Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Schultz, Emily B.

Committee Member

Matney, Thomas G.

Committee Member

Evans, David L.

Committee Member

Fan, Zhaofei.

Date of Degree

1-1-2014

Document Type

Dissertation - Open Access

Abstract

The Mississippi Institute for Forest Inventory (MIFI) is the only cost-effective large-scale forest inventory system in the United States with sufficient precision for producing reliable volume/weight/biomass estimates for small working circle areas (procurement areas). When forest industry is recruited to Mississippi, proposed working circles may overlap existing boundaries of bordering states leaving a gap of inventory information, and a remote sensing-based system for augmenting missing ground inventory data is desirable. The feasibility of obtaining acceptable cubic foot volume estimates from a Landsat-derived volume estimation model (Wilkinson 2011) was assessed by: 1) an initial study to temporally validate Landsat-derived cubic foot volume outside bark to a pulpwood top estimates in comparison with MIFI ground truth inventory plot estimates at two separate time periods, and 2) re-developing a regression model based on remotely sensed imagery in combination with available MIFI plot data. Initial results failed to confirm the relationships shown in past research between radiance values and volume estimation. The complete lack of influence of radiance values in the model led to a re-assessment of volume estimation schemes. Data outlier trimming manipulation was discovered to lead to false relationships with radiance values reported in past research. Two revised volume estimation models using age, average stand height, and trees per-acre and age and height alone as independent variables were found sufficient to explain variation of volume across the image. These results were used to develop a procedure for other remote sensing technologies that could produce data with sufficient precision for volume estimation where inventory data are sparse or non-existent.

URI

https://hdl.handle.net/11668/16713

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