, Volume 42, Issue 12, pp 2081–2097 | Cite as

Data mining algorithms for land cover change detection: a review

  • Sangram PanigrahiEmail author
  • Kesari Verma
  • Priyanka Tripathi


Land cover change detection has been a topic of active research in the remote sensing community. Due to enormous amount of data available from satellites, it has attracted the attention of data mining researchers to search a new direction for solution. The Terra Moderate Resolution Imaging Spectrometer (MODIS) vegetation index (EVI/NDVI) data products are used for land cover change detection. These data products are associated with various challenges such as seasonality of data, spatio-temporal correlation, missing values, poor quality measurement, high resolution and high dimensional data. The land cover change detection has often been performed by comparing two or more satellite snapshot images acquired on different dates. The image comparison techniques have a number of limitations. The data mining technique addresses many challenges such as missing value and poor quality measurements present in the data set, by performing the pre-processing of data. Furthermore, the data mining approaches are capable of handling large data sets and also use some of the inherent characteristics of spatio-temporal data; hence, they can be applied to increasingly immense data set. This paper stretches in detail various data mining algorithms for land cover change detection and each algorithm’s advantages and limitations. Also, an empirical study of some existing land cover change detection algorithms and results have been presented in this paper.


Data mining land cover change detection algorithm time series data high dimensional data 


  1. 1.
    Coppin P, Jonckheere I, Nackaerts K, Muys B and Lambin E 2004 Digital change detection methods in ecosystem monitoring: a review. Int. J. Remote Sens. 25(9): 1565–1596CrossRefGoogle Scholar
  2. 2.
    Panigrahi S, Verma K and Tripathi P 2016 An efficient approach to detect sudden changes in vegetation index time series for land change detection. IETE Tech. Rev. 33(5): 539–556CrossRefGoogle Scholar
  3. 3.
    Nemmour H and Chibani Y 2010 Support vector machines for automatic multi-class change detection in Algerian capital using Landsat TM imagery. J. Indian Soc. Remote Sens. 38(4): 585–591CrossRefGoogle Scholar
  4. 4.
    Singh K K, Nigam M J, Pal K and Mehrotra A 2014 A fuzzy Kohonen local information C-Means clustering for remote sensing imagery. IETE Tech. Rev. 31(1): 75–81CrossRefGoogle Scholar
  5. 5.
    Potter C, Tan P N, Kumar V, Kucharik C, Klooster S, Genovese V, Cohen W and Healey S 2005 Recent history of large-scale ecosystem disturbances in North America derived from the AVHRR satellite record. Ecosystems 8(7): 808–824CrossRefGoogle Scholar
  6. 6.
    Roy D P, Lewis P E and Justice C O 2002 Burned area mapping using multitemporal moderate spatial resolution data: a bi-directional reflectance model-based expectation approach. Remote Sens. Environ. 83(12): 263–286CrossRefGoogle Scholar
  7. 7.
    Huete A R, Justice C and Leeuwen W V 1999 MODIS vegetation index (MOD13) algorithm theoretical basis document. Version 3, Department of Environmental Sciences, University of VirginiaGoogle Scholar
  8. 8.
    Panigrahi S, Verma K and Tripathi P 2015 Review of MODIS EVI and NDVI data for data mining applications, communicatedGoogle Scholar
  9. 9.
    Lunetta R S, Knight J F, Ediriwickrema J, Lyon J G and Worthy L D 2006 Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens. Environ. 105(2): 142–154CrossRefGoogle Scholar
  10. 10.
    Keogh E, Chu S, Hart D and Pazzani M 2004 Segmenting time series: a survey and novel approach. In: Last M, Kandel A and Bunke H (Eds) Data mining in time series databases, vol. 57, pp. 1–22. Singapore: World ScientificCrossRefGoogle Scholar
  11. 11.
    Boriah S, Kumar V, Potter C, Steinbach M and Klooster S 2008 Land cover change detection using data mining techniques. TR 08-009, pp. 1–16Google Scholar
  12. 12.
    Page E S 1954 Continuous inspection schemes. Biometrika 41(1–2): 100–115MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Kucera J, Barbosa P and Strobl P 2007 Cumulative sum charts: a novel technique for processing daily time series of modis data for burnt area mapping in Portugal. In: Proceedings of IEEE MultiTemp 2007, pp. 1–6Google Scholar
  14. 14.
    Taylor W A 2000 Change-point analysis: a powerful new tool for detecting changes.
  15. 15.
    Boriah S 2010 Time series change detection: algorithms for land cover change. PhD Thesis, Department of CSE, University of MinnesotaGoogle Scholar
  16. 16.
    Boriah S, Kumar V, Steinbach M, Tan P N, Potter C and Klooster S 2008 Detecting ecosystem disturbances and land cover change using data mining. In: Next generation of data mining. CRC Press. Chapter 2, pp. 29–46Google Scholar
  17. 17.
    Potter C, Genovese V, Gross P, Boriah S, Steinbach M and Kumar V 2007 Revealing land cover change in California with satellite data. EOS Trans. Am. Geogr. Union 88(26): 269–276CrossRefGoogle Scholar
  18. 18.
    Jain A K and Dubes R C 1988 Algorithm for clustering data. In: Prentice Hall advanced reference series. Prentice Hall.
  19. 19.
    Salmon B P, Olivier J C, Wessels K J, Kleynhans W, Bergh F and Steenkamp K C 2011 Unsupervised land cover change detection: meaningful sequential time series analysis. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4(2): 327–335, CrossRefGoogle Scholar
  20. 20.
    Ward J 1963 Hierarchical grouping to optimize an objective function. J. Am. Statist. Assoc. 58(301): 236–244MathSciNetCrossRefGoogle Scholar
  21. 21.
    Mitchell T 1997 Machine learning. New York: McGraw HillzbMATHGoogle Scholar
  22. 22.
    Boriah S, Kumar V, Steinbach M, Potter C and Klooster S 2008 Land cover change detection: a case study. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 08, pp. 857–865Google Scholar
  23. 23.
    Boriah S, Mithal V, Garg A, Kumar V, Steinbach M, Potter C and Klooster S 2010 A comparative study of algorithms for land cover change. In: Proceedings of the 2010 Conference on Intelligent Data Understanding, pp. 175–187Google Scholar
  24. 24.
    Lucas J M and Saccucci M S 1990 Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32(1): 1–12MathSciNetCrossRefGoogle Scholar
  25. 25.
    Mithal V, Garg A, Boriah S, Steinbach M, Kumar V, Potter C, Klooste S and Castilla-Rubio J C 2011 Monitoring global forest cover using data mining. ACM Trans. Intell. Syst. Technol. 2(4): 36CrossRefGoogle Scholar
  26. 26.
    Mithal V, Garg A, Brugere I, Boriah S, Kumar V, Steinbach M, Potter C and Klooste S 2011 Incorporating natural variation into time series based land cover change detection. In: Proceedings of the Conference on Intelligent Data Understanding, pp. 45–59Google Scholar
  27. 27.
    Chamber Y, Mithal V, Garg A, Brugere I, Lau M, Krishna V, Boriah S, Potter C and Klooster S 2011 A novel time series based approach to detect gradual vegetation changes in forests. In: Proceedings of the 2011 Conference on Intelligent Data Understanding, pp. 248–262Google Scholar
  28. 28.
    Faghmous J H, Chamber Y, Boriah S, Vikeb F, Liess S, Mesquita M and Kumar V 2012 A novel and scalable spatio-temporal technique for ocean eddy monitoring. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI 12, pp. 281–287Google Scholar
  29. 29.
    Verbesselt J, Hyndman R, Newnham G and Culvenor D 2010 Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 114(1): 106–115CrossRefGoogle Scholar
  30. 30.
    Chen X C, Karpatne A, Chamber Y, Mithal V, Lau M, Steinhaeuser K, Boriah S, Steinbach M, Kumar V, Potter C, Klooster S A, Abraham T, Stanley J D and Castilla-Rubio J C 2012 A new data mining framework for forest fire mapping. In: Proceedings of the Conference on Intelligent Data Understanding, CIDU’ 12, pp. 104–111Google Scholar
  31. 31.
    Justice C O, Giglio L and Roy D 2011 MODIS-derived global fire products. In: Ramachandran B, Justice C O and Abrams M J (Eds) Land remote sensing and global environmental change. Berlin: Springer, pp. 661–679Google Scholar
  32. 32.
    Garg A, Manikonda L, Kumar S, Krishna V, Boriah S, Steinbach M, Toshnival D, Kumar V, Potter C and Klooster S A 2011 A model-free time series segmentation approach for land cover change detection. In: Proceedings of CIDU’11, pp. 144–158Google Scholar
  33. 33.
    Tan P N, Steinbach M and Kumar V 2006 Introduction to data mining. Cambridge: Addison-Wesley Longman, p. 769Google Scholar
  34. 34.
    Han J, Kamber M and Pei J 2011 Data mining: concepts and techniques, 3rd ed. Morgan Kaufmann, p. 744, ISBN:0123814790Google Scholar
  35. 35.
    Chen M S, Han J and Yu P S 1996 Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866–883CrossRefGoogle Scholar
  36. 36.
    Tan P, Steinbach M, Kumar V, Potter C, Klooster S and Torregrosa A 2001 Finding spatio-temporal patterns in earth science data. In: Proceedings of the KDD 2001 Workshop on Temporal Data Mining, vol. 19, pp. 1–12Google Scholar
  37. 37.
    Steinbach M, Tan P N, Kumar V, Potter C, Klooster S and Torregrosa A 2001 Clustering earth science data: Goals, issues and results. In: Proceedings of the Fourth KDD Workshop on Mining Scientific Datasets, pp. 1–8Google Scholar
  38. 38.
    Shekhar S, Zhang P and Huang Y 2009 Spatial data mining. In: Data mining and knowledge discovery handbook. USA: Springer, pp. 837–854CrossRefGoogle Scholar
  39. 39.
    Bogorny V and Shekhar S 2010 Spatial and spatio-temporal data mining. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), pp. 1217–1217Google Scholar

Copyright information

© Indian Academy of Sciences 2017

Authors and Affiliations

  • Sangram Panigrahi
    • 1
    Email author
  • Kesari Verma
    • 1
  • Priyanka Tripathi
    • 2
  1. 1.Department of Computer ApplicationsNational Institute of Technology RaipurRaipurIndia
  2. 2.Department of Computer Engineering and ApplicationsNational Institute of Technical Teachers’ Training and ResearchBhopalIndia

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