Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models
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...
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MDPI AG
2021-03-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/3/186 |
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| author | HuiHui Zhang Hugo A. Loáiciga LuWei Feng Jing He QingYun Du |
| author_facet | HuiHui Zhang Hugo A. Loáiciga LuWei Feng Jing He QingYun Du |
| author_sort | HuiHui Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:01:49Z |
| format | Article |
| id | doaj.art-040a1723b6e64926931b6a28a76eab1b |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T13:01:49Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-040a1723b6e64926931b6a28a76eab1b2023-11-21T11:26:01ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-03-0110318610.3390/ijgi10030186Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation ModelsHuiHui Zhang0Hugo A. Loáiciga1LuWei Feng2Jing He3QingYun Du4School of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaDepartment of Geography, University of California, Santa Barbara, CA 93106, USASchool of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaDetermining 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.https://www.mdpi.com/2220-9964/10/3/186multi-source satellite dataflow accumulation thresholdriver networksgeomorphological complexitycorrelation model |
| spellingShingle | HuiHui Zhang Hugo A. Loáiciga LuWei Feng Jing He QingYun Du Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models ISPRS International Journal of Geo-Information multi-source satellite data flow accumulation threshold river networks geomorphological complexity correlation model |
| title | Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models |
| title_full | Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models |
| title_fullStr | Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models |
| title_full_unstemmed | Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models |
| title_short | Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models |
| title_sort | setting the flow accumulation threshold based on environmental and morphologic features to extract river networks from digital elevation models |
| topic | multi-source satellite data flow accumulation threshold river networks geomorphological complexity correlation model |
| url | https://www.mdpi.com/2220-9964/10/3/186 |
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