Environmental Earth Sciences

, Volume 59, Issue 6, pp 1327–1336 | Cite as

Validating the demarcation of eco-geographical regions: a geostatistical application

  • Jiangbo Gao
  • Shuangcheng LiEmail author
  • Zhiqiang Zhao
Original Article


Eco-geographical regional system is important for the study of global environmental changes and sustainable development, and it serves as a scientific basis for rationally managing and sustainably utilizing ecosystems and natural resources, such as constructing healthy eco-environments and making policies of environmental management. This paper explained the necessity of validation in the demarcation of eco-geographical regions, which is difficult and may be achieved with some assumptions and presumptions because of the existence of transition zones. Also in this paper, we explored the use of geostatistics in validating regions and boundaries using a case study in Qinghai-Tibet Plateau on the basis of normalized difference vegetation index (NDVI) data. The results show that: (1) eco-geographical regions have different spatial complexity and spatial heterogeneity [i.e. different characteristic values (nugget/sill, fractal) of NDVI], and regions with similar patterns of temperature and moisture have similar mean NDVI values and spatial characteristics [i.e. similar spatial characteristic values (nugget/sill, fractal) of NDVI]. Thus, based on the similarity of spatial heterogeneity or spatial patterns of distribution, demarcations of eco-geographical regions with similar conditions of temperature and moisture, such as IID1 (Ngari montane desert-steppe and desert zone), IID2 (Qaidam montane desert zone), and IID3 (Northern slopes of Kunlun montane desert zone), meet the regional validation requirement. (2) Based on the comparison of spatial heterogeneity or spatial patterns of distribution, the boundary between IIA/B1 (Western Sichuan–eastern Xizang montane coniferous forest zone) and IB1 (Golog–Nagqu high-cold shrub–meadow zone) meets the boundary validation requirement. This boundary guarantees high similarities in an intra-region and high differences in inter-regions, because the value of fractal dimension is the minimum in buffer 1. Furthermore, this paper discussed the application of geostatistics in the choice of index system for boundaries of eco-geographical regions and the determination of region size. The results indicate that the application of geostatistics in eco-geographical regional system is broad, and such researches can serve for obtaining more reasonable and applicable eco-geographical regionalization schemes.


Eco-geographical regions Regional validation Boundary validation Geostatistics Spatial heterogeneity Qinghai-Tibet Plateau 



The research is supported by the National Key Research Development Plan (2005CB422000) and National Natural Science Foundation of China (40771001).


  1. Boer MM, Puigdefábregas J (2003) Predicting potential vegetation index values as a reference for the assessment and monitoring of dryland condition. Int J Remote Sens 24(5):1135–1141CrossRefGoogle Scholar
  2. Burrough PA (1983) Multiscale sources of spatial variation in soil I. The application of fractal concepts to nested levels of soil variations. Eur J Soil Sci 34:577–597CrossRefGoogle Scholar
  3. Cheng YQ, Zhang PY (2006) Progress on eco-geographical regionalization researches. Acta Ecol Sin 26(10):3424–3433Google Scholar
  4. Chiles JP, Pierre D (1999) Geostatistics: modeling spatial uncertainty. Wiley, New YorkGoogle Scholar
  5. Cressie NAC (1993) Statistics for spatial data. Wiley, New YorkGoogle Scholar
  6. Curran PJ, Foody GM, van Gardingen PR (1997) Scaling-up. In: van Gardingen PR, Foody GM (eds) Scaling-up from cell to landscape. Society for Experimental Biology Seminar Series 63. Cambridge University Press, CambridgeGoogle Scholar
  7. Deutsch CV, Journel AG (1998) GSLIB, Geostatistical software library and user’s guide, 2nd edn. Oxford University Press, New YorkGoogle Scholar
  8. Fan J (2007) The scientific foundation of major function oriented zoning in China. Acta Geogr Sin 62(4):339–350Google Scholar
  9. Gobron N, Pinty B, Verstraete MM et al (2000) Development of spectral indices optimized for the vegetation instrument.. Vegetation 2000 symposium, Belgirate, ItalyGoogle Scholar
  10. Guo D, Mou P, Jones RH et al (2002) Temporal changes in spatial patterns of soil moisture following disturbance: an experimental approach. J Ecol 90(2):338–347CrossRefGoogle Scholar
  11. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  12. Holben BN (1986) Characteristics of maximum-value composite images for temporal AVHRR data. Int J Remote Sens 7(11):1435–1445CrossRefGoogle Scholar
  13. Irvin BJ, Ventura SJ, Slater BK (1997) Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin. Geoderma 77:137–154CrossRefGoogle Scholar
  14. Jarlan L, Mangiarotti S, Mougin E et al (2008) Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model. Remote Sens Environ 112:1381–1394CrossRefGoogle Scholar
  15. Kallimanis AS, Sgardelis SP, Halley JM (2002) Accuracy of fractal dimension estimates for small samples of ecological distributions. Landscape Ecol 17(3):281–297CrossRefGoogle Scholar
  16. Kumar D, Ahmed S, Krishnamurthy NS et al (2007) Reducing ambiguities in vertical electrical sounding interpretations: a geostatistical application. J Appl Geophys 62:16–32CrossRefGoogle Scholar
  17. Lasaponara R (2006) On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from SPOT/VEGETATION NDVI temporal series. Ecol Model 194:429–434CrossRefGoogle Scholar
  18. Li SC, Zhao ZQ, Gao JB (2008) Identifying eco-geographical boundary using spatial wavelet transform. Acta Ecol Sin 28(9):4313–4322Google Scholar
  19. Mandelbrot BB (1982) The fractal geometry of Nature. Freeman, San FranciscoGoogle Scholar
  20. Moore ID, Lewis A, Gallant JC (1993) Terrain attributes: estimation methods and scale effects. In: Jakeman AJ, Beck MB, Mcaleer MJ (eds) Modelling changes in environmental systems. Wiley, ChichesterGoogle Scholar
  21. Moukana JA, Koike K (2008) Geostatistical model for correlating declining groundwater levels with changes in land cover detected from analyses of satellite images. Comput Geosci 34(11):1527–1540CrossRefGoogle Scholar
  22. Nielsen DR, Wendroth O (2003) Spatial and temporal statistics: sampling field soils and their vegetation. Catena Verlag, ReiskirchenGoogle Scholar
  23. Overmars KP, De Koning GHJ, Veldkamp A (2003) Spatial autocorrelation in multi-scale land use models. Ecol Model 164:257–270CrossRefGoogle Scholar
  24. Palmer MW (1988) Fractal geometry: a tool for describing spatial patterns of plant communities. Plant Ecol 75:91–102CrossRefGoogle Scholar
  25. Phillips JD (1986) Measuring complexity of environmental gradients. Plant Ecol 64(22):95–102CrossRefGoogle Scholar
  26. Quteiro L, Aspero F, Ubeda X (2008) Geostatistical method to study spatial variability of soil cations after a prescribed fire and rainfall. Catena 74(3):310–320CrossRefGoogle Scholar
  27. Tarnavsky E, Garrigues S, Brown ME (2008) Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sens Environ 112:535–549CrossRefGoogle Scholar
  28. Wu SH, Zheng D (2000) New recognition on boundary between tropical and subtropical zone in the middle section of eco-geographical system. Acta Geogr Sin 55(6):689–697Google Scholar
  29. Wu SH, Zheng D (2001) Delineation of boundary between tropical/subtropical in the middle section for eco-geographical system of South China. J Geogr Sci 11(1):80–86CrossRefGoogle Scholar
  30. Wu SH, Yang QY, Zheng D (2003a) Comparative study on eco-geographical regional systems between China and USA. Acta Geogr Sin 58(5):686–694Google Scholar
  31. Wu SH, Yang QY, Zheng D (2003b) Delineation of eco-geographic regional system of China. J Geogr Sci 13(3):309–315CrossRefGoogle Scholar
  32. Yang X, Wang MX, Huang Y, Wang YS (2002) A one-compartment model to study soil carbon decomposition rate at equilibrium situation. Ecol Model 151:63–73CrossRefGoogle Scholar
  33. Zheng D (1996) The system of physic-geographical regions of the Qinghai-Xizang (Tibet) plateau. Sci China Ser D 39(4):410–417Google Scholar
  34. Zheng D (1998) A study on the regionality and regional differentiation of geography. Geogr res 17(1):4–9Google Scholar
  35. Zheng D (2008) Eco-geographical regional system in China. Commercial Press, BeijingGoogle Scholar
  36. Zheng D, Fu XF (1999) A preliminary study on issues of integrated geographical regionalization. Sci Geogr Sin 19(3):193–197Google Scholar
  37. Zheng D, Ge QS, Zhang XQ et al (2005) Regionalization in China: retrospect and prospect. Geogr res 24(3):330–344Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.College of Urban and Environmental SciencesPeking UniversityBeijingChina
  2. 2.The Key Laboratory for Environmental and Urban Sciences, Shenzhen Graduate SchoolPeking UniversityShenzhenChina

Personalised recommendations