نشریه علوم و فنون نقشه برداری
سال یازدهم شماره 3 (پیاپی 43، زمستان 1400)

The growing trend of technology and telecommunication industry has led to the use of optical systems to enable the transmission of high bandwidth information in remote locations. Due to the advantages of fiber optic communication platform over copper wire, fiber to home technology has recently emerged and many citizens are applying for fiber optic based internet. Installation and development of fiber-to-the-house technology requires consideration of several parameters, which are location-based and enable the electronic participation of citizens. On the other hand, this technology has expensive equipment that, if not properly distributed, can lead to financial losses of telecommunications and dissatisfaction of citizens. These problems can be reduced by using location-based analysis and a web-based system. The most important innovation of this research is the location and optimal allocation of fiber optic terminal in the form of a web-based spatial system. To optimize FAT location, it will be done in such a way that with the lowest number of FATs, the highest demand (coverage issue) is covered at the shortest distance from the fiber optic joints (middle issue). One of the advantages of the proposed system is the reduction of equipment installation time without face-to-face visits, reduction of telecommunication costs, honoring the client and optimal coverage of the service applicant. The results show that the applicants were 87% satisfied with the mentioned system and the range that was previously covered with two FATs and about 20 working days, was covered with a FAT in 7 working days using the proposed model.

Groundwater level measurement and monitoring are one of the basic and essential steps in groundwater studies. Data in geographical studies, especially in groundwater studies, are generally measured at points in monitoring stations and play an important role in analyzing temporal and spatial variations of phenomena. optimization is one of the main issues in reducing the cost and increasing the quality of the extracted data. The main purpose of this research is to optimize the groundwater monitoring network of the Arak plain aquifer, in which a new metaheuristic method called Colliding Bodies Optimization (CBO) is used. For this purpose, the groundwater wells data are collected and refined. Then the CBO method is implemented and the inverse distance weighting (IDW) method is used to calculate the cost function in CBO. In order to evaluate the accuracy of the output, the results were compared with the ant colony optimization (ACO) method. Arak plain is located in the watershed of Miqan desert wetland and its groundwater level has been decreasing in recent years. Continuous and accurate monitoring of groundwater levels in Markazi province and especially the Arak aquifer is one of its main needs. For this purpose, the groundwater wells data are collected and refined. Then the CBO method is implemented and the inverse distance weighting (IDW) method is used to calculate the cost function in CBO. The CBO algorithm is based on simulating the search space with an environment in which the kinetic energy and momentum of the colliding particles are decreasing. In the proposed approach, using the IDW method, a continuous surface was created from the selected stations, and the error generated in the unselected stations was calculated based on the Root Mean Square Error (RMSE) formula. IDW is one of the simplest spatial interpolation methods that has been used extensively in network optimization. In this study, the average annual data of 57 groundwater level monitoring stations in 1397 were used. Out of 57 monitoring stations, only 43 were active and in 14 stations, measurements were not recorded. Evaluation of outlier data based on Grubbs test showed that well data No. 15 was as outlier data which was excluded from the calculations. In this study, different scenarios were evaluated for removal of 1 to 12 monitoring wells and the curve of the number of removed wells against the amount of error created in the wells was drawn. It was obvious that the error value would increase as the number of deleted stations increased. Comparison of the optimized error percentage shows that the CBO method always has a lower error than the ACO. In general, based on the location of the unselected stations in each scenario, it was observed that the study area is divided into three general parts, north, south, and southeast. In the first three scenarios, the unselected wells are located in the northern part of the aquifer. From scenario four, station No. 6 from the southeastern part is always removed, and from scenario five, station No. 40 from the southern part is always removed as a priority. According to the error diagram, the location of the unselected wells in different scenarios, and also expert opinions, it was found that by removing 6 wells (wells with numbers 5, 6, 9, 30, 36, and 40) from the groundwater monitoring network of Arak aquifer, a maximum of 35 cm of accuracy will be reduced in the well. On the other hand, it saves money and time for data collection. The location of the unselected wells shows that the wells around Miqan Wetland are of great importance in estimating the groundwater level of the aquifer and most of the removed wells are located in the northern and central part of the aquifer. This study also showed that the object collision optimization method is a suitable method in optimizing groundwater monitoring networks.

Due to the value of time in disaster relief, access to current information about the geographical location and physical condition of the injured and the use of new technologies and methods of GIS is important. The purpose of this study is to create a platform that uses MobileGIS, WebGIS and VGI technology to transfer and collect immediate and accurate information that leads to reducing relief time, increasing the speed of sending online or offline assistance requests by victims and reducing casualties. Real-time information on the physical condition and position of the injured person according to the required hardware facilities and the importance of time in responding to the needs of the injured is provided by each victim and used in this system .In this research, the information sent by the victims and those around them has been used. The information sent in the geoserver is analyzed .At the disaster, due to solving the problem of disconnecting the Internet, the output of this research was used as a mobile application with Android and IOS operating systems. In addition, the web version of the system was available when connected to the Internet. After the implementation of this system, with the obtained information, an Instant map was made available to the rescue manager, which included mapping the type of assistance and relief for each person or in groups for a group of injured in a specific geographical area, which help to assistance-and-relief managerschr('39') decision-making process.

The Earth Orientation parameters (EOP) including the Length of Day (LOD) are vital to widely used applications of Satellite Geodesy. The precise satellite positioning and navigation and the Earth system monitoring satellites are a few applications with a near real time request for the EOP values. Data gathering from a globally distributed co-located geodetic sensors and processing the collected data for estimation of the parameters are time demanding tasks which makes delayed access to a real-time processed values unavoidable. Consequently, accurate prediction of the EOP parameters time-series has been defined as a highly demanding task in geodesy. Many different techniques have already been employed either for short- or long-term prediction of the time-series. However, the implemented methods for short-term prediction of the EOP temporal changes is in central focus of the research centers and institutes due to its applications in the Earth Centered inertial (ECI) and the Earth Centered Earth Fixed (ECEF) reference frames. Moreover, combined methods with the ability of simultaneous functional and stochastic behavior modeling of the time-series are more interested due to their functionality for one-step accurate prediction. For instance, the Least squares auto regressive (LS+AR) is the most recent published article for the LOD forecasting. In this paper, we will address an innovative approach for complicated and challenging time-series prediction. The proposed methodology consists of the Singular Spectrum Analysis (SSA) combined with the Auto Regressive Moving Average (ARMA) enabling to model the functional and stochastic constituents respectively. For more precise forecasting, the proposed method is equipped with two pre analysis statistical tests in order to detect and identify any possible outliers. Moreover, the Fast Fourier transform (FFT) is employed to give a first guess of the possible periodic pattern of the data with its later application in the SSA appropriate window length selection. The SSA setup consists of the lag-covariance matrix computation, Eigen value and Eigen vector decomposition of the lag-covariance matrix, the Eigen values clustering and the component reconstruction. Trend and offset removal before utilizing the SSA method and its restoration after performing perdition is also worth mentioning. Selection an optimal number of deterministic components plays a key role in effective implementation of the SSA approach which is fully explained in the methodology part of the paper. The stochastic behavior of LOD signal is characterized using the ARMA technique whose successful implementation is highly depend on the right selection of the Moving Average (MA) and Auto Regressive (AR) orders. The Akaike information criterion (AIC) as an estimator of prediction error as the well-known order selection criteria is used. Moreover, the Mean Absolute Error (MAE) is computed and different prediction scenarios are compared. The suggested approach has dominantly outperformed eight already published method. Publicly available LOD data from International Earth Reference System (IERS) is used for the method evaluation and numerical comparison. Daily LOD data for the years of 2005-2009 is used for model training while the year 2010 is taken for validation. A ten-day interval prediction during the whole year of 2011 is considered for evaluation. On average, accuracy improvement rate is about 1.6 and 1.84 for the 5th and 10th ahead day of prediction.

Today, the rapid physical growth and development of cities has caused significant changes in their physical and functional characteristics, and as a result, many problems have arisen. The cities of Tehran and Tabriz, as the two metropolises of Iran, are no exception to this rule, because the reason for their development in recent years and the concentration and overcrowding of various uses, especially commercial uses and medical services in the central sector, issues and problems for transportation network failure. It has created two cities. Accordingly, in the present study, these two metropolises have been studied. Remote sensing observations at night provide us with an explicit and timely measurement of human activities. Numerous studies have shown that night light (NTL) can be used as a proxy for a number of variables, including urbanization, density, and economic growth. Accordingly, in this study, we examined urban growth and its effects using remote sensing at night. For this purpose, satellite images of SOUMI NPP, LANDSAT 8 and LANDSAT 7 as well as traffic information obtained from Google map were used, using ENVI 5.3, QGIS 3.10, ARC GIS 10.3 software, Google Earth Engine system and MATLAB software. This data was done. First, the physical development of the studied cities was investigated using the BUNTUS algorithm (urban built-up areas, night light image and travel time for the city limits). The results showed that both cities had a slight slope growth during this nine-year period (2020-2012). After calculating urban growth to study urban traffic, regression was performed between traffic information and numerical value of image pixels (DN) using MATLAB software, which showed the correlation between these two layers of information.

Micro-Aerial Vehicles (MAVs) are ideal platforms for indoor and outdoor applications because to their small size and light weight [1, 2]. Obstacles, on the other hand, may cause MAVs to crash. Cameras collect a lot of evidence about their surroundings. Through the use of grayscale values [3], point features [4], and edge details [5], vision-based techniques detect obstacles. They are divided into monocular and stereo types. There are four types of monocular approaches: appearance-based [6], motion-based [7], depth-based [8], and expansion-based [4]. Expansion-based techniques are based on the same principle as human vision. The majority of expansion-based systems identify obstacles by recognizing object points [4, 5, 9-11]. However, relying solely on points may not be sufficient; as a result, the MAV may collide with unseen impediments. To overcome this obstacle, we describe a novel technique based on the same concept but employing region-enlarging rates.

Various steps of our obstacle detection technique above can be summarised in (a) data acquisition and preparation, (b) region extraction and their area calculation, and (c) obstacle detection.

 We took four pairs of images with an LG 360 CAM fisheye camera, two with the camera moving forward and two with the camera moving to the sides. In the forward direction, recall accuracy is 82% for the first data and 52% for the second data. The new technique detects only a portion of the obstacle region. This problem emerges because some regions lack at least three matching points. While moving to the right, recall accuracy for the third and fourth data is 69% and 39%, respectively. This accuracy is lower in the fourth data set than in the other data sets due to the aforementioned description, the absence of at least three corresponding points, and the possibility of inaccurate corresponding points, particularly along the fisheye image's edges, which have a low quality in these places.

 The proposed method extracts regions of close obstacles from outdoor fisheye images. The findings demonstrate the method's efficacy in a variety of complex environments. Thus, on average, 60% of the obstacles are detected in two modes of forward movement and right movement. Additionally, a comparison of the suggested method to that of Al-Kaff et al. (2017) [4] demonstrates that it is more efficient than the existing algorithm.The proposed algorithm, however, has some gaps in terms of obstacle detection.One of these limitations is that certain regions lack at least three corresponding points. Also, the presence of incorrect corresponding points causes incorrect detection of obstacles. The second limitation is that the obstacle is the same color as the background, which leads to errors in the correct detection of obstacles. The third constraint is the long processing time required by the suggested approach. These constraints can be solved in the future with the use of more accurate and faster algorithms.

One of the natural disasters of all is drought. Drought has long been one of the problems of Iran and has always been mentioned as a serious threat to the country. Drought is one of the natural and repetitive features of the climate and part of the climate that cannot be observed without a specific limit of occurrence and impact using high-speed ground station information. Drought rate is divided into severe, mild, moderate drought and long-term drought and short-term drought based on time components. Drought has had effects on land degradation, forest fires, reduced air and water quality, and reduced agricultural production. Frequent droughts have been a concern for many years, and on a global scale, in recent decades, especially in arid and semi-arid regions, the frequency, severity and duration of droughts have increased significantly. Drought according to different definitions in each area is divided into four main categories, including meteorological drought, agricultural drought, hydrological drought and socio-economic drought. In meteorology, drought, or in other words dry period, is the reduction of loss in a period of time compared to the average amount of rainfall in that period or the amount of long-term rainfall in the same period. Of course, if this amount of rainfall does not meet the needs such as economic and social needs. The duration or period of drought varies according to the climate of each region and depends on the location and time. In Iran, the drought period is approximately equal to the crop year. The severity of drought also varies in each climate and country. The same amount of rainfall in one country may be considered drought, while in another country, the same amount of rainfall does not indicate drought. Agricultural drought damages the economy, social conditions, agricultural products and consequently food security, so monitoring it is essential. For this purpose, agricultural drought in Markazi province has been studied. One of the factors affecting agricultural drought, soil moisture and plant evapotranspiration as a cause of water loss is the difference between the available water and its wastage. Therefore, to measure drought, an indicator that shows evapotranspiration is needed. In this research, RWDI index is used as an indicator of drought based on evapotranspiration. To obtain this index, one must first obtain the actual evapotranspiration and potential. Due to the advantages of remote sensing methods compared to traditional methods, remote sensing methods based on energy balance and SEBAL2 image processing model as well as Landsat 8 images, in dry and wet seasons, along with meteorological data to obtain heat flux Hidden, which depends on the parameters of soil heat flux, tangible heat flux and net radiation flux, is used. Using latent heat, instantaneous evapotranspiration is calculated daily and using the Penman 3 method, reference evapotranspiration is calculated. Potential method of Taylor 4, evapotranspiration and potential transpiration are obtained, and as a result, after calculating the actual evapotranspiration, the RWDI index is prepared, which shows the water shortage and the severity of agricultural drought. The results show that the values ​​of evapotranspiration are obtained with acceptable accuracy. Meteorological stations are obtained in REF-ET software. In addition, based on the results of the water deficit index, the values ​​of this index are expected to be higher in dry seasons than in wet seasons.

With the spread of the COVID 19 virus and the threat of a pandemic, the World Health Organization (WHO) proposed quarantine and setting social distancing restrictions as a means of disease management. Despite their effectiveness, implementing and administering these restrictions is prohibitively expensive and not suitable for a long time. Contact tracing systems can be used as an alternative to public quarantine. Smart cameras are an easily accessible structure for developing such a system due to their widespread use. However, there are numerous challenges in designing and implementing the mentioned system. The architecture of an effective object detection model, a large enough dataset, and a method for positioning and tracing people are some of the challenges that have been examined and compared in this article.

More than 70 percent of the Earth is covered by water, and water bodies are the main features in the globe. Hence, studies of water depth and bathymetry are of great importance. Hydrography is the most accurate method in bathymetry science to measure the seafloor surface and water depth for defense and security, development, and environmental purposes. Mainly, maritime transportation and safety rely on spatial information databases and accurate hydrography of the seafloor in ports and coastal areas as well as periodic sedimentation monitoring in ports and approaches channels. Regular sedimentation monitoring allows for sedimentation distribution mapping, precise maintenance dredging, and safe navigation in coastal zones. To determine spatial and temporal trends of sedimentation, periodic hydrography of coastal areas is in demand. Therefore, in the present study, sedimentation rates were estimated based on precise hydrographic datasets in the port basin and approaching channel of Amirabad port (one of the most important Iranian ports in the Caspian Sea) in the period 2018-2021. Using the Ceeducer Pro (a 200 kHz hydrographic survey system) in 10 separate hydrographic surveys (with 2 to 6 months intervals), the three dimensional (3D) datasets were acquired with accuracies of higher than 0.5 m. After data processing (based on different dates), the digital surface models of the seafloor were produced for every hydrographic surveying. Then, the sedimentation volumes were calculated and compared in pairs. To estimate the final volume of the sedimentation by considering the dredging operations in the study area, the amount of sedimentation transferred by the dredging processes was also calculated and added to the final volume of the sedimentation. The outcomes confirmed the average rates of annual and monthly sedimentation (m3 / m2) in the channel by 0.122m and 0.010m and in the basin by 0.160m and 0.013m, respectively. Generally, a homogeneous distribution in sedimentation was observed in the whole region. However, a pattern of the highest volume of sedimentation occurred in the entrance of the channel (in the north and northeast direction), in the south of the main basin, and the western and eastern parts of the basin. Moreover, the highest rates of sedimentation were predominantly observed in the warmer seasons in Amirabad port. According to the finding of this research, the maintenance dredging system was performed at a satisfying level, resulting in successful removal of sediment from the seabed in the study area. Besides, undertaking a maintenance dredging system at longer intervals (i.e., more than four months) might threaten the navigation safety of ships in the region. The results of this research will help private and government sectors for more accurate planning and optimal dredging of the region in the future. In addition, these datasets are reliable and accurate reference and source for the validation and evaluation of other common empirical and semi-empirical methods in estimating sedimentation rates in Amirabad port.

Land Surface Temperature (LST), as a key parameter in environmental interaction, has been studied in several researches. Urban thermal condition depends on land cover types that composed of different materials with various thermal properties. In this study we analyzed diurnal temperature of vegetation, stone, water, cement, asphalt, and soil land cover types (LCT), as main components of urban structure. The Diurnal Temperature Cycle (DTC) method used to model daily behavior of different material temperature’s by implementation of precise sensors at a weather station in Tehran, of Iran. Then, comparisons between maximum diurnal temperatures of different LCT with LST at the day and night time of MODIS were conducted. Result showed that in warm days the discrepancies between materials were larger and soil, asphalt, and cement had higher temperatures than stone, vegetation, and water. Also, water had little fluctuations and some phase shift to reach maximum amplitude. Also, time series of MODIS LST in the study area were extracted and compared with maximum diurnal temperature of different LCT. Maximum correlation was estimated between Terra daytime LST and T_max of soil and cement material with 0.948 values for "R^2"  and 2.89 and 4.2^C"  for RMSE.