Estimating Above-Ground Biomass and Above-Ground Carbon by Remote Sensing Data

Dear Colleagues,

Today, forests stand as a powerful ally in the battle against climate change and, if managed properly, they can prevent emissions from deforestation and forest degradation and act as critical carbon sinks. The urgency of safeguarding these ecosystems has never been greater and accurately measuring their above-ground biomass (AGB) and carbon storage (AGC) is a major step toward this goal. While traditional field-based methods for quantifying AGB and AGC pose certain challenges, recent advancements in remote sensing and computational capabilities have come to offer efficient and innovative new alternatives, enabling increasingly accurate estimates at a wide range of spatial and temporal scales. Recent estimation methods rely on data fusion from different sensors and advanced machine learning algorithms; yet, these new possibilities raise questions regarding accuracy and precision. In a framework where greater and greater precision is required, we can no longer rely on “static” metrics to assess the performance of models and maps. Understanding spatial uncertainties is paramount and imperative to making remote sensing estimates more robust and reliable, and our continued efforts should focus on complementing maps of forest variables with the related error maps.

This Special Issue aims to bring together scientists and specialists developing and applying new remote sensing approaches in an effort to improve our understanding of the biomass and carbon dynamics of forest ecosystems. The topics covered in this Special Issue include, but are not exclusive to, the following:

• New methods to assess biomass and carbon in forest ecosystems using remote sensing;
• New sensors and new data fusion approaches;
• Temporal assessment of biomass and carbon dynamics;
• Biomass and carbon assessment via proximal sensing, i.e., TLS, photogrammetry, etc.;
• Spatial and temporal uncertainty assessments;
• Impact of forest disturbances on biomass and carbon balance;
• Large-scale monitoring of biomass and carbon dynamics;
• Cloud computing approaches.

Dr. Elia Vangi
Prof. Dr. Gherardo Chirici
Dr. Alessio Collalti
Guest Editors

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