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Land cover change detection using focused time delay neural network

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Abstract

The development of improved satellite technology generates a huge amount of remote sensing data, these data play the crucial role in natural resource management. The land use and land cover (LULC) change intensely affects local environment, as well as the global environment. Therefore, the quantifiable knowledge about LULC changes occur in global scale is important to make effective planning for conservation and precise use of natural resources, that has motivated the scientists to develop the various land cover change detection techniques. In this paper, we have proposed neural network-based approach, i.e., focused time delay neural network (FTDNN)-based approach for land cover change detection, which is a time series prediction-based approach and detect the sudden change in the enhanced vegetation index (EVI) time series. The performance of the proposed method has been addressed by using quantitative and qualitative analysis techniques. For the quantitative evaluation, the proposed algorithm is applied to the standard synthetic data set, which are analogous to EVI time series data set. The performance result of the proposed method compares with the four previously existing data mining-based benchmark techniques. The analysis was shown that the FTDNN-based method significantly outperforms than other techniques. For qualitative analysis, the San Francisco Bay Area data set has been used, which comprises real EVI time series. The proposed FTDNN-based method is applied to the San Francisco Bay Area data set and observe the interesting land cover changes. These outcomes indicate the effectiveness of data mining techniques for the land cover change detection problem.

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References

  • Alcock RJ, Manolopoulos Y (1999) Time-series similarity queries employing a feature-based approach. In: 7th Hellenic conference on informatics, pp 27–29

  • Amato F, Lpez A, Pea-Mndez EM, Vahara P, Hampl A, Havel J (2013) Artificial neural networks in medical diagnosis. J Appl Biomed 11:47–58

    Article  Google Scholar 

  • Anava O, Hazan E, Mannor S, Shamir O (2013) Online learning for time series prediction. In: Conference on learning theory, pp 172–184

  • Baraldi A, Parmiggiani F (1995) A neural network for unsupervised categorization of multivalued input patterns: an application to satellite image clustering. IEEE Trans Geosci Remote Sens 33(2):305–316

    Article  Google Scholar 

  • Bogorny V, Shekhar S (2010) Spatial and spatio-temporal data mining. In: IEEE international conference on data mining, ICDM, p 1217

  • Boriah S (2010) Time series change detection: algorithms for land cover change. PhD thesis, Department of CSE, University of Minnesota, pp 1–160

  • Boriah S, Kumar V, Steinbach M, Potter C, Klooster S (2008a) Land cover change detection: a case study. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining KDD, vol 08, pp 857–865

  • Boriah S, Kumar V, Steinbach M, Tan PN, Potter C, Klooster S (2008b) Detecting ecosystem disturbances and land cover change using data mining. In: Next generation of data mining. CRC Press, CH 2, pp 29–46

  • Boriah S, Kumar V, Potter C, Steinbach M, Klooster S (2008c) Land cover change detection using data mining techniques. Technical report

  • Boriah S, Mithal V, Garg A, Kumar V, Steinbach M, Potter C, Klooster S (2010) A comparative study of algorithms for land cover change. In: The proceeding of the 2010 conference on intelligent data understanding, pp 175–187

  • Box GEP, Jenkins GM (1968) Some recent advances in forecasting and control. Appl Stat 17(2):91–109

    Article  MathSciNet  Google Scholar 

  • Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis, forecasting and control, 3rd edn. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Briassoulis H (2004) Land-use, land-cover changes and global aggregate impacts. In: Land cover and land use, encyclopedia of life support systems. EOLSS-UNESCO Publ., Oxford

  • Center for continuing study of the California economy (2016) 1–8. http://www.ccsce.com/PDF/Numbers-Jan-2016-Bay-Area-Population-Trends.pdf. Accessed Aug 2017

  • Chamber Y, Mithal V, Garg A, Brugere I, Lau M, Krishna V, Boriah S, Potter C, Klooster S (2011) A novel time series based approach to detect gradual vegitation changes in forests. In: The proceeding of the 2011 conference on intelligent data understanding, pp 248–262

  • Chan K, Ling S, Dillon T, Nguyen H (2011) Diagnosis of hypoglycemic episodes using a neural network based rule discovery system. Expert Syst Appl 38:9799–9808

    Article  Google Scholar 

  • Charaniya NA, Dudul SV (2012) Focused time delay neural network model for rainfall prediction using Indian ocean dipole index. In: Computational intelligence and communication networks, CICN, pp 851–855

  • Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinform 12(4):458–473

    Article  Google Scholar 

  • Chellasamy M, Chinnasamy U, Ramaswamy SK (2015) A neural-evidence pooling approach to predict urban sprawl using multi-temporal remote sensing data. Int J Geomat Geosci 5(3):459–473

    Google Scholar 

  • Coppin P, Jonckherre I, Nackaerts K, Muys B (2004) Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25:1565–1596

    Article  Google Scholar 

  • De Vries B, Prncipe JC (1990) A theory for neural networks with time delays. In: NIPS, pp 162–168

  • Demuth H, Beale M (1993) Neural network toolbox for use with MATLAB - User’S Guide Verion 3.0

  • Fkirin MA, Badwai SM, Mohamed SA (2009) Change detection using neural network with improvement factor in satellite images. Am J Environ Sci 5(6):706–713

    Article  Google Scholar 

  • Garg A, Manikonda L, Kumar S, Krishna V, Boriah S, Steinbach M, Toshnival D, Kumar V, Potter C, Klooster SA (2011) A model-free time series segmentation approach for land cover change detection. In: Proceedings of CIDU11, pp 144–158

  • Gillanders SN, Coops NC, Wulder MA, Gergel SE, Nelson T (2008) Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends. Prog Phys Geogr 35(5):502–528

    Google Scholar 

  • Grekousis G, Manetos P, Yorgos NP (2013) Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities 30:193–203

    Article  Google Scholar 

  • Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann, Los Altos, p 744. ISBN:0123814790

  • Helmy AK, El-Taweel GS (2010) Using textural and spectral characteristics. Am J Eng Appl Sci 3(4):604–610

    Article  Google Scholar 

  • Houghton RA, House JI, Pongratz J, van der Werf GR, DeFries RS, Hansen MC, LeQuere C, Ramankutty N (2012) Carbon emissions from land use and land cover change. Biogeosciences 9:5125–5142

    Article  Google Scholar 

  • Htike KK, Khalifa OO (2010) Rainfall forecasting models using focused time-delay neural networks. In: Computer and communication engineering, ICCCE, pp 1–6

  • Huete AR, Justice C, Leeuwen WV (1999) MODIS vegetation index (MOD13) algorithm theoretical basis document, Ver. 3. Vegetation Index and Phenology Lab, Department of Environmental Sciences, University of Virginia. https://pdfs.semanticscholar.org/2204/b55a9ad69e8b69d19e88ad1f0e1f81a5d72b.pdf. Accessed Aug 2017

  • Hussain M, Chen D, Cheng A, Wei H, Stanley D (2013) Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens 80:91–106

    Article  Google Scholar 

  • Karpatne A, Jiang Z, Vatsavai RR, Shekhar S, Kumar V (2016) Monitoring land-cover changes: a machine-learning perspective. IEEE Geosci Remote Sens Mag 4(2):8–21

    Article  Google Scholar 

  • Kucera J, Barbosa P, Strobl P (2007) Cumulative sum charts: a novel technique for processing daily time series of modis data for burnt area mapping in Portugal. In: International workshop on the analysis of multi-temporal remote sensing images. IEEE, pp 1–6

  • Kumar V, Steinbach M, Tan P-N, Klooster S, Potter C, Torregrosa A (2001) 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

  • Land Processes Distributed Active Archive Center, LP DAAC (2017). https://www.lpdaac.usgs.gov/products/modis_products_table. Accessed Aug 2017

  • Liu HQ, Huete AR (1995) A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans Geosci Remote Sens 33(2):457–465

    Article  Google Scholar 

  • Lu D, Mausel P, Bronzdizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25:2365–2407

    Article  Google Scholar 

  • Lucas JM, Saccucci MS (1990) Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32(1):1–12

    Article  MathSciNet  Google Scholar 

  • Lunetta RS, Knight JF, Ediriwickrema J, Lyon JG, Worthy LD (2006) Land cover change detection using multi-temporal MODIS NDVI data. Remote Sens Environ 105(2):142–154

    Article  Google Scholar 

  • Mahmood R, Pielke RA, Hubbard KG, Nigoyi D, Dirmeyer PA, McAlpine C, Carleton AM, Hale R, Gameda S, Beltran-Przekurat A, Baker B, McNider R, Legates DR, Shepherd M, Du J, Blanken PD, Frauenfeld OW, Nair US, Fall S (2014) Land cover changes and their biogeophysical effects on climate. Int J Climatol 34:929–953

    Article  Google Scholar 

  • Mas JF (1999) Monitoring land-cover changes: a comparison of change detection techniques. Int J Remote Sens 20(1):139–152

    Article  Google Scholar 

  • Meher-Homji VM (1988) Effects of forests on precipitation in India. NRTS-United Nations University (UNU), Tokyo

  • Mithal V, Garg A, Boriah S, Steinbach M, Kumar V, Potter C, Klooste S, Castilla-Rubio JC (2011) Monitoring global forest coverusing data mining. ACM TIST 2(4):36

    Google Scholar 

  • Ngai E, Hu Y, Wong Y, Chen Y, Sun X (2011) The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis Support Syst 50(3):559–569

    Article  Google Scholar 

  • Page ES (1954) Continuous inspection schemes. Biometrika 41(1/2):100–115

    Article  MathSciNet  MATH  Google Scholar 

  • Panigrahi S, Verma K, Tripathi P (2016a) An efficient approach to detect sudden changes in vegetation index time series for land change detection. IETE Tech Rev 33(5):539–556

    Article  Google Scholar 

  • Panigrahi S, Verma K, Tripathi P (2016b) Optimal threshold value determination for land change detection. IAJIT 16(2):1–10

    Google Scholar 

  • Panigrahi S, Verma K, Tripathi P (2016c) Review of MODIS EVI and NDVI data for data mining applications (communicated)

  • Panigrahi S, Verma K, Tripathi P (2017) Data mining algorithms for land cover change detection: a review. Sdhana J 42(12):2081–2097

    Article  MathSciNet  MATH  Google Scholar 

  • Pham DT, Chan AB (1998) Control chart pattern recognition using a new type of self organizing neural network. Proc Inst Mech Eng Part I J Syst Control Eng 212(2):115–127

    Article  Google Scholar 

  • Qiu F, Jensen JR (2004) Opening the black box of neural networks for remote sensing image classification. Int J Remote Sens 25(9):1749–1768

    Article  Google Scholar 

  • Running SW, Baldocchi DD, Turner DP, Gower ST, Bakwin PS, Hibbard KA (1999) A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens Environ 70:108–127

    Article  Google Scholar 

  • Salmon BP, Olivier JC, Wessels KJ, Kleynhans W, Bergh F, Steenkamp KC (2011) Unsupervised land cover change detection: meaningful sequential time series analysis. IEEE J Sel Top Appl Earth Obs Remote Sens 4(2):327–335

    Article  Google Scholar 

  • Shekhar S, Zhang P, Huang Y (2009) Spatial data mining. In: Data mining and knowledge discovery handbook. Springer, pp 837–854

  • Shumway RH, Stoffer DS (2006) Time series analysis and its applications: with R examples, 2nd edn. Springer, New York

    MATH  Google Scholar 

  • Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003

    Article  Google Scholar 

  • Steinbach M, Tan PN, Kumar V, Potter C, Klooster S, Torregrosa A (2001) Clustering earth science data: goals, issues and results. In: Proceedings of the fourth KDD workshop on mining scientific datasets, pp 1–8

  • Synthetic Control Chart Time Series (2017) Available: http://kdd.ics.uci.edu/databases/synthetic_control/synthetic_control.html. Accessed Aug 2017

  • Tan P, Steinbach M, Kumar V, Potter C, Klooster S, Torregrosa A (2001) Finding spatio-temporal patterns in earth science data. In: KDD 2001 Workshop on temporal data mining, vol 19, pp 1–12

  • Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-Wesley Longman Publishing, Boston, pp 1–769

    Google Scholar 

  • Taormina R, Chau KW (2015) Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and extreme learning machines. J Hydrol 529(3):1617–1632

    Article  Google Scholar 

  • Taylor WA (2000) Change-point analysis: a powerful new tool for detecting changes. http://www.variation.com/cpa/tech/changepoint.html. Accessed Aug 2017

  • Tewkesbury AP, Comber AJ, Tate NJ, Lamb A, Fisher PF (2015) A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens Environ 160:114

    Article  Google Scholar 

  • Turkson RE, Baagyere EY, Wenya GE (2016) A machine learning approach for predicting bank credit worthiness. In: Artificial intelligence and pattern recognition, AIPR, pp 1–7

  • Verburg PH, Crossman N, Ellis EC, Heinimann A, Hostert P, Mertz O, Nagendra H, Sikor T, Erb KH, Golubiewski N, Grau R (2015) Land system science and sustainable development of the earth system: a global land project perspective. Anthropocene 12:29–41

    Article  Google Scholar 

  • Wang WC, Chau KW, Qiu L, Chen YB (2015) Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition. Environ Res 139:46–54

    Article  Google Scholar 

  • Weigend AS (1994) Time series prediction: forecasting the future and understanding the past. In: Weigend AS, Gershenfeld NA (eds) Conference proceedings edition: proceedings of the NATO advanced research workshop on comparative time series analysis, held in Santa Fe, New Mexico, May 14–17 1992

  • Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389(1–2):146–167

    Article  Google Scholar 

  • Zhang WJ (2007) Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks. Environ Monit Assess 130(1–3):415–422

    Article  Google Scholar 

  • Zhang S, Chau KW (2009) Dimension reduction using semi-supervised locally linear embedding for plant leaf classification. Emerg Intell Comput Technol Appl 5754:948–955

    Google Scholar 

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Correspondence to Sangram Panigrahi.

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Panigrahi, S., Verma, K. & Tripathi, P. Land cover change detection using focused time delay neural network. Soft Comput 23, 7699–7713 (2019). https://doi.org/10.1007/s00500-018-3395-3

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