فصلنامه سنجش از دور و GIS ایران
سال هشتم شماره 1 (پیاپی 29، بهار 1395)

Dust storm has been one of the most important challenges of western Asia. This phenomenon has been intensified due to the drought and has many negative effects on people's lives. Since this region located in a dust belt in the world, it is necessary to explore different aspects of this phenomenon is well. Predictive and modeling of this phenomenon can be prevented of jeopardizing the lives of millions of people. So present a Regional Model to assess different aspects of this phenomenon is necessary. Since climate and weather elements are constantly changing, the spatiotemporal model should be used for modeling and visualization. Hence, a model for estimating dust emission has been designed and developed and Geographic Information System (GIS) spatial modeling capabilities and remote sensing (RS) data (wind speed, soil moisture, soil texture and digital elevation model) are used. The model which is called DustEM calculates horizontal dust emission. In this study, modeling is done for 2001 to 2007 and model’s output is evaluated by MODIS AOD and for dictating hot spot area output is clustered in 3 categories contain high, medium and low with threshold 0.3 and 0.6 for AOD. Accuracy index mean for the study period was 73.6% and show high precision of model in detecting hot spot area.

In normal images, which resampled according to epipolar geometry, all of spatial displacements of points in the space of stereo images occur only in one direction of the digital image coordinate system. This prominent characteristic makes normalized imagery as an important prerequisite for many photogrammetric activities such as image matching, automatic aerial triangulation, automatic digital elevation model and orthophoto generation, and stereo viewing. In this paper, a novel approach for epipolar resampling of linear pushbroom satellite imagery is proposed based on Orbital Parameters Model (OPM). The proposed method is developed based on modifying the exterior orientation parameters of OPM in the object space. The most prominent advantage of this method is the capability of the correction of off-nadir viewing of the sensor through the physical interpretation of its parameters. Also, there is the capability of implementation of the proposed method by means of other common physical or interpolative mathematical models used in geometric correction of satellite imagery. According to the results, the average reminded vertical parallax x in the digital image coordinate system is determined 0.73 pixels with respect to the independent check points that demonstrates the high performance of the proposed method.

Developing intelligent and novel methods for crash prevention or reducing crash severity in regional highway corridor is one of the major goals of road safety research. This study presents a comprehensive approach using geospatial information systems and data mining to analyze the severity of highway corridors crashes and identify the most spatial contributing factors. The approach implements Fuzzy Classification and Regression Tree (FCART) on a database of spatial data and four year period accident records in the study corridor (Qazvin-Rasht). The proposed method is tested on the crash data using a 10-fold cross validation process and the results are compared with Classification and Regression Tree (CART) model. The results show that FCART model inducts crash severity better than CART model and its overall accuracy is higher than CART model. Moreover, the sensitivity analysis of FCART model indicates that beside vehicle failure, using seatbelt and weather condition factors, curve and the spatial distribution and prevalence of activities and land uses in the proximity of highway corridors are among the most important factors affecting the severity of injuries and increase opportunities for crash occurrences.

Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Worldview satellites were used. Pieces with different land applications were cropped from these images to serve as test images. The assessment of the metrics tested on these images resulted in the categorization of metrics into three groups as per their capability for displaying spectral and spatial distortions. The first group included methods that functioned on the basis of noise for overall assessment of images with respect to their noise; these methods included ERGAS, MSE, PSNR, WSNR, and SNR indices. The second group were those aligned with Spectral Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.

Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.

Knowledge of temporal and spatial distribution of LST to determine the amount of earth energy is much applicable for climatology studies, examination of vegetation and also determination of urban structure. With respect to deriving LST from the studied area and its relationship with urban structure and vegetation, the present study illustrates that climate conditions specially wind, urban structure and vegetation are some of the effective factors on LST. According to the importance of heat islands at pixel scale in this study, and the ability of Newton Interpolation Polynomial in this respect, urban construction and vegetation are derived by the stated polynomial and their relationship with LST is examined and the areas concluding heat island are known. In this study, Newton Interpolation Polynomials have presented two equations of grade 7 by received DN from 200 points of image including vegetation and the areas with urban structure. The produced error rate from deriving vegetation by using Newton Interpolation Polynomial in 100 locations of the studied area and in urban construction are calculated as 10.1 and 12.02 respectively. It should be stated that no research with similar method has been done yet. The use of mathematical techniques in remote sensing and the amount of accuracy and ability of them are considered some of the main purposes in this research.

In the fast growing world of today, land use planners frequently face situations in which various uses compete for the same piece of land. Hence, the final result heavily depends on the decision maker’s capabilities to select the best use among different conflicting land uses. Taking this approach, the present study aims at providing the best allocation solution for multiple land uses including agriculture, forest, range and development in Gorgan Township, Golestan Province of Iran with respect to minimizing allocating cost and maximizing compactness and contiguity as shape criteria of landscape metrics. To aim these objectives Linear Programming as an exact method in combination with Ant Colony as metaheuristic algorithm have been used. Since land use planning is NP-Hard problem with respect to its size (132 rows in 127 columns) and the mentioned objectives, LP-Relaxation and Branch & bound method have been used to solve it. Results indicate the superiority of the hybrid model (linear programming in combination with ant colony) to employment of each of the models separately in every objectives including allocating cost, compactness and contiguity. Additionally, comparing the results of proposed hybrid model with the results of MOLA algorithm in IDRISI shows the superiority of hybrid model against MOLA. In hybrid model cost, compactness and contiguity levels after standardization are respectively 0.03, 0.1 and 0.07 better than MOLA. Furthermore, using the proposed approach, it is possible to consider both suitability and landscape metrics or even more objectives.