فصلنامه سنجش از دور و GIS ایران
سال هشتم شماره 4 (پیاپی 32، زمستان 1395)

The work aimed in this paper investigates the applicability of forest parameters retrieval using the polarimetric interferometric synthetic aperture radar (PolInSAR) techniques. The application of PolInSAR in the forest area is based on the separation of backscattering phase centers associated to the top of canopy and the terrain. In this regards, one important and widely used technique is the random volume over ground model in the two-layer forest scenario. The unknown parameters of the model such as terrain topography, forest height, extinction, and effective ground to volume ratio, should be solved through a full nonlinear algorithm. However, due to the nonlinear nature of the problem and its complexity as well, the obtained solution depends strongly on the choice of the starting value and the considered polarizations. In the literature, to simplify the problem some important assumptions have been considered. However, the result is also highly affected by the considered assumptions. To address these problems, a simple model-based strategy is presented to obtain the forest parameters in the well-known random volume over ground model. The method obtains the solution without any assumption regarding the parameters. The experimental results show that the framework provides a promising solution for forest parameters extraction in comparison with the inversion and SPIRIT methods. The root mean square error of forest height estimation is decreased from 13.26 and 11.41 in the inversion and SPIRIT methods, respectively, to 7.34 meters in the proposed model.

To use the information of multi-source data, numerical or analytical fusion techniques are used in combination. The fusion makes for better interpretation and reduce the uncertainty and improve the classification. Several methods have been developed for fusion, including methods based on IHS, wavelet and PCA. In this study, the capabilities of fusion methods based on the IHS and PCA have been examined. In this regard, the methods FFT-PCA, Wavelet-PCA, FFT-IHS and Wavelet-IHS are used in order to fusion on Hyperion L1R data with spatial resolution of 30 meters and panchromatic image Cartosat-1 of IRS satellite with spatial resolution of 2.5 meters. To evaluate the results of the methods some criteria such as correlation coefficient, filtered correlation coefficient, error of least squares, ERGAS and SAM index were used. results of least square error criteria, respectively for, FFT-PCA, Wavelet-PCA, FFT-HIS and Wavelet-IHS for band 15 is 0/020, 0/022, 0/025 and 0/024 and for band 25 is 0/032, 0/035, 0/038 and 0/036 and for band 34 is 0/040, 0/043, 0/044 and 0/041.The results of this criteria and other criterias showed high accuracy of PCA-based methods to fusion of images. The PCA-based methods has advantages over methods based on IHS, such as no band limitation. Of all these methods, the FFT-PCA method provides more accurate results and preserve the spectral and spatial information better than the other methods.

Increasing gas consumption due to development of residential areas and industrial sites has been lead to emergence of a dense pipeline network. Denser pipeline network increases the risks and the potential risks. In order to prevent and reduce these risks providing a framework for assessing and analyzing risk is essential. Quantitative method is one of the most detailed and the most sophisticated methods in pipeline risk assessment. In this method the individual risk is calculating whit use of probability of pipeline failure and the potential consequences of accidents caused by gas leakage. The main purpose of this study is to presenting a suitable framework for quantitative risk assessment of transmission gas pipeline failure. In most studies on the risk of pipelines, the calculation is done only for one pipeline. Innovation of this study is the quantitative risk assessment of adjacent areas of gas pipeline whit the use of spatial analysis in GIS environment and evaluating the Cumulative effects of multi section of pipelines in magnitude of risk at a certain position. The final result has been prepared in a risk map for the pipeline and the surrounding area. According generated Maps, risk of some Parts of the area is higher than 1E-05, which is due to being in line with average local wind direction and the cumulative effect of multi sections of the pipeline. Produced maps can be used to minimize the level of risk and safety management of existing pipelines.Thus detection and visualization of hazard distances, vulnerability limits and high-risk areas due to pipeline gas leakage consequences was provided by implementation of a quantitative risk assessment method in GIS environment.

Sumer et al., (2008, 2013) proposed a new approach based on an adaptive fuzzy-genetic algorithm for detecting buildings using high-resolution satellite imagery. This approach improved the accuracy of building detection by decreasing the problem of untimed genetic convergence. The lack of geometric controllers to enhance the output of the algorithm was one of the main limitations of this method. In order to resolve this limitation, a geometric controller is integrated to the method which was proposed by Sumer et al., (2013). Training samples are selected to initiate the approach. The attribute plane was revealed by chromosomes carrying image processing operations. These planes are then reduced to one optimal plane for discriminating between two classes. The fitness value is calculated through the detection and miss-detection rate. Performance of GA operators is finally controlled by an adaptive- fuzzy module. This process repeats until a specified number of generations have been reached. Also a complementary area based genetic algorithm is proposed in order to enhance the algorithm results. By considering the hypothesis that buildings are similar in characteristics within an urban block or neighborhood unit, the complementary algorithm tries to find the optimal areas. Then regions with areas lower or upper than the determined optimal limits are labeled as ‘non-building’ class. The approach was tested on twelve dense and dispersed test scenes of the Varzaghan city of Tabriz, Iran. The kappa statistics computed for the proposed evolutionary genetic approach were between 0.59 and 0.91, which shows a better performance in comparison to other similar methods. The performance of the algorithm for building extraction was better for urban and suburban buildings than for buildings in rural scenes.

Features extracted from single-date satellite images can not lead to high accuracy in crop classification. As a result, in this article use of multi-temporal images and textural information is assessed. This research evaluates discrimination of four crops i.e. alfalfa, wheat, potato and cucumber in Ghorveh county (Kordestan province) with single-date and multi-temporal images (SPOT5 & ASTER). Seven images of four different months were obtained and separability analyses determined optimal scene combination (i.e. July 2nd image for single-date image and July 2nd – October 21st for two-date image) for classification. GLCM method has been used for extracting textural information. Approximate window size was determined with variogram, then extracted features were stacked with single-date and two-date spectral bands. The best single-date image overall accuracy (classification without texture) was 24% higher than the worst single-date image accuracy (76.02% versus 52.28%). Accuracy in two-date image reached to the highest levels (89.61%) and only five-date image has higher accuracy than two-date image, but with adding texture features to five-date image, accuracy decreased 5% lower than two-date image with texture features. This can indicate that texture has more importance than multi-date images. The highest classification accuracy was reached with the best two-date image (97.48%). In single-date images, July 2nd image reached to highest classification accuracy (95.2%). Results indicated using multi-temporal images along with texture features has very higher accuracy in comparison to conventional methods (without texture features or multi-temporal images).

The aim of studying of sedimentary in a reservoir dam is finding of a comprehensive attitude about the measurement of the reservoir lost volume. In this middle, studying and anticipating of the volume of input sediment into the reservoir is significant. In this paper by using of sub-scaling, we have anticipated long term precipitation and temperature in Sattarkhan reservoir by the area of 950 km2 located in East Azerbaijan province. According to anticipated precipitation and temperature and Water Assessment Tools (SWAT) input sediments into the dam has simulated. The results show that although SWAT model can simulate with upper than 80% the procedure of input flow into the reservoir, but it couldn’t assessment of the simulation of flow maximum value. To resolve of this problem, it must be add erosion parameter. Therefore we have used of SDSM model and Multi-Layer Perceptron (MLP) and then land use map has made in GIS environment to recognition of application effect and erosion rate. Composing and integrating of user parameter in GIS environment and anticipated precipitation can optimize SWAT outcome and make it close to real value. Results indicated that Correlation between flows are anticipated and value of error has decreased in 95% accuracy by integrated using of above models.

The Sarcheshmeh and Darrehzar mining regions are located in the Central Iranian area `and Urumieh-Dokhtar of Volcano-Plutonic Belt. This area is covered by the Eocene volcanic rocks that are intruded by the Oligo-Miocene intrusive bodies. Copper mineralization is mainly of porphyry type and is associated with extensive alterations. The area has a semi-arid climate due to the low vegetation, low levels of aerosols and atmospheric water vapor as with rocks clearly exposed that makes it suitable for testing the results of satellite remote sensing techniques. IARR calibration for normalize the images to be used in a scene. Crosstalk is an effect in ASTER imagery caused by signal leakage from band 4 in to adjacent bands 5 and 9. In this article, visible–near infrared and short wave infrared spectral bands of the level 1B and AST_07XT datasets of the ASTER product were used. The level 1B dataset was converted to earth surface reflectance using internal average relative reflectance method, whereas the AST_07XT datasets were previously calibrated and using crosstalk correction. In order to evaluate and identify the best methods of calibration, Spectral Feature Fitting (SFF) algorithm was implemented on the datasets and images output based on area geological map and field observations were compared. Z profile method were used to extract the spectra of the pure image of the both datasets. Spectra from field samples was examined by Analytical Spectral Device (ASD). Then, the extracted spectra from the samples were resampled in to 9 ASTER bands. Spectral library JPL1 was used as a reference to analyze the pure image spectra and the spectra obtained from field samples related to minerals of alteration index in the investigated region. Thus, kaolinite was used as an index mineral of phyllic-argillic alteration, alunite was used as an index mineral of advanced argillic alteration and epidote while it was used for enhancement of propylitic alteration. Results showed that L1B dataset calibrated by IARR method due to removal of restrictions such as the effects of atmospheric water vapor and additional absorption and reflection features displayed for enhancement argillic-phylic advanced argillic and propylitic alterations more accurately than AST_07XT by crosstalk corrected standard datasets.