Digital Elevation Models

1993 ◽  
Author(s):  
Keyword(s):  
Digital Elevation Models ◽  
Digital Elevation

Digital Elevation Models

10.1596/34445 ◽  
2020 ◽  
Author(s):  
Louise Croneborg ◽  
Keiko Saito ◽  
Michel Matera ◽  
Don McKeown ◽  
Jan van Aardt
Keyword(s):  
Digital Elevation Models ◽  
Digital Elevation

scholarly journals The performance of landslide susceptibility models critically depends on the quality of digital elevation models

2020 ◽  
Vol 11 (1) ◽  
pp. 1075-1092 ◽  
Author(s):  
Jonas Brock ◽  
Patrick Schratz ◽  
Helene Petschko ◽  
Jannes Muenchow ◽  
Mihai Micu ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Digital Elevation Models ◽  
Digital Elevation

scholarly journals Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

2021 ◽  
Vol 10 (3) ◽  
pp. 186
Author(s):  
HuiHui Zhang ◽  
Hugo A. Loáiciga ◽  
LuWei Feng ◽  
Jing He ◽  
QingYun Du
Keyword(s):  
Spatial Scales ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Drainage Density ◽  
River Network ◽  
Landscape Patterns ◽  
Estimation Methods ◽  
River Networks ◽  
Flow Accumulation ◽  
Digital Elevation

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

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