فهرست مطالب یونس نعیمی

In this paper, the performance of 8 change indices including Euclidean Distance (ED), Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), image regression, ERGAS, spectral-spatial correlation, mutual information (MI), and Jeffries-Matusita Distance (JMD) has been compared on two different datasets from the accuracy and computational time points of view. The first dataset includes a pair of bi-temporal images taken by Landsat TM5 and ETM+ sensors over the southern shores of Lake Urmia, and the second dataset is taken by Landsat TM4 and TM5 sensors overs the Maragheh city and sorrounding area. Implementing the mentioned indices on the first dataset indicates the SAM's significant superiority compared to the other indices. False alarm (FA), missed error (ME), and total error (TE) of the change map resulting from SAM are 3.40%, 13.91%, and 8.86%, respectively. The change map resulting from SCM is in the second order, its FA, ME, and TE values are almost twice the corresponding values derived from SAM. JMD, regression, MI, ED, and ERGAS indices were in the next ranks respectively with 20.17%, 20.61%, 20.84%, 21.22%, and 21.47% TE on their change maps. In the first dataset, the worst change detection result was obtained from the correlation index (TE=27.80%). In the second dataset, the best results have been obtained first from the ERGAS and then from the magnitude of change, which were at the top compared to others. The FA, ME, and TE values of the change map resulting from the ERGAS were 0.63%, 26.54%, and 7.5%, respectively, and the FA, ME, and TE values of the change map resulting from the ED were 0.63%, 32.23%, and 9.01%, respectively. In the lower ranks, SCM, SAM, regression, spectral-spatial correlation, and JMD have led to 11.41%, 12.62%, 14.45%, 17.34%, and 18.03% total errors in change detection, respectively. The worst result with 26.56% TE was achieved from the MI. It is noted should be that the significance of the differences in accuracy between the mentioned indices was tested and verified by McNemar’s test. In terms of computation time, the ED was the most efficient, while the MI was time-consuming on the analysed datasets.

Urmia Lake; the Turquoise Gem of Northwestern Iran is the second-largest permanent hypersaline lake in the world. It is situated in the northwestern corner of Iran surrounded by East and West Azerbaijan provinces, near the Turkish border. This lake was declared a Wetland of International Importance by the Ramsar Convention in 1971 and designated a UNESCO Biosphere Reserve in 1976. Its surface, unfortunately, has declined sharply in recent decades. Scientists have warned that this continuous shrinking would lead to increased salinity, the collapse of the lake’s ecosystem, loss of wetland habitat, wind-blown salt storms, alteration of local climate, and serious negative impacts on local agriculture and livelihoods as well as regional health in Iran and neighbor countries including Turkey, Iraq, and Azerbaijan.Accurate and comprehensive spatial information (such as the lake’s surface and shorelines) and descriptive information (such as salinity of water and soil) of the lake are essential to deal with current and emerging crises. This information can be obtained by ground-based and space-based measurements. While daily measurement of the lake level at ground stations is one of the most important ground-based observations, but ground measurements of the lake's surface are not a practical method. Thanks to satellite remote sensing data, space observations have been provided for accurate and continuous monitoring of the Lake's surface and its water level. Since each of the ground and space observations has its pros and cons, many efforts have been made to estimate the correlation of these observations (ground-based observations of lake’s level and space-based observations of lake’s surface). Although many studies have been conducted in this field, however, no efficient model has been proposed to relate the surface area and the water level of Urmia Lake, which is an important factor in various hydrological and environmental analyzes. This paper aims to extract the level-surface characteristics of Urmia Lake using space observations of the lake and ground-based measurements of level and analytical models. Level-surface models have been determined based on ground observation of water level and simultaneous space observation of lake surface area. As a result, daily estimates of the lake surface will be provided without the space-based observations. In the present study, 6 proposed models including Polynomials, Exponential function, Fourier transformation, Gaussian function, rational function (RF), and artificial neural network (ANN) along with the two existing models including The EAWO (Eastern Azerbaijan Water Organization) model and the Sahand University model. To evaluate the effectiveness of mentioned methods, 44 samples include the lake’s surface (from Mar. 2005 to Dec. 2015) and corresponding water levels on the same date were used in this study. All of these models were calibrated by training data and evaluated based on validation data. Used data records include water level fluctuation from 1270.43 m to 1273.9 m above mean sea level and lake area (surface) fluctuation from 1837 sq. km to 4508 sq. km.Comparing the RMSE, MAE, MAPE, and NSE of all mentioned models, it was found that the proposed RF-based model (S=(3026×L^2-3.84×〖10〗^6×L-6051)/(L^2-1991×L+9.17×〖10〗^5 )) estimates the area of Urmia Lake only with an error of RMSE = 57.8 sq. km, MAE= 46.2, MAPE=1.5%, and NSE=0.9958, which is five times better than the best previous models in terms of RMSE measure. One level below is ANN whose RMSE equals 62.3 sq. km and NSE= 0.9951. Two-degree Gaussian function (with RMSE= 86.53 sq. km and NSE= 0.9907), Fourier transformation (with RMSE= 145.30 sq. km and NSE= 0.9251), two-degree Exponential function (with RMSE= 173.46 sq. km and NSE= 0.9626) and three-degree polynomial (with RMSE= 173.68 sq. km and NSE= 0.9625) were in the lower levels, respectively. It is worth noting that all of these proposed models are more effective than the state-of-the-art models. The worst results were obtained by the Sahand University model with an error of RMSE = 856.8 sq. km and NSE= 0.088). Despite the similar accuracy of ANN and RF models, the design and computational complexity of the ANN model are much greater than the RF model. Therefore, the RF model with higher accuracy and less complexity than the ANN is introduced and recommended as the best model for estimating the level-surface characteristic of Urmia Lake. According to the proposed level-surface model and daily ground-based measurement of the water level of the lake, it is possible to estimate the daily area of the Urmia Lake surface without any space measurements. The findings of this study can be used in various hydrological and environmental analyzes and monitoring and rehabilitation programs of Urmia Lake.

In this paper, first the method of solving the linear weighted total least squares, and then its generalization to nonlinear state is discussed; as the problem-solving model for determining the coordinates with pseudo-range GPS observations is fully consistent with this model. Available techniques for solving the TLS are based on the SVD and have a high computational burden. Furthermore, the other presented methods that do not use SVD, need large matrices, and there is need for placing zero in the covariance matrix of the design matrix, corresponding to the errorless columns, which increases the matrix size and, as a result, raises the volume of the calculations. But in the proposed method, problem-solving is done without need for SVD, without introducing Lagrange multipliers, and avoiding the error-free introducing of some columns of the design matrix by entering zero in the covariance matrix of the design matrix. It is performed only using easy equations and on the basis of summation principles, which results in less computing effort and high speed. In the following, an optimal method for weighting the design matrix is presented, which can yield a much better answer to the unknowns in the presence of many failures (here, up to three failures are assumed and tested). Besides, it can estimate the residuals vector so that the failure observations would have larger magnitudes than the others, and could help with detecting them in a safer and more feasible way with respect to any other method.