Hassan Emami; Seyyed Ghasem Rostami
Abstract
Extended Abstract
Introduction
Unmanned Aerial System (UAS) photogrammetry now provides a low-cost, fast, and effective approach to real-time acquisition of high resolution and digital geospatial information, as well as automatic 3D modeling of objects, for a variety of applications including topographical ...
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Extended Abstract
Introduction
Unmanned Aerial System (UAS) photogrammetry now provides a low-cost, fast, and effective approach to real-time acquisition of high resolution and digital geospatial information, as well as automatic 3D modeling of objects, for a variety of applications including topographical mapping, 3D city modelling, orthophoto generation, and cultural heritage preservation. UASs are known by a variety of names and acronyms, including aerial robots or simply drones, with UAV and drone being the most commonly used terminology. Because of the versatility of their on-board Global Navigation Satellite System (GNSS) navigation systems and inertial measurement unit (IMU) sensors, UASs open up new options for photogrammetric projects. In this research, the ability of four different state-of-the-art and professional drone-based software packages, including AgisoftMetashape, InphoUASmaster, Photomodeler UAS, and Pix4D Mapper, to generate a high density point cloud as well as a Digital Surface Model (DSM) and true orthoimage over barren, residential, green space, and uniform textured areas in urban and exurban areas is investigated.
Methodology
The following are the major processes in this study: image acquisition, point cloud, DSM, DEM generation, and accuracy assessment. Data planning and acquisition are the initial steps in commencing any project. The overlapping images are initially obtained using four data sets with distinct surface feature attributes and camera kinds with different shooting situations. The data sets that must be acquired include pictures taken with FC6310 (8.8 mm), NEX-5R (5.2 mm), and Canon IXUS 220HS (4.3 mm) cameras at varied flight heights and spatial resolutions ranging from 52 to 246 m. The four data sets, two of which are connected to Iran and two of which are related to other nations, were chosen from barren, residential, green space, and uniform texture areas. GPS coordinates for these photos must also be recorded using a GPS device. This is done to geo-reference the images for improved model accuracy. The calibration of the camera must also be addressed, and its characteristics and readings must be determined at the start of the project. The images will be calibrated first in order to determine camera pose estimate. The following stage is to compare survey measurements to model measurements in order to assess the overall correctness of the 3D model. The correctness of the point cloud, DSM, and 3D textured model is next evaluated. The accuracy evaluation evaluates the orientation correctness, and measurement uncertainties in the various modeling procedures. Finally, the various products of the mentioned software packages were statistically and qualitatively evaluated.
Results and discussion
The outcomes of this study demonstrate the ability of commercial photogrammetric software packages to do automatic 3D reconstruction of numerous attributes across urban and exurban regions using high quality aerial imagery. This assessment employs a variety of visual and geometric measurements to assess the quality of produced point clouds as well as the performance of the four software packages. According to the visual quality findings, AgisMesh software performs better in 3D modeling of all varieties of surfaces in all locations, but badly in the reconstruction of building edges in urban regions. Pix4D software, on the other hand, performs poorly in areas with uniform texture but excels at recognizing height changes and reconstructing building site boundaries. In terms of visual outcomes, the other software falls somewhere in the middle. In quantitative tests, they were tested first with checkpoints and then with randomly selected points in three distinct classes of urban and exurban regions. Check point findings revealed that the root mean square error (RMSE) in AgisMesh, UASmas, Pix4D, and PhUAS software packages was 2.82, 2.63, 5.84, and 3.03 cm, respectively. Furthermore, quantitative findings obtained by choosing random locations revealed that UASmas had an accuracy of 1.83, 1.20, and 2.74 cm, respectively, in three residential, barren, and green space zones. In addition to the 6.90, 2.96, and 7.24 cm accuracy of the PhUAS, the Pix4D was 4.72, 3.46, and 3.59 cm more accurate than AgisMesh software in the three stated classes. Table 1 displays the assessment findings based on the RMSE criterion.
Conclusions
The findings of this study indicate the capacity of specialist drone-based photogrammetric software packages to automatically reconstruct 3D features from high quality aerial images over desolate, residential, green space, and uniform texture environments. In this study, all conditions and parameters in all software were regarded the same, and owing to the similarity of statistical parameters, number of points, and so on in various products, only the discrepancies and their differences were discussed in depth. Various visual and geometric parameters are utilized in this evaluation to analyze the quality of generated 3D point clouds, DSM, and true orthophoto. AgisMesh offers a simple and easy user interface in general and visual assessment, and it is possible to describe and execute data from any camera, even unknown models, without utilizing coordinate images by utilizing powerful processing methods. In contrast, the UASmas program has a highly complex user interface, and the user must be familiar with all of the concepts of photogrammetry as well as the camera parameters file, which is not readily set. It is possible to manually alter restricted processing results in Pix4D. As a result, faulty results are not obtained in regions with the same texture, while production points in other areas are of poor quality. When compared to the other three applications, PhUAS fared poorly aesthetically and geometrically. The user must enter many parameters or thresholds in the processing phases. Therefore, the user must be sufficiently informed of the specifics of photogrammetric and machine vision algorithms to understand that the quality of software output is largely reliant on these factors. Furthermore, check point findings revealed that theRMSE in AgisMesh, UASmas, Pix4D, and PhUAS software packages was 2.82, 2.63, 5.84, and 3.03 cm, respectively. Furthermore, quantitative findings obtained by picking random points revealed that UASmas has an accuracy of 3.51 cm, PhUAS has 10.45 cm, and Pix4D was 6.87 cm more accurate than AgisMesh in three residential, barren, and green space regions. Taking into account all of the benefits and evaluations of visual and geometric correctness, the performance and accuracy of AgisMesh, UASmas, Pix4D, and PhUAS may be ranked from one to four, accordingly.
Amir Hosein Shokri; Saied Sadeghian
Abstract
Introduction Recently, cadastre has become a suitable platform for global partnership in management of land and its assets. Due to ever- increasing population, spatial organization of citiesis considered to be one of the most important issues in national development planning. This indicates the necessity ...
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Introduction Recently, cadastre has become a suitable platform for global partnership in management of land and its assets. Due to ever- increasing population, spatial organization of citiesis considered to be one of the most important issues in national development planning. This indicates the necessity of using 3D land information systems since theenvironment, quality, ownership and other benefits of lands do not only change horizontallyany moreand height is also a decisive and vital factor.Therefore, 3D cadastreis used as abasis for integrating information into a complete and efficient information storage system. This system is usedtomanage scarce land resources and plays a key role in achieving future legal and managerial success in the field ofreal-estate. Designing and implementing a system capable of displaying the third dimension (height) is very complex. Common methods of producing 3D cadastral models include land surveying, classical aerial photogrammetry, high-resolution satellite imagery, and so on. Recently, the advent of drones has provided a suitable platform for large-scale cadastral mapping. Collecting high resolution images, processing withstructure from motion(SFM) method, multi-stereo vision (MSV), and dense 3D point cloud with a high resolution of about a few centimeters are the main advantages of these tools. Recentstudies in this field indicate high capabilities of UAV-based photogrammetry method for the production and updating of cadastral maps. Materials and Methods Due to the applied nature of the present study, guideline for the spatial information production using photogrammetric method published by Tehran Municipality and other Surveying and Mapping guidelines published by the National Cartographic Center of Iran have been used to produce 3D cadastral modeland reach relatively real results. The study area is Khosban village in MiyanTaleqan rural district, in the central district of Taleqan County, Alborz Province, Iran. Necessary information was collected using an eBee Plus survey drone with a SODA camera (designed for professional photogrammetric applications). Besides, exterior orientation parameters were measured using the preciseinertial measurement unit (IMU), global navigation satellite system (GNSS) Antenna withreal-time kinematic (RTK) and post-processing kinematic (PPK) techniques and triangulation was performed using these parameters. To increase the accuracy, reduce hidden areas and achieve more accurate 3D models, 75%longitudinal and transverse overlappingwere considered for the images. Image processing was performed using Pix4dmapper and Metashape software and products such as orthomosaic, dense 3D point cloud, and digital surface model were produced. To prove thegeometric accuracy of triangulation, 8 ground control points were used, and32 checkpoints were also used for the final evaluation of 3D models. Results and Discussion 3D cadastre implementation was performedin the present paperusing UAV based photogrammetry without any ground control points. According to the results of triangulation, the maximum root mean square error in the X-component was reported 3.21 cm, the Y-componentwas reported2.86 cm, and the Z-component was reported 3.96 cm using Pix4dmapper and Metashape software. Moreover, 32 sample checkpoints were used for the final evaluation of the 3D models and data collected from these points were compared with the reference data. Results indicated the occurrence of maximum root mean square error in the horizontal components (X, Y) of 0.2 and 0.21 meter respectively, and 0.27 meter in the height component (Z). A correlation coefficient of about 1 represents high geometric accuracy of the 3D models produced using UAV based photogrammetry. Conclusion 3D cadastre can be used as a tool for improving land management and related issues. Due to structural complexity and ownership issues,most developed countrieshave not yet fully implemented 3D cadastre. However, these countries are always looking for ways to achieve such a system. So far in our country, the issue of 3D cadaster has only been pursued in academic studies and no practical stephas been taken to implement this system. Unfortunately, technical dimension and preparation of 3D models are only a part of 3d cadastre and legal issues occurring due to insufficient understanding of the third dimensionand its complexity alsolead to failure in the implementation of 3D cadaster.
Mahvash Naddaf; Seyyed Reza Hosseinzadeh; Jose Martin; Naser Hafezi; Mahnaz Jahadi; Kapil Malik
Abstract
Extended AbstractIntroductionMining (especially surface) is one of the major causes of land and environmental degradation globally. Environmental impacts such as deforestation, landscape degradation, alteration of stream and river morphology, widespread environmental pollution, siltation of water bodies, ...
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Extended AbstractIntroductionMining (especially surface) is one of the major causes of land and environmental degradation globally. Environmental impacts such as deforestation, landscape degradation, alteration of stream and river morphology, widespread environmental pollution, siltation of water bodies, biodiversity loss, etc., have been noted to be associated with mining. Surface deformation is the biggest problem in open cast mines and their surrounding areas due to mining activities. Surveying engineers study the amount of displacement in open pit mines by using leveling to calculate the amount of displacement and determine it. These methods are expensive and time consuming. Satellite images are considered as an important tool for land resource management due to the wide view that provide of an area and also due to its regular repetitive coverage. Interferometric Synthetic Aperture Radar (InSAR) is a useful tool in the study of surface displacements. The SAR interferometry concept has been introduced in the last 1980s.The objective of this study as an academic research is monitoring deformation using Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) method for managing a very rich iron ore resource in the eastern part of Iran named Sangan, near the Afghanistan boundary. MethodologyIn this paper, surface deformation calculation based on the processing of PS-InSAR technique (Persistent Scatterers SAR Interferometry) have been carried out. For this study, according to the availability of data for study area 47 SLC images of Sentinel-1A covering the study area during the period of October 7, 2014 –July 7, 2020 are downloaded from European Space Agency website. Sentinel-1A acquired images with a swath width of 250 by 180, with revisiting time 12 days within the IW data acquisition mode, it is reduced to six days if the images acquired by the Sentinel-1B satellite are available. Sentinel-1 has launched on 4th April 2014 by ESA.PS includes following steps:Master image selection,Co-registration data,Reflectivity map generationAmplitude stability index,Persistent Scatterers Candidate selection (PSC),PS point selection,Multi-image sparse grid phase unwrapping,Atmospheric phase screen estimationRemoval and PS phased readingDisplacement estimation. Study areaSangan Iron Ore Complex (SIOC) is located at latitude N 34°24’ to 34°55’ longitude E 60°16’ to 60°55’ in the Khorasan-e-Razavi Province, North-Eastern Iran. The iron ore deposit is about 20 km Northeast of Sangan town at about 1650 meters above sea level. Sangan Iron Ore Mines (SIOM) is one of the largest mineral areas in Iran, and also considered to be one of the Middle East’s richest deposits which are located in a rectangular area with 26km length and 8km width. Results and DiscussionIn this paper, the 47 scenes of IW SLC Sentinel-1A images, spanning the period from October 7, 2014–July 7, 2020 are accumulated displacement map and the time series of the deformation derived. The PS were selected on the basis of the ASI threshold value of 0.7, which signifies the stability of target points. The LOS displacement was improved by using APS and atmospheric phase delay correction. Later, the LOS displacement velocity on PS locations was estimated. The temporal coherence of all the selected PS was also tested. The PS points having ASI value of 0.7 and above, and temporal coherence of 0.9 and above, gave a relatively stable estimation of LOS velocity. We have identified 215377 Scatterers points. By imposing the standard threshold of 0.7 on ensemble coherence value, this amount decreased dramatically to 52449 PS points. These factors make the chosen technique suitable for studies of surface deformations. The results showed that the deformation velocity in this area is -4.8 mm/yrs and maximum displacement-30mm. In order to verify the results, we collected the Total Station data and PS data for analysis and comparison. Due to the lack of data in the plain, the Total Station data is related to downslope areas and as a result, uplift of area has been used to validation the results. It has been observed that for the same area the Total Station value shows good agreement with the PS- InSAR result. However, there may be some errors due to the fact that the data are not synchronous and that the nature of the impression is different. ConclusionIn the present study, PS-InSAR technique and C-band sentinel-1 data have been used for surface deformation monitoring in open cast mines of Sangan-Khaf, Khorasan Razavi. It can be concluded that monitoring the deformation of mined surfaces using traditional monitoring techniques such as field surveys and using Total Station, especially in large study areas, is time consuming. Since in using the interferometry methods in the study of open pit mines, the area covered by SAR images is much larger, so the use of this method will reduce costs. The results were assessed and validated using leavening data has been observed that, for the same area, the levelling value shows good agreement with the PS- InSAR result.
Sara Attarchi; Najmeh Poorakbar
Abstract
Extended Abstract
Introduction
Free access to the Landsat dataset and Sentinel 2 images has provided a great opportunity for long-term monitoring of resources. Landsat 8 was launched in 2013 to continue the mission of the previous Earth observation satellites. Landsat 8multi-spectral sensor, Operational ...
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Extended Abstract
Introduction
Free access to the Landsat dataset and Sentinel 2 images has provided a great opportunity for long-term monitoring of resources. Landsat 8 was launched in 2013 to continue the mission of the previous Earth observation satellites. Landsat 8multi-spectral sensor, Operational Land Imager (OLI), provides multi-spectral images with 30-meter resolution. Sentinel 2 was launched in 2015 with a multispectral sensor called MSI which captures images with different spatial resolutions (10m to 60m). The secret mission of Landsat satellites started in the 1970s and they have the longest archive of satellite images collected from the Earth. Sentinel 2 offers higher spatial, spectral and temporal resolutions and therefore it is important to compare the compatibility of Sentinel 2 and Landsat 8 images. OLI and MSI sensors both operate in the optical region, thus weather conditions can impose some limitations on their data acquisition. In such circumstances, data collected by a compatible and similar sensor can replace the cloud-covered images.
Generally, spectral features of new sensors are designed in such a way toconform to the corresponding bands of the previous sensors. The present study compares the corresponding bands of MSI and OLI sensors. The efficiency of both sensors in the classification of a heterogeneous and complex region has also been investigated.
Materials & Methods
Three near-simultaneous pairs of Landsat 8 and Sentinel-2 scenes were obtained to conduct a comparative study. Images were acquired in August 2017, November 2017, and July 2018.Minudasht - in northern Iran- was selected as the study area because of the presence of different land cover classes including rainfed agricultural lands, irrigated agricultural lands, forests, residential areas, and bare lands.Thescenes were processed for further analysis. First, the scenes were atmospherically corrected. In the next step, spatial resolution of MSI bands was resampled to 30 m, and each pair of mages were geometrically co-registered. To do so, 10 tie points were selected, and scenes were co-registered usingthe first-degree polynomial method. RMSE values were reported 2.5 m, 2.4 m, and 2.8 m for August 2017, November 2017, and July 2018, respectively. To investigate the similarities and differences of the sensors’ spectral content, the correlation between corresponding bands of the two sensors was estimated.
Then, images were classified using the support vector machine (SVM) algorithm. Five distinct land cover classes were found in the region including rainfed agricultural land, gardens and irrigated agricultural land, forests, residential areas, and bare lands. The training samples were selectedfromthe land use map and high-resolution Google Earth images. Approximately 300 training samples were selected for each land cover class. The accuracy of classification results was compared to verify the efficiency of two sensors in land cover mapping. Independent validation samples were selected for each class. Overall accuracy, commission error, and omission error were calculatedbased on the confusion matrices.
Results & Discussion
The reported correlation coefficientfor all corresponding bands was higher than 0.8. Results indicate a high level of similarity between the two sensors. Similar findings were reported by previous studies. Overall classification accuracy ofOLIimagescollected in August 2017, November 2017, and July 2018 was 91. 35 %, 89.60 %, and 93.12%, respectively. Overall classification accuracy ofMSI images collected inAugust 2017, November 2017, and July 2018 was 94.76 %, 95.55 %, and 94.07%, respectively. As it is obvious, Sentinel 2showed a higher performance in comparison to Landsat’s, because of its higher spatial resolution. A medium spatial resolution image collected from a complex landscape is often composed of mixed pixels, since different land cover types exist in one pixel. As the image’s spatial resolution improves, the dimensions of each pixeldecrease. Therefore, the number of mixed pixels will decrease and a higher classification accuracy will be expected.
Conclusion
Results confirm the similarity of two sensors in land cover classification. However, the findings could not be extended to other applications. MSI sensorslacka thermal bandand thus are not applicable when such a feature is needed (for an instance inthe retrieval of land surface temperature). In such applications, MSI cannot substitute OLI. For further studies, it is necessary to compare the performance of these sensors in different regions, since different land cover types may impactclassification results. Findings of the present study may raise attention to the differences between Landsat 8- OLI and Sentinel 2 MSI. Further studies can be conducted to investigate the differences between these two sensors. The possible similarities of othersimilar sensors can also be a topic for further investigations.
Saied Sadeghian; Asghar Milan Lak; Hamed Ahmadi Masine; Roohollah Karimi
Abstract
Extended Abstract
Introduction
Applying GPS/IMU data in aerial triangulation has increased the strength of photogrammetric block and reduced the number of ground control pointsneededfor block adjustment. Systematic errors in data used fortriangulation reduce the accuracy of the process and make ...
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Extended Abstract
Introduction
Applying GPS/IMU data in aerial triangulation has increased the strength of photogrammetric block and reduced the number of ground control pointsneededfor block adjustment. Systematic errors in data used fortriangulation reduce the accuracy of the process and make ground control pointsnecessarydespitetheexistenceof GPS/IMU data. Therefore, reducing systematic errorsin data naturally increases the accuracy of triangulation and reduces the number of ground control points required forblock adjustment andthe number of crossstrips used to eliminate systematic errorsin GPS data.
Materials
Digital images captured by the National Cartographic Centerof Iran from an area in Fars province usingUltraCam-Xpcamera in2010 were used in the present study to investigate the roleof self-calibration parameters in the reduction of ground control points and cross strips requiredfor block adjustmentin aerial triangulation. The intended block consists of 58 images and four strips; two of which are cross strips. Control points in this block include eight horizontal control points, eight vertical control points and eight full control points. Each image has a dimension of 11310 by 17310 pixels, a pixel dimensionof 6 microns, afocal length of 10500 microns, an end lap of 70%, and a side lap of 30%. Theregion has an average elevation of 760 m. Given the focal length, flight height and pixel dimensions, ground resolution is around 12 centimeters. Each image covers anarea of 2077.2 mlength and 1357.2 mwidth on the ground.
Methodology
The present study investigates theroleof self-calibration parameters, such as elimination of systematic error in GPS/IMU data and image sensor,in increased accuracy oftriangulation, and reduced number of ground control points and cross strips required for block adjustment. To reach this aim, optimal self-calibration parameters are determined using a genetic algorithm and the identified parameters are used in the bundle block adjustment. Variance components estimation method was used to solve the problem of equationsinstability. This method not only stabilizes the equation, but also determines the optimal weight matrix during the adjustment process.
Results and Discussion
Since images at a scale of 1:2000 were used in the present study, maximum RMSE equals 60 cm and maximum residual errorsequal 1.2 m. Using additional parameters to eliminate systematic errors results in an acceptable maximum error at the control points, but absence of additional parameters results in an unacceptable maximum error at the horizontal and vertical control points even in the presence of crossstrips. In addition to the evaluation of horizontal and vertical errors at the ground control points, horizontal and vertical RMSE of the checkpointsare also used to evaluate the geometric accuracy of aerial triangulation. Again, applying additional parameters keeps the RMSE at a much lower level than the accepted limit, while absence of additional parameters results in a horizontal and verticalRMSE higher than the accepted limit even in the presence of cross strips. It should be noted that using cross strips reduces RMSE at the vertical component.
Conclusion
Results indicated that using self-calibration parameters and reducing errorsin data used for the adjustment process decreases the number of control points and cross strips required for block adjustment.Using optimal self-calibration parameters(even in the absence of control points) resultsin a maximum RMSE of 0.143 m at the checkpoints, while absence of these parameters results in a maximum RMSE error of around one meter with or without cross strips. Genetic algorithm is capable of determining optimal self-calibration parameters. It is also capable of optimizing nonlinear functions. Therefore, it is not necessary to linearize the equations before determination of self-calibration parameters, which reduces the amount of necessary calculations. Variance components estimation can also be used along with the bundle block adjustment method to stabilize the equations and determine the optimal weight matrix. As a result, it is suggested to take advantage of these three methods, i.e. block adjustment, stabilization and optimal weight matrixdetermination, simultaneously.
Mohsen Pourkhosravani; Ali Mehrabi; Sadegh Karimi; Mina Azizi
Abstract
Extend AbstractIntroductionEnergy is considered to be one of the most important factors affecting the development of human societies and also an essential parameter in economic and social development along with the quality of life. Population growth, rising living standards, the risk of global ...
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Extend AbstractIntroductionEnergy is considered to be one of the most important factors affecting the development of human societies and also an essential parameter in economic and social development along with the quality of life. Population growth, rising living standards, the risk of global warming caused by greenhouse gas, acid rain, environmental problems and threats to human health, lack of fossil energy sources and rising energy consumption have increased interests in renewable energies. Solar energy has been used as a source of renewable energy for a long time. As one of the safest, most efficient and most economical sources of energy, it has the potential to become the main energy source in the near future (Dincer, 2000: 157). Due to the high number of sunny days, Iran is among the countries receiving the highest level of solar radiation in the world. With 240 to 250 sunny days per year, approximately 80 percent of the country receives an average annual solar radiation of 4.5 - 5.4 kWh / m² (Moghadam et al, 2011: 107). In this regard, the present study seeks to evaluate and monitor radiant energy reaching the surface of Sirjan basin. Materials & MethodsThe study area, Sirjan Basin, is located between 28 degrees and 46 minutes and 50 seconds to 29 degrees and 58 minutes and 1 second northern latitude, and 55 degrees and 11 minutes and 20 seconds to 56 degrees and 32 minutes and 40 seconds eastern longitude. It includes 18481 square kilometers with an average altitude of 1710 meters above sea level. Descriptive-analytical method has been used in the present applied research. Data are collected using library and documentary research methods (from information and statistics offered by different organizations) or extracted from satellite images. Solar radiation energy reaching to the surface of the study area has been evaluated using three methods including Angstrom experimental model, Solar Analyst method in GIS and Remote Sensing. Results & DiscussionAngstrom experimental model indicates that the maximum amount of energy directly received by the basin at low latitudes (28 degrees and 50 minutes) is 73370-73436 watts per square meter. This decreases as we move toward higher latitudes reaching 72836-72903 watts per square meter in the northern parts of the basin (latitude 29 degrees and 50 minutes). Monitoring solar radiation energy reaching the surface with GIS Solar Analyst (solar radiation analysis method) shows that the lowest amount of radiant energy reached the surface in January (between 14000 to 144039 watts per square meter). Also, the maximum amount of radiant energy reached the surface in July (between 111000 to 252000 watts per square meter). Remote sensing technique also shows that the amount of instantaneous radiation received in Sirjan basin reaches its minimum during winters and only a limited part in the west of the study area receives 4.498 to 8.436 watts per square meter. The maximum amount of instantaneous radiation received in summers is 597.6 to 845.6 watts per square meter, which is received in a large part of the west, northwest and southwest of the basin. ConclusionMonitoring radiant energy reaching the surface of Sirjan basin using experimental Angstrom model shows that the highest level of energy received in the southern parts of the basin is around 733370 to 73436 watts per square meter. This is reduced moving toward the northern parts of the basin. Moreover, solar radiation analysis method (Solar Analyst in GIS) shows that the highest amount of solar energy in Sirjan Basin is received in July with 200000 to 252000 watt-hours per square meter , June with 170000 to 248341 watt-hours per square meter, May with 190000 to 247627 watt-hours per square meter and August with 190000 to 234500 watt-hours per square meter, respectively. These values are recorded in eastern, northeastern and southeastern parts of the basin. Results indicate that the eastern half of the basin in which the cities of Balvard, Tekiye, Saadatabad and Pariz are located, receives the highest amount of solar radiation energy especially in summer. Remote sensing technique shows that the highest amount of instantaneous radiation received in summer is 597.6to 845.6 watts per square meter which is recorded in the western, northern, northwestern, southern and southern parts of the region including the villages of Pariz, Saadatabad, Balvard in the central strip and Khatunabad, Mahmoudabad, Najafabad, Malekabad and Golestan. The same is also recorded in other seasons, though with a decreasing trend. The highest level of instantaneous radiation is received in these parts of the basin.
Mehdi Bazargan; Mohammad Ajza Shokouhi
Abstract
Introduction Nowadays, theft -especially residential burglary-is considered as one of the most common and frequent crimes in many countries of the world, including Iran. As such, it has become a pervasive and serious problem with various social, economic, and security-related aspects. Investigating ...
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Introduction Nowadays, theft -especially residential burglary-is considered as one of the most common and frequent crimes in many countries of the world, including Iran. As such, it has become a pervasive and serious problem with various social, economic, and security-related aspects. Investigating geographical dimensions of this crime facilitates the process of exploring this phenomenon. Space and its special features play an important and undeniable role in crime commitment, because space has always been considered as one of the most important factors in commitment of financial crimes such as residential burglary. Spatial analysis and geographical investigation of crimes seek to provide a spatial presentation of criminal actions, crime dispersion, and crime hotspots. This type of crime analysis basically aims to provide a model for decreasing crime commitment in urban spaces. Accordingly, the present research seeks tomodel spatial diffusion of residential burglary crimes in MashhadusingHogstrand’s spatial diffusion theory. Materials and methods The present study is performed based on descriptive-analytic and qualitative methods. The research sample includes cases of residential burglary committed in Mashhad in the 2011-2017period. Data analysis was performed using ArcGIS software. Case study area includes Mashhad, with an area of about 35187 hectares, a population of more than 3057679, and a population density of 87 people per hectare. Results and discussion Police reports in Mashhad suggest that the highest crime rates belong to the 2nd and 3thdistricts, and the lowest rates belong toSamen (around Razavi Shrine), the 12th, and 8thdistricts. 70% of crimes in Mashhad are committed in informal settlements including the 2nd, 3th, 4th, 5th, 6th, 7th, and 10thdistricts. However, only 10.6% of the city area and 29.3% of its population belong to these districts. Furthermore, the highest crime rates have been reported in 2017. In 2011, only two major crime hotspots were observed in Mashahd (in the 2nd and 3thdistricts). Results suggest that crimes have spread from one place to anotherin Mashhad, which indicates a close relationship between crime and distance factor. In other words, proximity to a crime hotspothas resulted in rapid spread of crimes, and due to the short distance, nearby places have been affected more quickly. Informal settlements of Mashhad are located in eastern, northern, and northeastern districts,which contain 99% of crime hotspots. This indicates that spatial autocorrelation of crimes in informal settlements of Mashhad is relatively high, which has led to formation of crime hotspots in these districts. However, moving from marginalized areas towards southern districts of Mashhad (more prosperous regions), spatial correlation of crimes decreases, and lead to formation of 99% of cold spots. Conclusion The present research has investigated the spatial diffusion pattern of crimes in Mashhad in 2011-2017period.To reach this end, crime hotspots were investigated by quantitative methods such as Kernel density, Moran coefficient, and crime hotspot analysis. Results suggest that the highest crime rates are reported in the 2nd and 3thdistricts, while the lowest rates are reported in Samen (around Razavi Shrine), the 12th, and 8th regions. In fact, 70% of crimes in Mashhad are committed in informal settlements including the 2nd, 3th, 4th, 5th, 6th, 7th, and 10thdistricts. Moreover, statistics indicate that for every100000 people,anaverage of 75/2 cases of crimes have been reported in the 2011-2017period.Results of Moran coefficient for spatial diffusion of crimes indicated the presence of a cluster distribution of crimes in Mashhad. Meanwhile, spatial diffusion pattern of crimes in Mashhad suggests that the first crime hotspots were formed in northern, eastern, and northeastern districtsof Mashhad, and crimes have spread from these to other districts (more central and prosperous regions such as the 8th and 9thdistricts). In fact, investigations suggest that crimes are spreading from informal settlements to other regionsof Mashhad, and acompatible spatial diffusion pattern of crimes exists in this city.
Zahra Bahari Sojahrood; Mohammad Taleai
Abstract
Extended Abstract
Introduction
The most important challenge in urban land use planning is the spatial organization of urban activities and functions based on the needs of urban society. Extracting the current rules that exist in the city not only adds local conditions to the standard values mentioned ...
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Extended Abstract
Introduction
The most important challenge in urban land use planning is the spatial organization of urban activities and functions based on the needs of urban society. Extracting the current rules that exist in the city not only adds local conditions to the standard values mentioned in various instructions (Habib et al, 1999; Shiea 2018; Saeedinia 2004) but also makes it possible to analyze with comparing the existing conditions of the city with the standards. There is some research to examine the current situation of the city. Most of these studies have used statistical methods (Hosseinzadeh et al. 1399; Omidipour et al, 2017; Mohammadnejad et al. 2012).
A few of them have utilized data mining methods, but none of these studies examine existing patterns between one type of land use with other land uses. In addition, the method used in this research is a new method that tries to use the capabilities of association rules and decision trees in exploring co-located patterns by combining these methods.Therefore, considering the importance and necessity of addressing this issue, the purpose of this research is to explore the current situation of urban land use by using data mining methods to discover the current patterns in the location of land uses in the vicinity and at different distances.
Finally, providing rules derived from these models may help planners and managers to understand the current status of land use appropriately and improve urban land-use plans by utilizing them in combination with standards and rules based on expert knowledge.
Materials & Methods
Spatial association rules
Association rules discover the laws of interdependence between the data of a large database. In other words, patterns that are frequently repeated in the data set are identified and used to explain the rules of dependence (Han & et al, 2011: 54; Li 2015). The rules of the association in which one of the propositions in the premise or sequence contains a spatial relation are called spatial association rules (Geissen & et al, 2007: 277-287, Mennis & et al, 2005: 5-17).
Decision Tree
The decision tree is one of the most powerful and common techniques for classification and prediction. Among the algorithms used to construct the decision tree, the most important is the C5 algorithm which is the developed ID3 algorithm.
Methodology
A n*l transaction matrix is generated. Where n is the number of available features and l represents the number of types of land use studied, which is 19 in this article. The elements of this matrix can be zero or one.
To fill the transaction matrix, we first consider the distance and apply buffer analysis for all the features in the land use layer. Then, for each feature, we intersect the buffer layer of that feature with the land-use layer and extract all the features that appeared at the intersection. Arc GIS software was used to perform spatial analysis.
Then, to extract the current rules of land use in the urban environment, the a priori algorithm is selected as one of the association rules algorithms, and the C5 algorithm is selected as one of the decision tree algorithms.
In this research, the user data of neighborhood 4, district 5 of Tehran Municipality, including 1065 property plots, were used.
Results & Discussion
In this step, the proposed model for deriving the rules of land use dependence based on the current situation of land use in the study area is implemented step by step and the results are presented.
According to existing standards, three distances are considered to extract spatial rules with an apriori algorithm. After extracting the rules, they are compared with the values of approved standards in urban land use planning. Vicinity and compatibility are examples of indicators in common standards for locating and determining land use for the land. Using the extracted rules, the indicators are examined.
Due to the lack of extraction of some rules by association rules, for example, not extracted rules related to therapeutic land uses within 300 meters from residential land uses, we use the decision tree algorithm to extract related rules in more detail. The graphs obtain from the decision tree shows which land uses are effective for predicting and categorizing specific land uses, based on the current status of the land uses located in the case study area.
Conclusion
The purpose of this paper is to data mining the current status of urban land uses to extract the rules of neighborhood and proximity of different land uses. Using the proposed model in this article, it is possible to extract the existing rules of land uses in detail and as well as to evaluate its compliance with conventional standards and criteria in urban land use planning.
Akram Sadeghbeygi; Kamran Moravej; Mohammad Amir Delavar
Abstract
Extended Abstract Introduction In the last few decades, thematic maps and models were usually assessed using Kappa index of agreement. The index gives us the relative observed agreement among raters (identical to accuracy), but lacks any useful information to make practical decision making about ...
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Extended Abstract Introduction In the last few decades, thematic maps and models were usually assessed using Kappa index of agreement. The index gives us the relative observed agreement among raters (identical to accuracy), but lacks any useful information to make practical decision making about the model’svalidity easier. In other words, Kappa index does not provide an explanation about classification quality or an idea about increasing theaccuracyof the predicted map. Moreover, the index does not explain the causes of disagreement.Thus, giving indices of agreement without any interpretation will not be satisfactory. Today, new complementary methods are required to show the quantitative and spatialagreement and disagreement between two maps. It is necessary to show how a modeled map can be produced with better accuracy. The present study seeks to introduce and explain concepts of agreement and disagreement components with an example. Finally, these components are introduced as a useful method for the validation of digital maps. Materials and Methods An area of 410 hectares which belongs toZanjanUniversity was used to express the findings of this study. The area is located 5 km before the beginning of Zanjan-Miyaneh Road at 48.4° eastern latitude and 36.68° northern longitude. A digital soil mapin which probability distribution of different soil classes is obtained using multinomial logistic regression algorithm and a reference soil mapproduced with the conventional methods are usedto explain the concepts and investigate the spatial and quantitative agreement and disagreement indices. Validation and calculation of quantitative and spatial agreement and disagreements are performed using IDRISI software (SELVA version). To simplify the process, two maps with a grid structure (3 x 3) are introduced as a reference map and a predicted map. The reference map is used for spatial and quantitative evaluation and validation of the predicted map cells. Each map contains 9 cells and each grid cell has a membership value of either white or gray categories. Results and Discussion In the validation process of two maps, most researchers seek to find answers to two important questions: 1- How much agreement is there between the cells of each mapping class group? And 2- How much agreement is there between the map used in modeling and the reference map regarding the position of the cells in each class? The present study expresses agreement between the two soil maps using an index of (M (m)) which equals 60.69%. With an average level of quantitative and spatial information about different classes of the digital soil map (DSM), the H (m) index equals46.4%. Results indicate that if the produced map is modified or rearranged (provided that the level of quantitative information remains unchanged but the amount of spatial information increases), the agreement between the maps increases dramatically and reaches 87.17%. Quantitative and spatialagreement and disagreement between the digital and traditional soil maps also equal 61% (M(m) = 61%) and 39%, respectively. The DSM accuracy can be increased to 87% (P (m) = 87%) compared to thetraditional soil map through spatial modification of cells(without changing quantitative information). Conclusion Evaluating the accuracy and validity of digital maps are considered to be an important and sensitive stepof research projects. Therefore, introducing more accurate indices is very important. According to the results of the present study, displayingquantitative and spatialagreement and disagreement in the form of a matrix and according to the different levels of quantitative and spatial information can be a new strategy to verify modeling methods. The method presented here not only introduces and interprets sources of (quantitative and spatial)error, but also provides information on the possible ways of reducing these errors. Thus, introducing the amount of error without any scientific interpretation cannot be useful for predicted maps. Unfortunately, researchers does not concur on how to report agreement and disagreement. However, it seems thatwhen it comes to explaining errors and finding a method to reduce such errors,the components of disagreement and its related parameters are more useful than agreement component and its indices. Therefore, it is recommended tointerpretdisagreement components before other components of agreement. The advantage of this method is that complex analyses can be reported in a simple form. Finally, this assessment and validation method is expected to be used in different studies as an appropriate and alternative method.
Geographic Data
Keyvan Mohammadzdeh; Sayyed Ahmad Hosseini; Mehdi Samadi; Ilia Laaliniyat; Masoud Rahimi
Abstract
Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms ...
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Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms affect the flow in water bodies, sediment transport, soil production, and climate at a local and regional scale. Identification and classification of landforms are among the most important purposes of geomorphological maps and also a fundamental step in the process of producing such maps. Geomorphologists have always been interested in achieving a proper and accurate classification of landforms in which their morphometric properties and construction processes are clearly indicated. The present study has attempted to develop a new method and identify the relationship between morphometry of landforms and surface processes using a multi-scale and object-based analysis. Extraction and classification of landforms are especially important in mountainous areas, which are considered to be dynamic due to their special physical and climatic conditions. These areas are often remote and sometimes unknown. Mountainous topography has also made them difficult to access. However, they are of great importance due to their impact on the macro-regional system. Because of this significant importance, Maku County was selected as the study area.
Materials and methods
Maku County is located in northwestern Iran (West Azerbaijan Province) which borders Qarasu River and Turkey in the north, Aras River and the Republic of Azerbaijan in the east, Turkey in the west, and Shut County in the south. This County is located between 44° 17' and 44° 52' east longitude and 39° 8' and 39° 46' north latitude. The present study takes advantage of satellite images (sentinel-2A) with a spatial resolution of 10 m, derivatives of DEM layer (slope, maximum curvature, and minimum curvature, profile and plan curvature) and object-based methods to identify and extract landforms of the study area precisely.
Discussion and results
The present study applies various functions and capabilities of OBIA techniques to extract landforms precisely. These functions include texture features (GLCM), average bands in the image, geometric information (shape, compression, density, and asymmetry), brightness index, terrain roughness index (TRI), maximum and minimum curvature, texture, and etc. The image segmentation scale was first optimized in the present study using ESP tools and objects of the image were created on three levels (9, 17, and 27 scales). In the next step, sample landforms were introduced, membership weights were calculated and defined for the classes in accordance with the fuzzy functions, and finally, 14 types of landforms were extracted using object-oriented analysis.
Conclusion
Fuzzy method includes boundary conditions, defines membership function, and constantly considers landform changes in class definition. Thus, it seems to be ideal for the purpose of the present study. The present study used two types of data (data derived from satellite imagery and DEM layer) along with OBIA approach to extract landforms. Classification of landforms based on fuzzy theory makes it possible to collect more comprehensive information from the earth's surface. Results indicate that fuzzy object-based method has classified landforms with an accuracy of 87% and a kappa index of 85%. Considering the resolution of the images applied in the present study, all features were extracted with an acceptable accuracy except for debris. This can be attributed to the fact that debris is usually accumulated in a small area on steep mountainsides, and thus remains hidden from satellites in nadir images. OBIA approach shows a high efficiency because it can combine spectral characteristics of various types of data (i.e. images and DEM data) and their derivatives while analyzing the shape of the segment, and size, texture and spatial distribution of segments based on their class and other neighboring segments.
Seyyed Ghasem Rostami; Hassan Emami
Abstract
Extended AbstractIntroductionVarious religions, including Islam, Judaism, Hinduism, and Chinese, have utilized lunar calendars for chronology. Methods for forecasting the first sighting of the new lunar crescent existed as early as the Babylonians, and maybe earlier. The Babylonians reasoned that the ...
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Extended AbstractIntroductionVarious religions, including Islam, Judaism, Hinduism, and Chinese, have utilized lunar calendars for chronology. Methods for forecasting the first sighting of the new lunar crescent existed as early as the Babylonians, and maybe earlier. The Babylonians reasoned that the lunar crescent can be seen with the naked eye under two conditions at sunset. First, the moon is older than 24 hours, and the moon's lag time is greater than 48 minutes. Fotheringham and Maunder developed standards for the seeing of the crescent moon at the beginning of the nineteenth century, and Bruin used his own criteria in 1977. Schaefer recently addressed crescent visibility extensively and integrated weather conditions into his work. Yallop then utilized the same database that Shaffer developed in 1997, but he overhauled some of the observation records extensively. Furthermore, many Muslim astronomers had developed their own criteria and published them in their literature. Despite the fact that different study organizations have created different criteria, there are still mistakes in the best time to forecast the crescent moon sighting. The use of old and conventional observations in modeling is one of these limitations, as is the use of non-uniform and heterogeneous observations. The Yallop criterion, for example, forecasts the visibility of the crescent moon for older crescents pessimistically. The Odeh criterion, on the other hand, forecasts young crescents with optimism. New Iranian criteria, such as the phase and altitude criteria (Mirsaeed criterion) and the triangular model (Iran criterion), have been presented in Iran. The goal of these criteria is to find the best timing between sunset and the first sighting of the crescent moon. Bruin, Schaefer, and Yallop have spent the last four decades developing the notion of the best moment. Because, after sunset, the sky darkens and the conditions for seeing the narrow crescent improve, while the moon approaches the horizon and the conditions for viewing the crescent moon worsen. Because the thickness of the atmosphere along the horizon is 3.7 times more than that of the zenith, the moonlight travels a greater distance than it did just a few minutes before. As a result, the sky towards the horizon is red or orange, and the crescent is not visible in this part of the sky. Material and Methods The objective of this study is to verify the rate of sky darkening in various regions and its influence on modeling the crescent visibility parameters of the moon, as well as to identify the best time to find out. To that end, 268 observational reports gathered from different divisions of Iran during the previous 20 years (2000-2021) were used to model the lunar crescent sighting. The proposed models are based not only on an examination of 20-year data to provide all effective tidal frequencies of the moon (the minimum period of moon’s notation motion is 18.61 years), but also on the use of sky-changing parameters such as local darkening rate and local sun occultation epoch time, the effect of the moon's distance from Earth, and the altitude of the moon from the horizon. The darkening rate of the sky factor was confirmed using various parameters and variables such as each point's geodetic latitude. Furthermore, unlike prior studies, the proposed models are developed using categorized observational reports with the least amount of error and can forecast the crescent sighting time in the presence of the sun (daylight time). The statistical correlation between the waiting time of each observation and the effective parameters in the lunar crescent visibility was studied in the first step. Following that, the parameters with the highest correlation values were chosen as the key quantities for modeling. After that, 17 alternative mathematical models with 2, 3, 4, and 5 parameters were implemented and tested, and the coefficients of the final two models (two and five parameter models) were determined using the least squares method as the suggested models. Results As a simple model, the two-parameter model can forecast crescent visibility with an average root-mean-square error (RMSE) of 4.7 minutes. The five-parameter model, on the other hand, was a more full and accurate model than the prior model, which was tested in two separate situations. They were evaluated over data for perigee distances of moon orbit (less than 375 thousand km) and observations for apogee distances of moon orbit (distance more than 390 thousand km) in the first and second cases, respectively. The findings of the 5-parameter model revealed that the first and second forms of the model had an average RMSE of 3.6 and 4.0 minutes to forecast the best time to see the crescent moon with the naked eye, respectively. Conclusion The results revealed that the best period to observe the crescent moon is from 32 minutes after sunset to 12 minutes earlier than sunset owing to the angular separation of the moon from the sun (10 to 20 degrees) and the difference in the altitude of the moon from the sun (5 to 20 degrees). When a result, as the local darkening epoch time increases, so does the waiting epoch time. In other words, the lunar crescent appears earlier in the northern part of Iran than in the southern half.
Reza Parhizcar isalu; Khalil Valizadeh Kamran; Bakhtiar Faizizadeh
Abstract
Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting ...
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Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting and exploring sources of this energy using modern and low cost methods –as a replacement for land surveying methods-can help planners and authorities working in the field of energy. In this regard, thermal remote sensing with a vast coverage of the earth’s surface, and the possibilityof calculating land surface temperature using satellite imagery plays an important role as a new economic tool.Mapping land surface temperature is a key point in achieving geothermal anomalies and different algorithms play an important role in land surface temperature estimation. Therefore, identifying potential sources of geothermal energyusingremotely sensed thermal data is a challenging and yet interesting subject.
Materials and Methods
The present study takes advantage of images received from OLI and TIRS sensors (Landsat 8) to estimate land surface temperature, analyze thermal anomalies, and identify areas with potential geothermal resources in Meshkinshahr.The images were retrieved fromUSGSin Geo TIFF format.Envi 5.3, eCognition 9.1, MATLAB and ArcMap 10.4.1 were used to prepare, process and analyze the images.Moreover, meteorological data received fromMeshkinshahr station was collected from the General Department and Meteorological Center of Ardabil Provincewith the aim of identifying the optimal algorithm for calculation ofland surface temperature. Data wascollected for a one-day period (31/08/2017), i.e. the same day Landsat 8 passed over the areaunder study.
Results and Discussion
The present study sought to identify areas with potential geothermal resources using thermal remote sensing and a combination of surface temperature and thermal anomaly models. In order to calculate thermal anomaly, an observational thermal image is required, which is in fact the same land surface temperature calculated using Split Window and Mono Window algorithmsfor the image received from the satellite thermal band at the moment of collecting images. It should be noted that the land surface temperature calculated with these algorithms was evaluated using statistical data recorded in the temperature monitoring station. Results indicated higher accuracy of Split Window algorithm (3 ° C difference). Since, temperature obtained from this algorithm was more consistent with the actual temperature, its results were used as the observational thermal image.A thermal model was also defined to model factors responsible for heat variation from one pixel to another one. These two images were calculated and subtracted to reach the thermal anomaly image.In order to identify thermal anomalies caused by undergroundfactors heating the earthsurface, other factors responsible for increasing/decreasinglandsurfacetemperature should be normalized in the image. Thus, the effect of parameters such as solar energy, environmental degradation and evaporation on land surface temperature obtained from split window algorithm was investigated and finally, areas with heat anomalies and evidences indicating the presence of geothermal resources around themwere selected as areas with potential geothermal resources.Results indicate that inthe area surroundingSabalanmountains,two regions with 5.5 and 10.05 hectares in the northern and northeastern parts of Moyelvillage, a1.4 hectares area in the southwestern part of Qutursouli Spa, and the southern part of the Qinrjah Spa with an area of 1.1 hectare had potentialgeothermal resources and a high potential for exploration of geothermal resources.
Conclusion
The presence of hot springs, a geothermal power plant and other evidences shows that Ardabil Province and especially Meshkinshahr city has the potential for geothermal energy production as one of the major sources of new and environmentally friendly energies.However, no effective studies have been performed to identify these resources using modern and low-cost methods including thermal remote sensing.Therefore, the present study for the first time took advantage ofGIS and remote sensingto identify areas appropriate for geothermal energy extraction inMeshkinshahr city and concluded that remote sensing studies on Landsat 8 satellite images have a high efficiency for identifying areas with potential geothermal resources. Thus, areas identified in the present study have a strong spatial correlation with the geothermal evidences founded in the region.
Yousef Ebadi; Akram Eftekhary; Hekmatollah Mohammad Khanlu; Majid Fakhri
Abstract
Introduction As an important type of precipitation, snow is especially important in the hydrological cycle. This importance can be examined and analyzed from several aspects such as water supply in other seasons. The most important aspect is the possibility of creating hazards for human beings and human ...
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Introduction As an important type of precipitation, snow is especially important in the hydrological cycle. This importance can be examined and analyzed from several aspects such as water supply in other seasons. The most important aspect is the possibility of creating hazards for human beings and human infrastructure (snow avalanches, floods during seasonsof snowmelt). Therefore, it is necessary to study the snow phenomenon and its covered surfaces in winter. Monitoring the changes in this important climatic phenomenon has always been considered important by researchers and planners. Remote sensing methods have revolutionized the field of natural environment monitoring since their inception. Snow depth is an example of what can be monitored and evaluated by remotely sensed data and techniques. Materials & Methods The present study seeks to evaluate the efficiency of several important remote sensing indices in monitoring snow depth, andalso to introduce and evaluate a proposed spectral index. To reach this aim, satellite images of Landsat 8 and Sentinel 2 have been used. These images were received from the relevant portal and used to calculate snow indicesafterinitial corrections. Four spectral indices were usedto extract snow covered surfaces. These indices include: NDSI - S3 - NDSII - SWI. These indices are based on reflection from snow covered surfaces in light reflection and absorption spectra of snow covered surfaces.Light reflection from snow covered surfaces in the visible spectra and absorption in the short infrared spectrum allow automatic detection and extraction of snow covered surfacesin remote sensing multispectral images. The above mentioned indices have the ability to extract snow, but they fail to differentiatebetween snow and other related phenomena such as water (in the absorption band) and light-color salt marshes (in the reflection band) and thus, similarity of the spectra occurs. This spectral mixing which occurs due to the similarity of the reflections, cannot be eliminated even when threshold limits are defined. Thus, the extracted snow cover includes not only snow, but also other similar zones. To solve this problem and extract snow covered surfaces correctly,a new index is presented in this paper based on principal component analysis (PCA) and the first component of the set, and short wave infrared (SWIR) spectrum reflection.Using the first component of the set with the highest variance makes the difference between reflectance of snow and similar phenomena visible and thus, solves the issue of spectral mixing to a very large extent. The proposed new index called PCSWIRI is also evaluated and validated along with 4 other indices in the present paper. Results & Discussion Spectral indices introduced in the previous section were examined and evaluatedusing 7 sets of images (4 Landsat images and 3 sentinel 2images) captured in different days of winter from the main study area (Lake Urmia in the northwest) and two other study areas. The results indicate efficiency of the proposed index in the extractionof snow covered surfaces. The proposed index has improved the accuracy of snow cover extractionin the whole collection of images. This increased accuracy has been confirmed withstatistical evaluation criteria, such as kappa coefficient, overall accuracy and in the visual review of indices(comparing to the composition of the original image). The main study area includes Lake Urmia, an important geographic feature containing water and salt and a mixture of the two, which makes its spectrum similar to snow. This lake is incorrectly identified by other indices as a snow covered surface. Like the main study area, the first study and assessment area contains salt covered zones (salt lake). Despite the spectral similarity between snow and salt,the proposed index has been able to distinguish between this phenomena (in both regions) and snow and to extract only realsnow covered surfaces. In addition, visual review of existing water bodies (Dam Lake) and 5 evaluated indicesindicates higher accuracy of the proposed index. In order to automate the process of calculation in the proposed spectral indices, a software was also providedbased on MatLAB. Conclusion The findings of the present study indicates higher accuracy and efficiency of the proposed index (PCSWIRI) for snow cover extraction. Snow cover maps are very useful in various hydrological, climatic, precipitation-runoff modeling studies, and etc. Therefore, increasing the accuracy of snow cover maps is of great importance and results inimprovedaccuracy and reliability of modeling processes.
Hamid Bayat Barooni; Mojtaba Ezam; Abbasali Aliakbri Bidokhti; Masoud Torabi Azad
Abstract
Extended AbstractIntroductionThe Caspian Seaclassed as the world’s largest lake, lies between Europe and South Western Asia (between 45.43°to 54.20°longitude east and 36.33°to 47.07°latitude north). The Caspian Sea level has changed widely over time. These changes have occurred ...
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Extended AbstractIntroductionThe Caspian Seaclassed as the world’s largest lake, lies between Europe and South Western Asia (between 45.43°to 54.20°longitude east and 36.33°to 47.07°latitude north). The Caspian Sea level has changed widely over time. These changes have occurred gradually and incrementally leading to landward and seaward migration of the coastline. Therefore, it is very important to study and predict futurechanges of the Caspian Seacoastline. Today, experts in atmospheric and marine physics from all around the world consider the Caspian Sea as a natural dynamic model of oscillatory processes in watersurface.High annual rate of water level changeshas made oscillatory processes of this lake different from those of oceans. With the advent of satellite altimetry in 1973, highly accuratemonitoring of sea level has been made possible. The present study seeks to investigate the trend of dynamic topography changes in the Caspian Sea and determine the effects of changes in thesea level on the southern coastline. MethodologyVarious sets of satellite data have been used in the present study. Long-term average ofglobal sea level data was obtained from MSS_CNES.CLS15. Covering a period of 20 years (1993 to 2012),these datasets are produced based on information received from different satellitealtimeters. Mean sea level is calculated foreach point of the network created atthe Caspian Sea (with a distance of 0.25°). The correlation between altimetry data and sea level changes is calculated using gravity changes. Investigating these changes leads us to equipotentialgeomagnetic surfaces called geoid. Geoid is an equilibrium surface of the Earth’s gravitational field showingapproximately the average leveloffree water. Mean sea level does not coincide with geoid and theirdifference at any given point is called absolute dynamic topography. In this study, GOCE model was used to calculate geoid value at every point of the network created at 1′distance from the Caspian Sea. Aviso Altimetry dataset was used to obtain sea level anomaly data. Mean sea level was obtained by adding dynamic topography mean to geoid height.In order to obtain average dynamic sea topography,MDT values were calculated for all the points created in the Caspian Sea. Afterwards, sea level anomaly was added to the mean dynamic sea topography to obtain absolute dynamic topography. Daily SLA data of the Caspian Sea were extracted with a resolution of 0.25° from AVISO and CNES.CLS15 SLA ultrasound satellites and interpolated at the specific location created on the Caspian Seanetwork with a resolutionof 1′.Aabsolute dynamic topography were calculated on a daily basis. These calculations were repeated for a 20 year period (7305 days) from 1993 to 2012 using MATLAB and in this way, a complete database including the Caspian Sea surface topographic datawas obtained for this period. ResultFollowing the calculation of the mean ADT data obtained fromall over the Caspian Sea, time series of daily Sea Level Fluctuations were extracted. These time series indicated that despite the positive trend of the Caspian Sea water level changes in both 1993-1995 and 2000-2005 periods, the overall trend of water level changes over the 20-year period is negative. Moreover, examining sea level changes over this 20-year period shows thatthe highest altitude (-25.914m) has occurred on June 1st, 1995, while the lowest altitude (-27.20) has occurred on November 26th, 2012. In addition, March 20th, 2002 and June 29th, 2005 have experienced two abrupt changes of -26.843m and -26.26m in the time series. In this time series, an upward trend is observed until June 1st, 1995, while a decreasing trend of 93 cmis observed from March 20th, 2002 over a period of approximately 7 years. Between March 20th, 2002 to June 29th, 2005 (a period of approximately 3 years), we observe a decreasing trend of 61 cm. Over a 7-year period (until late 2012), we also observe a 97cm decreasing trend. Altimetry data received from three stations located in the Caspian Sea are used to verify the results obtained from the above mentioned method. Examination of these values and comparing them with the values obtained from the method used in the study confirms the resulting trend. In orderto investigate the shoreline changes caused by changesin the Caspian Sea water level,the southern shoreline of the Sea is mapped based on the obtained trend.Days with the highest and lowest sea level over the 20-year study period were extracted from satellite images. Mapping and overlayingthe coastlines based on the information related to these two time series, changes have been observedthroughthe Caspian coastlines. However, these changes are more significant in the South Eastern Gorgan Bay (Miankale) due to the smaller slope of the South Eastern Caspian Sea compared to other areas of the Sea. ConclusionInvestigating changes of the Caspian Sea level shows anegativetrend of changes, with a -1.287 m difference between thehighest and lowest altitudes. Of course, the trend has not always been negative over these years. For an instance, a positive trend was observed from 1993 to1995 and from 2000 to 2005. Results indicate that the Caspian Sea dynamics of water level fluctuations changes rapidly and long-term prediction of the Caspian Sea water level cannot be very accurate. However, it can be concluded that the Caspian water level changes will continue its decreasing trend in the future. This negative trend of sea level changes has resulted in the seaward migration of the Caspian coastline, which has began in 1995 and still is present today. This has resulted in drying up of more than 12850 hectares of the GorganGulf.
Hadi Fadaei; Mahdi Modiri
Abstract
Extended Abstract
Introduction
Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types ...
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Extended Abstract
Introduction
Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types of resolution in digital ground elevation models.
üArea resolution: The dimensions of the length and width of each cell in the pixel grid is a digital elevation model that shows the minimum dimensions of the topographic features taken on the ground.
ü Height resolution: represents the minimum elevation dimensions that the digital elevation model is able to display. For example, in the digital model of ground elevation with a resolution of 30 meters, elevation features less than 30 meters are not visible.
The digital elevation model can be prepared for a region with different accuracy. The high accuracy of the digital elevation map provides more accurate estimates of the physiographic characteristics of the basin, but the preparation of such maps is very costly. PRISM sensor from ALOS satellite with three cameras: 1- Forward 2- Vertical 3- Forward, which is captured earth surface with the characteristics of the earth (low and high). Therefore, an object that is high above the ground is shown with other points on a flat surface. As a result, by imaging points from different angles, the elevation of those points can be obtained through adaptive mathematical calculations. The purpose of this study is to evaluate the accuracy of the digital elevation model generated by the PRISM sensor of ALOS satellite in comparison with the digital elevation model of ASTER and SRTM for Sarakhs border region (between Iran and Turkmenistan).
Method
The study area is located in north-eastern Iran in the range of 35 to 38 degrees north latitude and 56 to 60 degrees east longitude and on the border between Iran and Turkmenistan in the border region of Sarakhs. The research method in this research has an exploratory aspect that the production and extraction of digital elevation model from PRISM sensor stereo images from Alves satellite and its evaluation is with digital model extracted from ASTER image. The digital SRTM model has a spatial resolution of 90meters, the digital ASTER model has a spatial resolution of 15 meters and the digital elevation model obtained from the PRISM sensor from the ALOS satellite is 5 meters. In this study, elevation control points using Google Earth and GPS have been examined. The algorithms used in this method to extract elevation information are the same as the algorithms used in the photogrammetric method. Elevation digital models are made from satellite images taken in pairs. The accuracy of digital elevation models of this method is perfectly proportional to the scale or resolution of satellite images.
Results & Discussion
In this study, we evaluated the digital elevation model from stereo satellite images of ALOS/PRISM satellite and compared it with the digital model of ASTER elevation and ground observations in the Sarakhs border region located on the border between Iran and Turkmenistan. In this study, the ability to generate a digital elevation model prepared from stereo images extracted from a PRISM sensor with a file of rational polynomial coefficients has been investigated, and we compared it with digital models extracted from stereo ASTER satellite and digital models extracted from SRTM. The results obtained from the digital elevation model are the accuracy of the digital elevation model produced by the pair of ASTER satellite images using a correlation between the two images of 0.47 pixels. Due to the spatial accuracy of the image pixels, which is about 15 meters, the accuracy of the digital model is less than the size of pixels, i.e. less than 15 meters, 6 meters horizontally and 7 meters vertically, which is a total of 13 meters. The results show that RMSE as error index for digital model of elevation extracted from ASTER and PRISM and ground observations are 7.46, 8.77, 3.66 and 6.8 meters, respectively. The results obtained from the stereo images of the PRISM sensor are the standard deviation of the pixels in the longitudinal direction of 1.9 meters and in the transverse direction of 2.3 meters and the distance between the pixels of the digital model is 3 meters high. Therefore, the accuracy of the digital model extracted from PRISM sensor images is higher than SRTM and ASTER. It is recommended to use a high-precision digital elevation model in all borders of the country, which uses a digital elevation model produced from stereo PRISM images from ALOS satellite, which is accompanied by polynomial logical coefficient (RPC) files for geometric correction of images.
Conclusion
The higher the accuracy of the DEM, the more efficient it will be and give border commanders the ability to make better decisions in different situations. The elevation accuracy obtained from the stereo images of the PRISM sensor is 3 meters. The accuracy of the digital model of SRTM elevation in the plains is about 30 meters, which can be used for studies of phase zero and one of the projects, as well as reducing the huge costs of studies. The results of this paper, shows that the accuracy of the digital elevation model produced from the stereo images of the PRISM sensor is higher than the digital elevation and SRTM digital models, i.e. the RMSE error and standard deviation are relatively lower. As a result, it is recommended for border studies that require higher accuracy, and the entire borders of the country, to use the digital elevation model with accuracy.
Milad Alizadeh Badresh; Farhad Hosseinali
Abstract
Extended Abstract
Introduction
Cultivation Pattern is a roadmap that shows which, how much, when, and where crops should be cultivated given the constraints and available resources. Cultivation pattern program determines appropriate crop types in accordance with the climatic condition of the province ...
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Extended Abstract
Introduction
Cultivation Pattern is a roadmap that shows which, how much, when, and where crops should be cultivated given the constraints and available resources. Cultivation pattern program determines appropriate crop types in accordance with the climatic condition of the province and thus ensures the sustainability of agricultural products, food security, and optimal utilization of resources, capabilities and potentials of each region. Review of the related literature indicates that AHP and TOPSIS methods are among the most widely used methods in decision making and prioritization. Moreover, previous studies have shown that AHP method is suitable for qualitative data and TOPSIS method is suitable for quantitative data, whereas both quantitative and qualitative factors are involved in determining the cultivation pattern. Therefore, the present study has utilized a larger number of criteria (nine criteria), and combined AHP and TOPSIS models in an attempt to make use of their strengths and avoid their weaknesses. Linear programming was also used with four scenarios. In one of the scenarios, two lowest ranking crops in TOPSIS method were eliminated. The present study has innovatively utilized these models and a larger number of criteria simultaneously to determine the cultivation pattern. It also has precisely identified the appropriate crop for each plot of land using SWOT tables.
Materials & Methods
The case study was located in Qeyghaj plain in west Azerbaijan province. In accordance with the geographical location and climate, wheat, barley, alfalfa, sugar beet, rapeseed, potato, maize and fodder corn are mostly cultivated in the area which have been considered as alternatives for cultivation in each plot of the present study. The present study has begun with evaluating the slope and aspect of each cultivation plot. Then, crops are ranked and optimal crops are selected based on various criteria and using a combination of AHP and TOPSIS models and different decision matrices. Afterward, the maximum and minimum appropriate volume of crop production is determined using linear programming in accordance with the maximum profit. Finally, the most suitable crops for each land parcel are determined using SWOT tables.
The present study has proposed a multicriteria decision model which includes the strong points of AHP and TOPSIS models and avoids their weaknesses. In order words, relative weights were obtained from AHP pairwise comparisons and also the compatibility index was evaluated using AHP model while crop alternatives were ranked using TOPSIS model. The hierarchical structure included the goal, nine criteria used to evaluate the strategies and eight strategies (options).
Matrices used in pairwise comparisons were all obtained from experts' opinions. These include comparisons made to determine the weight of each criteria to be used in the TOPSIS model, as well as pairwise comparisons made between options which could not be quantitatively compared. Then, the general structure of the hierarchical model was developed in Superdecision software and the final weights, compatibility index of each matrix and quantity of each product were obtained based on each of the indicators. The values were then entered into the TOPSIS model and used to rank the crops, compare different options and select the best crop.
Results and Discussions
In the first step, a slope map was produced for the study area using digital elevation model based on which an aspect map was also produced. In accordance with these maps, the physiological suitability of the study area for the cultivation of eight crop types was evaluated. Results indicate that the study area is physiologically very suitable for cultivation of alfalfa, suitable for wheat, barley and canola and fairly suitable for the other four remaining crops.
Then, pairs were compared in hierarchical analysis using expert opinions and the weights of criteria and crops were obtained. Then, weights were assigned to each alternative (crops) and decision criterion (nine selected criteria) using the TOPSIS model and more appropriate products were selected. A decision matrix was first created in TOPSIS. Some criteria such as economic index were initialized directly in accordance with the available quantitative values whilst the values of some other criteria (such as temperature whose quantitative values cannot be obtained) were initialized using the results of AHP.
In the next steps, a weighted normalized matrix was developed and positive and negative ideals were found. The distance between positive and negative ideals was calculated and then the ideal solutions were obtained. Finally, the score obtained by each alternative or similarity index was calculated. The closer similarity index is to one, the superior that alternative will be. Linear programming is a method in mathematics that finds the minimum or maximum value of a linear function on a polygon. The present study seeks to reach maximum profit under various restrictions such as water restriction, restrictions on area under cultivation and maximum and minimum amount of cultivated crop. Water restriction included all surface and subsurface resources for crop cultivation. Crop coefficients were defined as the need for crop irrigation. Water constraints included the constraints assigned to allocated water in spring, summer, autumn and total amount of allocated water.
Three scenarios were developed with or without the previously mentioned constraints. Then the goal function was changed in accordance with the MOTAD method and another scenario was developed. The scenarios are explained as follows:
Scenario 1 (Without any restrictions on the minimum and maximum crop yield): In this case, the goal was reaching the maximum profit and the restriction included the lowest amount of water consumption, regardless of the requirements in the study area. In this scenario, variables x1 (wheat), x5 (Canola) and x8 (fodder corn) were included in the cultivation pattern. Consequently, farmers' income was maximized and the amount of water consumption was reduced. However, obtained results were not acceptable in accordance with the regional and national policies since cultivation of most crop types will thus be stopped.
Scenario 2 (locally acceptable size and local farming customs and the restrictions assigned by the agriculture office): the present scenario seeks to maximize profit, satisfy requirements of the area and achieve the goals of the agriculture office. All crops are included in the cultivation pattern. Therefore, minimum and maximum cultivation restrictions have been used in addition to water and land restrictions.
Scenario 3 (not cultivating some water-intensive crops): As previously mentioned, Poldasht agriculture office has introduced reduced cultivation of some low yielding crops or even stopping the cultivation of such crop types as one of its main goals. Corn and potato are highly water -intensive with a low yield in the study area and thus gain one of the lowest ranks. Therefore, potato and corn were removed to determine the cultivation pattern of the region in their absence.
Scenario 4 (MOTAD approach): MOTAD is a linear programming approach aiming to maximize the profit whose objective function equals the sum of deviations between total gross income and the expected income based on the average gross income of the sample. Linear programming with MOTAD requires having access to income gained from each crop type in previous years. Restrictions such as fund and manpower restriction must also be considered. The statistical period used in MOTAD approach starts in 2011 and lasts till 2016.
Income values in MOTAD approach lead to a constraint relation. Just as the previous scenarios, water and land constraints are considered in this approach and fertilizers and pesticides restrictions have not been taken into account.
Conclusions
Based on the collected information, available parameters, SWOT analytical model and tables developed for each field, a suitable crop was selected for each farm (parcel). Accordingly, 112.3 hectares was identified as suitable for the cultivation of wheat, 59.9 hectares for barley, 32.1 hectares for alfalfa, 37.6 hectares for sugar beet, 85.7 hectares for Canola, 15.5 hectares for potato, 13.2 hectares for Maize and 63.7 hectares for fodder corn. In this case, the resulting profit equaled 23, 503,410,000 Rials and the water consumption equaled 2,542,293.8 cubic meters which shows 2,052,120,000 Rials increase in profit and 90,770.6 cubic meters decrease in water consumption as compared to the present cultivation pattern.
Comparing the profit and water consumption in each of the five models and the current cultivation pattern, it can be concluded that the pattern obtained from the SWOT analytical model is more feasible since it includes various parameters and particularly farmers' opinions.
Mojtaba Yamani; Arefeh Shabanieraghi; Seyed Mohammad Zamanzadeh; Abolghasem Goorabi; Nafiseh Ashtari
Abstract
Extended Abstract
Introduction
Climate changesare considered to be the most important event of the Quaternary period largely reflected in the geomorphology and sedimentology of the period.Paleogeomorphology helps us to understand past climate changes and predict future changes. Depending on the ...
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Extended Abstract
Introduction
Climate changesare considered to be the most important event of the Quaternary period largely reflected in the geomorphology and sedimentology of the period.Paleogeomorphology helps us to understand past climate changes and predict future changes. Depending on the Quaternary periods, closed pitlakes are called cold or rainy period lakes.Some of these lakes have completely dried up, others are temporary lakes that change into playas in the dry season, and others have been larger in the past. Researchers can identify pluvial lakes in today’s arid regions, because of the variety of factors and complex processes involved in their formation.Mighan Playa is located in the central and southwestern areas of Markazi province. It includes seasonal and saline Tozlogol Lake, and alluvial plains.
Methodology
The present study used evidences of playa lake sediments as well as geomorphological evidences(lake terrace) to investigate the extent of MighanLake in Quaternary period. Data included datacollectedfrom library sources, statistical data, field surveys, sedimentary samples, sedimentary evidences, climatic data, remote sensing data received from Landsat TM satellite, ETM, and SRTM digital elevation models(SRTM 90 meters, and Dem10 meters).Initially, previous studies and environmental characteristics of the area were analyzed. Then, lake terracewas investigated to find geomorphic evidences of Pluvial Lakes in Quaternary period. To do so, probable ranges of the lake Terrace were determined using satellite imagery, geological maps, and elevation data of digital models. Probable area was divided into several distinct zones, and finally an area was identified in the western part of the lake and based on the elevation of this zone, the extent of the lake catchment in Quaternary period was determined. During fieldwork, samples were collected from the mountain slope line toward the Playa and lake shore, and then granulometrytests were performed on the 14 collected samples to determine the amount and type of sediments.Sedimentary and graphical analysis were also performed based on Folk classification. The percentage of clay and sand in the new samples collected from the region containing this mountainous area, lake coast and deeper parts of the lake were determined and attributed to past sediments. In this way, the information could be used to determine the extension of lake sedimentsin the past.Based on sedimentary logs (Arak Groundwater Studies Report, Central Water department of Markzani Province), sedimentology studies and percentages (clay-sand-gravel) of present-day samples collected from deep sections of Playa andelevated areas of sediment pits were interpolated in GIS environment and a map of the lake extension in the Mighan catchment areawas prepared.Subsequently based onpaleogeographic studies, paleontological climate of the area and sedimentation rate calculated by Pedrami in 1993, a map was produced to show the extent of sediments and the lake progressions and regressions in the past.
Discussion
The stratigraphic and sedimentary evidences of logs in the margins of Mighanpit indicates changes in wet and dry periods. Type and size of sediments reflect the climatic conditions in each period, while high percentage of clay sediments reflects lake conditions. Paleontological sedimentological maps of the area show that the clay sediments were more concentrated in the southwestern, western and northwestern regions. Uplift of the Talkhab fault in the northeastern regionhas resulted in tectonic asymmetry of the pitand concentration of sediments in the western and southern parts. According to Krinsley, Bubeck, Pedrami and etc. Lake Mighan has been larger in the past. However, none of these researchers have determined the extent of lake water in the past. In this study, the extent of the lake was determined by reconstruction of clay sediments and using geomorphological evidencescollected from the lake shorelines (lake terrace) near Mighan village (Mashhad). Results indicated a height of 15 m in Quaternary period.
Conclusion
Sedimentary and geomorphologic evidences indicated that compared to the present playa level, the Lake fluvial was more permanent and vast in the past, but this extension differs in different directions and shows significant differences due to the tectonic location of the area.
Mojdeh Ebrahimikia; Ali HosseiniNaveh
Abstract
Extended Abstract
Introduction
Today, orthophotos are one of the most widely used products in the field of spatial information, and they are often created from aerial or satellite images, so paying attention to their accuracy and quality is essential in order to have both geometric and radiometric ...
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Extended Abstract
Introduction
Today, orthophotos are one of the most widely used products in the field of spatial information, and they are often created from aerial or satellite images, so paying attention to their accuracy and quality is essential in order to have both geometric and radiometric information. The point clouds and the digital surface model used to build them are the two most important aspects that affect the quality of these images. On true orthophotos, there are some distortions on the structural edges of buildings, which is due to defects in these areas in the point cloud used in the digital surface model. This problem is greater for orthophotos that have been made from UAV images in urban areas because of their lower altitude. Additionally, because of the presence of occluded regions and radiometric changes between overlapping images, approaches for creating point clouds based on image matching are unable to produce complete point clouds and contain flaws, particularly towards the outer edges of objects with high height differences. Before interpolation of the point cloud and preparation of the digital surface model and then preparation of orthophotos of it, it is necessary to complete the point cloud in areas with defects. Some studies have shown that adding edge points has the effect of decreasing the distortion of true orthophotos. In this study, a new method for completing point clouds is proposed and explained in detail.
Materials & Methods
In this study, the imaging of the Yazd region was done with a Phantom 4 drone equipped with a DJI camera. The SfM algorithm has been used to calibrate the camera, estimate the internal and external camera parameters, and produce images without distortion and low-density point clouds, and SGM has been used to produce dense point clouds. In the proposed method, the purpose is to complete the incomplete points of the building. Assuming that the points on the roof of each building are predetermined, without noise, and have incomplete edges, these point clouds were used to complete them, and then added to the existing point clouds in their actual coordinates. The final point cloud was used in the preparation of digital models to produce irregular and then regular surfaces and in the preparation of true orthophotos using camera parameters and undistorted images. One of the images with buildings marked as numbers 1 to 4 was selected to perform tests and prepare orthophotos.
Results & Discussion
The lack of structural edge points on any roof, which is the distance between severe height differences between levels, causes the greatest amount of distortion on the edge of the roof and around it. Adding these points with edge line recognition and reconstruction algorithms to the point cloud improves the resulting digital surface model. Since the quality and accuracy of the digital elevation model directly affects the resulting orthophoto, using a more accurate digital elevation model improves these images. These point clouds have been modified in the proposed method, and quantitative and qualitative comparisons demonstrate improved results in eliminating distortion in the majority of the buildings studied. The reasons for the superiority of the proposed method over previous methods include determining and calculating a more complete and precise form of the roof of each building and considering the outermost edges of the buildings.
Conclusion
The biggest amount of distortion on the edge of the roofs and their surroundings is caused by the lack of points on the structural edge of each roof, which is the boundary between dramatic height variations between the levels. By integrating these points with algorithms for recognizing and repairing edge lines, the resulting digital elevation model will be improved. This study presented a new method for completing the point cloud that enhanced the quality of true orthophoto edges, which was tested on a large number of building images and compared to the results of existing methods. In addition to implementing a new method for improving point clouds for orthophoto creation, the degree of distortion on the selected edge of four buildings has been greatly reduced when compared to the previous method. The success of the results with the latest proposed method of true orthophoto enhancement indicates an improvement of about 62% and 55% in the distortion decreasing of the structural edges and maintaining their coordinate accuracy.
The proposed method did not uniformly reduce the distortions at the structural edges, and future advanced models could possibly improve it.
Faeze Shoja; Mahmood Khosravi; Ali Akbar Shamsipour
Abstract
Introduction
North Indian Ocean (NIO), which includes the Bay of Bengal(BoB) and the Arabian Sea (AS),is one of the tropical oceans and therefore, prone to the formation of the tropical cyclones (TC). On a global scale, approximately 7% of the tropical cyclones are formed in this area. Studies ...
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Introduction
North Indian Ocean (NIO), which includes the Bay of Bengal(BoB) and the Arabian Sea (AS),is one of the tropical oceans and therefore, prone to the formation of the tropical cyclones (TC). On a global scale, approximately 7% of the tropical cyclones are formed in this area. Studies indicate an increase in the frequency of remarkably powerful cyclonesin the Arabian Sea in recent years.In the period between May 16 and 27, 2018, two very strong cyclones called Sagar and Mekunu, affected southwestern and western regions of the Arabian Sea. The present study aims to determine the role of large-scale environmental parameters affecting the tropical cyclogenesis during the life period of these two storms.
Data and Methodology
The current study collects data, including the location of cyclones occurrence, tropical cyclone track, the minimum sea level pressure, and maximum wind speed from the report prepared by the India Meteorological Department. Requiredoceanic and atmospheric parameters, including U and V components of wind (at 200 and 850 hPa levels), relative humidity (at 600 hPa level), sea surface temperature (SST), sea level pressure (SLP), air temperature, pressure, and specific humidity at 23 levels of pressure (levels of 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 700, 775, 850, 925, 1000 hPa) were also extracted from the reanalyzed dataof ECMWF (European Centre for Medium-Range Weather Forecasts)on a daily basis and with the spatial resolution of 0.5°longitude and 0.5° latitude. In order to achieve the goal of the research, first, the values of large-scale environmental parametersplaying a crucial role in TC formation, including absolute vorticity (at 850 hPa level), vertical wind shear, potential intensity, and relative humidity, were calculatedusingGRADS and MATLAB. The related maps were also plotted and analyzed. Then, the genesis potential index of days before the storms occurrence wascalculated for different regions of the Arabian Sea, and the likely areas for cyclone occurrence were predicted based on the index. Finally, some anomaly maps were produced for the atmospheric parameters affecting cyclogenesis, and changes in these parameters were examined in the life period of the storms as compared to the normal climatological conditions.
Results and Discussion
Results indicated that the storms track coincided with the regions in which maximum relative humidity and maximum absolute vorticity occur.During cycloneSagar, relative humidity in areas affected by the cyclone reached over 80%. During the formation period ofcycloneMekunu,maximum relative humidity was observed in the area between 0°N to 10°N and 50°E to 80°E- the area dominated byMekunucyclone. Spatial distribution of environmental variables, such as temperature, sea level pressure, and vertical wind shear indicates that the favorable values of these parameters have been concentrated in the areas affected by the cyclones in all three phases of their formation, intensification, and dissipation.
Although, vertical wind shear did not considerably change in different parts of the Arabian Seaduring the life cycle of Sagar, its minimum levelwas reported in the Gulf of Aden. Similarly, with the increase in wind speed duringcyclone Mekunu on May 25, the minimum vertical wind shear moved to the northern latitudes and its value ranged from 6 to 12 m/s in the western Arabian Sea. The maximum absolute vorticity is observed in the Gulf of Aden during the life cycle of Sagarcyclone, and these conditions continue until cyclone’s dissipation. Also duringcycloneMekunu, maximum absolute vorticity was observed in the areas affected by thecyclone. Affected by the maximum sea surface temperature, potential intensity indexreached a value of more than 70 m/s in regions affected by the storms (20-degree north latitude). Spatial distribution of GPI values collected from the days before the cyclones occurrence indicated that there is a strong correlation between the spatial distribution of this index and the occurrence of cyclones. Furthermore, the storm track also coincided with the increase in this index,so that highest GPI values were concentrated in areas dominated by cyclones Sagar and Mekunu.Analysis of anomaly maps revealed that compared to the long-term average,sea surface temperature and relative humidity have increased in the area affected by tropical cyclones and sea level pressure and vertical wind shear have decreased.
Conclusion
Findings of the present research indicated that dynamic and thermodynamic parameters have provided the most favorable cyclogenesis conditions in the areas affected by the storms. In other words, the cyclone had moved to the direction in whichenvironmental parametersexhibited the best threshold levels. Therefore, it is possible to predict the occurrence of tropical cyclones in the northern latitudes of the Arabian Sea, especially in the Gulf of Oman,based on the changes in large-scale environmental parameters in different parts of the Arabian Sea.
Geographic Data
Mojtaba Ghadiri Masoum; Hamid Afshari
Abstract
Extended AbstractIntroductionNowadays, tourism is widely accepted as a fundamental basis of development. As a sector of economy, tourism is considered to be one of the most important activities of contemporary human beings, which not only makes dramatic changes to the landscape, and political, economic, ...
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Extended AbstractIntroductionNowadays, tourism is widely accepted as a fundamental basis of development. As a sector of economy, tourism is considered to be one of the most important activities of contemporary human beings, which not only makes dramatic changes to the landscape, and political, economic, and cultural condition, but also transforms lifestyle of many individuals. The contemporary world considers tourism as one of the most important sectors of the tertiary industry which affects job creation and income generation, results in significant economic growth, and consequently provides the prerequisites for sustainable development of different societies. Iran is among the top 10 countries of the world in terms of tourist attractions, possessing many sites with potential attractions. Thus, tourism can be considered as an effective tool in dealing with economic problems of the country. As the basis of sustainable development, tourism can solve some problems of the country and thus, development of its infrastructure results in optimal allocation of available resources. The present study seeks to investigate the overall condition of tourism infrastructure in Markazi province. Previous studies in France, Austria, Switzerland, the United Kingdom, Ireland, Thailand and Japan indicate that their tourism sector has developed rapidly and now aids other sectors of the economy. Therefore, a comprehensive analysis of necessary infrastructure for the development of this industry can result in a more dynamic rural economy. Materials & MethodsThis applied study has a descriptive-comparative design and its study area includes all counties of Markazi Province. Library method and questionnaires were used for data collection. Statistical data and information were collected from the General Directorate of Cultural Heritage, Tourism and Handicrafts of Markazi Province and the statistical yearbook (2015) of this province. In accordance with Delfi method and targeted sampling, related indices were sent to 17 rural development experts and specialists via Email. It should be noted that some of these experts had previous experience in tourism. Finally, 10 completed questionnaires were received. PROMETHEE multifunctional decision-making model was also used to determine the overall condition of counties in Markazi Province in relation to tourism infrastructure.Since the present study seeks to classify counties in Markazi province, the first function of this technique has been used. An appropriate weight is first assigned to each of the 20 indices of tourism infrastructure using Delphi method. Then, these weights are evaluated and measured along with the value of each component and option in Visual PROMETHEE software. Structural equation modeling was used to investigate relations between variables more comprehensively. SPSS 26 and Smart PLS 3 software were also used to analyze the data. Results & DiscussionFindings indicate that Arak with a value of 0.7739 has ranked first among the counties. Several factors can be the reason: First, as the capital of the province, Arak possesses better facilities, larger population, etc. Second, as the main access road connecting neighboring provinces, Arak has developed more than other counties. With a value of 0.4673, Saveh has the second rank. Saveh also contains the access road connecting some of neighboring provinces and is located near Tehran. Thus, a strong industrial town has developed in this county attracting many workers with different ethnicities seeking employment and income. Due to these factors, relatively good facilities have developed in Saveh. With a value of 0.3536, Shazand has ranked third. Due to its proximity to Arak (the capital of the province), this county has attracted large industries such as petrochemical industry along with suitable facilities and infrastructure. Khomein (0.3166), Delijan (0.0168), Mahalat (-0.1023), Tafresh (-0.1634), Khandab (-0.3002), Zarandieh (-0.3266), Farahan (-0.3320), Ashtian (-0.3514) and Komijan (-0.3523) are next in rank.Analyzing the relationships between variables indicates that service-related components (0.279) and transportation-related components (0.096) have the most powerful direct influence on the level of development and other variables are next in rank. ConclusionFindings of the present study and previous studies indicate that centrality and population can be considered as influential factors resulting in easier access to desirable and appropriate facilities in different countries of the world. However, such a difference is not observed between different regions in developed countries due to their integrated development. Developing countries such as Iran lack such an integrated development environment and thus, the condition in provincial capitals is much more different from other counties. As indicated in the present study, the level of development in Arak was much higher than other counties of Markazi province. Therefore, an appropriate plan is required for other counties to achieve sustainable development, and especially sustainable tourism development.
Abolfazl Ghanbari; Sadra Karimzadeh; Sedighe Taraneh
Abstract
Extended AbstractIntroductionDespite higher standards of living in urban areas, rapid growth of urbanization has caused some problems such as development of dense and unplanned residential areas, environmental pollution, lack of access to services and amenities, increased gap between social classes and ...
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Extended AbstractIntroductionDespite higher standards of living in urban areas, rapid growth of urbanization has caused some problems such as development of dense and unplanned residential areas, environmental pollution, lack of access to services and amenities, increased gap between social classes and etc. Manifested as severe differences between living standards in different parts of cities, these affect the quality of urban life. Quality of life is considered to be one of the most important dimensions of sustainable urban development. The desire to improve the quality of life in a particular space, for a particular individual or group is one of the main concerns of planners. Failure to identify factors affecting the quality of life in various human settlements will have unexpected and unfortunate consequences. With a decrease in citizens' life satisfaction, society will gradually lose its productive and capable labour force. The present study primarily seeks to find a way to objectively study and evaluate the quality of life in urban areas using remote sensing technology and GIS. Therefore, it investigates the quality of life in Zahedan and identifies possible factors improving life quality. Methods and MaterialThe present study applies a descriptive-analytical methodology. Statistical data were collected from census data of Iranian Statistics Center and maps were retrieved from Zahedan detailed plan-related service centers. Satellite images were also used. The present study applies four indicators to study the quality of life: economic, social, and environmental indicators along with access to service providing centers. Cronbach's alpha method was used in SPSS to determine the reliability of the questionnaire resulting in a coefficient of 0.723 for the previously mentioned indicators which shows high reliability of the instrument. The validity of the questionnaire was also investigated using experts' opinions. Collected data and factor analysis for economic and social variables were performed using SPSS. Criteria were weighted using Super Decision software and ArcGIS was used to combine and model the layers. Satellite images were retrieved from Google Earth Engine. Results and DiscussionIn order to investigate the socio-economic inequalities affecting quality of life, 16 parameters were extracted from the available census data and used to assess the socio-economic situation in the study area. Correlated parameters were combined using factor analysis to represent a single index. A specific name was then assigned to each factor. Indicators were normalized and aligned for the modeling stage. Fuzzy membership functions (Large, Small and Liner) were used to normalize the indicators in ArcGIS. Each index is then multiplied by the weight obtained from ANP method, and integrated using GAMMA fuzzy command. Spatial distribution of urban blocks in the central parts of the first district ranked higher in terms of economic and social indexes of life quality. Environmental indexes and access to service providing centers have a more desirable status in the second district. Parameters such as economic participation rate , housing status, air pollution and health centers had the largest impact on quality of life. Moran's spatial autocorrelation index shows a cluster pattern for quality of life in the study area. ConclusionFinal results show that access to service providing centers has the largest impact on quality of life. In general, the second district ranks higher than the first district in terms of quality of life. This city faces various economic and social limitations, while having access to many facilities: Recent droughts, universities and higher education institutions, mutual borders with neighboring countries and a large number of immigrants from Afghanistan. It is also facing hot and dry climate, a decrease in vegetation cover and an increase in temperature level. The freeway located in the western part of the study area, urban expansion toward the western parts, increased constructions and increased urban density due to proximity to university centers and finally heavy traffic have caused air pollution. Also, public service centers are not evenly distributed. These are some of the most important causes of low quality of life in the study area.
Morteza Heidarimozaffar; Morteza Shahavand
Abstract
Introduction Iran is mostly located in arid and semi-arid regions, and groundwater is its only water resource. The present study introduces a method based on spatial zoning evaluation which takes advantage ofFuzzy Logic and Geospatial Information System to design possible sites for an underground dam, ...
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Introduction Iran is mostly located in arid and semi-arid regions, and groundwater is its only water resource. The present study introduces a method based on spatial zoning evaluation which takes advantage ofFuzzy Logic and Geospatial Information System to design possible sites for an underground dam, and ranks them according to their suitability. The usability of this method for the construction of an underground dam in Kabodarahang Plain in the north of Hamedan Provincewas evaluated in the present study. Materials & Methods Groundwater use is considered to be a solution of water scarcity in arid and semi-arid regions. Lack of sufficient financial resources and adequate technology as well as specific physical conditions make it difficult to provide clean water in arid areas of most developing countries. Over the past few years, underground dams has been considered as a way to overcome water scarcity in arid and semi-arid regions. The present study seeks to identify suitable locations for the construction of an underground dam in Kabodarahangplain in north of Hamadan province using fuzzy logic in GIS environment. As one of the case study areas of Qareh Chai River,KabodarahangPlain isthe largest plain of Hamedan province with an area of 3448 square kilometers and an average height of about 1789 meters above the sea level.It is located between 48°14ˊ 51 ̋ to 49° 5ˊ 11 ̋ eastern longitude and 34° 50ˊ 6 ̋ to36°14ˊ 31 ̋ Northern latitude. To reach the goal of the present study, effective parameters in the constructionof underground dam, such as land slope, positionof wells, springs and aqueducts, rivers channels, positionof faults, location of villages and cities, position of paths and the thickness of alluvium were collected from the study area. Based onthe possibility of performing different spatial analyses in geographic information system environment, zoning ofKaboodarahang plain was evaluated from the point of view of an underground dam construction usingfuzzy logic and GIS tools in the present study. Results & Discussion Similar to membership in classical series,“And” operatorin Fuzzy Logic is used when two or more different criteria can help in solving an issue. This operator extracts the minimum membership level of pixel units in a specified positionand use it in the final map.Fuzzy multiplication operator multiplies membership level of pixel units in specified positionsof different factors and use the result in the final map. This operator is used when mapsof different criteria have a subtractive effect on each other.Fuzzy gamma operator is the general form of algebraic multiplication of fuzzy multiplication and addition operators to the power of gamma. It is used when increasing and decreasing effects are present in the relations between different criteria. Following the preparation of layers in Arc Map software, Euclidean distance operator and interpolation based on triangulation method were used to convert parameters to raster layers. Based on the background research and standards used, the criteria maps were combined using fuzzy operators. Using Fuzzy membership operator, an area of 3342 hectares, using fuzzy multiplier operator an area of2393 hectare (around one percent of the study area) and using the fuzzy gamma operator, an area of 35574 hectares (10.32% of the study area) was selected as having a very good potential for underground dam construction.Slope Map is also one of the most important criteria in determining areas appropriate for underground dam construction. It is suggested to use a larger-scale topographic map to improve the accuracy and increase the possibility of errors. Intelligent algorithms can also be used to determine the threshold level for standardization of the criteria. Since different organizationswork in the field of data collection, it is also suggested to providea suitable mechanism to assess the potential of other plains through consultation and coordination with other relevant organizations. It is recommended to use other parameters and factors affecting the selection of suitable areas for the construction of underground dams, such as soil type or physical and chemical properties of soil in future studies. Conclusion Zoning maps prepared by fuzzy logic in GIS environment can be used to determine the appropriate location forthe constructionof underground dams. Fuzzy operators provide special conditions which make them more reliablecompared to traditional methods.Appropriate areas for construction of underground dam were identified in GIS environment. A decision making model can also be produced based on the input parameters.It is suggested to enter general information of the area to perform the initial investigation of potential areas and then add field study information to complete the model.
Mehrdad AhangarCani; Mohammad Reza Malek
Abstract
Extended Abstract
Introduction and Objective
Road traffic accidents impose numerous social, economic, and cultural costs upon various societies, especially developing countries. Identification of accident blackspots is a method proposed to deal with car accident risks. Among various events associated ...
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Extended Abstract
Introduction and Objective
Road traffic accidents impose numerous social, economic, and cultural costs upon various societies, especially developing countries. Identification of accident blackspots is a method proposed to deal with car accident risks. Among various events associated with transportation network, road traffic accidents play a significant role, because of their specific features, including high frequency, high intensity and the chance of direct involvement of all members of the community.This problem is more conspicuous in developing countries such as Iran. The present study aims to identifyaccidentblackspotsand to prepare risk map for road trafficaccidents in Babol city using volunteered geographic information.
Materials and methods
According to the characteristics of the study area, the present study takes advantage of criteria such as distance from population centers, proximity to city squares, distance from footbridges, and proximity to road intersections to identifyaccidentblackspotsand a prepare risk map for roadtraffic accidents in Babol city. Accident blackspots detected by volunteered geographic information, along with the criteria determined by applying analytic hierarchy process (AHP) and analytic network process (ANP) were compared in a pairwise manner, and their respective weight was calculated to showtheir specific level of impact. Ultimately, a risk map was produced for the risk of road traffic accidents obtained from each method. In order to evaluate the accuracy of the identified accident blackspots obtained from volunteered geographic information, as well as the accuracy of susceptibility maps, ROC curve and Kappa Coefficient were applied to police official records.
Results and Discussion
According to the findings, Jame Mosque shopping center, Shahabnia shopping center, intersection of Farhangstreet and Velayat square were identified as the most accident-prone areas in Babol city. Also, among the prespecified criteria, distance from population centers and distance from intersections are considered to be the most important criteria, respectively. Results obtained from the evaluation criteria indicatedhigh accuracy of volunteered geographic information, and thus it is concluded that this kind of information can be effective in determining the accident blackspotsinBabol city. Also, the ANP method works better than AHP method in preparing the risk map of accidents.
Conclusion and Future works
Due to the large number of road accidents, especially in developing countries,the issue of accident blackspotsand providing a risk map for road trafficaccidents are an essential part of roads safety. In the present study, volunteered geographic information was used, along with multivariate decision-making methods of analytic hierarchy process (AHP) and analytic network process (ANP) to identifyaccident blackspots based on number, causes and severity of accidents and to develop a risk map for driving accidents in Babol city. Moreover, the criteria of distance from population centers, proximity to the city squares, distance from the footbridges, and adjacency to intersections were used to determine accident blackspotsand to prepare a risk map for driving accidents in Babol city. According to the results, Jame Mosque shopping center, Shahabnia shopping center, Farhang intersection and Velayat square were identified as the most accident-prone points in Babol city. Also, distance from population centers and distance from intersectionswere identified as the most important criteria, respectively. Evaluation criteria demonstrated that volunteered geographic information can be effective and accurate in determining accident blackspotsinBabol city. Also, the ANP method worked better than AHP method in preparing the risk map of driving accidents. The method proposed in this study to identify accident blackspots and preparedriving accidents risk maps can be generalized to other areas. Basedon the characteristics of specific routes, other criteria such as arc radius, longitudinal slope can alsobe used. It is also suggested that the results of other methods used for investigation ofaccidentblackspotsand production of risk maps based onvolunteered geographic information (VGI) are compared with the results of the present study.
Najmeh Neisany Samany; Ali Asghar Alesheikh; Zahra Abedi
Abstract
Extended Abstract
Introduction
Since urban bus networkis considered to be the most important part of transportation system in developing countries, optimal design of this networkis crucial for improving the status of public transportation. To reach this aim, it is necessary to locate these stations ...
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Extended Abstract
Introduction
Since urban bus networkis considered to be the most important part of transportation system in developing countries, optimal design of this networkis crucial for improving the status of public transportation. To reach this aim, it is necessary to locate these stations in areas which increase users of this system in different parts of the city. The present study seeks to identify suitable places for the construction ofproposedbus stations in the 6th district of Tehran municipality using GIS functions, Analytic Network Process and Allen’s temporal model.Proposedstationswere then optimized.
Materials & Methods:
Based on necessary investigations about the 6th district of Tehran, 17 indicators were identified: access criterion (sub criteria: business, administrative, medical, religious, educational and sports centers, and urban facilities, subway, roads), demographic criterion (sub criteria:population and employeesdensity) and traffic status (sub criteria: BRT lines, one way and two way streets, street width, traffic load, slop of the area and kind of road).
At the first phase, questionnaires were distributed among 35 experts of transportation and traffic. Based on the results of DEMATEL questionnaires and their analysis in MATLAB, the severity of relationship between the criteria were calculated and pairwise comparison questionnaires were designed.
Using DEMATEL technique, the presence or absence of a relationship between the aforementioned criteria and sub criteria was investigated. As a decision makingtechnique based on pairwise comparison, DEMATEL uses experts’ judgments to extractelements of a system and find a systematic structure for them using the principles of graph theory. This technique provides a hierarchical structure of the factors of the system along with their corresponding relationship, and determines the effect of these relations in the format of numerical scores. DEMATEL technique is used to identify and investigate the mutual relationships between criteria and to produce a map of network relations.
The ANP model not only calculates the relationship between the criteria, but also the relative weight of each criterion. The result of these calculations make a supermatrix, from which it is possible to derive dependency between each criterion and selection and their weights. An increase in this weight shows higher priority, so it is possible to choose the best option. (Saa’ti, 2003)
It is possible to calculate ANP process in both Super Decision and and ANP-solver software. After calculating weight of the criteria, spatial layers are created in GIS software and finally suitable digital layer is created through integration of the criteria. The obtained digital layer shows the best spatial zones for the construction of bus stations in the study area.
Results & Discussion:
Time and place are inseparable parts of each phenomenon in our world. Since, the first step of processing and analyzing a phenomenon in spatial information systemsismodeling, creating a model with necessary capabilities to include temporal dimension is inevitable. One of the main requirements of spatio-temporal modelling is the ability to investigate the topological temporal -spatial relations betweendifferent phenomena. The present study used Allen’s Interval Algebra to extract all relations between different dimensions of time. These include 3 relations between two temporal events, 6 relations between one event and a time mode, and 13 relations between two time modes.
Based on Allen’s model, the rush hours were investigated and common temporal – spatial features of each station were obtained. New stations were proposed based on existing stations and the desirable layer, and a desirable time was determined for the buses to pass stations based on land uses around the stations, the rush hours of each land useand common temporal – spatial features of each station (based on Allen’s model).
Conclusion
Results indicate that the ANP and Allen model can only search a very small number of possible answers and reach the required answer. 6thdistrict of Tehran municipality covers an area of 1557.65 hectares, from which 18.10% are in a suitable condition, 21.41% are relatively suitable, 30.45% are moderate, 23.88% are relatively improper and 6.17% are completely improper.
281.923 hectares of the district has no problem regarding the access criterion and donot need a station. This district has 185 bus stations and 61 new stations are proposed (a total number of 246).
From the aforementioned 246 stations, 17 stations do not have a common schedule, 87 stations have a common point in their schedule, 89 stations have 2, 42 have 3, 10 stations have 4 and one station have 5 common points in their schedule.
In terms of time,42.28% stations are in a suitable condition, 36.18% are relatively suitable, 17.07% are moderate, 4.07% are relatively improper and 0.41% are completely improper.
Accordingly it is recommended that a bus should pass every 5 minutesfrom stations with 5 and 4 common points in their schedule.For stations with 4 common points in their schedule, this time reaches 10 minutes.Stations with two common points in their schedule need a bus every 15 minutes and stations with 1 common point in their schedule need a bus every 20 minutes.
Saeed Farzaneh; Mohammad Ali Sharifi; Seyedeh Samira Talebi
Abstract
Extended Abstract
Introduction
In recent years, the development of the country in the space industry and the ability of building, launching and infusion of satellites in the lower orbit has put the limited number of countries with such technology. In order to complete the entire cycle of the space ...
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Extended Abstract
Introduction
In recent years, the development of the country in the space industry and the ability of building, launching and infusion of satellites in the lower orbit has put the limited number of countries with such technology. In order to complete the entire cycle of the space industry, the satellite navigation and control, which has been neglected since the beginning of the movement of space science in the country, has to be considered specially. The attitude determination in one sentence is the application of a variety of techniques for estimating the attitude of spacecrafts. In dynamic astronomy, the attitude determination is the the process of controlling the orientation of an aerospace vehicle with respect to an inertial frame of reference or another entity such as the celestial sphere, certain fields, and nearby objects, etc.
A spacecraft attitude determination and control system typically uses a variety of sensors and actuators. Because attitude is described by three or more attitude variables, the di®erence between the desired and measured states is slightly more complicated than for a thermostat, or even for the position of the satellite in space. Furthermore, the mathematical analysis of attitude
determination is complicated by the fact that attitude determination is necessarily either underdetermined or overdetermined.
Materials and methods
Attitude determination typically requires finding three independent quantities, such as any minimal parameterization of the attitude matrix. The mathematics behind attitude determination can be broadly characterized into approaches that use stochastic analysis and approaches that do not. This paper considers a computationally efficient algorithm to optimally estimate the spacecraft attitude from vector observations taken at a single time, which is known as single-point or single-frame attitude determination method. There have been a number of attitude determination algorithms that compute optimal attitude of a spacecraft from various observation sources (known as the Wahba’s problem), and each of the methods has advantages and limitations in terms of accuracy and computational speed. The most popular are: the very important ˆq-Method, the most popular TRIAD and QUEST, SVD, FOAM, and ESOQ-1, the fastest ESOQ-2, and many others approaches introducing new insights or different characteristics, for instance, the EAA, Euler-2, Euler-ˆq, and OLAE.
Results and discussion
Since star detection algorithms can provide more than two stars, the star detector field of view often consists of two or more stars that are passed through the identification algorithms will be detected, those star vectors that have measurement errors can be compensated by using more than two stars. Methods such as the QUEST algorithm usually optimize an error function to the minimum optimal. In fact, the QUEST algorithm estimates the optimum specific eigenvalue and vector for the problem described in the Q_method method without the need for complex numerical calculations. The fact that the QUEST algorithm retains all the computational advantages of a fast definitive algorithm while maintaining the desired result efficiency underscores why it is typically used.
Conclusion
Simulation results showed that the traid and quest algorithms with shuster method attitude determination algorithm can be an efficient alternative over the eight tested algorithm in terms of computational efficiency for singularity-free attitude representation.
