ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume IV-5/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5/W2, 97–102, 2019
https://doi.org/10.5194/isprs-annals-IV-5-W2-97-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5/W2, 97–102, 2019
https://doi.org/10.5194/isprs-annals-IV-5-W2-97-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 Dec 2019

05 Dec 2019

SPATIO-TEMPORAL ANALYSIS AND MODELING OF URBAN GROWTH OF BIRATNAGAR CITY, NEPAL

S. Shrestha S. Shrestha
  • Photogrammetry and Remote Sensing Unit, Land Management Training Center, Government of Nepal

Keywords: Urban Growth, Image Classification, Spatial Metrics, Analytical Hierarchical Process, Multi-Criteria Analysis, Land Use Modeling

Abstract. Increasing land use land cover changes, especially urban growth has put a negative impact on biodiversity and ecological process. As a consequences, they are creating a major impact on the global climate change. There is a recent concern on the necessity of exploring the cause of urban growth with its prediction in future and consequences caused by this for sustainable development. This can be achieved by using multitemporal remote sensing imagery analysis, spatial metrics, and modeling. In this study, spatio-temporal urban change analysis and modeling were performed for Biratnagar City and its surrounding area in Nepal. Land use land cover map of 2004, 2010, and 2016 were prepared using Landsat TM imagery using supervised classification based on support vector machine classifier. Urban change dynamics, in term of quantity, and pattern was measured and analyzed using selected spatial metrics and using Shannon’s entropy index. The result showed that there is increasing trend of urban sprawl and showed infill characteristics of urban expansion. Projected land use land cover map of 2020 was modeled using cellular automata-based approach. The predictive power of the model was validated using kappa statistics. Spatial distribution of urban expansion in projected land use land cover map showed that there is increasing threat of urban expansion on agricultural land.