Extraction, processing, production and display of geographic data
Maryam Kouhani; Abbas Kiani; Yasser Ebrahimian Ghajari
Abstract
Extended AbstractIntroductionVegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of ...
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Extended AbstractIntroductionVegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of Iran is also highly considered important. Research suggests that the destruction and change of vegetation cover and forests are among the most important factors influencing natural hazards such as floods, erosion, and earthquakes. In addition to processing and presenting well-known spatial data, remote sensing can also be used to improve human understanding of annual changes in vegetation cover, from a local to a global scale. In this regard, the anomaly evaluation criterion with high differentiation can separate and display anomalous areas in order to recognize the change process and reveal the areas with anomalies over time. Thus, medium-resolution images, vegetation indices, and anomaly criteria can be used to evaluate long-term vegetation changes. Therefore, a positive step in reducing the environmental effects of a region can be made by locating the urban areas that have experienced changes over time and making decisions related to future planning.Material and methodsThis study utilized a time series of Landsat 5, 7, and 8 images downloaded from the Google Earth engine. To get the best representation of the vegetation in this study, spring and summer were chosen because vegetation at this time is at its greenest. The main focus of this study was on the evaluation of vegetation changes over time quantitatively and qualitatively, using remote sensing data from Google Earth Engine to prepare a map of vegetation changes over time. The general process of implementing this research can be summarized in 7 phases. The first phase involves taking Landsat images and preparing statistical meteorological data. In the second phase, the time series images were collected according to the specific period and in the third phase, the obtained images were corrected and pre-processed. As a next step, the EVI index is extracted from all Landsat images, and then to determine the anomaly of changes, a series of statistical analyses, including the mean and standard deviation, are applied. The next step involves generating the map of anomalous time series changes and extracting the map of vegetation changes to improve understanding. The end of the process also includes evaluating the results obtained from this research. Results and DiscussionSince vegetation and drought changes are non-uniform depending on location and distance from the sea and humid areas, and vegetation is destroyed to build villas, residential areas, commercial areas, and towns, several study areas were divided into smaller pieces. Then each area was analyzed and evaluated separately for its changes. It has been observed in the first and third study areas that vegetation has generally been on the rise in the past 36 years, although sometimes there have been anomalies and fluctuations in EVI value. It was significant to see the reduced vegetation in 2008 in both regions. For example, 262.5 mm of precipitation in the first region fell this year, indicating a rain shortage. The results obtained from the second region, considered one of the coastal regions, indicate that the anomaly graph in the region during the period had a downward slope in the direction of decreasing vegetation, and EVI values reached 0.14 in 2005 and 0.09 in 2013. The 4th and 5th regions have shown a lot of fluctuations in anomalous changes and EVI values, although the trend has generally been downward. Results obtained in the 4th region show that vegetation cover peaked in 2004 and 2011. Rainfall in the 5th region, a highland region, in 2008 was deficient, with 259.8 mm reported by the meteorological station. The anomaly value in this year was -1.96. According to the Department of Meteorology in Mazandaran province, most droughts that have affected the underground water in the province have taken place in coastal and plain areas in the province's east and center, and in western cities, they have mostly affected mountainous areas.ConclusionThirty-six years of EVI time series images obtained from Landsat images were utilized in this study to investigate the changes and identify anomalies. In order to conduct a more detailed investigation, the study area was divided into several different regions, and each region was evaluated separately. The results obtained with existing meteorological statistical data were analyzed because vegetation can be affected by climatic and environmental conditions such as weather conditions. According to the results from study area )4(, vegetation cover has consistently decreased over the last three decades due to various factors like tree cutting, landslides, or land use changes. As shown in the map showing the obtained changes, there appears to be an increase in the value of the vegetation index in some northern areas of Chalus city until around 2002, indicating an improvement in greenness. While In some areas close to the Caspian Sea and the coastline, because of the construction of villas and commercial areas, there has been a loss of vegetation, such as in area (2) based on the changed map, a major part of the vegetation in that area has been destroyed because of the establishment of a settlement and construction of a road. As a result of comparing the evaluation of two anomaly approaches, it has also been concluded that both modes show almost the same trend of changes, but the graphs in "Anomaly compared to the overall average" mode compared to "Anomaly compared to the average of each set" display the change process better.
Yousef Alipour; Naser Bayat; Ali Osanlu
Abstract
Extended AbstractIntroductionTemperature is considered to be an important element of climate whose changes have important consequences for human life. The present study seeks to detect trends and significant changes in the temperature at the 1000 hPa level in Iran. Due to its geographical location, Iran ...
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Extended AbstractIntroductionTemperature is considered to be an important element of climate whose changes have important consequences for human life. The present study seeks to detect trends and significant changes in the temperature at the 1000 hPa level in Iran. Due to its geographical location, Iran climate is affected by various patterns of sea level pressure such as subtropical high-pressure, Siberian high-pressure, Monsoon low-pressure, the Mediterranean low pressure, Black Sea low pressure and Sudan low pressure during warm and cold seasons. These patterns have changed in different time series leaving adverse effects such as decreased precipitation and increased temperature, while probably changing Iran climate from semi-arid to arid and causing climate hazards. Having enough information on the temperature characteristics and its future trends, it is possible to decide on macro politics and a comprehensive method for the management of an area. Therefore, the present study aims to detect trends and significant changes in air temperature at the 1000 hPa level. Materials & Methods45 ° to 64 ° Eastern longitude and 45 ° to 64 ° latitude were selected to study temperature changes at the 1000 hPa level in Iran. In this study, temperature data of 1000 hPa level recorded in a 70-year statistical period (1950 to 2020) and data retrieved from NCEP/NCAR with a spatial resolution of 2.5 by 2.5 degrees have been used to prepare time series and necessary maps. The Kendall Man test was used to analyze the trend of time series. The 70-year statistical period (1950 - 2020) was divided into 10 decades and average seasonal temperature was used. Results & DiscussionThe average temperature of Iran at the 1000 hPa level is rising by 1.34° C per century and its standard deviation has reached its maximum value in recent decades. In the last two decades of the statistical period, 30 ° C contour line has approached Iran from southwest. Temperature trend at the 1000 hPa level is investigated in 4 different seasons of Iran.Summer: according to the Mann-Kendall test, average temperature in summer shows a significant trend and has increased by 0.2 ° C every decade.Autumn: time series of temperature data in autumn shows a significant trend and the slope of the regression line (temperature) has increased with a rate of 0.0451 ° C every decade.Winter: average temperature has decreased at the beginning of the study series and increased at the end of the series. 15.26 ° C and 8.18 ° C (in 1966 and 1972) were the highest and the lowest average temperature recorded in winter, respectively.Spring:The average temperature in Iran has increased by 0.197 ° C every decade. In this 70-year statistical period, average temperature of Iran in this season was 24.37 ° C with the highest annual average temperature recorded as 27.18 ° C in 2008 and the lowest annual average temperature recorded as 21.83 ° C in 1972 and 1992. ConclusionAverage temperature in Iran is raising with a rate much higher than the global average (0.74 ° C per hundred years), due to wide fluctuations in the general circulation patterns of the atmosphere and changes in sea level pressure pattern. Thus, it can be predicted that the temperature in southern Iran may reach over 60 ° C by the end of the century threatening southern riparian provinces with dangerously rising water level and the risk of drowning. Wildfires will still be common in Iranian forests, the number and intensity of floods will increase sharply, and water resources will reach a critically low status.
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.
Monireh Shamshiri; Mahdi Akhondzadeh Hanzaei
Abstract
Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which ...
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Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which are recognizable by remote measurements (such as using Global Positioning System) are known as earthquake ionospheric precursors. In this study, two data sets from the ionospheric Total Electron Content (TEC) derived from the GPS data processing by Bernese software were used for two studies, Ahar earthquake, East Azerbaijan (2012/08/11) and Kaki earthquake,Bushehr (2013/4/9), and the results were compared with data obtained from the global stations. Because of the nonlinear behavior of TEC changes, in order to predict and detect its changes, integration of neural network (using multilayer Perceptron (MLP)) with particle swarm optimization algorithm (PSO) was used. Particle Swarm Optimization algorithm with a performance based on the population can be effective in improving estimatedweight by artificial neural network. By analyzing the causes of ionospheric anomalies including the geomagnetic fields and solar activities and their removal from the processes, the results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms were able to have appropriate efficiency for the prediction of nonlinear time series. The output resulted from the integration of artificial neural network and PSO shows that both positive and negative anomalies occur. The anomalies before earthquakes often occur close to the epicenter of the earthquake and are visible 3 days before the Ahar earthquake and 2 to 6 days before the Kaki earthquake are.
