Abstract
Spatial distribution of soil attributes is the basic information required for land surface process simulating and ecological modeling. Purposive sampling method based on typical points which employed environmental factors has been widely used in digital soil mapping (DSM) to acquire soil spatial properties at different scales. Clustering analysis of soil environmental covariates was performed to explore for sampling points representative of different grades of soil spatial distribution and to formulate a sampling designing method based on representativeness grade. This method was used to predict soil organic matter (SOM) content in the surface layer of grassland soil within a 4 km2 area of the Bayanbulak District, Xinjiang Uyghur Autonomous Region. Six terrain factors, including elevation, slope, aspect, planform curvature, profile curvature, and topographic wetness index, were clustered by fuzzy c-means method. Fuzzy membership distribution of 9 groups of environmental factors was derived to position 18 soil samples in the area with membership larger than 0.9. Then, SOM map was predicted with fuzzy membership model. Finally, 35 individual soil samples (16 regular sampling points, 9 cross-sectional sampling points, and 10 sampling points according to altitude) were collected as the verify point. The results showed that purposive sampling combined with FCM is a low cost and efficiency mapping method with satisfactory prediction precision and model stability and could be possibly applied to small-scale region with the similar landscape conditions.
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References
Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences 10 (2/3): 191–203.
Mcbratney AB, Mendonca Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117(1-2): 3–52.
McSweeney K, Slater BK, Hammer RD, Bell JC, Gessler PE, Petersen GW (1994) Towards a new framework for modeling the soil-landscape continuum. In: Amundson R. (ed.) Factors of Soil Formation: A Fiftieth Anniversary Publication. Madison, Wisconsin: Soil Science Society of America. 127–154.
Sun XL, Zhao YG, Liu F, Wang DC, Liang CP (2013) Digital Soil Mapping and Advance in Research. Chinese Journal of Soil Science 44(3): 752–759.
Yang L, Zhu AX, Li BL, Qin CZ, Pei T, Liu BY, Li RK, Cai QG (2007) Extraction of knowledge about soil-environment relationship for soil mapping using Fuzzy c-Means(FCM) clustering. ACTA PEDOLOGICA SINICA 44(5): 784–791.
Yang L, Zhu AX, Qin CZ, Li BL, Pei T, Liu BY (2009) Soil property mapping using fuzzy membership—a case study of a study area in Heshan Farm of Heilongjiang Province. ACTA PEDOLOGICA SINICA 46(1): 9–15.
Yang L, Zhu AX, Qin CZ, Li BL, Pei T, Qiu WL, Xu ZG (2010) A Purposive Sampling Design Method Based on Typical Points and Its Application in Soil Mapping. PROGRESS IN GEOGRAPHY 29(3): 279–286.
Zhang H, Zhang GL, Gong ZT (2004) The Progress of Quantitative Soil- Landscape Modeling - A Review. Chinese Journal of Soil Science 35(3): 339–346.
Zhang SJ, Zhu AX, Liu J, Yang L (2012) Sample-based Digital Soil Mapping Methods and Related Sampling Schemes. Soils 44(6): 917–923.
Zhu AX (2008) Detail Digital Soil Survey: Models and Methods (in Chinese) Science Press Beijing China. ISBN: 978-7-03-021521-5, 227 p.
Zhu AX, Band LE, Vertessy R, Dutton B (1997) Derivation of soil properties using a soil land inference model (SoLIM). Soil Science Society of America Journal 61: 523–533.
Zhu AX, Hudson B, Burt J, Lubich K, Simonson K (2001) Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of American Journal 65: 1463–1472.
Zhu AX, Li BL, Yang L, Pei T, Qin CZ, Zhang GL, Cai QG, Zhou CH (2005) Predictive soil mapping based on a GIS, expert knowledge, and fuzzy logic framework and its application prospects in China. ACTA PEDOLOGICA SINICA 42(5): 844–851.
Zhu, AX, Qi F, Moore A, Burt JE (2010) Prediction of soil properties using fuzzy membership values. Geodema 158: 199–206.
Acknowledgements
This study is funded by Postdoctoral Foundation of Xinjiang Agricultural University. The authors are grateful to researchers who contribute to the development and maintenance of the SoLIM software.
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Zhu, L., Sheng, J., Jia, H., Wu, H. (2016). An Unsupervised Fuzzy Clustering Approach for the Digital Mapping of Soil Organic Carbon in a Montaneous Region of China. In: Zhang, GL., Brus, D., Liu, F., Song, XD., Lagacherie, P. (eds) Digital Soil Mapping Across Paradigms, Scales and Boundaries. Springer Environmental Science and Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-0415-5_16
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