Advertisement

Journal of Mountain Science

, Volume 9, Issue 3, pp 362–371 | Cite as

Topographic influence on wetland distribution and change in Maduo County, Qinghai-Tibet Plateau, China

  • Jay GaoEmail author
  • Xilai Li
  • Gary Brierley
Article

Abstract

Accurate information on the spatial distribution and temporal change of wetlands is vital to devise effective measures for their protection. This study uses satellite images in 1994 and 2001 to assess the effects of topography and proximity to channels on wetland change in Maduo County on the Qinghai-Tibet Plateau, western China. In 1994 wetlands in the study area extended over 6,780.0 km2. They were distributed widely throughout the county, with a higher concentration in the south, and were especially prominent close to streams. The pattern of wetlands demonstrated a bell-shaped distribution curve with elevation, ranging over hill slopes with gradients from 0–19°, the commonest gradient being around 3°. Although the aspects of these hill slopes range over all directions, there is a lower concentration of wetlands facing east and southeast. The extent of wetlands in 2001 decreased to 6,181.1 km2. Marked spatial differentiation in the pattern of wetlands is evident, as their area increased by 1,193.3 km2 at lower elevations but decreased by 1,792.2 km2 at higher ground, resulting in a net decrease of 598.8 km2. In areas with a gradient <2° or >9° the area of wetlands remained approximately consistent from 1994–2001. Newly retained wetlands are situated in relatively flat lowland areas, with no evident preference in terms of aspect. Wetlands on north-, east- and northeast-facing hillslopes with a bearing of 1–86° were more prone to loss of area than other orientations. The altered pattern of wetland distribution from higher to lower elevation on north-facing slopes coincided with the doubling of annual temperature during the same period, suggesting that climate warming could be an important cause.

Keywords

Wetland change detection Topographic influence Remote Sensing GIS Qinghai-Tibet Plateau 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Butera MK (1983) Remote sensing of wetlands. IEEE Transactions on Geoscience and Remote Sensing GE-21(3): 383–392.CrossRefGoogle Scholar
  2. Brivio PA, Zilioli E (1996) Assessing wetland changes in the Venice lagoon by means of satellite remote sensing data. Journal of Coastal Conservation 2: 23–32.CrossRefGoogle Scholar
  3. Bwangoy JB, Hansen MC, Roy DP, Grandi GD, Justice CO (2010) Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices. Remote Sensing of Environment 114(1): 73–86.CrossRefGoogle Scholar
  4. Cai D, Guo N (2008) Dynamic monitoring of wetland in Maqu by means of remote sensing. IEEE International Geoscience and Remote Sensing Symposium, 2007. pp 4603–4605.Google Scholar
  5. Chopra R, Verma VK, Sharma PK (2001) Mapping, monitoring and conservation of Harike wetland ecosytem, Punjab, India, through remote sensing. International Journal of Remote Sensing 22(1): 89–98.CrossRefGoogle Scholar
  6. Feng QS, Shang ZH, Liang TG, Long RJ (2008). Remote sensing monitoring and dynamic change of marsh wetlands in Maqu county, the first turning area of Yellow River. Wetland Science 6(3): 379–385 (in Chinese with English abstract).Google Scholar
  7. Gong P, Niu Z, Cheng X, Zhao KY, Zhou D, Guo J, Liang L, Wang X, Li D, Huang H, Wang Y, Wang K, Li W, Wang X, Ying Q, Yang Z, Ye Y, Li Z, Zhuang D, Chi Y, Zhou H, Yan J (2010) China’s wetland change (1990–2000) determined by remote sensing. Science China Earth Sciences 53(7): 1036–1042.CrossRefGoogle Scholar
  8. Haack B (1996) Monitoring wetland changes with remote sensing: an East African example. Environmental Management 20(3): 411–419.CrossRefGoogle Scholar
  9. Jensen JR, Hodgson ME, Christensen E, Mackey Jr HE, Tinney LR, Sharitz R (1986) Remote sensing inland wetlands: a multispectral approach. Photogrammetric Engineering & Remote Sensing 52(1): 87–100.Google Scholar
  10. Jensen JR, Rutchey K, Koch MS, Narumalani S (1995) Inland wetland change detection in the Everglades Water Conservation Area 2A using a time series of normalized remotely sensed data. Photogrammetric Engineering & Remote Sensing 61(2): 199–209.Google Scholar
  11. Johnston RM, Barson MM (1993) Remote sensing of Australian wetlands: an evaluation of Landsat TM data for inventory and classification. Australian Journal of Marine and Freshwater Resources 44(2): 235–252.CrossRefGoogle Scholar
  12. Munyati C (2000) Wetland change detection on the Kafue Flats, Zambia, by classification of a multitemporal remote sensing image dataset. International Journal of Remote Sensing 21(9): 1787–1806.CrossRefGoogle Scholar
  13. Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetlands Ecology and Management 10(5): 381–402.CrossRefGoogle Scholar
  14. Pan JH, Wang J, Wang JH (2007) Dynamic change of frigid wetlands in source region of the Yangtze and Yellow Rivers. Wetland Science 5(4): 298–304 (In Chinese with English abstract).Google Scholar
  15. Pantaleoni E, Wynne RH, Galbraith JM, Campbell JB (2009) Mapping wetlands using ASTER data: A comparison between classification trees and logistic regression. International Journal of Remote Sensing 30(13): 3423–3440.CrossRefGoogle Scholar
  16. Qi DC, Li GY (2007) Status, causes and protection countermeasures of wetland degradation in Maqu county in the Upper Yellow River. Wetland Science 5(4): 341–347.Google Scholar
  17. Rebelo LM, Finlayson CM, Nagabhatla N (2009) Remote sensing and GIS for wetland inventory, mapping and change analysis. Journal of Environmental Management 90(7): 2144–2153.CrossRefGoogle Scholar
  18. Rundquist DC, Narumalani S, Narayanan RM (2001) A review of wetlands remote sensing and defining new considerations. Remote Sensing Reviews 20(3): 207–226.CrossRefGoogle Scholar
  19. Schmid T, Koch M, Gumuzzio J, Mather PM (2004) A spectral library for a semi-arid wetland and its application to studies of wetland degradation using hyperspectral and multispectral data. International Journal of Remote Sensing 25(13): 2485–2496.CrossRefGoogle Scholar
  20. Teferi E, Uhlenbrook S, Bewket W, Wenninger J, Simane B (2010) The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia. Hydrology and Earth System Sciences Discussions 7(4): 6243–6284.CrossRefGoogle Scholar
  21. Wang G, Li Y, Wang Y, Chen L (2007) Typical alpine wetland system changes on the Qinghai-Tibet Plateau in recent 40 years. Acta Geographica Sinica 62(5): 481–491.Google Scholar
  22. Wright C, Gallant A (2007) Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment 107(4): 582–605.CrossRefGoogle Scholar
  23. Xing Y, Qiao Z, Jiang Q, Li W (2010) Analysis of the wetland environment change in Qinghai-Tibet plateau using remote sensing. 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010.Google Scholar
  24. Zhang S, Na X, Kong B, Wang Z, Jiang H, Yu H, Zhao Z, Li X, Liu C, Dale P (2009) Identifying wetland change in China’s Sanjiang Plain using remote sensing. Wetlands 29(1): 302–313.CrossRefGoogle Scholar
  25. Zhang SQ, Zhang SK, Zhang JY (2000) A study on wetland classification model of remote sensing in the Sangjiang Plain. Chinese Geographical Science 10(1): 68–73.CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of EnvironmentUniversity of AucklandAucklandNew Zealand
  2. 2.College of Agriculture and Animal HusbandryQinghai UniversityXiningChina

Personalised recommendations