The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data

Hydrology and Earth System Sciences

Volumn 14, Issue 6, 2010, Pages 847-857

  Bou Kheir, R ^a   Bocher P K ^a   Greve, M B ^a   Greve, M H ^a  

^a AARHUS UNIVERSITY   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

CONSERVATION PLANNING; DIGITAL ELEVATION MODEL; GEOGRAPHICAL DISTRIBUTION; GEOMORPHOLOGY; GIS; LIDAR; RESOURCE MANAGEMENT; SOIL TYPE; SPATIAL DISTRIBUTION; SUSTAINABLE DEVELOPMENT; TERRAIN;

DENMARK;

EID: 77953095601     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-14-847-2010     Document Type: Article

References (50)

1. Baltsavias, E. P.: A comparison between photogrammetry and laser scanning, PRS, 54 (2-3), 83-94, 1999.
2. Bishop, T. F. A. and Minasny, B.: Digital soil-terrain modelling: the predictive potential and uncertainty, Environmental soillandscape modeling. Geographic information technologies and pedometrics, edited by: Grunwald, S., Taylor & Francis, Boca Raton, Florida, 185-213, 2005.
3. Bolstad, P. V., Vose, J. M., and McNulty, S. G.: Forest productivity, leaf area, and terrain in Southern Appalachian deciduous forests, Forest Sci., 47, 419-427, 2001.
4. Bou Kheir, R., Wilson, J., and Deng, Y.: Use of terrain variables for mapping gully erosion susceptibility in Lebanon, Earth Surf. Proc. Land, 32, 1770-1782, 2007.
5. Bou Kheir, R., Chorowicz, J., Abdallah, C., and Dhont, D.: Soil and bedrocks distribution estimated from gully form and frequency: a GIS-based decision-tree model for Lebanon, Geomorphology, 93, 482-492, 2008.
6. Breiman, L.: Decision-tree forests, Mach Learn, 45 (1), 5-32, 2001.
7. Brian, E., Roth, K., Clint Slatton, M., and Cohen, J.: On the potential for high-resolution lidar to improve rainfall interception estimates in forest ecosystems, Front Ecol. Environ., 5 (8), 421-428, 2007.
8. Bui, E. N.: Soil survey as a knowledge system, Geoderma, 120, 17-26, 2004.
9. Bui, T. H., Zwieu, J., Poel, M., and Nijholt, A.: Toward affective dialogue modeling using partially observable markov decision processes, Proceedings of workshop emotion and computing, 29th Annual German Conference on Artificial Intelligence, edited by: Reichardt, D., Levi, P., and Meyer, J. C., Bremen, Germany, 47-0, June 2006.
10. Chorowicz, J., Ichoku, C., Riazanoff, S., Kim, Y. J., and Cervelle, B.: A combined algorithm for automated drainage network extraction, Water Resour., 28 (5), 1293-1302, 1992.
11. Clarke, L. A. and Pregibon, D.: Tree-based models, In: Statistical Models, edited by: Chambers, J. M. and Hastie, T. J., Chapman and Hall, New York, 377-419, 1992.
12. Dalsgaard, K.: Examples on use of the 1844 land registration maps to illustrate the past environment, Aarhus Geoscience, 7, 141-146, 1997.
13. Danmarks Geologiske Undersøgelse: Foreløbige geologogiske kort (1:25, 000) over Danmark. DGU Serie A, 3, 1978.
14. Danner, A., Yi, K., MØlhave, Th., Agarwal, P. K., Arge, L., and Mitasova, H.: TerraStream: from elevation data to watershed hierarchies. Proceedings of the 15th International Symposium on Advances in Geographic Information Systems (ACM GIS), 2007.
15. Dobos, E., Micheli, E., Baumgardner, M. F., Biechl, L., and Helt, T.: Use of combined digital elevation model and satellite radiometric data for regional soil mapping, Geoderma, 97, 367-391, 2000.
16. Dobos, E., Carré, F., Hengl, T., Reuter, H. I., and Toth, G.: Digital soil mapping as a support to production of functional maps - EUR 22123 EN. Office for Official Publication of the European Communities, Luxembourg, 2006.
17. Egli, M., Mirabella, A., Sartori, G., and Fitze, P.: Weathering rates as a function of climate: results from a climosequence of the Val Genova (Trentino, Italian Alps), Geoderma, 111, 9-121, 2003.
18. Egli, M., Wernli, M., Kneisel, C., Biegger, S., and Haeberli, W.: Melting glaciers and soil development in the proglacial area Morteratsch (Swiss Alps) : II Modelling presentday and future soil state, Arctic, Antarctic, and Alpine Research, 38, 510-522, 2006a.
19. Egli, M., Mirabella, A., Sartori, G. Zanelli, R., and Bischof, S.: Effect of north and south exposition on weathering and clay mineral formation in Alpine soils, Catena, 67, 155-174, 2006b.
20. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P.: From data mining to knowledge discovery in databases, American Association for Artificial Intelligence Press, Menlo Park, 1-34, 1996.
21. Florinsky, I. V., Eilers, R. G., Manning, G. R., and Fuller, L. G.: Prediction of soil properties by digital terrain modelling, Environ. Modell. Softw., 17, 295-311, 2002.
22. Friedl, M. A. and Brodley, C. E.: Decision tree classification of land cover from remotely sensed data, Rem. Sens. Environ., 61, 399-409, 1997.
23. Friedl, M. A., Brodley, C. E., and Strahler, A. H.: Maximizing land cover classification accuracies produced by decision trees at continental to global scales, IEEE Geosci. Remote, 37, 969-977, 1999.
24. Gallant, J. C. and Wilson, J. P.: Primary topographic attributes, in: Terrain analysis: principles and applications, edited by: Wilson, J. P. and Gallant, J. C., Wiley, New York, 51-86, 2000.
25. Gessler, P. E., Moore, I. D., McKenzie, N. J., and Ryan, P. J.: Soillandscape modeling and spatial prediction of soil attributes, Int. J. Geogr. Inf. Syst., 9, 421-432, 1995.
26. Gessler, P. E., Chadwick, O. A., Chamran, F., Althouse, L. D., and Holmes, K. W.: Modelling soil-landscape and ecosystem properties using terrain attributes, Soil. Sci. Soc. Am. J., 64, 2046-2056, 2000.
27. Grunwald, S. (Ed.) : Environmental soil-landscape modelling - Geographic Information Technologies and Pedometrics, CRC Press, New York, 2006.
28. Hengl, T.: A practical guide to geostatistical mapping, 2nd Edt. University of Amsterdam, 291 p, ISBN 978-90-9024981-0, http://spatial-analyst. net/book/GstatIntro, 2009.
29. Henderson, B. L., Bui, E. N., Moran, C. J., and Simon, D. A. P.: Australia-wide predictions of soil properties using decision trees, Geoderma, 124 (3-4), 383-398, 2004
30. IPCC: Land-use, land-use change, and forestry, in: Land-use, landuse change, and forestry, edited by: Watson, R. T., Noble, I. R., Bolin, B., Ravindranath, N. H., Verardo, D. J., and Dokken, D. J., A special report to the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, UK, 1-51, 2000.
31. Lagacherie, P. and Holmes, S.: Addressing geographical data errors in a classification tree for soil unit prediction, Int. J. Geogr. Inf. Sci., 11, 183-198, 1997.
32. Lagacherie, P., Legros, J. P., and Burrough, P. A.: A soil survey procedure using the knowledge of soil pattern established on a previously mapped reference area, Geoderma, 65, 283-301, 1995.
33. Lawrence, R., Bunn, A., Powell, S., and Zambon, M.: Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis, Rem. Sens. Environ., 90, 331-336, 2004.
34. Lees, B. G. and Ritman, K.: Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in distributed hilly environments, J. Environ. Manage., 15, 823-831, 1991.
35. Loh, W. Y. and Shih, Y. S.: Split selection methods for classification trees, Stat Sinica, 7, 815-840, 1997.
36. Liu, X.: Airborne LiDAR for DEM generation: some critical issues, Progress Prog. Phys. Geog., 32 (1), 31-49, 2008.
37. Luoto M. and Hjort, J.: Evaluation of current statistical approaches for predictive geomorphological mapping, Geomorphology, 67, 299-315, 2005.
38. Madsen, H. B., Nørr, A. H., and Holst, K. A.: The Danish Soil Clasification. Atlas over Denmark I, 3. The Royal Danish Geographical Society, Copenhagen, 1992.
39. McBratney, A. B., Mendonca, M. L., and Minasny, B.: On digital soil mapping, Geoderma, 117, 3-52, 2003.
40. McKenzie, N. J. and Ryan, P. J.: Spatial prediction of soil properties using environmental correlation, Geoderma, 89, 67-94, 1999.
41. Moore, I. D., Gessler, P. E., Nielsen, G. A., and Peterson, G. A.: Soil attribute prediction using terrain analysis, Soil Sci. Soc. Am. J., 57, 443-452, 1993.
42. Moran, C. J. and Bui, E. N.: Spatial data mining for enhanced soil map modeling, Int. J. Geogr. Inf. Sci., 16, 533-549, 2002.
43. Pachepsky, Y. A., Timlin, D. J., and Rawls, W. J.: Soil water retention as related to topographic variables, Soil Sci. Soc. Am. J., 65, 1787-1795, 2001.
44. Qi, F. and Zhu, A. X.: Knowledge discovery from soil maps using inductive learning, Int. J. Geogr. Inf. Syst., 17 (8), 771-795, 2003.
45. Safavian, S. J. and Norvig, P.: A survey of tree classifier methodology, IEEE T. Syst. Man. Ct. A, 21, 660-674, 1991.
46. Scull, P., Franklin, J., and Chadwick, O. A.: The application of classification tree analysis to soil type prediction in a desert landscape, Ecol. Model., 181, 1-15, 2005.
47. Schmitt, N. P., Rehm, W. F., Pistner, T., Zeller, P., Diehl, H., and Navé, P.: The AWIATOR airborne LIDAR turbulence sensor, Aerospace Sci. Technol., 11 (7-8), 546-552, 2007.
48. Tarboton, D. G., Bras, R. L., and Rodriguez-Iturbe, I.: On the extraction of channel networks from digital elevation data, Hydrol. Processes, 5, 81-100, 1991.
49. Thompson, J. A. and Kolka, R. K.: Soil carbon storage estimation in a forested watershed using quantitative soil-landscape modelling, Soil Sci. Soc. Am. J., 69, 1086-1093, 2005
50. Wilson, J. P. and Gallant, J. C.: Secondary topographic attributes, in: Terrain analysis: principles and applications, edited by: Wilson, J. P. and Gallant, J. C., Wiley: New York, 87-132, 2000