Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China

doi:10.1007/s11707-018-0700-5

Front. Earth Sci.

2019, Vol. 13

Issue (1) : 151-168 https://doi.org/10.1007/s11707-018-0700-5

RESEARCH ARTICLE

Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China

Xin YANG¹(

), Jiaming NA¹, Guoan TANG¹, Tingting WANG², Axing ZHU^3,⁴

¹. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
². State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
³. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
⁴. Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA

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Abstract

As one of most active gully types in the Chinese Loess Plateau, bank gullies generally indicate soil loss and land degradation. This study addressed the lack of detailed, large scale monitoring of bank gullies and proposed a semi-automatic method for extracting bank gullies, given typical topographic features based on 5 m resolution DEMs. First, channel networks, including bank gullies, are extracted through an iterative channel burn-in algorithm. Second, gully heads are correctly positioned based on the spatial relationship between gully heads and their corresponding gully shoulder lines. Third, bank gullies are distinguished from other gullies using the newly proposed topographic measurement of “relative gully depth (RGD).” The experimental results from the loess hilly area of the Linjiajian watershed in the Chinese Loess Plateau show that the producer accuracy reaches 87.5%. The accuracy is affected by the DEM resolution and RGD parameters, as well as the accuracy of the gully shoulder line. The application in the Madigou watershed with a high DEM resolution validated the duplicability of this method in other areas. The overall performance shows that bank gullies can be extracted with acceptable accuracy over a large area, which provides essential information for research on soil erosion, geomorphology, and environmental ecology.

Keywords bank gully DEMs topographic features loess shoulder line relative gully depth

Corresponding Author(s): Xin YANG

Just Accepted Date: 05 March 2018 Online First Date: 18 April 2018 Issue Date: 25 January 2019

Cite this article:

Xin YANG,Jiaming NA,Guoan TANG, et al. Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China[J]. Front. Earth Sci., 2019, 13(1): 151-168.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0700-5
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I1/151

Fig.1 Study area and data used: (a) the location of the sample and training areas; (b) a picture of bank gully in a slope; (c) perspective view of bank gullies though DOM image.

Fig.2 Workflow of bank gully extraction.

Fig.3 Flow chart of iterative channel deepening algorithm.

Fig.4 The relationship between gully head and shoulder line.

Fig.5 Flow chart of the relative gully depth (RGD) algorithm.

Tab.1 Statistics of the upslope catchment area of bank gully heads in the sample areas

Fig.6 The result of gully network. (a) Gully network extracted by original DEM without channel deepening; (b) enlarged areas located in the downstream, middle, and upstream parts of the drainage area, respectively. First column shows the gullies extracted by original DEM using a threshold of 20; Second column shows comparisons between original and 4 iterative deepening results; third column shows comparisons of 4 and 10 iterative deepening results. (c) Gully networks by 2, 4, 10, and 14 iterative deepening. Different color lines describe the results under different iterations.

Fig.7 Results after gully head revision.

Tab.2 Performance and accuracy of different parameters for RGD in the training area

Tab.3 Characteristics of bank gullies in the Linjiajian watershed

Fig.8 Two example of comparison between our result and field survey: (a) and (c) are our result and measured gully heads by field survey; and (b) and (d) are the corresponding photos. It is clear that nearly all extraction results correspond to their realistic position measured by field surveys, and a few bank gullies are misidentified.

Tab.4 Statistics of bank gully sizes in the sample area

Fig.9 Comparison between our result and artificial interpolation.

Fig.10 Comparison of channel network with the flow accumulation of 20 cells between D8 (left) and Barnes’ algorithm (right).

Fig.11 Impact of flow accumulation threshold on channel network generation. (a) Flow direction in flat area; (b) channel network with flow accumulation greater than 3 cells; (c) channel network with flow accumulation greater than 5 cells.

Fig.12 Channel network with Barnes’ algorithm under flow accumulation threshold of 500 cells.

Fig.13 (a) Comparison between our results and Afana’s. (b) Curve relationship between R_A and A_S for Linjiajian.

Fig.14 Results of Madigou with 0.5 m DEMs. (a) Result of iterative channel deepening and shoulder line extraction. (b) Result of gully head revised and bank gully identified.

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