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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 postwar related events and several human activities. Landsat-8 data was used to predict and evaluate the spatial variation and map distributions of the salinity, SO 4 and CaCO 3 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, SO 4 and CaCO 3 are found to be minimal in winter and maximum in autumn. The values of correlation coefficient (R 2) between the real data and the equation results for the salinity, SO 4 and CaCO 3 during the two seasons are 0.95, 0.96 and 0.92, respectively.

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.