The urban thermal island phenomenon has intensified in recent years due to the changes in urban airspace along with the rise of urbanization. Spatial-temporal patterns of biophysical constituents, which include vegetation, impermeable surfaces and soil type in the city, have a significant impact on urban thermal islands. The purpose of this study is to investigate the role of effective urban parameters in the formation and clustering of Yazd urban thermal islands. In order to achieve the proposed goal, the thermal map was developed using the single-window algorithm on the thermal band of OLT sensor of Landsat ETM+ sensors for August, 2015 and 2017; Land surface temperature (LST) was calculated and using spatial correlation (LISA), hot and cold clusters of thermal islands of Yazd were extracted. In order to evaluate the surface temperature, with the intensity of LST, spatial heterogeneity of the clusters increases nonlinearly. The relationship between the thermal islands with NDVI and urban carrion layers were investigated. Cold clusters are around the places with more green space and hot clusters are in the arid areas and in areas without vegetation cover. The result of the correlation between the surface temperature and the NDVI, NDBI, and NDBaI indicated that the relationship between NDVI and LST is negative, and the relationship between NDBaI and LST is also nonlinear and negative. But the relationship between NDBI and LST is nonlinear and positive. A spatial correlation with the local index has emphasized the extent of thermal islands in the studied periods تفاصيل المقالة

The earth surface is itself a complex system, and land cover variation is a complexprocess influenced by the interference of variables. In this study, the data of Sentinel 2for 2017 and 2016 were processed and classified to study the changes in the Andikaarea. After discovering vegetation changes between two images over the mentionedtime, vegetation increased by 661.74 hectares. Multiple regressions have been used toidentify factors affecting vegetation changes. Multiple regressions can explain therelationship between vegetation changes and the factors affecting them. In order toinvestigate the factors affecting vegetation change, altitude data, distance from theroad, distance from residential areas of the village and river were introduced intoregression equation. Since this method uses three parameters such as Pseudo-R2 andRelative Operation Characteristic (ROC(, 0.23, and 0.696 values for the aboveparameters, which indicates that the model is in good agreement. The results ofregression analysis show that linear composition of height variable as independentvariables in comparison with other parameters has been able to estimate vegetationchange. Subsequently, by using two classified pictures of 2017 and 2016, the amountof vegetation changes was calculated, and Markov chain method was used for 2018forecast changes. تفاصيل المقالة