An appraisal of population growth and forest cover change in Rawalpindi using NDVI and Linear regression model
Paper Details
An appraisal of population growth and forest cover change in Rawalpindi using NDVI and Linear regression model
Abstract
Population growth and vegetation cover have an inverse relationship since population growth promotes human activities, urban expansion, land use changes, which consequently result in forest cover change. Decline in forest cover is a major factor behind climate change and a great environmental challenge for the present day world. The present study was an attempt to analyse the change in forest cover led by population growth in Rawalpindi division of Punjab-Pakistan during the last 25 years. The Data for population was obtained from Punjab Bureau of Statistics, Lahore. Landsat satellite images were used to assess the forest cover change temporally for the years 1990, 2000, 2010 and 2015. The normalized difference vegetation index (NDVI) technique was used to monitor the forest cover change and the linear regression was applied to find out the relationship between population growth and forest cover change within the study area. The results reveal that population grew up to 67.4% during 1990 to 2015 and due to this rapid population growth, the south western and south central part of the Rawalpindi division has witnessed great changes in vegetation cover and on overall basis the region is experiencing continual forest degradation especially after year 2010. The results of linear regression also confirm the strong relationship found between population growth and vegetation cover change within the region. The results of the study can help in further advancement for developing workable policies for forest cover conservation and management in the Rawalpindi region.
Agrawal A. 2012. Population pressure and forest degradation: an over simplistic equation. Forest Research Journal 23(3), 133-140.
Ahmad SS, Abbasi Q, Jabeen R, Shah MT. 2012. Decline of conifer forest in Pakistan: a GIS approach. Pakistan Journal of Botany 44(2), 511-515.
Ali J, Tor A, Benjaminsen L. 2004. Fuelwood, timber and deforestation in the Himalayas. Mountain Research and Development 24(4), 312-318.
Atzberger C. 2013. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing 5(2), 949-81.
Ellis S, Taylor DM, Masood KR. 1994. Soil formation and erosion in the Murree hills, northeast Pakistan. CATENA 22(1), 69-78.
Gandhi G, Meera SP, Thummalu N, Christy A. 2015. NDVI: vegetation change detection using remote sensing and GIS – a case study of Vellore district. Procedia Computer Science 57(1), 1199-1210.
Ghebrezgabher M, Mihretab G, Yang T, Yang X, Wang X, Khan M. 2016. Extracting and analyzing forest and woodland cover change in Eritrea based on Landsat data using supervised classification. The Egyptian Journal of Remote Sensing and Space Science 19(1), 37-47.
Government of the Punjab. 2015. Punjab Development Statistics 2015. Bureau of Statistics. Islamabad, Pakistan.
Ikehi EM. 2015. Forest resource degradation and sustainable practices in plateau state, Nigeria. Journal of Agriculture and Ecology Research International 2(1), 30-38.
Iqbal ZM, Iqbal JM. 2018. Land use detection using remote sensing and GIS:a case study of Rawalpindi division. American Journal of Remote Sensing 6(1), 39-51.
Jin S, Yang L, Danielson P, Homer C, Fry J, Xian G. 2013. A comprehensive change detection method for updating the national land cover database to Circa 2011. Remote Sensing of Environment 132(1), 159-175.
Kumar D. 2011. Monitoring forest cover changes using remote sensing and GIS: a global prospective. Research Journal of Environmental Sciences 5(2), 105-23.
Mehmood MM, Yaseen M, Badshah A, Khan J, Haroon A. 2017. Causes of deforestation and its geological impacts in Swat district, Khyber Pakhtunkhwa, Pakistan. Asian Journal of Environment & Ecology 5(4), 1-9.
Meneses-Tovar LC. 2012. NDVI as indicator of degradation. Unasylva 62(2), 201-215.
Mikwa FJ, Gossens R, Defourny P. 2016. Forest degradation: a methodological approach using remote sensing techniques: a review. International Journal of Innovation and Scientific Research 24(1), 161-78.
Misra AK, Lata K, Shukla JB. 2014. Effects of population and population pressure on forest resources and their conservation: a modeling study. Environment, Development and Sustainability 16(2), 361-74.
Mittal R, Mittal CG. 2013. Impact of population explosion on environment. The National Journal 1(1), 23-28.
Naim P, Abbasi A. 2005. Rapid environmental appraisal of developments in and around Murree Hills. IUCN-Pakistan 2(1), 1-16.
Nwakile TCT, Ejiofor E, Ali CC. 2017. Characterization of forest resources and their users for evolving management options for local users in Ozubulu community of Anambra state, Nigeria. International Journal of Multidisciplinary and Current Research 5(1), 837-80.
Saeed MA, Ashraf A, Ahmed B, Shahi M. 2011. Monitoring deforestation and urbanization in Rawal watershed area using remote sensing and GIS techniques. A Scientific Journal of COMSATS – SCIENCE VISION 16(1), 93-104.
Siddiqui Km, Mohammad I, Ayaz M. 2009. Forest ecosystem climate change impact assessmentand adaptation strategies for Pakistan. Climate Research 12(1), 195-203.
Tan KC, Lim HS, Jafri MZ, Abdullah K. 2010. Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences 60(7), 1509-1521.
Tanvir A, Shahbaz A, Suleri A. 2006. Analysis of myths and realities of deforestation in northwest Pakistan: implications for forestry extension. International Journal of Agriculture and Biology 8(1), 107-10.
Rubab Zafar Kahlon, Ibtisam Butt (2019), An appraisal of population growth and forest cover change in Rawalpindi using NDVI and Linear regression model; JBES, V15, N1, July, P76-85
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