Land Use Land Cover Change Detection and Its Impact on Land Surface Temperature of Malana Watershed Kullu, Himachal Pradesh, India

Abstract

Land Surface Temperature (LST) is an important phenomenon in global warming, forest fire, glacier melting, global environmental change, and human-environment exchanges. In the present study, an attempt has been made to quantitatively evaluate and detect the Land Use Land Cover (LULC) change analysis and the associated changes in the LST of Malana Watershed, in district Kullu, Himachal Pradesh of the Indian Himalayan Region (IHR). Due to various natural phenomena and anthropogenic activities. i.e., hydropower development and roads construction, the LULC pattern of the earth’s surface is undergoing a fleeting change. To assess the LULC change pattern, satellite imageries were acquired for the years 2001 of Landsat-7 Enhanced Thematic Mapper Plus (ETM+), 2011 of Landsat-5 Thematic Mapper (TM), and 2017 of Resourcesat-2, Linear Imaging Self-Scanning Sensor (LISS -IV). The Supervised classification method using the Support Vector Machine (SVM) algorithm was applied in Environmental Visualization Imagine (ENVI) 5.3 and ERDAS Imagine 2015 and the study area was classified in total 8 LULC classes viz. glacier, water bodies, built-up, forest green, vegetation, agricultural land, barren land, open/rocky land. From 2001–2017, the results revealed that the study area has undergone a substantial increase and decrease in the LULC change pattern. Glaciers, forest green, and vegetation areas have declined from 2001 to 2017; for which the main causing factors include an increase in atmospheric temperature and anthropogenic activities. The current research study indicate that the area has witnessed a substantial increase and decrease in the LULC for different categories. There has been a considerable decrease in the glacier area from 39.19 km2 (in 2001) to 32.97 km2 (in 2017) and the area under Built-up/settlements categories has been increased from 0.1 km2 (in 2001) to 0.14 km2 (in 2017). For the estimation of LST, thermal bands of the Landsat series satellites were used with both mathematical algorithm such as Single Channel (SC) and Split Window (SW) algorithm. Normalized Difference Vegetation Index (NDVI) was estimated for deriving Land Surface Emissivity (LSE). The spatial distribution of LST of Landsat-7 ETM+ (18th October 2001) ranged from minimum 263.54 Kelvin (K) to maximum of 292.23 K, Landsat-5 TM (22nd October 2011) ranged from minimum 265.13 K to maximum of 288.61 K and for Landsat-8 OLI, (06th October 2017) ranged from minimum 264.89 K to maximum of 294.75 K. A decrease in the glacial area attributes to an increase in the barren and open/rocky areas as reflected in the results. Built-Up and agricultural land witnessed an increase in the area owing to the developing tourism in Parbati valley and altitudinal expansion of the horticultural area. The area under water bodies also increased accounting for the variation in the flow of the river.KeywordsLand use land coverLand surface temperatureLandsat-8SVMSplit window