Date of Graduation

Summer 2019

Degree

Master of Science in Geospatial Sciences

Department

Geography, Geology, and Planning

Committee Chair

Robert Pavlowsky

Abstract

Climate change has increased the frequency of large floods in rivers draining the Ozark Highlands resulting in higher rates of channel sedimentation, bank erosion, and damage to infrastructure. This study assesses the effects of a large flood (>500-year RI) during April-May 2017 on riparian forests along six tributary streams in the North Fork of the White River watershed, Missouri. High-resolution (<8 >cm) Unmanned Aerial Vehicle (UAV) imagery collected after the flood was used to identify riparian forest flood damage. Measurements of riparian forest flood damage calculated from the UAV imagery were verified through field surveys of damaged riparian trees. Geomorphic variables of valley confinement, sinuosity, substrate, and stream power were evaluated and used to explain the spatial distribution of riparian forest flood damage. In total over 1,000 damaged trees were identified at the sample reaches and canopy cover was reduced by up to 63%. Regression analysis showed positive relationships between riparian forest damage (total volume of damaged trees, volume of damaged trees per hectare, and canopy loss) with geomorphic variables such as confinement, sinuosity, substrate, and stream power. Mean (R2 = 0.67) and cross-sectional (R2 = 0.90) stream power accounted for the greatest percentage of variance in volume of riparian forest damage. Riparian forest damage peaked in the reaches with the largest drainage areas. UAVs have the potential to accurately assess riparian forest damage due to floods. However, more research on UAV measurement errors is needed to better evaluate UAV forest data. This information can be used to understand ecological disturbance by floods and inform land management practices in Mark Twain National Forest.

Keywords

floods, riparian forests, Missouri Ozarks, fluvial geomorphology, UAV imagery

Subject Categories

Environmental Monitoring | Geomorphology

Copyright

© Joshua William Hess

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Open Access

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