Applying the change vector analysis technique to assess the desertification risk in the south-west of Romania in the period 1984–2011



The desertification risk affects around 40% of the agricultural land in various regions of Romania. The purpose of this study is to analyse the risk of desertification in the south-west of Romania in the period 19842011 using the change vector analysis (CVA) technique and Landsat thematic mapper (TM) satellite images. CVA was applied to combinations of normalised difference vegetation index (NDVI)-albedo, NDVI-bare soil index (BI) and tasselled cap greenness (TCG)-tasselled cap brightness (TCB). The combination NDVI-albedo proved to be the best in assessing the desertification risk, with an overall accuracy of 87.67%, identifying a desertification risk on 25.16% of the studied period. The classification of the maps was performed for the following classes: desertification risk, re-growing and persistence. Four degrees of desertification risk and re-growing were used: low, medium, high and extreme. Using the combination NDVI-albedo, 0.53% of the analysed surface was assessed as having an extreme degree of desertification risk, 3.93% a high degree, 8.72% a medium degree and 11.98% a low degree. The driving forces behind the risk of desertification are both anthropogenic and climatic causes. The anthropogenic causes include the destruction of the irrigation system, deforestation, the destruction of the forest shelterbelts, the fragmentation of agricultural land and its inefficient management. Climatic causes refer to increase of temperatures, frequent and prolonged droughts and decline of the amount of precipitation.


Risk desertification Landsat Albedo NDVI BI 



We would like to thank the USGS website for the Landsat imagery and Dr. Raluca Sinu and Claudia Ciubancan for language assistance. Also, the author would like to thank the two anonymous reviewers for their constructive observations and comments.


  1. Ahmed, M. A., & Ahmad, W. (2013). Barren land index to assessment land use-land cover changes in Himreen Lake and surrounding area east of Iraq. Journal of Environment and Earth Science, 3(4), 15–26.Google Scholar
  2. Bălteanu, D., Dragotă, C. S., Popovici, A., Dumitraşcu, M., Kucsicsa, G., & Grigorescu, I. (2013). Land use and crop dynamics related to climate change signals during the post-communist period in south Oltenia, Romania. Proceedings of the Romanian Academy, Series B, 15(3), 265–278.Google Scholar
  3. Becerril-Piña, R., Mastachi-Loza, C. A., González-Sosa, E., Díaz-Delgado, C., & Bâ, K. M. (2015). Assessing desertification risk in the semi-arid highlands of central Mexico. Journal of Arid Environments, 120, 4–13.CrossRefGoogle Scholar
  4. Becerril-Piña, R., Díaz-Delgado, C., Mastachi-Loza, C. A., & González-Sosa, E. (2016). Integration of remote sensing techniques for monitoring desertification in Mexico. Human and Ecological Risk Assessment: An International Journal, 22(6), 1323–1340.CrossRefGoogle Scholar
  5. Chen, S., & Rao, P. (2008). Land degradation monitoring using multi-temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China. International Journal of Remote Sensing, 29(7), 2055–2073.CrossRefGoogle Scholar
  6. Cohen, W. B., Fiorella, M., Gray, J., Helmer, E., & Anderson, K. (1998). An efficient and accurate method for mapping forest clear-cuts in the Pacific Northwest using Landsat imagery. Photogrammetry Engineering and Remote Sensing, 64(4), 293–300.Google Scholar
  7. Dawelbait, M., & Morari, M. (2011). LANDSAT, spectral mixture analysis and change vector analysis to monitor land cover degradation in a savanna region in Sudan (1987-1999-2008). International Journal of Water Resources and Arid Environments, 1(5), 366–377.Google Scholar
  8. Dumitraşcu, M., Grigorescu, I., Cuculici, R., Dumitraşcu, C., Năstase, M., & Geacu, S. (2014). Assessing long-term changes in forest cover in the South West Development Region. Romania. Geographical Forum. Geographical Studies and Environment Protection Research, XIII(1), 76–85.Google Scholar
  9. Eldvige, C. D., Yuan, D., Weerackoon, R. D., & Lunetta, R. S. (1995). Relative radiometric normalisation of Landsat Multispectral Scanner (MSS) data using an automatic scattergram-controlled regression. Photogrammetric Engineering and Remote Sensing, 61(10), 1255–1260.Google Scholar
  10. Escadafal, R., & Huete, A. (1991). Improvement in remote sensing of low vegetation cover in arid regions by correcting vegetation indices for soil “noise”. Comptes Rendus – Academie des Sciences, Serie II, 312(11), 1385–1391.Google Scholar
  11. Flores, E. S., & Yool, S. R. (2007). Sensitivity of change vector analysis to land cover change in an arid ecosystem. International Journal of Remote Sensing, 28(5), 1069–1088.CrossRefGoogle Scholar
  12. Greşiţă, C. I. (2011). Expert system used for monitoring the behaviour of hydrotechnical constructions. REVCAD-Journal of Geodesy and Cadastre, 11, 75–84.Google Scholar
  13. Greşiţă, C. I. (2013). Surveying methods to studying the behaviour of dams. Iasi: Tehnopress Publishing House (in Romanian).Google Scholar
  14. Huber, V. (2008). Researches regarding the parametric objective approach of the climatic year structure in hilly and mountainous regions, in the frame of climatic changes. In: Proceedings of the international conference Sustainable Forestry in a Changing Environment, Vol. I, Bucharest, Romania, pp. 21–27.Google Scholar
  15. Idrisi Kilimanjaro Tutorial. (2003). Clark University, USA.Google Scholar
  16. Jafari, R. (2007). Arid land condition assessment and monitoring using multispectral and hyperspectral imagery. PhD Thesis, The University of Adelaide: Adelaide, 141 p.Google Scholar
  17. Jensen, J. R. (2007). Introductory to digital image processing: A remote sensing perspective. New Jersey: Prentice Hall PTR Upper Saddle.Google Scholar
  18. John, R., Chen, J. Q., Lu, N., & Wilske, B. (2009). Land cover/land use change in semi-arid Inner Mongolia: 1992–2004. Environmental Research Letters, 4, 1–9.CrossRefGoogle Scholar
  19. Karnieli, A., Qin, Z., Wu, B., Panov, N., & Yan, F. (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, 9316–9339.CrossRefGoogle Scholar
  20. Khiry, M.A. (2007). Spectral mixture analysis for monitoring and mapping desertification processes in semi-arid areas in North Kordofan State, Sudan. PhD Thesis, Technische Universität: Dresden, 126 p.Google Scholar
  21. Kong, T.M. (2012). Understanding land management and desertification in the South African Kalahari with local knowledge and perspectives. PhD Thesis, The University of Arizona: Arizona, 228 p.Google Scholar
  22. Lambin, E. F., & Strahler, A. H. (1994). Indicators of land cover change for change vector analysis in multitemporal space at coarse spatial scales. International Journal of Remote Sensing, 15(10), 2099–2119.CrossRefGoogle Scholar
  23. Lamchin, M., Lee, W. K., Jeon, S. W., Lee, J. Y., Song, C., Piao, D., et al. (2017). Correlation between desertification and environmental variables using remote sensing techniques in Hogno Khaan, Mongolia. Sustainability, 9, 519.CrossRefGoogle Scholar
  24. Li, J., Lewis, J., Rowland, J., Tappan, G., & Tieszen, L. L. (2004). Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series. Journal of Arid Environments, 59(3), 463–480.CrossRefGoogle Scholar
  25. Lillesand, T. M., & Kiefer, R. W. (2000). Remote sensing and image interpretation (4th ed.). New York: Wiley.Google Scholar
  26. Ma, Z., Xie, Y., Jiao, J., Li, L., & Wang, X. (2011). The construction and application of an Aledo-NDVI based desertification monitoring model. Procedia Environmental Sciences, 10(C), 2029–2035.CrossRefGoogle Scholar
  27. Marinică, I. (2006). Adverse climatic risk in Oltenia. Craiova: MJM Publisher.Google Scholar
  28. Marinică, I., & Marinică, A. F. (2014). Consideration on desertification phenomenon in Oltenia. Geographical Forum. Geographical Studies and Environment Protection Research, XIII(2), 136–147.Google Scholar
  29. Mason, J. A., Swinehart, J. B., Lu, H. Y., Miao, X. D., Cha, P. E., & Zhou, Y. L. (2008). Limited change in dune mobility in response to a large decrease in wind power in semi-arid northern China since the 1970s. Geomorphology, 102, 351–363.CrossRefGoogle Scholar
  30. National Institute of Statistics. (2015). Accessed 15 January 2016.
  31. Otiman, P. I. (2012). Romania’s present agrarian structure a great (and unsolved) social and economic problem of our country. Economics and Rural Development, VIII(1), 3–23.Google Scholar
  32. Parmenter, A. P., Hansen, A., Kennedy, R., Cohen, W., Langner, U., Lawrence, R., et al. (2003). Land use and land cover change in the Greater Yellowstone Ecosystem: 1975–95. Ecological Applications, 13(3), 687–703.CrossRefGoogle Scholar
  33. Pedology and Agrochemistry Research Institute. (2013). The soils map in electronic format 1:200,000. Accessed 20 November 2015.
  34. Popa, B. (2003). Ecological reconstruction methods and procedures applied in Covurlui Plateau forest. Forest Magazine, 118(2), 13–17.Google Scholar
  35. Popa, B., & Nita, M. D. (2013). Overview of forestland investments opportunities in the context of forest restitution process in Romania. Studia Universitatis Vasile Goldis Arad, Engineering Sciences and Agrotourism Series, 8(2), 7–12.Google Scholar
  36. Prăvălie, R., Peptenatu, D., & Sîrodoev, I. (2013). The impact of climate change on the dynamics of agricultural systems in south-western Romania. Carpathian Journal of Earth and Environmental Sciences, 8(3), 175–186.Google Scholar
  37. Prăvălie, R., Sîrodoev, I., & Peptenatu, D. (2014). Changes in the forest ecosystems in areas impacted by aridisation in south-western Romania. Journal of Environmental Health Science and Engineering, 12(2), 1–15.Google Scholar
  38. Purkis, S. J., & Klemas, V. V. (2011). Remote sensing and global environmental change. Oxford: Wiley-Blackwell Ltd..CrossRefGoogle Scholar
  39. Qi, Y., Chang, Q., Jia, K., Liu, M., Liu, J., & Chen, T. (2011). Remote sensing-based temporal-spatial variability of desertification and driving forces in an agro-pastoral transitional zone of Northern Shaanxi Province, China. African Journal of Agricultural Research, 6, 1707–1716.Google Scholar
  40. Rouse, J.W., Haas, R.H., Schell, J.A. & Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings Third Earth Resources Technology Satellite-1 Symposium, Washinghton, USA, pp. 3010–3017.Google Scholar
  41. Runnstrom, M. C. (2003). Rangeland development of the Mu Us Sandy Land in semiarid China: An analysis using Landsat and NOAA remote sensing data. Land Degradation & Development, 14, 189–202.CrossRefGoogle Scholar
  42. Sandu, I., Pescaru, V., Poiana, I., Geicu, A., Cândea, I., & Ţâştea, D. (2008). Climate of Romania. Bucharest: Romanian Academy Press.Google Scholar
  43. Siwe, R. N., & Koch, B. (2008). Change vector analysis to categorise land cover change processes using the tasselled cap as a biophysical indicator. Environmental Monitoring and Assessment, 145, 227–235.CrossRefGoogle Scholar
  44. Teresneu, C. C. (2012). Automatic data processing of geodetic data. Brasov: Transilvania University Press (in Romanian).Google Scholar
  45. UNEP. (1992). World atlas of desertification. London: Edward Arnold.Google Scholar
  46. Wessels, K. J., Prince, S. D., Frost, P. E., & van Zyl, D. (2004). Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series. Remote Sensing of Environment, 91, 47–67.CrossRefGoogle Scholar
  47. WorldClim - global climate data (2015). Accessed 20 November 2015.
  48. Xu, D. Y., Kang, X. W., Qiu, D. S., Zhuang, D. F., & Pan, J. J. (2009). Quantitative assessment of desertification using Landsat data on a regional scale—a case study in the Ordos Plateau, China. Sensors, 9, 1738–1753.CrossRefGoogle Scholar
  49. Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker, C. J. (2014). The use of the Normalized Difference Vegetation Index (NDVI) to assess land degradation at multiple scales: a review of the current status, future trends, and practical considerations. Sweden: Lund University Centre Report for Sustainability Studies.Google Scholar
  50. Zaady, E., Karnieli, A., & Shachak, M. (2007). Applying a field spectroscopy technique for assessing successional trends of biological soil crusts in a semi-arid environment. Journal of Arid Environments, 70, 463–477.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Forest Engineering, Forest Management Planning and Terrestrial Measurements Department, Faculty of Silviculture and Forest EngineeringTransilvania University of BrasovBrasovRomania

Personalised recommendations