Small area estimation of county-level forest attributes using forest inventory data and remotely sensed auxiliary information

Author

Okikiola Michael Alegbeleye, Mississippi State UniversityFollow

ORCID

https://orcid.org/0000-0002-4521-5895

Issuing Body

Mississippi State University

Advisor

Poudel, Krishna P.

Committee Member

VanderSchaaf, Curtis L.

Committee Member

Yang, Yun

Date of Degree

8-8-2023

Document Type

Graduate Thesis - Open Access

Major

Forestry

Degree Name

Master of Science (M.S.)

College

College of Forest Resources

Department

Department of Forestry

Abstract

The Forest Inventory and Analysis (FIA) program of the United States Department of Agriculture Forest Service collects forest inventory data that provide estimates with reasonable accuracy at the national scale. However, for smaller domains, these estimates are often not as accurate due to the small sample size. Small area estimation improves the accuracy of the estimates at smaller domains by relying on auxiliary information. This study compared direct (FIA estimates), indirect (multiple linear regression), and composite estimators (Fay-Herriot) using auxiliary information derived from Landsat and Global Ecosystem Dynamics Investigation (GEDI) to obtain county-level estimates of forest attributes namely total and merchantable volume (m³ ha^-1), aboveground biomass (Mg ha^-1), basal area (m² ha^-1), and Lorey’s mean height (m). Compared with FIA estimates, the composite estimator reduced error by 75-78% for all the variables of interest. This shows that a reasonable amount of precision can be achieved with auxiliary information from Landsat and GEDI, improving FIA estimates at the county level.

Recommended Citation

Alegbeleye, Okikiola Michael, "Small area estimation of county-level forest attributes using forest inventory data and remotely sensed auxiliary information" (2023). Theses and Dissertations. 5906.
https://scholarsjunction.msstate.edu/td/5906