VTechWorks staff will be away for the winter holidays starting Tuesday, December 24, 2024, through Wednesday, January 1, 2025, and will not be replying to requests during this time. Thank you for your patience, and happy holidays!
 

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

Simple item page
dc.contributor.authorGillin, Cody Palmeren
dc.contributor.committeechairMcGuire, Kevin J.en
dc.contributor.committeememberBailey, Scott W.en
dc.contributor.committeememberPrisley, Stephen P.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2014-11-07T07:00:18Zen
dc.date.available2014-11-07T07:00:18Zen
dc.date.issued2013-05-15en
dc.description.abstractTopographic 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.    en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:805en
dc.identifier.urihttp://hdl.handle.net/10919/50818en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecthydropedologyen
dc.subjecttopographic metricsen
dc.subjectsoilen
dc.subjectLiDARen
dc.subjectdigital elevation modelen
dc.titleDigital terrain analysis to predict soil spatial patterns at the Hubbard Brook Experimental Foresten
dc.typeThesisen
thesis.degree.disciplineForestryen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Gillin_CP_T_2013.pdf
Size:
2.78 MB
Format:
Adobe Portable Document Format
Download

Collections

Masters Theses