Quantitative Assessment of Land Cover Change Using Landsat Time Series Data: Case of Chunati Wildlife Sanctuary (CWS), Bangladesh

Yıl 2016, Cilt 3, Sayı 2, 45 - 55, 02.08.2016

Kamrul Islam Mohammed Jasimuddin Biswajit Nath Tapan Kumar Nath

https://doi.org/10.30897/ijegeo.306471

Cited By: 13

Öz

Due to inappropriate management and absence of land use planning, land cover change of developing country like Bangladesh is a common phenomenon. This land cover change phenomenon will have its tremendous effect if found at a greater extent on natural habitat area for numerous animal and tree species. In this study, land cover change of Chunati Wildlife Sanctuary (CWS) was assessed from (2005-2015) using Landsat TM (Thematic Mapper) and Landsat 8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor). ArcGIS v10.1 and ERDAS Imagine v14 were used to process satellite derived imageries and assess other quantitative data for land cover change assessment of this study area. Land cover change of the area was identified using Normalized Difference Vegetation Index (NDVI) technique. Highest NDVI value was found in 2005 (0.71) which denotes presence of moderate-high vegetation cover at that time period. After 2005, highest NDVI value was found following a decreasing trend (0.56 in 2010 and 0.4 in 2015) which clearly represents the rapid vegetation cover change in the study area. Almost 7608 hectares of moderate to high density natural forest cover dominated the area during 2005 but was totally absent in 2015 which may be considered as a great threat regarding proper ecosystem functioning of CWS. From the findings of this research, it can be easily concluded that the sanctuary area has lost its valuable land cover both qualitatively and quantitatively.

Anahtar Kelimeler

Land Cover Change, Remote Sensing, NDVI, Chunati Wildlife Sanctuary, Landsat Satellite Imagery

Kaynakça

• Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Schweik, C. M. (2001). A Review and Assessment of Land-Use Change Models Dynamics of Space, Time, and Human Choice. CIPEC Collaborative Report Series No. 1, Center for the Study of Institutions Population, and Environmental Change Indiana University.
• Aspinall, R. (2004). Modelling land use change with generalized linear models - A multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana. Journal of Environmental Management, 72, 91–103.
• Campbell, J. B., Introduction to Remote Sensing, The Guilford Press, New York, 1987.
• Chuvieco, E., “El factor temporal enteledeteccio´n: evolutio´n fenomenolo´gica y ana´lisis de cambio, Revista de Teledeteccio´n, 10. 1 -9. 1998.
• Coffey, R., 2013. The difference between “land use” and “land cover” [www Document]. URL The difference between “land use” and “land cover”
• Ellis, E. (2013). Land-use and land-cover change. The Encyclopedia of Earth.
• FAO. (1995). Planning for sustainable use of land resources Towards a new approach. FAO Land and Water Bulletin 2, Rome.
• Geist, H. J. (2005). The Land-Use And Cover Change (Lulc) Project. Land Use, Land Cover And Soıl Sciences, I. Retrieved from http://www.eolss.net/sample-chapters/c19/E1-05.pdf
• Hoffer, R. M., Biological and Physical considerations in Applying Computer-aided analysis techniques to Remote sensor data, In Remote Sensing: The quantitative approach, edited by P. H. Swamand S.M. Davis Mc Graw-Hill.; U.S.A., 1978.
• Lambin, E. F. (1997). Modelling and monitoring land-cover change processes in tropical regions. Progress in Physical Geography, 21(3), 375–393.
• Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Folke, C., Veldkamp, T. A. (2001). The causes of land-use and land-cover change : moving beyond the myths. Global Envıronmental Change, 11, 261–269. Retrieved from www.elsevier.com/locate/gloenvcha.
• Lillesand, T. M., and Keifer, R. W., Remote Sensing and Image Interpretation; John Wiley and Sons, New York, 1979, 1994 and 2000.
• López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe. Landscape and Urban Planning, 55(4), 271–285.
• NSP. (2004). Site-Level Field Appraisal for Protected Area Co-Management: Chunati Wildlife Sanctuary. International Resources Group (IRG).
• Singh, A., “Review Article: Digital Change Detection Techniques using Remotely Sensed Data,” International Journal of Remote Sensing, 10. 989-1003. 1989.
• Tovar, C. L. Meneses., “NDVI as indicator of Degradation,” Unasylva, 62. 238. 2012.
• Veldkamp, A, and Lambin, E. . (2001). Predicting land-use change. Agriculture, Ecosystems & Environment, 85, 1–6.
• Veldkamp, A., L, F., 1997. Exploring land use scenarios, an alternative approach based on actual land use. Agric. Syst. 1–17.
• Vescovi, F. D., Park, S. J., and Vlek, P. L. (2002, September). Detection of human-induced land cover changes in a savannah landscape in Ghana: I. Change detection and quantification. In 2nd Workshop of the EARSeL Special Interest Group on Remote Sensing for Developing Countries. Bonn, Germany. Zeng, Y.N., Wu, G.P., Zhan, F.B., Zhang, H. H. (2008). Modeling spatial land use pattern using autologistic regression. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII, 115–118.