AMBIO

, Volume 40, Issue 7, pp 807–818 | Cite as

Remote Sensing Change Detection and Process Analysis of Long-Term Land Use Change and Human Impacts

Report

Abstract

This study investigates environmental change over a 30-year period and attempts to gain a better understanding of human impacts on an arid environment and their consequences for regional development. Multi-temporal remotely sensed imagery was acquired and integrated to establish the basis for change detection and process analysis. Land cover changes were investigated in two categories, namely categorical change using image classification and quantitative change using a vegetation index. The results show that human-induced land cover changes have been minor in this remote area. However, the pace of growth of human-induced change has been accelerating since the early 1990s. The analysis of the multi-temporal vegetation index also shows no overall trend of rangeland deterioration, although local change of vegetation cover caused by human activities was noticeable. The results suggest that the current trend of rapid growth may not be sustainable and that the implementation of effective counter-measures for environmentally sound development is a rather urgent matter.

Keywords

Remote sensing Land use change Change detection Human impact assessment Arid zone 

Notes

Acknowledgments

The research is supported by a National Key Basic Research and Development Program Grant (2006CB701305), a Hong Kong Research Grants Council General Research Fund Grant (HKBU 202907), and a Hong Kong Baptist University Faculty Research Grant (FRG/06-07/II-76). The authors would also like to thank Prof. Bernie Owen of Hong Kong Baptist University for editing and improving the text of the manuscript. The constructive comments and suggestions from anonymous reviewers are also highly appreciated.

References

  1. Chen, X. 2002. Using remote sensing and GIS to analyze land cover change and its impacts on regional sustainable development. International Journal of Remote Sensing 23(1): 107–124.CrossRefGoogle Scholar
  2. Chen, X., J. Yan, Z. Chen, G. Luo, Q. Song, and W. Xu. 2009. A spatial geostatistical analysis of impact of land use development on groundwater resources in the Sangong Oasis Region using remote sensing imagery and data. Journal of Arid Land 1(1): 1–8.Google Scholar
  3. Eckhardt, D.W., J.P. Verdin, and G.R. Lyford. 1990. Automated update of an irrigated lands GIS using SPOT HRV imagery. Photogrammetric Engineering and Remote Sensing 56(11): 1515–1522.Google Scholar
  4. Hall, F.G., D.E. Strebel, J.E. Nickeson, and S.J. Goetz. 1991. Radiometric reflection: toward a common radiometric response among multidate multisensor images. Remote Sensing of Environment 35: 11–27.CrossRefGoogle Scholar
  5. Han, L., H. Wang, and X. Cao. 2001. Situation, formation and measures of land desertification in Tarim Watershed. Journal of Arid Land Resources and Environment 15(2): 16–21. (in Chinese).Google Scholar
  6. Henebry, G.M., and H. Su. 1993. Using landscape trajectories to assess the effects of radiometric rectification. International Journal of Remote Sensing 14(12): 2417–2423.CrossRefGoogle Scholar
  7. Herrmann, S.M., A. Anyamba, and C.J. Tucker. 2005. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Global Environmental Change: 15: 394–404.CrossRefGoogle Scholar
  8. Jensen, J.R., K. Rutchey, M.S. Koch, and S. Narumalani. 1995. Inland wetland change detection in the Everglades Water Conservation Area 2A using a time series of normalized remotely sensed data. Photogrammetric Engineering and Remote Sensing 61(2): 199–209.Google Scholar
  9. Jiao, F., X. Zhang, H. Wei, and Y. Zhao. 2000. Discussion on some questions of soil and water loss in Xinjiang. Arid Zone Research 17(1): 49–53. (in Chinese).Google Scholar
  10. Lambin, E.F., and D. Ehrlich. 1997. Land-cover changes in sub-Saharan Africa, 1982–1991: Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale. Remote Sensing of Environment 61: 181–200.CrossRefGoogle Scholar
  11. Larsson, H. 2002. Analysis of variations in land cover between 1972 and 1990, Kassala Province, Eastern Sudan, using Landsat MSS data. International Journal of Remote Sensing 23(2): 325–333.CrossRefGoogle Scholar
  12. Li, B., and Q. Zhou. 2009a. Accuracy assessment on multi-temporal land cover change detection using a trajectory error matrix. International Journal of Remote Sensing 30(5): 1283–1296.CrossRefGoogle Scholar
  13. Li, B., and Q. Zhou. 2009b. Spatial pattern of land cover change in China’s semiarid environment. Journal of Arid Land 1(1): 16–25.Google Scholar
  14. Maldonado, F.D., J.R. dos Santos, and V.C. de Carvalho. 2002. Land use dynamics in the semi-arid region of Brazil (Quixaba, PE): Characterization by principal component analysis (PCA). International Journal of Remote Sensing 23(23): 5005–5013.CrossRefGoogle Scholar
  15. Mao, D. 1998. The water resources, environment and management of Tarim River Watershed. Beijing: China Environmental Science Press (in Chinese).Google Scholar
  16. Michener, W.K., and P.F. Houhoulis. 1997. Detection of vegetation changes associated with extensive flooding in a forested ecosystem. Photogrammetric Engineering & Remote Sensing 63(12): 173–181.Google Scholar
  17. Miller, A.B., E.S. Bryant, and R.W. Birnie. 1998. An analysis of land cover changes in the northern forest of New England using Landsat MSS data. International Journal of Remote Sensing 19(19): 245–265.CrossRefGoogle Scholar
  18. Olsson, H. 1993. Regression functions for multi-temporal relative calibration of thematic mapper data over Boreal forest. Remote Sensing of Environment 46: 89–102.CrossRefGoogle Scholar
  19. Olsson, L., L. Eklundh, and J. Ardö. 2005. A recent greening of the Sahel—trends, patterns and potential causes. Journal of Arid Environments 63: 556–566.CrossRefGoogle Scholar
  20. Petit, C., T. Scudder, and E. Lambin. 2001. Quantifying processes of land-cover change by remote sensing: resettlement and rapid land-cover changes in south-eastern Zambia. International Journal of Remote Sensing 22(11): 3435–3456.CrossRefGoogle Scholar
  21. Schott, J.R., C. Salvaggio, and W.J. Volchok. 1988. Radiometric scene normalization using psedoinvariant features. Remote Sensing of Environment 26: 1–16.CrossRefGoogle Scholar
  22. Tian, C., Y. Song, and M. Hu. 1999. The current situation, causes and combating on desertification in Xinjang. Journal of Desert Research 19(3): 214–217. (in Chinese).Google Scholar
  23. Wang, J. 1999. Analysis on Yuli county land development affecting ecological environment in middle and lower reaches of Tarim River and protective countermeasures. Environmental Protection of Xinjiang 21(2): 43–45. (in Chinese).Google Scholar
  24. Wang, S. 1996. Influence of agricultural activities on environmental evolution in the areas along Xinjiang section of the new Eurasian Continental Bridge. Arid Land Geography 19(1): 4–9.Google Scholar
  25. Xia, X., and H.E. Dregne. 1995. The past, present and future of desert. Proceedings of the scientific conference on the Taklimakan Desert, Arid Zone, supplement (in Chinese).Google Scholar
  26. Xinjiang Statistics Bureau. (1988, 1992–2000), Xinjiang statistical yearbook, Beijing: China Statistics Press.Google Scholar
  27. Yuli County Chorography Committee. (1993). Yuli County chorography, Urumqi: Xijiang University Press.Google Scholar
  28. China Investigation Group of National Statistics Bureau for Rural Economic (1993–2000) China rural statistics yearbook. Beijing: China Statistics Press.Google Scholar
  29. Yimit, H., T. Tiyip, and H. Xiong. 2000. Analysis on annual variation and seasonal change of runoff from water resources utilization in the interior rivers—the case of Tarim River. Geographical Research 19(3): 271–276. (in Chinese).Google Scholar
  30. Yang, X., and C.P. Lo. 2000. Relative radiometric normalization performance for change detection from multi-data satellite images. Photogrammetric Engineering and Remote Sensing 66(8): 967–980.Google Scholar
  31. Yang, X., and C.P. Lo. 2002. Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing 23(9): 1775–1798.CrossRefGoogle Scholar
  32. Zhang, Q., J. Wang, X. Peng, P. Gong, and P. Shi. 2002. Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data. International Journal of Remote Sensing 23(15): 3057–3078.CrossRefGoogle Scholar
  33. Zhou, Q. 1998. Use of GIS technology for land resource inventories and modelling for sustainable regional development. AMBIO 27(6): 444–450.Google Scholar
  34. Zhou, Q., M. Robson, and P. Pilesjö. 1998. On the ground estimation of vegetation cover in Australian rangelands. International Journal of Remote Sensing 19(9): 1815–1820.CrossRefGoogle Scholar
  35. Zhou, Q., and M. Robson. 2001. Automated rangeland vegetation cover and density estimation using ground digital images and a spectral-contextual classifier. International Journal of Remote Sensing 22(17): 3457–3470.CrossRefGoogle Scholar
  36. Zhou, Q., B. Li, and A. Kurban. 2008a. Trajectory analysis of land cover change in arid environment of China. International Journal of Remote Sensing 29(4): 1093–1107.CrossRefGoogle Scholar
  37. Zhou, Q., B. Li, and A. Kurban. 2008b. Spatial pattern analysis of land cover change trajectories in Tarim Basin, northwest China. International Journal of Remote Sensing 29(19): 5495–5509.CrossRefGoogle Scholar

Copyright information

© Royal Swedish Academy of Sciences 2011

Authors and Affiliations

  1. 1.Department of Geography, Centre for Geo-computation StudiesHong Kong Baptist UniversityKowloon TongHong Kong
  2. 2.State Key Laboratory of Environment and Resources Information System, Institute of Geographical Sciences and Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.School of Resource and Environment ScienceWuhan UniversityHubeiPeople’s Republic of China

Personalised recommendations