This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LANDSAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the measurements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the imaged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measurement taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LANDSAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.

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The research deals with the thermal island in Baghdad city and the effect of urban expansion on it. Cities are suffering from a marked rise in temperature, compared with the surrounding rural areas, this problem occurs in most cities of the world. The reason for their formation is the increasing of human activity in the form of city components, such as buildings and roads that replace green spaces and open spaces. This means that most of these materials have the ability to absorb solar radiation and convert it into heat energy that increases the heat of their environment. Remote sensing technique is used to determine the thermal island through using Landsat satellite images as well as the use of GIS technology in mapping and analysis of spatial variation for thermal island. Normalized Difference Vegetation Index (NDVI) is calculated to extract vegetation cover from 2003 to 2018. The land surface temperature is estimated from the thermal band of satellite images for the same period. The change in vegetation cover is linked to the change in land surface temperature to determine the effect of vegetation cover degradation on surface temperature, also to determine the relationship between thermal island and land uses. The values of NDVI was high in 2003 and it ranged from (-0.714 - 0.693); this indicates a high vegetal cover, while its value is decreased to (-0.22 - 0.509) in 2018, pointing out to the significant deterioration of vegetation cover in 15 years. The land surface temperature is increased from (10.93-36.26) in 2003 to (22.62-50.29) in 2018; all this at the expense of converting the green, open and agricultural areas to residential, commercial and industrial uses as well as the large number of random settlements that appeared in Baghdad after 2003.

The Iraqi Marshlands has natural and economic potential through its environment rich in various forms of lives. This region has suffered numerous setbacks due to human and natural factors, especially in the last two decades of the last century, which led to significant environmental degradation. The purpose of this paper is to prepare spatial data for the area of the marshes in Iraq as a base (Hour-al Hoveizah and central marshes and Hammar). Also, the other aim is to produce a digital geodatabase for the marshes for the years 1973, 1986, 1999, 2006 and 2016 by using ArcGIS. The process of building geodatabase has been through done in three stages: the first stage is including data collection. The second stage will be by merging the satellite images covering the Iraqi marshes and add to images in order to get the image mosaic process. Also, a georeferencing of satellite images is to be done with all the traditional maps of the same area of the marsh. Finally, complete the full geodatabase for the area of interest by using ArcGIS as the in Cartography Design. The results of this research would be a geodatabase for the Iraqi marshes.

A precise location in aerial surveying can only be achieved using Ground Control Points GCPs. At least three point should be used and as the number increases the model will be more precise in X, Y and Z positions for a certain extent. The distribution of the GCPs also affect the precision of the 3D model resulted from the aerial imaging. This study aims to find the optimum number and distribution of the GCPs to achieve the minimal error in points location. 1.5 km2 of longitudinal area was imaged with a commercial UAV named DJI Mavic 2 pro with at nadir camera orientation at height of 100 m above the ground. A total of 1515 images were taken with average ground sampling distance (GSD) of 2.3 cm. Deferential Global Positioning System DGPS Leica GS 15 receiver were used to observe the 62 ground control points with PPK fashion. The project area was divided into two regions the first region has a parallel distribution of GCPs and the second region has a zigzag distribution. The images were processed using Pix4Dmapper and Agisoft Metashape software by applying a bundle adjustment process with an incremental number of GCPs starting with 3 and finishes with 26 for each distribution pattern, the remaining points were used as a check points to determine the precision of the model at each trial. The resulted coordinates of check points were compared with the coordinates observed with the DGPS. The comparison depicts the optimum number of GCPs required for the best location precision and the best distribution pattern.