School Of Environment And Earth Sciences

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    Spatial variations of LST and NDVI in Muzaffarpur district, Bihar using Google earth engine (GEE) during 1990-2020
    (Association of Agrometeorologists, 2023-05-30T00:00:00) Sajan, Bhartendu; Kanga, Shruti; Singh, Suraj Kumar; Mishra, Varun Narayan; Durin, Bojan
    The aim of this study is to analyze land cover changes and their effects on land surface temperature (LST) and normalized difference vegetation index (NDVI) in Muzaffarpur district, Bihar, India. The research utilized Landsat 5 and 8 satellite images taken every five years from 1990 to 2020 to classify seven land cover types, namely built-up areas, wetlands, fallow lands, croplands, vegetation, and water bodies, using the Artificial Neural Network technique in ENVI 5.1. The resulting land cover maps reveal a significant decrease in cropland area during the studied period, while fallow land area decreased from 48.06% to 35.79%. Analysis of LST and NDVI data showed a strong negative correlation (R2 <-0.0057) for all years, except for a weak positive correlation (R2 > 0.006). NDVI values were highest in agricultural lands with the lowest LST values, while fallow land areas showed the opposite trend. The study suggests that vegetation and fallow land are crucial determinants of the spatial and temporal variations in NDVI and LST, relative to urban and water cover categories. � 2023, Association of Agrometeorologists. All rights reserved.
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    Spatial trends of surface urban heat island in Bathinda: a semiarid city of northwestern India
    (Institute for Ionics, 2021-10-25T00:00:00) Kaur, R.; Pandey, P.
    The rising global temperature coupled with the urban heat island has considerable adverse impacts on urban inhabitants and ecological integrity. An attempt has been made in the present study to monitor the surface urban heat island effect for the Bathinda District of Punjab, India. The surface urban heat island effect was monitored for the period of 5�years (2015�2019) using Landsat 8 OLI/TIRS data. The surface temperature distribution pattern was investigated by spatial extension and statistical analysis of land surface temperature dataset. The spatial autocorrelation among the data was analyzed using Moran�s Index and Getis-Ord Gi* statistics. Besides, the impact of land use land cover on land surface temperature was examined using correlation, covariance and multivariate analysis between land surface temperature and spectral indices. The results revealed that the vegetated and water surfaces accounted for low surface temperature (19.98�30.45��C), while built-up areas with high temperature (26.47�44.01��C) had amplified the heat island effect. The spatial autocorrelation with Moran�s Index (above 0.5) confirmed the spatial clustering with low p values (< 0.001) and high z values (> 2.58). Further, the hot spot analysis validated that the higher-temperature pixels lie in urban areas with dense infrastructure, while vegetated areas exhibit clusters with low-temperature values. Hence, the study inferred the occurrence of surface urban heat island with the urban heat island index of 0.7�1 for the urban cluster. The correlation between spectral indices and land surface temperature urges the need of adequate urban planning with vital urban greening, in order to achieve the urban sustainable development goals. Graphic abstract: [Figure not available: see fulltext.] � 2021, Islamic Azad University (IAU).