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Enhancing Carbon Storage Through Prescribed Burning Management

Our lab focuses on the management of forest lands using prescribed burning to conserve and restore native ecosystems. We are also exploring Nature-based Solutions (NbS) to address climate change by capturing atmospheric CO2 through plant and soil storage. This process, known as "carbon sequestration," offers ecological benefits like habitat conservation and can generate economic value through carbon credits.


The effects of fire on carbon (C) storage are complex and varied, with studies showing positive, neutral, and negative impacts. These mixed results often stem from short-term studies (less than 10 years), which can lead to misleading conclusions. Additionally, most research has concentrated on aboveground C storage, overlooking the significant role of belowground biomass in roots. Roots have a greater potential for long-term C storage and are less prone to decomposition.


Our goal is to inform prescribed burning practices to enhance land C storage. We will use an experimental site with different burning frequencies since 1983 to determine the optimal frequency for maximizing both above- and belowground C storage. Our ongoing 40-year vegetation survey will be complemented by cutting-edge technologies, including:

 

  • Drone-based LiDAR for measuring aboveground C storage.
  • Ground-penetrating radar for assessing belowground C storage in soil and roots with minimal disturbance.

As a RaMP fellow in our lab, you will:

 

  • Master advanced technologies like drone-based LiDAR and ground-penetrating radar.
  • Develop quantitative skills through data analysis.
  • Lead the writing and publication of a scientific manuscript based on project results.
  • Present findings at national or international conferences.
  • Network with diverse collaborators from state agencies, tribal nations, and the carbon and forestry industries.

These experiences will equip you with valuable skills and connections for your long-term career development.


Network mentor: Dr. Lu Zhai

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