Updating conventional soil maps by mining soil–environment relationships from individual soil polygons
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摘要
Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.The proposed method consists of three major steps.Firstly,the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.Secondly,for each environmental covariate,these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.And lastly,the extracted soil–environment relationships are applied to updating the conventional soil map with new,improved environmental data by adopting a soil land inference model(SoLIM)framework.This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County,Wisconsin,United States.The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.
Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.The proposed method consists of three major steps.Firstly,the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.Secondly,for each environmental covariate,these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.And lastly,the extracted soil–environment relationships are applied to updating the conventional soil map with new,improved environmental data by adopting a soil land inference model(SoLIM)framework.This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County,Wisconsin,United States.The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.
Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.The proposed method consists of three major steps.Firstly,the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.Secondly,for each environmental covariate,these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.And lastly,the extracted soil–environment relationships are applied to updating the conventional soil map with new,improved environmental data by adopting a soil land inference model(SoLIM)framework.This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County,Wisconsin,United States.The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.
引文
Brus D J,Bogaert P,Heuvelink G B M.2008.Bayesian maximum entropyprediction of soil categories using a traditional soil map as soft information.European Journal of Soil Science,59,166-177.
Bui E N,Loughhead A,Corner R.1999.Extracting soillandscape rules from previous soil surveys.Australian Journal of Soil Science,37,495-508.
Bui E N,Moran C J.2001.Disaggregation of polygons of surficial geology and soil maps using spatial modeling and legacy data.Geoderma,103,79-94.
Bui E N,Moran C J.2003.A strategy to fill gaps in soil survey over large spatial extents:An example from the MurrayDarling Basin of Australia.Geoderma,111,21-44.
Collard F,Kempen B,Heuvelink G B M,Saby N P A,de Forges A C R,Lehmann S,Nehlig P,Arrouays D.2014.Refining a reconnaissance soil map by calibrating regression models with data from the same map(Normandy,France).Geoderma Regional,1,21-30.
Diggle P.1985.A kernel method for smoothing point process data.Journal of the Royal Statistical Society(Series C:Applied Statistics),34,138-147.
Du F,Zhu A X,Band L E,Liu J.2015.Soil property variation mapping through data mining of soil category maps.Hydrological Processes,29,2491-2503.
Ehlschlaeger C R,Goodchild M F.1994.Dealing with uncertainty in categorical coverage maps:Defining,visualizing,and managing errors.In:Proceedings of the Workshop on Geographical Information Systems at the Conference on Information and Knowledge Management.Gaithersburg,Maryland.pp.86-91.
Fotheringham A S,Brunsdon C,Charlton M.2000.Quantitative Geography:Perspectives on Spatial Data Analysis.Thousand Oaks,Sage,CA.
Gatrell A C,Bailey T C,Diggle P J,Rowlingson B S.1996.Spatial point pattern analysis and its application in geographical epidemiology.Transactions of the Institute of British Geographers,21,256-274.
Grinand C,Arrouays D,Laroche B,Martin M P.2008.Extrapolating regional soil landscapes from an existing soil map:Sampling intensity,validation procedures,and integration of spatial context.Geoderma,143,180-190.
H?ring T,Dietz E,Osenstetter S,Koschitzki T,Schr?der B.2012.Spatial disaggregation of complex soil map units:A decision-tree based approach in Bavarian forest soils.Geoderma,185,37-47.
Heung B,Bulmer C E,Schmidt M G.2014.Predictive soil parent material mapping at a regional-scale:A Random Forest approach.Geoderma,214-215,141-154.
Heung B,Ho H C,Zhang J,Knudby A,Bulmer C E,Schmidt MG.2016.An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping.Geoderma,265,62-77.
Holmes K W,Griffin E A,Odgers N P.2015.Large-area spatial disaggregation of a mosaic of conventional soil maps:Evaluation over Western Australia.Soil Research,53,865-880.
Jenny H.1941.Factors of Soil Formation:A System of Quantitative Pedology.McGraw-Hill,New York.
Kerry R,Goovaerts P,Rawlins B G,Marchant B P.2012.Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale.Geoderma,170,347-358.
Law R,Illian J,Burslem D F R P,Gratzer G,Gunatilleke C VS,Gunatilleke I A U N.2009.Ecological information from spatial patterns of plants:Insights from point process theory.Journal of Ecology,97,616-628.
Li R K,Zhu A X,Song X F,Li B L,Pei T,Qin C Z.2012.Effects of spatial aggregation of soil spatial information on watershed hydrological modelling.Hydrological Processes,26,1390-1404.
McBratney A B,Mendon?a Santos M L,Minasny B.2003.On digital soil mapping.Geoderma,117,3-52.
Moran C J,Bui E N.2002.Spatial data mining for enhanced soil map modelling.International Journal of Geographical Information Science,16,533-549.
Odgers N P,Sun W,Mcbratney A B,Minasny B,Clifford D.2014.Disaggregating and harmonising soil map units through resampled classification trees.Geoderma,214,91-100.
Qi F.2004.Knowledge discovery from area-class resource maps:Sata preprocessing for noise reduction.Transactions in GIS,8,297-308.
Qi F,Zhu A X.2003.Knowledge discovery from soil maps using inductive learning.International Journal of Geographical Information Science,17,771-795.
Qi F,Zhu A X.2011.Comparing three methods for modeling the uncertainty in knowledge discovery from area-class soil maps.Computers&Geosciences,37,1425-1436.
Qi F,Zhu A X,Pei T,Qin C Z,Burt J E.2008.Knowledge discovery from area-class resource maps:Capturing prototype effects.Cartography and Geographic Information Science,35,223-237.
Qin C Z,Zhu A X,Pei T,Li B L,Scholten T,Behrens T,Zhou C H.2011.An approach to computing topographic wetness index based on maximum downslope gradient.Precision Agriculture,12,32-43.
Quinn T,Zhu A X,Burt J E.2005.Effects of detailed soil spatial information on watershed modeling across different model scales.International Journal of Applied Earth Observation and Geoinformation,7,324-338.
Silva S H G,de Menezes M D,Owens P R,Curi N.2016.Retrieving pedologist’s mental model from existing soil map and comparing data mining tools for refining a larger area map under similar environmental conditions in Southeastern Brazil.Geoderma,267,65-77.
Silverman B W.1986.Density Estimation for Statistics and Data Analysis.Chapman and Hall,London,UK.
SSDS(Soil Survey Division Staff).2017.Soil Survey Manual.USDA Handbook 18.Government Printing Office,Washington,D.C.
Subburayalu S K,Jenhani I,Slater B K.2014.Disaggregation of component soil series on an Ohio County soil survey map using possibilistic decision trees.Geoderma,213,334-345.
Subburayalu S K,Slater B K.2013.Soil series mapping by knowledge discovery from an Ohio County soil map.Soil Science Society of America Journal,77,1254-1268.
Vincent S,Lemercier B,Berthier L,Walter C.2016.Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships.Geoderma,311,130-142.
Yang L,Jiao Y,Fahmy S,Zhu A X,Hann S,Burt J E,Qi F.2011.Updating conventional soil maps through digital soil mapping.Soil Science Society of America Journal,75,1044-1053.
Zhang G L,Liu F,Song X D.2017.Recent progress and future prospect of digital soil mapping:A review.Journal of Integrative Agriculture,16,2871-2885.
Zhu A X.1997.A similarity model for representing soil spatial information.Geoderma,77,217-242.
Zhu A X.1999.A personal construct-based knowledge acquisition process for natural resource mapping.International Journal of Geographical Information Science,13,119-141.
Zhu A X.2008.Model and Method of Detail Digital Soil Survey.Science Press,Beijing.(in Chinese)
Zhu A X,Band L E,Dutton B,Nimlos T.1996.Automated soil inference under fuzzy logic.Ecological Modelling,90,123-145.
Zhu A X,Band L E,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 A X,Hudson B,Burt J,Lubich K,Simonson D.2001.Soil mapping using GIS,expert knowledge,and fuzzy logic.Soil Science Society of America Journal,65,1463-1472.
Zhu A X,Mackay D S.2001.Effects of spatial detail of soil information on watershed modelling.Journal of Hydrology,248,54-77.
Zhu A X,Yang L,Li B L,Qin C Z,Pei T,Liu B Y.2010.Construction of membership functions for predictive soil mapping under fuzzy logic.Geoderma,155,166-174.
Bui E N,Loughhead A,Corner R.1999.Extracting soillandscape rules from previous soil surveys.Australian Journal of Soil Science,37,495-508.
Bui E N,Moran C J.2001.Disaggregation of polygons of surficial geology and soil maps using spatial modeling and legacy data.Geoderma,103,79-94.
Bui E N,Moran C J.2003.A strategy to fill gaps in soil survey over large spatial extents:An example from the MurrayDarling Basin of Australia.Geoderma,111,21-44.
Collard F,Kempen B,Heuvelink G B M,Saby N P A,de Forges A C R,Lehmann S,Nehlig P,Arrouays D.2014.Refining a reconnaissance soil map by calibrating regression models with data from the same map(Normandy,France).Geoderma Regional,1,21-30.
Diggle P.1985.A kernel method for smoothing point process data.Journal of the Royal Statistical Society(Series C:Applied Statistics),34,138-147.
Du F,Zhu A X,Band L E,Liu J.2015.Soil property variation mapping through data mining of soil category maps.Hydrological Processes,29,2491-2503.
Ehlschlaeger C R,Goodchild M F.1994.Dealing with uncertainty in categorical coverage maps:Defining,visualizing,and managing errors.In:Proceedings of the Workshop on Geographical Information Systems at the Conference on Information and Knowledge Management.Gaithersburg,Maryland.pp.86-91.
Fotheringham A S,Brunsdon C,Charlton M.2000.Quantitative Geography:Perspectives on Spatial Data Analysis.Thousand Oaks,Sage,CA.
Gatrell A C,Bailey T C,Diggle P J,Rowlingson B S.1996.Spatial point pattern analysis and its application in geographical epidemiology.Transactions of the Institute of British Geographers,21,256-274.
Grinand C,Arrouays D,Laroche B,Martin M P.2008.Extrapolating regional soil landscapes from an existing soil map:Sampling intensity,validation procedures,and integration of spatial context.Geoderma,143,180-190.
H?ring T,Dietz E,Osenstetter S,Koschitzki T,Schr?der B.2012.Spatial disaggregation of complex soil map units:A decision-tree based approach in Bavarian forest soils.Geoderma,185,37-47.
Heung B,Bulmer C E,Schmidt M G.2014.Predictive soil parent material mapping at a regional-scale:A Random Forest approach.Geoderma,214-215,141-154.
Heung B,Ho H C,Zhang J,Knudby A,Bulmer C E,Schmidt MG.2016.An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping.Geoderma,265,62-77.
Holmes K W,Griffin E A,Odgers N P.2015.Large-area spatial disaggregation of a mosaic of conventional soil maps:Evaluation over Western Australia.Soil Research,53,865-880.
Jenny H.1941.Factors of Soil Formation:A System of Quantitative Pedology.McGraw-Hill,New York.
Kerry R,Goovaerts P,Rawlins B G,Marchant B P.2012.Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale.Geoderma,170,347-358.
Law R,Illian J,Burslem D F R P,Gratzer G,Gunatilleke C VS,Gunatilleke I A U N.2009.Ecological information from spatial patterns of plants:Insights from point process theory.Journal of Ecology,97,616-628.
Li R K,Zhu A X,Song X F,Li B L,Pei T,Qin C Z.2012.Effects of spatial aggregation of soil spatial information on watershed hydrological modelling.Hydrological Processes,26,1390-1404.
McBratney A B,Mendon?a Santos M L,Minasny B.2003.On digital soil mapping.Geoderma,117,3-52.
Moran C J,Bui E N.2002.Spatial data mining for enhanced soil map modelling.International Journal of Geographical Information Science,16,533-549.
Odgers N P,Sun W,Mcbratney A B,Minasny B,Clifford D.2014.Disaggregating and harmonising soil map units through resampled classification trees.Geoderma,214,91-100.
Qi F.2004.Knowledge discovery from area-class resource maps:Sata preprocessing for noise reduction.Transactions in GIS,8,297-308.
Qi F,Zhu A X.2003.Knowledge discovery from soil maps using inductive learning.International Journal of Geographical Information Science,17,771-795.
Qi F,Zhu A X.2011.Comparing three methods for modeling the uncertainty in knowledge discovery from area-class soil maps.Computers&Geosciences,37,1425-1436.
Qi F,Zhu A X,Pei T,Qin C Z,Burt J E.2008.Knowledge discovery from area-class resource maps:Capturing prototype effects.Cartography and Geographic Information Science,35,223-237.
Qin C Z,Zhu A X,Pei T,Li B L,Scholten T,Behrens T,Zhou C H.2011.An approach to computing topographic wetness index based on maximum downslope gradient.Precision Agriculture,12,32-43.
Quinn T,Zhu A X,Burt J E.2005.Effects of detailed soil spatial information on watershed modeling across different model scales.International Journal of Applied Earth Observation and Geoinformation,7,324-338.
Silva S H G,de Menezes M D,Owens P R,Curi N.2016.Retrieving pedologist’s mental model from existing soil map and comparing data mining tools for refining a larger area map under similar environmental conditions in Southeastern Brazil.Geoderma,267,65-77.
Silverman B W.1986.Density Estimation for Statistics and Data Analysis.Chapman and Hall,London,UK.
SSDS(Soil Survey Division Staff).2017.Soil Survey Manual.USDA Handbook 18.Government Printing Office,Washington,D.C.
Subburayalu S K,Jenhani I,Slater B K.2014.Disaggregation of component soil series on an Ohio County soil survey map using possibilistic decision trees.Geoderma,213,334-345.
Subburayalu S K,Slater B K.2013.Soil series mapping by knowledge discovery from an Ohio County soil map.Soil Science Society of America Journal,77,1254-1268.
Vincent S,Lemercier B,Berthier L,Walter C.2016.Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships.Geoderma,311,130-142.
Yang L,Jiao Y,Fahmy S,Zhu A X,Hann S,Burt J E,Qi F.2011.Updating conventional soil maps through digital soil mapping.Soil Science Society of America Journal,75,1044-1053.
Zhang G L,Liu F,Song X D.2017.Recent progress and future prospect of digital soil mapping:A review.Journal of Integrative Agriculture,16,2871-2885.
Zhu A X.1997.A similarity model for representing soil spatial information.Geoderma,77,217-242.
Zhu A X.1999.A personal construct-based knowledge acquisition process for natural resource mapping.International Journal of Geographical Information Science,13,119-141.
Zhu A X.2008.Model and Method of Detail Digital Soil Survey.Science Press,Beijing.(in Chinese)
Zhu A X,Band L E,Dutton B,Nimlos T.1996.Automated soil inference under fuzzy logic.Ecological Modelling,90,123-145.
Zhu A X,Band L E,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 A X,Hudson B,Burt J,Lubich K,Simonson D.2001.Soil mapping using GIS,expert knowledge,and fuzzy logic.Soil Science Society of America Journal,65,1463-1472.
Zhu A X,Mackay D S.2001.Effects of spatial detail of soil information on watershed modelling.Journal of Hydrology,248,54-77.
Zhu A X,Yang L,Li B L,Qin C Z,Pei T,Liu B Y.2010.Construction of membership functions for predictive soil mapping under fuzzy logic.Geoderma,155,166-174.