Remote Sensing (RS)
Seyedeh Kosar Hamidi; Asghar Fallah; Nastaran Nazaryani
Abstract
Extended AbstractIntroductionVarious Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro ...
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Extended AbstractIntroductionVarious Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro planning and information generation. Methods using air temperature recorded in weather stations to estimate the relative heat in urban areas are considered to be both time-consuming and costly. On the other hands, data with relatively high spatial resolution are capable of measuring ground surface parameters more efficiently and accurately. Thus, remote sensing technology is now considered to be a solution used to improve previously mentioned methods. Remotely sensed data are now widely used to find the quantitative relationship between patterns of vegetation cover and the elements of climate. Predicting the conditions of vegetation cover is considered to be essential for planners seeking an efficient plan for its exploitation and protection.Materials & MethodsThe present study seeks to investigate the effects of climatic factors on the vegetation trend observed in Frame forest in Mazandaran province using Sentinel 2 images and to determine the most suitable index for this area. Climatic Data collected from the nearest weather station in Farim City have been used to model climate factors (temperature and precipitation). Changes in the height above mean sea level were also considered. Following the pre-processing and processing of the Sentinel 2 images, the corresponding digital values were extracted from the spectral bands and applied as independent variables. ENVI software was used for image processing and STATISTICA and R software were used for modeling. 70% of the resulting data were used for training and the rest were used for testing or evaluating the model. Mean square error, correlation, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the presented models. Models with the highest correlation and the lowest standard error, the mean square error, the Akaike information evaluation criterion and the Bayesian evaluation criterion were selected as the best models for the studied variables.Results & Discussion A correlation coefficient of 0.43 and 0.56 was observed between temperature and precipitation and vegetation indices. AIC and BIC values equaled (565 and 3209) and (739 and 3383) respectively. Differential Vegetation Index (DVI) has proved to be the most effective parameter in relation to both temperature and precipitation factors in the region. Results indicated that differential vegetation index, green normalized difference vegetation index (GNDVI) and green difference vegetation index (GDVI) have a positive correlation with temperature, while there is a negative correlation between temperature and normalized vegetation index. Precipitation is considered to be one of the most important factors affecting vegetation. Results indicate that differential vegetation index, green difference vegetation index, green normalized difference vegetation index, non-linear vegetation index and normalized difference vegetation index have the highest impact on precipitation. In forest ecosystems, changes in climatic factors may affect trees differently. ConclusionCollecting information about the state of vegetation cover in forests is considered to be very important. Thus, the present study has endeavored to investigate the relationship between indices of vegetation cover and climatic variables. To reach this aim, satellite data are used as a suitable and efficient tool for investigating forest ecosystems with a relatively low cost. This provides the possibility of continuously monitoring land surface. Results indicated that climatic factors affect vegetation indices in the study area. Vegetation cover protects and stabilizes the environment and thus, many researchers have tried to investigate the growth and spatial patterns of vegetation cover in different regions. It is also suggested to study the effects of climatic factors on the vegetation cover of the study areas in different geographical directions. In addition, using other climatic factors such as relative humidity, wind speed, evaporation, transpiration, and higher resolution images can increase the accuracy of the study.
Seyed Ali Ebadinejad; Mohammad Reza Pourgholami-Sarvandani; Ali Asghar Mohammadpour; Ali Osanlu
Abstract
Extended Abstract Introduction Along with other environmental factors, climatic conditions are among the most important factors affecting social, moral and cultural problems. People behave differently in different climates. Quetelet and Gurreydeveloped crime statistics in Franceandinvestigatedits relationship ...
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Extended Abstract Introduction Along with other environmental factors, climatic conditions are among the most important factors affecting social, moral and cultural problems. People behave differently in different climates. Quetelet and Gurreydeveloped crime statistics in Franceandinvestigatedits relationship with physical environment.Thus, they studied the effects of geography and climatic conditions on human behavior, including criminal behavior. In Climate and Crime, Ellen J. Cohen argues that situational approaches, selected rationaltheories and routine activity theory all suggest that climate has a major impact on the rate of crimes and criminal behaviors. Based on their observations, Quetelet and Gurreyformulatedthethermic law of delinquencyin criminology. Based on statistical studies, they concluded that violent crimes are more frequent in hot seasons and hot regions, while in cold regions and cold seasons, more deceptive crimes such as crimes against property requiring thinking and imaginationoccurmore often. It should be noted that crime is a social phenomenon affected by various factors. Environmental conditions can also intensify the threat of human behaviors. The present study seeks to investigate the relationship between the climatic element of temperature and the occurrence of crime in Shiraz, Abadeh and Larestancounties of Fars province? Materials & Methods The present study is applied in nature and purpose, while taking advantage of an analytical-descriptive method. 3 meteorological stations of Shiraz, Abadeh and Larestan were studied here. Investigated data included the seasonal average temperature and seasonal rate of crimes for the2008-2013 period. Seasonal rate of crimes including social corruption, theft, forgery, strife, mischief, intimidation and coercion, smuggling, drug-related crimes, murder, and suspicious death were investigated in Shiraz, Abadeh and Larestan, which have a meteorological station. Crime statistics were collected from the Prevention Police Department of Fars province Law Enforcement Force and statistics related to the climatic elements of temperature were obtained from Fars Meteorological Department. Different descriptive and inferential statistical methods were used to analyze the data and Pearson correlation coefficient test was used in inferential statistics. Data analysis in the present study included two stages. First, the seasonal and annual percentage of various crimes were studied in each of the mentioned cities. In the second stage, the correlation coefficient between the average temperature and the total (seasonal) number of crime occurrence were investigated. Discussion Investigation of various crime occurrence in Shiraz, Abadeh and Larestancounties of Fars province revealed that in spring, strife and affray (47.11), theft (23.16) and social corruption (19.16) were the most frequently committed crimes in Shiraz. However, intimidation and coercion (0.32), smuggling (0.24), forgery (0.20) and murder (0.05) had the lowest frequency in Shiraz during spring. In summer, strife and affray (47.71), theft (24.64) and social corruption (20.95)are considered to be the most frequent crimes, while intimidation and reluctance (0.33), smuggling (0.23), forgery (0.20) and murder (0.03) arethe least frequent crimes, respectively. In autumn, strife and affray (44.36), theft (27.71) and social corruption (18.24) were more common, whileintimidation and coercion (0.33), smuggling (0.27), forgery (0.26) and murder (0.04) had the lowest frequency. In winter, strife and affray (43.92), theft (29.99) and social corruption (16.84) were the most frequently reported crimes,whileintimidation and coercion (0.35), smuggling (1.4), forgery (0.24) and murder (0.02) were the least frequently reported crimes. Findings indicate that during the 2008-2013 period, strife and affray (45.86), theft (28/28) and social corruption (18.84) were the most common crimesin Shiraz city, while smuggling (0.43), intimidation and coercion (0.33), forgery (0.22) and murder (0.03) were the least common crimes. Generally in the three counties, crimes against the person such as strife and affray, murder, mischief, intimidation and coercion were more frequently reported in warm seasons (spring and summer). However, crimes against property, such as theft, were more frequent in cold seasons (autumn and winter). Strife and affray(0.95) in Shiraz have the highest correlation with the seasonal average temperature. There is a negative correlation between the crime of strife and affray and the seasonal average precipitation in Shiraz. The same relationship existsbetweenstrife and affray and the seasonal average relative humidity in Shiraz. In Larestan, drug-related crimes (-0.97) have the highest negative correlation with the seasonal average temperature. In Abadeh city, social corruptions (0.99) have the highest correlation with the seasonal average temperature. Conclusion: In total, crimes against the person, such as strife, murder, mischief, intimidation and coercion were more commonly reported in the warm seasons of the year (spring and summer) in the three counties on the whole and separately. However, crimes against property such as theft had a higher rate of occurrencein the cold seasons (autumn and winter). Therefore, as crimes against the personare more common in warm seasons and crimes against property are more frequent in cold seasons, it can be concluded that QueteletandGurrey’s thermic law of delinquencyis in force in all the three specified counties. However, this law is not generalizable and it cannot be concluded that crimes against property occur more in cold regions and crimes against the person occurs more in warm regions of Fars province. In this respect, this law only applies to Larestan which is located in the warm region of the province.
Hossein Asakereh; hadis kiani
Abstract
Extended Abstract
Global warming and its consequence which occurs as climate change are of the world's major problems in the current century. Climate change and the warming of the earth have adverse effects on resources such as water, forests, pastures, agricultural land, industry and ultimately human ...
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Extended Abstract
Global warming and its consequence which occurs as climate change are of the world's major problems in the current century. Climate change and the warming of the earth have adverse effects on resources such as water, forests, pastures, agricultural land, industry and ultimately human life. The initial effect of climate change is on the atmospheric elements, particularly on the precipitation and temperature. Through evaluating long-term temperature trends we can be provided with a better insight as to how to plan for the upcoming years.
Temperature is one of the elements influencing this issue. That is why monitoring and assessing its behavior is very important to humans. Therefor the simulation of these variables can be vital to gain a perception of human future. There are various methods to simulate and predict climate variables. The most reliable one is using the data from the atmospheric general circulation models or GCM. The GCM models are only able to simulate the atmospheric general circulation data on large surfaces. The implementation of these models for long periods of time is time consuming and requires high processing speeds. To overcome this problem some simplifications should be done including a reduction in spatial resolution and removing some of the physical and thermodynamic processes at the micro scale. These simplifications increase the errors in the atmospheric circulation models and also they cause errors in the prediction and evaluation of the earth’s future climate. To solve this problem, the outputs of general circulation models are down-scaled through dynamical and statistical methods. In recent years, from the various methods of downscaling, researchers have been interested in the statistical downscaling method more than other methods. In the statistical downscaling, statistical methods such as regression and air generator models can be used. The statistical downscaling methods which also include the SDSM model, do the reducing scale based on the statistical history of large-scale predictors and the dependent variables. One of the most widely used models for downscaling GCM data, is the statistical model SDSM. In this study, the competency of this model for downscaling mean temperature was evaluated in Kermanshah station. Several data series including the mean daily temperature in Kermanshah station, data from the function of the national center for environmental prediction and the data from HadCM3 general circulation models were used under the A2 and B2 scenarios. Based on the A2 scenario a world is imagined in which the countries are operating independently, they are self-reliant, the world's population constantly increases, and economic development is region-based. And according to the B2 scenario, the population steadily increases but its growth rate is lower than the A2. The emphasis is on local solutions rather than having global solutions for economic, environmental and social stability, moderate economic development and Rapid technological changes. Kermanshah station data includes daily average from the beginning of 1961 until the end of 2010 which were used for calibration of the model. To this end, collecting the independent variables and the calibration of the model were done for the mean temperature by applying the daily temperature data of Kermanshah’s synoptic station and the data from the National Center for Environmental Prediction. In order to calibrate the observed data from Kermanshah’s station and the data from the National Center for Environmental Prediction (NCEP), it was divided into two 15-year periods (1975, 1961) and (1990 to 1976). The first 15 years was used to calibrate the model using the least square error method optimization. This work was done for the period of 40 years from 1961 to 2000. Then the mean temperature for the 10-year period 2010 -2001 data based on two basic periods of 15 years (1990-1961) and the 40 years (2000- 1961) under the two scenarios A2 and B2, were Predicted and were compared with the observed data of this period to evaluate the predicting performance of the model. The results of the evaluation period (2000-1961 and 1990-1976) using NCEP data showed that the SDSM model has an acceptable capability in simulating the variables such as the mean temperature in the evaluation period and the basic. It should be noted that with an increase in the prediction base period to 40 years, the differences according to the NCEP model and the observed data turned to zero. This can be considered as one of the model’s defects which is due to the use of linear regression because, by reducing the base period to simulate the mean temperature, the results of it, falls away from the average of the observed period, but by increasing the period duration, the outcomes will be valid. Also the amount of variance, the maximum and minimum temperature which are applied by the model to calculate the mean temperature, are not suitable and competence and it commits several errors. This can be caused by poor capability of the model to evaluate and reveal temperature fluctuations; this could be the consequence of adherence to linear regression of the model, although the station’s local conditions and the Hadcm3 model’s errors could intensify the inability.
Mahmood Davoodi; Naser Bay; Omid Ebrahimi
Volume 22, Issue 88 , January 2014, , Pages 100-105
Abstract
Traditional methods of climatic classification are very diverse. Despite traditional and comparative importance, these methods have weaknesses which impair their comprehensive performance. Natural potentials as the background of human activities form the basis and foundation of many environmental programs ...
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Traditional methods of climatic classification are very diverse. Despite traditional and comparative importance, these methods have weaknesses which impair their comprehensive performance. Natural potentials as the background of human activities form the basis and foundation of many environmental programs and land use plans. Sustainable development needs careful planning based on resource constraints and abundances, and local development potentials are determined by its climate. Due to the significant topographic diversity and geographic expansion of Iran, providing a logical classification based on this country's natural realities is quite difficult. Due to topographic diversity of Mazandaran province, its climatic classification is not easily executable. The present article seeks to determine the climate of Mazandaran province according to Litinsky model. We tried to use different methods for climatic classification of the province, yet finally we focused on Litinsky model and explained it. Litinsky model use three fundamental elements of temperature, precipitation and Berry coefficient. Then, it takes advantage of auxiliary indicators including adaptation, continuity of dry season and solar radiation condition to provide a comprehensive classification. To do so, data obtained from 10 synoptic and climatologic stations in Mazandaran during 1984-2005 statistical period was used in SPSS environment. Finally, climate of Mazandaran province stations were determined and proposed in table 4.
Fatemeh Zabol Abbasi; Morteza Asmari Sadabad
Volume 21, Issue 83 , November 2012, , Pages 70-72
Abstract
Precipitation and temperature can be considered among the most important climatic elements in any area. Phenomena like sudden increase or decrease in temperature and precipitation during a year or more can be considered a reason for climate change in the area. The present article applies usual time series ...
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Precipitation and temperature can be considered among the most important climatic elements in any area. Phenomena like sudden increase or decrease in temperature and precipitation during a year or more can be considered a reason for climate change in the area. The present article applies usual time series method to investigate annual and seasonal changes in temperature and precipitation in Bandar-e Lenge synoptic station during the statistical period (1966-2000). Analysis indicate that average precipitation in Bandar-e Lenge during the statistical time period is 154.7 mm. Standard deviation and coefficient of precipitation changes are respectively 96.7 and 63 percent. Seasonal trend of precipitation changes in the statistical period indicates that precipitation showed a decreasing trend in spring and summer and an increasing trend in autumn and winter. Seasonal distribution of precipitation in Bandar-e Lenge during the above mentioned period (spring to autumn) contains 7, 2, 21, 70 percent of the annual precipitation respectively. In order to determine annual temperature, frequency of decreasing periods, definite temperature increase and degree of changes in each thermal period were calculated. Temperature changes more severely in summer and autumn, while the frequency of changes in winter and spring is more orderly. Moreover, the statistical period of 1966-70 is introduced as the most arid period of the study years.
