An Efficient Approach to Detect Sudden Changes in Vegetation Index Time Series for Land Change Detection

References

• P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin, “Digital change detection methods in ecosystem monitoring: A review,” Int. J. Remote Sensing, Vol. 25, no. 9, pp. 1565–96, May 2004.
   Web of Science ®Google Scholar
• T. Aach, and A. Kaup, “Bayesian algorithms for adaptive change detection in image sequences using markov random fields,” Signal Process.: Image Commun., Vol. 7, no. 2, pp. 147–60, Aug. 1995.
   Web of Science ®Google Scholar
• T. Kasetkasem, and P. Varshney, “An image change detection algorithm based on markov random field models,” IEEE Trans. Geoscience Remote Sensing, Vol. 40, no. 8, pp. 1815–23, Aug. 2002. ISSN 0196-2892. DOI: 10.1109/TGRS.2002.802498.
   Web of Science ®Google Scholar
• H. Nemmour, Y. Chibani, “Support vector machines for automatic multi-class change detection in Algerian capital using landsat TM imagery,” J. Indian Soc. Remote Sensing, Vol. 38, no. 4, pp. 585–91, Dec. 2010.
   Web of Science ®Google Scholar
• K. K. Singh, M. J. Nigam, K. Pal, and A. Mehrotra, “A fuzzy Kohonen local information C-means clustering for remote sensing imagery,” IETE Tech. Rev., Vol. 31, no. 1, pp. 75–81, May 2014. DOI: 10.1080/02564602.2014.891375.
   Web of Science ®Google Scholar
• C. Potter, P.-N. Tan, V. Kumar, C. Kucharik, S. Klooster, V. Genovese, W. Cohen, and S. Healey. , “Recent history of large-scale ecosystem disturbances in North America derived from the AVHRR satellite record,” Ecosystems, Vol. 8, no. 7, pp. 808–24, Nov. 2005.
   Web of Science ®Google Scholar
• D. P. Roy, P. E. Lewis, and C. O. Justice, “Burned area mapping using multi temporal moderate spatial resolution data–A bi-directional reflectance model based expectation approach,” Remote Sensing Environ., Vol. 83, no. 1–2, pp. 263–86, Nov. 2002.
   Web of Science ®Google Scholar
• S. Boriah, V. Kumar, C. Potter, M. Steinbach, and S. Klooster, “Land cover change detection using data mining techniques,” Technical Report March 14, 2008. Available: https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/08-009.pdf
   Google Scholar
• R. S. Lunetta, J. F. Knight, J. Ediriwickrema, J. G. Lyon, and L. D. Worthy, “Land-cover change detection using multi-temporal MODIS NDVI data,” Remote Sensing Environ., Vol. 105, no. 2, pp. 142–54, Nov. 2006.
   Web of Science ®Google Scholar
• S. Boriah, “Time series change detection: algorithms for land cover change,” Ph.D. thesis, Department of Computer Science and Engineering, University of Minnesota, 2010. Available: http://www-users.cs.umn.edu/~sboriah/PDFs/BoriahB2010.pdf
   Google Scholar
• S. Boriah, V. Kumar, M. Steinbach, C. Potter, and S. Klooster, “Land cover change detection: A case study,” in KDD '08: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, Aug. 24–27, 2008, pp. 857–65.
   Google Scholar
• V. Mithal, A. Garg, S. Boriah, M. Steinbach, V. Kumar, C. Potter, S. A. Klooster, and J. C. Castilla-Rubio, “Monitoring global forest cover using data mining,” ACM Transactions on Intelligent Systems and Technology, Vol. 2, no. 4, pp. 36. 1–36. 24, Jul. 2011.
   Web of Science ®Google Scholar
• S. Boriah, V. Mithal, A. Garg, V. Kumar, M. Steinbach, C. Potter, and S. Klooster, “A comparative study of algorithms for land cover change,” in The Proceeding of the 2010 Conference on Intelligent Data Understanding, Mountain View, CA, Oct 5–6, 2010, pp. 175–88.
   Google Scholar
• A. R. Huete, and C. Justice, “MODIS vegetation index (MOD13) algorithm theoretical basis document,” Ver.3, 1999.
   Google Scholar
• S. Panigrahi, K. Verma, and P. Tripathi, Review of MODIS EVI and NDVI Data for Data Mining Applications, (communicated).
   Google Scholar
• US Geological Survey and NASA. Land Processes Distributed Active Archive Center (LP DAAC). Available: https://lpdaac.usgs.gov/products/modis_products_table
   Google Scholar
• S. Panigrahi, K. Verma, and P. Tripathi, “Review of land cover change detection using data mining algorithms for MODIS time series dataset,”J. Indian Soc. Remote Sensing, submitted for review.
   Google Scholar
• E. S. Page, “Continuous inspection schemes,” Biometrika, Vol. 41, no. 1/2, pp. 100–15, Jun. 1954.
   Web of Science ®Google Scholar
• J. Kucera, P. Barbosa, and P. Strobl, “Cumulative sum charts–A novel technique for processing daily time series of modis data for burnt area mapping in Portugal,” in Fourth International Workshop on the Analysis of Multitemporal Remote Sensing Images, Provinciehuis Leuven, Belgium, Jul. 18–20, 2007, pp. 1–6.
   Google Scholar
• W. A. Taylor, Change-point analysis: A powerful new tool for detecting changes, 2000. Available: http://www.variation.com/cpa/tech/changepoint.html
   Google Scholar
• J. M. Lucas, and M. S. Saccucci, “Exponentially weighted moving average control schemes: Properties and enhancements,” Technometrics, Vol. 32, no. 1, pp. 1–12, Feb. 1990.
   Web of Science ®Google Scholar
• V. Mithal, A. Garg, I. Brugere, S. Boriah, V. Kumar, M. Steinbach, C. Potter and S. Klooster, “Incorporating natural variation into time series based land cover change detection,” in Proceedings of the 2011 Conference on Intelligent Data Understanding, Mountain View, CA, Oct. 19–21, 2011, pp. 45–59.
   Google Scholar
• E. Keogh, S. Chu, D. Hart, and M. Pazzani, “Segmenting time series: A survey and novel approach,” in Data mining in time series databases. Vol. 57, M. Last, A. Kandel, and H. Bunke, Eds. Singapore: World Scientific, 2004, pp. 1–22.
   Google Scholar
• C. Chatfield, The Analysis of Time Series: An Introduction. Chapman & Hall/CRC, Jul. 2003, pp. 352. ISBN:9781584883173
   Google Scholar
• R. J. Alcock, and Y. Manolopoulos, “Time-series similarity queries employing a feature-based approach,” in Proceedings of the 7th Hellenic Conference on Informatics, Ioannina, Aug. 1999, pp. 1–9.
   Google Scholar
• D. T. Pham and A. B. Chan, “Control chart pattern recognition using a new type of self organizing neural network,” Proc. Instn, Mech, Engrs. Vol. 212, no. 1, pp. 115–27, Mar. 1998.
   Google Scholar
• Synthetic Control Chart Time Series, Sep. 10, 2015. Available: http://kdd.ics.uci.edu/databases/synthetic_control/synthetic_control.html
   Google Scholar
• P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Boston, MA: Addison-Wesley, Longman Publishing, 2006, pp. 769.
   Google Scholar
• J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann, 2011, pp. 744. ISBN:0123814790.
   Google Scholar
• V. Kumar, M. Steinbach, P.-N. Tan, S. Klooster, C. Potter, and A. Torregrosa, “Mining scientific data: Discovery of patterns in the global climate system,” in Proceedings of the Joint Statistical Meetings (Athens, GA, Aug. 5–9). American Statistical Association, Alexandria, VA, 2001.
   Google Scholar
• J. Weier and D. Herrieng, Measuring vegetation (NDVI & EVI), Aug. 30, 2002. Available: http://earthobservatory.nasa.gov/Features/MeasuringVegetation
   Google Scholar