Effect of error in the DEM on environmental variables for predictive vegetation modelling

Journal of Vegetation Science

Volumn 15, Issue 6, 2004, Pages 747-756

  Van Niel, Kimberly P ^a   Laffan, Shawn W ^b   Lees, Brian G ^c  

^a UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Air temperature; Aspect; DEM; Environmental modelling: Niche modelling; Slope; Topographic position; Topographic Wetness Index

Indexed keywords

DIGITAL ELEVATION MODEL; ENVIRONMENTAL CONDITIONS; ENVIRONMENTAL MODELING; ERROR ANALYSIS; PREDICTION; VEGETATION COVER;

AUSTRALASIA; AUSTRALIA; EASTERN HEMISPHERE; KIOLOA; NEW SOUTH WALES; WORLD;

EID: 10344232011     PISSN: 11009233     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1654-1103.2004.tb02317.x     Document Type: Article

References (55)

1. Anon. 1998. USGS National mapping program standards for digital elevation models. US Department of the Interior. Reston, VA, US.
2. Arbia, G., Griffith, D. & Haining, R. 1998. Error propagation and modelling in raster GIS: overlay operations. Int. J. Geogr. Inf. Sci. 12: 145-167.
3. Austin, M. 1985. Continuum concept, ordination methods and niche theory. Annu. Rev. Ecol. Syst. 16: 39-61.
4. Austin, M. 1987. Models for the analysis of species' response to environmental gradients. Vegetatio 69: 35-45.
5. Austin, M. & Cunningham, R. 1981. Observational analysis of environmental gradients. Proc. Ecol. Soc. Aust. 11: 109-119.
6. Austin, M. & Gaywood, M. 1994. Current problems of environmental gradients and species response curves in relation to continuum theory. J. Veg. Sci. 5: 473-482.
7. Austin, M., Cunningham, R. & Good, R. 1983. Altitudinal distribution of several eucalypt species in relation to other environmental factors in southern New South Wales. Aust. J. Ecol. 8: 169-180.
8. Austin, M., Nicholls, A., Doherty, M. & Meyers, J. 1994. Determining species response functions to an environmental gradient by means of a β-function. J. Veg. Sci. 5: 215-228.
9. Austin, M., Meyers, J., Belbin, L. & Doherty, M. 1995. Simulated data case study, Sub-project 5. In: Modelling of landscape patterns and processes using biological data. Division of Wildlife and Ecology, CSIRO, Canberra, AU.
10. Barling, R., Moore, I. & Grayson, R. 1994. A quasi-dynamic wetness index for characterising the spatial distribution of zones of surface saturation and soil water content. Water Resour. Res. 30: 1029-1044.
11. Brown, G. & Bara, T. 1994. Recognition and reduction of systematic error in elevation and derivative surfaces from 7.5-Minute DEMs. Photogramm. Eng. Remote Sens. 60: 189-194.
12. Burrough, P. & McDonnell, R. 1998. Principles of Geographical Information Systems. Oxford University Press, New York, NY, US.
13. Dirnböck, T., Dullinger, S., Gottfried, M., Ginzler, C. & Grabherr, G. 2003. Mapping alpine vegetation based on image analysis, topographic variables and Canonical Correspondence Analysis. Appl. Veg. Sci. 6: 85-96.
14. Dubayah, R. & Rich, P. 1995. Topographic solar radiation models for GIS. Int. J. Geogr. Inf. Syst. 9: 405-419.
15. Elston, D., Jayasinghe, G., Buckland, S., MacMillan, D. & Aspinall, R. 1997. Adapting regression equations to minimize the mean squared error of predictions made using covariate data from a GIS. Int. J. Geogr. Inf. Sci. 11: 265-280.
16. Emmi, P. & Horton, C. 1995. A Monte Carlo simulation of error propagation in GIS-based assessment of seismic risk. Int. J. Geogr. Inf. Syst. 9: 447-461.
17. Evans, I. 1972. General geomorphometry. In: Chorley, R. (ed.) Spatial analysis in geomorphology, pp. 17-90. Methuen, London, UK.
18. Fisher, P. 1998. Improved modeling of elevation error with geostatistics. GeoInformatica 2: 215-233.
19. Fitzgerald, R. & Lees, B. 1992. The application of neural networks to the floristic classification of remote sensing and GIS data in complex terrain. In: Proceedings of the 17th ISPKS, pp. 570-573. American Society for Photogrammetry and Remote Sensing, Washington DC, US.
20. Franklin, J. 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progr. Phys. Geogr. 19: 474-499.
21. Franklin, J. 1998. Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. J. Veg. Sci. 9: 733-748.
22. Gahegan, M. & Ehlers, M. 2000. A framework for the modelling of uncertainty between remote sensing and geographic information systems. ISPRS J. Photogramm. 55: 176-188.
23. Gallant, J. & Wilson, J. 1996. TAPES-G: A grid-based terrain analysis program for the environmental sciences. Comput. Geosci. 22: 713-722.
24. Guisan, A., Weiss, S.B. & Weiss, A.D. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecol. 143: 107-122.
25. Guisan, A., Theurillat, J. & Kienast, F. 1998. Predicting the potential distribution of plant species in an alpine environment. J. Veg. Sci. 9: 65-74.
26. Guisan, A. & Zimmerman, N. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135: 147-186.
27. Heuvelink, G. 1998. Error propagation in environmental modelling with GIS. Taylor and Francis, London, UK.
28. Heuvelink, G. & Burrough, P. 1993. Error propagation in cartographic modelling using Boolean logic and continuous classification. Int. J. Geogr. Inf. Syst. 7: 231-246.
29. Heuvelink, G., Burrough, P. & Stein, A. 1989. Propagation of errors in spatial modelling with GIS. Int. J. Geogr. Inf. Syst. 3: 303-322.
30. Holmes, K., Chadwick, O. & Kyriakidis, P. 2000. Error in a USGS 30-meter digital elevation model and its impact on terrain modelling. J. Hydrol. 233: 154-173.
31. Huebner, C.D. & Vankat, J.L. 2003. The importance of environment vs. disturbance in the vegetation mosaic of Central Arizona. J. Veg. Sci. 14: 25-34.
32. Hunter, G. & Goodchild, M. 1997. Modeling the uncertainty of slope and aspect estimates derived from spatial databases. Geogr. Anal. 29: 35-49.
33. Jones, K. 1998. A comparison of algorithms used to compute hill slope as a property of the DEM. Comput. Geosci. 24: 315-323.
34. Leathwick, J. 1995. Climatic relationships of some New Zealand forest tree species. J. Veg. Sci. 6: 237-248.
35. Leathwick, J. & Mitchell, N. 1992. Forest pattern, climate and vulcanism in central North Island, New Zealand. J. Veg. Sci. 3: 603-616.
36. Lees, B. 1999. The Kioloa GLCTS Pathfinder Site. Accessed: 21 October. URL: http://sres.anu.edu.au/associated/pathfinder/index.html
37. Lees, B. & Ritman, K. 1991. Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments. Environ. Manage. 15: 823-831.
38. Margules, C., Nicholls, A. & Austin, M. 1987. Diversity of Eucalyptus species predicted by a multi-variable environmental gradient. Oecologia 71: 229-232.
39. Matsumoto, M. & Nishimura, T. 1998. Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Trans. Model. Comput. Simul. 8: 3-30.
40. McKenney, D., Mackey, B. & Zavitz, B. 1999. Calibration and sensitivity analysis of a spatially-distributed solar radiation model. Int. J. Geogr. Inf. Sci. 13: 49-65.
41. Miller, J. & Franklin, J. 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol. Model.
42. Moore, I., Grayson, R. & Ladson, A. 1991. Digital terrain modelling: A review of hydrological, geomorphological biological applications. Hydrol. Proc. 5: 3-30.
43. Moore, I., Norton, T. & Williams, J. 1993. Modelling environmental heterogeneity in forested landscapes. J. Hydrol. 150: 717-747.
44. Næsset, E. 1999. Effects of delineation errors in forest stand boundaries on estimated area and timber volumes. Scand. J. For. Res. 14: 558-566.
45. Nicholls, A. 1989. How to make biological surveys go further with Generalised Linear Models. Biol. Conserv. 73: 51-75.
46. Openshaw, S. 1989. Learning to live with errors in spatial databases. In: Goodchild, M. & Gopal, S. (eds.) The accuracy of spatial databases, pp. 263-276. Taylor and Francis, London, UK.
47. Press, W., Teukolsky, S., Vetterling, W. & Flannery, B. 1992. Numerical recipes in C: The art of scientific computing. 2nd ed. Cambridge University Press, Cambridge, UK.
48. Shary, P. 1995. Land surface in gravity points classification by complete system of curvatures. Math. Geol. 27: 373-390.
49. Thuiller, W., Arajujo, M.B. & Lavorel, S. 2003. Generalised models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales. J. Veg. Sci. 14: 669-680.
50. Van Niel, K. & Laffan, S. 2003. Gambling with randomness: The use of pseudo-random number generators in GIS. Int. J. Geogr. Inf. Sci. 17: 49-68.
51. Vayssières, M., Plant, R. & Allen-Diaz, B. 2000. Classification trees: An alternative non-parametric approach for predicting species distributions. J. Veg. Sci. 11: 679-694.
52. Villers-Ruiz, L., Trejo-Vázquez, I. & Lopez-Blanco, J. 2003. Dry vegetation in relation to the physical environment in the Baja California Peninsula, Mexico. J. Veg. Sci. 14: 517-524.
53. Wilson, J. & Gallant, J. 2000. Terrain analysis: Principles and applications. John Wiley and Sons, Inc., New York, NY, US.
54. Zevenbergen, L. & Thorne, C. 1987. Quantitative analysis of land surface topography. Earth Surf. Proces. 12: 47-56.
55. Zimmermann, N. 2000. Tools for analyzing, summarizing, and mapping of biophysical variables. Accessed: 4/20/ 2001. URL: http://www.wsl.ch/staff/ niklaus.zimmermann/ progs.html