Several azo dyes were synthesized through coupling reaetion of some substituted phenols and B.naphthol with diazonium salt of 2- amino-1,3-4- thiadiazol -5- thiol. All the synthesized compounds during this work were characterized using some speetral data (F.TIRand UV)andM.P . 2-[4 --Hydroxy napthyl-azo ] -1,3,4-Thiadiazol -5-Thiol • 2- [2-- hydroxy –4- NO2 – phenyl- azo]- 1,3,4 - Thiadiazol –5-Thiol. • 2- [3--Amino-4-Hydroxy phenyl –azo]-1,3,4 - Thiadiazol –5-Thiol. . • 2-[2--Amino-4-Hydroxy phenyl -azo]-1,3,4 - Thiadiazol –5-Thiol . • 2- [3--Amino-6- Hydroxy phenyl -azo]-1,3,4 - Thiadiazol –5-Thiol. • 2-[2-- Hydroxy- 5 – chloro – Pheny - azo]- 1,3,4 - Thiadiazol –5-Thiol . • 2- [4-- Hydroxy phenyl -azo] -1,

Shiranish formation has been divided into two microfacies units: 1-Marly biowacke stone facies 2-Biogenic pack stone facies These microfacies reflected marine deep shelf margin in the upper part of the formation, the lower part was deeper. 238 slides were investigated depending on Mineralogical, compositional and Biological processes, which reflect deep shelf margin at upper part of the formation, but at the lower part open sea environment. The age of the formation is estimated depending on the recognized biostratigraphic zone using the index fossils to be Upper- Middle Maestrichtian.

This study appears GIS techniqueand remote sensing data are matching with the field observation to identify the structural features such as fault segments in the urban area such as the Merawa and Shaqlawa Cities. The use of different types of data such as fault systems, drainage patterns (previously mapped), lineament, and lithological contacts with spatial resolution of 30m was combined through a process of integration and index overlay modeling technique for producing the susceptibility map of fault segments in the study area. GIS spatial overlay technique was used to determine the spatial relationships of all the criteria (factors) and subcriteria (classes) within layers (maps) to classify and map the potential ar

One of the main environmental problems which affect extensively the areas in the world is soil salinity. Traditional data collection methods are neither enough for considering this important environmental problem nor accurate for soil studies. Remote sensing data could overcome most of these problems. Although satellite images are commonly used for these studies, however there are still needs to find the best calibration between the data and real situations in each specified area. Landsat satellite (TM & ETM+) images have been analyzed to study soil pollution (Exacerbation of salinity in the soil without the use of abandoned agricultural for a long time) at west of Baghdad city of Iraqi country for the years 1990, 2001 & 2007. All of the th

This study investigates the changes occurring in the province of Basra using geospatial methods and analyzes the variations in land surface temperature among the various types of land cover. For the months of July and December in the years 2013 and 2021, Landsat images were used in Landsat 8 OLI/TIRS, and satellite images were processed using ArcGIS 10.8 software. The study's categories for land use and land cover were generated through the application of supervised classification techniques, and the land surface temperature was calculated using data from a satellite sensor's brightness temperature. According to the study's findings, there has been an increase in urban areas (including barren land). From 2013 to 2021, a greater correlati

The normalized difference vegetation index (NDVI) is an effective graphical indicator that can be used to analyze remote sensing measurements using a space platform, in order to investigate the trend of the live green vegetation in the observed target. In this research, the change detection of vegetation in Babylon city was done by tracing the NDVI factor for temporal Landsat satellite images. These images were used and utilized in two different terms: in March 19th in 2015 and March 5th in 2020. The Arc-GIS program ver. 10.7 was adopted to analyze the collected data. The final results indicate a spatial variation in the (NDVI), where it increases from (1666.91 𝑘𝑚2) in 2015 to (1697.01 𝑘𝑚2)) in 2020 between the t