نشریه علوم و فنون نقشه برداری
سال یازدهم شماره 1 (پیاپی 41، تابستان 1400)

Foot-and-Mouth (FMD) is a viral and contagious disease that endangers both domestic and wild animals and this disease has caused a lot of social and economic damage to the livestock industry in Iran. Considering the endemism of this disease in Iran, the main purpose of this study is to investigate the temporal trend, discover spatial and spatio-temporal clusters and determine the impact of environmental factors and model the disease in order to better understand the spatio-temporal epidemiology of FMD in Iran. In this study, data related to the incidence of disease as well as environmental variables including meteorological variables, vegetation and topography of the region were used. This study was performed on 12442 cases of registered diseases in the years 1396-1387 in the city for the whole of Iran. First, general auto correlation indices including Moranchr('39')I index, General G index and Ripleychr('39')s K index were used to investigate the distribution of the spatial pattern of the disease. Local Moran and the Gatis-Ord G* index were used to detect hot and cold spots. Then, with the help of the points of occurrence and time of the disease, spatial and spatio-temporal clusters of the disease were discovered. Spearmanchr('39')s correlation and linear regression analysis were used to evaluate the type and severity of correlation between environmental parameters and disease incidence. The results showed that the maximum incidence is in spring (44%) and the minimum incidence is in summer (15%). The months of Ordibehesht, Farvardin, Khordad and Dey, with a total of 6650 cases, were the most riskful months of the year, respectively. Autocorrelation tests showed the distribution of FMD clusters for every 10 years studied. The results of the local Moran index and the Gatis-Ord G* showed that the foci of this disease are still active in Iran and parts of Semnan, Tehran, Markazi, Fars, Lorestan, Khorasan Razavi and East Azerbaijan provinces are hot spots of FMD. In spatial scan, 8 clusters were identified, which confirmed the results of Moran and the Gatis-Ord G* index. In the spatio-temporal scan, 5 clusters were discovered that were consistent with the results of the spatial scan. Spearman analysis showed that there is a positive correlation between the incidence of the disease and vegetation, which is decreasing from the beginning to the end of the year and has a negative relationship in winter. Also, a positive and increasing correlation was observed with wind speed and precipitation from the beginning to the end of the year. In addition, a positive correlation between the incidence of the disease and altitude and a negative relationship with the direction of the slope was calculated. Linear regression analysis was also used to evaluate the effect of variables, which resulted in confirming Spearman correlation outputs. The results of the model showed that vegetation and topography are the most important environmental factors that affect the prevalence of snow fever in the region. The results of this study identify areas that are at higher risk and need more planning and attention to control the disease.

The deployment of new technologies in digital devices and communications has increased social networkschr('39') popularity and pervasiveness. Geo-tagged content has connected cyberspace to the real world by adding a new dimension to social networks. Among geo-tagged content available on social networks, geo-tagged images show userschr('39') communication and interaction with the environment in a better way. Because users frequently photograph regions of interest, these images can be used in many applications, including discovering regions of interest. Compared to traditional methods such as censuses and surveys, geo-tagged images benefit from saving time and expense to discover and analyze regions of interest. Therefore, researchers can use them in urban management and tourist recommendation. The purpose of this study is to discover the region of interest using geo-tagged data. Also, extracting appropriate semantic information and analysis in different contexts to identify regions of interest and understand the reason for their attractiveness is another goal of this research. This paper uses the Flickr geo-tagged images taken from New York City between 2015 and 2018. In the preprocessing phase, noise and data redundancy was removed. Then the data were clustered by the HDBSCAN method, and adjacent clusters that were similar in terms of text tags were merged. As a result, 106 regions of interest were identified. At the next step, a concave surface was fitted to points by α-shape method, and semantic information including distinguished labels, names, and categories, was selected for regions of interest. Finally, attractive regions were analyzed based on the type of visitors, users’ sentiment and the number of visits in different contexts. The evaluation results show the discovery of regions of interest in different shapes, dimensions, and densities. Our result corresponded for 66% with TripAdvisorchr('39')s top attractions, while for the simple DBSCAN method this value was 53%. In regions that overlapped with TripAdvisor attractions, the naming was 76% similar.

Advances in Remote Sensing technologies provide various information regarding object detection problems. This information makes the interpretation of optical remote sensing images easier. Especial kinds of these interpretations relate to Object Detection approaches that most researches in this field are carried out using Neural Networks and Deep Learning techniques; Design of the network is an important process that affects detection accuracy. Recent researches in the deep learning field and convolutional neural networks show that deeper networks can achieve better accuracy. However, in previous researches, sometimes too deep networks are the reason for other problems such as increasing the number of trainable parameters, vanishing gradients, unused extracted features, etc. These problems decrease the accuracy of the network in recognition of objects. This issue has been mentioned in many types of researches in the field of convolutional networks, and they have tried to meet the challenge by examining different topologies or presenting new training methods. In this article, a model was developed and tried to keep extracted features and transfer them to the next layers. The proposed architecture is a combination of several blocks stacked in a row. The blocks receive their input from the previous block and perform the relevant calculations. Each block consists of several cells that have two layers of convolution. To efficiently use all the features of the training images, the filters used in the convolution layers have kernels with sizes of 1×1 and 3×3. The output of the 3×3 layer in the combining stage is integrated with the information of the previous layers. The architecture of each cell in the proposed network keeps all the extracted features from previous layers to be used in subsequent cells. With these connections between layers, the networks can be deeper with fewer effects of vanishing gradient. In addition to solving gradient problem, this architecture decreases the number of trainable parameters and duration of the training phase impressively. The result of this process is an increase in the ability of existing models to distinguish multi-class objectives. For this purpose, first, a collection of 320 training images is proposed and preprocessed. The proposed method is defined as feature extractor of Faster R-CNN model, and it is trained on image collection. To evaluate the proposed method, a part of Beijing International Airport and a part of Imam Khomeini International Airport were selected as the first and second case study areas. The F1-Measure criterion values for both regions are 97.9 and 93.7, respectively. While, ResNet architecture with 101 layers of convolution and 14.4 million more trainable parameters than the proposed architecture has achieved values of 96.7 and 93% for the mentioned criterion. Finally, the results of applying the proposed model were compared with different famous models of the existing network. The experimental results indicated the reliability and efficiency of the proposed method. To improve the proposed architecture in this paper, dilated convolution operators can be used to extract more prominent features. On the other hand, with the aim of development and generalization, the proposed method can be applied in two stages on high resolution remote sensing images; In the first step, the goal is to identify the location of the airport, and in the next step, the planes inside each airport will be identified by the proposed method.

Image registration is the technique of aligning two or more images with a reference image all acquired at the same geographical area with different acquisition dates, viewpoints, and imaging sensors. This technique is a fundamental process in photogrammetry and remote sensing tasks, and change detection is known as one of its important applications. Urban change detection with an arbitrary time difference between the two images faces limited access to the required datasets. The Google Earth software is known as an appropriate resource to achieve such datasets. Nevertheless, a significant limitation of this software is the co-registration error of the images acquired at different times. In this work, a combined image registration approach has been proposed to cope with this problem. The proposed method is made up of three major steps. In the first step, the KAZE algorithm is applied to extract and describe image features with high stability against illumination distortions. In the second step, the image matching and mismatch elimination procedures are conducted using the VFC (Vector Field Consensus) technique. Finally, in the third step, the geometric relation between the two images is established using the TPS (Thin-Plate Spline) adaptive transformation model. The results derived from the KAZE feature extraction and matching algorithm are compared with those of the SIFT and SURF algorithms in four satellite image pairs related to various urban areas. For datasets 1 to 4, the highest correct correspondence rates of 0.63, 0.81, 0.6, and 0.76 were obtained using the KAZE algorithm compared to the other algorithms. Moreover, the TPS transformation model established the most accurate and reliable geometric relation between the two images with RMSE values of 2.1, 1.8, 2.1, and 1.7 pixels for datasets 1 to 4, respectively. Accordingly, the results indicate the efficacy of the proposed integrated method for robust feature extraction and image registration of the Google Earth imagery with significant brightness and landscape differences.

Mangrove forests are a collection of trees and shrubs that are located in the tropics and this tidal plays an effective role in their growth, establishment and continuity of life. Mangrove ecosystems are found in tropical and subtropical regions of the world. Mangrove forests may exist in wide ranges between latitudes 35 ° N and 38 ° S. Mangroves are one of the most globally adaptable conservation ecosystems on the planet, providing invaluable services to coastal areas and the millions of people living in these communities, but because most of these services cannot be priced at market prices. As a result, the importance of these unique ecosystems is often underestimated. Due to the environmental role of mangrove forests, monitoring the changes in these forests is of particular importance. However, the location of these forests between water and land has made the direct study of this phenomenon difficult. In recent years, increasing attention to remote sensing science has removed barriers to access to these areas and made it possible to study these plants in any regional conditions. For this purpose, in this study, using telemetry data, the changes of Goiter estuary in the southeast of Sistan and Baluchestan province in a period of 30 years were investigated and Landsat satellite imagery (TM, ETM + and OLI sensors) from 1988, 1996, 2005, 2013 and 2018 were used. Developments were examined in the form of four study periods: 1988-1996, 1996-2005, 2005-2013 and 2013-2018. In order to calculate the amount of mangroves in this area each year and prepare a map of the distribution of mangrove masses, the Normalized Difference Vegetation Index was used. In the mentioned environmental conditions, only mangrove species are able to survive and the presence of other plant species in such conditions is far from the mind and as a result, this index can have a good performance in the field of mangrove studies. The study of mangrove values in the mentioned years showed a positive growth of mangrove values in the first three study periods. In the first period 32.31 hectares, in the second period 59.31 hectares and in the third period 1.17 hectares was added to the area of mangroves. This increase has more than doubled from 1988 to 2013. But from 2013 to 2018, there was a sudden decrease of 59.94. This reduction in area in tropical forests can be affected by various reasons. In this project, according to the comparison of the spectral diagrams of mangroves and the marginal waters of this cover, it seems that this reduction is false and is affected by the tidal phenomenon. The spectral behavior of two ranges proved that despite the differences between the two curves in reflection, their overall structure is the same and the red curve is a combination of water reflection and vegetation, indicating the presence of mangroves underwater when Is fashion. Therefore, it can be said that the decrease in 2018 was due to the placement of part of the mangroves under water and therefore there was no decrease in the area.

Recent developments in information and communication technologies, including the emergence of the Internet and the Web as a global infrastructure, have provided valuable opportunities for the active participation of ordinary people in the planning, decision-making, development and advancement of science and knowledge. The web and mobile GIS technologies have provided the necessary platform for people to actively participate in the production of spatial data by providing the ability to draw on the maps via smartphones equipped with GPS systems. Unlike the traditional process of data production, which is closed, expert-centered, and organizational, participatory spatial data is generated by each user and made available to others for free. In other words, each person is both a consumer and a producer of spatial data. The amount of effort made by people trying to produce spatial data is directly related to the quality of the spatial data produced. The low effort of a user in the production of spatial data, indicates that the user produces data with low quality or, conversely, users with high interest and motivation who spend more effort to produce data, improves data quality. Therefore, it is necessary for users of participatory spatial data (such as government organizations) to be aware of the amount of effort made in the production of spatial data. In other words, by measuring and evaluating the amount of effort made in data production, the quality of data can be ensured to some extent. Therefore, determining and evaluating various indicators that indicate the amount of effort made in the production of spatial data is an important and necessary issue. The purpose of this study is to investigate and evaluate the efforts of citizens in producing spatial data related to urban problems. For this purpose, five indicators including the time spent drawing features, the number of zooming performed, the number of features drawn, the difference between the number of complex and simple features and the number of spatial edits on the map were selected. To measure the above indicators, a participatory web and mobile GIS was implemented in District 6 of Tehran. The results show that most users spend about 16 to 32 seconds between drawing the first and last feature, 0 to 3 times zooming operation, 0 to 2 times drawing the feature and 0 to 3 times the activity of restoring the drawn feature to the previous state and redrawing (Undo-Redo) on the map. In addition, the number of polygon features drawn by most citizens is more than point features (high effort) while for 17 people the opposite is true (low effort). In general, the low number of the above activities shows that citizens have not spent much effort and accuracy in producing spatial data. It is suggested that future studies examine and evaluate the relationship between user characteristics and indicators of effort in producing spatial data. Moreover, the amount of effort of users in producing spatial data related to a geographical phenomenon is affected by the degree of complexity, user perception and knowledge about that phenomenon. Drawing some phenomena requires more thought, time and effort. For example, drawing a contaminated area whose boundary is not precisely defined is far more complex than drawing an urban green space that has a clear boundary. Therefore, it is suggested that future studies examine and analyze the relationship between user perception and understanding of a spatial phenomenon and the amount of effort in producing data of that phenomenon.

Due to the wide application of GPS time series such as the study of tectonic movements, land crust change and earthquake dynamics, etc., it is important to provide a method to increase the speed in the analysis of variance components. Researchers have shown that nearly 90% of GPS time series have a combination of white+flickr noise and a lower percentage have a combination of white+random-walk noise. Researchers have used a trigonometric relationship inside the maximum likelihood estimation (MLE) method, to reduce the dimensions; instead of calculating 3 unknowns (white, flicker and random-walk noise components) they calculated 2 unknowns, a phase such as phi introducing relationship between white noise and colored noise and a phase such as theta introducing relationship between two colored noise (flicker and random-walk noises). On the other hand, researchers have been able to estimate white noise by empirical mode decomposition (EMD). In this research first we try to estimate the white noise using EMD method and then with the mentioned trigonometric relation, we can estimate the flicker or random-walk noise. It is expected that if this project is successful, the noise components will be estimated immediately. Finally, the results of the proposed method are compared to the results of least square variance estimation (LS-VCE) method. In another new method, since white noise can be extracted and separated by EMD, first white noise is extracted and then GPS time series components including linear trend, periodic movements with annual and semi-annual frequencies are extracted by least squares. The type of colored noise (flicker or random noise) can be determined by the Hurst parameter and assuming that the residual is flicker or random-walk noise, the statistical information of colored noise can be estimated (standard deviation method). The methods were first tested on simulated series and after its success, real GPS time series were used for verification. In this study, extracting white noise by EMD has been fundamentally reviewed; including how to decompose and how to detect white noise through intrinsic mode functions (IMFs) by Hurst parameter. Among Hurst parameter estimation methods, 12 methods were evaluated and boxed-periodogram method had better results. The results of the new methods are very efficient for simulated and GPS time series with white+flicker noise, but challenging for series with white+random-walk noise.

In recent years, the worldchr('39')s population has been growing, reaching about 7.7 billion in 2018. In the next five years, the worldchr('39')s population will increase to about ten billion, thus increasing the demand for food, water resources and fertile land for food production. Therefore, precision agriculture is trying to improve the quantity and quality of agricultural products by using new technologies. Precision farming is a new concept in modern agriculture and is based on the existence of heterogeneity at the farm level. One of the reasons for the growth of precision agriculture among scientists and farmers is the advancement of technology in various fields such as global coordinate system, imaging sensors and spatial information management tools. The use of remote sensing allows us to control a very large space in a very short time, which will reduce heavy costs. Along with rice and wheat, potatoes are the third most important food crop in the world, fed by more than one billion people, and the demand for this crop has increased as the population has grown; Therefore, better methods of product protection and management need to be used to improve production. In order to build the whole model, the images of the region were considered during the months of March to October in a period of two years, and pre-processing such as removing the cloud and other errors were done in order to have images with the least error. The NDVI index was then calculated using red and infrared bands (Figure 3). By calculating the NDVI index, useful information about the plant such as health, plant vigor, presence or absence of fall, and proper irrigation were obtained. On the other hand, land information, agricultural lands have been collected through questionnaires. For modeling, we deal with two sets of terrestrial and satellite data. Separate use of each of them will be associated with shortcomings and shortcomings; Therefore, for modeling, a combination of terrestrial and satellite information was used to build a model with minimal deficiencies and defects. It is common for modeling to work repetitively to obtain the best algorithm, so linear regression is the best choice for modeling. Univariate method was used for modeling and the date of 28 June was selected as the modeling interval. Because using multivariate methods, due to the fact that there was a high correlation in the information of different time periods, a lot of duplicate information was generated, and for this reason, we used the univariate method. Four machine learning algorithms were used for modeling, which Gaussian methods and support vector showed the best results. According to the production estimation model, it is possible to predict the amount of potatoes produced in each agricultural land and its health status with 50% accuracy three months before the harvest. The results of this study showed that estimation models will provide very valuable information for better management of crops to be used by agricultural planners to avoid wasting resources and prevent imports.

Water vapor is the most important atmospheric greenhouse variable gas, both spatially and temporally. It involves a positive feedback loop in climate change. In addition, the atmospheric water vapor content is not only an important parameter to forecast precipitation and severe weather but also a central factor for studying the global water cycle. Therefore, in order to weather forecast and climate monitoring applications, study of spatio-temporal distribution of water vapor is valuable. Today, satellite sensors, due to their wide spectrum range, high -spatial resolution, and almost daily global coverage, have enabled them to observe the Earthchr('39')s atmosphere and constantly monitor its changes. One of the atmospheric parameters measured by satellite sensors is water vapor. Among the various sensors that measure water vapor, the MODerate resolution Imaging Spectroradiometer (MODIS) is one of the most famous. MODIS offers water vapor in the form of two separate products, one is the near-infrared product and the other is the infrared product. In addition to satellite sensors, water vapor can also be accessed through observations of ground-based devices such as Global Position System (GPS) and Sunphotometer. In this research, among the MODIS water products, the near-infrared product was used and the data measured at the Aerosol RObotic NETwork (AERONET) site along with the computed values from GPS observations were used to validate the satellite water vapor data. In this research, the method of Point Precise Positioning (PPP) has been used to process GPS observations. The main goal of this research is the investigation of MODIS and AERONET water vapor data quality using GPS observations at Zanjan city. In this study, standard MODIS near-infrared level-2 Integrated Water Vapor (IWV) data product and AERONET IWW retrievals from sunhpotometer at Institute for Advanced Studies in Basic Sciences (IASBS) site, were evaluated using estimated data from GPS permanent station of IASBS from 2011 to 2013. Intercomparison results showed that AERONET-IWV and GPS-IWV had high correlation (R2=0.95). The mean values of an Root Mean Square (RMSE) and Bias between AERONET-IWV and GPS-IWV (AERONET-GPS) were about 3.2 mm and 2.2 mm, respectively. The MODIS IWV product showed slightly higher correlation coefficient (R2 =0.92) with GPS-IWV compared with AERONET –IWV (R2 =0.90). In addition, MODIS-NIR IWV showed approximately 2 times larger RMSE and 2.5 times larger Bias from AERONET -IWV (RMSE =5.48 mm, Bias =4.88 mm) compared with GPS IWV (RMSE =2.76 mm, Bias =2 mm). Finally, the results of the presented study provide good information about the overall quality of the MODIS and AERONET IWV data for the areas of atmospheric researches. The results of this study were comparable to previous studies conducted in Kanpur India and Beijing, China. The results obtained in this research provide valuable information to researchers in the fields of meteorology, climate, hydrology.

Awareness and knowledge about the cultivation pattern and the area under cultivation play an important role in agricultural land management and estimating net production. Combining the results of ground observations and measurements with remote sensing data can provide timely maps of crops. This is valuable for defining management units and achieving accurate information needed by farmers and planners. Most of the methods used to separate agricultural products do not work well when the cultivation pattern of different crops, such as wheat and barley, is very similar. Therefore, the purpose of this paper is to provide a deep learning-based classification method on satellite time-series images to produce a map of the exact area under the cultivation of different types of agricultural products with high technological similarity. For this purpose, the Landsat8 satellite time-series images were selected based on the regionchr('39')s crop calendar. Using a normalized vegetative differential indication index (NDVI) as a time series and a set of training data from various agricultural farms, the Convolutional Neural Network (CNN) was used to automatically generate a product map in the Chenaran region of North Khorasan Province. In order to separate the agricultural products in this study, a combination of supervised classification and visual correction has been used. In order to estimate the accuracy of the results, the maps produced with the ground control points were examined and the Kapa coefficient and overall accuracy were calculated. The results showed that the use of satellite series time data is highly effective in identifying and distinguishing different types of agricultural products. Also, the classification method based on the convolutional neural network with an overall accuracy of 95.76 has higher accuracy than other conventional methods such as random forest methods (overall accuracy: 89.85), backup vector machine (overall accuracy: 88.78), network Perspective neurons (overall accuracy: 85.75) and K have the closest neighbor (overall accuracy: 89.60) in separating and identifying agricultural products. The present study was conducted to investigate the performance of the CNN classification method in producing the map of the area under cultivation of agricultural products in Chenaran city using Landsat8 satellite time-series images. The results showed that the use of the proposed CNN-based classification algorithm in identifying agricultural products according to the same training samples and two quantitative accuracy evaluation criteria showed better performance than other methods used and was able to effectively overcome to the existing challenges. It is very important to have accurate maps of the area under cultivation of various agricultural products in each region because this knowledge can be used in various fields such as improving the cultivation pattern of agricultural products, planning in the field of water resources required by the agricultural sector, Estimate the required budget to be used to allocate the machinery needed for each section. Therefore, using this method with optimal cost and time to produce a crop map is recommended. In future research, a combination of radar and optical images of Sentinel 1 and 2 satellites and deep learning networks will be used to achieve higher accuracy and better spatial resolution maps of the area under cultivation.

Todaychr('39')s cities are full of large structures that require very high cost and time to monitor in traditional ways. Existing engineering structures are deformed due to various factors such as landslides, land subsidence, earthquake, flood, explosion and etc. With increasing the number of large engineering structures in cities, experts are looking for a good solution for monitoring these structures to avoid great financial and human damages.  One of the most important huge structures in any country is the dam. In addition to providing drinking water and water for agriculture, dams prevent devastating floods and are able to generate a large amount of hydropower. Therefore, continuous monitoring and study of the displacement and deformation behavior of the dams is essential. From the past, leveling and ground surveying were carried out to measure the deformation of structures and ground displacements along the vertical direction; but these measurements are time-consuming and costly. Also, the use of precision instruments and deformation sensors are not suitable because of their high cost, time-consuming and complex. Due to the ability of Radar images and Radar interferometry techniques in the field of monitoring the ground displacement, in this research, we are looking for evaluating the potential of this method for monitoring the dam deformation and displacements. To achieve this goal, we used two sets of radar data which are CosmoSkyMed-X and Sentinel-1A.   The study area in this study is Daryan earth-fill dam which is located in Kermanshah province. Daryan Dam is a gravel dam with a clay core. The length of the crown of the dam is 368 meters, the width is 15 meters and the height of the foundation is 179 meters. The method used to estimate the displacement of the dam is the Persistent scatterers (PSI) technique. In the time series processing of these images, the PSI method was selected then the star graph and Deloney triangulation were used. In the next step, we used both linear and nonparametric models for displacement estimation. The results were evaluated by applying two different displacement models and finally, the model with higher temporal coherence was selected as the appropriate model and the other model was discarded. In processing the Daryan dam images, the appropriate model for monitoring the displacements with S-1A Radar data was the nonparametric model and for CSK data was linear model. The results of the accuracy assessment showed that by using CSK and S-1A radar data, we can monitor the earth-fill dam displacements by the precision of 2 and 4 mm which these precisions are acceptable and standard. It is important to note that the results of X-band data due to itchr('39')s higher resolution, has a higher density of PS points, and higher accuracy. Also, the larger size or greater area of the dam body makes more the density of the PS points and the displacements obtained for these points will be more accurate. Generally, we can say that the X and C-band radar data have the potential to monitor the earth-fill dam displacements by radar interferometry technique and this method can replace with traditional and costly methods.

Water reserves are considered as renewable resources and the process of renewal is due to the water cycle in nature. However, the amount of water that emerges in this way on the surface of the earth or in any given geographical area is fixed, regardless of the changes between the years. In other words, the amount of renewable water that the earthchr('39')s surface receives now and annually is equal to the water that it may have received thousands of years ago since the dawn of human civilization. This is while the spatial and temporal distribution of the amount of renewable water is completely variable and is not commensurate with the distribution of population and water needs of human societies. Obviously, the most accurate rainfall measurements are provided using traditional methods such as climatological stations, ground synoptics and rain gauges, but the accuracy of using this method in a dependent catchment Due to the spatial distribution of the stations and the sensitivity of its trustees in the maintenance of the stations, the realization of this important issue requires a lot of money and time. Accordingly, and since remote sensing is able to provide continuous, homogeneous and near-real-time information on the location and time in a particular area, even in inaccessible areas. There are several factors involved in evapotranspiration, the degree of effect of each of which depends on other factors, which makes it difficult to accurately estimate evapotranspiration. Some of these factors are air temperature, relative humidity, atmospheric pressure, amount of water-soluble substances, altitude and longitude. Thus it is impossible to use traditional methods and to directly calculate evapotranspiration without any degree of accuracy over a wide range. When the amount of rainfall in a catchment is more than the soil infiltration capacity, part of the rainwater flows as runoff on the surface of the catchment. The amount of runoff in a catchment depends on several factors including soil characteristics, catchment concentration time, geological conditions, climate and climate, slope and direction of slope, land use and drainage density. Various devices such as lysimeter and TDR device can be used to estimate the penetration rate directly; However, it is obvious that using these devices to estimate the infiltration rate in a large catchment area is very time consuming due to changes in the soil type of the catchment. Therefore, this component is calculated through methods, equations and relationships resulting from some hypotheses in hydrology. It is obvious that the accuracy of ground stations is much higher than satellite data, but in large basins due to the high cost of construction and maintenance of these stations and also the time consuming use of it. This study was conducted in the location of Tajan catchment area, which seems to be the ground stations located in it, both in terms of number and in terms of health due to the prosperity of agriculture and the location of Sari city with the majority of farmers in the region compared to other basins.

For the preparation of small-scale maps from large-scale maps, generalization of vector features is important. This method increases the quality of maps published, enables the analysis of data at various levels of detail, and reduces the volume needed to store them. The methods of linear and polygonal features generalization are performed with the aim of preserving their geometry and area while reducing their details. Various models have been used and evaluated by researchers in this field; However, most of them summarize the features with the aim of selecting a few points from them and deleting other points. Even so, the deleted points may contain valuable information for this complication and their removal will lead to a defect in its geometry and area. In this study, the generalization of multi-linear features was performed using minimizing the vertical distance from the main line. In order to study the proposed model, after its implementation on different shapes, the multi-lines of Lake Urmia and its islands were generalized and the results of the proposed model were compared with the common Douglas-Poker and Viswalingam methods. The results were then evaluated using the indices of area differences, the similarity of the mean curvature, the similarity of the amount of angle changes and the modified average Hausdorff distance. The results showed an average superiority of 99.91, 66.29 and 60.99% compared to conventional simplification approaches with proposed model (in the first three indicators). The proposed model has a advantage over the Douglas-Poker and Viswalingam methods 0.16 and 0.2 percent based on the area difference index, 7 and 5 percent based on the average curvature index, and 6 and 2 percent based on the  sharp change index. but In the the modified average Hausdorff distance index,  it was about 2 meters worse than the aforementioned methods, due to the lack of reliance on the initial points of the complication.

With the development of complex urban infrastructure such as high-rise buildings, the importance of geospatial information in the interior of buildings becomes more apparent. Routing is one of the most basic processes related to geospatial information that existence of a precise and complete network of the desired environment is necessary for its efficiency. In most cases, a data set from the target network is required before performing any analysis on a route network; Having such data outside the building, such as Google Map and OSM data, as well as accessing it is much easier than inside the building. Access to such information requires geometric information and in some cases accurate semantic information of the building. In this regard, building information modeling technology (BIM), by providing useful information about buildings can help to build a route network in its interior. On the other hand, in order to complete the routing process, in addition to the route network, efficient tools for analysis on the route network are needed, which are not available in BIM. In this regard, Spatial Information Systems (GIS) have an ability to perform the required analysis in the routing process. Therefore, it seems that by combining BIM and GIS, the ground can be provided for better management of the routing process inside the building. In this research, the combination of BIM and GIS has been done with the aim of building an indoor route network using FME Reader software. In this research, the campus of the Faculty of Engineering, University of Tehran has been considered as the study building. In order to prepare a three-dimensional model of these floors, Revit Architecture 2017 software has been used. First, the two-dimensional map related to the plan of these classes is entered into the software environment and then the three-dimensional process is implemented in accordance with the necessary standards. After extracting the required data in the BIM model, this data has become the standard format in GIS. In order to eliminate the errors resulting from the conversion of data format from BIM to GIS, such as overshoot and undershoot errors, concepts and topological analysis in GIS have been used. Finally, the two proposed methods Mesh and TIN have been used to create the route network. The Mesh method uses regular grading of the region to create the route network, while the TIN method uses the Delaunay triangulation concepts to construct the route network. In the Mesh method, the number of points involved in routing is very high. High number of points increases the accuracy of routing, but processing these points in order to find the optimal path requires high hardware. The TIN method uses triangulation to network the environment, and therefore, due to the low number of points involved in the path network, information processing in order to find the best path requires less hardware power. Also in this study, the stairs were connected to each other as a straight line, which reduces the accuracy of routing. Routing can be used in technologies such as in-house positioning. The results of the research show the efficiency of the information in BIM models as well as the proposed methods in constructing the route network in the routing process.

Malaria is a parasitic disease transmitted by mosquitoes that kills 1.5-2.7 million people per year.  Malaria remains a major concern for the World Health Organization despite extensive research. Computer simulation models have been used to research disease carrier management strategies in recent years. The development of a simulation model based on the Agent modeling of the malaria is being considered in this study. Given that the spread of this disease depends on various environmental, geographical and social factors. Therefore, this study focuses on evaluating the impact of effective environmental and geographical factors, on the spread of the disease in Bandzarak rural district of Hormozgan province and also examines the impact of control measures and policies to prevent the spread of the disease. Humans and female Anopheles mosquitoes were used as factors in this study to model the spread of malaria, and the land cover map of the study area was used as a simulation environment. Temperature, humidity, distance from stagnant water, distance from vegetation, and human population density were chosen as effective factors in the spread of malaria in this research. In order to test and evaluate the model, four experimental scenarios were designed, during which the effect of different factors in the model was investigated. The results showed the sensitivity of the model to these factors and the relative superiority of 20% of the recovery parameter in controlling this disease. It was also shown that simple scenarios can show and evaluate the effectiveness of control measures in reducing and controlling the disease. Control policies are meant to avoid disease transmission by drying stagnant water, using protective nets, killing mosquitoes at the start of the warm season, and deteriorating health conditions, with the result that using protective nets is the most effective way to prevent disease transmission.

The importance of information has led researchers to look for the least cost and time ways to acquiring information. One of the newest mechanisms of acquiring and compiling geographic data is Volunteered Geographic Information (VGI). This way saves time and money and can provide regularly updated geographic information that is difficult to obtain through remote sensing but can easily be collected by citizens on the ground. VGI refers to geographic information created by citizen volunteers. VGI has proliferated in recent years, mainly due to the technological advancements enabling the public to contribute geospatial data. One of the fields that we can use VGI is agriculture. In this way, every farmer, as a citizen volunteers, records the information of his agricultural lands. Now, field visits and satellite image processing are the two main ways to collect this information. Both of these methods are costly and time-consuming.  In this article, we propose a VGI system to gathering the type of cultivation information and the spatial boundaries of agricultural lands. For evaluation, the proposed approach was implemented for a study area and thatchr(chr('39')39chr('39'))s results were compared with the remote sensing method. The results show that the accuracy of the information gathered with the proposed method, which obtained 91% by the confusion matrix. This accuracy is approximately equal to the accuracy obtained from the remote sensing method; While the time and cost of this method are much less than the remote sensing method. Also, the precision of the collected data according to the confusion matrix for the four crops of corn, rice, wheat, and barley in this study were 0.91, 0.89, 0.86, and 0.91, respectively, the results were reliable. This shows the reliability of the results obtained from the VGI for different products.