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2 October 2019 Analysis of climate change-urban vegetation land cover interaction through time-series satellite and field data
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Abstract
This paper investigated the influences of urban growth on thermal environment in relationship with other biophysical variables for Bucharest and Timisoara cities in Romania. Satellite remote sensing provides a cost-effective and time-saving methodology for spatio-temporal analyses of land surface temperature (Ts) distribution and urban thermal environment monitoring. Time series satellite remote sensing data from Landsat TM/ETM+/OLI, MODIS Terra/Aqua and NOAA AVHRR were used to assess urban vegetation land cover– temperature interactions over period between 2000 and 2018 years. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. For this purpose, a set of meteorological and climatic data recorded in synoptic stations were used. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at city level. Based on these parameters, the urban growth, and urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results show that the city area ratio of impervious surface in both towns Bucharest and Timisoara increased significantly during investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively.
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Maria A. Zoran, Roxana S. Savastru, Dan M. Savastru, Marina N. Tautan, Laurentiu A. Baschir, and Adrian I. Dida "Analysis of climate change-urban vegetation land cover interaction through time-series satellite and field data", Proc. SPIE 11157, Remote Sensing Technologies and Applications in Urban Environments IV, 111570R (2 October 2019); https://doi.org/10.1117/12.2532888
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