Image of landsate-7 taken by thematic mapper was used and classified using supervised method. Results of supervised classification indicated presence of nine land cover classes. Salt-soils class shows the highest reflectance value while water bodies' class shows the lowest values. Also the results indicated that soil properties show different effects on reflectance. There was a high significant positive relation of carbonate, gypsum, electric conductivity and silt content, while there was a week positive relation with sand and negative relation with organic matter, water content, bulk density and cataion exchange capacity.

Cognitive radios have the potential to greatly improve spectral efficiency in wireless networks. Cognitive radios are considered lower priority or secondary users of spectrum allocated to a primary user. Their fundamental requirement is to avoid interference to potential primary users in their vicinity. Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. In addition, reliable sensing creates spectrum opportunities for capacity increase of cognitive networks. One of the key challenges in spectrum sensing is the robust detection of primary signals in highly negative signal-to-noise regimes (SNR).In this paper ,

The core interval at the K.H5\6 and K.H5\8 Wells in the West of Rutba provinces reveals a significant succession across the Late Cretaceous–Early Paleocene transition. The sampled interval encompasses a series of carbonates belonging to Digma Formation of Latest Cretaceous age, which underlies the Akashat Formtion of Danian age. Fifty-five species belonging to thirty-five genera were recognized. Based on the distribution of these species, eight biozones were distinguished, three biozones are recorded from the K.H 5\6 studied section and two biozones are documented from the K.H 5\8 studied section which refers to Late Maastrichtian age of Digma Formation. Five biozones are recorded from Akashat Formation in the K.H 5\6 studied section and

Background: Tap waters play an important role in fulfilling the people needs for drinking and domestic purposes. Contaminate the tap water with different pollutants has become an issue of great concern for 90% of people who are depended on the tap water as the main source of drinking. Pollutants can make their way easily into the delivering pipes which suffer from the leaking resulting in decreasing the quality of water. Objective: Therefore, assess the water quality for drinking purpose by calculating the water quality index is an important tool to ascertain whether the water is suitable for human consumption or not. Methods: In the present work, the water quality of the Al-Salam, western region of Baghdad city, Iraq was investigated for 7

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

The marshes are one of the important environmental features affecting human and animal systems, so the studying of changes they undergo is one of the important topics. This study is concerned with the changes occurring in the Al Saadya marsh for the period from 1987 to 2017 exclusively in the winter season (the marshes’ revival season in Iraq revive). In order to inspect the changes in this marsh, we choose 7 years to cover the study period as a criterion years, namely 1987, 1990, 1995, 2000, 2007, 2014 and 2017. The “Maximum Likelihood” classifier was used to separate the stacked land cover features, where the minimum overall accuracy ratio that recorded for all years of study was 96%. The results revealed that Al-Saadya marsh went t

In this study, the Earth's surface was studied in Razzaza Lake for 25 years, using remote sensing methods. Images of the satellites Landsat 5 (TM) and 8 (OLI) were used to study and determine the components of the land cover. The study covered the years 1995-2021 with an interval of 5 years, as this region is uninhabited, so the change in the land cover is slow. The land cover was divided into three main classes and seven subclasses and classified using the maximum likelihood classifier with the help of training sets collected to represent the classes that made up the land cover. The changes detected in the land cover were studied by considering 1995 as a reference year. It was found that there was a significant reduction in the water mass

In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa

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