Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs
Selecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents the MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when extracting drainage from DEMs. This work employs 36 s...
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/11/2024 |
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| author | Wei Zhang Wenkai Li Hugo A. Loaiciga Xiuguo Liu Shuya Liu Shengjie Zheng Han Zhang |
| author_facet | Wei Zhang Wenkai Li Hugo A. Loaiciga Xiuguo Liu Shuya Liu Shengjie Zheng Han Zhang |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | Selecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents the MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when extracting drainage from DEMs. This work employs 36 sample sub-basins in Hubei (China) province. Firstly, topography, the normalized difference vegetation index (NDVI), and water storage change are used in building multiple regression models to calculate the drainage length. Power functions are fit to calculate the FAT of each sub-basin. Nine randomly chosen regions served as test sub-basins. The results show that: (1) water storage change and NDVI have high correlation with the drainage length, and the coefficient of determination (R<sup>2</sup>) ranges between 0.85 and 0.87; (2) the drainage length obtained from the Multiple Regression model using water storage change, NDVI, and topography as influence factors is similar to the actual drainage length, featuring a coefficient of determination (R<sup>2</sup>) equal to 0.714; (3) the MR-AP method calculates suitable FATs for each sub-basin in Hubei province, with a drainage length error equal to 5.13%. Moreover, drainage network extraction by the MR-AP method mainly depends on the water storage change and the NDVI, thus being consistent with the regional water-resources change. |
| first_indexed | 2024-03-10T11:12:28Z |
| format | Article |
| id | doaj.art-a3000d8b5a69431db3ea213de8555fff |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:12:28Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a3000d8b5a69431db3ea213de8555fff2023-11-21T20:43:19ZengMDPI AGRemote Sensing2072-42922021-05-011311202410.3390/rs13112024Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMsWei Zhang0Wenkai Li1Hugo A. Loaiciga2Xiuguo Liu3Shuya Liu4Shengjie Zheng5Han Zhang6School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaDepartment of Geography, University of California, Santa Barbara, CA 93106, USASchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaChina Petroleum Pipeline Engineering Co., Ltd., Langfang 065000, ChinaSchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaSelecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents the MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when extracting drainage from DEMs. This work employs 36 sample sub-basins in Hubei (China) province. Firstly, topography, the normalized difference vegetation index (NDVI), and water storage change are used in building multiple regression models to calculate the drainage length. Power functions are fit to calculate the FAT of each sub-basin. Nine randomly chosen regions served as test sub-basins. The results show that: (1) water storage change and NDVI have high correlation with the drainage length, and the coefficient of determination (R<sup>2</sup>) ranges between 0.85 and 0.87; (2) the drainage length obtained from the Multiple Regression model using water storage change, NDVI, and topography as influence factors is similar to the actual drainage length, featuring a coefficient of determination (R<sup>2</sup>) equal to 0.714; (3) the MR-AP method calculates suitable FATs for each sub-basin in Hubei province, with a drainage length error equal to 5.13%. Moreover, drainage network extraction by the MR-AP method mainly depends on the water storage change and the NDVI, thus being consistent with the regional water-resources change.https://www.mdpi.com/2072-4292/13/11/2024flow accumulation threshold (FAT)multiple regressionpower functiondrainage networks |
| spellingShingle | Wei Zhang Wenkai Li Hugo A. Loaiciga Xiuguo Liu Shuya Liu Shengjie Zheng Han Zhang Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs Remote Sensing flow accumulation threshold (FAT) multiple regression power function drainage networks |
| title | Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs |
| title_full | Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs |
| title_fullStr | Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs |
| title_full_unstemmed | Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs |
| title_short | Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs |
| title_sort | adaptive determination of the flow accumulation threshold for extracting drainage networks from dems |
| topic | flow accumulation threshold (FAT) multiple regression power function drainage networks |
| url | https://www.mdpi.com/2072-4292/13/11/2024 |
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