Journal of the Indian Society of Remote Sensing

, Volume 38, Issue 3, pp 535–547 | Cite as

Geospatial modeling of Brown oak (Quercus semecarpifolia) habitats in the Kumaun Himalaya under climate change scenario

  • S. SaranEmail author
  • R. Joshi
  • S. Sharma
  • H. Padalia
  • V. K. Dadhwal
Research Article


The study explores the use of multiple criteria decision techniques in predicting spatial niche of Brown oak (also known as Kharsu oak, Quercus semecarpifolia Sm.) formation in midaltitude (2,400–3,500 meter amsl) Kumaun Himalaya. Predictive models using various climatic and topographical factors influencing Brown oak’s growth and survival were developed to define its current ecological niche. Analytical Hierarchical Process (AHP) method involving Saaty’s pair-wise comparison was performed to rank the explanatory powers of each compared variable. Variables were suitably weighted using fuzzy factor standardization scheme to reflect their relative importance in defining species niche. An optimum indicator was then chosen for deriving a site suitability map of brown oak. This study establishes the role of aspect in the current distribution of the species along with known influence of altitude. Future niches of oak has been tracked in the projected climate change scenario of +1°C and +2°C rise in temperature and 20 mm in precipitation. The results show that on predicted +1°C and +2°C increase in temperature, present habitat of brown oak distribution may be reduced by 40 per cent and 76 per cent respectively.


MCDM AHP Quercus semecarpifolia Kumaun Himalaya 


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  1. Allen CD and Breshears DD (1998) Drought-induced shift of a forest-woodland ecotone: Rapid landscape response to climate variation. Proceedings of the National Academy of Sciences USA. 95: 14839–14842CrossRefGoogle Scholar
  2. Aspinall R (1992) An Inductive Modelling Procedure Based on Bayes’ Theorem for Analysis of Pattern in Spatial Data, International Journal of Geographical Information Systems 6(2):105–121CrossRefGoogle Scholar
  3. Baker MB Jr (1975) Modeling Management of Ponderosa Pine Forest Resources. In Proceedings of Watershed Management Symposium, ASCE Irrigation and Drainage Division, Logan, UT, August 11–13. pp. 478–493Google Scholar
  4. Baker MB Jr (1982) Influence of Clearing Ponderosa Pine on Timing of Snowmelt Runoff. In Proceedings of the Western Snow Conference, Reno NV, April 19–23. pp. 20–26Google Scholar
  5. Baker MB Jr (1986) Effects of Ponderosa Pine Treatments on Water Yield in Arizona. Water Resources Research 21(1):67–73CrossRefGoogle Scholar
  6. Bakkenes M, Alkemade JRM, Ihle F, Leemans R and Latour JB (2002) Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050. Global Change Biology 8: 390–407CrossRefGoogle Scholar
  7. Bartlein PJ, Prentice IC and Webb T (1986) Climatic response surfaces from pollen data for some eastern North American taxa. Journal of Biogeography 13:35–57CrossRefGoogle Scholar
  8. Beinat E (2001) Multi-Criteria Analysis for Environmental Management”. Journal of Multi-Criteria Decision Analysis 10:51CrossRefGoogle Scholar
  9. Charnes A and Cooper W (1961) Management Models and Industrial Applications of Linear Programming. John Wiley and SonsGoogle Scholar
  10. Charnes A and Cooper WW (1961) Management Models and Industrial Applications of Linear Programming. Wiley & Sons, New YorkGoogle Scholar
  11. Chen K, Blong R and Jacobson C (2001) MCE-RISK: Integrating Multicriteria Evaluation and GIS for Risk Decision-making in Natural Hazards, Environmental Modelling Software 16:387–397CrossRefGoogle Scholar
  12. Deshingkar P, Bardley PN, Chadwick MJ and Leach G (1997) Adapting to climate change in a Forest-Based Land Use System: A Case Study of Himachal Pradesh, India. Report submitted to the SEIGoogle Scholar
  13. Deng H (1999) Multi-criteria analysis with fuzzy pair wise comparison. International Journal of Approximate Reasoning 21: 215–231CrossRefGoogle Scholar
  14. Dirnböck T, Hobbs RJ, Lambeck RJ and Caccetta PA (2002) Vegetation distribution in relation to topographically driven processes in southwestern Australia. Appl Veg Sci 5:147–158CrossRefGoogle Scholar
  15. Ellenberg H (1988) Floristic changes due nitrogen deposition in central Europe. In: Nilsson J., Grennfelt P. (eds.). Critical loads for sulphur and nitrogen, Report from a workshop held at Skokloster, Sweden 19–24 March, 1988. Miljörapport/Nord 15 Nordic Council of Ministers, Kopenhagen.Google Scholar
  16. Frescino TS, Edwards Jr TC and Moisen GG (2001) Modeling spatially explicit forest structural attributes using Generalised Additive Models. Journal of Vegetation Science 12:15–26Google Scholar
  17. Gottfried MD, Rogers RR and Curry RK (2004) First record of Late Cretaceous coelacanths from Madagascar. In: Arratia G, Wilson M.V.H, Cloutier R, editors. Recent advances in the origin and early radiation of vertebrates. Dr Pfeil Verlag; Munich: 2004. 687-691 pGoogle Scholar
  18. Gonzalez P (2001) Desertification and a shift of forest species in the West African Sahel. Climate Res 17: 217–228CrossRefGoogle Scholar
  19. Heikkinon RK and Birks HJB (1996) Spatial and environmental components of variation in the distribution patterns of sub-arctic plant species at Kovo, N. Finland — a case study at the mesoscale level. Ecography 19:341–351Google Scholar
  20. Huntley B, Berry PM, Cramer W and McDonald AP (1995) Modelling present and potential future ranges of some European higher plants using climate response surfaces. Journal of Biogeography 967–1001Google Scholar
  21. Huston MA (1994) Biological Diversity: The Coexistence of Species on Changing Landscape. Cambridge University Press, Cambridge 708 pGoogle Scholar
  22. IPCC (2006) Climate Change, Accessed on 5 November 2009
  23. Jensen ME and Everett R (1993) An overview of ecosystem management principles. pp. 9–18 in Jensen M.E. and P.S. Bourgeron (eds.) Eastside Forest Ecosystem Health Assesment: vol. II Ecosystem Management: Principles and Applications. USDA Forest Service Google Scholar
  24. Kapetsky JM and Aguilar-Manjarrez J (2007) Geographic Information Systems, Remote Sensing and Mapping for the Development and Management of Marine Aquaculture, FAO, Rome, 97–102Google Scholar
  25. Kirschbaum, MUF, Cannell MGR, Cruz RVO, Galinski W and Cramer WP (1996) Climate change impacts on forests. In: Climate Change 1995, Impacts, Adaptation and Mitigation of Climate Change: Scientific-Technical Analyses, Cambridge University Press, CambridgeGoogle Scholar
  26. Louis R, Iverson I, Schwartz MW, Anantha M Prasad (2004) Potential colonization of newly available tree-species habitat under climate change: an analysis for five eastern US species, Landscape Ecology 19: 787–799CrossRefGoogle Scholar
  27. Malczewski J (1999) GIS and Multi-Criteria Decision Analysis, John Wiley and Sons, New YorkGoogle Scholar
  28. Midgley GF, Hannah L, Millar D, Ruther-ford MC and Powrie LW (2002) Assessing the vulnerability of species richness to anthropogenic climate change in a biodiversity hotspot. Global Ecology and Biogeography 11: 445–451CrossRefGoogle Scholar
  29. McDaniels TL, Gregory RS and Fields D (1999) Democratizing Risk Management: Successful public involvement in local water management decisions. Risk Analysis 19(3):497–510Google Scholar
  30. Moisen GG Edwards TCJ (1999) Use of generalized linear models and digital data in a forest inventory of Utah. Journal of Agricultural, Biological and Environmental Statistics 4(4): 372–390CrossRefGoogle Scholar
  31. Nute D, Rosenberg, G, Nath S, Verma B, Rauscher HM, Twery MJ and Grove M (2000) Goals and goal orientation in decision support systems for ecosystem management. Computers and Electronics in Agriculture 27:355–375CrossRefGoogle Scholar
  32. Penuelas J and Boada M (2003) A global changeinduced biome shift in the Montseny Mountains (NE Spain). Global Change Biology 9: 131–140CrossRefGoogle Scholar
  33. Pomerol JC and Sergio BR (2000) Multicriterion Decision in Management, Principles and Practice. Kluwer Academic Publishers, LondonGoogle Scholar
  34. Rauscher HM (1999) Ecosystem management decision support for federal forests in the United States: A review. Forest Ecology and Management 114:173–197CrossRefGoogle Scholar
  35. Ravindranath NH, Joshil, NV, Sukumar R and Saxena A (2006) Impact of climate change on forest in India. Current Science 90(3):354–361Google Scholar
  36. Saran S, Ghosh S, Shrivastava G, Roy PS, Talukdar G and Prasad N (2003) Spatial Decision Support System for Biodiversity Conservation Prioritization: A Case Study for Web Based Approach. Asian Journal of Geoinformatics 4(1):21–30Google Scholar
  37. Schlesinger WH (1991) Biogeochemistry: An analysis of global change. Academic Press, San Diego. pp. 443Google Scholar
  38. Saaty TL (1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill Comp., New York 54–55Google Scholar
  39. Singh JS, Rawat YS and Chaturvedi OP (1983) Replacement of oak forest with pine in the Himalaya affects the nitrogen cycle. Nature 311:54–56CrossRefGoogle Scholar
  40. Singh JS and Singh SP (1986) Structure and function of central Himalayan Oak forests. Proceedings of Indian Academic of Science 96: 156–189Google Scholar
  41. Singh JS and Singh SP (1992) Forest of Himalaya; Structure, functioning and impact of man, Gyanodaya Prakashan, Nainital, IndiaGoogle Scholar
  42. Sukumar R, Ramesh R, Pant RK and Rajagopalan G (1993) A d13C record of late Quaternary climate change from tropical peats in southern India. Nature 364:703–706CrossRefGoogle Scholar
  43. Sukumar R, Suresh HS and Ramesh R (1995) Climate change and its impacts on tropical montane ecosystems in southern India. Journal of Biogeography 22:533–536CrossRefGoogle Scholar
  44. Stockwell DRB and Peters D (1999) The GARP Modeling System: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13(2)143–158CrossRefGoogle Scholar
  45. Tecle A and Lucien (1993) Concepts of Multi-Criterion Decision Making.” Chapter 3 in H.P. Nachtnebel (ed.) Decision Support System in Water Resource Management. Paris, France: UNESCO Press Google Scholar
  46. Thuiller W, Lavorel S, Araújo MB (2005) Niche properties and geographic extent as predictors of species sensitivity to climate change. Global Ecology and Biogeography 14:347–357CrossRefGoogle Scholar
  47. Troup RS (1921) The silviculture of Indian trees Clarendon Press, Oxford, UK pp. 1195Google Scholar
  48. Upreti N, Tewari JC and Singh SP (1985) The oak forests of Kumaun Himalaya (India): Composition, Diversity, and regeneration. Mountain Research and Development 5:163–174CrossRefGoogle Scholar
  49. Woodward FI (1987) Climate and Plant Distribution. New York: Cambridge University Press. 174 pGoogle Scholar
  50. Zeleny M (1982) Multiple Criteria Decision Making. McGraw-Hill Book Company, New YorkGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2010

Authors and Affiliations

  • S. Saran
    • 1
    Email author
  • R. Joshi
    • 1
  • S. Sharma
    • 2
  • H. Padalia
    • 3
  • V. K. Dadhwal
    • 4
  1. 1.Indian Institute of Remote Sensing (NRSC)DehradunIndia
  2. 2.G. B. Pant Institute of Himalayan Environments and DevelopmentAlmoraIndia
  3. 3.Regional Remote Sensing Service CentreDehradunIndia
  4. 4.National Remote Sensing CentreHyderabadIndia

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