Remote sensing is one of the most important tools for monitoring and assisting to estimate and predict Water Quality parameters (WQPs). The traditional methods used for monitoring pollutants are generally relied on optical images. In this paper, we present a new approach based on the Synthetic Aperture Radar (SAR) images which we used to map the region of interest and to estimate the WQPs. To achieve this estimation quality, the texture analysis is exploited to improve the regression models. These models are established and developed to estimate six common concerned water quality parameters from texture parameters extracted from Terra SAR-X data. In this purpose, the Gray Level Cooccurrence Matrix (GLCM) is used to estimate several regression models using six texture parameters such as contrast, correlation, energy, homogeneity, entropy and variance. For each predicted model, an accuracy value is computed from the probability value given by the regression analysis model of each parameter. In order to validate our approach, we have used tow dataset of water region for training and test process. To evaluate and validate the proposed model, we applied it on the training set. In the last stage, we used the fuzzy K-means clustering to generalize the water quality estimation on the whole of water region extracted from segmented Terra SAR-X image. Also, the obtained results showed that there are a good statistical correlation between the in situ water quality and Terra SAR-X data, and also demonstrated that the characteristics obtained by texture analysis are able to monitor and predicate the distribution of WQPs in large rivers with high accuracy. Downloaded From: http://proceedings.spiedigitallibrary.org/ on 07/15/2015 Terms of Use: http://spiedl.org/terms Proc. of SPIE Vol. 9243 924319-3 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 07/15/2015 Terms of Use: http://spiedl.org/terms

No abstract

Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that use of textural data during the object image classification approach can considerably enhance land use classification performance. Moreover, the results showed higher overall accuracy (86.02%) in the o object based method than pixel based (79.06%) in urban extractions. The object based performed much more capabilities than pixel based.

The health of the individual is one of the most important indicators of good living and quality of life for the community. Therefore, the contribution of developing of public health sector management and monitoring of diseases related to the cultural, economic, and social progress of any society. Moreover, the diseases occur from spatial factors where the distribution and concentration differ in diverse positions. Hence, GIS can be used as a decision support system in order to help the mangers, assess and monitoring of various types of diseases. Thus, this research aims to define a spatial distribution, prediction of risks and analysis of disease hazard areas in Kirkuk city, north east of Iraq using two models evidential belief function (EBF) and Inverse distance weighting (IDW). IDW determines the correlation between conditioning factors and disease occurrence. Consequently, EBF can be used to assess the effect of each class of conditioning factors on diseases occurrence. The result shows that Al-Wasity quarter reports the highest range of the patients who have the blood diseases (D89-D50) in 2017. Contrary, the northern parts of the city and some quarters in the center of the city (Tessen, Bagdad road, Al-Mansor) reflect the lowest range of the patients in blood diseases. Eye diseases (H59-H00) and its accessories have the same spatial distribution. The result also demonstrated that the GIS based spatial techniques is provided a prospect to simplify and measure the epidemic state of different diseases within specific areas(minor part of Kirkuk city), and lay a base to pursue future surveys into the environmental factors responsible for the augmented disease threat.

In this paper, a new method has been presented to estimate of the Water Quality Parameters (WQPs) such as Electrical Conductivity (EC), salinity and Total Dissolved Salt (TDS). This method is mainly depended on the thermal and SAR images as an active and passive input satellite data. A Small Perturbation Method (SPM) using the Elfouhaily spectrum has been used as a physical model to calculate the electromagnetic scattering by the river surface. This method is basically used in the inversion process to find the physical parameters of water. The inversion process is fallen into three steps: first step includes, the estimation of the reflectivity coefficients via SAR image; then, the second step, the polarization coefficient (in VV or HH polarizations) is used to find the water dielectric constant; finally, the Debye formulation is used to find the salinity value. In this study, the thermal band is used to obtain the temperature of the water surface which we used in our proposed method. Total Dissolved Salts (TDS) is estimated by calculation of the electrical conductivity (EC) which is directly derived from the salinity value. To validate our methodology, a dataset of in situ data is used and the comparison between the WQP estimated using the proposed method and those obtained from in situ data is achieved. The obtained results shows, that the using of thermal image is helped to estimate salinity ranges close to in situ, consequently, enhance the produced TDS. The results also show that the thermal-TSX is helpful to improve the estimation and mapping of WQP for the practical use in the study area with high accuracy. Keywords-active and passive microwave; salinity; in situ water quality; SAR images; thermal images; SPM method.I.

No abstract

LULC factors in Tikrit city (Iraq) and the neighboring municipalities are studied among 1989, 2002 and 2015 using various techniques of remote sensing, geographical information system (GIS), and fuzzy analytical hierarchy process (FAHP). Satellite imagery with GIS helped to assess the standard LULC changes in the long term period. FAHP permitted estimating the importance of various LULC by determination of the suitable weight for used factors and then producing the evaluating models. Using different techniques, two models were created (1) to estimate the degradation of the land (2) is generated to determine the reclamation of the area. The finding reveals that the a overall accuracy of 97.0939%, 98.9199% and 99.5817% or 1989, 2002 and 2015 respectively. The outcomes also revealed that urban, vegetation, and water features area are developed in the long term (1989–2015) about 4.35%, 4.28%, and 1.49%, respectively, while barren area is reduced about 5.57%.The degradation map index showed that the lands strongly debased are these converted from vegetation to barren, followed by moderate to high these changed from water areas to urban, while moderate degradation is noticed of urban transformed to barren soil. Contrary, the reclamation map index illustrated that the lands are powerfully transformed from barren to the vegetation and followed by those converted from barren to the water, while barren transformed to the urban is marked as moderate reclamation. The transformation from urban to vegetation or water was classified as the low and deficient class to evaluate the area. The study is also revealed that the integration of remote sensing and GIS produces a successful method for LULC monitoring and managing the environment.

No abstract