Growth of Dehradun city: An application of linear spectral unmixing (LSU) technique using multi-temporal landsat satellite data sets

Remote Sensing Applications: Society and Environment

Volumn 1, Issue , 2015, Pages 98-111

  Dutta, Dipanwita ^a   Rahman, Atiqur ^b   Kundu, Arnab ^c  

^a VIDYASAGAR UNIVERSITY   (India)

^b JAMIA MILLIA ISLAMIA CENTRAL UNIVERSITY   (India)

^c UNIVERSITY OF ALLAHABAD   (India)

Author keywords

Dehradun city India; GIS technique; Landsat and IRS Satellite data sets; Linear spectral unmixing; Urban growth

Indexed keywords

EID: 84944080875     PISSN: None     EISSN: 23529385     Source Type: Journal    
DOI: 10.1016/j.rsase.2015.07.001     Document Type: Article

References (66)

1. Adams J.B., Smith M.O., Johnson P.E. Spectral mixture modelling: a new analysis of road and soil types at the Viking Lander site. J. Geophys. Res. 1986, 91:8098-8112.
2. Adams J., Sabol D., Kapos V., Filho R., Roberts D., Smith M. Classification of multispectral images based on fractions of endmembers: applications to land cover change in the Brazilian Amazon. Remote Sens. Environ. 1995, 52:137-154.
3. Alam Md.Ejaz, Mallick Javed, Bhaskaran Sunil, Rahman A., Dutta D. Spatio-temporal assessment of vegetation covers in the upper catchment of Kosi River basin using MODIS data. Int. J. Remote Sens. Geosci. (IJRSG) 2013, 2(6):33-40. ISSN No: 2319-3484.
4. Annez P.C., Buckley R. Urbanization and growth: setting the context. Urbanization and Growth 2009, 1:1-45. Commission on Growth and Development, The World Bank, Washington, DC.
5. Asselman N.E.M., Middelkoop H., Van Dijk P.M. The impacts of changes in climate and land use on soil erosion, transport and deposition of suspended sediment in the River Rhine. Hydrol. Process. 2003, 17:3225-3244.
6. Banerjee A., Reddy K.N., Paul P. Application of remote sensing and GIS in demographic and socio-economic analysis of Dehradun city. Indian Cartogr. 2002, 22:274-281.
7. Bhatta B. Analysis of Urban Growth and Sprawl from Remote Sensing 2010, Springer-Verlag, Berlin/Heidelberg, Germany.
8. Dare P. Shadow analysis in high-resolution satellite imagery of urban areas. Photogramm. Eng. Remote Sens. 2005, 71:169-177.
9. Davis K., Golden H.H. Urbanization and the development of pre-industrial areas. Econ. Dev. Cult. Chang. 1954, 3:6-26.
10. De Asis A.M., Omasa K. Estimation of vegetation parameter for modeling soil erosion using linear spectral mixture analysis of Landsat ETM data. ISPRS J. Photogramm. Remote Sens. 2007, 62:309-324.
11. Deng C., Wu C. Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach. Remote Sens. Environ. 2013, 131:262-274.
12. Dennison P., Roberts D. Endmember selection for multiple endmember buhspectral mixture analysis using endmember average RMSE. Remote Sens. Environ. 2003, 87:123-135.
13. Elizabeth A.W., Nelson D., Rahman A., Stefanov W.L., Roy S.S. Expert system classification of urban land use/cover for Delhi, India. Int. J. Remote Sens. 2008, 29(15):4405-4427.
14. Elvidge C.D., Imhoff M.L., Baugh K.E., Hobson V.R., Nelson I., Safran J., Dietz J.B., Tuttle B.T. Night-time lights of the world: 1994-1995. ISPRS J. Photogramm. Remote Sens. 2001, 56:81-99.
15. Fisher P. The pixel: a snare and a delusion. Int. J. Remote Sens. 1997, 18:679-685.
16. Forman Richard T.T. Some general principles of landscape and regional ecology. Landsc. Ecol. 1995, 10(3):133-142.
17. Han H., Lai S., Dang A., Tan Z., Wu C. Effectiveness of urban construction boundaries in Beijing: an assessment. J. Zhejiang Univ. Sci. A 2009, 10:1285-1295.
18. Herold M., Goldstein N., Clarke K. The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens. Environ. 2003, 86:286-302.
19. Huang H., Margulis S., Chu C., Tsai H. Investigation of the impacts of vegetation distribution and evaporative cooling on synthetic urban daytime climate using a coupled LES-LSM model. Hydrol. Process. 2011, 25:1574-1586.
20. Hussain M., Chen D., Cheng A., Wei H., Stanley D. Change detection from digital images: from pixel-based to object-oriented approaches. ISPRS J. Photogramm. Remote Sens. 2013, 80:91-106.
21. Jacobson C.R. The effects of endmember selection on modeling impervious surfaces using spectral mixture analysis: a case study in Sydney, Australia. Int. J. Remote Sens. 2014, 35:715-737.
22. Jain S., Jain R.K. A remote sensing approach to establish relationships among different land covers at the micro level. Int. J. Remote Sens. 2006, 27:2667-2682.
23. Ji M., Jensen J.R. Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper Imagery. Geocarto Int. 1999, 14:33-41.
24. Kardi T. Remote sensing of urban areas: linear spectral unmixing of Landsat Thematic Mapper images acquired over Tartu (Estonia). Proc. Estonian Acad. Sci. Biol. Ecol. 2007, 56:19-32.
25. Kotkin, J. 2010. The World's Fastest-Growing Cities.New Geography. Feb. 25, 2011. , last accessed 20.01.15. http://www.newgeography.com/content/001801-the-worlds-fastest-growing-cities.
26. Kumar U., Kerle N., Ramachandra T.V. Constrained linear spectral unmixing technique for regional land cover mapping using MODIS data. Innovations and advanced techniques in systems, computing sciences and software engineering 2008, 416-423. Springer-Verlag, Berlin. K. Elleithy (Ed.).
27. Kundu A., Patel N.R., Saha S.K., Dutta D. Monitoring the extent of desertification processes in western Rajasthan (India) using geo-information science. Arab. J. Geosci. 2014, http://dx.doi.org/10.1007/s12517-014-1645-y.
28. Li X., Yeh A.G.O. Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta. Int. J. Remote Sens. 1998, 19:1501-1518.
29. Linard C., Tatem A.J., Gilbert M. Modelling spatial patterns of urban growth in Africa. Appl. Geogr. 2013, 44:23-32.
30. Liu J., Liu M., Tian H., Zhuang D., Zhang Z., Zhang W., Tang X., Deng X. Spatial and temporal patterns of China's cropland during 1990-2000: an analysis based on Landsat TM data. Remote Sens. Environ. 2005, 98:442-456.
31. Liu J., Tian H., Liu M., Zhuang D., Melillo J.M., Zhang Z. China's changing landscape during the 1990s: large-scale land transformation estimated with satellite data. Geophys. Res. Lett. 2005, 32:L02405. 10.1029/2004GL021649.
32. Lu D., Weng Q. Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+ imagery. Photogramm. Eng. Remote Sens. 2004, 70:1053-1062.
33. Lu D., Weng Q. Urban classification using full spectral information of Landsat ETM+ imagery in Marion county, Indiana. Photogramm. Eng. Remote Sens. 2005, 71:1275-1284.
34. Lu D., Weng Q. Use of impervious surface in urban land use classification. Remote Sens. Environ. 2006, 102:146-160.
35. Lu D., Moran E., Hetrick S. Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier. ISPRS J. Photogramm. Remote Sens. 2011, 66:298-306.
36. Lu D., Tian H., Zhou G., Ge H. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sens. Environ. 2008, 112:3668-3679.
37. Mallick, J., Rahman, A., Hoa, P.V., Joshi, P.K., 2009. Assessment of night-time urban surface temperature-land use/cover relationship for thermal urban environment studies using optical and thermal satellite data. In: Proceedings of the 7th FIG Regional Conference Spatial Data Serving People: Land Governance and the Environment - Building the Capacity, Hanoi, Vietnam, 19-22 October 2009. TS 1G-Remote Sensing for Sustainable Development.
38. Madhavan B.B., Kubo S., Kurisaki N., Sivakumar T.V.L.N. Appraising the anatomy and spatial growth of the Bangkok metropolitan area using a vegetation-impervious soil model through remote sensing. Int. J. Remote Sens. 2001, (22):789-806.
39. Maktav D., Erbek F.S. Analysis of urban growth using multi-temporal satellite data in Istanbul, Turkey. Int. J. Remote Sens. 2005, 26:797-810.
40. McGranahan G., Martine G. Urbanization and Development: Policy Lessons from the BRICS Experience 2012, 1-25. International Institute for Environment and Development, London, (IIED Discussion Paper).
41. Phinn S., Stanford M., Scarth P., Murray A.T., Shyy P.T. Monitoring the composition and form of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. Int. J. Remote Sens. 2002, 23:4131-4153.
42. Rahman A., Kumar Sunil, Fazal Shahab, Siddiqui Masood A. Assessing land use/land cover change in the North-West District of Delhi using remote sensing and GIS techniques. J. Indian Soc. Remote Sens. 2012, 40(4). (ISSN: No. 0255-660).
43. Rahman A., Aggarwal S.P., Netzband M., Fazal S. Monitoring Urban Sprawl Using Remote Sensing and GIS Techniques of a Fast Growing Urban Centre, India. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (JSTARS) 2011, 4:56-64.
44. Ramachandra, T.V., Kumar, U., 2004. Geographic resources decision support system for land use, land cover dynamics analysis. In: Proceedings of the FOSS/GRASS Users Conference, 12-14 September 2004, Bangkok, Thailand.
45. Rajeshwari S. Management of the urban environment using remote sensing and geographical information systems. J. Hum. Ecol. 2006, 20:269-277.
46. Ridd M.K. Exploring a V-I-S (vegetation.impervious surface.soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int. J. Remote Sens. 1995, 16:2165-2185.
47. Ridd M.K., Hipple J.M., 2006. Remote sensing of human settlements. 3rd ed. Manual of Remote Sensing, Vol. 5 (pp. 752). Bethesda, Maryland: American Society of Photogrammetry and Remote Sensing.
48. Roberts D.A., Gardner M., Church R., Ustin S., Scheer G., Green R.O. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. Remote Sens. Environ. 1998, 65:267-279.
49. Schulz J.J. Monitoring land cover change of the dryland forest landscape of Central Chile (1975-2008). Appl. Geogr. 2010, 30:436-447.
50. Settle J.J., Drake N.A. Linear mixing and the estimation of ground cover proportions. Int. J. Remote Sens. 1993, 14:1159-1177.
51. Singh O., Arya P., Chaudhary B.S. On rising temperature trends at Dehradun in Doon valley of Uttarakhand, India. J. Earth Sys. Sci. 2013, 122:613-622.
52. Singh A. Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 1989, 10:989-1003.
53. Small C. Multitemporal analysis of urban reflectance. Remote Sens. Environ. 2002, 81:427-442.
54. Small C. High spatial resolution spectral mixture analysis of urban reflectance. Remote Sens. Environ. 2003, 88:170-186.
55. Song C. Spectral mixture analysis for subpixel vegetation fractions in the urban environment: how to incorporate endmember variability?. Remote Sens. Environ. 2005, 95:248-263.
56. Tang J., Wang L., Myint S.W. Improving urban classification through fuzzy supervised classification and spectral mixture analysis. Int. J. Remote Sens. 2007, 28:4047-4063.
57. Tompkins S., Mustard J., Pieters C., Forsyth D. Optimization of endmembers for spectral mixture analysis. Remote Sens. Environ. 1997, 59:472-489.
58. Van der Meer F.D. Spectral unmixing of Landsat thematic Mapper data. Int. J. Remote Sens. 1995, 16:3189-3194.
59. Wang W., Yao X., Ji M., Zhang J. Spectral data treatments for impervious endmember derivation and fraction mapping from Landsat ETM+ imagery: a comparative analysis. Front. Earth Sci. 2015, 9:179-191.
60. Weng F., Pu R. Mapping and assessing of urban impervious areas using multiple endmember spectral mixture analysis: a case study in the city of Tampa, Florida. Geocarto Int. 2013, 28:594-615.
61. Wilson E.H., Hurd J.D., Civeo D.L., Prisloe M.P., Arnold C. Development of a geo-spatial model to quantify describe and map urban growth. Remote Sens. Urban Environ. 2003, 86:275-285.
62. Wu C., Murray A.T. Estimating impervious surface distribution by spectral mixture analysis. Remote Sens. Environ. 2003, 84:493-505.
63. Yang J., He Y., Oguchi T. An endmember optimization approach for linear spectral unmixing of fine-scale urban imagery. Int. J. Appl. Earth Obs. Geoinf. 2014, 27:137-146.
64. Yang J., He Y., Caspersen J. Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areas. Int. J. Appl. Earth Obs. Geoinf. 2015, 38:88-98.
65. Zhang Y., Guindon B. Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover. Int. J. Digit. Earth 2009, 2:89-108.
66. Zhang Q., Wang J., Peng X., Gong P., Shi P. Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data. Int. J. Remote Sens. 2002, 23:3057-3078.