{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/50818"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/50818","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Digital terrain analysis to predict soil spatial patterns at the Hubbard Brook Experimental Forest","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Gillin, Cody Palmer"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Forestry","degree_department":"Forest Resources and Environmental Conservation","school":null,"contributors":[],"advisors":[],"committee_chairs":["McGuire, Kevin J."],"committee_members":["Bailey, Scott W.","Prisley, Stephen P."],"year":2013,"date_issued":"2013-05-15","date_published":"2013-05-15","updated_at":"2026-07-22T22:19:51Z","subjects":["hydropedology","topographic metrics","soil","LiDAR","digital elevation model"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:805"],"render_values":[{"text":"vt_gsexam:805","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/50818","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["McGuire, Kevin J."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Bailey, Scott W.","Prisley, Stephen P."]},{"key":"dc:contributor.department","label":"Department","values":["Forest Resources and Environmental Conservation"]},{"key":"dc:creator","label":"Author","values":["Gillin, Cody Palmer"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-11-07T07:00:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-11-07T07:00:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2013-05-15"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Forestry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["hydropedology","topographic metrics","soil","LiDAR","digital elevation model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:805"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/50818"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Digital terrain analysis to predict soil spatial patterns at the Hubbard Brook Experimental Forest"]}]}],"canonical_facts":{"dc:contributor.committeechair":["McGuire, Kevin J."],"dc:contributor.committeemember":["Bailey, Scott W.","Prisley, Stephen P."],"dc:contributor.department":["Forest Resources and Environmental Conservation"],"dc:creator":["Gillin, Cody Palmer"],"dc:date.accessioned":["2014-11-07T07:00:18Z"],"dc:date.available":["2014-11-07T07:00:18Z"],"dc:date.issued":["2013-05-15"],"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."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:805"],"dc:identifier.uri":["http://hdl.handle.net/10919/50818"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["hydropedology","topographic metrics","soil","LiDAR","digital elevation model"],"dc:title":["Digital terrain analysis to predict soil spatial patterns at the Hubbard Brook Experimental Forest"],"dc:type":["Thesis"],"thesis:degree_discipline":["Forestry"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:51Z"}