Speaker
Description
Accurately estimating carbon stock in forest ecosystems is crucial for ensuring sustainable management of ecosystem services and addressing global climate change challenges. Over the past decade, methods for assessing carbon stored in soil and forest biomass have evolved considerably. Traditional approaches, primarily based on direct field measurements and simple statistical models, provided fundamental insights but often lacked spatial and temporal coverage. Recent advances have introduced new technologies and analytical frameworks that significantly improve the precision and scale of carbon stock estimation. Spectroscopy techniques now enable rapid and non-destructive assessment of soil organic carbon, while remote sensing tools, including satellite and airborne data, offer the ability to monitor forest biomass dynamics over large areas. Additionally, the integration of machine learning and advanced analytical methods allows researchers to analyze complex datasets, identify patterns, and develop predictive models of carbon sequestration processes across diverse forest landscapes. Current research trends reveal three main areas of innovation: (1) the use of spectroscopy combined with soil organic carbon assessment to improve measurements of belowground carbon; (2) the application of remote sensing and forest inventory data to quantify aboveground biomass; and (3) the deployment of machine learning and advanced statistical techniques to model carbon stock variations and predict future trends. These methodological advancements provide essential scientific tools for policymakers and forest managers, supporting the development of effective strategies for climate change adaptation and mitigation, while enhancing our understanding of the role of forests as vital carbon sinks in the global carbon cycle.
