The Response of Land Surface Temperature to the Changing Land-Use Land-Cover in a Mountainous Landscape under the Influence of Urbanization: Gilgit City as a case study in the Hindu Kush Himalayan Region of Pakistan - Aftab Ahmed Khan, Syed Najam ul Hassan, Saranjam Baig, Muhammad Zafar Khan, Amin Muhammad (International Journal of Economic and Environment Geology, 2019)
With growing urbanization in mountainous landscapes, the built-up areas dominateother land use classes resulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendous urban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the near future because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be the commercial hub of the northern region of Pakistan. The objective of present study is to explore the influence of land use and land cover variations on LST and to evaluate the relationship between LST with normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/cover classes are determined through supervised classification and LST maps are created using the Mono-window algorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitized vector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI maps are computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, and NDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and land cover play a substantial role in LST variability. The maximum temperatures are connected with built-upareas and barren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimum temperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016 respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI and NDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners to strategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainous landscapes