Analysising Land Use/ Land Cover Changes Classification Using Remote Sensing and GIS in Sikkim Himalayas

Authors

  • Prasansha Dahal Research Scholar, Department of Geography, Nagaland University, Nagaland, India Author
  • Prof. Wangshimenla Jamir Faculty of Department of Geography, Nagaland University, Nagaland, India Author
  • R. Supongtula Research Scholar, Department of Geography, Nagaland University, Nagaland, India Author
  • Prof. Lanusashi Longkumer Faculty of Department of Geography, Nagaland University, Nagaland, India Author

DOI:

https://doi.org/10.32628/IJSRSET2512546

Keywords:

Landuse/Landcover, Remote Sensing, GIS

Abstract

Land refers to a region of the Earth’s surface that is composed of all or most of the components of the biosphere, which includes both biotic and abiotic components. Some of these includes vegetation, geothermal features, and animal life, soil, weather etc. However over the course of time land has undergone changes mainly due to anthropogenic factors. With the use of RS data, efficient evaluations and monitoring of large areas can be done concerning LU/LC conditions, which further empower planners and decision makers to impact the environment positively by making better decisions. This study focuses on classifying and mapping landuse/landcover of the state of Sikkim using Remote Sensing and Geographic Information System techniques . In this study supervised classification was performed between the period of 2003-2023. The important lulc classifications were vegetation, agriculture, barren land, water bodies and built up areas.

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References

Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. US Geological Survey Professional Paper 964. Link: https://pubs.er.usgs.gov/publication/pp964 . DOI: https://doi.org/10.3133/pp964

Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. DOI: 10.1016/0034-4257(91)90048-B DOI: https://doi.org/10.1016/0034-4257(91)90048-B

Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185-201.DOI: 10.1016/S0034-4257(01)00295-4 DOI: https://doi.org/10.1016/S0034-4257(01)00295-4

Giri, C. P. (2012). Remote sensing of land use and land cover: Principles and applications. CRC Press.

Gong, P., Li, X., & Zhang, W. (2019). 40-year (1978–2017) global urban expansion estimated by a fully convolutional network. Science Advances, 5(6), eaav8913. DOI: 10.1126/sciadv.aav8913

Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective. 3rd Edition. Pearson Prentice Hall.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. DOI: 10.2307/2529310 DOI: https://doi.org/10.2307/2529310

Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. DOI: 10.1080/01431160600746456 DOI: https://doi.org/10.1080/01431160600746456

Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57. DOI: 10.1016/j.rse.2014.02.015 DOI: https://doi.org/10.1016/j.rse.2014.02.015

Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007-1011. DOI: 10.1080/01431160512331314083 DOI: https://doi.org/10.1080/01431160512331314083

Richards, J. A., & Jia, X. (2006). Remote sensing digital image analysis: An introduction. 4th Edition. Springer. DOI: https://doi.org/10.1007/3-540-29711-1

Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77-89. DOI: 10.1016/S0034-4257(97)00083-7 DOI: https://doi.org/10.1016/S0034-4257(97)00083-7

Verburg, P. H., Neumann, K., & Nol, L. (2011). Challenges in using land use and land cover data for global change studies. Global Change Biology, 17(2), 974-989. DOI: 10.1111/j.1365-2486.2010.02307.x DOI: https://doi.org/10.1111/j.1365-2486.2010.02307.x

Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation mapping: A review. Journal of Plant Ecology, 1(1), 9-23. DOI: 10.1093/jpe/rtm005 DOI: https://doi.org/10.1093/jpe/rtm005

prasanshadahal2@gmail.com

wangshi@nagalanduniversity.ac.in

tutumoli16@gmail.com

lanusashi@nagalanduniversity.ac.in

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Published

06-09-2025

Issue

Section

Research Articles

How to Cite

[1]
Prasansha Dahal, Prof. Wangshimenla Jamir, R. Supongtula, and Prof. Lanusashi Longkumer, “Analysising Land Use/ Land Cover Changes Classification Using Remote Sensing and GIS in Sikkim Himalayas”, Int J Sci Res Sci Eng Technol, vol. 12, no. 5, pp. 16–22, Sep. 2025, doi: 10.32628/IJSRSET2512546.