Abstract

The increasing need for real-time data on the various land surface form phenomena led to the rising research interest in the area. Among the applications making use of such data are national, regional and global monitoring systems resource monitoring platforms, changes in land use and land cover monitoring and various studies on environmental issues. One of the techniques used for affording such real-time data is remote sensing. Remote sensing satellite platforms capture environmental data at different resolutions, which are extensively utilized today for detecting changes occurring in the environment. The real-time detection of precise changes occurring on the surface of the earth constitutes a vital component in understanding the interrelation between the interactions of humans with nature, which is important for suitable decision-making. In real life scenarios, change detection is a complex phenomenon which includes different procedures such as identifying the specific change detection problem, image preprocessing and variables and algorithm selection for the computations. Over the years, quite a wide range of techniques have been developed for analyzing remote sensing data and yet newer methods are still being developed. As such, this study seeks to provide a comprehensive review of the fundamental processes required for change detection. The study also seeks to provide a concise summary of the main techniques of change detection and to discuss the need for development of new, enhanced change detection methods.

Keywords:

Change detection analysis, classification, land use and land cover, object base method, remote sensing, sub pixel method,

References

1. Ardila, J.P., W. Bijker, V.A. Tolpekin and A. Stein, 2012. Multitemporal change detection of urban trees using localized region-based active contours in VHR images. Remote Sens. Environ., 124: 413-426.
   CrossRef    
2. Bergen, K.M., D.G. Brown, J.F. Rutherford and E.J. Gustafson, 2005. Change detection with heterogeneous data using ecoregional stratification, statistical summaries and a land allocation algorithm. Remote Sens. Environ., 97(4): 434-446.
   CrossRef    
3. Bontemps, S., P. Bogaert, N. Titeux and P. Defourny, 2008. An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sens. Environ., 112(6): 3181-3191.
   CrossRef    
4. Bontemps, S., A. Langner and P. Defourny, 2012. Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series. Int. J. Remote Sens., 33(15): 4673-4699.
   CrossRef    
5. Cassidy, L., J. Southworth, C. Gibbes and M. Binford, 2013. Beyond classifications: Combining continuous and discrete approaches to better understand land-cover change within the lower Mekong River region. Appl. Geogr., 39: 26-45.
   CrossRef    
6. Chander, G., B.L. Markham and D.L. Helder, 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote Sens. Environ., 113(5): 893-903.
   CrossRef    
7. Chavez, P.S., 1996. Image-based atmospheric corrections-revisited and improved. Photogramm. Eng. Rem. S., 62(9): 1025-1035.
   
8. Chen, J., P. Gong, C. He, R. Pu and P. Shi, 2003. Land-use/land-cover change detection using improved change-vector analysis. Photogramm. Eng. Rem. S., 69(4): 369-379.
   CrossRef    
9. Chen, J., M. Lu, X. Chen, J. Chen and L. Chen, 2013. A spectral gradient difference based approach for land cover change detection. ISPRS J. Photogramm., 85: 1-12.
   CrossRef    
10. Chen, X., J. Chen, Y. Shi and Y. Yamaguchi, 2012. An automated approach for updating land cover maps based on integrated change detection and classification methods. ISPRS J. Photogramm., 71: 86-95.
    CrossRef    
11. Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ., 37(1): 35-46.
    CrossRef    
12. Congalton, R.G. and K. Green, 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press, Boca Raton.
    CrossRef    
13. Coppin, P., I. Jonckheerea, K. Nackaertsa, B. Muysa and E. Lambinb, 2004. Review article digital change detection methods in ecosystem monitoring: A review. Int. J. Remote Sens., 25(9): 1565-1596.
    CrossRef    
14. Coppin, P.R. and M.E. Bauer, 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sens. Rev., 13(3-4): 207-234.
    CrossRef    
15. Dai, X. and S. Khorram, 1998. The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE T. Geosci. Remote, 36(5): 1566-1577.
    CrossRef    
16. DeFries, R., 2012. Why Forest Monitoring Matters for People and the Planet. Global Forest Monitoring from Earth Observation, pp: 15-28.
    
17. Demir, B., F. Bovolo and L. Bruzzone, 2012. Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification. IEEE T. Geosci. Remote, 50(5): 1930-1941.
    CrossRef    
18. Deng, J.S., K. Wang, Y.H. Deng and G.J. Qi, 2008. PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int. J. Remote Sens., 29(16): 4823-4838.
    CrossRef    
19. Desclée, B., P. Bogaert and P. Defourny, 2006. Forest change detection by statistical object-based method. Remote Sens. Environ., 102(1): 1-11.
    CrossRef    
20. Du, P., S. Liu, J. Xia and Y. Zhao, 2013. Information fusion techniques for change detection from multi-temporal remote sensing images. Inform. Fusion, 14(1): 19-27.
    CrossRef    
21. Eckardt, R., C. Berger, C. Thiel and C. Schmullius, 2013. Removal of optically thick clouds from multi-spectral satellite images using multi-frequency SAR data. Remote Sens., 5(6): 2973-3006.
    CrossRef    
22. Foody, G.M., 2002. Status of land cover classification accuracy assessment. Remote Sens. Environ., 80(1): 185-201.
    CrossRef    
23. Foody, G.M., 2010. Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sens. Environ., 114(10): 2271-2285.
    CrossRef    
24. Ghosh, A., N.S. Mishra and S. Ghosh, 2011. Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inform. Sciences, 181(4): 699-715.
    CrossRef    
25. Grimm, N.B., S.H. Faeth, N.E. Golubiewski, C.L. Redman, J. Wu, X. Bai and J.M. Briggs, 2008. Global change and the ecology of cities. Science, 319(5864): 756-760.
    CrossRef    PMid:18258902    
26. Gungor, O. and O. Akar, 2010. Multi sensor data fusion for change detection. Sci. Res. Essays, 5(18): 2823-2831.
    
27. Haertel, V., Y.E. Shimabukurob and R. Almeida-Filho, 2004. Fraction images in multitemporal change detection. Int. J. Remote Sens., 25(23): 5473-5489.
    CrossRef    
28. Hansen, M.C., Y.E. Shimabukuro, P. Potapov and K. Pittman, 2008. Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover. Remote Sens. Environ., 112(10): 3784-3793.
    CrossRef    
29. Hansen, M.C. and T.R. Loveland, 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ., 122: 66-74.
    CrossRef    
30. Herold, M., P. Mayaux, C.E. Woodcock, A. Baccini and C. Schmullius, 2008. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sens. Environ., 112(5): 2538-2556.
    CrossRef    
31. Howarth, P.J. and G.M. Wickware, 1981. Procedures for change detection using Landsat digital data. Int. J. Remote Sens., 2(3): 277-291.
    CrossRef    
32. Huang, C., K. Song, S. Kim, J.R.G. Townshend and P. Davis, 2008. Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sens. Environ., 112(3): 970-985.
    CrossRef    
33. Hussain, M., D. Chen, A. Cheng, H. Wei and D. Stanley, 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS J. Photogramm., 80: 91-106.
    CrossRef    
34. Im, J. and J.R. Jensen, 2005. A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens. Environ., 99(3): 326-340.
    CrossRef    
35. Im, J., J.R. Jensena and J.A. Tullis, 2008. Object-based change detection using correlation image analysis and image segmentation. Int. J. Remote Sens., 29(2): 399-423.
    CrossRef    
36. Im, J., J. Rhee and J.R. Jensen, 2009. Enhancing binary change detection performance using a Moving Threshold Window (MTW) approach. Photogramm. Eng. Rem. S., 75(8): 951-961.
    CrossRef    
37. Jensen, J.R., 2005. Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice-Hall Inc., Upper Saddle River, NJ.
    
38. Jianya, G., S. Haiganga, M. Guoruia and Z. Qiming, 2008. A review of multi-temporal remote sensing data change detection algorithms. Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci., 37(B7): 757-762.
    
39. Jin, S. and S.A. Sader, 2005. Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens. Environ., 94(3): 364-372.
    CrossRef    
40. Jones, P.D., D.H. Lister and Q. Li, 2008. Urbanization effects in large-scale temperature records, with an emphasis on China. J. Geophys. Res-Atmos., 113(D16).
    CrossRef    
41. Kennedy, R.E., P.A. Townsend, J.E. Gross, W.B. Cohen and P. Bolstad, 2009. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens. Environ., 113(7): 1382-1396.
    CrossRef    
42. Kim, D.Y., J. Thomas, J. Olson, M. Williams and N. Clements, 2013. Statistical trend and change-point analysis of land-cover-change patterns in East Africa. Int. J. Remote Sens., 34(19): 6636-6650.
    CrossRef    
43. Lefsky, M.A. and W.B. Cohen, 2003. Selection of remotely sensed data. Remote Sensing of Forest Environments, Springer, pp: 13-46.
    CrossRef    
44. Li, D., 2010. Remotely sensed images and GIS data fusion for automatic change detection. Int. J. Image Data Fusion, 1(1): 99-108.
    CrossRef    
45. Lippitt, C.D., J. Rogan, Z. Li, J.R. Eastman and T.G. Jones, 2008. Mapping selective logging in mixed deciduous forest. Photogramm. Eng. Rem. S., 74(10): 1201-1211.
    CrossRef    
46. Lu, D., P. Mauselb, E. Brondízioc and E. Moran, 2004. Change detection techniques. Int. J. Remote Sens., 25(12): 2365-2401.
    CrossRef    
47. Lu, D., P. Mauselb, M. Batistellac and E. Moran, 2005. Land-cover binary change detection methods for use in the moist tropical region of the Amazon: A comparative study. Int. J. Remote Sens., 26(1): 101-114.
    CrossRef    
48. Lu, D. and Q. Weng, 2007. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens., 28(5): 823-870.
    CrossRef    
49. Lu, D., M. Batistella and E. Moran, 2008. Integration of Landsat TM and SPOT HRG images for vegetation change detection in the Brazilian Amazon. Photogramm. Eng. Rem. S., 74(4): 421-430.
    CrossRef    PMid:19789721 PMCid:PMC2752897    
50. Lu, D., S. Hetrick, E. Moran and G. Li, 2010. Detection of urban expansion in an urban-rural landscape with multitemporal QuickBird images. J. Appl. Remote Sens., 4(1): 041880-041880-041817.
    
51. Lu, D., G. Li, E. Moran, M. Batistella and C.C. Freitas, 2011. Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban-rural landscape in the Brazilian Amazon. ISPRS J. Photogramm., 66(6): 798-808.
    CrossRef    
52. Lu, D., G. Li, E. Moran and S. Hetrick, 2013. Vegetation Change Detection in the Brazilian Amazon with Multitemporal Landsat Images. In: Guangxing, W. and W. Qihao (Eds.), Remote Sensing of Natural Resources. CRC Press/Taylor and Francis, Boca Raton, Florida, pp: 127-140.
    CrossRef    
53. Lunetta, R.S., D.M. Johnson, J.G. Lyon and J. Crotwell, 2004. Impacts of imagery temporal frequency on land-cover change detection monitoring. Remote Sens. Environ., 89(4): 444-454.
    CrossRef    
54. Michishita, R., Z. Jiang and B. Xu, 2012. Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing. Remote Sens. Environ., 117: 3-18.
    CrossRef    
55. Nascimento Jr, W.R., P.W.M. Souza-Filhoa, C. Proisyc, R.M. Lucasd and A. Rosenqvist, 2013. Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery. Estuar. Coast. Shelf S., 117: 83-93.
    CrossRef    
56. Nemmour, H. and Y. Chibani, 2006. Fuzzy neural network architecture for change detection in remotely sensed imagery. Int. J. Remote Sens., 27(4): 705-717.
    CrossRef    
57. Nordberg, M.L. and J. Evertson, 2005. Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery. Land Degrad. Dev., 16(2): 139-149.
    CrossRef    
58. Olofsson, P., G.M. Foody, S.V. Stehman and C.E. Woodcock, 2013. Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens. Environ., 129: 122-131.
    CrossRef    
59. Reiche, J., C.M. Souzax, D.H. Hoekman and J. Verbesselt, 2013. Feature level fusion of multi-temporal ALOS PALSAR and Landsat data for mapping and monitoring of tropical deforestation and forest degradation. IEEE J. Sel. Top. Appl., 6(5): 2159-2173.
    CrossRef    
60. Riaño, D., E. Chuvieco, J. Salas and I. Aguado, 2003. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEE T. Geosci. Remote, 41(5): 1056-1061.
    CrossRef    
61. Rogan, J. and D. Chen, 2004. Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog. Plann., 61(4): 301-325.
    CrossRef    
62. Shi, W. and M. Hao, 2013. Analysis of spatial distribution pattern of change-detection error caused by misregistration. Int. J. Remote Sens., 34(19): 6883-6897.
    CrossRef    
63. Singh, A., 1986. Change Detection in the Tropical Forest Environment of Northeastern India using Landsat. In: Eden, M.J. and J.T. Parry (Eds.), Remote Sensing and Tropical Land Management. John Wiely and Sons, London, pp: 237-254.
    
64. Singh, A., 1989. Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens., 10(6): 989-1003.
    CrossRef    
65. Song, C. and C.E. Woodcock, 2003. Monitoring forest succession with multitemporal Landsat images: Factors of uncertainty. IEEE T. Geosci. Remote, 41(11): 2557-2567.
    CrossRef    
66. Song, C., C.E. Woodcock, K.C. Seto, M.P. Lenney and S.A. Macomber, 2001. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sens. Environ., 75(2): 230-244.
    CrossRef    
67. Souza, J., C. Achard et al., 2012. Monitoring of Forest Degradation: A Review of Methods in the Amazon Basin. Global Forest Monitoring Earth Observation, pp: 185.
    
68. Stow, D.A., 2010. Geographic Object-based Image Change Analysis. In: Fischer, M.M. and A. Getis (Eds.), Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Springer-Verlag, Berlin, Heidelberg, pp: 565-582.
    CrossRef    
69. Stow, D.A. and D.M. Chen, 2002. Sensitivity of multitemporal NOAA AVHRR data of an urbanizing region to land-use/land-cover changes and misregistration. Remote Sens. Environ., 80(2): 297-307.
    CrossRef    
70. Van Oort, P., 2007. Interpreting the change detection error matrix. Remote Sens. Environ., 108(1): 1-8.
    CrossRef' target='_blank'>CrossRef    
71. Vicente-Serrano, S.M., F. Perez-Cabello and T. Lasanta, 2008. Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sens. Environ., 112(10): 3916-3934.
    CrossRef    
72. Volpi, M., D. Tuia, F. Bovolo, M. Kanevski and L. Bruzzone, 2013. Supervised change detection in VHR images using contextual information and support vector machines. Int. J. Appl. Earth Obs., 20: 77-85.
    CrossRef    
73. Wang, Y. and T.R. Allen, 2008. Estuarine shoreline change detection using Japanese ALOS PALSAR HH and JERS-1 L-HH SAR data in the Albemarle-Pamlico Sounds, North Carolina, USA. Int. J. Remote Sens., 29(15): 4429-4442.
    CrossRef    
74. Whittle, M., S. Quegan, Y. Uryu, M. Stuewe and K. Yulianto, 2012. Detection of tropical deforestation using ALOS-PALSAR: A Sumatran case study. Remote Sens. Environ., 124: 83-98.
    CrossRef    
75. Wilson, E.H. and S.A. Sader, 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens. Environ., 80(3): 385-396.
    CrossRef    
76. Xian, G., C. Homen and J. Fry, 2009. Updating the 2001 national land cover database land cover classification to 2006 by using landsat imagery change detection methods. Remote Sens. Environ., 113(6): 1133-1147.
    CrossRef    
77. Yang, L., G. Xian, J.M. Klaver and B. Deal, 2003. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogramm. Eng. Rem. S., 69(9): 1003-1010.
    CrossRef    
78. Zanotta, D.C. and V. Haertel, 2012. Gradual land cover change detection based on multitemporal fraction images. Pattern Recogn., 45(8): 2927-2937.
    CrossRef    
79. Zeng, Y., J. Zhang, J.L. van Genderen and Y. Zhang, 2010. Image fusion for land cover change detection. Int. J. Image Data Fusion, 1(2): 193-215.
    CrossRef    
80. Zhou, W., A. Troy and M. Grove, 2008. Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors, 8(3): 1613-1636.
    CrossRef    

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