Date of Award

Spring 5-3-2024

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Master of Science (MS)

Department

Forest Resources

Advisor

Daniel J. Hayes

Second Committee Member

Aaron Weiskittel

Third Committee Member

Shawn Fraver

Additional Committee Members

Sean Healey

Abstract

The challenges associated with accurate carbon reporting, particularly in tropical regions, stem from limited reference data and spatial heterogeneity. Remote sensing systems play a crucial role in carbon accounting by providing comprehensive and spatially explicit information on forest biomass and carbon stocks over large areas. These systems utilize advanced methodologies to estimate biomass and monitor changes, integrating data from multiple sources to capture the complexities of ecosystem processes accurately. Committee on Earth Observation Satellites (CEOS) supports developing countries with carbon reporting by facilitating access to satellite data and promoting collaboration among space agencies and international organizations. Google Earth Engine (GEE) processing power and accessibility to remote sensing data and tools like the Continuous Change Detection and Classification (CCDC) and Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithms, as well as datasets like the Hansen dataset, further enhance the capabilities of researchers to analyze and report carbon dynamics. By leveraging these remote sensing technologies and platforms, researchers can enhance our understanding of carbon dynamics and support evidence-based decision-making for effective forest conservation and management strategies. The study reveals significant differences in aboveground biomass (AGB) estimates across various forest types through a comparative analysis of different datasets and monitoring systems. Shrubland, Lowland Pine, and Submontane-broadleaf forests are identified as sinks, showing a net gain in carbon stocks. In contrast, Lowland Savanna and Lowland Broadleaf forests are sources, experiencing a net loss. The comparison of net changes in AGB between 2010 and 2020 underscores the complexity of forest biomass dynamics, with LandTrendr and CCDC data suggesting a reduction in net AGB change, while both Hansen and CCDC datasets indicate a general rise. These findings emphasize the importance of utilizing multiple monitoring technologies to comprehensively assess changes in AGB and carbon stocks over time, enhancing our understanding of carbon cycling within terrestrial ecosystems and informing effective conservation and management strategies. Challenges persist in tropical regions due to limited reference data and spatial heterogeneity, but integrating GEDI data offers promising opportunities for improved accuracy. Calibration and validation with independent reference data remain critical for ensuring the reliability of AGB estimates. and enhance the accuracy of carbon stock assessments.

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