A climatological study of the spatio-temporal variability of land surface temperature and vegetation cover of Vadodara district of Gujarat using satellite data

Land use land cover (LULC) of city regions is strongly affected by urbanization and affects the thermal environment of urban centers by influencing the surface temperature of core city areas and their surroundings. These issues are addressed in the current study, which focuses on two provincial capitals in Pakistan, i.e., Lahore and Peshawar. Using Landsat data, LULC is determined with the aim to (a) examine the spatio-temporal changes in LULC over a period of 20 years from 1998 to 2018 using a CA-Markov model, (b) predict the future scenarios of LULC changes for the years 2023 and 2028, and (c) study the evolution of different LULC categories and investigate its impacts on land surface temperature (LST). The results for Peshawar city indicate the significant expansion in vegetation and built-up area replacing barren land. The vegetation cover and urban area of Peshawar have increased by 25.6%, and 16.3% respectively. In contrast, Lahore city urban land has expanded by 11.2% while vegetation cover decreased by (22.6%). These transitions between LULC classes also affect the LST in the study areas. Transformation of vegetation cover and water surface into built-up areas or barren land results in the increase in the LST. In contrast, the transformation of urban areas and barren land into vegetation cover or water results in the decrease in LST. The different LULC evolutions in Lahore and Peshawar clearly indicate their effects on the thermal environment, with an increasing LST trend in Lahore and a decrease in Peshawar. This study provides a baseline reference to urban planners and policymakers for informed decisions.

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Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2011.09.007 ◽

2013 ◽

Vol 20 ◽

pp. 4-19 ◽

Cited By ~ 41

Author(s):

R. Van De Kerchove ◽

S. Lhermitte ◽

S. Veraverbeke ◽

R. Goossens

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Temporal Variability ◽

Land Surface ◽

Remotely Sensed ◽

Altay Mountains ◽

Spatio Temporal

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Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

Remote Sensing ◽

10.3390/rs11172016 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2016

Author(s):

Lijuan Wang ◽

Ni Guo ◽

Wei Wang ◽

Hongchao Zuo

Keyword(s):

Surface Temperature ◽

Diurnal Variation ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Regional Scale ◽

Pso Algorithm ◽

Variation Characteristics ◽

Split Window Algorithm ◽

Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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