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

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