Natural Hazards

, Volume 62, Issue 3, pp 1137–1153 | Cite as

Ranking desertification indicators using TOPSIS algorithm

Original Paper


Desertification is the result of natural and anthropogenic processes, leading to degradation or loss of the land’s productivity and complexity. To assess the desertification status, integrated set of indicators must be identified. Indicators must provide synthetic information on threshold levels, status and evolution of relevant physical, chemical, biological and anthropogenic processes. Multi-criteria decision-making (MCDM) is a collection of methodologies to compare, select, or rank multiple alternatives that involve incommensurate attributes. Technique for order preference by similarity to ideal solution (TOPSIS) method is a multiple criteria method to identify solutions from a finite set. TOPSIS is an algorithm for determining the most preferable choices among the possible indicators that can be developed. The aim of this paper is to introduce TOPSIS as a decision-making method for the selection and integration of desertification indicators. The simulation case study presented here is related to the selection of the best set of indicators to monitor land degradation by remote sensing in three different countries (Brazil, Mozambique and Portugal), within the framework defined by the DesertWatch Extension project.


MCDM TOPSIS Desertification Indicator system DesertWatch 



The data used were kindly provided by the DesertWatch Extension project, a project funded by the European Space Agency (ESA) and coordinated by Advanced Computer System (ACS Spa). Data have been collected by the implementing Consortium lead by Critical Software SA.

Authors would like to thank Prof. S. Madrau of the Sassari University for his assistance in the preparation of this paper.


  1. Armas R, Caetano M, Carrão H, Soares A, Pereira MJ, Gutierrez A, Rocha A, Pace G, Zucca C, del Barrio G, Paganini M (2010) Earth observation from space to support the unccd:the Desertwatch Extension project. Paper presented to the Living Planet Symposium, Bergen (Norway), July 1, 2010Google Scholar
  2. Brans JP, Mareschal B, Vincke Ph (1984) PROMETHEE: A new family of outranking methods in multi criteria analysis. Journal of Operational Research. 84:477–490Google Scholar
  3. Gentile AR (1998) From national monitoring to European reporting: the EEA framework for policy relevant environmental indicators. In: Enne G, d’Angelo M, Zanolla C (eds), Proceedings of the International Seminar on Indicators for Assessing Desertification in the Mediterranean, Porto Torres (Italy) pp 18–20, September, 16-26, 1998Google Scholar
  4. Hwang CL, Yoon K (1981) Multiple attributes decision making methods and applications. Springer, New YorkCrossRefGoogle Scholar
  5. Kosmas C, Kirkby M, Geeson N (1999) The MEDALUS project Mediterranean desertification and land use; Manual on key indicators of desertification and mapping environmentally sensitive areas to desertification. European Commission, BrusselsGoogle Scholar
  6. Niemeijer D, de Groot RS (2008) Framing environmental indicators: moving from causal chains to causal networks. Environ Dev Sustain 10:89–106CrossRefGoogle Scholar
  7. Olson DL (2001) Comparison of three multi criteria methods to predict know outcomes. Eur J Oper Res 130(3):576–587CrossRefGoogle Scholar
  8. Olson DL (2004) Comparison of weights in TOPSIS models. Mathematical and Computer Modeling. 40:721–727CrossRefGoogle Scholar
  9. Opricovic S (1998) Multi criteria Optimization of Civil Engineering Systems. Faculty of Civil Engineering, BelgradeGoogle Scholar
  10. Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455CrossRefGoogle Scholar
  11. Reed MS, Dougill AJ, Baker TR (2008) Participatory indicator development: what can ecologists and local communities learn from each other? Ecol Appl 18:1253–1269CrossRefGoogle Scholar
  12. Roy B (1990) The outranking approach and the foundations of ELECTRE methods. In: Bana e Costa CA (ed) Readings in multiple criteria decision aid. Springer, Berlin, pp 155–183CrossRefGoogle Scholar
  13. Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New YorkGoogle Scholar
  14. Shen Q, Jensen R (2007) Rough Sets. Their Extensions and Applications, International Journal of Automation and Computing 4(3):217–228Google Scholar
  15. Sommer S, Zucca C, Grainger A, Cherlet M, Zougmore R, Sokona Y, Hill J, Della Peruta R, Roehrig J, Wang G (2011) Application of indicator systems for monitoring and assessment of desertification from national to global scales. Land Degrad Dev 22:184–197CrossRefGoogle Scholar
  16. Thomas DSG (1997) Science and desertification debate. J Arid Environ 37:599–608CrossRefGoogle Scholar
  17. Triantaphyllou E, Lin C (1996) Development and Evaluation of Five Fuzzy Multi-Attribute Decision-Making Methods. Approximate Reasoning 14(4):281–310CrossRefGoogle Scholar
  18. UNCCD (1994) United Nations convention to combat desertification in countries experiencing serious drought and/or desertification, particularly in Africa. A/AC.241/27, ParisGoogle Scholar
  19. Zeleny M (1982) Multiple Criteria Decision Making. McGraw-Hill, New YorkGoogle Scholar
  20. Zucca C, Previtali F, Enne G (2004) Ongoing research and concentration activities on desertification in Northern Mediterranean Countries (UNCCD Annex IV). In: Zdruli P, Steduto P, Kapur S, Akca E (eds), Proceedings of the International Seminar “Ecosystem-based assessment of soil degradation to facilitate land users’ and land owners’ prompt actions”, Adana, Turkey, 2–7 June 2003. MEDCOASTLAND publication 1. IAM Bari, Italy. pp 315–327Google Scholar
  21. Zucca C, Della Peruta R, Salvia R, Cherlet M, Sommer S (2010) Evaluation and integration of baseline indicators for assessing and monitoring desertification. European Communities, EUR Report in press, ISSN 1018-5593, Luxembourg, Office for Official Publications of the European CommunitiesGoogle Scholar
  22. Zucca C, Della Peruta R, Salvia R, Sommer S, Cherlet M (2012) Towards a World Desertification Atlas. Relating and selecting indicators and datasets to represent complex issues. Ecol Ind 15:157–170CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Assistant Professor at the Faculty of Natural Resources and EnvironmentFerdowsi University of Mashhad (FUM)MashhadIran
  2. 2.Department of Territorial Engineering, NRD (Nucleo Ricerca Desertificazione)University of SassariSardiniaItaly

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