This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

In this paper is investigating correlation between land surface temperature and vegetation indices (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index 2 - EVI2 and Modified Soil Adjusted Vegetation Index - MSAVI) using Landsat images for august, the warmest month, for study area. Iaşi county is considered as study area in this research. Study Area is geographically situated on latitude 46°48'N to 47°35'N and longitude 26°29'E to 28°07'E. Land surface temperature (LST) can be used to define the temperature distribution at local, regional and global scale. First use of LST was in climate change models. Also LST is use to define the problems associated with the environment. A Vegetation Indices (VI) is a spectral transformation what suppose spatial-temporal intercomparisons of terrestrial photosynthetic dynamics and canopy structural variations. Landsat5 TM, Landsat7 ETM+ and Landsat8 OLI, all data were used in this study for modeling. Landsat images was taken for august 1994, 2006 and 2016. Preprocessing of Landsat 5/7/8 data stage represent that process that prepare images for subsequent analysis that attempts to compensate/correct for systematic errors. It was observed that the “mean” parameter for LST increased from 1994 to 2016 at approximately 5°C. Analyzing the data from VI, it can be assumed that the built-up area increased for the Iasi county, while the area occupied by dense vegetation has decreased. Many researches indicated that between LST and VI is a linear relationship. It is noted that the R2 values for the LST-VI correlations decrease from 1994 (i.g.R2= 0.72 for LST-NDVI) in 2016 (i.g.R2= 0.23 for LST-NDVI). In conclusion, these correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur.

This study investigates the relationship between land surface temperature (LST) and urban indices (such as: Normalized Difference Bareness Index (NDBaI), Normalized Difference Buildup Index (NDBI) and Urban Index (UI)) using Landsat-8 OLI imagery, for Iasi municipality area. Landsat data were used to estimate the LST, NDBI, NDBaI and UI for Iasi municipality area, between 2013-2016. In that period, the mean temperature rose by about 2 0 C, so as to 2016 the mean temperature was of over 30 0 C and the maximum temperature exceeded 40 0 C. Analyzing the relationship between LST and urban indices it can be noticed that, whilst NDBI and UI indices have high a correlation with LST, the correlation between NDBaI and LST is lower. The R 2 fluctuates significantly from over 0.4 for NDBI and UI to about zero for NDBaI. Therefore, NDBI and UI are accurate indicators of SUHI effects, as against NDBaI and can be used as a complementary metric to the traditionally applied NDVI for analyzing surface urban heat island studies.

Identification of drought extent using NVSWI and VHI in Iaşi county area, Romania. Drought is a stochastic natural phenomenon that appears from considerable lacking in precipitation. Among natural hazards, drought is known to provoke extensive damage and affects a important number of people. Techniques for observing agricultural drought from R.S. are indirect. These depend on using images based parameters to exemplifed soil moisture condition when the soil is often obscured by a vegetation cover. The procedure are mainly based on determing vegetation health or greenness using VI , often in combination with canopy temperature anomalies using thermal infrared wavebands. In this study were used remote sensing images from the Landsat 8 OLI, taken in may and june 2017. The study area was the county of Iasi. To evaluate drought in this study, for Iasi county, Normalized Vegetation Supply Water Index (NVSWI) and Vegetation Health Index (VHI), were used. VSWI is derived from The Vegetation Supply Water Index (VSWI). This index was developed to combine the NDVI and the land surface temperature (LST) to detect the moisture condition. VHI was developed through a combination of Vegetation Condition Index (VCI), one of the important vegetation indicators when monitoring weather-related variations, such as droughts, and Temperature Condition Index (TCI), which reflects the stress of temperature, that both indicies can be successfully used to determine the spatiotemporal extent of agricultural drought. After applying NVSWI to determine the degree of drought we noticed that for the satellite image of May prevailed "slight drought" and for june "normal". Second index, VHI indicate that in both months, may and june, is "no drought". It can be concluded that VHI is a very good indicator for studing extreme drought and NVSWI offer information about areas "normal" and "wet".

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