Land Use/Land Cover Change Detection Study Using Remote Sensing and GIS Technique in Puthimari River Basin-A Transboundary Basin Between Bhutan and India
DOI:
https://doi.org/10.32628/IJSRSET21817Keywords:
Supervised classification, LULC, Maximum likelihood, Landsat, Puthimari, Change detectionAbstract
Using remote sensing and GIS technique, we analyse the change detection of different land use/land cover (LULC) types that has taken place in Puthimari river basin during a two-decade period from 1999 to 2019. Supervised classification method with maximum likelihood algorithm have been applied to prepare the LULC maps. The LULC change detection has been performed employing a post-classification detection method. Puthimari is a north bank sub-catchment of River Brahmaputra, the northern part of which falls in Bhutan and the rest falls in the Assam state of India. The primary LULC types of the basin are, dense vegetation which is predominant in the upper catchment, crop land and rural settlement. Thus, five different classes have been considered for the analysis, viz., dense vegetation, water bodies, silted water, cropland and rural settlement. The results showed that the rural settlement and water bodies in the basin increased by 42.70% and 30.31% from 1999 to 2019. However, dense vegetation, silted water and cropland decreased by 9.24%, 27.47% and 28.10% during these two decades.
References
- Shetty, A., Nandagiri, L., Thokchom, S., and Rajesh, M.V.S. 2005. Land use-Land cover mapping using satellite data for a forested watershed, Udupi district, Karnataka state, India. J. Indian Society of Remote Sensing 33(2), 233-238.
- Turner, M.G., and Ruscher, C.L. 2004. Change in landscape patterns in Georgia, USA. Land. Ecol. 1 (4), 251–421.
- Ruiz-Luna, A., and Berlanga-Robles, C.A. 2003. Land use, land cover changes and costal lagoon surface reduction associated with urban growth in northwest Mexico. Land. Ecol. 18, 159–171.
- El-Raey, M., Nasr, S., El-Hattab, M., and Frihy, O. 1995. Change detection of Rosetta Promontory over the last forty years. Int. J. Remote Sen., 16, 825–834.
- Kumar, M., and Singh, R.K. 2013. Digital Image Processing of Remotely Sensed Satellite Images for Information Extraction. Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013), 406-410.
- Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. 2004. Remote Sensing and Image Interpretation. 2004; Ed. 5. John Wiley & Sons Ltd.
- DeFries, R.S., and Chan, J. 2000. Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment 74, 503-515.
- Hansen, M., DeFries, R.S., Townshend, J.R.G., and Sohlberg, R. 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing 21, 1331–1364.
- Kruse, R., and Gebhardt, J. 1993. Fuzzy Probability Theory and Fuzzy Statistics. In F. Faulbaum, Hrsg., Softstat 93: Advances in Statistical Software 605-610. Gustav Fischer Verlag, Stuttgart, 1993.
- Jaisawal, R.K., Saxena, R., and Mukherjee, S. 1999. Application of Remote Sensing technology for Landuse/landcover change analysis. J.Indian Soc. Remote Sens. 27(2),123-128.
- Jensen, J.R. 1996. Digital Image Processing: a Remote Sensing Perspective. Englewood Cliffs, New Jersey: Prentice Hall.
- Pilon, P.G., Howarth, P.J., and Bullock, R.A.Q. 1988. An enhanced classification approach to change detection in semi-arid environments. Photogram Eng. Rem. Sens. 54,1709-1716.
- Fung, T., and Zhang Q. 1989. Land use change detection and identification with Landsat digital data in the Kitchener-Waterloo area. Indian Remote Sensing and Methodologies of Land use Change Analysis, eds C.R. Bryant, E.F. LeDrew, C.
- Daniel, L.C., James, D.H., Emily, H.W., Mingjun, S., and Zhenkui, Z. 2002. A Comparison of Land use and Land cover Change Detection Methods. ASPRS-ACSM Annual Conference and FIG 22nd Congress, p. 2.
- Pandy, A.C., and Nathawat, M.S. 2006. Land Use Land Cover Mapping Through Digital Image Processing of Satellite Data – A case study from Panchkula, Ambala and Yamunanagar Districts, Haryana State, India. Geospatial World.
- Bhagawat, R. 2011. Application of remote sensing and GIS, land use/land cover change in Kathmandu metropolitan city. Nepal. J. Theor. Appl. Inform. Technol., 23 (2), 80–86.
- Jaelani, L.M. 2018. Analysis of land cover change due to gold mining in Bombana using Sentinel 1A Radar data. International Journal of Geoinformatics 14(2), 1-7.
- Shareef, M.A., Hassan, N.D., Hasan, S.F., and Khenchaf, A. 2020. Integration of Sentinel 1A and Sentinel 2B data for land use and land cover mapping of the Kirkuk Governorate, Iraq. International Journal of Geoinformatics 16(3), 87-96.
- Chander, G., Markham, B.L., and Helder, D.L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Rem. Sen. Envi., 113 (5), 893–903.
- El Bastawesy, M. 2014. Hydrological Scenarios of the Renaissance Dam in Ethiopia and Its Hydro-Environmental Impact on the Nile Downstream. J. Hydro. Engin., http://dx.doi.org/10.1061/ (ASCE)HE.1943-5584.0001112.
- Yuan, F., Sawaya, K.E., Loeffelholz, B., and Bauer, M.E. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Rem. Sen. Envi. 98, 317–328.
- Adepoju, M.O., Millington, A.C., and Tansey, K.T. 2006. Land Use/ Land Cover Change Detection in Metropolitan Lagos (Nigeria): 1984–2002. American Society for Photogrammetry and Remote Sensing, Annual Conference, Reno, Nevada, May 1–5.
- Nori, W., Elssidig, N., and Niemeyer, I. 2008. Detection of land cover changes using multi-temporal satellite imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII. Part B7. Beijing 2008.
- Sarma, K., and Kushwaha, S.P.S. 2005. Coal mining impact on land use/land cover in Jaintia hills district of Meghalaya, India using Remote Sensing and GIS technique. University School of Environment Management, Guru Gobind Singh Indraprastha University, 2005.
- Kuldeep, T., and Kamlesh, K. 2011. Land Use / Land cover change detection in Doon valley (Dehradun Tehsil), Uttarakhand: using GIS& Remote Sensing Technique. International Journal of Geomatics and Geosciences 2(1), 34-41.
- Mehta, A., Sinha, V.K., and Ayachit, G. 2012. Land use/land cover study using remote sensing and GIS in an arid environment. Bull. Envi. Sci. Res. 1 (3–4), 4–8.
- Sharma, L.K., Pandey, P.C., and Nathawat, M.S. 2012. Assessment of land consumption rate with urban dynamic changes using Geospatial approach. J. Land Use Sci. 7 (2), 131–148.
- Singh, P., Gupta, P., and Singh, M. 2014. Hydrological inferences from watershed analysis for water resource management using remote sensing and GIS techniques. Egypt. J. Rem. Sens. Space Sci. 17, 111–121.
- Bora, M., and Goswami, D.C. 2016) Spatio-temporal landuse/landcover (LULC) change analysis of Kolong River basin, Assam, India using Geospatial technologies. International Journal of Geomatics and Geosciences 6(3),1676-1684.
- Roy, P.K., and Qureshi, Z.H. 2014. Magnitude of floods and its consequences in Puthimari river basin of Assam, India. European Academic Research II(2), 2665-2685.
- Hassan, Z, Shabbir, R, Ahmed, S.S., Malik, A.H., Aziz, A., Butt, A., and Erum, S. 2016. Dynamics of land use and land cover change (LULCC) using geospatial techniques: A case study of Islamabad Pakistan. Springer Plus 2016; 5, 812 (2016). https://doi.org/10.1186/s40064-016-2414-z.
- Veeraswami, G., Nagaraju, A., Balaji, E., and Sreedhar, Y. 2017. Land use and land cover analysis using remote sensing and GIS: A case study in Gudur area, Nellore district, Andhra Pradesh, India. International Journal of Research, 4(17).
- Hu, Y, Batunacun, Z.L. et al. 2019. Assessment of Land-Use and Land-Cover Change in Guangxi, China. Sci Rep.2019; 9, 2189.https://doi.org/10.1038/s41598-019-38487-w.
- Giannaros, T.M., Melas, D., Daglis, I.A., Keramitsoglou, I., and Kourtidis. K. 2013. Numerical study of the urban heat island over Athens (Greece) with the WRF model. Atmospheric Environment 73, 103-111.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET

This work is licensed under a Creative Commons Attribution 4.0 International License.