Initial delineation of prospecting zones of groundwater was conducted in the present studyusing remote sensing and geographic information system (GIS) techniques. It has been preparingan integrated geographic database of spatial and non-spatial data for the study area. The spatialdata were generated by using image processing software (Erdas 8.3) and GIS software (Arc view3.3) enhanced by real frequent field visits of the study area. These data include: surface featureswhich give a direct and indirect indicators of the existence of groundwater and affect to thegroundwater movement such as hydrogeomorphological, drainage density, slope, landuse andsoil maps. The non spatial data were derived primarily from real views during field visits to thestudy area and from the existing writing or previous studies. All the data generated were saved inthe GIS databank for the purpose of digitization, computational and generate the best possibleoutput results to determine the extent of possible areas where the water that exists for the purposeof prospecting. Results showed that more areas could be have very good categories of prospectzones are the southern parts of the study area, which covers about 375 Km2 while the northernareas, which covers about 164 Km2 of the study area are grouped as runoff zone. Accordingly thepossibilities of the presence of groundwater are poor to negligible in this zone. The current studydemonstrated that a remote sensing and GIS technique are very effective tools that can give theinitial predictions on the presence or probability of the presence of ground water in areas whichhave the same considered geological deposits for the study area.
This research represents part of the current attempts to employ remote sensing data in the scopes of the civil engineering and the geotechnical engineering applications. There is great need to know the kinds of soil and their geotechnical properties, to create recent maps which have the capability and high flexibility to deal with them in digitizing way. Therefore GIS techniques are employed in the soil of area of study . By using ArcView software, a geographical database and information about soil chemical properties analysis have been registered and constructed digitally to represent the geotechnical soil characteristics maps . The work includes the digital image processing ( digital classification techniques) by using ERDAS, ver.,8.4 package, and classify the soil of study area by using the supervise and unsupervised techniques . The geotechnical maps by using GIS techniques depend on remote sensing data are the better to represent the ground truth regarding the characteristics of soil , in comparison with the traditional method, because they are easy way to produce, use, store and update, in addition they save in efforts, time and cost . The results of this study have shown that the soil of study area is gypsum where it ratio exceeded the allowable ratio ( 10.75 % ) for all samples . In addition the total Soluble Salts ratio and SO4 ratio high compared to allowable ratio (10 % , 5 %) respectively .
The progress in technological earth observation field , using of satellite data and the development of computer software, lead to reduce effort and time to control the change in land uses especially after the increases in both accuracy and resolution of image data. This paper studies the urban development of falluja city since its construction (depending on the fact of land uses) and future directions for the development of the city. The above two trends are very important in supporting decisions of governorates, municipalities and government departments in the land uses management and control of building by using a new technique which treated with data and uses maps. The present study concluded that the use of remote sensing, geographic information system and mathematical models are very important to prepare master plan of cities with high efficiency.
Satellites may provide data with various spectral and spatial resolutions. The spatial resolution of panchromatic (PAN) images is higher, but the spectral resolution of multispectral (MS) images is greater. There is Satellite sensors limitation for capturing an image with high spatial and spectral resolution, due to the hardware design of the sensors. Whereas many remote sensing, as well as GIS applications, need high spatial and spectral resolution. Image fusion merges images of different spectral and spatial resolutions based on a certain algorithm. It can be used to overcome the sensor's limitation and play an important role in the extraction of information. The standard image fusion approaches lose spatial information or distort spectral characteristics. Optimizations of fusion rules can overcome and degrade the distortions as the fusion core is the image fusion rules. In this paper, the Grey Wolf Optimizer (GWO) is used to find the optimal injection gain, as most distortions in image fusion are caused by the extraction and injection of spatial detail. Both qualitative and quantitative metrics were utilized to evaluate the quality of the merged image. The mentioned metrics that were used commonly for evaluation of image fusion results support the proposed algorithm for image fusion as the output image was qualitatively and quantitatively growth. In the future the proposed method can be updated by increasing the objective function dimensions to two or three for getting a best fused image.
This research provides a comparison between the performances of Sugeno type versus Mamdani-type fuzzy inference systems. The main motivation behind this research was to assess which approach provides the best performance for satellite image classification. The performance of each approach has been evaluated for six bands (from Landsat-5) for West Iraq image classification and compared with traditional method (Maximum likelihood), based on pixel-by-pixel technique. Due to the importance of performance in online systems we compare the Mamdani model, used previously, with a Sugeno formulation using four types of membership function (MF) generation methods. The first method triangular membership function using the mean, minimum and maximum of the histogram attribute values. The second approach generates triangular membership function using the peak and the standard deviation of attributes values. The third procedure generates Gaussian membership function using the mean and the standard deviation of the histogram attributes values. The fourth approach generates Gaussian membership function using the peak and the standard deviation of the histogram attributes values. The results show that the Mamdani models perform better in most of the case under study.