Spatial Similarity between Soil Erosion and Its Influencing Factors Based on Information Entropy Theory
Received date: 2008-06-01
Revised date: 2008-12-01
Online published: 2009-03-25
Soil erosion, affected by climate, landforms, soil, vegetation and human activities, is an important element influencing the environment. In order to find out the main influencing factors of soil erosion, the related data of the Yellow River Basin is collected, and the spatial similarity between soil erosion and the influencing factors is analyzed based on information entropy theory. It is indicated that, at a scale of 1∶100 000, the order of the factors' importance to soil erosion in the Yellow River Basin is as follows: (1) in the water eroded area weighted precipitation > topographic relief > vegetation coverage > soil type > gully density; (2) in the wind eroded area topographic relief > wind erosion climatic factor > vegetation coverage > soil type > gully density; and (3) in the freezing -thawing eroded area gully density > topographic relief > temperature difference > vegetation coverage > soil type. A quantitative spatial similarity analysis method between the qualitative and quantitative variables is constructed. The main influencing factors for the water eroded area, wind eroded area and freezing-thawing eroded area in the Yellow River Basin are presented. The research result is of great significance to the soil erosion process study and soil conservation in the basin.
LI Xiuxia, NI Jinren . Spatial Similarity between Soil Erosion and Its Influencing Factors Based on Information Entropy Theory[J]. PROGRESS IN GEOGRAPHY, 2009 , 28(2) : 161 -166 . DOI: 10.11820/dlkxjz.2009.02.001
[1] Mitra B, Scott H D, Dixon J C, et al. Application of fuzzy logic to the prediction of soil erosion in a large watershed. Geoderma, 1998, 86: 183~209.
[2] 申曙光. 灾害生态经济研究. 长沙: 湖南教育出版社, 1992.
[3] 中华人民共和国水利部. 全国水土流失公告， 2002.
[4] Wischmeier W H, Smith D D. Predicting rainfall erosion losses. In: Agric. Handbook 537, Washington DC: USDA, 1978，58.
[5] 李钜章, 景可, 李凤新. 黄土高原多沙粗沙区侵蚀模型 探讨. 地理科学进展, 1999, 18(1): 46~53.
[6] 蔡强国, 刘纪根. 关于我国土壤侵蚀模型研究进展. 地 理科学进展, 2003, 22(3): 242~250.
[7] 刘光. 土壤侵蚀模型研究进展. 水土保持研究, 2003, 10 (3): 73~76.
[8] Shannon C E. A mathematical theory of communication. The Bell System Technical Journal, 1948, 27: 379~423.
[9] 盛骤, 谢式千, 潘承毅. 概率论与数理统计(3 版). 北京: 高等教育出版社, 2004.
[10] Meyer W B, Turner B L. Change in Land Use and Land Cover: A Global Perspective (Ⅱ). London: Cambridge University Press, 1994.
[11] 檀满枝, 詹其厚, 陈杰. 基于信息熵原理的土壤PH 影响 因素空间相关性分析. 土壤, 2007, 39(6): 953~957.
[12] 马蔼乃. 中国水土流失灾害的分类分级和危险度评价方 法研究. 见: 王劲峰, 等. 中国自然灾害影响评价方法研 究. 北京: 科技出版社, 1993.
[13] 景可, 卢金发, 梁季阳等. 黄河中游侵蚀环境特征和变 化趋势. 郑州: 黄河水利出版社, 1997.
[14] UNFAO. A Provisional Methodology for Soil Degradation Assessment. Rome: FAO, 1979.
[15] 程天文等. 农田蒸发与蒸发力的测定及其计算方法. 地 理集刊第12 号. 北京: 科学出版社, 1980.
[16] 汤国安, 陈正江, 赵牡丹等. ArcView 地理信息系统空 间分析方法. 北京: 科学出版社, 2002.
[17] 刘新华, 张晓萍, 杨勤科等. 不同尺度下影响水土流失 地形因子指标的分析与选取. 西北农林科技大学学报 (自然科学版), 2004, 32(6): 107~111.
[18] 中国科学院南京土壤研究所. 中国土壤. 北京: 科学出 版社, 1978.
[19] 全国土壤普查办公室. 中国土壤. 北京: 中国农业出版 社, 1998.
[20] 牛宝茹, 刘俊荣, 王政伟. 干旱半干旱地区植被覆盖度 遥感信息提取研究. 武汉大学学报(信息科学版), 2005, 30(1): 27~30.
[21] 党安荣, 王晓栋, 陈晓峰等. ERDAS IMAGINE 遥感图 像处理方法. 北京: 清华大学出版社, 2003, 112.
[22] 顾祝军, 曾志远. 遥感植被盖度研究. 水土保持研究, 2005, 12(2): 18~21.
[23] 倪晋仁, 李秀霞. 基于最小图斑的土壤侵蚀评估方法. 应用基础与工程科学学报, 2007, 15(4): 425~434.
[24] 中华人民共和国水利部标准. 土壤侵蚀分类分级标准 (SL190-96). 北京: 水利电力出版社, 1997.
[25] 盛海洋. 黄土高原水土流失的地质环境研究. 人民黄河. 2006, 28(1): 76~78.
[26] 倪晋仁，李秀霞，韩鹏. 试论水利部土壤侵蚀分级方法的 适用性. 北京大学学报(自然科学版), 2008, 44(6): 965~ 969.
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