A remote sensing and GIS based approach for land use/cover, inundation and vulnerability analysis in Moscow, Russia
Choudhary K., Boori M.S., Kupriyanov A.V.

 

Samara National Research University, 443086, Russia, Samara, Moskovskoye Shosse 34;
American Sentinel University, 2260 South Xanadu Way, Suite 310, Aurora, Colorado 80014, USA;
IPSI RAS - Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, 443001, Samara, Russia;

The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

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Abstract:
Monitoring of land use/cover (LULC) change is very important for sustainable development planning study. This research work is to understand natural and environmental situation and its cause such as intensity, distribution and socio and economic effects in Moscow, Russia based on remote sensing and Geographical Information System techniques. A model was developed by following thematic layers: land use/cover, vegetation, soil, geomorphology and geology in ArcGIS 10.2 software using multi-spectral satellite data obtained from Landsat 7 and 8 for the years of 1995, 2005 and 2016 respectively. Increasing scientific and political interest in regional aspects of global environmental changes, there is a strong stimulus to better understand the patterns causes and environmental consequences of LULC expansion in the elevation of Moscow state, one of the areas in the nation with fast economic growth and high population density. A 70 to 300 m inundation land loss scenarios for surface water and sea level rise (SLR) were developed using digital elevation models of study site topography through remote sensing and GIS techniques by ASTER GDEM and Landsat OLI data. The most severely impacted sectors are expected to be the vegetation, wetland and the natural ecosystem. Improved understanding of the extent and response of SLR will help in preparing for adaptation.

Keywords:
LULC, Sea level rise, Landsat data, remote sensing and GIS.

Citation:
Choudhary K, Boori MS, Kupriyanov A.V. A remote sensing and GIS based approach for land use/cover, inundation and vulnerability analysis in Moscow, Russia. Computer Optics 2019; 43(1): 90-98. DOI: 10.18287/2412-6179-2019-43-1-90-98.

References:

  1. Amiri F, Rahdari V, Najafabadi SM, Pradhan B, Tabatabaei T. Multi-temporal Landsat images based on eco-environmental change analysis in and around Chah Nimeh reservoir, Balochestan (Iran). Environmental Earth Sciences 2014; 72(3): 801-809. DOI: 10.1007/s12665-013-3004-9.
  2. Bai J, Chen X, Yang L, Fang H. Monitoring variations of inland lakes in the arid region of Central Asia. Frontiers of Earth Science 2012; 6(2): 147-156. DOI: 10.1007/s11707-012-0316-0.
  3. Choudhary K, Boori MS, Kupriyanov A. Spatial modelling for natural and environmental vulnerability through remote sensing and GIS in Astrakhan, Russia. Egypt J Remote Sensing Space Sci 2017; 21(2): 139-147. DOI: 10.1016/j.ejrs.2017.05.003.
  4. Barbosa CCF. < lgebra de mapas e suas aplicações em Sensoriamento Remoto e Geoprocessamento. Dissertação (Mestrado em Sensoriamento Remoto). Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, 1997.
  5. Cui L, Ge Z, Yuan L, Zhang L. Vulnerability assessment of the coastal wetlands in the Yangtze Estuary, China to sea-level rise. Estuarine, Coastal and Shelf Science 2015; 156: 42-51. DOI: 10.1016/j.ecss.2014.06.015.
  6. Sweet WV, Park J. From the extreme to the mean: Acceleration and tipping points of coastal inundation from sea level rise. Earth’s Future 2014; 2(12): 579-600. DOI: 10.1002/2014EF000272.
  7. Yabuki H, Park H, Kawamoto H, Suzuki R, Razuvaev VN, Bulygina ON, Ohata T. Baseline Meteorological Data in Siberia (BMDS). Version 5.0, RIGC, JAMSTEC, Yokosuka, Japan, Distributed by CrDAP, Digital Media 2011. Source: < https://ads.nipr.ac.jp/kiwa/Graph.action?owner_site=ADS&fileIdentifier=A20131107-002&version=5.00# >.
  8. Choudhary K, Boori MS, Kupriyanov A. Landscape analysis through remote sensing and GIS techniques: a case study of Astrakhan, Russia. Proc SPIE 2017; 10225: 102251U. DOI: 10.1117/12.2266245.
  9. Grigio AM, De Castro AF, Souto MVS, Amaro VE, Vital H, Diodato MA. Use of remote sensing and geographical information system in the determination of the natural and environmental vulnerability of the Guamaré municipal district – Rio Grande do Norte – northeast of Brazil. Journal of Coastal Research 2004; III(SI 39): 1427-1431.
  10. Choudhary K, Boori MS, Kupriyanov AV. Mapping and evaluating urban density patterns in Moscow, Russia. Computer Optics 2017; 41(4): 528-534. DOI: 10.18287/2412-6179-2017-41-4-528-534
  11. Padgett J, Tapia C. Sustainability of natural hazard risk mitigation: Life cycle analysis of environmental indicators for bridge infrastructure. J Infrastruct Syst 2013; 19(4): 395-408. DOI: 10.1061/(ASCE)IS.1943-555X.0000138.
  12. Romero AF, Abessa DMS, Fontes RFC, Silva HG. Integrated assessment for establishing an oil environmental vulnerability map: Case study for the Santos Basin region, Brazil. Marine Pollution Bulletin 2013; 74(1): 156-164. DOI: 10.1016/j.marpolbul.2013.07.012.
  13. Nigel R, Rughooputh SDDV. Soil erosion risk mapping with new datasets: an improved identification and prioritization of high erosion risk areas. Catena 2010; 82(3): 191-205. DOI: 10.1016/j.catena.2010.06.005.
  14. Thakur AK, Sing S, Roy PS. Orthorectification of IRS-P6 LISS IV data using Landsat ETM+ and SRTM datasets in the Himalayas of Chamoli district, Uttarakhand. Curr Sci 2008; 95(10): 1458-1463.
  15. Courchamp F, Hoffmann BD, Russell JC, Leclerc C, Bellard C. Climate change, sea-level rise, and conservation: keeping island biodiversity afloat. Trends in Ecology and Evolution 2014; 29(3): 127-130. DOI: 10.1016/j.tree.2014.01.001.
  16. Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME. Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sens Environ 2005; 98(2-3): 317-328. DOI: 10.1016/j.rse.2005.08.006.
  17. Saboori B, Sulaiman J, Mohd S. Economic growth and CO2 emission in Malaysia: A cointegration analysis of the Environmental Kuznets Curve. Energy Policy 2012; 51: 184-191. DOI: 10.1016/j.enpol.2012.08.065.
  18. Mendoza ME, Granados EL, Geneletti D, Pérez-Salicrup DR, Salinas V. Analysing land cover and land use change processes at watershed level: a multitemporal study in the Lake Cuitzeo Watershed, Mexico (1975-2003). Appl Geogr 2010; 31(1): 237-250. DOI: 10.1016/j.apgeog.2010.05.010.
  19. Stefanov WL, Ramsey MS, Christensen PR. Monitoring urban land cover change: an expert system approach to land cover classification of semiarid to arid urban centers. Remote Sens Environ 2001; 77(2): 173-185.
  20. Thinh NX. Contemporary spatial analysis and simulation of the settlement development of the Dresden city region. 17th International Conference Informatics for Environmental Protection 2003: 253-261.
  21. Thinh NX, Arlt G, Heber B, Hennersdorf J, Lehmann I. Pin-pointing sustainable urban land-use structures with the aid of GIS and cluster analysis. In Book: Hilty LM, Gilgen PW, eds. Sustainability in the information society, 15th symposium informatics for environmental protection, metropolis. Marburg: Metropolis Verlag; 2013: 559-567.
  22. Weng QH. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management 2002; 64(3): 273-284. DOI: 10.1006/jema.2001.0509.
  23. Wilson EH, Hurd JD, Civco DL, Prisloe MP, Arnold C. Development of a geospatial model to quantify, describe and map urban growth. Remote Sens Environ 2003; 86(3): 275-285. DOI: 10.1016/S0034-4257(03)00074-9.
  24. Saaty TL. The analytic hierarchy process: Planning, priority setting, resource allocation. New York: McGraw Hill; 1980. ISBN: 978-0-07-054371-3.
  25. Wu KY, Ye ZY, Qi ZF, Zhang H. Impacts of land use/land cover change and socioeconomic development on regional ecosystem services: The case of fast-growing Hangzhou metropolitan area, China. Cities 2013; 31: 276-284. DOI: 10.1016/j.cities.2012.08.003.
  26. Yagoub M. Monitoring of urban growth of a desert city through remote sensing: Al-Ain, UAE, between 1976 and 2000. International Journal of Remote Sensing 2004; 25(6): 1063-1076. DOI: 10.1080/0143116031000156792.
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