DOI: https://doi.org/10.14710/geoplanning.5.1.1-16

SPATIAL EXPLICIT MODELING TO UNDERSTAND THE DYNAMICS OF LANDUSE SWITCH USING OPEN SOURCE SATELLITE DATA

Saifudheen Kalluvetty  -  Indian Institute of Remote Sensing (Indian Space Research Organisation), India, India
*Subhajit Bandopadhyay orcid  -  Meteorology Department, , Faculty of Environmental Engineering and Spatial Management, Poznan University of Life Sciences, Poland

Citation Format:
Abstract
Restless global urbanization needs to monitor in order to design a stable and sustainable urban habitat. In this regard, remote sensing and GIS are considered as an efficient monitoring and decision-support tool in sustainable urban planning and practices. In this paper we accumulate the results of a research undertaken to measure the urban sprawl and land use dynamics of the Dehradun city, Uttarakhand using vast sixteen years data and spatially explicit cellular automata CA-Markov model. Furthermore, future scenario of the city and land use was also examined. To achieve the desired goal, sixteen years large temporal images of Landsat were used to analyze the spatial decoration of land use change in the study area. The outcome of this study was clearly reviled that there was a substantial change was take place in the Dehradun city and its surroundings in last sixteen years. Modeling proposed a clear trend of various land use classes’ transformation in the area of urban built up expansions and urban encroachment whereas agricultural lands and forest covers are reduced at an alarming rate over the time. Dynamically increasing population of the city can be approximated by the predicted future scenarios. In order to promote a balance in between urban growth and environmental protection towards a sustainable urban habitat and environmental, local community involvement and capacity building program can be an efficient drive in this regard.
Fulltext View|Download
Keywords: Land-use change; urban sprawl; CA-Markov; sustainable urban habitat; Dehradun City

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
ESTIMATING MANGROVE FOREST DENSITY USING GAP FRACTION METHOD AND VEGETATION TRANSFORMATION INDICES APPROACH APPLICATION OF UAV WITH FISH-EYE LENSES CAMERA FOR 3D SURFACE MODEL RECONSTRUCTION INSIGHT ANALYSIS ON DYKE PROTECTION AGAINST LAND SUBSIDENCE AND THE SEA LEVEL RISE AROUND NORTHERN COAST OF JAVA (PANTURA) INDONESIA More recent articles
Most cited articles
MODEL SPASIAL STATISTIK KEPEMILIKAN SEPEDA MOTOR DI KECAMATAN BANYUMANIK, KOTA SEMARANG GIS-BASED LANDSLIDE SUSCEPTIBILITY ASSESSMENT AND FACTOR EFFECT ANALYSIS BY CERTAINTY FACTOR IN UPSTREAM OF JENEBERANG RIVER, INDONESIA SLUM REVITALIZING PLAN OF BAGHDADIYAH BY SPATIAL RE-MODELING CONFIGURATION MONITORING AND PREDICTING LAND USE-LAND COVER (LULC) CHANGES WITHIN AND AROUND KRAU WILDLIFE RESERVE (KWR) PROTECTED AREA IN MALAYSIA USING MULTI-TEMPORAL LANDSAT DATA DETEKSI PERUBAHAN PENGGUNAAN LAHAN DENGAN CITRA LANDSAT DAN SISTEM INFORMASI GEOGRAFIS: STUDI KASUS DI WILAYAH METROPOLITAN BANDUNG, INDONESIA More cited articles

  1. Aithal, B. H., Vinay, S., Durgappa, S., & Ramachandra, T. V. (2013). Modeling and Simulation of Urbanisation in Greater Bangalore, India. In Proc. of National Spatial Data Infrastructure 2013 conference, IIT Bombay (pp. 34–50).

  2. Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world’s largest megacity during the last 40years. Remote Sensing of Environment, 127, 210–222. [https://doi.org/10.1016/j.rse.2012.09.011">Crossref]

  3. Bandopadhyay, S. (2016). Does elevation impact local level climate change? An analysis based on fifteen years of daily diurnal data and time series forecasts. Pacific Science Review A: Natural Science and Engineering, 18(3), 241-253.

  4. Bandopadhyay, S. (2017). Integrated Spatial Platform for Better Flood Management and Mitigation for Local Governments: A Geospatial Approach. Geo-spatial Data in Natural Resources, 1-12 [https://doi.org/10.21523/gcb5.1701">Crossref]  

  5. Bardhan, R., Debnath, R., & Bandopadhyay, S. (2016). A conceptual model for identifying the risk susceptibility of urban green spaces using geo-spatial techniques. Modeling Earth Systems and Environment, 2(3). [https://doi.org/10.1007/s40808-016-0202-y">Crossref]

  6. Brown, D. G., Pijanowski, B. C., & Duh, J. D. (2000). Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59(4), 247–263. [https://doi.org/10.1006/jema.2000.0369">Crossref]

  7. Deep, S., & Saklani, A. (2014). Urban sprawl modeling using cellular automata. The Egyptian Journal of Remote Sensing and Space Science, 17(2), 179–187. [https://doi.org/10.1016/j.ejrs.2014.07.001">Crossref]  

  8. Glaeser, E., & Kahn, M. (2003). Sprawl and Urban Growth. National Bureau of Economic Research. [https://doi.org/10.3386/w9733">Crossref]

  9. Islam, M. S., & Ahmed, R. (2012). Land Use Change Prediction In Dhaka City Using Gis Aided Markov Chain Modeling. Journal of Life and Earth Science, 6. [https://doi.org/10.3329/jles.v6i0.9726">Crossref]

  10. Jat, M. K., Choudhary, M., & Saxena, A. (2017). Application of geo-spatial techniques and cellular automata for modelling urban growth of a heterogeneous urban fringe. The Egyptian Journal of Remote Sensing and Space Science, 20(2), 223–241. [https://doi.org/10.1016/j.ejrs.2017.02.002">Crossref]

  11. Jat, M. K., Garg, P. K., & Khare, D. (2008). Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10(1), 26–43. [https://doi.org/10.1016/j.jag.2007.04.002">Crossref]

  12. Memarian, H., Balasundram, S. K., Talib, J. Bin, Sung, C. T. B., Sood, A. M., & Abbaspour, K. (2012). Validation of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia. Journal of Geographic Information System, 04(06), 542–554. [https://doi.org/10.4236/jgis.2012.46059">Crossref]

  13. Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151–157.

  14. Myint, S. W., & Wang, L. (2006). Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing, 32(6), 390–404. [https://doi.org/10.5589/m06-032">Crossref]

  15. Nouri, J., Gharagozlou, A., Arjmandi, R., Faryadi, S., & Adl, M. (2014). Predicting Urban Land Use Changes Using a CA-Markov Model. Arabian Journal for Science and Engineering, 39(7), 5565–5573. [https://doi.org/10.1007/s13369-014-1119-2">Crossref]

  16. Park, M.-J., Park, G.-A., Lee, Y.-J., & Kim, S.-J. (2010). Application of the Modified CA-Markov Technique for Future Prediction of Forest Land Cover in a Mountainous Watershed. Journal of The Korean Society of Agricultural Engineers, 52(1), 61–68. [https://doi.org/10.5389/ksae.2010.52.1.061">Crossref]

  17. Pontius, G. R., & Malanson, J. (2005). Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2), 243–265. [https://doi.org/10.1080/13658810410001713434">Crossref]

  18. Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Mathematical and Computer Modelling, 54(3–4), 938–943. [https://doi.org/10.1016/j.mcm.2010.11.019">Crossref]

  19. Sun, H., Forsythe, W., & Waters, N. (2007). Modeling Urban Land Use Change and Urban Sprawl: Calgary, Alberta, Canada. Networks and Spatial Economics, 7(4), 353–376. [https://doi.org/10.1007/s11067-007-9030-y">Crossref]

  20. Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., & Dech, S. (2012). Monitoring urbanization in mega cities from space. Remote Sensing of Environment, 117, 162–176. [https://doi.org/10.1016/j.rse.2011.09.015">Crossref]

  21. Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2006). Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape and Urban Planning, 75(1–2), 69–80. [https://doi.org/10.1016/j.landurbplan.2004.12.005">Crossref]

  22. Yin, J., Yin, Z., Zhong, H., Xu, S., Hu, X., Wang, J., & Wu, J. (2011). Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979--2009) in China. Environmental Monitoring and Assessment, 177(1–4), 609–621.

  23. Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2–3), 317–328. [https://doi.org/10.1016/j.rse.2005.08.006">Crossref]


Last update:

  1. Enhancing Carbon Sequestration: A Systematic Literature Review of Spatial Decision Support Tools

    Ekaterina Tarasova, Riccardo Valentini, Giulio Di Lallo, Alexander Cotrina-Sanchez, Maria Vincenza Chiriacò. Sustainability, 16 (12), 2024. doi: 10.3390/su16125045

  2. Multi-approach synergic investigation between land surface temperature and land-use land-cover

    Prasenjit Saha, Subhajit Bandopadhyay, Chandan Kumar, Chandana Mitra. Journal of Earth System Science, 129 (1), 2020. doi: 10.1007/s12040-020-1342-z

Last update: 2024-11-21 07:10:37

  1. Multi-approach synergic investigation between land surface temperature and land-use land-cover

    Prasenjit Saha, Subhajit Bandopadhyay, Chandan Kumar, Chandana Mitra. Journal of Earth System Science, 129 (1), 2020. doi: 10.1007/s12040-020-1342-z