Skip to main content
SpringerLink
Log in

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

  • Published:
Environmental Management Aims and scope Submit manuscript

Abstract

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

Literature Cited

  • Abella S. R., V. B. Shelburne, N. W. Macdonald. 2003. Multifactor classification of forest landscape ecosystems of Jocassee Gorges, Southern Appalachian Mountains, South Carolina. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 33:1933–1946

    Article  Google Scholar 

  • Austin M. P., C. R. Margules. 1986. Assessing representativeness. In M. B. Usher (ed.), Wildlife conservation evaluation. Chapman and Hall, London. Pp: 45–67

    Google Scholar 

  • Austin M. P., T. M. Smith. 1989. A new model for the continuum concept. Vegetatio 83:35–47

    Article  Google Scholar 

  • Bailey R. G. 1996. Ecosystem geography. Springer-Verlag, New York

    Google Scholar 

  • Bailey R. G. 2004. Identifying ecoregion boundaries. Environmental Management 34(Suppl 1):S14–S26

    Article  Google Scholar 

  • Balastegui A., P. Ruiz-Lapuente, R. Canal. 2001. Reclassification of gamma-ray bursts. Monthly Notices of the Royal Astronomical Society 328:283–290

    Article  Google Scholar 

  • Ball G. H., D. J. Hall. 1965. A novel method of data analysis and pattern classification. Stanford Research Institute, Menlo Park, California

    Google Scholar 

  • Bedward M., D. A. Keith, R. L. Pressey. 1992. Homogeneity analysis: assessing the utility of classifications and maps of natural resources. Australian Journal of Ecology 17:133–139

    Article  Google Scholar 

  • Belbin L. 1993. Environmental representativeness: regional partitioning and reserve selection. Biological Conservation 66:223–230

    Article  Google Scholar 

  • Belbin L., C. McDonald. 1993. Comparing three classification strategies for use in ecology. Journal of Vegetation Science 4:341–349

    Article  Google Scholar 

  • Bennani Y., F. Fogelman-Soulié, and P. Gallinari. 1990. Text-dependent speaker identification using learning vector quantization. Pages 1087–1090 in Proceedings of INNC’90, International Neural Network Conference II, Kluwer Academic Press

  • Brandt J., E. Holmes, and D. Larsen. 1994. Monitoring ‘Small Biotopes’. Pages 251–274 in F. Klijn (ed.), Ecosystem classification for environmental management. Kluwer Academic Press, Dordrecht

    Google Scholar 

  • Bryan, B. A. 2000. Strategic revegetation planning in an agricultural landscape: a spatial information technology approach. PhD dissertation, University of Adelaide, South Australia

  • Bunce R. G. H., C. J. Barr, R. T. Clarke, D. C. Howard, A. M. J. Lane. 1996. Land classification for strategic ecological survey. Journal of Environmental Management 47:37–60

    Article  Google Scholar 

  • Bunce R. G. H., P. D. Carey, R. Elena-Rossello, J. Orr, J. Watkins, R. Fuller. 2002. A comparison of different biogeographical classifications of Europe, Great Britain and Spain. Journal of Environmental Management 65:121–134

    Article  CAS  Google Scholar 

  • Burrough P. A., P. F. M. van Gaans, R. A. MacMillan. 2000. High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems 113:37–52

    Article  Google Scholar 

  • Burrough P. A., J. P. Wilson, P. F. M. van Gaans, A. J. Hansen. 2001. Fuzzy k-means classification of topo-climatic data as an aid to forest mapping the Greater Yellowstone Area, USA. Landscape Ecology 16:523–546

    Article  Google Scholar 

  • Carmean W. H. 1996. Forest site-quality estimation using forest ecosystem classification in Northwestern Ontario. Environmental Monitoring and Assessment 39:493–508

    Article  Google Scholar 

  • Carter R. E., M. D. MacKenzie, D. H. Gjerstad. 1999. Ecological land classification the Southern Loam Hills of South Alabama. Forest Ecology and Management 114:395–404

    Article  Google Scholar 

  • Cheeseman P., and J. Stutz. 1996. Bayesian classification (AutoClass): theory and results. Pages 153–180 in U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances knowledge discovery and data mining. AAAI Press/MIT Press

  • Chon T., Y. Park, J. H. Park. 2000. Determining temporal pattern of community dynamics by using supervised learning algorithms. Ecological Modelling 132:151–166

    Article  Google Scholar 

  • Christian C. S., G. A. Stewart. 1968. Methodology of integrated surveys. UNESCO, Paris

    Google Scholar 

  • Claessen F. A. M., F. Klijn, J. Flip, P. M. Witte, J. G. Nienhaus. 1994. Ecosystem classification and hydro-ecological modelling for national water management. In F. Klijn (ed.), Ecosystem classification and environmental management. Kluwer Academic Press, Dordrecht. Pages 199–222

    Google Scholar 

  • Dhawan, A. P., and L. Arata. 1993. Segmentation of medical images through competitive learning. Pages 1277–1282 in Proceedings of ICNN’93, International Conference on Neural Networks III, IEEE Service Center

  • Dolnicar S. 1997. The use of neural networks marketing: market segmentation with self organising feature maps. Pages 38–43 in Proceedings of WSOM’97, Workshop on Self-Organizing Maps, Helsinki University of Technology, Neural Networks Research Centre, June 4–6, 1997, Espoo, Finland

  • Franklin J. 2003. Clustering versus regression trees for determining ecological land units in the Southern California Mountains and Foothills. Forest Science 49:354–368

    Google Scholar 

  • Gallant A. L., T. R. Loveland, T. L. Sohl, D. E. Napton. 2004. Using an ecoregion framework to analyze land-cover and land-use dynamics. Environmental Management 34(Suppl 1):S89–S110

    Article  Google Scholar 

  • Hanson R., J. Stutz, and P. Cheeseman. 1991. Bayesian classification theory. Technical report FIA-90-12-7-01, NASA Ames Research Center, Artificial Intelligence Branch

  • Hargrove W. W., F. M. Hoffman. 2004. Potential of multivariate quantitative methods for delineation and visualization of ecoregions. Environmental Management 34(Suppl 1):S39–S60

    Article  Google Scholar 

  • Hill M. O. 1979. TWINSPAN: a FORTRAN program for arranging multivariate data in an ordered two-way table by classification of the individual and attributes. Cornell University Press, Ithaca, New York

    Google Scholar 

  • Hirvonen H. 2001. Canada’s national ecological framework: an asset to reporting on the health of Canadian forests. Forestry Chronicle 77:111–115

    Google Scholar 

  • Host G. E., P. L. Polzer, D. J. Mladenoff, M. A. White, T. R. Crow. 1996. A quantitative approach to developing regional ecosystem classifications. Ecological Applications 6:608–618

    Article  Google Scholar 

  • Hutto C. J., V. B. Shelburne, S. M. Jones. 1999. Preliminary ecological land classification of the Chauga Ridges region of South Carolina. Forest Ecology and Management 114:385–393

    Article  Google Scholar 

  • Kanefsky B., J. Stutz, and P. Cheeseman. 1991. An automatic classification of a Landsat/TM image from Kansas (FIFE). Technical report FIA-91-26, NASA Ames Research Center, Artificial Intelligence Branch

  • Kanefsky B., J. Stutz, P. Cheeseman, and W. Taylor. 1994. An improved automatic classification of a Landsat/TM image from Kansas (FIFE). Technical report FIA-94-01, NASA Ames Research Center, Artificial Intelligence Branch

  • Kirkpatrick J. B., M. J. Brown. 1994. A comparison of direct and environmental domain approaches to planning reservation of forest higher plant communities and species Tasmania. Conservation Biology 8:217–224

    Article  Google Scholar 

  • Kohonen T. 1995. Self-organizing maps. Springer Series Information Science 30

  • Kohonen T., J. Hynninen, J. Kangas, and J. Laaksonen. 1996. SOM_PAK: the self-organizing map program package. Report A31. Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland

  • Kraft J., J. W. Einax, C. Kowalik. 2004. Information theory for evaluating environmental classification systems. Analytical and Bioanalytical Chemistry 380:475–483

    Article  CAS  Google Scholar 

  • Kupfer J. A., S. B. Franklin. 2000. Evaluation of an ecological land type classification system, Natchez Trace State Forest, Western Tennessee, USA. Landscape and Urban Planning 49:179–190

    Article  Google Scholar 

  • Lathrop R. G., J. A. Bognar. 1998. Applying GIS and landscape ecological principles to evaluate land conservation alternatives. Landscape and Urban Planning 41:27–41

    Article  Google Scholar 

  • Laut P., P. C. Heyligers, G. Keig, E. Loffler, C. Margules, R. M. Scott, M. E. Sullivan. 1977. Environments of South Australia, Province 3 Mt. Lofty Block. CSIRO Division of Land Use Research, Canberra

    Google Scholar 

  • Leathwick J. R., J. M. Overton, M. Mcleod. 2003. An environmental domain classification of New Zealand and its use as a tool for biodiversity management. Conservation Biology 17:1612–1623

    Article  Google Scholar 

  • Legendre P., and A. Vaudor. 1991. Le progiciel R. analyse multidimensionnelle, analyse spatiale. User’s guide. Université de Montréal, Montréal

  • Lek S., J. F. Guégan. 1999. Artificial neural networks as a tool in ecological modelling: an introduction. Ecological Modelling 120:65–73

    Article  Google Scholar 

  • Lindenmayer D. B., R. B. Cunningham. 1996. A habitat-based microscale forest classification system for zoning wood production areas to conserve a rare species threatened by logging operations south-eastern Australia. Environmental Monitoring and Assessment 39:543–557

    Article  Google Scholar 

  • Lioubimtseva E., P. Defourny. 1999. GIS-based landscape classification and mapping of European Russia. Landscape and Urban Planning 44:63–75

    Article  Google Scholar 

  • Loveland T. R., J. M. Merchant. 2004. Ecoregions and ecoregionalization: geographical and ecological perspectives. Environmental Management 34(Suppl 1):S1–S13

    Article  Google Scholar 

  • Mabbutt J. A. 1968. Aeolian landforms in Central Australia. Australian Geographical Studies 6:139–150

    Article  Google Scholar 

  • Mackey B. G. 1993. A spatial analysis of the environmental relations of rainforest structural types. Journal of Biogeography 20:303–336

    Article  Google Scholar 

  • Mackey B. G., H. A. Nix, M. F. Hutchinson, J. P. MacMahon, P. M. Fleming. 1988. Assessing representativeness of places for conservation reservation and heritage listing. Environmental Management 12:501–514

    Article  Google Scholar 

  • Mackey B. G., H. A. Nix, J. A. Stein, S. E. Cork, F. T. Bullen. 1989. Assessing the representativeness of the wet tropics of Queenslands World Heritage property. Biological Conservation 50:279–303

    Article  Google Scholar 

  • MacMillan R. A., W. W. Pettapiece, S. C. Nolan, T. W. Goddard. 2000. A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems 113:81–109

    Article  Google Scholar 

  • MacQueen J. B. 1967. Some methods for the classification and analysis of multivariate observations. Pages 281–297 in L. Le Cam, J. Neyman (eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press, Berkeley, CA

    Google Scholar 

  • McKenney D. W., B. G. Mackey, R. A. Sims. 1996. Primary databases for forest ecosystem management—examples from Ontario and possibilities for Canada: NatGRID. Environmental Monitoring and Assessment 39:399–416

    Article  Google Scholar 

  • Nadeau L. B., C. Li, H. Hans. 2004. Ecosystem mapping in the lower foothills subregion of Alberta: application of fuzzy logic. Forestry Chronicle 80:359–365

    Google Scholar 

  • Neily, P. D., E. Quigley, L. Benjamin, B. Stewart, and T. Duke. 2003. Ecological land classification for Nova Scotia: volume 1—mapping Nova Scotia’s terrestrial ecosystems. Report DNR 2003-2, April 2003, Nova Scotia Department of Natural Resources, Renewable Resources Branch

  • Nix H. A. 1982. Environmental determinants of biogeography and evolution Terra Australis. pages: 47–66 In W. R. Barker and P. J. M. Greenslade (eds.), Evolution of the flora and fauna of arid Australia. Peacock Publications.

  • Nolet P., G. Domon, Y. Bergeron. 1995. Potentials and limitations of ecological classifications as a tool for forest management: a case study of disturbed deciduous forests, Québec. Forest Ecology and Management 78:85–98

    Article  Google Scholar 

  • Omernik J. M. 2004. Perspectives on the nature and definition of ecological regions. Environmental Management 34(Suppl 1):S27–S38

    Article  Google Scholar 

  • Park Y., R. Céréghino, A. Compin, S. Lek. 2003. Applications of artificial neural networks for patterning and predicting aquatic insect richness running waters. Ecological Modelling 160:265–280

    Article  Google Scholar 

  • Pressey R. L., M. Bedward. 1991. Mapping the environment at different scales: benefits and costs for nature conservation. In C. R. Margules, M. P. Austin (eds.). Nature conservation: cost effective biological surveys and data analysis. CSIRO, Australia. Pp: 7–13

    Google Scholar 

  • Pressey R. L., V. S. Logan. 1994. Level of geographical subdivision and its effects on assessments of reserve coverage—a review of regional studies. Conservation Biology 8:1037–1046

    Article  Google Scholar 

  • Pressey R. L., V. S. Logan. 1995. Reserve coverage and requirements relation to partitioning and generalization of land classes—analyses for western New South Wales. Conservation Biology 9:1506–1517

    Article  Google Scholar 

  • Richards J. A. 1986. Remote sensing digital image analysis: an introduction. Springer-Verlag, New York

    Google Scholar 

  • Runhaar H. J., H. A. Udo de Haes. 1994. The use of site factors as classification characteristics for ecotopes. In F. Klijn (ed.), Ecosystem classification for environmental management. Kluwer Academic Press, Dordrecht. Pp: 169–172

    Google Scholar 

  • Sims R. A., I. G. W. Corns, K. Klinka. 1996. Global to local: ecological land classification. Environmental Monitoring and Assessment 39:1–10

    Article  Google Scholar 

  • Stutz J., P. Cheeseman. 1994. AutoClass—a Bayesian approach to classification. In J. Skilling and S. Sibisi (eds.), Maximum entropy and Bayesian methods. Kluwer Academic Press, Dordrecht

  • Thompson R. S., S. L. Shafer, K. H. Anderson, L. E. Strickland, R. T. Pelltier, P. J. Bartlein, M. W. Kerwin. 2004. Topographic, bioclimatic, and vegetation characteristics of three ecoregion classification systems in North America: comparisons along continent-wide transects. Environmental Management 34(Suppl 1):S125–S148

    Article  Google Scholar 

  • Ultsch A. 1993. Self organized feature maps for monitoring and knowledge acquisition of a chemical process. In S. Gielen, B. Kappen (eds.), Proceedings of the international conference on artificial neural networks (ICANN93). Springer-Verlag, London. Pp: 864–867

    Google Scholar 

  • Walley W. J., M. A. O’Connor. 2001. Unsupervised pattern recognition for the interpretation of ecological data. Ecological Modelling 146:219–230

    Article  Google Scholar 

  • Watts D. 1970. Principles of biogeography. McGraw-Hill, New York

    Google Scholar 

  • Wolock D. M., T. C. Winter, G. McMahon. 2004. Delineation and evaluation of hydrologic-landscape regions in the United States using geographic information system tools and multivariate statistical analyses. Environmental Management 34(Suppl 1):S71–S88

    Article  Google Scholar 

  • Zonneveld I. 1994. Basic principles of classification. In F. Klijn (ed.), Ecosystem classification for environmental management. Kluwer Academic Press, Dordrecht. Pp: 23–48

    Google Scholar 

Download references

Acknowledgments

The author gratefully acknowledges the use of data supplied by the South Australian Department of Primary Industries and Resources and PlanningSA. I am grateful to Professor Hugh Possingham for reading an early version of this work and to Neville Crossman and Dave Gerner for comments on the final draft. Infrastructure and support from GISCA, the National Centre for Social Applications of GIS, University of Adelaide is gratefully acknowledged. This research was supported by an Australian Postgraduate Research Award scholarship and an Australian Research Council Discovery Project grant DP0343036. CSIRO Land and Water also contributed to the final revision of this article.

Author information

Authors and Affiliations

  1. Policy and Economic Research Unit, CSIRO Land and Water, Private Bag 2, Glen Osmond, South Australia, 5064, Australia

    Brett A. Bryan

Authors
  1. Brett A. Bryan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brett A. Bryan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bryan, B.A. Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes. Environmental Management 37, 126–140 (2006). https://doi.org/10.1007/s00267-004-0058-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00267-004-0058-1

Keywords

Search

Navigation