Regional features of topographic relief over the Loess Plateau, China: evidence from ensemble empirical mode decomposition

doi:10.1007/s11707-020-0819-z

Front. Earth Sci.

2020, Vol. 14

Issue (4) : 695-710 https://doi.org/10.1007/s11707-020-0819-z

RESEARCH ARTICLE

Regional features of topographic relief over the Loess Plateau, China: evidence from ensemble empirical mode decomposition

Yongjuan LIU^1,³, Jianjun CAO^2,^3,⁴(

), Liping WANG¹, Xuan FANG^2,³, Wolfgang WAGNER⁴

¹. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
². School of Geography Science/Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, China
³. School of Environmental Science, Nanjing Xiaozhuang University, Nanjing 211171, China
⁴. Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna 1040, Austria

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Abstract

Landforms with similar surface matter compositions, endogenic and exogenic forces, and development histories tend to exhibit significant degrees of self-similarity in morphology and spatial variation. In loess hill–gully areas, ridges and hills have similar topographic relief characteristics and present nearly periodic variations of similar repeating structures at certain spatial scales, which is termed the topographic relief period (TRP). This is a relatively new concept, which is different from the degree of relief, and describes the fluctuations of the terrain from both horizontal and vertical (cross-section) perspectives, which can be used for in-depth analysis of 2-D topographic relief features. This technique provides a new perspective for understanding the macro characteristics and differentiation patterns of loess landforms. We investigate TRP variation features of different landforms on the Loess Plateau, China, by extracting catchment boundary profiles (CBPs) from 5 m resolution digital elevation model (DEM) data. These profiles were subjected to temporal-frequency analysis using the ensemble empirical mode decomposition (EEMD) method. The results showed that loess landforms are characterized by significant regional topographic relief; the CBP of 14 sample areas exhibited an overall pattern of decreasing TRPs and increasing topographic relief spatial frequencies from south to north. According to the TRPs and topographic relief characteristics, the topographic relief of the Loess Plateau was divided into four types that have obvious regional differences. The findings of this study enrich the theories and methods for digital terrain data analysis of the Loess Plateau. Future study should undertake a more in-depth investigation regarding the complexity of the region and to address the limitations of the EEMD method.

Keywords catchment boundary profile topographic relief period ensemble empirical mode decomposition Loess Plateau

Corresponding Author(s): Jianjun CAO

Online First Date: 14 September 2020 Issue Date: 08 January 2021

Cite this article:

Yongjuan LIU,Jianjun CAO,Liping WANG, et al. Regional features of topographic relief over the Loess Plateau, China: evidence from ensemble empirical mode decomposition[J]. Front. Earth Sci., 2020, 14(4): 695-710.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-020-0819-z
https://academic.hep.com.cn/fesci/EN/Y2020/V14/I4/695

Tab.1 Geographic descrition of the study sites

Fig.1 Process flow for extracting catchment boundary profiles. DEM: digital elevation model.

Fig.2 Morphological parsing of catchment boundary profiles (CBP). (a) Catchment boundary on the surface with shaded relief as background. (b) CBP (unfold with an anticlockwise direction).

Sample area	Parameter	IMF1	IMF2	IMF3	IMF4	IMF5	IMF6	IMF7	IMF8	IMF9	IMF10	IMF11	RES
Yulin	Variance contribution rate/%	0.0257	0.0094	0.0063	0.0134	0.1559	0.3753	1.0603	0.8886	2.1906	1.3194	0.0023	93.95
	Period/m	3.2069	6.5619	13.3292	31.1404	69.1558	156.62	287.84	760.71	1331.25	2662.50	2662.50	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Jiaxian	Variance contribution rate/%	0.0245	0.0088	0.0053	0.0144	0.1950	0.7115	1.9095	2.4140	1.9461	1.1858	0.0057	91.58
	Period/m	3.2024	6.5572	13.2939	28.1280	64.5175	124.68	307.53	1153.25	1537.67	2306.50	4613	--
	Confidence level	>99%	<95%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Hengshan	Variance contribution rate/%	0.0215	0.0078	0.0055	0.0252	0.6429	0.7879	1.1816	5.8280	3.7891	0.1401	--	87.57
	Period/m	3.2074	6.3490	13.1131	34.2028	77.3125	140.04	412.33	742.20	1855.50	1855.50	--	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--	--
Suide	Variance contribution rate/%	0.0218	0.0076	0.0043	0.0096	0.0623	0.1532	0.4667	3.0651	2.6405	2.1114	--	91.46
	Period/m	3.1559	6.4885	13.2582	26.7483	57.5188	131.90	273.21	956.25	1912.50	2550	--	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--	--
Jingbian	Variance contribution rate/%	0.0303	0.0110	0.0065	0.0175	0.1872	0.3911	2.8338	51.0685	5.9935	0.5473	--	38.91
	Period/m	3.1832	6.5402	13.4409	30.2124	82.2651	126.44	401.65	1707	2276	3414	--	--
	Confidence level	>99%	<95%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--	--
Yan'an	Variance contribution rate/%	0.0257	0.0104	0.0132	0.1480	1.7661	4.2753	5.5753	21.8382	14.5225	1.4284	0.0000	50.40
	Period/m	3.1779	6.5835	14.0599	38.5887	81.7798	185.71	318.36	1114.25	2228.50	4457	Inf	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Yichuan	Variance contribution rate/%	0.0212	0.0081	0.0094	0.0319	0.2527	0.6003	0.5286	1.0666	2.0095	0.6038	0.0111	94.86
	Period/m	3.1532	6.5275	14.2943	32.4964	67.9248	148.10	301.13	645.29	1129.25	2258.50	4517	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Fuxian	Variance contribution rate/%	0.0228	0.0083	0.0086	0.0618	0.4890	2.3066	3.2257	1.4275	8.2622	0.7899	0.2519	83.15
	Period/m	3.2153	6.6503	13.7288	36.9815	74.2370	169.86	357.93	626.38	1670.33	5011	5011	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Huanglong	Variance contribution rate/%	0.0303	0.0115	0.0154	0.1243	1.2210	6.3551	4.3871	33.7958	24.4366	3.1237	0.0000	26.50
	Period/m	3.1993	6.4540	13.9381	37.3712	83.9020	171.16	534.88	1069.75	2852.67	4279	4279	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Luochuan	Variance contribution rate/%	0.0253	0.0090	0.0084	0.0980	0.4167	0.4735	1.0070	0.7850	2.6711	1.6407	0.0002	92.87
	Period/m	3.2607	6.6089	14.1743	34.1984	69.5000	130.58	331.46	538.63	2154.50	4309	Inf	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Xunyi	Variance contribution rate/%	0.0228	0.0081	0.0056	0.0180	0.0740	0.1037	0.2634	2.2865	3.3380	0.1924	0.0001	93.69
	Period/m	3.2047	6.4654	13.3469	33.7381	61.1655	130.80	354.25	850.20	2125.50	4251	8502	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Pucheng	Variance contribution rate/%	0.0293	0.0117	0.0135	0.0235	0.1500	0.3681	1.0154	1.5289	1.6306	0.9683	--	94.26
	Period/m	3.2175	6.4375	13.4799	27.7034	61.8000	133.90	365.18	1004.25	2008.50	2678	--	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--	--
Yongshou	Variance contribution rate/%	0.0229	0.0090	0.0077	0.0358	0.7214	2.1104	2.8088	4.2091	2.6032	0.3255	0.0001	87.15
	Period/m	3.1928	6.5444	14.1130	32.8986	87.7297	173.89	347.79	973.80	1623	4869	9738	--
	Confidence level	>99%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--
Qianyang	Variance contribution rate/%	0.0283	0.0103	0.0051	0.0074	0.0681	0.1433	0.5041	4.0998	7.5288	0.8433	0.0041	86.76
	Period/m	3.2012	6.4856	13.2217	27.7538	53.1149	128.36	770.17	924.20	2310.50	3080.67	4621	--
	Confidence level	>99%	<95%	<95%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	>99%	--

Tab.2 Parameters of catchment boundary profiles (CBP) from the ensemble empirical mode decomposition (EEMD) for the 14 sample areas

Fig.3 Significance test for the catchment boundary profiles from the ensemble empirical mode decomposition (EEMD) for (a) Yulin and (b) Jiaxian. IMFs: intrinsic mode functions.

Fig.4 Significance test for the catchment boundary profiles from the ensemble empirical mode decomposition (EEMD) for (a) Hengshan and (b) Suide. IMFs: intrinsic mode functions.

Fig.5 Significance test for the catchment boundary profiles from the ensemble empirical mode decomposition (EEMD) for (a) Jingbian and (b) Yan’an. IMFs: intrinsic mode functions.

Fig.6 Significance test for the catchment boundary profiles from the ensemble empirical mode decomposition (EEMD) for (a) Yichuan and (b) Fuxian. IMFs: intrinsic mode functions.

Fig.7 Significance test for the catchment boundary profiles from the ensemble empirical mode decomposition (EEMD) for (a) Huanglong and (b) Luochuan. IMFs: intrinsic mode functions.

Fig.8 Significance test for the catchment boundary profiles from the ensemble empirical mode decomposition (EEMD) for (a) Xunyi and (b) Pucheng. IMFs: intrinsic mode functions.

Fig.9 Significance test for the catchment boundary profiles from the ensemble empirical mode decomposition (EEMD) for (a) Yongshou and (b) Qianyang. IMFs: intrinsic mode functions.

Fig.10 Multi-fractal spectrum of the catchment boundary profile topographic-relief variation feature of sampling sites on the Loess Plateau, (a)–(n) are Yulin, Jiaxian, Hengshan, Suide, Jingbian, Yan’an, Yichuan, Fuxian, Huanglong, Luochuan, Xunyi, Pucheng, Yongshou, and Qianyang, respectively.

Fig.11 Singularity strength of the catchment boundary profile topographic-relief variation of sampling sites on the Loess Plateau, where the x-axis represents the sampling sites: 1. Yulin, 2. Jiaxian, 3. Hengshan, 4. Suide, 5. Jingbian, 6. Yan’an, 7. Yichuan, 8. Fuxian, 9. Huanglong, 10. Luochuan, 11. Xunyi, 12. Pucheng, 13. Yongshou, and 14. Qianyang.

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