Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 13 4 2022 02 20 Prediction of Wheat Fusarium Head Blight Severity by Using Random Forest Prediction of Wheat Fusarium Head Blight Severity by Using Random Forest 1 14 102113 10.52547/gisj.13.4.1 FA Elham Khodabandehloo Space Research Institute, Iranian Space Research Center Mohsen Azadbakht Assistant Prof., Dept. of RS & GIS, Shahid Beheshti University Soheil Radiom Iranian Space Research Center, Tehran, Iran Davood Ashourloo Assistant Prof., Dept. of RS & GIS, Shahid Beheshti University Abas Alimohammadi Prof., Dept. of GIS Engineering, Faculty of Geodesy & Geomatic Engineering, K.N. Toosi Uniersity of Technology Journal Article 2022 02 22 Rapid increase of the world population growth and the demand for food security makes increasing yield as an essential strategy for solving the food supply problem. What is more, because of the restrictions in increasing crop cultivation areas and the decrease in some crops such as wheat in Iran, increasing the yield potential can be an effective way to respond to this requirement. Fusarium Head Blight (FHB) is one of the most important wheat diseases and for prediction FHB some methods have already been developed in the USA, Canada, Argentina and Brazil. As there is no model for predicting FHB in Iran, in this study, a method for predicting severity of FHB based on spatial analysis and using environmental parameters and meteorological data was developed for the Moghan, which is in the northwest of Iran. An Internet of Things (IoT) network was established in the study area for measurement of environmental data, including relative humidity, rainfall and air temperature for evaluating the developed model. Random Forests (RF) and extracted indices were used for predicting FHB severity and calculating the relative importance of the indices. We evaluated FHB for the period of 1389 to 1396 and the results show the effectiveness of the developed model and the capability of IoT and spatial analysis for predicting FHB. Rapid increase of the world population growth and the demand for food security makes increasing yield as an essential strategy for solving the food supply problem. What is more, because of the restrictions in increasing crop cultivation areas and the decrease in some crops such as wheat in Iran, increasing the yield potential can be an effective way to respond to this requirement. Fusarium Head Blight (FHB) is one of the most important wheat diseases and for prediction FHB some methods have already been developed in the USA, Canada, Argentina and Brazil. As there is no model for predicting FHB in Iran, in this study, a method for predicting severity of FHB based on spatial analysis and using environmental parameters and meteorological data was developed for the Moghan, which is in the northwest of Iran. An Internet of Things (IoT) network was established in the study area for measurement of environmental data, including relative humidity, rainfall and air temperature for evaluating the developed model. Random Forests (RF) and extracted indices were used for predicting FHB severity and calculating the relative importance of the indices. We evaluated FHB for the period of 1389 to 1396 and the results show the effectiveness of the developed model and the capability of IoT and spatial analysis for predicting FHB.

https://gisj.sbu.ac.ir/article_102113_d95222648063e566f3b21e59ecbae905.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 13 4 2022 02 20 Determining the Land Suitability for Dryland Wheat Cultivation Using GIS Based VIKOR Decision Model: A Case Study of Fars Province Determining the Land Suitability for Dryland Wheat Cultivation Using GIS Based VIKOR Decision Model: A Case Study of Fars Province 15 34 101571 10.52547/gisj.13.4.15 FA Mehrdad Gobal M.Sc. Student of Engineering, School of Industrial Engineering (SIE), Iran University of Science & Technology MirSaman Pishvaee Associate Prof., School of Industrial Engineering (SIE), Iran University of Science & Technology Barat Mojaradi Assistant Prof., School of Civil Engineering, Iran University of Science & Technology Journal Article 2019 05 28 From the beginning of the Earth until now, humans have affected their environment more than any other creature. With increasing population, water and soil constraints, and climate change, food supply has faced serious challenges. Among agricultural products, wheat is one of the most widely used strategic crops in Iran and many countries in the world, which can be grown in hot and dry climates with good yields. In Iran, about 10% of the demand for agricultural products is supplied from Fars province. Fars province also has the second rank of wheat production among the provinces of Iran. This study intends to evaluate the suitability of lands in this province for dryland wheat cultivation. In the first step, climatic, soil and topographic data in the area of Fars province are reviewed and analyzed. In the next step, the information layers are entered into the GIS software and the suitability map of wheat cultivation lands is determined using the VIKOR multi-criteria decision analysis method. The results of this study have shown that about 32% of the Fars province lands is in the first and second rank of land suitability for wheat cultivation. Also, the distribution of suitable areas for wheat cultivation is higher in the western and northwestern regions of Fars province. From the beginning of the Earth until now, humans have affected their environment more than any other creature. With increasing population, water and soil constraints, and climate change, food supply has faced serious challenges. Among agricultural products, wheat is one of the most widely used strategic crops in Iran and many countries in the world, which can be grown in hot and dry climates with good yields. In Iran, about 10% of the demand for agricultural products is supplied from Fars province. Fars province also has the second rank of wheat production among the provinces of Iran. This study intends to evaluate the suitability of lands in this province for dryland wheat cultivation. In the first step, climatic, soil and topographic data in the area of Fars province are reviewed and analyzed. In the next step, the information layers are entered into the GIS software and the suitability map of wheat cultivation lands is determined using the VIKOR multi-criteria decision analysis method. The results of this study have shown that about 32% of the Fars province lands is in the first and second rank of land suitability for wheat cultivation. Also, the distribution of suitable areas for wheat cultivation is higher in the western and northwestern regions of Fars province.

https://gisj.sbu.ac.ir/article_101571_748162f98ad42768ff7a07d4e4559af4.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 13 4 2022 02 20 Anlysis of Spatiotemporal Behavior of Mashhad Metropolis’s Urban Heat Island Anlysis of Spatiotemporal Behavior of Mashhad Metropolis’s Urban Heat Island 35 50 101572 10.52547/gisj.13.4.35 FA Moslem Torky Ph.D. Student of Climatology, University of Isfahan, Isfahan Seyed Abolfazl Masoodian Prof. of Climatology, University of Isfahan, Isfahan Journal Article 2019 10 21 The expansion of urbanization and the increase of population in metropolises and the growth of industrial activities of cities, It has caused changes in urban area climate. One result of these changes is the city's heat islands. The city of Mashhad has also grown rapidly in recent years. This study investigates the heat/cold island of Mashhad metropolis based on the background climate in order to identify its spatiotemporal behavior. For this purpose The MODIS Terra and Aqua land surface temperature (LST) data were obtained and the heat island was examined accordingly. A new was used to measure the heat island. In this method, Modis land use data was used to determine the urban and suburban boundaries as well as to determine the land use type of the study area. The background climate was determined based on Far-side temperature and the representative non urban area was selected based on the most frequent temperature and the heat island was calculated. Survey of heat/cold island in the daily period showed that during the day the average temperature of city is lower than non urbun temperature and at night is higher. Also the seasonal survey of heat island/could island of Mashhad metropolitan shows that daily cold island is the highest during the warm seasons and lowest in the cold seasons and the seasonal variability of nightly heat island is less than the daily cold island. The core of the daily cold island is located between the Haram and the Shahid Fehmidah Square towards the western area of Mashhad. The day time cold island matches the areas of the city with high vegetation coverage. The core of the nightly heat island is consistent with the old texture and dense area around the Haram towards the northwest of the city. The heat/cold island intensity is also directly related to the wind speed. The role of land use in intensifying or reducing the intensity of the heat island of Mashhad is well seen. In the development of the city, more attention can be paid to the use of urban land use in order to moderate the temperature of the city. The expansion of urbanization and the increase of population in metropolises and the growth of industrial activities of cities, It has caused changes in urban area climate. One result of these changes is the city's heat islands. The city of Mashhad has also grown rapidly in recent years. This study investigates the heat/cold island of Mashhad metropolis based on the background climate in order to identify its spatiotemporal behavior. For this purpose The MODIS Terra and Aqua land surface temperature (LST) data were obtained and the heat island was examined accordingly. A new was used to measure the heat island. In this method, Modis land use data was used to determine the urban and suburban boundaries as well as to determine the land use type of the study area. The background climate was determined based on Far-side temperature and the representative non urban area was selected based on the most frequent temperature and the heat island was calculated. Survey of heat/cold island in the daily period showed that during the day the average temperature of city is lower than non urbun temperature and at night is higher. Also the seasonal survey of heat island/could island of Mashhad metropolitan shows that daily cold island is the highest during the warm seasons and lowest in the cold seasons and the seasonal variability of nightly heat island is less than the daily cold island. The core of the daily cold island is located between the Haram and the Shahid Fehmidah Square towards the western area of Mashhad. The day time cold island matches the areas of the city with high vegetation coverage. The core of the nightly heat island is consistent with the old texture and dense area around the Haram towards the northwest of the city. The heat/cold island intensity is also directly related to the wind speed. The role of land use in intensifying or reducing the intensity of the heat island of Mashhad is well seen. In the development of the city, more attention can be paid to the use of urban land use in order to moderate the temperature of the city.

https://gisj.sbu.ac.ir/article_101572_e8d959f5a5ede9c378984b470f371a2d.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 13 4 2022 02 20 Evaluation of the Efficiency of Spectral Data and Indices Derived from OLI and TIRS Sensors in Estimating Soil Salinity in Arid Regions of Southern Ilam Province Evaluation of the Efficiency of Spectral Data and Indices Derived from OLI and TIRS Sensors in Estimating Soil Salinity in Arid Regions of Southern Ilam Province 51 66 101573 10.52547/gisj.13.4.51 FA Hamidreza Matinfar Associate Prof. of Soil Science, College of Agriculture, Lorestan University, Lorestan Foziyeh Kohani Ph.D. Student of Soil Science, College of Agriculture, Lorestan University, Lorestan Ali Akbar Asilian Mahabadi Graduate Master of Soil Science, College of Agriculture, Ilam University, Ilam Journal Article 2020 04 21 Soil salinity is one of the most important environmental problems, and the identification and zoning of saline soils is difficult due to the need for sampling and laboratory analysis, as well as having temporal and spatial variability. In recent years, the use of satellite imagery has always been of interest to experts due to its ease of use and ability to detect phenomena. Remote sensing information greatly aids the study of soil salinity and can be helpful in identifying salinity values. In this study, 220 soil samples were collected from Meymeh area of Dehloran, south of Ilam province, according to the type of study and physiographic types and soil units. Then, pH and EC values were measured using standard methods. Soil salinity values were evaluated using correlations between EC electrical conductivity values obtained from ground data and variables obtained from Landsat 8 satellite images including bands, salinity indices, vegetation indices and soil indices. Finally, the soil surface salinity estimation model was obtained using stepwise regression method. This method involves the automatic selection of independent variables, and with the availability of statistical software packages, it is possible to do so even in models with hundreds of variables. In previous studies, indicators and bands have been used separately and in a limited way, but in this study, an attempt has been made to use a combination of different indicators more widely, and finally to achieve the best relationship by eliminating the indicators that have the least impact on soil salinity estimation. Using significant level analysis and correlation between the output of models and ground data, the best model with a value of (R2 = 0.882) was selected and a soil salinity map was prepared based on it. In the study area, the highest area belonged to non-saline class which comprises 75% of the total study area and about 1% of the soils belong to the saline class. Soil salinity is one of the most important environmental problems, and the identification and zoning of saline soils is difficult due to the need for sampling and laboratory analysis, as well as having temporal and spatial variability. In recent years, the use of satellite imagery has always been of interest to experts due to its ease of use and ability to detect phenomena. Remote sensing information greatly aids the study of soil salinity and can be helpful in identifying salinity values. In this study, 220 soil samples were collected from Meymeh area of Dehloran, south of Ilam province, according to the type of study and physiographic types and soil units. Then, pH and EC values were measured using standard methods. Soil salinity values were evaluated using correlations between EC electrical conductivity values obtained from ground data and variables obtained from Landsat 8 satellite images including bands, salinity indices, vegetation indices and soil indices. Finally, the soil surface salinity estimation model was obtained using stepwise regression method. This method involves the automatic selection of independent variables, and with the availability of statistical software packages, it is possible to do so even in models with hundreds of variables. In previous studies, indicators and bands have been used separately and in a limited way, but in this study, an attempt has been made to use a combination of different indicators more widely, and finally to achieve the best relationship by eliminating the indicators that have the least impact on soil salinity estimation. Using significant level analysis and correlation between the output of models and ground data, the best model with a value of (R2 = 0.882) was selected and a soil salinity map was prepared based on it. In the study area, the highest area belonged to non-saline class which comprises 75% of the total study area and about 1% of the soils belong to the saline class.

https://gisj.sbu.ac.ir/article_101573_d2a63359aabd363d1e098a99e4665c19.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 13 4 2022 02 20 Commercialization o f Remote Sensing in Agriculture Commercialization o f Remote Sensing in Agriculture 67 88 101654 10.52547/gisj.13.4.67 FA Manouchehr Manteghi Ph.D. of Systems Management, Manager of Development of Space Technologies and Advanced Transportation Yazdan Rahmatabadi M.Sc of Business Management Journal Article 2019 10 02 Remote sensing is the science of obtaining information from the surface of the earth without explicit contact with the components studied. Commercialization is a set of activities that converts an innovation into a product or service that brings economic benefits. Given the widespread use for measurement and the high importance of its application in agriculture, commercialization of this technology in agriculture has been a top priority and investigated in this study. The target population of this research is active and passive companies in this field to use their experience to provide suitable field for cultivation of remote sensing technology through in-depth interviewing and snowball sampling. The catch is used. In this research, using product and technology life cycle diagrams, examining the challenges of technology and infrastructure commercialization, commercialization elements, types of software used in the world agricultural industry, remote sensing investment charts and analysis The viability of remote sensing in agriculture as a business has been scrutinized. As a result, the best way to commercialize the product is to reduce constraints for active companies, build the necessary infrastructure, especially timely data, and be independent in deploying this technology to allow users to use a variety of business methods. Provide. Remote sensing is the science of obtaining information from the surface of the earth without explicit contact with the components studied. Commercialization is a set of activities that converts an innovation into a product or service that brings economic benefits. Given the widespread use for measurement and the high importance of its application in agriculture, commercialization of this technology in agriculture has been a top priority and investigated in this study. The target population of this research is active and passive companies in this field to use their experience to provide suitable field for cultivation of remote sensing technology through in-depth interviewing and snowball sampling. The catch is used. In this research, using product and technology life cycle diagrams, examining the challenges of technology and infrastructure commercialization, commercialization elements, types of software used in the world agricultural industry, remote sensing investment charts and analysis The viability of remote sensing in agriculture as a business has been scrutinized. As a result, the best way to commercialize the product is to reduce constraints for active companies, build the necessary infrastructure, especially timely data, and be independent in deploying this technology to allow users to use a variety of business methods. Provide.

https://gisj.sbu.ac.ir/article_101654_1277320d21cf4bf7ec32afea3498e023.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 13 4 2022 02 20 Finding Optimal Contextual Parameters for Real-Time Vessel Position Prediction Using Deep Learning Finding Optimal Contextual Parameters for Real-Time Vessel Position Prediction Using Deep Learning 89 100 101688 10.52547/gisj.13.4.89 FA Ali Asghar Alesheikh Full Prof., Dep. of Geomatics Engineering, K.N. Toosi University of Technology, Tehran Saeed Mehri K. N. Toosi University of Technology Journal Article 2019 09 20 About 80% of world transportation happens at sea. Therefore the safety of vessels, in particular during vessels’ movement, is crucially important. As different contextual parameters affect vessels’ movement, selecting optimal contextual parameters is one of the main changes in vessels’ Context- Aware movement analysis. Toward this end, a Long Short-Term Memory (LSTM) network is used for wrapper feature selection to identify optimal contextual parameters for vessels’ movement prediction. To do this, the Automatic Identification System (AIS) dataset from the eastern coast of the United States of America collected from December 2017 is used. All possible combinations of three contextual parameters, including speed, course and vessels’ presence probability in different positions at sea, were evaluated using the wrapper method in the LSTM network. In all evaluations, 70% of data was used for training and the remaining for cross-validation. The results selected speed and presence probability as optimal contextual parameters for vessel movement prediction. The model trained with optimal contextual parameters is 26.98% more accurate than a model trained with all available contextual parameters and 16.14% better than a model without contextual parameters. Therefore, selecting optimal parameters from available contextual parameters can help improve the accuracy of vessels’ predictions. Keywords: Context-Aware, Long Short-Term Memory, Automatic Identification System, wrapper, Movement prediction, Context. About 80% of world transportation happens at sea. Therefore the safety of vessels, in particular during vessels’ movement, is crucially important. As different contextual parameters affect vessels’ movement, selecting optimal contextual parameters is one of the main changes in vessels’ Context- Aware movement analysis. Toward this end, a Long Short-Term Memory (LSTM) network is used for wrapper feature selection to identify optimal contextual parameters for vessels’ movement prediction. To do this, the Automatic Identification System (AIS) dataset from the eastern coast of the United States of America collected from December 2017 is used. All possible combinations of three contextual parameters, including speed, course and vessels’ presence probability in different positions at sea, were evaluated using the wrapper method in the LSTM network. In all evaluations, 70% of data was used for training and the remaining for cross-validation. The results selected speed and presence probability as optimal contextual parameters for vessel movement prediction. The model trained with optimal contextual parameters is 26.98% more accurate than a model trained with all available contextual parameters and 16.14% better than a model without contextual parameters. Therefore, selecting optimal parameters from available contextual parameters can help improve the accuracy of vessels’ predictions. Keywords: Context-Aware, Long Short-Term Memory, Automatic Identification System, wrapper, Movement prediction, Context.

https://gisj.sbu.ac.ir/article_101688_99a1b8cea5f967844a24000153bf4347.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 13 4 2022 02 20 Application of Spatial Autocorrelation Techniques in Spatial-Temporal Analysis of Domestic Water Consumption in the City of Qom at the Household Level Application of Spatial Autocorrelation Techniques in Spatial-Temporal Analysis of Domestic Water Consumption in the City of Qom at the Household Level 101 118 101911 10.52547/gisj.13.4.101 FA Zahra Barkhordari M.Sc. Student of Remote Sensing & GIS Dep., Tarbiat Modarres University Jalal Karami Associate Prof. of Remote Sensing & GIS Dep.,Tarbiat Modarres University Hojatolah Mahboobi Ph.D. Student of Remote Sensing & GIS Research Center, Shahid Beheshti University Journal Article 2021 11 21 Due to the scarcity and crisis of water resources, the issue of optimal use and management of it is of particular importance. Improper pattern of water consumption in different areas of a city can be one of the cases that cause water crisis in a city. Therefore, there is necessary to apply methods in order to identify consumption patterns in different areas of the city. The purpose of this study is to investigate the spatial pattern of water consumption in Qom city using spatial autocorrelation techniques. For this reason, the consumption of 117 neighborhoods of Qom city during 2017 was collected and the average household water consumption for each neighborhood was calculated. Moran index was used to identify the type of consumption pattern and local Moran index and hot spot technique were used for spatial distribution of the consumption pattern. The results of spatial autocorrelation showed that the largest cluster pattern of water consumption in Qom city occurred in summer with the value of Moran index (I = 0.24). Also, the highest significance of the index (z = 7.02) was observed in this season. In both local and hot spot analysis, it was observed that high consumption has a high cluster pattern compared to low consumption. Spatially, high consumption clusters were observed in the central and western neighborhoods of the city and low consumption clusters were observed in the southern, eastern and northern neighborhoods of the city. Temporally, high consumption clusters were observed in central and western neighborhoods in summer and winter, respectively and low consumption clusters were observed in cold seasons. Due to the scarcity and crisis of water resources, the issue of optimal use and management of it is of particular importance. Improper pattern of water consumption in different areas of a city can be one of the cases that cause water crisis in a city. Therefore, there is necessary to apply methods in order to identify consumption patterns in different areas of the city. The purpose of this study is to investigate the spatial pattern of water consumption in Qom city using spatial autocorrelation techniques. For this reason, the consumption of 117 neighborhoods of Qom city during 2017 was collected and the average household water consumption for each neighborhood was calculated. Moran index was used to identify the type of consumption pattern and local Moran index and hot spot technique were used for spatial distribution of the consumption pattern. The results of spatial autocorrelation showed that the largest cluster pattern of water consumption in Qom city occurred in summer with the value of Moran index (I = 0.24). Also, the highest significance of the index (z = 7.02) was observed in this season. In both local and hot spot analysis, it was observed that high consumption has a high cluster pattern compared to low consumption. Spatially, high consumption clusters were observed in the central and western neighborhoods of the city and low consumption clusters were observed in the southern, eastern and northern neighborhoods of the city. Temporally, high consumption clusters were observed in central and western neighborhoods in summer and winter, respectively and low consumption clusters were observed in cold seasons.

https://gisj.sbu.ac.ir/article_101911_e057c7d6a5faba40f67142fc181e3606.pdf