Al-Hawizeh Marsh is considered a major marsh in the south of Iraq with a longitude of 47° 32″–47° 45″ and latitude of 31° 30″–31° 42″, length of 80 km and width of 30 km, while the depth is from 1.5 to 4 m. It has a significant impact on the ecosystem and provides habitat for several types of living creatures. The surface water and the agricultural lands surrounding this marsh suffered from high level degrees of salinity. In the last decades, radical deterioration of water quality in this marsh happened because of the post-war-related events and several human activities. Landsat-8 data was used to predict and evaluate the spatial variation and map distributions of the salinity, SO₄ and CaCO₃ within Al-Hawizeh Marsh during the two seasons in the year 2017 based on the development of salinity and minerals mathematical equations. The evaluated values for salinity, SO₄ and CaCO₃ are found to be minimal in winter and maximum in autumn. The values of correlation coefficient (R²) between the real data and the equation results for the salinity, SO₄ and CaCO₃ during the two seasons are 0.95, 0.96 and 0.92, respectively.

Using solely an optical remotely sensed dataset to obtain an accurate thematic map of land use and land cover (LU/LC) is a serious challenge. The dataset fusion of multispectral and panchromatic images play a big role and provide an accurate estimation of LU/LC map simply because using a dataset from different spectrum portions with different spatial and spectral characteristics will improve image classification. For this study, the Landsat operational land imager multispectral and panchromatic images were adopted. This study aimed to investigate the effectiveness of using a panchromatic highly spatial resolution to refine the methodology for LU/LC mapping in Baghdad city, Iraq, by performing a comparison of classifications using different algorithms on multispectral and fused images. Different classification algorithms were employed to classify the data set; minimum distance (MD) and the maximum likelihood classifier (MLC). A suitable classification method was proposed to map LU/LC based on the outcome results. The result evaluation was conducted by applying a confusion matrix. An overall accuracy of a fused image using a principal component-based spectral sharpening algorithm and classified by the MLC classifier reveals the highest accurate results with an overall accuracy and kappa coefficient of 98.90% and 0.98, respectively. Results showed that the best methodology for LU/LC mapping of the study area is found from fusion of multispectral with panchromatic images via principal component-based spectral algorithm with MLC approach for classification.

Remote sensing has become a central factor in approaches to managing natural resources and observing environmental fluctuations. Urban development has brought severe losses of farming land, vegetation land and water bodies. Urban sprawl is responsible for a variety of urban environmental issues including reduced air quality, increased local temperature and reduction in water quality. In this study, we have taken the city of Baghdad as a case study and explore the land use and land cover variation that took place over approximately 28 years from 1990 to 2018. Remote sensing practice was implemented to analyse the city of Baghdad’s land cover and land use changes throughout the study period. Landsat TM and OLI 8 images of Baghdad were collected from the USGS Earth Explorer website. Having pre-processed the image, we used supervised classification to categorize the images in different land cover classes. The study region was classified into five categories: urban area, water bodies, vegetative area, barren land and wetland. The accuracy assessment of classification we obtained was 85.11% and 88.14%. From these results, change detection analysis shows that urban area and soil land levels have gone up by 3% and 20%, respectively. In another area the vegetation has diminished, wetlands and water bodies have also decreased by 5%, 17%, and 1% respectively.

Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.

Embankment dams have many advantages; however, they frequently develop seepage problems which may cause dams’ failure. In this study, comparison analysis of seepage through embankment dams was conducted using three different methods, namely experimental tests, mathematical calculations and numerical modeling. Three homogeneous embankment dam models with different downstream drainage filters were considered. Results revealed that SEEP/W model is inappropriate to compute the water flow volume if there is an intersection between the seepage flow line and the downstream slope of the embankment dam due to the appearance of pipes. Numerical modeling based on SEEP/W software was found to be compatible to the rest physical models. The findings also demonstrated that for all scenarios, both the Casagrande equations and the SEEP/W model produced seepage lines that closely matched the observed seepage lines. These results highlight the significance of managing the seepage line’s location to ensure the stability of embankment dams through the implementation of horizontal drains.

Feature extraction plays an important role in pattern recognition because band-to-band registration and geometric correction from different satellite images have linear image distortion. However, new near-equatorial orbital satellite system (NEqO) images is different because they have nonlinear distortion. Conventional techniques cannot overcome this type of distortion and lead to the extraction of false features and incorrect image matching. This research presents a new method by improving the performance of the Scale-Invariant Feature Transformation (SIFT) with a significantly higher rate of true extracted features and their correct matching. The data in this study were obtained from the RazakSAT satellite covering a part of Penang state, Malaysia. The method consists of many stages: image band selection, image band compression, image sharpening, automatic feature extraction, and applying the sum of absolute difference algorithm with an experimental and empirical threshold. We evaluate a refined features scenario by comparing the result of the original extracted SIFT features with corresponding features of the proposed method. The result indicates accurate and precise performance of the proposed method from removing false SIFT extracted features of satellite images and remain only true SIFT extracted features, that leads to reduce the extracted feature from using three frame size: (1) from 2000 to 750, 552 and 92 for the green and red bands image, (2) from 678 extracted control points to be 193, 228 and 73 between the green and blue bands, and (3) from 1995 extracted CPs to be 656, 733, and 556 between the green and near-infrared bands, respectively.