Open AccessAssessing the impact of spatio-temporal land cover changes on land surface temperature using satellite data in Beas Valley, Himachal Pradesh, India
Author(s) -
Renu Lata,
Sayanta Ghosh
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/986/1/012050
Subject(s) - normalized difference vegetation index , land cover , environmental science , correlation coefficient , physical geography , remote sensing , land use , orchard , vegetation (pathology) , geography , satellite , agricultural land , hydrology (agriculture) , climate change , agriculture , geology , ecology , medicine , oceanography , statistics , mathematics , archaeology , geotechnical engineering , pathology , aerospace engineering , engineering , biology
Here, the impact of spatio-temporal Land Use Land Cover Changes (LULC) on the variations of Land Surface Temperature (LST) has been estimated using Landsat 7 ETM+, Landsat 8 TIRS/OLI and Sentinel-2 data for Beas basin of Kullu district, Indian Himalayan Region (IHR). Also, the relationship of various remote sensing indices such as NDVI, NDSI, NDBI and NDMI with LST were established using coefficient of correlation to monitor the role of natural ecosystem alteration on LST. LST was calculated using Single Window algorithm (SW) for eight major land cover categories extracted from Landsat-7 ETM+ and Landsat-8 (Path-147 and Row-38) Thermal Infrared Sensor (TIRS) data for October, 2000 and October, 2020 respectively. LULC change detection revealed that there was a huge increase in agricultural land including orchard expansion of 123 % during the year 2020 than that of 2000. Also there’s a sharp increase of 40.63 % in settlement areas which includes the tourism activities. These are the significant factors for the changes in LST. Further, it shows that negative correlation with the strong correlation coefficients of R 2 = 0.7072 and R 2 = 0.5642 between NDSI and LST in Pre-winter season of 2000 and that of 2020 respectively. Whereas, the correlation between NDVI and LST showed positive correlation with the coefficient of R 2 = 0.2577 in 2000, which increased to R 2 = 0.5959 in 2020. This positive relationship highlighted the fact that an increase in LST provides favourable conditions for the vegetation growth in the valley during the pre-winter season 2000 and 2020 respectively. The main outcome of the paper, we believe, will be helpful in analysing the dynamics of land cover changes and sustainable environmental planning in the Beas Valley, which is crucial for livelihood sustainability of the people residing in the already fragile IHR.