Spatial data mining for enhanced soil map modelling

References

• AUSLIG . 1996 . GEODATA 9 sec DEM (DEM-9S) , Belconnen, ACT : Australian Survey and Land Information Group .
   Google Scholar
• BONHAM-CARTER , G. F. 1994 . Geographic Information Systems for Geoscientists: Modelling with GIS , Oxford : Pergamon Press .
   Google Scholar
• BUI , E. N. , LOUGHHEAD , A. and CORNER , R. 1999 . Extracting soil-landscape rules from previous soil surveys . Australian Journal of Soil Science , 37 : 495 – 508 .
   Web of Science ®Google Scholar
• BUI , E. N. and MORAN , C. J. forthcoming Spatial modelling to generate a soil map and assess its quality: an example from the Murray-Darling basin of Australia submitted Geoderma
   Google Scholar
• DRUCKER , H. , CORTES , C. , TECHEL , L. P. , LECAN , Y. and VAPRIK , V. 1994 . Boosting and other ensemble methods . Neural Computation , 6 : 1289 – 1301 .
   Web of Science ®Google Scholar
• FREUND , Y. and SCHAPIRE , R. Experiments with a new boosting algorithm . Machine Learning: Proceedings of the Thirteenth International Conference . July 1996 . San Mateo, California : Morgan Kaufmann .
   Google Scholar
• FRIEDMAN , J. , HASTIE , T. and TIBSHIRANI , R. 2000 . Additive logistic regression: A statistical view of boosting . Annals of Statistics , 28 : 337 – 374 .
   Web of Science ®Google Scholar
• GEOIMAGE . 1995 . Australian Landsat MSS mosaic, (100 m resolution) , Brisbane : GEOIMAGE .
   Google Scholar
• GESSLER , P. E. , MOORE , I. D. , McKENZIE , N. J. and RYAN , P. J. 1995 . Soil-landscape modelling and spatial prediction of soil attributes . International Journal Geographical Information Systems , 9 : 421 – 431 .
   Web of Science ®Google Scholar
• HARUNO , M. , SHIRAI , S. and OOYAMA , Y. 1999 . Using decision trees to construct a practical parser . Machine Learning , 43 : 131 – 149 .
   Web of Science ®Google Scholar
• HUDSON , B. D. 1992 . The soil survey as a paradigm-based science . Soil Science Society America Journal , 56 : 836 – 841 .
   Web of Science ®Google Scholar
• KEARNS , K. and MANSOUR , Y. 1999 . On the boosting ability of top-down decision tree learning algorithms . Journal of Computer and System Sciences , 58 : 109 – 128 .
   Web of Science ®Google Scholar
• LAGACHERIE , P. 1992 . Formalisation des lois de distribution des sols pour automatiser la cartographie pedologique a partir d'un secteur pris comme reference , Institut National de la Recherche Agronomique, Laboratoire de science du sol, Montpellier . Ph.D. Thesis
   Google Scholar
• LAGACHERIE , P. , LEGROS , J. P. and BURROUGH , P. A. 1995 . A soil survey procedure using the knowledge of soil pattern established on a previously mapped reference area . Geoderma , 65 : 283 – 301 .
   Web of Science ®Google Scholar
• LAGACHERIE , P. and HOLMES , S. 1997 . Addressing geographical data erroes in a classification tree for soil unit prediction . International Journal of Geographical Information Science , 11 : 183 – 198 .
   Web of Science ®Google Scholar
• MINASNY , B. , MCBRATNEY , A. B. and WHELAN , B. M. 1999 . VESPER version 1.0. Australian Centre for Precision Agriculture, McMillan Building A05 , NSW 2006 The University of Sydney . http://www.usyd.edu.au/su/agric/acpa
   Google Scholar
• MOORE , I. D. , GESSLER , P. E. , NIELSEN , G. A. and PETERSON , G. 1993 . Soil attribute prediction using terrain analysis . Soil Science Society of America Journal , 57 : 443 – 452 .
   Web of Science ®Google Scholar
• McBRATNEY , A. B. and ODEH , I. O. A. An overview of pedometric techniques for use in soil survey. (Keynote Paper, Symposium 17) Paper 622 . 16th World Congress of Soil Science . August 20–26 1998 , Montpellier, France.
   Google Scholar
• McKENZIE , N. J. and RYAN , P. J. 1999 . Spatial prediction of soil properties using environmental correlation . Geoderma , 89 : 67 – 94 .
   Web of Science ®Google Scholar
• PRESS , W. H. , FLANNERY , B. P. , TENKOLSKY , S. A. and VETTERLING , W. T. 1988 . Numerical recipes in C: The art of scientific computing , Cambridge : Cambridge University Press .
   Google Scholar
• QUINLAN , J. R. 1993 . C4.5: Programs for machine learning , San Mateo, California : Morgan Kaufmann .
   Google Scholar
• SCHAPIRE , R. E. , FREUND , Y. , BARTLETT , P. and LEE , W. S. 1998 . Boosting the margin: A new explanation for the effectiveness of voting methods . Annals of Statistics , 26 : 1651 – 1686 .
   Web of Science ®Google Scholar
• SCHETSELAAR , E. M. , CHUNG , C-J. F. and KIM , K E. 2000 . Integration of Landsat TM, Gamma-ray, magnetic, and field data to discriminate lithological units in vegetated granite-gneiss terrain . Remote Sensing of the Environment , 71 : 89 – 105 .
   Web of Science ®Google Scholar
• SWITZER , P. 1980 . Extensions of linear discriminant analysis for statistical classification of remotely sensed satellite imagery . Mathematical Geology , 12 : 367 – 376 .
   Google Scholar
• THOMPSON , G. G. and BECKMANN , C. H. 1959 . Soils and Land Use in the Toowoomba Area, Darling Downs , Queensland, Soils and Land Use Series No 28 Australia : CSIRO .
   Google Scholar
• VOLTZ , M. , LAGACHERIE , P. and LOUCHART , X. 1997 . Predicting soil properties over a region using sample information from a mapped reference area . European Journal of Soil Science , 48 : 19 – 30 .
   Web of Science ®Google Scholar