Research Article
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Year 2022, Volume: 7 Issue: 2, 191 - 207, 10.07.2022
https://doi.org/10.26833/ijeg.975222

Abstract

References

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  • Foody G M (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environ-ment, 80(1), 185–201. https://doi.org/https://doi.org/10.1016/S0034-4257(01)00295-4
  • Fortin M (2003). On the role of spatial stochastic mod-els in understanding landscape indices in ecology. Oikos, 102(1), 203–212. https://doi.org/https://doi.org/10.1034/j.1600-0706.2003.12447.x
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  • Gong P, Ledrew E F & Miller J R (1992). Registration-noise reduction in difference images for change de-tection. International Journal of Remote Sensing, 13(4), 773–779. https://doi.org/10.1080/01431169208904151
  • Ha T V, Tuohy M, Irwin M & Tuan P V (2018). Monitor-ing and mapping rural urbanization and land use changes using Landsat data in the northeast sub-tropical region of Vietnam. The Egyptian Journal of Remote Sensing and Space Sciences, 23(1), 11–19. https://doi.org/10.1016/j.ejrs.2018.07.001
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  • Im J, Rhee J, Jensen J R & Hodgson M E (2007). An au-tomated binary change detection model using a cal-ibration approach. Remote Sensing of Environment, 106(1), 89–105. https://doi.org/10.1016/j.rse.2006.07.019
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Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study

Year 2022, Volume: 7 Issue: 2, 191 - 207, 10.07.2022
https://doi.org/10.26833/ijeg.975222

Abstract

Mapping, analysis, and monitoring of landuse and landcover in micro region is necessary for sustainable land development, planning and management. The present study is, therefore, aimed to identify the spatio-temporal change of LULC in two central administrative C.D. blocks of North 24 Parganas in West Bengal, India during period 1987-2020. To figure out the essence of the transition, the supervised classification along with post-classification change detection using the 'From'-'To' approach was employed. Furthermore, hotspot analysis has been utilized to identify all of the areas that are the most variable in terms of change potentiality. Besides, cellular automata were also introduced to find out the character of urban growth and future trend of LULC change. The results show that between 1987 and 2020, agricultural area and vegetation with settlement decreased by -11.60 % and -4.34 %, respectively, while dense set-tlement increased by +15.69 % due to significant population growth and overcrowding from neighboring countries. The prediction model also supports this argument. So, the very high and uncontrolled growth of urban settlement in the study area, may become a big challenge for the district authority to control the unplanned urban expansion.

Supporting Institution

West Bengal State University

References

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  • Butt A, Shabbir R, Ahmad S S & Aziz N (2015). Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islama-bad, Pakistan. The Egyptian Journal of Remote Sensing and Space, 18(2), 251–259. https://doi.org/10.1016/j.ejrs.2015.07.003
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  • Dewan A M & Yamaguchi Y (2009). Using remote sens-ing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960 – 2005. Environmental Monitoring and Assessment, 150(1–4), 237–249. https://doi.org/10.1007/s10661-008-0226-5
  • Dhali M K, Chakraborty M & Sahana M (2019). As-sessing spatio-temporal growth of urban sub-centre using Shannon’s entropy model and principal com-ponent analysis: A case from North 24 Parganas, lower Ganga River Basin, India. Egyptian Journal of Remote Sensing and Space Science, 22(1), 25–35. https://doi.org/10.1016/j.ejrs.2018.02.002
  • Dhar R B, Chakraborty S, Chattopadhyay R & Sikdar P K (2019). Impact of Land-Use / Land-Cover Change on Land Surface Temperature Using Satellite Data: A Case Study of Rajarhat Block, North 24-Parganas District, West Bengal. Journal of the Indian Society of Remote Sensing, 47(2), 331–348. https://doi.org/10.1007/s12524-019-00939-1
  • Foody G M (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environ-ment, 80(1), 185–201. https://doi.org/https://doi.org/10.1016/S0034-4257(01)00295-4
  • Fortin M (2003). On the role of spatial stochastic mod-els in understanding landscape indices in ecology. Oikos, 102(1), 203–212. https://doi.org/https://doi.org/10.1034/j.1600-0706.2003.12447.x
  • Getis A & Ord J K (1996). Spatial analysis and modeling in a GIS environment. In R. B. McMaster & E. L. Us-ery (Eds.), A research agenda for geographic infor-mation science (pp. 157–160). CRC Press,Taylor & Francis Group.
  • Gong P, Ledrew E F & Miller J R (1992). Registration-noise reduction in difference images for change de-tection. International Journal of Remote Sensing, 13(4), 773–779. https://doi.org/10.1080/01431169208904151
  • Ha T V, Tuohy M, Irwin M & Tuan P V (2018). Monitor-ing and mapping rural urbanization and land use changes using Landsat data in the northeast sub-tropical region of Vietnam. The Egyptian Journal of Remote Sensing and Space Sciences, 23(1), 11–19. https://doi.org/10.1016/j.ejrs.2018.07.001
  • Handbook D C (2011). West Bengal.
  • Hazra S & Saradar J (2014). Monitoring of landuse and landcover – a case study of Bidyadhari basin, North 24 Parganas, West Bengal. Geographical Review of India.
  • Hussain M, Chen D, Cheng A, Wei H & Stanley D (2013). Change detection from remotely sensed im-ages: Frompixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80(1), 91–106. https://doi.org/10.1016/j.isprsjprs.2013.03.006
  • Im J, Rhee J, Jensen J R & Hodgson M E (2007). An au-tomated binary change detection model using a cal-ibration approach. Remote Sensing of Environment, 106(1), 89–105. https://doi.org/10.1016/j.rse.2006.07.019
  • Jensen J R (2015). Introductory digital image pro-cessing a Remote Sensing Perspective (4th ed.).
  • Jing Y & Yue Z (2016). Change and prediction of the land use /cover in Ebinur Lake Wetland Nature Re-serve based on CA-Markov model. Chinese Journal of Applied Ecology, 27(11), 3649–3658. https://doi.org/1001-9332.201611.027
  • Jogun T, Lukić A & Gašparović M (2019). Simulation model of land cover changes in a post-socialist pe-ripheral rural area: Požega-slavonia county, croatia. Hrvatski Geografski Glasnik, 81(1), 31–59. https://doi.org/10.21861/HGG.2019.81.01.02
  • Kefalas G, Xofis P, Lorilla R S & Martinis A (2018). The use of vegetation indices and change detection techniques as a tool for monitoring ecosystem and biodiversity integrity. International Journal of Sus-tainable Agricultural Management and Informatics, 4(1), 47–67. https://doi.org/10.1504/IJSAMI.2018.10013626
  • Kuldeep T & Kamlesh K (2011). Land Use / Land cover change detection in Doon valley (Dehradun Tehsil), Uttarakhand: using GIS & Remote Sensing Tech-nique. International Journal of Geomatics and Geo-sciences, 2(1), 34–41.
  • Kumar C (2009). Migration and refugee issue between India and Bangladesh. Scholar’s Voice: A New Way of Thinking, 1(1), 62–84.
  • Kushwaha S P S (1990). Forest-type mapping and change detection from satellite imagery. Journal of Photogrammetry and Remote Sensing, 45(3), 175–181. https://doi.org/https://doi.org/10.1016/0924-2716(90)90057-I
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Details

Primary Language English
Journal Section Articles
Authors

Swapan Paul 0000-0002-9373-6310

Publication Date July 10, 2022
Published in Issue Year 2022 Volume: 7 Issue: 2

Cite

APA Paul, S. (2022). Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study. International Journal of Engineering and Geosciences, 7(2), 191-207. https://doi.org/10.26833/ijeg.975222
AMA Paul S. Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study. IJEG. July 2022;7(2):191-207. doi:10.26833/ijeg.975222
Chicago Paul, Swapan. “Change Detection and Future Change Prediction in Habra I and II Block Using Remote Sensing and GIS – A Case Study”. International Journal of Engineering and Geosciences 7, no. 2 (July 2022): 191-207. https://doi.org/10.26833/ijeg.975222.
EndNote Paul S (July 1, 2022) Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study. International Journal of Engineering and Geosciences 7 2 191–207.
IEEE S. Paul, “Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study”, IJEG, vol. 7, no. 2, pp. 191–207, 2022, doi: 10.26833/ijeg.975222.
ISNAD Paul, Swapan. “Change Detection and Future Change Prediction in Habra I and II Block Using Remote Sensing and GIS – A Case Study”. International Journal of Engineering and Geosciences 7/2 (July 2022), 191-207. https://doi.org/10.26833/ijeg.975222.
JAMA Paul S. Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study. IJEG. 2022;7:191–207.
MLA Paul, Swapan. “Change Detection and Future Change Prediction in Habra I and II Block Using Remote Sensing and GIS – A Case Study”. International Journal of Engineering and Geosciences, vol. 7, no. 2, 2022, pp. 191-07, doi:10.26833/ijeg.975222.
Vancouver Paul S. Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study. IJEG. 2022;7(2):191-207.