate scenarios into predictive models; the adoption of machine learning for complex datasets; and the validation of silvicultural practices across diverse forest types. However, challenges remain, particularly in scaling localized models to larger regions and reconciling field data with remote sensing outputs. Future research should prioritize the following topics:

Cross-scale validation: Testing models’ generalizability across biogeographic regions.

Dynamic modeling: Incorporating temporal shifts in climate, species composition, and disturbance regimes.

Policy integration: Translating scientific findings into adaptive management frameworks for carbon markets and wildfire mitigation.

As global forest ecosystems face unprecedented pressures from climate change and land-use shifts, the methodologies and insights presented here provide a robust foundation for evidence-based stewardship. By fostering interdisciplinary collaboration and technological innovation, we can ensure that forests continue to serve as vital carbon sinks and biodiversity havens for generations to come.