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An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm

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Abstract

Detection of snow cover changes is vital for avalanche hazard analysis and flood flashes that arise due to variation in temperature. Hence, multitemporal change detection is one of the practical mean to estimate the snow cover changes over larger area using remotely sensed data. There have been some previous studies that examined how accuracy of change detection analysis is affected by different topography effects over Northwestern Indian Himalayas. The present work emphases on the intercomparison of different topography effects on discrimination performance of fuzzy based change vector analysis (FCVA) as change detection algorithm that includes extraction of change-magnitude and change-direction from a specific pixel belongs multiple or partial membership. The qualitative and quantitative analysis of the proposed FCVA algorithm is performed under topographic conditions and topographic correction conditions. The experimental outcomes confirmed that in change category discrimination procedure, FCVA with topographic correction achieved 86.8% overall accuracy and 4.8% decay (82% of overall accuracy) is found in FCVA without topographic correction. This study suggests that by incorporating the topographic correction model over mountainous region satellite imagery, performance of FCVA algorithm can be significantly improved up to great extent in terms of determining actual change categories.

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References

  • Almutairi A, Warner TA (2010) Change detection accuracy and image properties: a study using simulated data. Remote Sensing 2(6):1508–1529. doi:10.3390/rs2061508

    Article  Google Scholar 

  • Butt M, Bilal M (2011) Application of snowmelt runoff model for water resource management. Hydrol Process 25(24):3735–3747. doi:10.1002/hyp.8099

    Article  Google Scholar 

  • Campbell JB (1987) Introduction to remote sensing. Guilford, New York

    Google Scholar 

  • Chen J, Gong P, He C, Pu R, Shi P (2003) Landuse/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering and Remote Sensing 69(4):369–379. doi:10.14358/PERS.69.4.369

    Article  Google Scholar 

  • Chen J, Chen X, Cui X, Chen J (2011) Change vector analysis in posterior probability space: a new technique for land cover change detection. IEEE Geo-sci Remote Sensing Lett 8(2):317–321. doi:10.1109/LGRS.2010.2068537

    Article  Google Scholar 

  • Chunyang H, Yuanyuan Z, Jie T, Peijun S, Qingxu H (2013) Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion. Int J Remote Sens 34(4):1127–1145. doi:10.1080/01431161.2012.718458

    Article  Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Edu Psychol Measure 20:37–46. doi:10.1177/001316446002000104

    Article  Google Scholar 

  • Congalton RG, Green K (1999) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, Boca Raton

    Google Scholar 

  • Congalton RG, Plourde L (2002) Quality assurance and accuracy assessment of information derived from remotely sensed data. In: Bossler J (ed) Manual of geospatial science and technology London. Taylor & Francis, 349–361. doi:10.1201/9780203305928.ch21

  • Coppin PR, Bauer ME (1996) Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Rev 13(3–4):207–234. doi:10.1080/02757259609532305

    Article  Google Scholar 

  • ERDAS (1999) ERDAS: Field Guide. ERDAS Inc., Atlanta, p 671

    Google Scholar 

  • Foody GM (1995) Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data. ISPRS J Photogram Remote Sensing 50:2–12. doi:10.1016/0924-2716(95)90116-V

    Article  Google Scholar 

  • Foody GM (1996) Approaches for the production and evaluation of fuzzy land cover classification from remotely sensed data. Int J Remote Sens 17:1317–1340. doi:10.1080/01431169608948706

    Article  Google Scholar 

  • Gao Y, Zhang W (2009) A simple empirical topographic correction method for ETM + imagery. Int J Remote Sens 30(9):2259–2275. doi:10.1080/01431160802549336

    Article  Google Scholar 

  • 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 32:503–528. doi:10.1177/0309133308098363

    Article  Google Scholar 

  • Gopal S, Woodcock C (1994) Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogram Eng Remote Sensing 60:181–188

    Google Scholar 

  • Gurung DR, Kulkarni AV, Giriraj A, Aung KS, Shrestha B, Srinivasan J (2011) Changes in seasonal snow cover in Hindu Kush-Himalayan region. Cryosphere Discuss 5:755–777. doi:10.5194/tcd-5-755-2011

    Article  Google Scholar 

  • Hoffmann B (1975) About Vectors. Dover Publications Inc., New York, p 134

    Google Scholar 

  • Holland PG, Steyne DG (1975) Vegetation responses to latitudinal variations in slope angle and aspect. J Biogeogr 2:179–183. doi:10.2307/3037989

    Article  Google Scholar 

  • Jensen JR (1996) Introductory digital image processing: a remote sensing perspective. Prentice-Hall, Englewood Cliffs. doi:10.1080/10106048709354084

    Google Scholar 

  • Lambin EF, Strahler AH (1994) Change-vector analysis in multi-temporal space: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sens Environ 48(2):231–244. doi:10.1016/0034-4257(94)90144-9

    Article  Google Scholar 

  • Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2407. doi:10.1080/0143116031000139863

    Article  Google Scholar 

  • Lu D, Batistella M, Moran EF (2008) Integration of Landsat TM and SPOT HRG images for vegetation change detection in the Brazilian Amazon. Photogram Eng Remote Sensing 74(4):421–430

    Article  Google Scholar 

  • Malila WA (1980) Change vector analysis: an approach for detecting forest changes with Landsat, In Proceedings of the 6th Annual Symposium on Machine Processing of Remotely Sensed Data, 3–6 June 1980, West Lafayette, IN (West Lafayette: Purdue University), 326–335. http://docs.lib.purdue.edu/lars_symp/385

  • Mather PM (2004) Computer processing of remotely-sensed images: an Introduction, Wiley, 2, Chichester

  • Melesse AM, Jordan JD (2002) A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloud-shadow discrimination from Landsat images. Photogram Eng Remote Sensing 68:905–911

    Google Scholar 

  • Meng L, Tao L, Li J (2008) A system for automatic processing of MODIS L1B Data. In: Wan Y et al. (eds), International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, World Academic Union (Press), Shanghai, 8:335–343

  • Mishra VD, Sharma JK, Singh KK, Thakur NK, Kumar M (2009a) Assessment of different topographic corrections in AWiFS satellite imagery of Himalaya terrain. Journal of Earth System Sciences 118(1):11–26. doi:10.1007/s12040-009-0002-0

    Article  Google Scholar 

  • Mishra VD, Sharma JK, Khanna R (2009b) Review of topographic analysis techniques for the western Himalaya using AWiFS and MODIS satellite imagery. Ann Glaciol 51(54):1–8. doi:10.3189/172756410791386526

    Google Scholar 

  • Nackaerts K, Vaesen K, Muys B, Coppin P (2005) Comparative performance of a modified change vector analysis in forest change detection. Int J Remote Sens 26(5):839–852. doi:10.1080/0143116032000160462

    Article  Google Scholar 

  • Nichol J, Hang LK, Sing WM (2006) Empirical correction of low sun angle images in steeply sloping terrain: a slope matching technique. Int J Remote Sens 27(3–4):629–635. doi:10.1080/02781070500293414

    Article  Google Scholar 

  • Ramamoorthi AS, and Haefner H (1991) Runoff modeling and forecasting of river basins, and Himalayan snow cover Information (HIMSIS). Proceedings of the Vienna Symposium, IAHS Publ. No. 201:347–355

  • Sharma JK, Mishra VD, Khanna R (2013) Impact of topography on accuracy of land cover spectral change vector analysis using AWiFS in Western Himalaya. Journal of the Indian Society of Remote Sensing 41(2):223–235. doi:10.1007/s12524-011-0180-5

    Article  Google Scholar 

  • Sharma V, Mishra VD, Joshi PK (2014) Topographic controls on spatiotemporal snow cover distribution in Northwest Himalaya. Int J Remote Sens 35(9):3036–3056. doi:10.1080/01431161.2014.894665

    Article  Google Scholar 

  • Singh S, Talwar R (2014) A comparative study on change vector analysis based change detection techniques. SADHANA-Acad Proceed Eng Sci 39(6):1311–1331. doi:10.1007/s12046-014-0286-x

    Google Scholar 

  • Singh S, Talwar R (2015a) Assessment of different CVA based change detection techniques using MODIS dataset. MAUSAM Journal 66(1):77–86

    Google Scholar 

  • Singh S, Talwar R (2015b) Performance analysis of different threshold determination techniques for change vector analysis. J Geol Soc India 86:52–58. doi:10.1007/s12594-015-0280-x

    Article  Google Scholar 

  • Singh S, Sharma JK, Mishra VD (2011) Comparison of different topographic correction methods using AWiFS satellite data. International Journal of Advanced Engineering Sciences and Technologies 7(1):85–91

    Google Scholar 

  • Vanonckelen S, Lhermitte S, Balthazar V, Rompaey AV (2014) Performance of atmospheric and topographic correction methods on Landsat imagery in mountain areas. Int J Remote Sens 35(13):4952–4972. doi:10.1080/01431161.2014.933280

    Article  Google Scholar 

  • Varshney A, Arora MK, Ghosh JK (2012) Median change vector analysis algorithm for land-use land-cover change detection from remote-sensing data. Remote Sensing Letters 3(7):605–614. doi:10.1080/01431161.2011.648281

    Article  Google Scholar 

  • Wacker AG and Landgrebe DA (1972) Minimum distance classification in remote sensing. LARS Technical Reports

  • Wood TF, Foody GM (1993) Using cover-type likelihoods and typicalities in a geographic information system data structure to map gradually changing environments. In: Green DR, Cousins SH (eds) R Haines Young. Taylor and Francis, Landscape Ecology and GIS, pp 141–146

    Google Scholar 

  • Xian G, Collin H, Fry J (2009) Updating the 2001 National land cover database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sensing Environment 113(6):1133–1147. doi:10.1016/j.rse.2009.02.004

    Article  Google Scholar 

  • Zhang J, Foody GM (1998) A fuzzy classification of sub-urban land cover from remotely sensed imagery. Int J Remote Sens 9(14):2721–2738. doi:10.1080/014311698214479

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to the anonymous referees and the editor for their constructive comments and valuable suggestions, that helped to significantly improve the earlier version of manuscript. The authors are also thankful to NASA and United States Geological Survey (USGS) for making the MODIS and ASTER Global DEM version 2 data, respectively, available to us for research and educational purposes.

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Correspondence to Sartajvir Singh.

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Responsible Editor: A. P. Dimri.

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Singh, S., Talwar, R. An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm. Meteorol Atmos Phys 130, 125–136 (2018). https://doi.org/10.1007/s00703-016-0494-5

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  • DOI: https://doi.org/10.1007/s00703-016-0494-5

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