Quantifying Greenhouse Gas Exchange and Ecosystem Services in Ecosystems Managed for Nature-Based Solutions using Eddy Covariance, Remote Sensing, and Models

Author

Lucas Clay, Clemson UniversityFollow

Date of Award

12-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Forestry and Environmental Conservation

Committee Chair/Advisor

Thomas L. O'Halloran

Committee Member

Marzieh Motallebi

Committee Member

Robert Baldwin

Committee Member

James Anderson

Abstract

As global temperatures rise due to climate change, nature-based solutions are increasingly being used to restore nature and to remove carbon from the atmosphere. Nature-based solutions draw on methodologies such as ecological forestry and improved forest management to reduce emissions and maximize resilience. These protocols are still in their infancy and additional measurements and case studies are important to optimizing these protocols. Herein this dissertation we describe multiple measurement methodologies for both greenhouse gas (GHG) emissions and for ecosystem services. In coastal wetland ecosystems, the need for GHG measurements is paramount to understanding the ability of these ecosystems to reduce emissions. We show herein a novel study of adjacent, coastal wetlands and their impact on the climate. In forested ecosystems, the quantification of ecosystem services along with carbon benefits has been lacking. We developed two methods to assess where forest management would be best implemented to increase sediment and water retention and also if certain forest management practices such as ecological forestry and low basal areas could increase albedo and reduce warming. These protocols specifically translate these additional co-benefits into units similar to carbon projects so they can be integrated and scaled with current methodologies.

Recommended Citation

Clay, Lucas, "Quantifying Greenhouse Gas Exchange and Ecosystem Services in Ecosystems Managed for Nature-Based Solutions using Eddy Covariance, Remote Sensing, and Models" (2024). All Dissertations. 3800.
https://open.clemson.edu/all_dissertations/3800

Author ORCID Identifier

0000-0002-3077-7860