نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 113 (بهار 1399)

Cloud is a special phenomenon formed by dynamic and thermodynamic changes of the general atmospheric circulation. Through dispersion and reflection of solar radiation, cloudschange energy balance of the Earth and affect its hydrologic cycleby producing rainfall in various forms. Determining the state of clouds (in terms of clouds being liquid or ice) is crucial, sinceitaffects the atmosphere feedback mechanism. Moreover, the state of clouds is related with itsheight, i.e., higher clouds tend to have an icy state. Therefore, determiningtheir statusis especially important for the accuracy of elevation estimation. The present study seeks toinvestigatetemporal and spatial variation of liquid clouds in the geographical range of Iran using information received from meteorological stations and remote sensing techniques. It aims to find the feedback of cloudsin liquid phase and theirdominant condition.

Data received from MODIS Sensor of TERRA Satellite (2001-2015) and Cloud mask (CM) algorithm were used in the present study. Moreover, long-term data of 31 synoptic meteorological stations collected during the period of 1960–2015 were used to compare satellite data. Followingdata decoding and required calculations, maps of each season were produced using Kriging method.

Results indicate that maximum number of liquid clouds occurs in winter, while their minimum number occurs in summer. In winter, Rasht, Ramsar, Babolsar and Gorgan stations (with cumulative frequency of 174.33 to 305.66 days) have maximum frequency of liquid clouds.This country almost lacks liquid clouds in summer. Only in the coastal zone of the Caspian Sea, Rasht, Ramsar, Babolsar and Gorganstations with 153, 93.33, 77.66 and 26 days, respectively,had the maximum frequency of liquid clouds. The average thickness of liquid clouds in Iran was calculated on a seasonal scale. In winter, spring, summer and autumn, it was 22.23, 17.13, 14.11 and 16.7 microns, respectively. Results indicate that the average thickness of liquid clouds decreases in warm seasons. Maximum thickness of liquid clouds in winter, spring, summer and autumn was 33.04, 24.56, 24.85, 22.84 and minimum thickness of liquid clouds was 13.98, 6.82, 6.27, 8.09, respectively. In winter,maximum frequency of liquid clouds occurred in western Iran and the Caspian coastline, while maximum thickness of liquid clouds occurredin northwestern and western Iran.Moving from north to south and west to east,the frequency of liquid and icy clouds decreases. In contrast, maximum frequency of liquid clouds occurs in summer.

Results indicated that maximum frequency of winter and autumn liquid clouds mainly occur in high latitudes of northern regions, southern hillside of Alborz(west to east direction), and northwestern and western regions of the country. Maximum frequency of summer liquid clouds occurs in the Caspian Coasts, while maximum frequency of spring liquid clouds occursin the northern half and southeast regions of the country. This is well-justified due toactivities of the expected systems and local factors in each season. Liquid clouds of Iran have a nonlinear and possibly complex relationship, and factors such as hillside orientation, precipitation systems, distance from sources ofmoisture, lack of ascending factor, lack of sufficient moisture and many other factors contribute to this relationship.Evaluation of liquid clouds thickness indicated that elevated regions of central and western Zagros have the highest amount of liquid clouds in cold seasons, since low-pressure systems, fronts and mid-latitudewaves of atmosphere play a decisive role in the growthof cloud numbers in these seasons. This is also in consistencywith Masoudian (2011) results. Northwestern Iran and the Alborz belt are almost always affected by the western winds. Western winds pass over the Mediterranean Sea and its sufficient moisture resource, which play a significant role in the cloudiness of this area. Results are consistent with Alijani’sstudy(2010) that reported 120 cloudy days in Alborz Mountains, Khorasan and northern Azerbaijan altitudes. Increased cloudiness of southern and southeastern Iran during warm seasons is related with the monsoon system in July-September,which is also confirmed by Ghasemifar et al. (2018) and its mechanism is discussed by Yadva (2016). Results are also in consistency with the results of Ahmadi et al. (2018), which examined the cloud optical thickness (COT) and the total cloud cover (TCC) of Iran. In other words, results of Ahmadi et al.(2018) also confirm our findings.

Components of verticaldeflection, i.e., North-South component  and East-West component ,are used for accurate determination of geoid or quasigeoid. Moreover, vertical deflection components area useful source for determination of variations in subsurface density and geophysical interpretations. Generally, there are two definitions for verticaldeflection. According to Helmert definition, vertical deflection at any given pointis the angle between the actualgravity vector (actual plumb line) and a line that is normal to the reference ellipsoid(a straight line perpendicular to the surface of reference ellipsoid). Another definition of vertical deflection is proposed by Molodensky. According this definition, vertical deflection at any given point is the angle between actualgravity vector and normal gravity vector (normal plumb line). Some relations have been introduced to convert Molodensky vertical deflection to Helmert vertical deflection. Helmert vertical deflection is estimated using astrogeodetic observations (combination of astronomical and geodetic observations). Presently, global geopotential models (GGMs) have been expanded to the degree of2190, which is equivalenttoabout 5-min spatial resolution. Vertical deflectionat any point on the Earth can be calculated using the GGM. The resulting vertical deflection is consistent with Molodensky definition.Unfortunately, accuracy of GGMs is not sufficient for estimation of verticaldeflection.In other words, since GGMs are expanded up to a limited degree due to their resolution, omission error(or truncation error) occurs in computation of the earth’s various gravity field functionals, such as the geoidal height and verticaldeflection. Combining GGM with a digital terrain model (DTM) is a method used to reduce omission error.It should be noted that DTM has a higher spatial resolution as compared to GGM.In this method, the omitted signals of GGM can be modeled using residual terrain model (RTM) derived from subtracting high resolution DTM from a reference smooth surface. The reference smooth surface is obtained from eitherapplying average operator to DTM or expanding global topography into spherical harmonics. Fortunately, DTMs with spatial resolution of 3seconds or more,and reference smooth surface based on 2190 degree spherical harmonics are publicly available. The present study seeks to assess vertical deflectionderived from a combination of GGM and DTM in Iran. Previously, Jekeli(1999) has studied EGM96 geopotential model with the aim of computingvertical deflection in the USA. Hirt(2010) and Hirt et al. (2010a) have assessed vertical deflection in Europe and the Alps using a combination of EGM2008 and RTM models.In Iran, GO_CONS_GCF_2_TIM_R4, a GOCE-only model, and EGM2008 geopotential model have been used toobtain vertical deflection and the results have been evaluated byKiamehr and Chavoshi-Nezhad(2014).

To implement the present study,a EGM2008 model with a spatial resolution of about 5-min is selected asGGM and a SRTM model with 3-sec spatial resolution is considered as DTM. To obtain RTM, DTM2006 model based on2190 degree spherical harmonicsis selected as the reference smooth surface.To compute the residual topography effect, prism method was used in an ellipsoidalmulti-cylindrical equal-area map projection system. First, we compute vertical deflectionusing EGM2008 model. It is also calculated using a combination of EGM2008 model and RTM(EGM2008/RTM method). In the next step, vertical deflection derived from the first method (EGM2008 model) and the second one (combination of EGM2008 model and RTM) are compared with vertical deflectionderived from astrogeodetic observations in 10 available Laplace stations in Iran.

Results indicate that there is a 1.2sec difference between North-South component of vertical deflection (i.e.) obtained from EGM2008 model and astrogeodetic observations.With RTM, this will reach 1 sec, which shows a 15% improvement. Moreover, there is a5.7secdifference between East-West component of vertical deflection () obtained from EGM2008 model and astrogeodetic observations, while this value will reach 5.6sec using RTM. Improvement in East-West component () is1.4%, which is smaller than the improvement of North-South component (). Based on the computations, we found that values of  and  in the Laplace stations canreach 17sec (RMS=7sec) and 15sec (RMS=8sec), respectively. Therefore, it is concluded that the relative error ofNorth-South component ()computation using EGM2008/RTM method is about 6% and the relative error ofEast-West component ()computation is about 37%.

The present research has studied the RTM effect on the improvement of GGM used for the determination of vertical deflectionin Iran. To performthe study, EGM2008 model with around 5-min spatial resolution was selected as GGM. RTM is also derived from subtracting the DTM2006 model (based on2190 degree spherical harmonics)from the 3-sec spatial resolutionSRTM model. Numerical findings indicate that a combination of RTM and GGM can improve the results of vertical deflectioncomputation, as compared to the results obtained from GGM-only approach. The improvement in North-South component of vertical deflection () is about15%and East-West component of the vertical deflection () undergoes about 1.4% improvement. In general, EGM2008 model and its combination with RTM have been more successful in the computation of  component as compared to computationin the geographical region of Iran. There is no clear explanation for this difference, but it can be due to errors in theastronomical or geodetic observations oflongitude in Laplace stations.

Natural hazards have always been a part of our surrounding environment and human life would be unimaginable without considering these hazards. With the development of social life, and particularly with urbanization and increasing expansion of cities, the dimensions of such incidents have become more complicated. Earthquake is one of the most important natural hazards that takes the lives of many people every year. Although definite prediction of earthquake is not still possible, high-risk areas can be identified by zoning earthquake hazard using new technologies such as GIS, and measures can be taken to deal with the critical situation of identified regions during an earthquake. Planning of temporary accommodation with the aim of crisis management and reduction of secondary damages caused by the earthquake have always been amongmajor concerns of urban planners and managers. In the past, the policy of creating temporary accommodation centers and disaster relief sites lacked a specific program, so that locating a vacant land, with no owner was the most important principle for the creation of these centers in urban areas. It is now proved that these methods lack efficiency. However, recent advances in modern technologies such as GIS have improved planning process. This kind of planning procedure takes effective parameters and criteriainto account, many of which have spatial nature. Urban resiliency is one of the most important branches of urban crisis management, thus risk assessment and risk reduction planning, including site selection for temporary accommodation (as a principle of urban resiliency),are highly essential.

The study area of the present research is Babol, one of the major and central cities of Mazandaran Province. Babol is located in BabolCounty, 14 km from the Caspian Sea and 10 km from the Alborz Mountains. With a total area of approximately 32 km2 and a population of250,217 (at the2016 census), it is the second most populous city in Mazandaran province.The 600 km long Caspian faults and 680 km long Alborz faults are among the effective faults of the study area. In the present study, effective measures for selectionof temporary accommodation siteswere extracted and weighted using expert opinions specialized in structural engineering, earthquake, urban planning, crisis management, passive defense, traffic and transportation. Identified criteria included distance from the river, distance from the fault, land use, distance from installations network, access to the transit network, distance from fire stations, population density, distance from tall buildings, distance from police stations and distance from health centers. Then, using GIS analytic functions, standard maps were produced and combined to identify the best areas for temporary accommodation (after a possible earthquake) in Babol. Criteria were weighted using fuzzy analytic hierarchy process and weighted overlay method was also used to combine them.

Analyzing the results indicated that only 7% of the total study area (Babol City) is appropriate for temporary accommodation. Identified areas were examined according to other temporary accommodation standards. Finally, six sites and a total of 107 hectares (less than 4% of the study area) were identified as suitable sitesfor temporary accommodation. With a very large area (37 hectares) and full access to water, electricity and gas facilities,the first site is locatednear eastern beltway of Baboland Lotus PondRecreational Complex. The second proposed site is a 11-hectarevacant arealocated in the northeastern part of Babol City, between Ramenet and Pari Kola Villages. With a total area of 22 hectares,the third proposed site is located in the south-east of Babol City and near Babol-Qa’emShahr Road. Unlike the previous three sites, the fourth proposed site is located almost inside the city. It is a vacant 5-hectarearea in the northern side of the Motamedi Martyrs’ Cemetery. The next site, also located inside the city, is Aminian Dormitory (Noushirovani University of Technology) with a total area of 4 hectares. Although the last proposed site was ranked lower than the other five sites in the final analysis, it has the highest score among available sites inwestern side of Babol river. With a total area of 28 hectares, this site is located within a short distance of Imam Khamenei Highway.

According to the international standards, per capita area for temporary accommodation is approximately 4 m2. Therefore,with a population of about 250,217,Babol needs an average space of 100 hectares for temporary accommodation. Although, the proposed space for temporary accommodation (107 hectares) in Babol almost equals the required space (100 hectares), with the present rate of population growth inBabol, increasedconstructions, and consequently, reduction of appropriate space for temporary accommodation, Babol will definitely face a shortage of suitable space for temporary accommodation of earthquake victimsin near future. Moreover, the spatial distribution of suitable sites for temporary accommodation is not reasonable, as most of the suitable sites are located in the eastern part and within the boundaries of the city. While, these sites are expected to be scattered throughout the city with an equal access for all residents.Finally, it can be concluded that temporary accommodation of earthquake victimswas not considered in urban planning of Babol, and as a result, the city does not have a suitable status regarding temporary accommodation of earthquake victims.

Foehn is thedecending of hot and dry air that occurs under certain conditions in the lee of a mountain range.In an adiabatic process, the humid air rises toward mountain peaks on the windward hillside. With sufficient humidity, it is saturated and thus, forms clouds or precipitation. In this way, it loses moisture, and passing over the lee of maintain, descends and heat upin an adiabatic process. Thus, the air in the lee side gets warmer and drier than the air in the windward hillside. Moving upward toward the mountain peak, the air loses temperature. At the mountain peak, the saturated air hasreached dew point temperature, and begins to rain to discharge its moisture. This dry air descends, and cross the leeward hillside with increasing velocity, and at the base of the mountain, its temperature is higher than the initial air temperature (Haji Mohammadi, 1396).

In order to extract the frequency of days with foehn windsin the present study, daily temperature, relative and hourly humidity and wind speed were prepared for a 10-year statistical period (2015-2006) and then heat wave index was used to extract the number of days with foehn winds. To investigate the effect of foehn on thermal stress of plants using Landsat 8 OLI images, factors affecting thermal stress inplants,such as albedo, short wavelength radiations reaching the Earth surface, long wavelengthradiations emitted from the Earth surface, long wavelength radiations entering the earth surface, net radiation flux and soil heat flux were analyzed. ENVI 5.3 and Arc GIS 10.1 wereused to perform calculations and produce the aforementioned maps.

 The present study was conducted to investigate thefoehn phenomenon in the west Alborz Mountains and its effect on the amount of thermal stress in the vegetation cover.First, the frequency of foehn wind occurrence in the statistical period of 2006 to 2015, in stations under study was extracted using wind direction, baldiindex (heat wave index) and increasing temperature and decreasing relative humidity compared to the previous day. In other words, days with temperature higher than 0 degree Celsius were considered as a heat wave. Based on wind direction, temperature increase and relative humidity decrease compared to the previous day (which in some cases is twice or even more), days are associated with foehn wind. In order to investigate the effect of foehn on thermal stressin plants, a sample of images with better atmospheric conditions (lacking clouds) collected by Landsat 8 OLI sensor on September 24, 2015 –in which foehn phenomenon had taken place-was received from the website of US Geological Survey (Earth Explorer)in the present study.The study area (West Alborz Mountains) was selected and cut out ofthese images and radiometric corrections were performed on the resulting images using ENVI 5.3 software. Afterwards, parameters like atmospheric thickness (atmospheric conductivity), Top of AtmosphereAlbedo, Earth’s surface albedo, Earthdistancefrom the Sun, solar altitude, Normalized difference vegetation index (NDVI), leaf area index (LAI), Fracture value, brightness temperature, ground surface temperature were determined and net radiation flux reaching vegetation cover and soil heat fluxwere calculated using these parameters. The output maps were produced in ARCGIS 10.1 environment.

According to the study sample (September 4, 2015), results indicated that areas with dense forest cover (eastern hillsides of the Alborz Range) receives the highest values of net radiation.The effect of foehn infiltration on these hillsides has increased the amount of radiation received up to 600 or 700 W / m 2. In contrast, the net radiation received on the downstream of thewindwardhillsides (western hillsides) is about 75 and at higher altitudes 150 W / m 2less than areas under the influence offoehn.Due to lower vegetation densityand lower heat transfer,soil heat flux in the western hillsides is much higher than the eastern hillsides.Most of windward hillsides has a heat flux of between 80 and 120 W / m2, while in leeward hillsides,sunlight is absorbed by the canopy and the soil heat flux is between 20 and 40 W / m2.Thus, most of solar radiation is used to raise the temperature around the vegetation crown, provide the necessary conditions for higher evaporation from the vegetation and create thermal stressin the vegetation organs. Therefore, descending of air mass on trees and plants causes severe evapotranspiration.This will lead to rapid drying of the leaves, which will cause thermal stress in the plant’s organs and intensify the likelihood of forest fires.

Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries.

Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed:  a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters.

The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).

Nowadays, theft -especially residential burglary-is considered as one of the most common and frequent crimes in many countries of the world, including Iran. As such, it has become a pervasive and serious problem with various social, economic, and security-related aspects. Investigating geographical dimensions of this crime facilitates the process of exploring this phenomenon. Space and its special features play an important and undeniable role in crime commitment, because space has always been considered as one of the most important factors in commitment of financial crimes such as residential burglary. Spatial analysis and geographical investigation of crimes seek to provide a spatial presentation of criminal actions, crime dispersion, and crime hotspots. This type of crime analysis basically aims to provide a model for decreasing crime commitment in urban spaces. Accordingly, the present research seeks tomodel spatial diffusion of residential burglary crimes in MashhadusingHogstrand’s spatial diffusion theory.

The present study is performed based on descriptive-analytic and qualitative methods. The research sample includes cases of residential burglary committed in Mashhad in the 2011-2017period. Data analysis was performed using ArcGIS software. Case study area includes Mashhad, with an area of about 35187 hectares, a population of more than 3057679, and a population density of 87 people per hectare.

Police reports in Mashhad suggest that the highest crime rates belong to the 2nd and 3thdistricts, and the lowest rates belong toSamen (around Razavi Shrine), the 12th, and 8thdistricts. 70% of crimes in Mashhad are committed in informal settlements including the 2nd, 3th, 4th, 5th, 6th, 7th, and 10thdistricts. However, only 10.6% of the city area and 29.3% of its population belong to these districts. Furthermore, the highest crime rates have been reported in 2017. In 2011, only two major crime hotspots were observed in Mashahd (in the 2nd and 3thdistricts). Results suggest that crimes have spread from one place to anotherin Mashhad, which indicates a close relationship between crime and distance factor. In other words, proximity to a crime hotspothas resulted in rapid spread of crimes, and due to the short distance, nearby places have been affected more quickly. Informal settlements of Mashhad are located in eastern, northern, and northeastern districts,which contain 99% of crime hotspots. This indicates that spatial autocorrelation of crimes in informal settlements of Mashhad is relatively high, which has led to formation of crime hotspots in these districts. However, moving from marginalized areas towards southern districts of Mashhad (more prosperous regions), spatial correlation of crimes decreases, and lead to formation of 99% of cold spots.

The present research has investigated the spatial diffusion pattern of crimes in Mashhad in 2011-2017period.To reach this end, crime hotspots were investigated by quantitative methods such as Kernel density, Moran coefficient, and crime hotspot analysis. Results suggest that the highest crime rates are reported in the 2nd and 3thdistricts, while the lowest rates are reported in Samen (around Razavi Shrine), the 12th, and 8th regions. In fact, 70% of crimes in Mashhad are committed in informal settlements including the 2nd, 3th, 4th, 5th, 6th, 7th, and 10thdistricts. Moreover, statistics indicate that for every100000 people,anaverage of 75/2 cases of crimes have been reported in the 2011-2017period.Results of Moran coefficient for spatial diffusion of crimes indicated the presence of a cluster distribution of crimes in Mashhad. Meanwhile, spatial diffusion pattern of crimes in Mashhad suggests that the first crime hotspots were formed in northern, eastern, and northeastern districtsof Mashhad, and crimes have spread from these to other districts (more central and prosperous regions such as the 8th and 9thdistricts). In fact, investigations suggest that crimes are spreading from informal settlements to other regionsof Mashhad, and acompatible spatial diffusion pattern of crimes exists in this city.

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.

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 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.

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.

Remote sensing data provide valuable information for the agricultural section and natural resources managers. Nowadays, performance management and estimation via using various methods such as classification and mapping have gained great significance. An example of such data is the mapping of crops cultivation and orchards at national and regional levels, which is one of the key tools in sustainable agricultural planning and management. These studies appear necessary especially in the field of strategic commodities such as rice and citrus which are among the most important food items for the Iranian people. The spatial information on agricultural lands in the field of agricultural planning and management can help the prevention of the spread of pests, management of the environmental stresses, crop performance estimation and vulnerability assessment in crop production. Field surveys and observations for crops mapping in the growing season in different years are very time-consuming, costly, and only suitable for small-scale studies. In contrast, over the past decades, remote sensing has been recognized as a suitable method for crops mapping for large areas in the shortest time and at low cost. Due to the climatic conditions of the areas in North of Iran, green spaces including vegetation and orchards, and rice fields are located near each other. At the time of the maximum growth of rice products, the spectral characteristics of these land covers are very similar. Therefore, the separation of these two land covers using satellite image classification process faces serious challenges. The aim of this study is to investigate the efficiency of the satellite images and the optimization algorithms for separating green spaces and rice fields from each other at the time of maximum growth. The present study differs from others in this field from two aspects; first, the study compares the capabilities of multispectral and hyperspectral satellite images with each other; additionally, it aims at comparing and evaluating the efficiency of the Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) so as to determine the optimal features for increasing the separation accuracy of green spaces and rice fields.

This research was carried out based on the two objectives of studying the capabilities of the Hyperion and Landsat images and comparing the efficiency of the PSO and GSA to determine optimal features for the separation of green spaces and rice fields. For this purpose, the two Landsat and Hyperion satellite images as well as ground data sets of the case study in North of Iran were employed. In the first step, preprocessing of the Hyperion and Landsat images was performed. In the second step, various features were extracted from the Hyperion and Landsat images using different spectral indices and transformations. In the third step, the Support Vector Machine (SVM) classifier was applied with two strategies, i.e. the usage of spectral bands and the usage of spectral bands as well as indices as the features in the classification process to extract green spaces and rice fields. In the fourth step, PSO and GSA were employed to extract optimal features from the Hyperion image to distinguish between green spaces and rice fields; then, classification was done with the extracted optimal features; and finally, the efficiency of PSO and GSA were compared to determine the optimal features for the separation of green spaces and rice fields using ground data sets.

The results indicate that the use of Landsat image is not effective for the separation of rice fields and green spaces. In other words, due to the high spectral similarity of these land covers, a large percentage of pixels related to the two classes are mistakenly classified in another class. However, the accuracy of the producer and user relating to each class has increased by an average of 10 percent with the addition of spectral indices to the classification process. Using Hyperion image is more effective than Landsat image for the separation of rice fields and green spaces. Moreover, the accuracy for the separation of rice fields and green spaces has increased with the simultaneous consideration of the bands and spectral indices in the classification process. It should be noted that one of the key factors in the efficiency evaluation process of the classification methods is the processing time. The results of using optimization algorithms for determining the optimal features indicate that out of the 150 spectral features (including 140 Hyperion image bands and 10 spectral indices and transformations), using PSO and GSA, only 25 and 31 optimal features were selected for the separation of green spaces and rice fields, respectively.The use of optimal features in the classification increases the accuracy for the separation of green spaces and rice fields more, compared to the use of all features in the classification. Additionally, GSA is superior to PSO when used for extracting optimal features for the separation of green spaces and rice fields. 

The results of this research indicate that the separation accuracy of green spaces and rice fields using Landsat image,is less than that of Hyperion image. With the addition of spectral indices to the classification process, the separation accuracy in both Landsat and Hyperion data increases. Moreover, using an optimization algorithm to determine the optimal features in the classification process will increase the separation accuracy of green spaces and rice fields. Given the overall accuracy values, the efficiency of GSA for separating green spaces and rice fields is higher than PSO.

Evapotranspiration is one of the most important parts of the water cycle (Boegh and Soegaard 2004). Precise prediction of actual evapotranspiration () is essential for various fields, such as agriculture, water resource management, irrigation planning and plant growth modeling. Therefore, accurate determination of actual evapotranspiration has always been a major concern of experts in these fields. Due to the limited number of weather stations and the fact that collecting ground information is both time consuming and expensive, remote sensing and satellite imagerycan be a suitable tool in determination of actual evapotranspiration (Brisco et al., 2014). Satellite productions are usually divided into images with low, medium and high spatial resolution (Rao et al., 2017). Surface energy balance is a method usually used in combination withremotely sensed spatial data for estimation. Information collected from various sources, such as remotely sensedimageries and meteorological data, are used in this method. The present studyinvestigatesspatial distribution on different scales (from field- to regional-) using remotely sensed imagerieswithdifferent spatial and temporal resolution. TheSurface Energy Balance System (SEBS) is one of the most important methods used for the estimation of in remotely sensed images (Ochege et al., 2019). This model needs thermal maps produced using satellite images. Daily maps produced with RS are usually very large, and their pixelsize is usually so large that it can provide the spatial diversity found in the basins with respect to the errors (Mahour et al., 2017).

In order to estimate the actual evapotranspirationin satellite images collected from Zayanderud basin,the effects of Co-Kriging downscaling of surface temperature (LST) were investigated in June 2017 using two different methods.To reach this aim, we first applied a co-kriging downscaling method to a low-power LST product collected from MODIS at 1000 meters. Then based on the results and using the SEBS system, the daily  was obtained from images with average spatial resolution (250 m).In the second method, map produced usinghigh resoultion satellite imageswas downscaled to medium resolution (250 m). For both methods, 250 m resolutionMODIS NDVI products were used as co-variables.Then, validation was performed using Landsat-8 imagery, and land surface temperature was extracted from its thermal bands. SEBS algorithm was used to determine in Landsat 8 30-meter resolutionimagery. Accuracy of measurements wasexamined based on a comparison between down scaledLST and maps (250 meterresolution).

In the present study, mean LST equals 3/312 K (SD = 1.74) and average daily equals 12.5 mm / day (SD = 0.86). In the downscaling phase, the relationship between LST parameters and and vegetation index(as a co-variable)was investigated.Moreover, to investigate the relation betweenhigh resolution variables and NDVI, we re-sampled LST and   variables from a 1000 mresolution to 250 mresolution.In250 mresolution, there is a negative linear relation (r=-0.85) between LST and NDVI, but the relation betweenand NDVI is positive (r = 0.80). Thus, lower LST (> 305k) indicates more vegetation (NDVI >0.3) inthe region, while higher LST results in lower NDVI or lack of vegetation. As a result, more vegetation can be observed in regions with higher(12 mm/day). Results indicated that the difference between average  downscaled-SEBS (12.56 mm/day) and reference  (13.11 mm/day) is negligible. The RMSE between the reference and the downscaled  equaled 1.66 mm/day (r = 0.73), and RMSE between the reference LST and the downscaled LST equaled4.36 K (r = 0.78). Thus,values obtained from two downscaling methods were similar, but the  obtained from downscaled LST showed a higher spatial variation. Therefore, LST has greatly influenced the production of maps using remotely sensing images, and Co-Kriging downscaling has been useful for providing daily  maps with intermediate spatial resolution.

Evapotranspiration downscaling using the co-kriging method is not significantly different from the SEBS product and the results are similar. The results of -SEBS method isalso acceptable, but the  derived from the SEBS algorithm is more variable due to the LST downscaling.

 Investigation of vegetation changes can provide valuable information on global warming, the carbon cycle,water cycle and energy exchange. Satellite imagery timeseriesandremote sensing techniques offers a great deal of information on variations and dynamics of vegetation. Harmonic ANalysis of Time Series (HANTS) has been effectively used to eliminate missing and outliers in time series of vegetation indices and land surface temperature (LST). However, the algorithm has been less frequently used to detect changes in vegetation and phenology. HANTSalgorithm decomposes periodic phenomena into their components(different sines and cosineswith different amplitudes and phases). The value of phases and amplitudes contains valuable information that can be used to investigate variations and identify different characteristics of vegetation such as growth and phenology. The present study aims to determine changes in each componentof vegetation time series in Iranin the past (1982, 1983, 1984 and 1985) and in recent years (2015, 2016, 2017 and 2018).

 A daily NDVI product of AVHRR sensor, with a resolution of 0.05 at 0.05 ° (i.e. AVH13C1) was used in the present study. To obtain reliable harmonic components (amplitude and phase images), a reliable curve has to be fitted on the primary time series data. To do so, first,parameters of HANTS algorithm were determined and then Root Mean Square Error (RMSE) of the curves fitted on data related to four one-year time series in the past year’s category (1982, 1983, 1984 and 1985) and four one-year time series in recent year’s category (2015, 2016, 2017 and 2018) was estimated. This classification (i.e. four one-year time series in the past and recent years) was used for two reasons. First, extraction and comparison of harmonic components in a single time series in the past and recentyears’ categories cannot reflect real changes, as these changes may occur under the influence ofimpermanent dynamics of vegetation, such as dryor wet periods. Second, with four one-year time series in the past category (1982, 1983, 1984 and 1985), and four one-year time series (2015, 2016, 2017 and 2018) in recent years, statistical comparison of the harmonic components through one-way analysis of variance becomes possible. Following the production of reliable harmonic components, variations of the harmonic components in recent years were compared with their variations in the past using difference method, and mean difference ​​of the harmonic components’value in four one-year time seriesin the past and present categories wasdetermined using one-way analysis of variance. Finally, some maps were produced to exhibitthe significance of differenceinmeans.

According to the findings of the present study, mean RMSE of the fitted curves in the four one-year periods ofpresent and past time series were always less than 0.1 unit of NDVI. Moreover, mean RMSEof total area of Iranin the past and present time series were 0.037 and 0.039, respectively. This demonstrates high efficiency of the HANTS algorithm in elimination of missing and outlier data in the daily-NDVI time series ofNOAA-AVHRR. Results indicate thatrange of zero amplitude (the mean value of NDVI or the average vegetation coverage) decreasesin the central, eastern and northeastern regions of Iran atthe 95% probability level (F-value <0.05), whileit increases significantly (F-value <0.05)in the north, northwestern and western regions (especially, the Alborz and Zagros mountains). The meandifferenceof phases value in the four-time series of the past and recent years’categories wassignificant at the 95% probability level (F-value <0.05). Compared to the past time series, first harmonic phase average of total area of Iran in the new time series has decreased by almost 14 degrees. This decrease in the value of the annual and 6-month phases indicates a quicker growth phase and phenological processes of plants compared to past times.

 Results indicated that HANTS algorithm can effectively eliminateand reconstruct outliers in the NDVI time series. Zero harmonic (mean value) represents the overall level of vegetation cover and the firstharmonic phase in a one-year time series determines the starting time of growth in seasonal plants or thosewith agrowth period of6-month or less. Annual Phase indicates the angular starting position of the annual cycles and the 6-month phase inherently indicates the fluctuation and angular position of a half-year or 6-month curve. However, interpreting 6-month amplitude and phases are difficult. As most changes are controlled by the first harmonic phase, the first harmonic phase in a one-year time series contains important information about the beginning of growth and the phenological processes of plants. Therefore, harmonic components of a periodic time series canbeusedto identify and determine changes in vegetation coverage and phenological processes.

LiDAR (Light Detection and Ranging) employs pulse models which penetrates vegetation cover easilyand provides the possibility of retrieving data related to Digital Terrain Model (DTM).Pulses sent by the Lidar sensorhitdifferent geographical features on the surfaceground and scatter inall directions. Distance to the object is determined by recording the time between transmitted and backscattered pulses and by using the speed of light to calculate the distance traveled by the small portion of pulses backscattered. Most LiDAR receivers at least record the first and last backscattered pulses. The first backscattered pulses are used to produce Digital Surface Models (DSMs) and the last ones are used to produce DTMs. Despite the fact that these data can provide a valuable source for DTM generation, the volume of vegetation (vegetation density) in forest areas reducesthe accuracyof DTMs. Onthe other hand, ground surveying of forest areas is rather expensive and time consuming, especially in largerforests. Aerial images are also used as a source for DTM generation, but this approach requires a 60–80% overlap between images which along with canopy height reduce the potential of this method for DTM generation. Also, low spatial resolution of satellite images collected from forest areas increases errors in DTM generation to a large degree. The present study investigates the accuracy and precision of DTMsproduced from LiDAR data in a forest area. Furthermore, the effect of different methods of filtering and DTM interpolation was explored. Different methods of DTM generation were also closely analyzed and evaluated.

 The case study area is located in Doroodforests, a part of Zagros forests, in the southeastern regions of Lorestan province in Iran (48°51’19’’E to 48°54’31’’E and 33°19’21’’N to 33°21’15’’N). Minimum and maximum altitude above sea level were 1143 and 2413m, respectively. The study area covers 100 hectares of mountains with an average slope of 38%. Approximately 50% of the area is covered by forests in which Brant’s oak (Quercusbrantii Lindley) is the most frequent species. LiDAR data were collected by the National Cartographic Center of Iran (NCC) in 2012 using a Laser scanner system (Litermapper 5600) fixed on an aircraft flying at an average altitude of 1000m. LiDAR data consisted of the first and last returns (backscattered pulses), distance and their intensity value. Collected data had an irregular structure and included an average of more than four points per square meter. A DTM was produced using a two-step filtering. First, a morphological filter removed most of non-ground points, and then a slope-based filter detected remaining points. Inforest areas with rough-surface, DTM was producedthrough processing ofLiDAR data with statistical methods likekriging and inverse distance weighting (IDW). These methods apply third and fourth power to detect and remove non-ground points. To assess the accuracy of DTMs produced by different approached, 5 percent of the LiDAR point cloudswererandomly separated as the test data. Amongst these data sets, 62 points with a suitable dispersion were selected and measured using a GPS-RTK. An error matrix, along with accuracy indices (including correlation and Root Mean Square Error (RMSE)) were calculated based on these data.

Results indicated that 44-degree slope is the best threshold for isolation of non-ground points and inverse distance weighting (IDW) is the best third power interpolation method with the highest correlation (0.9986) and the lowest RMSE (0.204 meter). Amongst the filtering methods, slope-based filter used for separation of ground and non-ground points had the best performance. Since this filter combines two parameters of slope and radius, it can remove cloud points related to the vegetation cover and results in high efficiency for steep forest areas. Slope-based filter is suitable for processing of near-surface vegetation, whilst statistical filter is well-suited for vegetation cover consisting of tall trees.

The present study proposed and investigated different scenarios for the production offorest areas’ DTM using LiDAR data and two interpolation methods. These algorithms were practicallyassessed using LiDAR data collected from Dorood forest areas. The best scenario was slope-based filter with inverse distance weighting (IDW) interpolation. Based on accurate assessment, this approach can produce reliable DTM in forest areas.

The sun is known as the source of energy, the origin of life, and the origin of all other energies. The global solar radiation is one of the fundamental structures of any climatic range. Hence, recognition of the features and the prediction of these basic structures have a great impact on energy-related planning. One way to gainaccess to the solar energy information is the direct measurements of solar energy by measuring devices such as Pyranometer and Pyrheliometer Unfortunately, the measurement of the solar radiation is not always carried out in many parts due to the high cost, maintenance and the need for the equipment calibration. Remote sensing techniques ​​can be an appropriate alternative to the experimental and old methods in this field due to the high accuracy and speed in predicting the net radiation values. In general, remote sensing models have a better performance in estimating solar radiation, and can be used as one of the suitable and low cost tools for estimating solar radiation. Considering the importance of solar radiation as a clean, availableand free of any environmental destructive pollutants, identifying the radiation areas to be introduced to the relevant authorities is essential and the aim of the research. In this research, it was attempted to study the feasibility of utilizing solar energy in the region of Alashtar County using the SEBALalgorithm and remote sensing technology.

To investigate and study the feasibility of using solar radiation energy, the Landsat-8 satellite images over a 12-month period of the year 2017, 1: 50,000 digital topographic maps of the Armed Forces Geographic Organization and the climatic data of the study area including temperature, precipitation, wind speed and the number of sunny days were used. The ENVI software was used to perform the calculations related to SEBALmodel and the ArcGIS software was used to implement the model. In this study, the feasibility of using solar energy in Salsala city was studied using SEBALalgorithm and remote sensing technology. In this method, the instantaneous values of pure radiation are obtained by measuring the sun’s incident radiation from the cloudless images and using surface albedo, surface emission and surface temperature. In this method, instantaneous values of pure radiation are obtained by measuring the sun’s incident radiation from cloudless images using surface albedo, surface emission and surface temperature. After calculating the parameters of the SEBAL algorithm, the net surface radiation flux was calculated.

The results showed that the average maximum short-wave radiation was 996 watts per square meter in June and the minimum was 460 watts per square meter in January, while the highest amount of net radiation in September was calculated to be 602 watts per square meter and the lowest amount in January was calculated to be 261 watts per square meter. Also, the highest percentages of net radiation distribution in the ranges of 0-200, 200-400, 400-600, 600-800 and 800-1000 watts per square meter were in August, November, April, September and June. The highest percentage of net radiation distribution was in the range of 600-800 watts per square meter with 69.86% of total net radiation in September and the lowest percentage was in the range of 800-800 watts per square meter in January.

In order to carry out the research, the Landsat 8 ETM satellite images for the 12 month period of the year 2017 were provided. But, since the images of February, March and December were completely cloudy, they were not used. Then the preprocessing operation in ENVI software was used on all bands of images. The amount of pure radiation in the study area was calculated in watts per square meter in January to November in ENVI software environment and by the utilization of SEBAL algorithm, using the prepared images (Table 2). The results of Table (2) show that the average maximum input shortwave radiation is 996 watts per square meter in June, the lowest amount input is 460 watts per square meter in January, the highest output long wave radiation is 539 watts per square meter in July and the lowest output is 391 watts per square meter in January. Finally, the highest amount of net radiation reaching the surface of the Earth was 602 watts per square meter in September and the lowest amount was 261 watts per square meter in January. The highest percentage of net radiation in the range of 600-800 watts per square meter was 69.86% in September 2017 and the highest percentage of net radiation in the range of 600-400 watts per square meter was 60.12% in January 2017. The difference in the amount of net radiation reaching the ground in the study area is due to the difference in the angle of the sunlight and the number of sunny hours in different months of the year.The results obtained from of the information in Tables 2 to 11 prove this fact. Also, given the sensitivity of the photovoltaic cells that are sensitive to the solar radiation from the radiation threshold of up to 1000 watts per square meter and receive them, it can be concluded that solar radiation in the city of Alshtar has the potential to implement the solar photovoltaic plans in 9 months of January to November.