Semester of Graduation

Summer 2021

Degree

Master of Science (MS)

Department

Geography

Document Type

Thesis

Abstract

Analysis of satellite imagery combined with Geographic Information Systems (GIS), often allows for observing the increasing changes in land cover dynamics. These changes are often on a macro scale buoyed by natural hazards such as wildfires. This study examines the vegetation recovery dynamics by using multispectral data and also gleaned insight in the applications of Hyperspectral satellite imagery in the study of vegetation dynamics. Both Landsat 8 Operational Land Imager (OLI) (multispectral) and the Earth-Observing One (EO-1) (hyperspectral) data are freely available; however the former has a limited spatial and temporal coverage. The relationship between vegetation recovery, elevation, aspect and slope are explored in the study as well as the applications of hyperspectral remote sensing in the study of forest health and fire fuels. Vegetation recovery dynamics of the burn scar as a whole and stratified was studied in a chronological manner using normalized difference vegetation index (NDVI) from the multispectral imagery. Fire fuel and forest health analysis (only possible with hyperspectral imagery) were performed on segments of the burn scar due to spatio-temporal paucity of hyperspectral datasets. The indices used were Normalized Difference Lignin Index (NDLI) and Cellulose Absorption Index (CAI). Results suggest a nine-year recovery period to pre-fire NDVI levels. The rate of recovery was shown to be inversely related to the burn severity. Also, the propensity for future fires, based on fire fuels, is shown to be inversely related to forest health. Generally, this study provides important insight into the understanding of the responses of vegetation indices over the period under study and post-fire vegetation regeneration as well as the critical role hyperspectral remote sensing can play in the understanding of post-fire vegetation dynamics and in forest management.

Committee Chair

Wang, Lei

Available for download on Thursday, May 25, 2023

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