SPATIOTEMPORAL CHANGE OF LAND USE LAND COVER: A CASE STUDY OF NARAYANGANJ SADAR UPAZILA, BANGLADESH

Authors

  • Md. Abdur Rahman Urban and Rural Planning Discipline, Khulna University, Khulna 9208, Bangladesh
  • Kazi Saiful Islam Urban and Rural Planning Discipline, Khulna University, Khulna 9208, Bangladesh
  • Samiul Islam Siam Urban and Rural Planning Discipline, Khulna University, Khulna 9208, Bangladesh
  • Sazzadul Islam Urban and Rural Planning Discipline, Khulna University, Khulna 9208, Bangladesh

DOI:

https://doi.org/10.53808/KUS.2022.ICSTEM4IR.0017-se

Keywords:

LULC classification, MLC algorithm; change detection, Sentinel-2, Narayanganj Sadar Upazila.

Abstract

Major cities of Bangladesh have been experiencing rapid urbanization and industrialization. These are incurring positive externalities to national economy at the expense of environmental degradation and deterioration of living environment. The ambivalent sequel of industrialization has made it necessary to study urban areas and monitor spatiotemporal changes to facilitate decision-making process regarding land use planning, resource distribution, priority setting for planning interventions. Thus, this study aims to classify land use land cover (LULC) of Narayanganj Sadar Upazila and detect spatiotemporal changes within the period of 2015 to 2020 using MLC algorithm based supervised classification method. To serve this purpose, sentinel-2 satellite imagery are used. The results derived from the study elicit an increase (24.14 acre) in industrial land in 2020 compared to 2015. Approximately 1,538-acres land transformed into built-up area in 2020. Decrease in vegetation (15.85%) and water body (7.65%) are also evident from the findings. The results of the study will be conducive to have an initial glimpse of the LULC changing trend in the study area due to rapid urbanization and thus, will be instrumental for performing further research.

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References

Al-Saady, Y., Merkel, B., Al-Tawash, B., & Al-Suhail, Q. (2015). Land use and land cover (LULC) mapping and change detection in the Little Zab River Basin (LZRB), Kurdistan Region, NE Iraq and NW Iran. FOG - Freiberg Online Geoscience, 43, 1–32.

Andualem, T., Belay, G., & Guadie, A. (2018). Land Use Change Detection Using Remote Sensing Technology. Journal of Earth Science & Climatic Change, 9. https://doi.org/10.4172/2157-7617.1000496

BBS (Bangladesh Bureau of Statistics). (2011). Bangladesh population and housing census 2011. Community report Zila: Narayanganj. Bangladesh Bureau of Statistics, Statistics and Informatics Division, Ministry of Planning, Government of the People’s Republic of Bangladesh.

Batisani, N., & Yarnal, B. (2009). Urban expansion in Centre County, Pennsylvania: Spatial dynamics and landscape transformations. Applied Geography, 29(2), 235–249. https://doi.org/10.1016/j.apgeog. 2008.08.007

Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1990). Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 540–552. https://doi.org/10.1109/TGRS.1990.572944

Al-Saady, Y., Merkel, B., Al-Tawash, B., & Al-Suhail, Q. (2015). Land use and land cover (LULC) mapping and change detection in the Little Zab River Basin (LZRB), Kurdistan Region, NE Iraq and NW Iran. FOG - Freiberg Online Geoscience, 43, 1–32.

Andualem, T., Belay, G., & Guadie, A. (2018). Land Use Change Detection Using Remote Sensing Technology. Journal of Earth Science & Climatic Change, 9. https://doi.org/10.4172/2157-7617.1000496

BBS (Bangladesh Bureau of Statistics). (2011). Bangladesh population and housing census 2011. Community report Zila: Narayanganj. Bangladesh Bureau of Statistics, Statistics and Informatics Division, Ministry of Planning, Government of the People’s Republic of Bangladesh.

Batisani, N., & Yarnal, B. (2009). Urban expansion in Centre County, Pennsylvania: Spatial dynamics and landscape transformations. Applied Geography, 29(2), 235–249. https://doi.org/10.1016/j.apgeog. 2008.08.007

Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1990). Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 540–552. https://doi.org/10.1109/TGRS.1990.572944

Elhag, M., & Boteva, S. (2016). Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data. IOP Conference Series: Earth and Environmental Science, 44(4), 042032. https://doi.org/10.1088/1755-1315/44/4/042032

Heydari, S. S., & Mountrakis, G. (2018). Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sensing of Environment, 204, 648–658. https://doi.org/10.1016/j.rse.2017.09.035

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. https://doi.org/10.1080/01431160600746456

Mancino, G., Nolè, A., Ripullone, F., & Ferrara, A. (2014). Landsat TM imagery and NDVI differencing to detect vegetation change: Assessing natural forest expansion in Basilicata, southern Italy. IForest - Biogeosciences and Forestry, 7(2), 75. https://doi.org/10.3832/ifor0909-007

Mandanici, E., & Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sensing, 8(12), 1014. https://doi.org/10.3390/rs8121014

Nguyen, H. T. T., Doan, T. M., Tomppo, E., & McRoberts, R. E. (2020). Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam. Remote Sensing, 12(9), 1367. https://doi.org/10.3390/rs12091367

Pielke Sr., R. A., Pitman, A., Niyogi, D., Mahmood, R., McAlpine, C., Hossain, F., Goldewijk, K. K., Nair, U., Betts, R., Fall, S., Reichstein, M., Kabat, P., & de Noblet, N. (2011). Land use/land cover changes and climate: Modeling analysis and observational evidence. WIREs Climate Change, 2(6), 828–850. https://doi.org/10.1002/wcc.144

Prakasam, C. (2010). Land use and land cover change detection through remote sensing approach: A case study of Kodaikanal Taluk, Tamil Nadu. International Journal of Geomatics and Geosciences, 1, 150–158.

Prakash, A., & Gupta, R. P. (1998). Land-use mapping and change detection in a coal mining area—A case study in the Jharia coalfield, India. International Journal of Remote Sensing, 19(3), 391–410. https://doi. org/10.1080/014311698216053

Sarma, V. V. L. N., Krishna, G. M., Malini, B. H., & Rao, K. N. (2001). Landuse/Landcover change detection through remote sensing and its climatic implications in the godavari delta region. Journal of the Indian Society of Remote Sensing, 29(1), 85–91. https://doi.org/10.1007/BF02989918

Tewabe, D., & Fentahun, T. (2020). Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science, 6(1), 1778998. https://doi.org/10.1080/23311843.2020.1778998

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Published

18-10-2022

How to Cite

[1]
M. A. Rahman, K. S. . Islam, S. I. . Siam, and S. . Islam, “SPATIOTEMPORAL CHANGE OF LAND USE LAND COVER: A CASE STUDY OF NARAYANGANJ SADAR UPAZILA, BANGLADESH”, Khulna Univ. Stud., pp. 233–243, Oct. 2022.

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