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You are here: Home » Latest Issue » Volume 15, 2020 - Number 2 »
EVALUATION OF URBAN AREAS BY REMOTE SENSING METHODS IN RELATION TO CLIMATIC CONDITIONS: CASE STUDY CITY OF TIMISOARA, Carpathian Journal of Earth and Environmental Sciences, August 2020, Vol. 15, No. 2, p. 327 – 337; Doi:10.26471/cjees/2020/015/133


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Mihai Valentin HERBEI1 & Florin SALA2*
Banat University of Agricultural Sciences and Veterinary Medicine "King Michael I of Romania" from Timisoara, Timișoara, 300645, Romania; 1Remote Sensing and GIS, 2Soil Science and Plant Nutrition
E-mail: florin_sala@usab-tm.ro


EVALUATION OF URBAN AREAS BY REMOTE SENSING METHODS IN RELATION TO CLIMATIC CONDITIONS: CASE STUDY CITY OF TIMISOARA, Carpathian Journal of Earth and Environmental Sciences, August 2020, Vol. 15, No. 2, p. 327 – 337; Doi:10.26471/cjees/2020/015/133


Full text

Abstract:

The study used methods based on remote sensing to evaluate the urban area of Timisoara City in relation to the climatic conditions. Satellite images were taken from the Landsat 8 system. The study interval was between August 9, 2013 and August 7, 2018. The images were taken in August, an expressive month in thermal aspect for the studied area. The spectral information from the satellite images was analyzed using specific indices, such as: Land Surface Temp - LST, Normalized Difference Built-Up Index – NDBI, and Normalized Difference Vegetation Index – NDVI, respectively. For the interpretation of the values of the indices, the climatic data were taken into account for the period January - July of each analyzed year (P1 - P7, precipitation in January-July; T1-T7, average monthly temperature in January-July). There were registered very strong, negative and positive correlations (NDVI with NDBI, r = -0.998; LST with P7, r =-0.976; LST with T4, r =-0.984; NDVI with P7, r =0.900). Also, strong negative or positive correlations were recorded (LST with P6, r=-0.891; LST with T5, r =-0.889; NDVI with LST, r=0.824; NDVI with T4, r =0.883). Depending on the time factor (T), the variation of indices was described by smoothing spline model (LST vs. T), or by models of the type of polynomial equations of degree 2 (NDBI vs T, R2 =0.965, p <0.05; NDVI vs. T, R2 =0.986, p <0.01). Multiple regression analysis led to obtaining 3D and isoquant variation models of NDVI and LST indices depending on T7 and P6.



Keyword: isoquant, LST, mathematical model, NDVI, NDBI, urban ecosystem


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