Geospatial assessment of tourism impact on land environment of Dehradun, Uttarakhand, India
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India’s tourism industry has emerged as a leading industry with a potential to grow further in the next few decades. Dehradun, one of the famous tourist places in India located in the state of Uttarakhand, attracts tourist from all over the country and abroad. The surge in tourist number paved the way for new infrastructure projects like roads, buildings, and hotels, which in turn affects the topography of the mountainous region. In this study, remote sensing and GIS techniques have been used to assess the impact of tourism on the land environment of Dehradun. Satellite images of the years 1972, 2000, and 2016 were analyzed using object-based image analysis (OBIA) to derive land use and land cover (LULC) and ASTER-DEM (Digital Elevation Model) was used to determine the topography of the study area. LULC classification includes built-up, vegetation, forest, scrub, agriculture, plantation, and water body. The slope of the region was categorized as gentle, moderate, strong, extreme, steep, and very steep. To assess the sprawl of built-up on high terrain land, built-up class of LULC was overlaid on slope classes. The overlay analysis reveals that due to increase in tourism, the land use in terms of the built-up area has been extended from gentle slope to very steep slope. The haphazard construction on the extreme, steep, and very steep slope is prone to landslide and other natural disasters. For this, landslide susceptibility maps have also been generated using multicriteria evaluation (MCE) techniques to prevent haphazard construction and to assist in further planning of Dehradun City. This study suggests that a proper developmental plan of the city is essential which follows the principles of optimum use of land and sustainable tourism.
KeywordsGIS Land use/cover DEM OBIA Sustainable tourism
Authors are thankful to the Director, CSIR-NEERI, Nagpur for providing necessary infrastructure and support to carry out this research study.
- Anderson, J.R. (1976) A land use and land cover classification system for use with remote sensor data. vol. 964. US Government Printing Office.Google Scholar
- Antonellini, M., Dentinho, T., Khattabi, A., Masson, E., Mollema, P. N., Silva, V., & Silveira, P. (2014). An integrated methodology to assess future water resources under land use and climate change: an application to the Tahadart drainage basin (Morocco). Environment and Earth Science, 71(4), 1839–1853.CrossRefGoogle Scholar
- Atik, M., Altan, T., & Artar, M. (2010). Land use changes in relation to coastal tourism developments in Turkish Mediterranean. Pol. J Environmental Studies, 19(1), 21–33.Google Scholar
- Baatz, M. (2000). Multi resolution Segmentation: an optimum approach for high quality multi scale image segmentation. Beutrage zum AGIT-Symposium (pp. 12–23). Salzburg: Heidelberg.Google Scholar
- Blaschke, T., & Strobl, J. (2001). What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GIS—Zeitschrift für Geoinformations Systeme, 14(6), 12–17.Google Scholar
- Bualhamam, M. R. (2009). The study of urban growth impact in tourism area using remote sensing and GIS technique for north part of the UAE. Journal of Geography and Regional Planning, 2(6), 166–175.Google Scholar
- Census of India (2011). District census handbook, Dehradun, Directorate of Census Operations, Series-06, Part XII-B http://www.censusindia.gov.in/2011census/dchb/0505_PART_B_DCHB_DEHRADUN.pdf.
- Chandel, V. B., Brar, K. K., & Chauhan, Y. (2011). Remote sensing & GIS based landslide hazard zonation of mountainous terrains a study from middle Himalayan Kullu district, Himachal Pradesh, India. Journal of Geoscience and Environment Protection, 2(1), 121–132.Google Scholar
- City Development Plan: Dehradun Revised (2007). Jawaharlal Nehru national urban renewal mission, Urban Development Department, Govt. of Uttarakhand https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwj44Jj0wdXQAhXFrY8KHdovAcoQFggaMAA&url=http%3A%2F%2Fwww.jnnurmmis.nic.in%2Ftoolkit%2FCDP_DEHRADUN.PDF&usg=AFQjCNFwNl56DToKruPlgCygcXl_oYMhNQ&sig2=myqDtDy8XWspaaHq84_tsA&bvm=bv.139782543,d.c2I.
- DMMR; 2017 State disaster management action plan for the state of Uttarakhand, Disaster Mitigation & Management Centre Uttarakhand Secretariat Rajpur Road, Dehradun, India.Google Scholar
- Hay, G.J. and Castilla, G. (2006). Object based image analysis: strength, weakness, opportunities and threats. The international archives of the photogrammetry, remote sensing and spatial information science.Google Scholar
- Irons, J.R., and Taylor, M.P., Laura, R. (2016). Landsat1. Landsat Science. NASA.Google Scholar
- Kuniyal, J. C., Vishvakarma, S. C. R., Badola, H. K., & Jain, A. P. (2004). Tourism in Kullu valley—an environmental assessment. Uttaranchal: GBPIHED.Google Scholar
- Navulur, K. (2007). Multispectral image analysis using the object-oriented paradigm. Boca Raton: CRC Press.Google Scholar
- NRC. (1998). Soil classification working group, the Canadian system of soil classification (3rd ed.). Canada: National Research Council (NRC) Research Press, Ottawa.Google Scholar
- Sarkar, S., & Gupta, P. K. (2005). Techniques for landslide hazard zonation-application to Srinagar-Rudraprayag area of Garhwal Himalaya. Journal of The Geological Society of India, 65(2), 217–230.Google Scholar
- Sharma, A., Singh, O.P., Saklani, M.M. (2012). Climate of Dehradun. Indian Meteorological Department, Ministry of Earth Sciences https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjg3PjJvNXQAhVEr48KHUKvDb0QFggaMAA&url=http%3A%2F%2Famssdelhi.gov.in%2Fnews_events%2FDehradun_Climate.pdf&usg=AFQjCNFOCUuRstPNrozkuiaaxyHo654Jg&sig2=UbtC5YjAwiWGIoo08PfCcQ&bvm=bv.139782543,d.c2I.
- Yin K. L., Yan T. Z. (1988) Statistical prediction model for slope instability of metamorphosed rocks. In: Proceedings of the 5th International Symposium on Landslides, Lausanne, Switzerland (Vol. 2, pp. 1269-1272).Google Scholar