ABSTRACT:Urban Heat Island (UHI) refers to the phenomena of higher surface temperature occurring in urban areas as compared to the surrounding countryside attributable to urbanization. Spatio-temporal changes in UHI can be quantified through Land Surface Temperature (LST) derived from satellite imageries. Spatial variations in LST occur due to complexity of land surface -combination of impervious surface materials, vegetation, exposed soils as well as water surfaces. Jaipur city has observed rapid urbanization over the last decade. Due to rising population pressure the city has expanded considerably in areal extent and has also observed substantial land use/land cover (LULC) changes. The paper aims to determine changes in the LST and UHI phenomena for Jaipur city over the period from 2000 to 2011 and analyzes the spatial distribution and temporal variation of LST in context of changes in LULC. Landsat 7 ETM+ (2000) and Landsat 5 TM (2011) images of summer season have been used. Results reveal that Jaipur city has witnessed considerable growth in built up area at the cost of greener patches over the last decade, which has had clear impact on variation in LST. There has been an average rise of 2.99 ºC in overall summer temperature. New suburbs of the city record 2º to 4⁰ C increase in LST. LST change is inversely related to change in vegetation cover and positively related to extent of built up area. The study concludes that UHI of Jaipur city has intensified and extended over new areas.
A host of remote-sensing and mapping applications require both high spatial and high spectral resolutions. Availability of high spatial and spectral details at different resolutions from a suite of satellite sensors has necessitated the development of effective image fusion techniques that can effectively combine the information available from different sensors and take advantage of their varied capabilities. A common problem observed in the case of multi-sensor multi-temporal data fusion is spectral distortion of the fused images. Performance of a technique also varies with variation in scene characteristics. In this article, two sets of multi-temporal CARTOSAT-1 and Indian Remote Sensing satellite (IRS-P6) Linear Imaging and Self Scanning sensor (LISS-IV) image sub-scenes, with different urban landscape characteristics, are fused with an aim to evaluate the performance of five image fusion algorithms -high-pass filtering (HPF), Gram-Schmidt (GS), Ehlers, PANSHARP and colour-normalized Brovey (CN-Brovey). The resultant fused data sets are compared qualitatively and quantitatively with respect to spectral fidelity. Spatial enhancement is assessed visually. The difference in the performance of techniques with variation in scene characteristics is also examined. For both scenes, GS, HPF and PANSHARP fusion techniques produced comparable results with high spectral quality and spatial enhancement. For these three methods, the variation in performance over different scenes was not very significant. The Ehlers method resulted in spatially degraded images with a more or less constant negative offset in data values in all bands of one scene and in the first two bands in the other. The CN-Brovey method produced excellent spatial enhancement but highly distorted radiometry for both sub-scenes.
Land use land cover (LULC) changes on the surface of the earth are classic manifestation of the relationship between man and his environment. Various studies have analyzed LULC changes and land surface temperatures (LST) to study the environmental livability and sustainability, especially of urban areas. The combination proves reasonable to understand the variations in surface heat fluxes due to changing landscape dynamics. The present study investigates LST variations over Bhilwara district in correspondence to the land cover distribution. Multi-spectral satellite data of Landsat 8 OLI and TIRS (October, 2017) have been used to derive LULC and LST patterns in the region. Supervised classification using maximum likelihood classifier has been employed to map seven LULC classes: water body, agriculture cropped, agriculture fallow, vegetation/grass, built-up, scrub and barren. Thermal bands of the satellite data have been used to estimate LST by applying NDVI threshold methods. Results show a high correlation between spatial patterns of LULC and LST. 'Agriculture fallow' and 'barren' classes correspond to highest surface temperatures followed by 'scrub' and 'built-up' while the lowest temperatures are recorded over 'water' and 'vegetation/grass'. The study underlines immense potential of geospatial technique to address dynamic environmental issues at regional level.
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