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Landscape Structure Through Depression Hierarchy Analysis in the Continental United States
Xuezhi Cang,
(He/him/his),University of Illinois Chicago Chicago Illinois, United States. xuezhicang@gmail.com
Kerry Callaghan, University of Illinois Chicago Chicago Illinois, United States. kerryc@uic.edu
Landscape morphology is the fingerprint of natural processes and human activities and is commonly represented by Digital Elevation Models (DEMs). The spatial structure of a landscape encodes essential information, making it fundamental for analyzing and understanding their responses to various influencing factors. In this study, we utilize the hierarchical relations of topographic depressions—features once dismissed as artifacts or errors in DEMs—to characterize landscape structure. We extract the hierarchical relationships using the Depression Hierarchy algorithm applied to the DEMs.
In this study, we extract depression hierarchies from the SRTM DEM at 30-meter resolution for the continental United States, initially representing them as binary trees. To enhance real-world applicability, we convert these binary trees into general trees. In the depression hierarchy general tree, the top-level depressions are the depressions traditionally defined by DEM filling algorithms, while the bottom-level depressions generally are the small and independent pits in the DEM. We extract the top three and bottom three depression levels and analyze the relationship between depression parameters, such as median depression volume, and various environmental variables, including land use, lithology, soil type, climate classification, glacier zones, aridity index, and mean annual precipitation at a HUC-8 watershed scale.
We use ridge regression to analyze the correlation between median depression volumes and environmental variables. The strongest correlation occurs at the bottom hierarchical level (leaf depression). The three variables most strongly correlated with depression volume are land use, climate classification, and aridity index. The R² values range from 0.30 to 0.45. When all variables, which include land use, lithology, soil type, climate classification, glacier zones, aridity index, and mean annual precipitation, are used in the ridge regression, the model has an R² of approximately 0.6. These results highlight the significance of depression structures in shaping the landscape and reveal the connections between depressions and both natural processes and human activities.
