Spatiotemporal Dynamics of Urban Green Spaces in Dehradun City during 2000-2020
DOI:
https://doi.org/10.36808/if/2026/v152i6A/171343Keywords:
Land Use Land Cover (LULC), Remote sensing, Urbanisation, Landsat, Decadal change.Abstract
This study assessed the decadal changes in Urban Green Spaces (UGS) of Dehradun city, Uttarakhand, India, using Landsat satellite images for the years 2000, 2010, 2020. The study aimed to classify major land cover classes, map UGS, and assess temporal changes caused by rapid urbanisation. Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI datasets were processed using a hybrid classification approach integrating Maximum Likelihood Classification and Iterative Self Organising (ISO) data clustering techniques. The imagery was classified into agriculture, built-up, forest, scrub, and water classes. Accuracy assessment using 350 field verification points produced an overall classification accuracy of 90.9% with a Kappa coefficient of 0.886. The results showed a rapid increase in built-up area from 14.85% in 2000 to 47.33% in 2020, while agricultural land and scrub areas declined significantly. The study also revealed that institutional campuses and roadside corridors showed noticeable greening trends. The study demonstrates the usefulness of remote sensing and GIS techniques for monitoring urban expansion and sustainable management of UGS. The findings can support future urban planning and ecological conservation strategies.
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Bertram C. and Rehdanz K. (2015). The role of urban green space for human well-being. Ecological Economics, 120: 139–152.
Bhat P.A., ul Shafiq M., Mir A.A. and Ahmed P. (2017). Urban sprawl and its impact on landuse/land cover dynamics of Dehradun City, India. International Journal of Sustainable Built Environment, 6(2) : 513–521.
Budruk M., Thomas H. and Tyrrell T. (2009). Urban green spaces: A study of place attachment and environmental attitudes in India. Society and Natural Resources, 22(9): 824–839.
Dixon B. and Candade N. (2008). Multispectral landuse classification using neural networks and support vector machines: One or the other, or both? International Journal of Remote Sensing, 29(4): 1185–1206. https://doi.org/10.1080/ 01431160701294661
Dobermann A., Ping J.L., Adamchuk V.I., Simbahan G.C. and Ferguson R.B. (2003). Classification of Crop Yield Variability in Irrigated Production Fields. Agronomy Journal.
Dutta D., Rahman A. and Kundu A. (2015). Growth of Dehradun city: An application of linear spectral unmixing (LSU) technique using multi-temporal landsat satellite data sets. Remote Sensing Applications: Society and Environment, 1: 98–111.
FAO. (2009). How to Feed the World in 2050. Insights from an Expert Meeting at FAO. https://doi.org/10.1111/j.17284457.2009.00312.x
FAO. (2021). Food and Agriculture Organization report/ :State of. 82. https://www.fao.org/3/cb7654en/cb7654en.pdf
Gupta K. (2013). Unprecedented growth of Dehradun urban area: a spatio-temporal analysis. International Journal of Advancement in Remote Sensing, GIS and Geography, 1(2): 47–56.
Gupta P. and Goyal S. (2014). Urban expansion and its impact on green spaces of Dehradun city, Uttarakhand, India. International Journal of Environment, 3(4): 57–73.
Holl K.D. and Aide T.M. (2011). When and where to actively restore ecosystems? Forest Ecology and Management, 261(10): 1558–1563.
Ipcc. (2000). Summary for Policymakers: Emissions Scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Group, 20. https://doi.org/92-9169113-5
ISFR. (2021). India State of Forest report. Forest Survey of India, Ministry of Environment, Forest and Climate Change, Government of India, Dehradun.
Jana C., Mandal D., Shrimali S.S., Alam N.M., Kumar R., Sena D.R. and Kaushal R. (2020). Assessment of urban growth effects on green space and surface temperature in Doon Valley, Uttarakhand, India. Environmental Monitoring and Assessment, 192(4): 257.
Jat M.L., Dagar J.C., Sapkota T.B., Yadvinder S., Govaerts B., Ridaura S.L., Saharawat Y.S., Sharma R.K., Tetarwal J.P., Jat R.K., Hobbs H. and Stirling C. (2016). Climate change and agriculture: Adaptation strategies and mitigation opportunities for food security in South Asia and Latin America. In Advances in Agronomy, (Vol. 137). Elsevier Inc. https://doi.org/10.1016/bs.agron.2015.12.005
Johnson C.A. and Krishnamurthy K. (2010). Dealing with displacement: Can “ social protection” facilitate long-term adaptation to climate change? Global Environmental Change, 20(2010): 648–655. https://doi.org/10.1016/j.gloenvcha.2010.06.002
Joshi P.K.K., Roy P.S., Singh S., Agrawal S. and Yadav D. (2006). Vegetation cover mapping in India using multi-temporal IRS Wide Field Sensor (WiFS) data. Remote Sensing of Environment, 103(2): 190–202.
Keenan R.J., Reams G.A., Achard F., de Freitas J.V., Grainger A. and Lindquist E. (2015). Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. In Forest Ecology and Management. https://doi.org/10.1016/j.foreco.2015.06.014
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