基于分融策略的土壤采样设计方法
|
摘要
采样设计方法在地理要素空间分布推测中起着关键作用。采集的样点数量尽可能少且推测精度较高通常是采样设计的目标。此外,高效合理的采样方案应保证较高的推测可信度,同时尽可能避免冗余样点。传统的采样方法大多依靠增加样点个数来提高推测精度,且对样点集内部的冗余情况考虑较少。为获取更加高效合理的样点集,在环境条件越相似、地理要素越相似的假设下,通过环境相似度分析计算,得到样点的推测可信度和样点集内部的冗余度,并提出一种基于分融策略的样点设计方法。该方法在分化阶段将推测可信度低的样点进行分化,增加样点以降低推测不确定性,在融合阶段将环境条件过于相似的样点进行融合以降低冗余,通过多次分化融合最终使得推测可信度和冗余度均达到一定的预设标准,得到最佳样点方案。将该方法应用于美国Raffelson研究区的土壤采样,结果表明,该方法在分化阶段可以有效提高样点的推测可信度,在融合阶段能够有效去除冗余样点,最终可得到用于推测的高效样点。将本方法与传统的规则采样和分层随机采样进行对比,结果反映本方法获得的样点在同等数量下推测可信度更高,冗余度更低,更高效。
【Objective】Quality of mapping based on prediction of geographic variables is greatly affected by the layout of the sampling sites.Due to the limitation of sampling cost,it is generally expected to have fewer sampling sites that will be able to provide more information for accurate prediction.To achieve such a target of efficient sampling,it is advisable to proceed from the following two point:first,set up sampling sites that are highly representative of the area of interest for better prediction accuracy,and second,reduce the number of sampling sites as many as possible without risking any loss of required accuracy.Based on the assumption that the more similar the two sites in geographic environment,the more similar their geographic elements,it is held that every sampling site can be used to represent an area similar to the site in environment,and the similarity between the sampling site and the sites to be predicted can be used to determine reliability of the prediction,meanwhile,the similarity within the sampling site set can be used to determine redundancy of the sampling site set.So,the layout of efficient sampling sites needs to keep balance between reliability of the prediction and redundancy of the sampling site set.【Method】 In this paper,a sample differentiation and fusion strategy is set forth for designing of sampling.The differentiation strategy is to increase the number of sampling sites so as to improve reliability of later on predictions,while the fusion strategy is to merge over-similar sampling sites,so as to reduce redundancy of the sampling site set.Through repeated differentiation and fusion,a preset requirement is finally met for prediction reliability and sampling site redundancy.The method has been tested in a case study of a small watershed in Raffelson,Wisconsin of USA.First,a comparative analysis was done of sampling sites varying in prediction reliability with 99 validation sampling sites to determine relationship between prediction reliability and validation accuracy.Then,verification was performed of effectiveness of the proposed strategy improving prediction reliability in its first phase and reducing redundancy of the sampling site set in its second phase.And in the end,comparison was done of the proposed method in this paper with other sampling methods(grid sampling and stratified simple random sampling)using the same number of sampling sites(15,20 and 25,separately).【Result】Results show that prediction reliability is positively related to prediction accuracy,so the former can be used as a better indicator of the latter.From the specific processes of the strategy,it can be discerned that,the differentiation can effectively raise the prediction reliability,while the fusion reduce the redundancy of the sampling site set,and what is more,have little impact on the prediction reliability.The comparisons show that the proposed method is higher in prediction reliability and lower in redundancy,and is 17.3%(n=15),14.8%(n=20)and 16.2%(n=25)lower than the grid sampling method,and 16.5%(n=15),15.3%(n=20)and 17.0%(n=25)higher than the stratified simple random sampling method in lowest prediction reliability,respectively,while 8.8%(n=15),12.8%(n=20)and 20.3%(n=25)lower than the grid sampling method,and 6.4%(n=15),12.4%(n=20)and 19.6%(n=25)lower than the stratified simple random sampling method,respectively,in redundancy.【Conclusion】Therefore,it can be concluded that the proposed method provides a means for obtaining a high prediction reliability and low sampling redundancy in sampling,and hence is a more efficient method for designing sampling schemes than the grid sampling and stratified simple random sampling methods.
【Objective】Quality of mapping based on prediction of geographic variables is greatly affected by the layout of the sampling sites.Due to the limitation of sampling cost,it is generally expected to have fewer sampling sites that will be able to provide more information for accurate prediction.To achieve such a target of efficient sampling,it is advisable to proceed from the following two point:first,set up sampling sites that are highly representative of the area of interest for better prediction accuracy,and second,reduce the number of sampling sites as many as possible without risking any loss of required accuracy.Based on the assumption that the more similar the two sites in geographic environment,the more similar their geographic elements,it is held that every sampling site can be used to represent an area similar to the site in environment,and the similarity between the sampling site and the sites to be predicted can be used to determine reliability of the prediction,meanwhile,the similarity within the sampling site set can be used to determine redundancy of the sampling site set.So,the layout of efficient sampling sites needs to keep balance between reliability of the prediction and redundancy of the sampling site set.【Method】 In this paper,a sample differentiation and fusion strategy is set forth for designing of sampling.The differentiation strategy is to increase the number of sampling sites so as to improve reliability of later on predictions,while the fusion strategy is to merge over-similar sampling sites,so as to reduce redundancy of the sampling site set.Through repeated differentiation and fusion,a preset requirement is finally met for prediction reliability and sampling site redundancy.The method has been tested in a case study of a small watershed in Raffelson,Wisconsin of USA.First,a comparative analysis was done of sampling sites varying in prediction reliability with 99 validation sampling sites to determine relationship between prediction reliability and validation accuracy.Then,verification was performed of effectiveness of the proposed strategy improving prediction reliability in its first phase and reducing redundancy of the sampling site set in its second phase.And in the end,comparison was done of the proposed method in this paper with other sampling methods(grid sampling and stratified simple random sampling)using the same number of sampling sites(15,20 and 25,separately).【Result】Results show that prediction reliability is positively related to prediction accuracy,so the former can be used as a better indicator of the latter.From the specific processes of the strategy,it can be discerned that,the differentiation can effectively raise the prediction reliability,while the fusion reduce the redundancy of the sampling site set,and what is more,have little impact on the prediction reliability.The comparisons show that the proposed method is higher in prediction reliability and lower in redundancy,and is 17.3%(n=15),14.8%(n=20)and 16.2%(n=25)lower than the grid sampling method,and 16.5%(n=15),15.3%(n=20)and 17.0%(n=25)higher than the stratified simple random sampling method in lowest prediction reliability,respectively,while 8.8%(n=15),12.8%(n=20)and 20.3%(n=25)lower than the grid sampling method,and 6.4%(n=15),12.4%(n=20)and 19.6%(n=25)lower than the stratified simple random sampling method,respectively,in redundancy.【Conclusion】Therefore,it can be concluded that the proposed method provides a means for obtaining a high prediction reliability and low sampling redundancy in sampling,and hence is a more efficient method for designing sampling schemes than the grid sampling and stratified simple random sampling methods.
引文
[1]Brus D J,Gruijter J J D.Random sampling or geostatistical modelling?Choosing between designbased and model-based sampling strategies for soil.Geoderma,1997,80(1/2):1—44
[2]Gregoire T G,Valentine H T.Sampling strategies for natural resources and the environment.International Journal of Environmental Analytical Chemistry,2008,88(8):596—597
[3]Mcbratney A B,Santos M L M,Minasny B.On digital soil mapping.Geoderma,2003,117(1/2):3—52
[4]Stein A,Ettema C.An overview of spatial sampling procedures and experimental design of spatial studies for ecosystem comparisons.Agriculture Ecosystems&Environment,2003,94(1):31—47
[5]Hartemink A E,Mcbratney A,Mendon?a-Santos M D L.Digital soil mapping with limited data.New York:Springer-Verlag,2008
[6]朱阿兴,李宝林,裴韬,等.精细数字土壤普查模型与方法.北京:科学出版社,2008Zhu A X,Li B L,Pei T,et al.Model and method of detail digital soil survey(In Chinese).Beijing:Science Press,2008
[7]Cochran W G.Sampling techniques,3rd ed.New York:John Wiley&Sons,1977
[8]Kish L.Survey sampling.New York:John Wiley&Sons,1985
[9]Webster R,Oliver M A.Statistical methods in soil and land resource survey.Oxford:Oxford University Press,1990
[10]Mcbratney A B,Webster R.How many observations are needed for regional estimation of soil properties?Soil Science,1983,135(3):177—183
[11]Brus D J,Gruijter J J,Groenigen J W.Designing spatial coverage samples using the k-means clustering algorithm//Digital soil mapping.An introductory perspective.New York:Elsevier,2006:183—192
[12]Minasny B,Mc Bratney A B.A conditioned Latin hypercube method for sampling in the presence of ancillary information.Computers&Geosciences,2006,32(9):1378—1388
[13]Minasny B,Mc Bratney A B,Walvoort D J J.The variance quadtree algorithm:Use for spatial sampling design.Computers&Geosciences,2007,33(3):383—392
[14]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
[15]Brus D J,Heuvelink G B M.Optimization of sample patterns for universal kriging of environmental variables.Geoderma,2007,138(1/2):86—95
[16]Zhu A,Yang L,Li B L,et al.Construction of membership functions for predictive soil mapping under fuzzy logic.Geoderma,2010,155(3/4):164—174
[17]Qin C Z,Zhu A X,Qiu W L,et al.Mapping soil organic matter in small low-relief catchments using fuzzy slope position information.Geoderma,2012,171/172(2):64—74
[18]杨琳,朱阿兴,秦承志,等.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.地理科学进展,2010,29(3):279—286Yang L,Zhu A X,Qin C Z,et al.A purposive sampling design method based on typical points and its application in soil mapping(In Chinese).Progress in Geography,2010,29(3):279—286
[19]刘京,朱阿兴,张淑杰,等.基于样点个体代表性的大尺度土壤属性制图方法.土壤学报,2013,50(1):12—20Liu J,Zhu A X,Zhang S J,et al.Mapping soil properties using individual representativeness of samples over large area(In Chinese).Acta Pedologica Sinica,2013,50(1):12—20
[20]Zhang S J,Zhu A X,Liu J,et al.An heuristic uncertainty directed field sampling design for digital soil mapping.Geoderma,2016,267:123—136
[21]Zhu A X.A similarity model for representing soil spatial information.Geoderma,1997,77(2/4):217—242
[22]Zhu A X,Band L E,Dutton B,et al.Automated soil inference under fuzzy logic.Ecological Modelling,1996,90(2):123—145
[23]Zhu A X.Measuring uncertainty in class assignment for natural resource maps under fuzzy logic.Photogrammetric Engineering&Remote Sensing,1997,63(10):1195—1202
[24]Zhu A X,Liu J,Du F,et al.Predictive soil mapping with limited sample data.European Journal of Soil Science,2015,66(3):535—547
[25]Zhu A X,Hudson B,Burt J,et al.Soil mapping using GIS,expert knowledge,and fuzzy logic.Soil Science Society of America Journal,2001,65(5):1463—1472
[26]Qi F,Zhu A.Knowledge discovery from soil maps using inductive learning.International Journal of Geographical Information Science,2003,17(8):771—795
[27]Zhu A,Band L E.A knowledge-based approach to data integration for soil mapping.Canadian Journal of Remote Sensing,1994,20(4):408—418
[28]朱阿兴,李宝林,杨琳,等.基于GIS、模糊逻辑和专家知识的土壤制图及其在中国应用前景.土壤学报,2005,42(5):844—851Zhu A X,Li B L,Yang L,et al.Predictive soil mapping based on a GIS,expert knowledge,and fuzzy logic framework and its application prospects in China(In Chinese).Acta Pedologica Sinica,2005,42(5):844—851
[2]Gregoire T G,Valentine H T.Sampling strategies for natural resources and the environment.International Journal of Environmental Analytical Chemistry,2008,88(8):596—597
[3]Mcbratney A B,Santos M L M,Minasny B.On digital soil mapping.Geoderma,2003,117(1/2):3—52
[4]Stein A,Ettema C.An overview of spatial sampling procedures and experimental design of spatial studies for ecosystem comparisons.Agriculture Ecosystems&Environment,2003,94(1):31—47
[5]Hartemink A E,Mcbratney A,Mendon?a-Santos M D L.Digital soil mapping with limited data.New York:Springer-Verlag,2008
[6]朱阿兴,李宝林,裴韬,等.精细数字土壤普查模型与方法.北京:科学出版社,2008Zhu A X,Li B L,Pei T,et al.Model and method of detail digital soil survey(In Chinese).Beijing:Science Press,2008
[7]Cochran W G.Sampling techniques,3rd ed.New York:John Wiley&Sons,1977
[8]Kish L.Survey sampling.New York:John Wiley&Sons,1985
[9]Webster R,Oliver M A.Statistical methods in soil and land resource survey.Oxford:Oxford University Press,1990
[10]Mcbratney A B,Webster R.How many observations are needed for regional estimation of soil properties?Soil Science,1983,135(3):177—183
[11]Brus D J,Gruijter J J,Groenigen J W.Designing spatial coverage samples using the k-means clustering algorithm//Digital soil mapping.An introductory perspective.New York:Elsevier,2006:183—192
[12]Minasny B,Mc Bratney A B.A conditioned Latin hypercube method for sampling in the presence of ancillary information.Computers&Geosciences,2006,32(9):1378—1388
[13]Minasny B,Mc Bratney A B,Walvoort D J J.The variance quadtree algorithm:Use for spatial sampling design.Computers&Geosciences,2007,33(3):383—392
[14]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
[15]Brus D J,Heuvelink G B M.Optimization of sample patterns for universal kriging of environmental variables.Geoderma,2007,138(1/2):86—95
[16]Zhu A,Yang L,Li B L,et al.Construction of membership functions for predictive soil mapping under fuzzy logic.Geoderma,2010,155(3/4):164—174
[17]Qin C Z,Zhu A X,Qiu W L,et al.Mapping soil organic matter in small low-relief catchments using fuzzy slope position information.Geoderma,2012,171/172(2):64—74
[18]杨琳,朱阿兴,秦承志,等.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.地理科学进展,2010,29(3):279—286Yang L,Zhu A X,Qin C Z,et al.A purposive sampling design method based on typical points and its application in soil mapping(In Chinese).Progress in Geography,2010,29(3):279—286
[19]刘京,朱阿兴,张淑杰,等.基于样点个体代表性的大尺度土壤属性制图方法.土壤学报,2013,50(1):12—20Liu J,Zhu A X,Zhang S J,et al.Mapping soil properties using individual representativeness of samples over large area(In Chinese).Acta Pedologica Sinica,2013,50(1):12—20
[20]Zhang S J,Zhu A X,Liu J,et al.An heuristic uncertainty directed field sampling design for digital soil mapping.Geoderma,2016,267:123—136
[21]Zhu A X.A similarity model for representing soil spatial information.Geoderma,1997,77(2/4):217—242
[22]Zhu A X,Band L E,Dutton B,et al.Automated soil inference under fuzzy logic.Ecological Modelling,1996,90(2):123—145
[23]Zhu A X.Measuring uncertainty in class assignment for natural resource maps under fuzzy logic.Photogrammetric Engineering&Remote Sensing,1997,63(10):1195—1202
[24]Zhu A X,Liu J,Du F,et al.Predictive soil mapping with limited sample data.European Journal of Soil Science,2015,66(3):535—547
[25]Zhu A X,Hudson B,Burt J,et al.Soil mapping using GIS,expert knowledge,and fuzzy logic.Soil Science Society of America Journal,2001,65(5):1463—1472
[26]Qi F,Zhu A.Knowledge discovery from soil maps using inductive learning.International Journal of Geographical Information Science,2003,17(8):771—795
[27]Zhu A,Band L E.A knowledge-based approach to data integration for soil mapping.Canadian Journal of Remote Sensing,1994,20(4):408—418
[28]朱阿兴,李宝林,杨琳,等.基于GIS、模糊逻辑和专家知识的土壤制图及其在中国应用前景.土壤学报,2005,42(5):844—851Zhu A X,Li B L,Yang L,et al.Predictive soil mapping based on a GIS,expert knowledge,and fuzzy logic framework and its application prospects in China(In Chinese).Acta Pedologica Sinica,2005,42(5):844—851