Sampling methods of soil erosion at county scale based on spatial autocorrelation

doi:10.12302/j.issn.1000-2006.202103028

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (3): 177-184.doi: 10.12302/j.issn.1000-2006.202103028

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Sampling methods of soil erosion at county scale based on spatial autocorrelation

CHEN Chao¹(), QI Fei¹, XU Yannan¹^,^*(), LI Jiazuo², ZHAO Chuanpu², SU Xinyu²

1. College of Forestry, Nanjing Forestry University, Nanjing 210037, China
2. Monitoring Center Station of Soil and Water Conservation, Huaihe River Commission, Ministry of Water Resources, Bengbu 233001, China

Received:2021-03-11 Accepted:2021-05-06 Online:2022-05-30 Published:2022-06-10
Contact: XU Yannan E-mail:15996311752@163.com;nfuxyn@126.com

Abstract

Abstract:

【Objective】Soil erosion is one of the main ecological and environmental problems in China. To take effective measures to control soil erosion, field investigations must be undertaken. It is important to maintain the ecological balance and promote sustainable development of the social economy. 【Method】In China, the method of soil erosion investigation combining sampling surveys and a Chinese soil loss equation (CSLE) experience model is one of the most important methods to obtain the soil erosion status at different scales. Stratified sampling with unequal probability and the CSLE experience model were used in the First National Water Census from 2010 to 2012. To further explore the influence of different spatial sampling methods and spatial interpolations on estimation precision in soil erosion areas, and to provide technological support for fast regional evaluation of soil erosion, this paper uses Mengyin County of Yimeng Mountain as the study area. Based on the spatial autocorrelation theory, using GF-1 and GF-6 satellite images, a CSLE model, 1∶10 000 topographic map and other data, three spatial sampling methods of spatial random sampling, spatial systematic sampling, spatial stratified sampling and four spatial interpolation methods of inverse distance weight, spline function method, ordinary Kriging, and co-Kriging was applied to predict the soil erosion area in Mengyin County in the Yimeng Mountain area. 【Result】(1) According to the global Moran’ I index, Z score and average pattern type, 400 m × 400 m square is the optimal size for sampling units. (2)The efficiency and accuracy of the sampling survey were affected by the sampling methods. Compared with spatial random samples and spatial systematic sampling, which had more than 250 units, spatial stratified sampling by slope had the highest sampling efficiency with the minimum sample size (150 units) and the highest sampling accuracy (96.69%). (3) Using spatial stratified sampling, the percentage of the soil erosion area for both ordinary Kriging and co-Kriging was similar to the grid calculation with a relative error of 8.25% and 9.85%, while that of the inverse distance weight and the spline function method were quite different from the grid calculation with a relative error of more than 25%. The spatial distribution of soil erosion-based co-Kriging was more detailed and is mainly influenced by the slope, but there were some differences between the spatial interpolation and the grid calculation. 【Conclusion】In view of the precision and workload in soil erosion surveys at the county scale in Yimeng Mountain area, stratified sampling, ordinary Kriging or co-Kriging are advisable for rapid prediction of soil erosion to provide an important data support for regional ecological environment protection.

Key words: soil erosion, spatial autocorrelation, spatial sampling method, spatial interpolation, sampling unit size, Yimeng Mountain area

CLC Number:

S157

CHEN Chao, QI Fei, XU Yannan, LI Jiazuo, ZHAO Chuanpu, SU Xinyu. Sampling methods of soil erosion at county scale based on spatial autocorrelation[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(3): 177-184.

Figures/Tables 7

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References 32

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空间抽样方法 sampling methods	土壤侵蚀面积/km² area					比例/% ratio					相对误差/% relative error
空间抽样方法 sampling methods	合计 total	轻度 mild	中度 moderate	强烈 intense	极强烈 extreme	合计 total	轻度 mild	中度 moderate	强烈 intense	极强烈 extreme	总体 total	轻度 mild	中度 moderate	强烈 intense	极强烈 extreme
栅格计算法GC	506.76	492.19	12.64	1.67	0.26	31.58	30.67	0.79	0.10	0.02
随机抽样SRS	610.34	605.68	4.39	0.24	0.03	38.02	37.74	0.27	0.01	0.00	20.44	23.06	65.31	85.80	90.21
系统抽样SSS_系统	578.22	576.20	2.02	0.00	0.00	36.04	35.91	0.13	0.00	0.00	14.10	17.07	84.05	100.00	100.00
分层抽样SSS_分层	548.58	529.74	17.95	0.75	0.14	34.21	33.01	1.14	0.05	0.01	8.25	7.63	42.00	55.05	44.50

方法method	δ_ME	δ_RMSE	δ_MSE	δ_RMSSE	δ_ASE	\|δ_ASE-δ_RMSE\|
反距离权重法IDW	39.42	493.79
样条函数法SFM	20.32	500.49
普通克里金法o-Kriging	0.06	1.56	0.02	1.18	1.29	0.27
协同克里金法co-Kriging	0.09	1.50	0.05	1.15	1.28	0.22

插值方法 interpolation methods	水土流失面积/km² area					比例/% ratio					相对误差/% relative error
插值方法 interpolation methods	合计 total	轻度 mild	中度 moderate	强烈 intense	极强烈 extreme	合计 total	轻度 mild	中度 moderate	强烈 intense	极强烈 extreme	合计 total	轻度 mild	中度 moderate	强烈 intense	极强烈 extreme
栅格计算法GC	506.76	492.19	12.64	1.67	0.26	31.58	30.67	0.79	0.10	0.02
反距离权重法IDW	885.76	819.08	61.51	4.36	0.82	55.20	51.04	3.83	0.27	0.05	74.79	66.41	386.62	160.88	215.76
样条函数法SFM	633.65	546.35	78.39	6.48	2.43	39.49	34.05	4.88	0.40	0.15	25.04	11.00	520.15	288.29	833.02
普通克里金法o-Kriging	548.58	529.74	17.95	0.75	0.14	34.18	33.01	1.12	0.04	0.01	8.25	7.63	42.00	55.05	44.50
协同克里金法co-Kriging	556.66	519.87	34.47	1.98	0.33	34.69	32.40	2.15	0.12	0.02	9.85	5.62	172.74	18.37	28.81

Sampling methods of soil erosion at county scale based on spatial autocorrelation

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