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Analyses are the power point of GIS because GIS can process and analyze different spatial and attribute data, leading to new results for supporting decision makers. The research aims to study advanced hydrologic analyses for the western part of Karbala province, Iraq. The hydrologic analysis is done based on where that DEM creates from the field survey method. This analysis gives digital maps and tables showing the region's main and minor hydrological properties, such as flow direction, flow accumulation, stream order, stream to feature, basin, and watershed maps. Also, it can be calculated as area, perimeter, lengths of streams, and numbers of stream orders for the main watersheds that are effective in the study area. These analyses are very important for making decisions in studying the hydrologic properties of any region in Iraq, and the GIS technique saves time, cost, and effort.

Irrigation has the largest share of water exploitation. Therefore, it is vital to access the irrigation performance in any region of the world. Furthermore, estimation of the evapotranspiration and evaluation of the Irrigation performance by using the traditional methods represented by the field measurements is very difficult. The integrated monitoring, diagnosing and assessment of the irrigation systems required the use of effective tools that enable the decision makers to study large area and analyses large number of the variables that concerns the processes of monitoring and assessment. The remote sensing and GIS techniques are considered as an efficient techniques that can be used for achieving such works. This paper reviews main topics directly related to diagnose irrigation water resources in Iraq using remote sensing and GIS techniques. It reviews and discusses the previous studies on diagnosing water resources using remote sensing and GIS. Also, it presents and synthesizes the methods and approaches used for estimation of evapotranspiration (ET) from remote sensing data and identifies the advantages and disadvantages of the previous studies on the evaluation of performance of irrigation systems. More ever, it reviews the important studies on rainfall-runoff modelling using GIS technique.&nbsp

Two common techniques for classifying satellite imagery are pixel-based and Feature extraction image analysis methods. Typically, for agreements reached imaging, pixel-based analysis is used, whereas high-resolution imagery is suitable for Feature extraction analysis. However, In the classification of moderate images, image segmentation's ability depending on criteria such as shape, color, texture, and spatial features in Feature extraction image analysis implies it can perform better than pixel-based analysis. A comparative study of the two methods was performed using Sentinel-2 imagery from 18 May 2020 to categorize LU/LC in the City of Baghdad, Iraq. After calculating LU/LC for Baghdad images' capital, a supervised classification was performed using the two methods. The images used have been the support vector machines (SVM) and the maximum likelihood classification (MLC) for pixel-based method and Feature extraction method, which is available in ENVI and ArcGIS software packages, respectively. Land cover and land use classes included five Groups (vegetation area, asphalt road, soil area, water body, and built-up) was found that the Feature extraction methodology produced higher overall accuracy and Kappa index in the city of Baghdad image. The highest achieved accuracy for the Feature extraction technique was overall accuracy 95% with Kappa index 0.94 of SVM and overall accuracy of 92% with Kappa index 0.90 of MLC. In comparison, the highest accuracy for the pixel-based was overall accuracy 88% with Kappa index 0.84 of SVM and overall accuracy 86% with Kappa index 0.82 of MLC.

Analyses are the power point of GIS because GIS can process and analyze different data such as spatial and attribute data, leading to obtaining new results for supporting decision-makers. This research aims to study two types of advanced analyses include; topographic and hydrologic analyses for the western part of Karbala in Iraq using GIS. The topographic analysis aims to know surface terrain and obtain the results of digital maps that show the simulation of the study area based on some methods and tools in GIS after creating DEM for study area from field survey methods such as slope, aspect, hill shades, and contour maps. After that, hydrologic analysis is done based on DEM of the study area, where this analysis gives digital maps that show the main and minor hydrological properties of the region, such as flow direction, flow accumulation, stream order, and basin maps. These analyses are very important for making decisions in studying the topographic and hydrologic properties of any region in Iraq, and the GIS technique offers a save in time, cost and efforts.

In this research support vector machine (SVM) method apply to classify the satellite image and produce land use and land cover (LULC) map. The used data is the multispectral Landsat-8 OLI satellite image with a spatial resolution of (30 × 30)m 2. However, the Karbala city was the study area. The SVM Applied with the default parameters of Kernel type, gamma in kernel function, penalty parameter and classification probability threshold. The SVM method achieved high accuracy in separating the categories of the study area based on the test samples collected from the study area in the Karbala province, Iraq. The classification training sites were selected based on visual interpretation and Google Earth Program. The image classification carried for six classes of the study area (Urban Area, Vegetation Area, Soil-1, Soil-2, Water Bodies and Roads). The results show a good accuracy of using SVM method based on relying on the capabilities and the precision of each pixel within the categories. The result evaluation was performed using the confusion matrix, the Kappa coefficient and the overall were 0.89 and 90.61% respectively. The SVM method is able to classify the land use and land cover of the study area with good and accurate results.

Flood is one of the most widely distributed hazards around the world and its management is an important concern for all decision maker this phenomenon. In Iraq, especially in Salah Al-Din Governorate, during the Spring 2019, the amount of water flowing has been increased into Tigris River, this led to a rise in the river level as a result of rains in close range and snow melting, as well as an increase in the drainage of the Small Zab, which is flowed into Tigris River at Salah Al-Din Governorate. Therefore, the flood hazard mapping and flood shelter analyses have been accomplished through the assessment of damage and determination the optimal location to make flood escape for mitigation the flood hazard processing. The Remote Sensing (R.S) technique and Geographic Information System (GIS) have been utilized to assess the damage by using the NDVI and NDWI indices. Arc Hydro tool in GIS software with the hydraulic model HEC-Ras software have been also utilized to find the flood escape and modeling the flood shelter. The result shows that the damaged areas at Salah Al-Din Governorate for agriculture land and flood inundation map.

Investigation of rainfall–run-off modelling is an important subject to develop any available means to water supply, which maintains human life such as run-off harvesting method. This study aims to analyse and understand the rainfall–run-off relationship in a part of Babil city, Iraq. Curve number which is a function of land use, soil texture, soil moisture and land slope is used in this study. Remote sensing and GIS are used to analyse the data and to produce the run-off depth map for the study area. Then, the run-off depth is used with rainfall to investigate the relationship between them using linear correlation. This study showed a strong linear relationship between rainfall and run-off (R2 = 0.992). It indicates that in the absence of rainfall data, run-off data can be used to estimate rainfall amount. Also, the study revealed through water balance analysis that there is an average monthly change in storage.

Remotely sensed imagery identifying as the best type of data has information throughout the world. The imagery has a importance information, since it can show up-date-to day information, and provide a truly information. Different kinds of classifiers have been used to perform that. However, there is no once test for Land cover and Land use in Hilla city. The study aims to create land use classification by making a comparison between different algorithms in Hilla city, Babylon, Iraq. The WorldView-2 imagery is used to perform the per-processing, analysing of our comparison. Under the steps of pre-processing, the several corrections were made and performed on the imagery. For processing stages, two approaches were used; (1) Support Vector Machine and (2) Maximum Likelihood. The result reveals, that the Support Vector Machine method has the most significant of overall accuracy equal to 94.48% with kappa coefficient equal to 0.90, and these values much better and higher than those of Maximum Likelihood algorithm in estimating and extracting of Land cover/Land use. Therefore, this algorithm has been suggested to be applied as an optimal classifier for extraction of land use maps due to its higher accuracy and better consistency within the study area.

Atmospheric correction is a main problem in visible or near-infrared remote sensing images since the existence of the atmosphere continuously influences the radiation from the ground to the sensor. Hence, atmospheric correction is necessary. Remotely sensed imagery has noise affected by atmospheric particles that can unclear the image and make quantitative analysis unreliable. The aim of this research is to evaluate atmospheric correction methods for remotely sensed imagery using ENVI software to get accurate results. In this research, three methods of atmospheric correction have been selected depend on the essential parameters whether from the image or field for hyperspectral imagery by using ENVI software. These methods include: IAR Reflectance, Flat field correction, and Empirical Line Correction. The results showed the corrected images from three methods give better interpretation of Z-profile than original image which helps to determine the absorption feature and increase the possibility to get a good result after processing. IAR Reflectance method gives easier spectral curve to be interpreted when comparison between radiance spectrum and reflectance spectra than other correction methods.

Because of the importance of the Marshlands and their vital role in economic and environmental aspects in the south of Iraq, it is necessary to know the changes in their land cover, especially water and natural vegetation for current period depending on image processing techniques for Landsat images. Hence, this research aimed to produce a land cover map for the marshes area using GIS and remotely sensed data. In this research, Landsat satellite images and set of maps were used to analyze and extract the information using GIS and remote sensing techniques. Two essential indices were extracted from satellite images: Transformed Normalized Difference Vegetation Index (TNDVI) and Normalized Difference Water Index (NDWI). A supervised classification was also used to produce a land cover map for the study area. After conducting all the necessary analyses, the results showed that there is an increase in the surface area of waters and natural vegetation of the marshes in the current period.

The classification of hyperspectral images is an interesting job since the data dimension is huge for conventional classification procedures; normally several hundreds of spectral bands are attained for each image. These spectral bands can supported very rich spectral data of each pixel to find objects material .The objective of this research is to classify hyperspectral images for detection and production of detailed minerals mapping using geological map and Environment for Visualizing Images (ENVI) software. In this research, ASTER data and geological map have been used. Some techniques on these data are used such as enhancement, matching (linking), De-correlation, Band Ratio, stacking image and classification. The results showed that comparison of the two classification results showed the classification of stack image with the aspect and the slope provide more information than classification of ASTER image alone. Also, using ENVI software to generate 3D surface views.It concluded that capability of hyperspectral and its differentiation with multispectral data to extract detailed features from ASTER image.