方法模型与应用

基于典型点的目的性采样设计方法及其在土壤制图中的应用

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  • 1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室|北京 100101; 2. 北京师范大学地理学与遥感科学学院|北京 100875|3. 公安部禁毒情报技术中心|北京 100193; 4. 中国科学院遥感应用研究所|北京 100101
杨琳(1982- )|女|山东文登人|博士|助理研究员|主要从事数字土壤制图研究。 E-mail: yanglin@lreis.ac.cn

网络出版日期: 2010-03-25

基金资助

国家自然基金项目项目(40971236);国家重点基础研究发展计划资助项目(2007CB407207);资源与环境信息系统国家重点实验室自主创新项目;“国家科技攻关”支撑计划项目(2007BAC15B01)

A Purposive Sampling Design Method Based on Typical Points and Its Application in Soil Mapping

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  • 1. State Key Laboratory of Environment and Resources Information System,
    Institute of Geographic Sciences and Resources Research, CAS, Beijing 100101, China|
    2. College of Geography and Remote Sensing Sciences, Beijing Normal University, Beijing 100875, China|
    3. Narcotics Control Information Technology Center, Bureau of the Ministry of Public Security, Beijing 100193, China|
    4. |Institute of Remote Sensing Applications, CAS, Beijing 100101, China

Online published: 2010-03-25

摘要

鉴于经典采样和空间采样存在的局限性,提出了一种旨在寻找典型点的目的性采样设计方法。该方法通过分析与目标地理要素空间分布具有协同变化关系的环境因子,提取地理要素空间变化的典型模式,进而在典型模式上布设样点,即可获得典型点,从而减少所需样本量。以位于黑龙江鹤山农场的2个研究区为例,分别选择土壤厚度和表层有机质2个土壤属性,通过对土壤属性空间变化的4个协同环境因子进行模糊c均值聚类,获得对应土壤属性空间变化模式的环境因子组合;根据其模糊隶属度结果设计典型点并进行采样,最后结合典型点的属性值与环境因子组合模糊隶属度结果,采用加权平均模型得到土壤属性空间分布图,反映了土壤属性随地形变化的连续性分布。基于独立野外验证点,选择了4个评价指标对所得属性图进行定量评价。结果表明:2个研究区验证点集的预测值和观测值一致性指数均较高,可见本研究提出的方法是一种有效的样点布设方法。研究还对在每一环境组合类设计不同数量典型点所得土壤属性制图结果的差异进行了讨论,认为典型点增多并不一定能提高土壤属性空间推测的精度。

本文引用格式

杨琳1|朱阿兴1|秦承志1|李宝林1|裴韬1|邱维理2|徐志刚3,4 . 基于典型点的目的性采样设计方法及其在土壤制图中的应用[J]. 地理科学进展, 2010 , 29(3) : 279 -286 . DOI: 10.11820/dlkxjz.2010.03.004

Abstract

In consideration of limitations of classical sampling and spatial sampling, this paper proposed a purposive sampling method based on typical points. This method employed environmental factors which co-vary the target geographical element to find typical patterns of the geographical element over space. Field samples were then designed based on the locations of these typical patterns. It is believed that these field samples capture the overall pattern of spatial variation of the target variable well. With this approach, the number of field samples needed to characterize the spatial pattern of the target variable was reduced. The approach was applied in two study areas in Heshan Farm in Heilongjiang Province of China. Two soil properties were chosen, i.e. A-horizon organic matter and soil thickness (each property for one study area). Environmental combinations were generated by a fuzzy c-means clustering on four local environmental data layers and typical points were then designed with help of fuzzy membership maps of environmental combinations. Spatial variation of the two soil properties was inferred using a linear weighted average model with typical points’ soil property values and fuzzy membership maps of environmental combinations. The mapping results reflected continuous changing of soil properties with terrain changing. Four indices were set up for evaluation of mapping results by using independently validation points. The evaluation results showed that, the agreement coefficients between observed values and predicted values of validation points were high for both of the study areas. We then concluded that the proposed sampling design approach was effective. Analysis was also conducted on difference between soil property mapping results by using different amount of typical points when different number of typical points representing each environmental combination. It was showed that using more points would not necessarily result in a more accurate map.

参考文献


[1]   Cochran W G. Sampling Techniques. 3rd ed. New York: John Wiley & Sons, 1977.

[2]   Kish L. Survey Sampling. New York: John Wiley & Sons, 1985.

[3]   王劲峰, 姜成晟, 李连发, 等. 空间抽样与统计推断. 北京: 科学出版社, 2009.

[4]   Cressie N. Statistics for Spatial Data. New York: Wiley & Sons, 1991.

[5]   Wang J F, Liu J Y, Zhuang D F. Spatial sampling design for monitoring the area of cultivated land. International Journal of Remote Sensing, 2002, 23(2): 263-284.

[6]  Haining R. Spatial Data Analysis. Cambridge: Cambridge University Press, 2003.

[7]   Wang X J, Qi F. The effects of sampling design on spatial structure analysis of contaminated soil. The Science of the Total Environment, 1998, 224(1-3): 29-41.

[8]   杨贵羽, 陈亚新. 土壤水分盐分空间变异性与合理采样数研究. 干早地区农业研究, 2002, 20 (4): 64-66.

[9]  Simbahan G C, Dobermann A. Sampling optimization based on secondary information and its utilization in soil carbon mapping. Geoderma, 2006, 133(3-4): 345-362.

[10] 任振辉, 吴宝忠. 精细农业中最佳土壤采样间距确定方法的研究. 农机化研究, 2006(6): 82-85.

[11] 齐文虎, 谢高地, 丁贤忠. 精准农业土壤采样密度研究: 以上海精准农业试验示范基地为例. 中国生态农业学报, 2003, 11(1): 48-52.

[12] 姚荣江, 杨劲松, 姜龙. 黄河三角洲土壤盐分空间变异性与合理采样数研究. 水土保持学报,2006,20(6): 89-94.

[13] Webster R. Quantitative and Numerical Methods in Soil Classification and Survey. Oxford:  Clarendon Press, 1979.

[14] Webster R, Oliver M A. Statistical Methods in Soil and Land Resource Survey. Oxford: Oxford University Press, 1990.

[15] Patton M Q. Qualitative Research and Evaluation Methods. 3rd ed. Thousand Oaks: Sage, 2002.

[16] Trochim W, Donnelly, J P. Research Methods Knowledge Base. 3rd ed. Phoenix, AZ: Atomic Dog Publication Inc., 2006.

[17] Zhu A X, Band L E. A knowledge-based approach to data integration for soil mapping. Canadian Journal of Remote Sensing, 1994, 20(4): 408-418.

[18] Mcbratney A B, Mendonca Santos M L, Minasny B. On digital soil mapping. Geoderma, 2003, 117: 3-52.

[19] Hudson B D. The soil survey as paradigm-based science. Soil Science Society of America Journal, 1992, 56: 836-841.

[20] Bezdek J C, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 1984,10(2-3):191- 203.

[21] 杨琳, 朱阿兴, 李宝林, 等. 应用模糊c均值聚类获取土壤制图所需土壤—环境关系知识的方法研究. 土壤学报, 2007, 44(5): 16-23.

[22] Zhu A X, Band L E, Vertessy R, et al. Derivation of soil properties using a soil land inference model (SoLIM). Soil Science Society of America Journal, 1997, 61: 523-533.

[23] 秦承志, 朱阿兴, 李宝林, 等. 基于栅格DEM的多流向算法述评. 地学前缘, 2006, 13(3): 91-98.

[24] 秦承志, 李宝林, 朱阿兴, 等. 水流分配策略随下坡坡度变化的多流向算法. 水科学进展, 2006, 17 (4): 450-456.

[25] Qin C Z, Zhu A X, Pei T, et al. An approach to computing topographic wetness index based on maximum downslope gradient. Precision Agriculture, 2009
[2009-08-29]. http://www.springerlink.com/content/n44jk10568123375/.

[26] 秦承志, 杨琳, 朱阿兴, 等. 平缓地区地形指数的计算方法. 地理科学进展, 2006, 25 (6): 87-93.

[27] Willmott C J. On the evaluation of model performances in physical geography//Gaile G L, Willmott C J. Spatial Statistics and Models. Dordrecht: D. Reidel Publi., 1984: 43-460.

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