Microsoft Word 孙亚洲.doc

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1 土壤 (Soils), 2017, 49(6): DOI: /j.cnki.tr 基于模糊 k- 均值算法模型的土壤数值化分类以河南省境内分布的雏形土为例 1 1* ( () ) k k- S159.9 A [1-6] ph [7-9] (numerical soil classification) - [10-14] 3 VLADIMIR [15-20] c- (FCM fuzzy c-means algorithm)( k-) c- ( k-) [21-25] [25] (2008FY110600) ( ) * (jchen@zzu.edu.cn) (1992 ) sunyazhou00@hotmail.com

6 k- 1263 [26-28] 7. 7 km 2 60% [29] 1.2 ( 2008FY110600) 1 2009 12 2013 3 97 [29] 图 1 Fig.1 河南省境内雏形土土系调查样点分布示意图 Spatial distribution of sampled cambisol series in Henan Province 1.

2 6 k [26-28] 7. 7 km 2 60% [29] 1.2 ( 2008FY110600) [29] 图 1 Fig.1 河南省境内雏形土土系调查样点分布示意图 Spatial distribution of sampled cambisol series in Henan Province 1.3 ( ) 16 ( 1) [26-27] 表 1 用于划分河南省境内雏形土各级分类单元的诊断层诊断特性 Table 1 Diagnostic horizons and characteristics of cambisols in Henan province ~ 50 cm 12() 10% 3 50 ~ 100 cm ~ 100 cm [25] ( 2) 表 2 数值化土壤分类因子赋值举例 Table 2 Value assignment examples of indexes of soil family in numerical soil classification

3 ( 3) 表 3 数值化分类因子在用于不同分类层级时的权重分配 Table 3 Factor weights of different classification hierarchies in Numerical Soil Classification k- k- k- (fuzzy memberships) (taxonomic distance) k- [23-24, 30-32] (Eucilidean distance) n d ( X X ) 12 1k 2k k 1 2 d 12 X 1k X 2k k R k- [33] k ( ) [27] - ( 1) ( 2) ( 3) - ( 4) - ( 5) - ( 6) ( - - ) 4 5(- - )

4 6 k 表 4 研究区雏形土聚类类别发生学属性质心值 Table 4 Centroid values of soil genetic properties of different cambisol clustering categories in study region ~ 50 cm ~ 100 cm () 10% 表 5 研究区雏形土中心土族之间的分类距离 Table 5 Taxonomic distance matrix between centroid soil families of cambisol in study region 表 6 研究区雏形土样点土壤与各中心土族之间的分类距离 Table 6 Taxonomic distance matrix between sampled and centroid soil families of cambisol in study region

5 ( - ) 47 ( 7) ( 7) 表 7 研究区雏形土样本土壤数值化分类结果与中心土族参比 Table 7 Reference between results of numerical soil classification and centroid soil families of cambisol in study region ) 97 k- ( ) ) - [1]. [D]. :, 2007 [2]. [D]. :,2011 [3]. [D]. :, 2009 [4]. [D]. :, 2014 [5]. GIS [D]. :,2009 [6]. [D]. :, 2013 [7]. [D]. :,2011 [8],,,. [J]., 2013, 50(1): [9],,. [J]. ( ), 2006, 32(3): [10] Carré F. and McBratney A. B. Digital terron mapping[j]. Geoderma, 2005, 128: [11] Carré F. and Jacobson M. Numerical classification of soil profile data using distance metrics[j]. Geoderma, 2009, 148: [12],,,. [J]., 2015, 47(4):

6 6 k [13],,,. [J]., 2016, 48(5): [14],,,. [J]., 2016, 53(1): [15] Rayner J H. Classification of soils by numerical methods[j]. Journal of Soil Science, 1966, 17:79 92 [16] Carré F. and Girard M.C. Quantitative mapping of soil types based on regression kriging of taxonomic distances with landform and land cover attributes[j]. Geoderma, 2002, 110: [17],,,. k- [J]., 2012, 32(6): [18]. [D]. :,2013 [19],,. [J]., 2011, 26(2): [20]. [D]. :,2014 [21] De Gruijter J J, McBratney A B. A modified fuzzy k-means method for predictive classification[c] // Bock H H. Classification and related methods of data analysis. Amsterdam: Elsevier,, 1988: [22] Verheyen K, Dries A, Hermy M, et al. High-resolution continuous soil classification using morphological soil profile descriptions[j]. Geoderma, 2001, 101:1 48 [23],. [J]., 2009, 46(1): [24],. c- [J]., 2009, 46(4): [25]. [D]. :,2017 [26],,,. [M]. :, 2007 [27],,( )[M]. :, 2001: [28],. [J]., 2016, 53(6): 1 8 [29],,,. [M]. :, 2017 [30] McBratney A B, De Gruijter J J. A continuum approach to soil classification by modified fuzzy k-means with extragrades[j]. Journal of Soil Science, 1992, 43: [31] De Gruijter J J, Walvoort D J J, van Gamsb P F M. Continuous soil maps-a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models[j]. Geoderma, 1997, 77: [32] McBratney A B, Odeh, et al. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurement, and fuzzy decisions[j]. Geoderma, 1997, 77: [33]. [M]. :, 2002: Numerical Soil Classification Based on Fuzzy k-means Algorithm Model A Case Study of Cambisols in Henan Province SUN Yazhou 1, CHEN Jie 1*, WU Kening 2, LI Ling 3, HAN Xingxing 1, WANG Haiyang 1 (1 College of Water Conservancy and Environment, Zhengzhou University, Zhengzhou , China; 2 School of Land Science and Technology, China University of Geosciences, Beijing , China; 3 College of Resource and Environment, Henan Agricultural University, Zhengzhou , China) Abstract: According to Chinese Soil Taxonomy(CST), we selected 16 soil diagnostic horizons and diagnostic characteristics, which can be used to classify cambisols at different levels based on 97 cambisol soil profiles in Soil Series of China (Henan), and we built a data matrix with different characteristics and hierarchy weights of these indexes. Then we extracted 6 centroid soil families by fuzzy k-means algorithm model, and calculated the taxonomic distance between sampled and centroid soil families, meanwhile numerically classified the 97 cambisol soil profiles. We find that the rationality of the traditional hierarchic CST can be evaluated according to the consistence of traditional hierarchic classifying units and the numerical soil classification derived from the taxonomic distance between sampled and centroid soil families. Key words: Fuzzy k-means algorithm model; Numerical soil classification; Cambisols

Microsoft Word 任辉_new_.doc

Microsoft Word 任辉_new_.doc 土壤 (Soils), 2014, 46(2): 373 378 1 基于地物驱动要素的住宅地价空间模拟研究以南京市为例 1, 2, 3 (1 410128 2 210095 3 210098) GIS F301.4 [1] Bruechner [2] Capozza Sick [3] 20 90 [4 6] [7 9] GIS 1 1.1 GIS [10] [11] (12QN57) (09&ZD046)