Iranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-59669120171222Measurement of Surface Changes and Velocity fields of Alam-chal Glacier Using Satellite Imagery and AerialphotosMeasurement of Surface Changes and Velocity fields of Alam-chal Glacier Using Satellite Imagery and Aerialphotos11696217FA, YRezaeiAssistant Prof., Dep. Of Civil Engineering, Faculty of Engineering, Bu-Ali Sina University, M.J.Valadan ZoujProf. of Photogrammetry and Remote Sensing Department, Geodesy and Geomatics Faculty, K.N. Toosi University of Technology0000-0003-4325-8741, M.RSahebiAssociate prof., Dep. of Photogrammetry and Remote Sensing, Geodesy and Geomatics Faculty, K.N. Toosi University of Technology0000-0001-7742-3974Journal Article20171025Mountain Glaciers are pertinent indicators of climate change and their surface velocity changes, are an essential climate variable. In order to retrieve the climatic signature from surface velocity, large scale study of glacier changes is required. Satellite remote sensing is an effective way to derive mountain glacier surface velocities. In this research, we have conducted a comprehensive assessment of Alam-Chal glacier surface changes (include displacement and velocity), all based on remotely-sensed data. All datasets include aerial photos and satellite images were ortho rectified, normalized and co-registered. By using an aerial photograph collected in 1955 as a baseline and comparing it against a 2003 image collected by the SPOT satellite, the glacier retreat, in direct response to changes in local climate conditions were extracted. Furthermore, we have assessed short-term changes over two-time scales (1988-2003, 2003-2005),using an aerial photo acquired in 1988, a 2003 SPOT image, and a high-resolution Quick Bird image collected over the study area in 2005. We have derived accurate glacier surface velocity vectors (RMSE~2m), based on an FFT-based image cross-correlation technique. Our results point to the capability of the proposed method in accurately retrieving glacier surface changes at a high level of spatial detail, which is important for studies of regional climate change.Mountain Glaciers are pertinent indicators of climate change and their surface velocity changes, are an essential climate variable. In order to retrieve the climatic signature from surface velocity, large scale study of glacier changes is required. Satellite remote sensing is an effective way to derive mountain glacier surface velocities. In this research, we have conducted a comprehensive assessment of Alam-Chal glacier surface changes (include displacement and velocity), all based on remotely-sensed data. All datasets include aerial photos and satellite images were ortho rectified, normalized and co-registered. By using an aerial photograph collected in 1955 as a baseline and comparing it against a 2003 image collected by the SPOT satellite, the glacier retreat, in direct response to changes in local climate conditions were extracted. Furthermore, we have assessed short-term changes over two-time scales (1988-2003, 2003-2005),using an aerial photo acquired in 1988, a 2003 SPOT image, and a high-resolution Quick Bird image collected over the study area in 2005. We have derived accurate glacier surface velocity vectors (RMSE~2m), based on an FFT-based image cross-correlation technique. Our results point to the capability of the proposed method in accurately retrieving glacier surface changes at a high level of spatial detail, which is important for studies of regional climate change.https://gisj.sbu.ac.ir/article_96217_26309f6d73f685bf51d59b79647aff99.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-59669120171222Provide New Discomfort Indices at Fire Time Using Results of Agent-Based Geosimulation (Case Study: Hafte-Tir Subway Station)Provide New Discomfort Indices at Fire Time Using Results of Agent-Based Geosimulation (Case Study: Hafte-Tir Subway Station)173696223FA, A.AMatkanProf. in Remote Sensing & GIS Research Center, Shahid Beheshti University, A.AlimohammadiAssistant prof., Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology0000-0002-9641-1204, BMirbagheriP.Hd. Student of GIS, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, KAkbariM.Sc. of Remote Sensing And GIS, Remote Sensing & GIS Research Center, Shahid Beheshti University, MTanasanM.Sc. of Remote Sensing And GIS, Remote Sensing & GIS Research Center, Shahid Beheshti UniversityJournal Article20171025Commensurate with the complexity of human behavior, social systems are complicated. Population management in these systems are crucial and need to spend too much cost. Because of the interaction between humans and the environment and then the impact of these interactions on social systems in the process of population movements, there is a need to identify and study these interactions, especially in emergency situations.In this study, the results of agent based geosimulation of pedestrian movements and fire simulation at Hafte-Tir subway station were used to investigate the behavior of individuals and the environment during fire. Then, the discomfort indices, including environmental and human-environmental indicators, were calculated to examine the effect of the environment and agents on the movement process. This research has introduced two new discomfort indices i.e. environmental index AM1 and environmental-humanity index AM2 to evaluate the behavior of individuals and the environment during the fire. The innovation of these indices relates to the integration of the results of the agent based simulation and the fire simulation in the environment and after that using of visibility, in addition to the interactions of individuals with each other and their interactions with the physical components of the environment. Calculating results of indices and the results of people movement’s simulation in the station represented an inverse relationship between the level of discomfort and speed of crowd in the station. Also, the discomfort induces in the successful environmental scenario shows a reduction in the discomfort in hot spots rather than current situation scenario. The use of agent based geosimulations and the result of discomfort indices in different periods of crisis, can contribute population management strategies and emergency evacuation.Commensurate with the complexity of human behavior, social systems are complicated. Population management in these systems are crucial and need to spend too much cost. Because of the interaction between humans and the environment and then the impact of these interactions on social systems in the process of population movements, there is a need to identify and study these interactions, especially in emergency situations.In this study, the results of agent based geosimulation of pedestrian movements and fire simulation at Hafte-Tir subway station were used to investigate the behavior of individuals and the environment during fire. Then, the discomfort indices, including environmental and human-environmental indicators, were calculated to examine the effect of the environment and agents on the movement process. This research has introduced two new discomfort indices i.e. environmental index AM1 and environmental-humanity index AM2 to evaluate the behavior of individuals and the environment during the fire. The innovation of these indices relates to the integration of the results of the agent based simulation and the fire simulation in the environment and after that using of visibility, in addition to the interactions of individuals with each other and their interactions with the physical components of the environment. Calculating results of indices and the results of people movement’s simulation in the station represented an inverse relationship between the level of discomfort and speed of crowd in the station. Also, the discomfort induces in the successful environmental scenario shows a reduction in the discomfort in hot spots rather than current situation scenario. The use of agent based geosimulations and the result of discomfort indices in different periods of crisis, can contribute population management strategies and emergency evacuation.https://gisj.sbu.ac.ir/article_96223_5a9b83a7e0cf9a19e657598045f24de4.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-59669120171222Determine Potential Fishing Zones in the Persian Gulf Using Remote Sensing and GISDetermine Potential Fishing Zones in the Persian Gulf Using Remote Sensing and GIS374896231FA, Z.FazilatpourM.Sc. of RS & GIS Dep. of Remote Sensing and GIS, Chamran University in Ahvaz, KRangzanAssociate Prof., Dep. of Remote Sensing and GIS, Chamran University in Ahvaz0000-0002-4576-6275G.REskandari,Assistant Prof. of Southern Research Center Aquaculture, ASaberiLecturer, Dep. of Remote Sensing and GIS, Chamran University in AhvazJournal Article20171025Marine environment, including the ocean and coastal areas provide enormous opportunities for the growth in fisheries and the exploitation of natural resources. Fishing is an important source for the food production industry and in Iran. Due to the high demand to find fish resources, data from satellites play an important role in fisheries applications. Remote sensing of ocean color can be used in many applications, such as commercial fishing, marine transport, ocean mining, oil and gas exploration, hydrography, etc. Since the satellite images can provide a wide area coverage and good temporal resolution, they can be of great help in detecting potential fishing zones. The MODIS sensor that has the capability of high spectral resolution and multiple thermal bands make it a superior sensor compared to other types. In this research, the correlation coefficient value between MODIS satellite images and in-site water samples were at 0.84, which indicates a high accuracy of SST MODIS Images. This research aims to determine potential fishing zones in the Persian Gulf by using layers of sea surface temperature, sea surface height, chlorophyll - a and sea surface temperature gradient of water surface. Fuzzy methods provide a good decision making algorithm to determine the location of important areas, including uncertain and unclear ones. In this decision was given weighting layer through FAHP procedure was performed and the highest weight in the sea surface temperature as a parameter. After overlaying layers, the results indicate that 82% of the selected areas coincide with that of commercial fishing zones. The application of targeted fishing can be used to increase fishing efficiency at lower time compared to the current longer time that is possible by integrating remote sensing and Global geographic information system (GIS). Marine environment, including the ocean and coastal areas provide enormous opportunities for the growth in fisheries and the exploitation of natural resources. Fishing is an important source for the food production industry and in Iran. Due to the high demand to find fish resources, data from satellites play an important role in fisheries applications. Remote sensing of ocean color can be used in many applications, such as commercial fishing, marine transport, ocean mining, oil and gas exploration, hydrography, etc. Since the satellite images can provide a wide area coverage and good temporal resolution, they can be of great help in detecting potential fishing zones. The MODIS sensor that has the capability of high spectral resolution and multiple thermal bands make it a superior sensor compared to other types. In this research, the correlation coefficient value between MODIS satellite images and in-site water samples were at 0.84, which indicates a high accuracy of SST MODIS Images. This research aims to determine potential fishing zones in the Persian Gulf by using layers of sea surface temperature, sea surface height, chlorophyll - a and sea surface temperature gradient of water surface. Fuzzy methods provide a good decision making algorithm to determine the location of important areas, including uncertain and unclear ones. In this decision was given weighting layer through FAHP procedure was performed and the highest weight in the sea surface temperature as a parameter. After overlaying layers, the results indicate that 82% of the selected areas coincide with that of commercial fishing zones. The application of targeted fishing can be used to increase fishing efficiency at lower time compared to the current longer time that is possible by integrating remote sensing and Global geographic information system (GIS). https://gisj.sbu.ac.ir/article_96231_fd7dc6e1fc6cb14600f98d484650fc74.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-59669120171222Assessment of SVM and MLC Algorithms on Landuse/ Landcover Mapping of Riparian Forest, Using OLI Sensor (Case Study: Riparian Forest of Maroon, Behbahan)Assessment of SVM and MLC Algorithms on Landuse/ Landcover Mapping of Riparian Forest, Using OLI Sensor (Case Study: Riparian Forest of Maroon, Behbahan)496296239FA, A.ATorahiAssociate prof. of Dep. of Geoinformatics, Faculty of Geographical Sciences, University of Kharazmi, TehranMFiroziNejad,M.Sc. of Silviculture and Forest Ecology, Shahid Chamran University, Ahwaz, AAbdolkhaniM.Sc. of Remote Sensing and GIS, Shahid Chamran University, AhwazJournal Article20171025Obtaining more accurate and updated information about the forest area is one of the basic factors in sustainable management of this area. Acquiring this information is more beneficial in terms of time and cost through classification of remote sensing data. In this paper, Landsat8 (OLI) data from Maroons Behbahan riparian forest that is located in Khoozestan province of Iran were used for mapping and better management of riparian forest. Preprocessing operation including radiometric and atmospheric correction was applied to the data. Supervised classification algorithms including maximum likelihood (MLC) and support vector machine (SVM) with seven and three classes were used for classification. In order to evaluate the capability of support vector machine, three categories of training data with 241, 141 and 41 numbers with four kernels of SVM (linear, radial basic function, sigmoid and polynomial) were used. The results indicate that mapping of Maroons riparian forest using Landsat images is possible and the best result was acquired using SVM –polynomial method by three classes with overall accuracy and kappa coefficient of (99/24) % and (0/97) respectively. Also, the findings showed that with reduction of number of classes from seven to three, the accuracy of classification is increased. By reducing the number of samples to moderate, significant difference in accuracy of classification was not observed, but by more reduction of samples, the accuracy of results is reduced. Obtaining more accurate and updated information about the forest area is one of the basic factors in sustainable management of this area. Acquiring this information is more beneficial in terms of time and cost through classification of remote sensing data. In this paper, Landsat8 (OLI) data from Maroons Behbahan riparian forest that is located in Khoozestan province of Iran were used for mapping and better management of riparian forest. Preprocessing operation including radiometric and atmospheric correction was applied to the data. Supervised classification algorithms including maximum likelihood (MLC) and support vector machine (SVM) with seven and three classes were used for classification. In order to evaluate the capability of support vector machine, three categories of training data with 241, 141 and 41 numbers with four kernels of SVM (linear, radial basic function, sigmoid and polynomial) were used. The results indicate that mapping of Maroons riparian forest using Landsat images is possible and the best result was acquired using SVM –polynomial method by three classes with overall accuracy and kappa coefficient of (99/24) % and (0/97) respectively. Also, the findings showed that with reduction of number of classes from seven to three, the accuracy of classification is increased. By reducing the number of samples to moderate, significant difference in accuracy of classification was not observed, but by more reduction of samples, the accuracy of results is reduced. https://gisj.sbu.ac.ir/article_96239_7254ea9a18b7d4d03d7303aa54cb1a28.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-59669120171222Comparing the Results of Actual Evapotranspiration from SEBAL and METRIC Models Using MODIS and ETM+ Sensor ImagesComparing the Results of Actual Evapotranspiration from SEBAL and METRIC Models Using MODIS and ETM+ Sensor Images637496247FARNazari,M.Sc. Graduate, Irrigation and Drainage Engineering, Imam Khomeini International University, QazvinAKaviani, AAssistant prof., Dep. of Water Engineering, Imam Khomeini International UniversityJournal Article20171025Increasing crop production depends on the supply crop water demands, thus accurate estimation of crop water demands helps not only to crop production, but also is effective in the management of water resources. SEBAL and METRIC algorithms are the most widely used methods for estimating evapotranspiration as a residual of the energy balance with using remote sensing data. Based on this, the purpose of the present research was to investigate the results of actual evapotranspiration of crop from SEBAL and METRIC models in the Qazvin plain. To evaluate the results of actual evapotranspiration, two sensors with different temporal and spatial resolution (images of MODIS sensor Terra satellite and ETM+ sensor Landsat 7 satellite) were used. In this regard, Qazvin weather station data as well as data Lysimeter were used in order to verify the results of METRIC and SEBAL algorithm. The results of METRIC and SEBAL models with a total of 10 images obtained from MODIS sensor Terra satellite and ETM+ sensor Landsat 7 satellite were evaluated with data Lysimeter for grass reference crop in 1380. MODIS sensor with r=0.88, RMSE=1.91 and SE=0.85 mm/day with r=1.00, RMSE=0.91 and SE=0.09 in METRIC model compared with the SEBAL model estimates are more accurate than the lysimeter operation and in this research recommended as a top model for estimating actual evapotranspiration in the Qazvin plain.Increasing crop production depends on the supply crop water demands, thus accurate estimation of crop water demands helps not only to crop production, but also is effective in the management of water resources. SEBAL and METRIC algorithms are the most widely used methods for estimating evapotranspiration as a residual of the energy balance with using remote sensing data. Based on this, the purpose of the present research was to investigate the results of actual evapotranspiration of crop from SEBAL and METRIC models in the Qazvin plain. To evaluate the results of actual evapotranspiration, two sensors with different temporal and spatial resolution (images of MODIS sensor Terra satellite and ETM+ sensor Landsat 7 satellite) were used. In this regard, Qazvin weather station data as well as data Lysimeter were used in order to verify the results of METRIC and SEBAL algorithm. The results of METRIC and SEBAL models with a total of 10 images obtained from MODIS sensor Terra satellite and ETM+ sensor Landsat 7 satellite were evaluated with data Lysimeter for grass reference crop in 1380. MODIS sensor with r=0.88, RMSE=1.91 and SE=0.85 mm/day with r=1.00, RMSE=0.91 and SE=0.09 in METRIC model compared with the SEBAL model estimates are more accurate than the lysimeter operation and in this research recommended as a top model for estimating actual evapotranspiration in the Qazvin plain.https://gisj.sbu.ac.ir/article_96247_e28889646616aba1a1b60a20bde0597a.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-59669120171222Standardization and Upgrading the Collection and Updating Process of Urban Property Survey Data Using a Context- Aware Mobile GIS and Based on ISO Spatial StandardsStandardization and Upgrading the Collection and Updating Process of Urban Property Survey Data Using a Context- Aware Mobile GIS and Based on ISO Spatial Standards659296261FAZFazli,Instructor, Dep. of Surveying Engineering, Molana Institute of Higher Education, Abyek, QazvinM.RDelavar,Full Prof., GIS Division, Dep. of Surveying and Geomatics Engineering, College of Eng., University of Tehran, TehranM.RMalek,Associate Prof., Dep. of GIS, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, TehranJournal Article20171025Urban property survey is one the of municipality activities for development and updating of the spatial database of the urban properties. With the emergence of mobile geospatial information system, new methods were developed to collect and update the location information. In mobile environment the calculations depend on to tasks undertaken and user dynamic environment. Reduction of user direct interaction with the system is one of the most important factors for system automation and leads to reduction of the human errors in collection and updating information. This can be done by understanding user situation using context information so that suitable maps and attribute information can be provided to the user at right place. Urban property survey information is a sample of spatial information that can benefit from such systems.The objective of this paper is to identify the role of context-aware mobile GIS in urban property survey, standardization and optimization of urban property survey process and identification of the implementation methods to present suitable spatial information relevant to the user’s context and also intelligent user interfaces for enhancement of the system capabilities. In addition to the client-server architecture, a stand-alone architecture is used in the system design and implementation for this research to prevent the survey process failure in case of disconnecting from server. Finally, Tehran urban blocks are used to test the system and the obtained results compared to the results of two former municipality urban property surveys. The results indicate their required time for data collection in the proposed method compared to the two Tehran urban property survey periods has been reduced to 50% and the time between spatial and attribute data collection and their upload to municipality database reduced nearly 100%. By using this system, direct interaction of the surveyor and the system application is reduced and it has helped to upgrade automation in data collection and updating which results in enormous improvements in urban property survey process. By using the proposed method, the block and property data based on paper maps in traditional urban property survey, have been directly corrected and updated in field. Urban property survey is one the of municipality activities for development and updating of the spatial database of the urban properties. With the emergence of mobile geospatial information system, new methods were developed to collect and update the location information. In mobile environment the calculations depend on to tasks undertaken and user dynamic environment. Reduction of user direct interaction with the system is one of the most important factors for system automation and leads to reduction of the human errors in collection and updating information. This can be done by understanding user situation using context information so that suitable maps and attribute information can be provided to the user at right place. Urban property survey information is a sample of spatial information that can benefit from such systems.The objective of this paper is to identify the role of context-aware mobile GIS in urban property survey, standardization and optimization of urban property survey process and identification of the implementation methods to present suitable spatial information relevant to the user’s context and also intelligent user interfaces for enhancement of the system capabilities. In addition to the client-server architecture, a stand-alone architecture is used in the system design and implementation for this research to prevent the survey process failure in case of disconnecting from server. Finally, Tehran urban blocks are used to test the system and the obtained results compared to the results of two former municipality urban property surveys. The results indicate their required time for data collection in the proposed method compared to the two Tehran urban property survey periods has been reduced to 50% and the time between spatial and attribute data collection and their upload to municipality database reduced nearly 100%. By using this system, direct interaction of the surveyor and the system application is reduced and it has helped to upgrade automation in data collection and updating which results in enormous improvements in urban property survey process. By using the proposed method, the block and property data based on paper maps in traditional urban property survey, have been directly corrected and updated in field. https://gisj.sbu.ac.ir/article_96261_34be5ea416ccc9646c9dd2e6dcd3981f.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-59669120171222Investigation of Atmospheric Correction Methods in Estimation of Forest Canopy Density of Guilan Province Using Vegetation Indices of Landsat 8 DataInvestigation of Atmospheric Correction Methods in Estimation of Forest Canopy Density of Guilan Province Using Vegetation Indices of Landsat 8 Data9311096266FAS.A.RNourediniPh.D. Student, Faculty of Natural Resources, University of GuilanA.ABonyadProf., Faculty of Natural Resources, University of GuilanJournal Article20171025Reflectance of different of land surface phenomena on remote sensing data was influenced by different conditions including atmospheric conditions. Variety methods of atmospheric correction have been developed for remove and reduction of its effects. In this study three atmospheric correction methods: Dark Object Subtraction (DOS), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubus (FLLASH) and Second Vector Simulation of Satellite Signal in the Solar Spectrum (6SV) have been applied on OLI sensor of Landsat8 inthe forest regions of Guilan province. Numbers of 10 vegetation indices were extracted from each image. Forest area was extracted on various indices detected by global land cover layer. Forest areas segmented on Landsat8 image by object-based method. In the total 91 segments, randomly were selected. Forest canopy density of any segment plot estimated on Google images using 20×20 m network dotted. Person test was used for correlation between indices and training samples and two linear and nonlinear regression models were used for forest canopy density estimation. The results confirmed that 6SV method dominates than other methods in the forest regions of Guilan province. The lowest root means square error (RMSE) with 17.72 was shown in the green atmospherically resistant vegetation index (GARI) extracted from DOS. The results indicated that the lowest RMSE was in atmospherically resistant vegetation index (ARVI) using 6SV, FLAASH and OLI original image with 18.38, 15.87 and 21.78 respectively. The results of this study were shown that use of atmospheric correction methods in preparing vegetation indices is cause of increasing information accuracy from satellite images. Reduction of atmosphere effects in preprocessing before modeling is necessary and suggestible. Reflectance of different of land surface phenomena on remote sensing data was influenced by different conditions including atmospheric conditions. Variety methods of atmospheric correction have been developed for remove and reduction of its effects. In this study three atmospheric correction methods: Dark Object Subtraction (DOS), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubus (FLLASH) and Second Vector Simulation of Satellite Signal in the Solar Spectrum (6SV) have been applied on OLI sensor of Landsat8 inthe forest regions of Guilan province. Numbers of 10 vegetation indices were extracted from each image. Forest area was extracted on various indices detected by global land cover layer. Forest areas segmented on Landsat8 image by object-based method. In the total 91 segments, randomly were selected. Forest canopy density of any segment plot estimated on Google images using 20×20 m network dotted. Person test was used for correlation between indices and training samples and two linear and nonlinear regression models were used for forest canopy density estimation. The results confirmed that 6SV method dominates than other methods in the forest regions of Guilan province. The lowest root means square error (RMSE) with 17.72 was shown in the green atmospherically resistant vegetation index (GARI) extracted from DOS. The results indicated that the lowest RMSE was in atmospherically resistant vegetation index (ARVI) using 6SV, FLAASH and OLI original image with 18.38, 15.87 and 21.78 respectively. The results of this study were shown that use of atmospheric correction methods in preparing vegetation indices is cause of increasing information accuracy from satellite images. Reduction of atmosphere effects in preprocessing before modeling is necessary and suggestible. https://gisj.sbu.ac.ir/article_96266_772800c641bbbf4f700f2bb365294695.pdf