Ecoregion Classification



After a brief introduction, in this chapter, the spatial interpolation methods and regression models for main climate variables for the Lhasa area at the central Tibetan Plateau are developed. Subsequently, based on the comprehensive analysis on regional environmental characteristics and climatic conditions in the study area, seven key variables from topography and climate are selected as main indicators affecting ecological region, and then ecoregion classification is implemented based on these indicators using principal component analysis (PCA) and artificial neural networks (ANN) techniques, and main results are presented. The chapter ends with conclusion and discussion.


Spatial interpolation Climate variables Ecoregion classification PCA ANN Central Tibetan Plateau 


  1. Agricultural and Pastoral Bureau of Lhasa Municipality. 1993. Land Resources in Lhasa Area, 16–17. Beijing: China Agricultural Science and Technology Press.Google Scholar
  2. Borges, P.D.A., J. Franke, H. Weiss, and C. Bernhofer. 2016. Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil. Theoretical and Applied Climatology 123 (1–2): 335–348.CrossRefGoogle Scholar
  3. Chu, D., P. Da, L. Xiang, et al. 2015. Application of GeoSFM model in the Lhasa River basin, Tibet. Mountain Research 33 (6): 751–758.Google Scholar
  4. Chu, D., T. Pubu, G. Norbu, et al. 2011. The validation of satellite-derived rainfall product over the Tibet. Acta Meteorologica Sinica 25 (6): 734–741.CrossRefGoogle Scholar
  5. Civco, D. 1993. Artificial neural networks for land-cover classification and mapping. International Journal of Geographical Information Systems 7 (2): 173–186.CrossRefGoogle Scholar
  6. Comprehensive Scientific Expedition Team to TP of CAS. 1982. Physical Geography of Tibet. Beijing: Science Press.Google Scholar
  7. Guan, Z.H., C.Y. Chen, Y.X. Ou, et al. 1984. Rivers and Lakes in Tibet, 176–182. Beijing: Science Press.Google Scholar
  8. Han, Z., W. Song, X. Deng, et al. 2018. Grassland ecosystem responses to climate change and human activities within the Three-River Headwaters region of China. Scientific Reports 8 (1): 9079.CrossRefGoogle Scholar
  9. Ishida, T., and K. Kawashima. 1993. Use of cokriging to estimate surface air temperature from elevation. Theoretical and Applied Climatology 47 (3): 147–157.CrossRefGoogle Scholar
  10. Jolliffe, I. 2005. Principal Component Analysis. New York: Springer.Google Scholar
  11. Kleynhans, C.J., C. Thirion, and J. Moolman. 2005. A level I river ecoregion classification system for South Africa, Lesotho and Swaziland. Report no. N/0000/00/req0104.Google Scholar
  12. Li, X., G. Cheng, and L. Lu. 2000. Comparison of spatial interpolation methods. Advance in Earth Sciences 15 (3): 260–265.Google Scholar
  13. Lin, R., C. Li, and Y. Zhang. 2001. Climatic Resources for Agriculture in Lhasa Area, Tibet, 19–68. Beijing: Meteorological Press.Google Scholar
  14. Lin, Z., G. Mo, H. Li, et al. 2002. Comparison of three spatial interpolation methods for climate variables in China. Acta Geographica Sinica 57 (1): 047–056.Google Scholar
  15. Liu, N., and Y. Guo. 1994. Integrated Physical Geography, 102–103. Beijing: Science Press.Google Scholar
  16. Lussana, C., M.R. Salvati, U. Pellegrini, and F. Uboldi. 2009. Efficient high-resolution 3-D interpolation of meteorological variables for operational use. Advances in Science and Research 3: 105–112.CrossRefGoogle Scholar
  17. Mather, P.M. 2003. The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing 24 (23): 4907–4938.CrossRefGoogle Scholar
  18. Nalder, I.A., and R.W. Wein. 1998. Spatial interpolation of climate normals: Test of a new method in the Canadian boreal forest. Agricultural and Forest Meteorology 92 (4): 211–225.CrossRefGoogle Scholar
  19. Omernik, J.M. 1995. Ecoregions: A spatial framework for environmental management. In Biological Assessment and Criteria, ed. W.S. Davis and T.P. Simon. Boca Raton/London/Tokyo: Lewis Publishers.Google Scholar
  20. ———. 1987. Ecoregions of the conterminous United States. Annals of the Association of American Geographers 77: 118–125.CrossRefGoogle Scholar
  21. Pan, Y., D. Gong, L. Deng, et al. 2004. Smart distance searching-based and DEM-informed interpolation of surface air temperature in China. Acta Geographica Sinica 59 (3): 366–374.Google Scholar
  22. Pio, C.A., T.V. Nunes, C.S. Borrego, and J. Martins. 1989. Assessment of air pollution sources in an industrial atmosphere using principal component and multilinear regression analysis. Science of the Total Environment. 80 (2–3): 279–292.CrossRefGoogle Scholar
  23. Price, D.T., D.W. Mckenney, I.A. Nalder, et al. 2000. A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agricultural and Forest Meteorology 101 (2): 81–94.CrossRefGoogle Scholar
  24. Rahmani, M., and G.K. Atia. 2017. Coherence pursuit: Fast, simple, and robust principal component analysis. IEEE Transactions on Signal Processing 65 (23): 6260–6275.CrossRefGoogle Scholar
  25. Robeson, M.S. 1994. Influence of spatial sampling and interpolation on estimates of air temperature change. Climate Research 4 (2): 119–126.CrossRefGoogle Scholar
  26. Smith, M. 1992. Expert Consultation on Revision of FAO Methodologies for Crop Water Requirements. Rome: Land and Water Development Division, Food and Agriculture Organisation.Google Scholar
  27. Song, X., Z. Duan, and X. Jiang. 2012. Comparison of artificial neural networks and support vector machine classifiers for land cover classification in northern china using a SPOT-5 HRG image. International Journal of Remote Sensing 33 (10): 3301–3320.CrossRefGoogle Scholar
  28. Three-river Development and Construction Committee of Tibet. 1997. Eco-environmental Planning for Integrated Development in the central Tibet. 1997.4.Google Scholar
  29. Uboldi, F., C. Lussana, and M. Salvati. 2010. Three-dimensional spatial interpolation of surface meteorological observations from high-resolution local networks. Meteorological Applications 15 (3): 331–345.CrossRefGoogle Scholar
  30. You, X. 1996. Experimental comparisons among several numerical interpolation methods. Meteorological Monthly 22 (4): 3–7.Google Scholar
  31. Yue, W., J. Xu, and L. Xu. 2005. A study on spatial interpolation methods for climate variables based on geostatistics. Plateau Meteorology 24 (6): 974–980.Google Scholar
  32. Zhang, Z., and D. Chu. 1998. Integrated Environmental Assessment in the Central Tibet using Remote Sensing and GIS. Beijing: Yuhang Press.Google Scholar
  33. Zhou, C., J. Luo, X. Yang, et al. 1999. Remote Sensing Imaging and Analysis, 228–238. Beijing: Science Press.Google Scholar
  34. Zhu, L.P., M.P. Xie, and Y.H. Wu. 2010. Quantitative analysis of lake area variations and the influence factors from 1971 to 2004 in the Namtso Basin of the Tibetan Plateau. Chinese Science Bulletin 55: 1294–1303.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Duo Chu
    • 1
  1. 1.Tibet Institute of Plateau Atmospheric and Environmental SciencesTibet Meteorological BureauLhasaChina

Personalised recommendations