Journal of Arid Land

, Volume 10, Issue 1, pp 12–26 | Cite as

Monitoring desertification processes in Mongolian Plateau using MODIS tasseled cap transformation and TGSI time series

  • Qingsheng LiuEmail author
  • Gaohuan Liu
  • Chong Huang


Most remote sensing studies assess the desertification using vegetation monitoring method. But it has the insufficient precision of vegetation monitoring for the limited vegetation cover of the desertification region. Therefore, it offers an alternative approach for the desertification research to assess sand dune and sandy land change using remote sensing in the desertification region. In this study, the indices derived from the well-known tasseled cap transformation (TCT), tasseled cap angle (TCA), disturbance index (DI), process indicator (PI), and topsoil grain size index (TGSI) were integrated to monitor and assess the desertification at the thirteen study sites including sand dunes and sandy lands distributed in the Mongolian Plateau (MP) from 2000 to 2015. A decision tree was used to classify the desertification on a regional scale. The average overall accuracy of 2000, 2005, 2010 and 2015 desertification classification was higher than 90%. Results from this study indicated that integration of the advantages of TCA, DI and TGSI could better assess the desertification. During the last 16 years, Badain Jaran Desert, Tengger Desert, and Ulan Buh Desert showed a relative stabilization. Otindag Sandy Land and the deserts of Khar Nuur, Ereen Nuur, Tsagan Nuur, Khongoryn Els, Hobq, and Mu Us showed a slow increasing of desertification, whereas Bayan Gobi, Horqin and Hulun Buir sandy lands showed a slow decreasing of desertification. Compared with the other 11 sites, the fine sand dunes occupied the majority of the Tengger Desert, and the coarse sandy land occupied the majority of the Horqin Sandy Land. Our findings on a three or four years’ periodical fluctuated changes in the desertification may possibly reflect changing precipitation and soil moisture in the MP. Further work to link the TCA, DI, TGSI, and PI values with the desertification characteristics is recommended to set the thresholds and improve the assessment accuracy with field investigation.


desertification MODIS desert sand dune sandy land Mongolian Plateau 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This research was jointly supported by the Innovation Project of State Key of Laboratory of Resources and Environmental Information System (O88RA20CYA), the National Natural Science Foundation of China (41671422), the International Cooperation in Science and Technology Special Project (2013DFA91700), and the National Science-Technology Support Plan Project (2013BAD05B03). The authors would like to thank Miss ZHANG Yunjie and Miss GUO Yushan for MODIS MCD43A4 data downloading and mosaicking.


  1. Albalawi E K, Kumar L. 2013. Using remote sensing technology to detect, model and map desertification: A review. Journal of Food, Agriculture & Environment, 11(2): 791–797.Google Scholar
  2. Baumann M, Ozdogan M, Wolter P T, et al. 2014. Landsat remote sensing of forest windfall disturbance. Remote Sensing of Environment, 143: 171–179.CrossRefGoogle Scholar
  3. Bremborg P. 1996. Desertification mapping of Horqin Sandy Land, Inner Mongolia, by means of remote sensing. MSc Thesis. Sweden: Lund University.Google Scholar
  4. Ci L J, Wu B. 1997. Climatic type division and the potential extent determination of desertification in China. Journal of Desert Research, 17(2): 107–111. (in Chinese)Google Scholar
  5. Collado A D, Chuvieco E, Camarasa A. 2002. Satellite remote sensing analysis to monitor desertification processes in the croprangeland boundary of Argentina. Journal of Arid Environments, 52(1): 121–133.CrossRefGoogle Scholar
  6. Cui L L, Fan W Y, Shi J, et al. 2006. Assessment of aeolian desertification in Korqin sand, China. In: Proceedings of SPIE Volume 6298, Remote Sensing and Modeling of Ecosystems for Sustainability III. San Diego, California, USA: SPIE, 62981L.Google Scholar
  7. Czerwinski C J, King D J, Mitchell S W. 2014. Mapping forest growth and decline in a temperate mixed forest using temporal trend analysis of Landsat imagery, 1987–2010. Remote Sensing of Environment, 141: 188–200.CrossRefGoogle Scholar
  8. Dorjsuren M, Liou Y A, Cheng C H. 2016. Time series MODIS and in situ data analysis for Mongolia Drought. Remote Sensing, 8(6): 509.CrossRefGoogle Scholar
  9. Duan H C, Wang T, Xue X, et al. 2014. Dynamics of Aeolian desertification and its driving forces in the Horqin Sandy Land, Northern China. Environmental Monitoring and Assessment, 186(10): 6083–6096.CrossRefGoogle Scholar
  10. Eckert S, Hüsler F, Liniger H, et al. 2015. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments, 113: 16–28.CrossRefGoogle Scholar
  11. Elhadi E M, Zomrawi N, Hu G D. 2009. Landscape change and sandy desertification monitoring and assessment. American Journal of Environmental Sciences, 5(5): 633–638.CrossRefGoogle Scholar
  12. El-Magd I A, Hassan O, Arafat S. 2013. Quantification of sand dune movements in the south western part of Egypt, using remotely sensed data and GIS. Journal of Geographic Information System, 5(5): 498–508.CrossRefGoogle Scholar
  13. Fang J Y, Bai Y F, Wu J G. 2015. Towards a better understanding of landscape patterns and ecosystem processes of the Mongolian Plateau. Landscape Ecology, 30(9): 1573–1578.CrossRefGoogle Scholar
  14. Gómez C, White J C, Wulder M A. 2011. Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation. Remote Sensing of Environment, 115(7): 1665–1679.CrossRefGoogle Scholar
  15. Guo J, Wang T, Xue X, et al. 2010. Monitoring Aeolian desertification process in Hulun Buir grassland during 1975–2006, Northern China. Environmental Monitoring and Assessment, 166(1–4): 563–571.CrossRefGoogle Scholar
  16. Hereher M E. 2010. Sand movement patterns in the Western Desert of Egypt: an environmental concern. Environmental Earth Sciences, 59(5): 1119–1127.CrossRefGoogle Scholar
  17. Hermas E, Leprince S, El-Magd I A. 2012. Retrieving sand dune movements using sub-pixel correlation of multi-temporal optical remote sensing imagery, northwest Sinai Peninsula, Egypt. Remote Sensing of Environment, 121: 51–60.CrossRefGoogle Scholar
  18. Hu Y M, Jiang Y, Chang Y, et al. 2002. The dynamic monitoring of Horqin sand land using remote sensing. Chinese Geographical Science, 12(3): 238–243.CrossRefGoogle Scholar
  19. Huang L. 2017. Spatial distribution of Agriophyllum squarrosum Moq. (Chenopodiaceae) in the straw checkerboards at a revegetated land of the Tengger Desert, northern China. Journal of Arid Land, 9(2): 176–187.CrossRefGoogle Scholar
  20. Huang S, Siegert F. 2006. Land cover classification optimized to detect areas at risk of desertification in North China based on SPOT VEGETATION imagery. Journal of Arid Environments, 67(2): 308–327.CrossRefGoogle Scholar
  21. Hugenholtz C H, Levin N, Barchyn T E, et al. 2012. Remote sensing and spatial analysis of Aeolian sand dunes: A review and outlook. Earth-Science Reviews, 111(1–4): 319–334.CrossRefGoogle Scholar
  22. Javzandulam T, Tateishi R, Sanjaa T. 2005. Analysis of vegetation indices for monitoring vegetation degradation in semi-arid and arid areas of Mongolia. International Journal of Environmental Studies, 62(2): 215–225.CrossRefGoogle Scholar
  23. Jin S M, Sader S A. 2005. Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment, 94(3): 364–372.CrossRefGoogle Scholar
  24. John R, Chen J Q, Lu N, et al. 2008. Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia. Remote Sensing of Environment, 112(5): 2018–2032.CrossRefGoogle Scholar
  25. Karnieli A, Qin Z H, Wu B, et al. 2014. Spatio-temporal dynamics of land-use and land-cover in the Mu Us Sandy Land, China, using the change vector analysis technique. Remote Sensing, 6(10): 9316–9339.CrossRefGoogle Scholar
  26. Kawamura K, Akiyama T. 2010. Simultaneous monitoring of livestock distribution and desertification. Global Environmental Research, 14: 29–36.Google Scholar
  27. Lam D K, Remmel T K, Drezner T D. 2010. Tracking desertification in California using remote sensing: a sand dune encroachment approach. Remote Sensing, 3(1), 1–13.CrossRefGoogle Scholar
  28. Lamchin M, Lee J Y, Lee W K, et al. 2016. Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia. Advances in Space Research, 57(1): 64–77.CrossRefGoogle Scholar
  29. Li E J. 2011. Comparison of characteristics of deposits of Badain Jaran Desert and Tengger Desert. PhD Dissertation. Xi’an: Shaanxi Normal University. (in Chinese)Google Scholar
  30. Liu Q S, Liu G H, Huang C, et al. 2016. Comparison of tasselled cap components of images from Landsat 51. Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus. Journal of Spatial Science, 61(2): 351–365.Google Scholar
  31. Lobser S E, Cohen W B. 2007. MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data. International Journal of Remote Sensing, 28(22): 5079–5101.CrossRefGoogle Scholar
  32. Lozano F J, Suárez-Seoane S, de Luis E. 2007. Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling. Remote Sensing of Environment, 107(4): 533–544.CrossRefGoogle Scholar
  33. Masek J G, Huang C Q, Wolfe R, et al. 2008. North American forest disturbance mapped from a decadal Landsat record. Remote Sensing of Environment, 112(6): 2914–2926.CrossRefGoogle Scholar
  34. Powell S L, Cohen W B, Healey S P, et al. 2010. Quantification of live aboveground forest biomass dynamics with Landsat timeseries and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment, 114(5): 1053–1068.CrossRefGoogle Scholar
  35. Shafie H, Hosseini S M, Amiri I. 2012. RS-based assessment of vegetation cover changes in Sistan Plain. International Journal of Forest, Soil and Erosion, 2(2): 97–100.Google Scholar
  36. Sternberg T, Tsolmon R, Middleton N, et al. 2011. Tracking desertification on the Mongolian steppe through NDVI and fieldsurvey data. International Journal of Digital Earth, 4(1): 50–64.CrossRefGoogle Scholar
  37. Sternberg T. 2012. Piospheres and pastoralists: vegetation and degradation in steppe grasslands. Human Ecology, 40(6): 811–820.CrossRefGoogle Scholar
  38. Sternberg T, Rueff H, Middleton N. 2015. Contraction of the Gobi Desert, 2000–2012. Remote Sensing, 7(2): 1346–1358.CrossRefGoogle Scholar
  39. UNCCD. 2016a. Is desertification a global problem? [2016–12–08]. Scholar
  40. UNCCD. 2016b. Combating desertification in Asia. 1. [2016–12–08]. Scholar
  41. Vova O, Kappas M, Renchin T, et al. 2015. Land degradation assessment in Gobi-Altai province. In: Proceeding of the Trans- Disciplinary Research Conference: Building Resilience of Mongolian Rangelands. Ulaanbaatar, Mongolia. =1&isAllowed=y.Google Scholar
  42. Wang X M, Cheng H, Li H, et al. 2017. Key driving forces of desertification in the Mu Us Desert, China. Scientific Reports, 7: 3933, doi: 10. 1038/s41598-017-04363-8.CrossRefGoogle Scholar
  43. Wu B, Ci L J. 2002. Landscape change and desertification development in the Mu Us Sandland, Northern China. Journal of Arid Environments, 503: 429–444.CrossRefGoogle Scholar
  44. Xiao J, Shen Y, Tateishi R, et al. 2006. Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. International Journal of Remote Sensing, 27(12): 2411–2422.CrossRefGoogle Scholar
  45. Xu D Y, Kang X W, Qiu D S, et al. 2009. Quantitative assessment of desertification using Landsat data on a regional scale–A case study in the Ordos Plateau, China. Sensors, 9(3): 1738–1753.CrossRefGoogle Scholar
  46. Yang X, Zhang K, Jia B, et al. 2005. Desertification assessment in China: An overview. Journal of Arid Environments, 63(2): 517–531.CrossRefGoogle Scholar
  47. Yang X P, Rost K T, Lehmkuhl F, et al. 2004. The evolution of dry lands in northern China and in the Republic of Mongolia since the last glacial maximum. Quaternary International, 118–119: 69–85.CrossRefGoogle Scholar
  48. Yao Z Y, Wang T, Han Z W, et al. 2007. Migration of sand dunes on the northern Alxa Plateau, Inner Mongolia, China. Journal of Arid Environments, 70(1): 80–93.CrossRefGoogle Scholar
  49. Yu H N, Lee J Y, Lee W K, et al. 2013. Feasibility of vegetation temperature condition index for monitoring desertification in Bulgan, Mongolia. Korean Journal of Remote Sensing, 29(6): 621–629.CrossRefGoogle Scholar
  50. Yu X N, Huang Y M, Li E G, et al. 2017. Effects of vegetation types on soil water dynamics during vegetation restoration in the Mu Us Sandy Land, northwestern China. Journal of Arid Land, 9(2): 188–199.CrossRefGoogle Scholar
  51. Zha Y, Gao J. 1997. Characteristics of desertification and its rehabilitation in China. Journal of Arid Environments, 37(3): 419–432.CrossRefGoogle Scholar
  52. Zhang G L, Dong J W, Xiao X M, et al. 2012. Effectiveness of ecological restoration projects in Horqin Sandy Land, China based on SPOT-VGT NDVI data. Ecological Engineering, 38(1): 20–29.CrossRefGoogle Scholar
  53. Zhang Y Z, Chen Z Y, Zhu B Q, et al. 2008. Land desertification monitoring and assessment in Yulin of Northwest China using remote sensing and geographic information systems (GIS). Environmental Monitoring and Assessment, 147(1–3): 327–337.CrossRefGoogle Scholar
  54. Zhao X, Hu H F, Shen H H, et al. 2015. Satellite-indicated long-term vegetation changes and their drivers on the Mongolian Plateau. Landscape Ecology, 30(9): 1599–1611.CrossRefGoogle Scholar

Copyright information

© Xinjiang Institute of Ecology and Geography, the Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina

Personalised recommendations