Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Cilt: 7 Sayı: 2, 191 - 207, 10.07.2022
https://doi.org/10.26833/ijeg.975222

Öz

Kaynakça

  • Amaral G, Bushee J, Cordani U G, KAWASHITA K, Reynolds J H, ALMEIDA F F M D E, … Junho M. do C B (2013). Change detection of urban body. Journal of Petrology, 369(1), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004
  • Bagchi K (1944). The Ganges delta. Calcutta: University of Calcutta.
  • Barnsley M J, Mollar-Jensen L & Barr S L (2001). Infer-ring Urban Land Use by Spatial and Structural Pat-tern Recognition. In J. P. Donnay, M. J. Barnsley, & P. A. Longley (Eds.), Remote Sensing and Urban Analysis (pp. 102–130). London: Taylor & Francis.
  • Basu T & Saha S K (2017). The Analysis of Land Use Land Cover Changes Using Geoinformatics and Its Relation to Changing Population Scenariosin Ba-rasat Municipality in North Twenty-Four Parganas, West Bengal. International Journal of Humanities and Social Science Invention, 6(8), 1–13.
  • Bera S & Das Chatterjee N (2019). Mapping and moni-toring of land use dynamics with their change hotspot in North 24-Parganas district, India: a geo-spatial- and statistical-based approach. Modeling Earth Systems and Environment, 5(4), 1529–1551. https://doi.org/10.1007/s40808-019-00601-2
  • Bhatta B (2018). Remote sensing and GIS (2nd ed.). Oxford University Press.
  • Bhattacharjee D & Hazra S (2014). Distribution of Land Surface Temperature Over Built-up Area by Web-GIS Techniques. Indian Journal of Spatial Science, 5(2), 70–76.
  • Boschetti L, Flasse S P & Brivio P A (2004). Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic prod-ucts: The Pareto Boundary. Remote Sensing of En-vironment, 91(1), 280–292. https://doi.org/10.1016/j.rse.2004.02.015
  • 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
  • Cai L, Shi W, Miao Z & Hao M (2018). Accuracy assess-ment measures for object extraction from remote sensing images. Remote Sensing, 10(2), 303. https://doi.org/10.3390/rs10020303
  • Canty M J (2014). Image analysis, classification and change detection in remote sensing With Algo-rithms for ENVI/IDL and Python (3rd ed.). Boca Ra-ton: CRC Press, Taylor & Francis Group.
  • Chaurasia R, Loshali D C, Dhaliwal S S, Sharma P K, Kudrat M & Tiwari A K (1996). Land use change analysis for agricultural management—A case study of Tehsil Talwandi Sabo, Punjab. Journal of the Indi-an society of remote sensing, 24(2), 115-123.https://doi.org/doi.org/10.1007/BF03016124
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  • Civco D L, Hurd J D, Wilson E H, Song M & Zhang Z (2002). A comparison of land use and land cover change detection methods. ASPRS-ACSM Annual Conference and FIG XXII Congress.
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  • Congalton R G (2001). Accuracy assessment and vali-dation of remotely sensed and other spatial infor-mation. International Journal of Wildland Fire, 10(3–4), 321–328. https://doi.org/10.1071/wf01031
  • Coppin P R & Bauer M E (1996). Digital Change Detec-tion in Forest Ecosystems with Remote Sensing Im-agery. Remote Sensing Reviews, 13(3–4), 207–234. https://doi.org/https://doi.org/10.1080/02757259609532305
  • Datta P (2004). Push-pull factors of documented migra-tion from Bangladesh to west Bengal: a perception study.
  • 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
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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
  • Jensen J R (2015). Introductory digital image pro-cessing a Remote Sensing Perspective (4th ed.).
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  • 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.
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  • Liping C, Yujun S & Saeed S (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques:A case study of a hilly area, Jiangle, China. PLoS ONE, 13(7), 1–23. https://doi.org/https://doi.org/10.1371/journal.pone.0200493
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Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study

Yıl 2022, Cilt: 7 Sayı: 2, 191 - 207, 10.07.2022
https://doi.org/10.26833/ijeg.975222

Öz

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.

Destekleyen Kurum

West Bengal State University

Kaynakça

  • Amaral G, Bushee J, Cordani U G, KAWASHITA K, Reynolds J H, ALMEIDA F F M D E, … Junho M. do C B (2013). Change detection of urban body. Journal of Petrology, 369(1), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004
  • Bagchi K (1944). The Ganges delta. Calcutta: University of Calcutta.
  • Barnsley M J, Mollar-Jensen L & Barr S L (2001). Infer-ring Urban Land Use by Spatial and Structural Pat-tern Recognition. In J. P. Donnay, M. J. Barnsley, & P. A. Longley (Eds.), Remote Sensing and Urban Analysis (pp. 102–130). London: Taylor & Francis.
  • Basu T & Saha S K (2017). The Analysis of Land Use Land Cover Changes Using Geoinformatics and Its Relation to Changing Population Scenariosin Ba-rasat Municipality in North Twenty-Four Parganas, West Bengal. International Journal of Humanities and Social Science Invention, 6(8), 1–13.
  • Bera S & Das Chatterjee N (2019). Mapping and moni-toring of land use dynamics with their change hotspot in North 24-Parganas district, India: a geo-spatial- and statistical-based approach. Modeling Earth Systems and Environment, 5(4), 1529–1551. https://doi.org/10.1007/s40808-019-00601-2
  • Bhatta B (2018). Remote sensing and GIS (2nd ed.). Oxford University Press.
  • Bhattacharjee D & Hazra S (2014). Distribution of Land Surface Temperature Over Built-up Area by Web-GIS Techniques. Indian Journal of Spatial Science, 5(2), 70–76.
  • Boschetti L, Flasse S P & Brivio P A (2004). Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic prod-ucts: The Pareto Boundary. Remote Sensing of En-vironment, 91(1), 280–292. https://doi.org/10.1016/j.rse.2004.02.015
  • 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
  • Cai L, Shi W, Miao Z & Hao M (2018). Accuracy assess-ment measures for object extraction from remote sensing images. Remote Sensing, 10(2), 303. https://doi.org/10.3390/rs10020303
  • Canty M J (2014). Image analysis, classification and change detection in remote sensing With Algo-rithms for ENVI/IDL and Python (3rd ed.). Boca Ra-ton: CRC Press, Taylor & Francis Group.
  • Chaurasia R, Loshali D C, Dhaliwal S S, Sharma P K, Kudrat M & Tiwari A K (1996). Land use change analysis for agricultural management—A case study of Tehsil Talwandi Sabo, Punjab. Journal of the Indi-an society of remote sensing, 24(2), 115-123.https://doi.org/doi.org/10.1007/BF03016124
  • Chowdhury M, Emran M & Abdullah-al-mamun M M (2018). Land use/land cover change assessment of Halda watershed using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Sci-ences, 23(1), 63–75. https://doi.org/10.1016/j.ejrs.2018.11.003
  • Civco D L, Hurd J D, Wilson E H, Song M & Zhang Z (2002). A comparison of land use and land cover change detection methods. ASPRS-ACSM Annual Conference and FIG XXII Congress.
  • Comber A J, Wadsworth R A & Fisher P F (2008). Using semantics to clarify the conceptual confusion be-tween land cover and land use: the example of ‘for-est.’ Journal of Land Use Science, 3(2–3), 185–198. https://doi.org/10.1080/17474230802434187
  • Congalton R G (2001). Accuracy assessment and vali-dation of remotely sensed and other spatial infor-mation. International Journal of Wildland Fire, 10(3–4), 321–328. https://doi.org/10.1071/wf01031
  • Coppin P R & Bauer M E (1996). Digital Change Detec-tion in Forest Ecosystems with Remote Sensing Im-agery. Remote Sensing Reviews, 13(3–4), 207–234. https://doi.org/https://doi.org/10.1080/02757259609532305
  • Datta P (2004). Push-pull factors of documented migra-tion from Bangladesh to west Bengal: a perception study.
  • 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
  • Lambin E F, Geist H J & Lepers E (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28(1), 205–241. https://doi.org/10.1146/annurev.energy.28.050302.105459
  • Lambin E F, Turner B L, Geist H J, Agbola S B, Angelsen A, Folke C, … Veldkamp T A (2001). The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change, 11(4), 261–269. https://doi.org/https://doi.org/10.1016/S0959-3780(01)00007-3
  • Liping C, Yujun S & Saeed S (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques:A case study of a hilly area, Jiangle, China. PLoS ONE, 13(7), 1–23. https://doi.org/https://doi.org/10.1371/journal.pone.0200493
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Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Swapan Paul 0000-0002-9373-6310

Yayımlanma Tarihi 10 Temmuz 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 7 Sayı: 2

Kaynak Göster

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. Temmuz 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, sy. 2 (Temmuz 2022): 191-207. https://doi.org/10.26833/ijeg.975222.
EndNote Paul S (01 Temmuz 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, c. 7, sy. 2, ss. 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 (Temmuz 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, c. 7, sy. 2, 2022, ss. 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.