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Virginia Tech

Digital terrain analysis to predict soil spatial patterns at the Hubbard Brook Experimental Forest

Abstract

dc:description.abstract

Topographic analysis using digital elevation models (DEMs) has become commonplace in soil and hydrologic modeling and analysis and there has been considerable assessment of the effects of grid resolution on topographic metrics using DEMs of 10 m resolution or coarser. However, examining fine-scale (i.e., 1-10 m) soil and hydrological variability of headwater catchments may require higher-resolution data that has only recently become available, and both DEM accuracy and the effects of different high-resolution DEMs on topographic metrics are relatively unknown. This study has two principle research components. First, an error analysis of two high-resolution DEMs derived from light detection and ranging (LiDAR) data covering the same headwater catchment was conducted to assess the applicability of such DEMs for modeling fine-scale environmental phenomena. Second, one LiDAR-derived DEM was selected for computing topographic metrics to predict fine-scale functional soil units termed hydropedological units (HPUs). HPU development is related to topographic and surface/subsurface heterogeneity resulting in distinct hydrologic flowpaths leading to variation of soil morphological expression. Although the two LiDAR datasets differed with respect to data collection methods and nominal post-spacing of ground returns, DEMs interpolated from each LiDAR dataset exhibited similar error. Grid resolution affected DEM-delineated catchment boundaries and the value of computed topographic metrics. The best topographic metrics for predicting HPUs were the topographic wetness index, bedrock-weighted upslope accumulated area, and Euclidean distance from bedrock. Predicting the spatial distribution of HPUs may provide a more comprehensive understanding of hydrological and biogeochemical functionality of headwater systems.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Forestry
Department dc:contributor.department
Forest Resources and Environmental Conservation
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2013

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Gillin, Cody Palmer
Chair dc:contributor.committeechair
  • McGuire, Kevin J.
Committee members dc:contributor.committeemember
  • Bailey, Scott W.
  • Prisley, Stephen P.

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:805
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/50818

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Gillin, Cody Palmer. Digital terrain analysis to predict soil spatial patterns at the Hubbard Brook Experimental Forest. masters thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/50818