Advertisement

New Forests

, Volume 44, Issue 2, pp 163–181 | Cite as

Autologistic regression and multicriteria evaluation models for the prediction of forest expansion

  • Dimitrios P. Triantakonstantis
  • Dionissios P. Kalivas
  • Vassiliki J. Kollias
Article

Abstract

Land use changes are complex ecological processes driven by the interaction of biophysical and human related factors. The prediction of forest land use changes is important for sustainable forest management and biodiversity conservation. This study investigates the modelling process of the spatial dynamics of a forest ecosystem in north eastern Greece. For the prediction of forest expansion, based on land use data of the study area, a deterministic approach using logistic regression and heuristic methods of multi-criteria evaluation is adopted. The set of factors driving forest expansion are: the slope, the distance to roads, the distance to urban areas, the distance to forest, the soil depth, the soil erosion and the influence from the land uses of the neighbourhood. The spatial autocorrelation of driving factors is addressed using an autologistic regression model. The multicriteria evaluation approach is developed using weighted linear combination (WLC) and ordered weighted averaging (OWA) methods. In WLC method the relative importance of each factor was estimated using the analytical hierarchy process. In the OWA method, decision strategies are generated using a selection of relative linguistic quantifiers, which allow different Risk in decisions. The accuracy of the models produced was tested with real data for the year 2001 using the ROC validation method. All the methods produced satisfactory results. Autologistic regression showed slightly better performance than multicriteria evaluation methods due to higher degree of objectivity in defining the importance of driving factors for forest expansion.

Keywords

Forest expansion Land use changes Autologistic regression Analytic hierarchy process Ordered weighted averaging ROC 

Notes

Acknowledgments

The authors would like to express their deep appreciation to WWF-Greece for providing the digitized satellite image. We also thank the Forest Research Institute of the Ministry of Agriculture for providing the geological and soil data.

References

  1. Adamakopoulos T, Gatzoyannis S, Poirazidis K (1995) Study on the assessment, the enhancement of the legal infrastructure and the management of the protected area in the forest of Dadia. Specific environmental study, conducted by the WWF Greece pursuant to the Ministerial Decret No. 6926/1990, Athens (in Greek)Google Scholar
  2. Ananda J, Herath G (2003) The use of analytic hierarchy process to incorporate stakeholder preferences into regional forest planning. For Policy Econ 5:13–26CrossRefGoogle Scholar
  3. Augustin NH, Mugglestone MA, Buckland ST (1996) An autologistic model for the spatial distribution of wildlife. Appl Geogr 33:339–347Google Scholar
  4. Bayala J, Kindt R, Belem M, Kalinganire A (2010) Factors affecting the dynamics of tree diversity in agroforestry parklands of cereal and cotton farming systems in Burkina Faso. New For 41:281–296CrossRefGoogle Scholar
  5. Boroushaki S, Malczewski J (2008) Implementation an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Comput Geosci 34:399–410CrossRefGoogle Scholar
  6. Boyd DS, Foody GM, Ripple WJ (2002) Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing. Appl Geogr 22:375–392CrossRefGoogle Scholar
  7. Briassoulis H (2000) Analysis of land use change: theoretical and modelling approaches. The Web Book of Regional Science, Regional Research Institute, West Virginia University, Morgantown. Online at http://www.rri.wvu.edu/regscweb.htm
  8. Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, OxfordGoogle Scholar
  9. Burton PJ (1993) Some limitations inherent to static indices of plant competition. Can J For Res 23:2141–2152CrossRefGoogle Scholar
  10. Campbell KA, Dewhurst SM (2007) A hierarchical simulation-through-optimization approach to forest disturbance modelling. Ecol Model 202:281–296CrossRefGoogle Scholar
  11. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46CrossRefGoogle Scholar
  12. D’Amato AW, Puettmann KJ (2004) The relative dominance hypothesis explains interaction dynamics in mixed species Alnus rubra/Pseudotsuga menziesii stands. J Ecol 92:450–463CrossRefGoogle Scholar
  13. Domenrich TA, McFadden D (1975) Urban travel demand: behavioural analysis. North-Holland, AmsterdamGoogle Scholar
  14. Eastman RJ (2003) Idrisi Kilimanjaro, Manual. Clark Labs, Clark University, WorcesterGoogle Scholar
  15. ESRI (2008) ArcGIS 9.3. RedlandsGoogle Scholar
  16. Freeman EA, Moisen GG (2008) A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol Model 217:48–58CrossRefGoogle Scholar
  17. Goldberg DE (1987) Neighborhood competition in an old-field plant community. Ecology 68:1211–1223CrossRefGoogle Scholar
  18. Gutierrez F, Navines R, Navarro P, Garcia-Esteve L, Subira S, Torrens M, Martin-Santos R (2008) What do all personality disorders have in common? Ineffectiveness and uncooperativeness. Compr Psychiatry 49:570–578PubMedCrossRefGoogle Scholar
  19. Hanspach J, Kuhn I, Pysek P, Boos E, Klotz S (2008) Correlates of naturalization and occupancy o introduced ornamentals in Germany. Perspect Plant Ecol 10:241–250CrossRefGoogle Scholar
  20. Ismail Z, Herrmann N, Rothenburg LS, Gotter A, Leibovitch FS, Rafi-Tari S, Blach SE, Lanctot KL (2008) A functional neuroimaging study of appetite loss in Alzheimer’s disease. J Neurol Sci 271:97–103PubMedCrossRefGoogle Scholar
  21. Jelinski D, Wu J (1996) The modifiable areal unit problem and implications for landscape ecology. Landsc Ecol 11(3):129–140CrossRefGoogle Scholar
  22. Jiang H, Eastman JR (2000) Application of fuzzy measures in multi-criteria evaluation in GIS. Int J Geogr Inf Sci 14(2):173–184CrossRefGoogle Scholar
  23. Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, LondonGoogle Scholar
  24. Lichstein JW, Simons TR, Shriner SA, Franzreb KE (2002) Spatial and autoregressive models in ecology. Ecol Monogr 72:445–463CrossRefGoogle Scholar
  25. Lopez AS, Civil RS, Jimenez JG, Ayanz JSM (2008) Integration of socio-economic and environmental factors for modelling long-term fire danger in Southern Europe. Eur J For Res 127:149–163CrossRefGoogle Scholar
  26. Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New YorkGoogle Scholar
  27. Malczewski J (2006) Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land use suitability analysis. Appl Earth Obs Geoinf 8:270–277CrossRefGoogle Scholar
  28. Malczewski J, Chapman T, Flegel C, Walters D, Shrubsole D, Healy MA (2003) GIS-multicriteria evaluation with ordered weighted averaging (OWA): case study of developing management strategies. Environ Plan 35(10):1769–1784CrossRefGoogle Scholar
  29. Marrocco C, Molinara M, Tortorella F (2006) Exploiting AUC for optimal linear combinations of dichotomizers. Pattern Recognit Lett 27:900–907CrossRefGoogle Scholar
  30. Mertens B, Lambin EF (1997) Spatial modelling of deforestation in southern Cameroon. Appl Geogr 17:143–162CrossRefGoogle Scholar
  31. Miller C, Urban DL (2000) Modeling the effects of fire management alternatives on Sierra Nevada mixed-conifer forests. Ecol Appl 10(1):85–94CrossRefGoogle Scholar
  32. Osborne PE, Alonso JC, Bryant RG (2001) Modelling landscape-scale habitat use using GIS and remote sensing: a case study with great bustards. Appl Ecol 38:458–471CrossRefGoogle Scholar
  33. Overmars KP, de Koning GHJ, Veldkamp A (2003) Spatial in multi-scale land use models. Ecol Model 164:257–270CrossRefGoogle Scholar
  34. Poirazidis K (2002) Systematic monitoring plan of Dadia-Leykimi-Soufli nature reserve. Record of landscape characteristic of Dadia forest in 2001, WWF-Greece, Athens, 38 pp (unpublished study—in Greek)Google Scholar
  35. Reyer C, Guericke M, Ibisch PL (2009) Climate change mitigation via afforestation, reforestation and deforestation avoidance: and what about adaptation to environmental change? New For 38:15–34CrossRefGoogle Scholar
  36. Saaty TL (1977) A scaling method for priorities in hierarchical structures. Math Psychol 15:234–281CrossRefGoogle Scholar
  37. Saaty RW (1987) The analytic hierarchy process-what it is and how it is used. Math Model 9:161–176CrossRefGoogle Scholar
  38. Saaty TL (1994a) Highlights and critical points in the theory and application of the analytical hierarchy process. Eur J Oper Res 74:426–447CrossRefGoogle Scholar
  39. Saaty TL (1994b) The fundamentals of decision making and priority theory with the analytic hierarchy process. AHP series, vol VI. RWS Publication, Pittsburgh, p 527Google Scholar
  40. Schmoldt DL, Kangas J, Mendoza GA, Pesonen M (2001) The analytic hierarchy process in natural resource and environmental decision making. Kluwer, AmsterdamGoogle Scholar
  41. Serneels S, Lambin EF (2001) Proximate causes of land use change in Narok District Kenya: a spatial statistical model. Agric Ecosyst Environ 85:65–81CrossRefGoogle Scholar
  42. Simard SW, Zimonick BJ (2005) Neighborhood size effects on mortality, growth and crown morphology of paper birch. For Ecol Manag 214:251–265CrossRefGoogle Scholar
  43. Tortorella F (2005) A ROC-based reject rule for dichotomizers. Pattern Recognit Lett 26:167–180CrossRefGoogle Scholar
  44. Triantakonstantis DP, Kollias VJ, Kalivas DP (2006) Forest re-growth since 1945 in the Dadia forest nature reserve in northern Greece. New For 32:51–69CrossRefGoogle Scholar
  45. Tsai Y (2005) Quantifying urban form: compactness versus ‘sprawl’. Urban Stud 42(1):141–161CrossRefGoogle Scholar
  46. Tzeng HM, Yin CY (2008) Crisis management systems: staff nurses demand more support form their supervisors. Appl Nurs Res 21:131–138PubMedCrossRefGoogle Scholar
  47. Valente ROA, Vettorazzi CA (2008) Definition of priority areas for forest conservation through the ordered weighted averaging method. For Ecol Manag 256:1408–1417CrossRefGoogle Scholar
  48. Vardakis G, Ziangas E, Nakos G (1996) Land resource map of Greece. Map sheets of Mega Dereion and Soufli, scale 1:50.000. Forest Research Institute of AthensGoogle Scholar
  49. Veldkamp A, Verburg PH (2004) Modelling land use change and environmental impact: introduction to the spatial issue. Environ Manag 72:1–3CrossRefGoogle Scholar
  50. Verburg PH, Veldkamp A (2005) Editorial: spatial modeling to explore land use dynamics. Int J Geogr Inf Sci 19:99–102CrossRefGoogle Scholar
  51. White R, Engelen G (2000) High resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput Environ Urban Syst 23:383–400CrossRefGoogle Scholar
  52. Wu F (2002) Calibration of stochastic cellular automata: the application to rural-urban land conversions. Int J Geogr Inf Sci 16(8):795–818CrossRefGoogle Scholar
  53. Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190CrossRefGoogle Scholar
  54. Yager RR (1996) Quantifier guided aggregation using OWA operators. Intell Syst 11:49–73CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Dimitrios P. Triantakonstantis
    • 1
  • Dionissios P. Kalivas
    • 1
  • Vassiliki J. Kollias
    • 1
  1. 1.Laboratory of Soils and Agricultural ChemistryAgricultural University of AthensAthensGreece

Personalised recommendations