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Baghdad city has experienced a rapid urban expansion over the last decades due to accelerated economic growth. This paper reports an investigation into the application of the integration of remote sensing and geographic information systems (GIS) for detecting urban built up growth for the period 1961-2002, and evaluate its impact on surface temperature in Baghdad city. The purpose of this study is to analysis and verifies the spatial distribution property of the surface temperature with urban spatial information, related with land cover / land use and NDVI using remotely sensed data and GIS. Surface temperature, land cover pattern and NDVI were extracted from Landsat7 ETM + data. Then surface temperatures, was linked to land use data of Baghdad region for further investigations of the relationship between temperature behavior and urban structures. The Normalized Difference Vegetation Index (NDVI) was used to examine the relation between thermal behavior and vegetation cover amount. The results showed that the negative average correlation more than 85 % was identified by the results from the correlation and regression analysis of the extracted surface temperature from Landsat data image with NDVI. Also this research verified the distribution of urban surface temperature was very different depends on various land cover type of surrounding areas. Water and forest cover types show low day temperature differences compared to residential, commercial cover types. The integration of remote sensing and GIS was found to be effective in monitoring and analyzing urban growth patterns and in evaluating urbanization impact on surface temperature.

The aim of this paper is to study the water quality indices and their classifications for irrigation use at many stations along the Euphrates River inside the Iraqi lands and to try to correlate the results with the satellite image analyses for the purpose of making a colored model for the Euphrates that can be used to predict the quality classifications of the river for irrigation use at any point along the river. The Bhargava method was used to calculate the water quality index for irrigation use at sixteen stations along the river from its entrance to the Iraqi land at Al-Qaim in Anbar governorate to its union with the Tigris River at Qurna in Basrah governorate. Coordinates of the sixteen stations of the Euphrates River were projected at the mosaic of Iraq satellite image which was taken from LANDSAT satellite for bands 1, 2 and 3.It was noticed that there was a strong negative correlation between the water quality index and the digital numbers at band 2 for the mosaic satellite image. A regression model was built between the water quality index at December, 2009 and the digital number at band 2 so as to build a colored model which was used to predict a water quality classification for irrigation use at any point along the river.

The aim of this work is to produce an empirical model for the determination of particulate matter (PM10) concentration in the atmosphere using visible bands of Landsat 8 OLI satellite image over Kirkuk city-Iraq.The suggested algorithm is established on the aerosol optical reflectance model. The reflectance model is a function of the optical properties of the atmosphere, which can be related to its concentrations.The concentration of PM10 measurements was collected using Particle Mass Profiler and Counter in a Single Handheld Unit (Aerocet 531) meter simultaneously by the Landsat 8 OLI satellite image date. The PM10 measurement locations were defined by a handheld global positioning system (GPS).The obtained reflectance values for visible bands (Coastal aerosol, Blue, Green and blue bands) of landsat 8 OLI image were correlated with in-suite measured PM10.The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and rootmean-square error (RMSE) compared with the PM10 ground measurement data. A choice of our proposed multispectral model was founded on the highest value correlation coefficient (R) and lowest value of the root mean square error (RMSE) with PM10 ground data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM10 concentration to an acceptable level of accuracy.