Date on Master's Thesis/Doctoral Dissertation

5-2019

Document Type

Master's Thesis

Degree Name

M.S.

Department

Geography and Geosciences

Degree Program

Geography (Applied), MS

Committee Chair

Gaughan, Andrea

Committee Co-Chair (if applicable)

Stevens, Forrest

Committee Member

Stevens, Forrest

Committee Member

Priope, Narcisa

Author's Keywords

UAS; UAV; structure from motion; drone; vegetation

Abstract

Unmanned Aerial Systems (UAS) have emerged as a capable platform for measuring vegetation health, structure and productivity. Products derived from UAS imagery typically have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity of vegetation to be mapped and monitored with more detail. This study uses UAS-captured imagery from the Chobe Enclave of northern Botswana. Flights were conducted across a gradient of savanna sites classified as grass-, shrub-, or tree-dominated. We compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. Sensor types were also compared, to determine whether multispectral data improves estimates of vegetation structure at the expense of spatial resolution. We found that leveraging multispectral reflectance information aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. Comparisons are made between two crown delineation techniques, and the efficacy of each technique within savanna environments is discussed. The methods presented hold potential to inform field sampling protocols and UAS-based techniques for autonomous crown delineation in future dryland systems research. These findings advance research for field and remote sensing analyses assessing degradation in heterogeneous landscapes where varying vegetation structure has implications on land use and land functions.

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