Optimized ground penetrating radar methods can account for landscape variance in properties informing soil carbon distribution in boreal forest hillslopes

Gates, Zachary W. (2022) Optimized ground penetrating radar methods can account for landscape variance in properties informing soil carbon distribution in boreal forest hillslopes. Masters thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (13MB)

Abstract

This thesis entailed developing optimized ground penetrating radar (GPR) methods for estimating soil horizon thickness and bulk density to determine soil carbon (C) distribution across forest hillslopes. A review of forest GPR studies was conducted to synthesize optimized system settings, survey parameters, and data processing steps. Recommended GPR survey settings (> 500 MHz antenna frequency, > 32 stacks, 5 cm sampling interval) and data processing tools were compiled for forest soil surveys and demonstrated to improve the interpretability of specific soil targets (ex. soil horizon boundaries, rock, and root content) in forest soil radargrams. Physical soil sampling and GPR surveying methods were conducted across a boreal forest hillslope in Pynn’s Brook, Newfoundland to collect small (1 m² soil pits) and large (80 m GPR survey lines) spatial scale soil horizon thickness and bulk density estimates. This allowed for comparisons between physical soil sampling and GPR estimates of soil horizon thickness, soil bulk density and resulting soil C distribution calculated using soil C stocks. Furthermore, large spatial scale GPR surveying revealed landscape trends in soil bulk density, such as increasing density downslope and high variability across the slope, which informs our understanding of forest soil C distribution and its landscape controls.

Actions (login required)

View Item