نشریه پژوهش های تجربی در مهندسی عمران
سال چهارم شماره 2 (پیاپی 8، پاییز و زمستان 1396)

The earthquake is the most dangerous natural hazard, which can target thousands of people and cause massive destruction of infrastructure or buildings.After a earthquake,recognizing the location and extent of damage buildings is necessary to take emergency measures and temporary accommodation operations.Remote sensing is an excellent tool due to reasonable costs and rapid data capture.The recent earthquake in Sarpol-Zahab is one of the most recent destructive earthquakes that destroyed infrastructures.The purpose of this study was to use multi-time detection techniques to investigate the changes caused by this earthquake.In this research,different image classification methods are used to compare their results with the ability to identify damaged buildings on high-resolution satellite data (Pleiades1) and with mid-resolution (Landsat-8 and Sentinel-2). The first goal of this study is to obtain the best map of the proper destruction for the structure of Iran by comparing the output change maps with the reference data and localization of the change detection algorithms. In the final step, we examined the various applications of the map of changes. The results show that the SVM has been more accurate in comparison of image classification and also showing changes in the neural network method. Overall accuracy in using the SVM classification for the change maps obtained in the Sentinel is 0.85 and for the Landsat is 0.64. However, the overall accuracy using the neural network for change maps The obtained results are 0.80 for Sentinel and 0.61 for Landsat, which indicates the effectiveness of Sentinel images along with SVM techniques in Iran.

Increasing volatility and decreasing water depths in Lake Urmia along with the rapid progress of desertification during the last decade has become one of the most important and controversial environmental challenges in the country. In this study, from the viewpoint of the components that affect the elevation level of Lake Urmia water, the water level is analyzed and using the neural networks, its amount is foreseen for the coming years, so that an appropriate management plan for the program Design and implement necessary measures to prevent drought in Lake Urmia. Thus, in order to achieve the highest accuracy in predicting and simulating the water level, five scenarios were investigated, in which the input data were considered as evaporation, temperature, precipitation, temperature-precipitation and evapotranspiration respectively. The water level was selected as the output for network training. Finally, reaching the value of the value 0.026 for RMSE in the first scenario and 0.023 for RMSE in the scenario Second, as well as 0.093 for RMSE in the third scenario, we find that the effect of precipitation from temperature and evaporation and the effect of temperature on evaporation is greater, but given the results of 0.068 for RMSE in the fifth scenario. The most accurate scenario is the fifth scenario, which is the result of concurrent import of all three variables of temperature, evaporation and precipitation into the neural network.

According to the strategic position of Iran in the Middle East region and the continued presence of foreign threats it is essential to take different measure to protect military installations.One of the measures that can prevent internal damage is passive defense. One of the important principles in passive defence is the proper site selection.For this purpose, Geographic Information System (GIS) equipped with high processing power and capability of voluminous information management can help commanders and designers.The aim of this research is to find a suitable site for military installations and equipment considering the principles of military safety and passive defense. This research was carried out at two levels of detail in Mazandaran province. At the large level, the effective factors were determined by the experts and the factor maps were prepared. Then, the maps were weighted using AHP method and were overlayed using Index Overlay approach. The results of this level showed that the most appropriate area is Chahardange district located in Kiasar county. Therefor, with the goal of taking uncertainty into account, fuzzy method was used. Therefore, the factor maps were fuzzified and by adopting a strict approach, fuzzy product function was used. Consequently the maps were combined using fuzzy product and the proper site was detertmined. Evaluation of the results by field researches revealed that the determined site has the proper conditions for the purposes of the research.

Having a lot of wondrous benefits, Unmanned Aerial Vehicles (UAVs) are widely used for an array of applications in engineering and military sciences for several purposes. Without a shadow of doubt, the merits of using UAVs is not comparable with other Surveying techniques. The main aim of utilizing these kinds of technology would be gathering field data with high accuracy which will otherwise is colossal waste of time and expenditure. Digital elevation model is one of the most significant part of each project. Producing this data with old methods is not acceptable due to time and price of these kind of old approaches. DEM can be produced with manual, semi-automatic and automatic techniques by applicability of UAVs technology gathering images in very short time and convert them into DEM maps with the usage of photogamarty methods. The main purpose of this study is generating new method or approach for producing digital elevation model (DEM) with the use of UAVs data. In this research 11 control point (GCP) were gathered by RTK GPS method for improving the accuracy of final results. The accuracy of this model is 2.23 cm. With regard to the accuracy of the result, this approach can be utilized instead of many surveying techniques, or as a supplement data, or it can help organizations to use it as an approach for monitoring and assessing the accuracy of each project.

Road recognition and extraction based on remotely sensed data is efficient and applicable in much urban management studies. In this research, the capabilities of SPOT and SAR images are investigated for road recognition. Spectral and textural similarities between roads and other urban objects such as building’s roofs may cause some difficulties in road recognition based on SPOT image. On the other hand, SAR images are good for small road recognition but, may have some difficulties for detecting roads among vegetation. The proposed method in this paper is a decision level fusion of SPOT and SAR classification results in order to modify extracted road regions. This method has three main steps; 1) texture feature extraction from each of the SPOT and SAR images, 2) classifying each of the SPOT and SAR images based on SVM classifier, 3) decision level fusion of classification results in order to reduce road recognition difficulties and having optimum road regions. Performing the capabilities of the proposed decision level fusion algorithm for road recognition can improve the quality of the classification for about 21%.

Studying the extent of resource changes in the past years and assessing and predicting these changes in the coming years can be an important step in environmental planning, land preparation and sustainable development in the future. The catastrophic earthquake at the Sarpole-Zahab occurred on November 21, 2006 in Kermanshah province. This earthquake has caused changes in land cover and consequently changes in natural vegetation and rangelands of the region, in addition to life and infrastructure damage. It is usually difficult and limited to study and monitor vegetation on a global scale and areas of access to field or field data. It is also time-consuming to estimate vegetation in a conventional way that includes an overall estimate of vegetation and does not provide very accurate information. Therefore, remote sensing is a very useful technology that has features such as providing a wide and integrated view of an area, repeatability, easy access to information, high accuracy of information obtained and time saving over other methods. Is preferred. Therefore, satellite data is one of the most effective tools in the field of rangeland and vegetation science. Vegetation indices such as NDVI as well as SAVI soil effect modifier vegetation index were calculated from the VRR vegetation improvement index to evaluate the effect of earthquake on vegetation area and to evaluate natural vegetation recovery rate. The natural recovery rate of VRR plants for the year after the earthquake was 40% for the study area using NDVI index and 39% using SAVI index.