فهرست مطالب hamid enayati

Healthy diet has an important role in the success of military operations, so the commanders and all forces must understand the relationship between proper diet and the physical ability to perform physical activity. The aim of the current study is to evaluate the nutritional status of the military in order to provide strategies to improve the health status and increase the efficiency of individuals.

This study has been done on 89 navies in the cities of Bandar Abbas and Bandar Lengeh, south of Iran in 2019. Demographic information, IPAQ and FFQ168 questionnaires were used to collect data.

There were 89 navy men with an average age of 32.6 years. The average daily calorie intake was 2973 kcal. The average daily intake of milk and dairy products, vegetables, fruits, cereals and meats were 1.2, 3.15, 3.55, 1.2 and 1.6 units per day, respectively. The highest deficiency of the main groups of the pyramid was related to the dairy group (77.5%). In the evaluation of micronutrient status, deficiency of calcium (48.3%), vitamin A (83%), zinc (52.8%), omega 3 (46.1%) and vitamin C (33.7%) were reported.

This study showed that the navy in the field of food intake, especially in the consumption of dairy products and sources of calcium, vegetables, fruits, protein sources, omega 3 and energy intake needs to intervene to increase access and consumption of food items.

Iran is faced with limited freshwater resources and, on the other hand, is facing increasing demand for water for various uses. One of the most important challenges in planning water resources is the determination of the environmental flow in the rivers. The environmental flow describes the time, quality, and amount of flow needed to maintain the aquatic ecosystem. Protected rivers are widely protected with a high conservation value, aimed at preserving and restoring plant habitats. Protected areas are suitable environments for conducting educational and environmental studies. Protected River Kashafrood is part of the Qara-e-Ghom basin of Iran located in the northeast of the country and in the north of Khorasan Razavi province. The basin has 15650 km of area. To maintain ecosystems in this area, it is essential to determine the minimum environmental flow. Today, various methods for estimating the environmental flow of the river are presented in the world, which hydrological approaches are the most widely used ones. In this paper, the environmental flow of the Kashafrood Protected River is estimated on the basis of the 39-year statistical period in the Olang Asadi station using hydrostatic methods of Tessman and Transmission curve continuity flow (Smakhtin). The required flows were obtained and then, using SWOT analysis, according to the internal and external characteristics of each method, the most suitable approach for determining the environmental flow of Kashafrood River was presented. The minimum environmental flow in the Tessman method was 1. 57 and in Smakhtin method, the continuity curve was 1.85. Using the SWOT analysis, internal and external evaluation matrices of the internal and external assessment matrix were used to convey the continuity curve of 3.64 and 3.53, which was selected as the appropriate method in comparison to the method.

Rapid damage mapping after an earthquake in order to produce damage map is important for relief and rescue operations. Recently, the use of remote sensing images for producing damage maps is considered due to their synoptic view and low cost. In this research, a rapid damage mapping approach according to change detection is proposed, which is applied to the 2018 Sarepol-e Zahab earthquake. In order to assess results, outcomes of the change detection were evaluated using ground truth, which show high accuracy in detecting change areas. On the other hand, our damage map was evaluated using damage map produced by the European Space Agency (ESA), which outcomes depict our proposed method can detect damage areas by an overall accuracy of 84 %. Using the proposed method, damage map of the Sarepol-e Zahab was generated less than 30 minutes. 

Remote sensing is a useful science and technology for different applications, especially disaster management. Remote sensing can be used to produce building damage maps after the earthquake. Recently, researchers used remote sensing data for producing building damage maps [1-4]. However, the used approaches are based on training samples. Preparing training samples is a time consuming task. For this reason, scientists would like to develop rapid damage mapping. Tiede et al. proposed a method to map damage areas of the Haiti earthquake using a shadow analysis approach. The proposed approach can produce damage areas after 12 hours [5]. The main goal of this paper is to develop a rapid damage mapping approach based on pre- and post-event images in Sarpol-e Zahab. The developed method benefits from decision making approaches to make a rapid map.

The proposed method is done in four steps according to Figure 1. In the first step, some essential pre-processing tasks including georeferencing and radiometric correction are performed. In the second step, difference image is produced and some textural features are extracted from it. In the third step, change and unchanged areas are identified using three change detection approaches. Finally, TOPSIS decision making approach is employed to make a damage map.
 
Fig. 1.  Workflow of the proposed

Since the proposed method is based on change detection, we applied it to two data sets. Results of change detection over two case studies present in Figure 2. According to validation results, the proposed approach can detect changed and unchanged areas with about 95 % accuracy.


Fig. 2. Results of change detection approaches over two study areas
Using pre- and post-event Sentinel-2 images and our proposed approach, damage map of Sarpol-e Zahab was produced. Figure 3 shows pre- and post-event Sentinel-2 images and damage map of the study area.


Fig. 3. Pre- and post-event Sentinel-2 images and damage map of the study area
The accuracy of our damage detection approach is assessed using damage map produced by European space agency (ESA). Table 1 depicts the confusion matrix regarding the accuracy of our proposed method. Based on this table, the overall accuracy of our proposed approach is about 70 %.
Table 1. the confusion matrix of our proposed approach

In this paper, a rapid damage mapping approach is proposed to detect damage areas from Sarpol-e Zahab earthquake. The proposed method is based on change detection and unsupervised. From the perspective of change detection, our proposed approach is robust. To assess the capability of the proposed method, it was applied in Sarpol-e Zahab earthquake. Using pre- and post-event Sentinel-2 images, the proposed approach can detect damaged areas with an accuracy of 80 %.

Hydrography is a science used for regular measurement of parameters such as depth of water, geophysical geology, tide, water flow, waves and other physical properties of seawater. It is also used for the production of maritime maps. Hydrography contributes significantly to the internal infrastructure of coastal countries. Providing proper hydrographic services ensures safe and efficient sailing. Thus, development of hydrographic services on the national level can improve safety of mariners, and protect people’s lives and belongings on the sea, while providing some facilities for the protection of marine environment. The advancement of space technologies in recent years has increased the speed of spatial information production and facilitated sea monitoring. Different methods are used for bathymetry. Lyzanga et al (1978) used a linear combination of the logarithm of corrected radiance ratio. This method is based on the simplification of Beer's physical model in which a linear equation of five unknowns is obtained for two bands. In 2006, Lyzanga et al. presented an improved version of their model. Using Tow-Bands Reflection Ratio, Stampf et al (2003) not only reduced the number of unknown variables in Lyzenga method, but also decreased the sensitivity of depth determination to different substrates. In this method, the difference between absorption properties of green and blue bands is used. TCarta is a global supplier of geospatial products. The company generated Satellite Derived Bathymetry (SDB) dataset by accurately extracting water depth from multispectral imageries received from the European Space Agency’s Sentinel-2 Satellite. The resulting bathymetric data had a point spacing of 10 meters, while measuring up to a depth of 15 meters. Data covered a 30-square kilometer area around Preparis Island on the Bay of Bengal.
The present article used images received from Sentinel-2 in 7 different periods for depth determination, and 1: 25,000 ADMIRALTY Nautical Charts for accuracy evaluation. Following the assessment of water transparency in received images, the 12/15/2018 image was used for depth determination. Case study area contains around 130 km along the Port of Salalah, Oman. In order to implement the model, it is necessary to separate land from water in images using NDVI, NDWI, MNDWI and AWEI indices. The NDVI index has been used in this project. NDVI is primarily used to estimate vegetation cover, but since this index exhibits a negative value in areas covered with water, this property is used to provide a mask for separating land from water. In this step, 68 control points and 68 check points were selected from the existing ADMIRALTY map. The DN values of the corresponding pixels of the selected points were extracted from four 10-meter bands of Sentinel-2 images. The control and checkpoints and the DN value of their corresponding pixels were extracted in 4 separate files, then these 4 files were logged into the Bathymetry software and the parameters of LMR and Stumpf methods were calculated. The root mean square error (RMSE) and correlation coefficient (CC) were used to assess geometric accuracy. In order to extract necessary parameters for each model, RMSE= 2.15 m and CC= 92.5% were calculated at depth distances of 0 to 20m. Results indicates higher accuracy and stronger correlation of LMR findings. Therefore, this method was used for depth determination between 0 to 20 meters. The 5 parameters extracted from the Bathymetry software and the corresponding pixel values of the four bands with 10-meter resolution extracted from the Sentinel-2 image (received from the on 12-15-2018) were used as input. Linear Regression Model was applied to transform 4 bands of Sentinel-2 image into depth. The output of the model (depth) was presented as the Substrate DEM of the coasts of Port of Salaleh, Oman. Hence, it can be concluded that Remote Sensing technologies can be used for depth determination and sea monitoring at critical times (during wars or other periods of insecurity) for an acceptable time period. It also provides an appropriate context for bathymetry of inaccessible coastlines and monitoring of strategic widespread water zones. In this way, the depth of sea bed in shallow areas is extracted using spectral analysis of satellite data and different models.

A Digital Elevation Model or DEM is a physical representation of terrain and topography that is modeled by a digital 3D model. DEMs have various applications in many fields. Today, with respect to improvements in technology and importance of generating DEM from every region in our country, the importance of satellite remote sensing is more sensible.  One of the main topics in satellite remote sensing is radar remote sensing. In recent years, a number of satellites have been launched to capture SAR information from the surface of the Earth. The last project is Sentinel, and Sentinel-1generates SAR data. It generates images with medium spatial resolution from the Earth every 12 days. DEMs are generated through multiple methods, one of which is SAR interferometry.
Material and Methods: The area under study in this research for conducting experiments and generating the DEM is Iran and the city of Tehran. Tehran is located in the north of the country and south of the Alborz Mountains, 112 kilometers south of the Caspian Sea. Its elevation ranges from 2000 meters in the highest points of the north to1200 meters in the center and 1050 meters in the south. In this paper, the Sentinel-1 stereo images are used to generate DEM. Tehran is located on part of these images. These images are shown in Figure (1). In order to evaluate the digital model generated by these images, a reference digital model which has been prepared from the city of Tehran with an accuracy of 1 meter is used. This elevation data was collected using terrestrial surveying and aerial photogrammetry. In this paper, radar interferometry was used to generate digital elevation model from the Sentinel-1 images. In SAR interferometry, the phase of images taken from various imaging positions or various imaging times is compared pixel by pixel. The new image is produced by differentiating between these values which is called interferogram. Interferogram is a Fringe interference pattern. Fringes are lines with the equal phase differences similar to contours in topographic maps. The phase difference obtained from SAR interferometry is affected by several components. Some of the most important components are orbital paths, topographic, displacement and atmospheric components. By eliminating the major part of the orbital component (and calculating the effect of other components or assuming their insignificance effects comparing with orbital and topographic components), since the topographic radar observes the Earth from two different points, the stereoscopic effect is revealed. This topographic component leads to fringes which encompasses the topography like contours. These patterns are called topographic fringes.   In order to conduct the experiments considered in this paper, two mountainous and flat areas in Tehran are picked out and separated from the main image. The mountainous area is selected from the north and the flat one from the south of Tehran. The aforementioned technique is implemented and executed on these images. The generated DEM in these two areas is shown in Figure (2). After generating the Earth DEM using the Sentinel-1 images, and comparing it with the reference DEM having an elevation accuracy of 1 meter, the accuracy of the generated DEM was determined. As expected, the results in the flat area were more desirable compared to the mountainous area. The accuracy of the generated DEM was evaluated by creating a network with the dimensions of 138761 points from the flat area and a network with the dimensions of 78196 points from the mountainous area, from both generated and reference DEMs and comparing the corresponding elevations of the network points. Digital numbers of images represent the magnitude of error occurring in the generation of DEM. After testing the 3 error (blunder detection) and eliminating large errors occurred in DEM, a standard deviation error of 1.26 meters for the flat area (South of Tehran), and 10.32 meters for the mountainous area (North of Tehran) were obtained.  Considering the development of technology and the launch of new satellite imagery projects from the Earth and the importance of the existence of a digital elevation model from the country, it is possible to recognize the importance of studying these images more and more. One of the latest satellite remote sensing projects is the Sentinel project. The Sentinel-1 radar images with medium spatial resolution capabilities provide the possibility of generating a Digital Elevation Model (DEM) from the country.  This research is the first study on the accuracy of Digital Elevation Model resulted from the Sentinel-1 radar images in Iran. An elevation accuracy of 10.32 meters in the mountainous area, and 1.26 meters in the flat area were obtained. The results show that these satellite images have the capability of generating a relatively optimal DEM, particularly in non-mountainous area.

The Digital Terrain Model (DTM) is a statistical presentation of the earth surface using some points with predefined 3D coordinates. Extracting the DTM as an important product of photogrammetry and remote sensing that is the basis of many practical projects, has always been considered for experts. LiDAR is a powerful equipment that can provide 3D point cloud with high accuracy from the earth. Nowadays, advances in technology make the generating 3D point cloud from the digital aerial images by dense matching feasible. These point clouds represents an approximate Digital Surface Model (DSM) of the earth. The DSM contains both terrain points and off-terrain points, but the DTM contains only the terrain points. In other words, the DTM presents a bare earth without any natural and artificial objects. Generating the DTM using the DSM is a challenging topic in photogrammetry and remote sensing. In this paper an algorithm with two independent approaches is proposed. Before beginning the process, the irregular point clouds was gridded, interpolate and convert to the image by specifying a point interval.
The first proposed approach was a progressive morphological filter that detect the off-terrain points from the DSM. This approachused the simple morphological filter in a specific procedure with four steps. In the first step, a minimal surface was generated by identifying the points which have minimum elevation in predefine scan windows. The structural element of the morphological filters should be determined. As it is a progressive filter, a vector that contains the structural elements sizes was determined in the second step. In the third step, a morphological opening was applied on the point cloud with a structural element accordance with the produced vector in step1. For each window size in the vector, an elevation threshold was calculated by multiplying the interval distance and supplied slope parameter. Then, the difference between initial surface and the result of applying the morphological opening was computed. The points with difference value more than the calculated elevation threshold was selected as off-terrain points.
In the second approach an iterative procedure was designed which was based on morphological filters. The geodesic dilation was a combination of traditional morphological filter. Although the morphological filters operated based on the image and structural element, geodesic dilation operated with two images including the mask and the marker. In geodesic dilation of size one the marker image was dilated by an elementary isotropic structural element and the resulting image was forced to remain below the mask image. In other words, the mask image acts as a limitation for the dilated marker image. Image reconstructing using geodesic dilation on an image was done by performing some successive geodesic dilations on the image. The results of geodesic dilation was depending on the elevation value. If this value was low, only the building ridge line was extracted andoff-ground. Moreover, if the elevation value was high, some of the bare earth was cut as off-terrain, wrongly. Hence, an iterative procedure was proposed to make the extracting the most of the object possible. In this way, the probability of error was reduced. In each loop, the elevation value was increased and some objects was extracted using geodesic dilation. Among the extracted parcels in each loop, the parcels which have local range variation more than a threshold were selected and the others were removed. The local range variation for each point was computed by specifying a window and calculating the difference between maximum and minimum elevation value in that window. This procedure was repeated till analyzing all of the elevation values.
Finally, the results of detecting the off terrain points using the both of approach were accumulated to acquire the final class of off-terrain points. Then this points were removed and the cubic interpolation was employed in order to retrieval the elevation of the losses points and generate the DTM.
In order to analyze the performance of the proposed algorithm, 7 test area was used. The point cloud of area 1, 2 and 3 were produced using dense matching of digital aerial images (Ultracam) by National GeographyOrganization of Iran. The point spacing of these areas is about 0.5 meter. The point cloud of area 4, 5, 6 and 7 were captured using LiDAR by International Society for Photogrammetry and Remote Sensing. The point spacing of these areas were 3, 1, 2.5 and 3 meter, respectively. The data set covered the most of the features such as steep slopes, mixture of vegetation and building, bridge underpass, road and building with various roof shape. Evaluating the performance of proposed algorithm represented the 4.85% error for extracting the off-terrain points and 0.68 meter error for generated DTM in all test areas, averagely. The evaluation results clarify the ability of proposed practical algorithm in generating the DTM using the DSM

Analyzing multi-temporal remotely sensed images is a powerful technique for monitoring land use and land cover changes in urban areas. Apart from the change detection (CD) technique, the features space have an enormous impact on the CD accuracy.To achieve satisfactory CD results in urban area, optimum selection of textural and spectral features is necessary. Although an exhaustive search guarantees the optimality of the selected features, but it is computationally prohibitive. Data reduction techniques such as PCA considers the independence of the data to find a smaller set of variables with less redundancy without intending to improve the CD accuracy. Difficulty in setting the best threshold for JM distance in separability analysis algorithm (SAA) reduces its efficiency. The aim of this paper is finding the optimal textural and spectral features to enhance the CD accuracy using genetic algorithms (GA) and Bayesian classifier. To evaluate the effectiveness of the proposed approach, a case study using IRS-P6 and GeoEye1 satellite imagery acquired on July 15, 2006, and September 1, 2013, respectively, from Sahand New Town (Northwest of Iran) was performed. All the mentioned feature selection methods (PCA, SAA and proposed GA-based method) were implemented in MATLAB R2013a. Results show that, textural features provides a complementary source of data for CD in urban areas. Results show that features selection is an effective procedure in change detection based on textural and spectral features. Each of feature selection methods has its own limitation and advantages, but in general they increase the CD accuracy. The proposed GA-based feature selection approach was found to be relatively effective when compared to PCA and SSA approaches. Overall accuracy and Kappa coefficient of CD were increased from 53.66% to 88.49% and 58.94% to 90.39%, respectively using proposed method in compared with that using only spectral information.

Availability of spatial information and decision making based on the analyses performed by geographic information system are among the fundamental components of sustainable development. For this purpose, an organization or more is responsible for the production of spatial information in different countries. In recent years and in the era of information technology and public access to internet, creating a platform for providing spatial information in cyberspace is considered to be much more important. As a result, the new generation of spatial information products have the capability of being presented and disseminated in the cyberspace. Some organization have proceeded and not only pioneered for dissemination of spatial information in the Internet, but also have provided users with the possibility of performing some basic spatial analyses. In developed countries, these possibilities are available and spatial information is being provided in cyberspace. In order to take advantage of developed countries knowledge and experience in producing and disseminating spatial information, it is necessary to evaluate the procedure of producing spatial data in these countries. USGS in United States of America and Survey Ordnance in UK are responsible for producing spatial data. Therefore, the present article investigates the procedure of producing spatial information.

Nowadays, energy use has increased dramatically in Iran. Gas is one of the most important energy resources. Gas use will grow in future and its pipe lines are quickly developing. The length of this gas distributing network reaches thousands of miles, so validating and timely defect detection in each part of pipe line is important and essential. The present article reviews current technology of leakage detection in pipe lines and points out the role of remote sensing and aerial methods in this detection.
In the first section, different leakage detection methods applied on gas pipe lines are shortly reviewed and the second section focuses on optical methods and applying remote sensing and aerial methods with the aim of decreasing costs and time of leakage detection in vast areas. Finally, different methods of leakage detection are compared in the conclusion.

Summarizing DTM received from LIDAR data is performed to generalize (decrease details) and strengthen important features. In order to incarnate ground surface, we need to strengthen some especial features like routes and dames. The present article seeks to prepare an algorithm to extract routes. Using summarization algorithm, it is possible to estimate route network and LIDAR filtered cloud points.

In recent years the use of aerial laser scanners for determining the topography of water beds has been introduced in the world and has found practical aspect. Depth measurement using laser scanners is a more precise, cost- efficient, and faster method than other depth-measuring methods, which is based on accurate measurement of the travel time of two light signals transmitted to the surface and bed of water. Consequently, the use of appropriate hardware and software, in which the source of the major errors is detected and minimized, is very effective on the result of the flight. This paper presents a variety of depth-measuring laser scanners, various techniques used in each of them, and a description of how depth-measuring operations are performed. In addition to expressing the natural causes of error as well as noise causes in operational data, an algorithm for data correction and a method for noise cancellation is presented.

Nowadays, accelerating the production of three-dimensional (3D) maps applying new photogrammetric methods using automated technology is not completely realized. For executives and users in the implementation of various projects that have serious need of maps, the time of production of map is a matter of importance. Therefore, considering the issue raised, it is a question of whether another high-speed product can be produced that could be an appropriate alternative to certain maps and be able to meet the needs of users?