جستجوی مقالات مرتبط با کلیدواژه "principal component analysis" در نشریات گروه "جغرافیا"

This research examines the influencing factors in the formation and growth of informal settlements in Mashhad metropolis. The results of the factor analysis model showed that macro and local management factor with 20.9% of the total variance was the first determining factor in the formation and growth of informal settlements in Mashhad,. The results of the t-test showed that the indicators of the faster growth of the areas from the plan and plan, the weakness of urban management, the inefficiency of the government's plans and policies, unemployment and not having a suitable job (at the origin), obtaining job opportunities and more income (at the destination) and the cheapness of land and the low price of rent (at the destination) have been the most important influencing indicators in the formation and growth of informal settlements in Mashhad. In addition, the ownership sub-indices of Astan Quds Razavi and the lack of proper monitoring of land acquisition and occupation, proximity to workplaces and activities, the presence of villages with a high population and the possibility of integration and absorption in the region and the city, the presence of elements and projects that drive development, better access to Welfare facilities, religious and cultural attraction of Mashhad, migration of foreign nationals especially from Afghanistan, closeness to relatives and ethnic and cultural similarities and lack of suitable background for growth and development at the origin of residence were also identified as differentiating indicators in the formation and growth of informal study areas.

Aerosol optical depth (AOD) is a dimensionless quantity that shows the amount of light passing through the atmosphere and expresses the amount of absorption and scattering caused by aerosols in the path of light passage. Knowing AOD is necessary to understand its effects on air quality and provide a strategy of confrontation with it. South Baluchistan basin is affected by dust and high concentrations of aerosol due to its geographical location. Therefore, in this study, the tempo-spatial changes of AOD in this basin have been analyzed. In this research, the data of the AOD product (MOD 04 L2) and, the Deep Blue algorithm of MODIS sensor of Terra satellite during the period of 2002-2019 were used. Principal component analysis (PCA) and cluster analysis were used for data processing. The results showed that the first component alone explains 84% of the variance of the data. The pattern of temporal changes shows that this component exists throughout the year, but it decreases in the cold period and increases in the warm period of the year. Based on the temporal changes of the AOD, the basin can be divided into four temporal periods: winter, spring-autumn, transitional, and summer. The mean AOD reaches 0.69 in the summer pattern. This means that the basin has relatively dusty weather during the summer. Based on spatial distribution, the basin can be divided into three areas: mountainous, piedmont, and plains. The average AOD in the basin is 0.38, which reaches 0.62 in the plain zone. The AOD regime is the same in all three zones, but the plain zone is significantly different from the other two zones in terms of quantity. The high deal of AOD in this basin is related to regional factors that are active in the warm period of the year (Indian monsoon) in addition to local factors.

Shamal wind is one of the local winds with high continuity and diversity in the Middle East and the Persian Gulf region, which often causes dust storms during the summer. In this research, the hourly data of sea level pressure, geopotential height, U and V winds were collected for a period of 44 years from the Copernicus database. First, these data were arranged as a long-term mean, secondly. In order to process the data, principal component analysis and cluster analysis were used. Applying the PCA showed that more than 97% of the variation of the data is explained by three components. The score map of the first component revealed the spatial mechanism of the Shamal summer wind and the eigenvector diagram of the first component of the time period of the Shamal summer wind activity. In addition, the hierarchical cluster analysis on the time-space matrix of SLP showed three-time clusters that represent the time period of the north wind in summer and winter. The analysis of the maps of the monthly pressure distribution pattern showed that the most important active and influential system in the region is the low pressure of the Persian Gulf in the summer season, and the low pressure of the Red Sea in the winter season. The results suggest that the changes in the patterns of the earth's surface at the levels of 500 and 700 hPa are not much significant while it shows a better existence in the 850 and 1000 hPa maps.

Carbon dioxide is one of the main greenhouse gases that affect the world's air temperature. Small changes in the amount of carbon dioxide emissions from the soil have a significant effect on the concentration of this gas in the atmosphere. Soil respiration, the process that emits carbon dioxide from the soil to the atmosphere, is one of the most important carbon flows in the ecosystem and includes two components of heterotrophic respiration (microbial respiration) and autotrophic respiration (root respiration). Researchers measure the rate of soil respiration for every 10 degrees Celsius of temperature change with an index called temperature sensitivity of soil respiration (Q10). The evidence shows that the Q10 value of the soil is not constant and has a negative correlation with temperature and a positive correlation with soil moisture. Also, the amount of soil organic carbon, incubation temperature and the interaction of these two have a significant effect on soil organic carbon decomposition. Accordingly, this research measures the temperature sensitivity (Q10) in soil under tea cultivation and investigates its relationship with some soil chemical characteristics and topographic indices.

After surveying the east and west tea gardens in Guilan province in the north of Iran, 200 samples were taken at a depth of 0 to 40 cm. The experiments were conducted to determine Organic Carbon, Labile carbon, Bulk density, PH, Cation Exchange Capacity, Microbial Biomass and soil microbial respiration. To measure Q10, two temperature treatments of 25 and 35 °C were used. Elevation, slope and aspect were obtained using a DEM map in ArcGIS 10.5 and other topographical indicators such as wetness index, slope length, relative slope position, catchment area, channel network base level, vertical distance to channel network, convergence index, profile curvature and plan curvature were extracted from DEM map in Saga GIS 2.1.0. Pearson correlation was used to investigate whether there is any relationship between soil temperature sensitivity with other soil properties. Then, principal component analysis (PCA) was performed to determine a minimal data set. All the statistical analyses were done with SPSS 24. Regression charts were also drawn using Excel software.

The Q10 values varied from 1.19 to 1.58. This index has the most negative correlation with organic carbon (-0.863), Labile carbon (-0.863), microbial biomass (-0.837), respiration at 25 °C (-0.831) and 35 °C (-0.8) at 1% level and negative correlation with elevation at 5% (-0.159). The principal component analysis showed that the first six components (PC1, PC2, PC3, PC4, PC5 and PC6) have special values of more than one and were able to describe 73% of the total variance. The first main component (PC1) describes 23.125% of the total variance and includes soil organic carbon, labile carbon, microbial biomass and Q10 which have the highest factor loading in this component. The second one (PC2), which explains about 12.99% of the total variance, has the highest factor loading with the vertical distance to the channel network (0.880). The third component (PC3) explains about 12.22% of the total variance. In PC3, clay has the highest factor loading. In the fourth component catchment area, convergence profile and slope length have the highest factor loading, respectively. Finally, the fifth and the sixth components are related to the elevation, slope and plan curvature.

The highest positive factor loading is related to soil organic carbon (0.981). Therefore, the first main component can be "part of the role of organic carbon in microbial biomass, labile carbon and temperature sensitivity". The results showed that Q10 has the highest negative correlation with soil microbial biomass and organic and labile carbon. In other words, the higher the soil organic and microbial biomass carbon, the lower the amount of Q10. Also, the second component can be considered as topographic indicators related to the channel network. Topographic indices can be used very strongly to model making soil organic carbon. The third component is related to clay properties. Several studies have indicated that the amount of clay has a high relationship with cation exchange capacity and it is a good indicator to determine the quality of soil. According to the results, although the correlation between some characteristics obtained from soil topographical analysis can prove the possibility of using them as auxiliary variables in predicting soil organic carbon, this point should be taken into account that other factors also play a role in the process of soil formation and development.

One of the important issues in the sustainable management of soils in order to optimize the agricultural production and preserve natural resources is the assessment of soil quality.  Soil quality is the capacity of a specific kind of soil to function to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation. It is also considered one of the most important factors investigated in the assessment of soil management. Knowledge of quality changes is necessary for sustainable soil management. Since soil quality cannot be measured directly, it must be obtained from the relevant characteristics. Soil quality characteristics are sets of measurable soil characteristics that are sensitive to land use change, management, or conservation operations. Using soil quality indicators is a useful tool to determine and compare soil quality. The purpose of this research is to quantify the soil quality index using multivariate analysis in different soil types in Urmia Plain.

In this research, according to the semi-detailed soil studies of Urmia plain, 24 soil profiles from different soil units of this area, which are mostly under garden and agricultural use, were excavated, described, sampled, and classified, and 96 samples were also collected from soil solum. 2 profiles in Typic Haploxerepts (TH1) soil type, 4 profiles in Fluvaquentic Endoaquepts (FE) soil type, 6 profiles in Typic Calcixerepts (TC) soil type, 2 profiles in Typic Endoaquepts (TE) soil type, 5 profiles in Fluventic soil type Haploxerepts (FH) and 2 profiles were located in Typic Halaquept (TH2) soil type. At a certain distance from the excavation site of the profiles, four soil samples were taken from four directions of the profile. Using principal component analysis (PCA), among 22 characteristics affecting soil quality (TDS), the minimum characteristics affecting soil quality (MDS) were determined. Then the soil quality in different soil types of region was evaluated using two cumulative soil quality indices (IQI) and Nemuro Quality Index (NQI) and each of them was evaluated in two sets of TDS and MDS in different soil types.

Among the measured parameters, pH had the lowest (2.5%) and salinity (EC) had the highest coefficient of variation (154.6%) in the region. Among the 22 measured soil properties, sodium absorption ratio (SAR) in the first component, nickel element (Ni) in the second component, cation exchange capacity (CEC) in the third component, sand percentage in the fourth component, active lime (ACCE) in the fifth component and absorbable phosphorus (PAW) in the sixth component were selected as MDS. The examination of the soil quality index showed that the soils of this region mainly have quality grade II (57%). The highest average value of selected soil quality index related to MDS mode in IQI model was calculated with a value of 0.79 in Fluventic Haploxerepts soil type and the lowest average value related to TDS mode in NQI model was calculated with 0.28 value in Typic Halaquepts soil type. The correlation coefficient between the soil quality index with the total category and minimum data in both IQI and NQI models was equal (R2=0.48).

The sequence order of both soil quality indices (IQIa, NQI) in both TDS and MDS conditions in the types of the studied area was as FE>TE>TH1>TC>FH>TH2. Therefore, the highest soil quality was observed in the Fluventic Endoaquepts type and the lowest soil quality was observed in the Typic Halaquepts type. Based on the results, both indicators were classified in three classes (good, medium and poor) in TDS and MDS sets. In the case of TDS, 57.83% of land (equivalent to 19731 hectares) had good class (II), 30.48% of land (equivalent to 10400 hectares) had medium class (III) and 11.69% of land (equivalent to 3990 hectares) they had poor class (IV); (very good (I) and very poor (V) class were not observed). In MDS mode, 18.56% of lands (equivalent to 6333 hectares) had very good class (I), 27.15% of lands (equivalent to 9264 hectares) had good class (II) and 54.29% of lands (equivalent to 18524 hectares) They had medium class (III); (weak class (IV) and very weak (V) were not observed). The degrees of IQIa and NQI indices were similar and were divided into three classes (good, medium and poor). As a result, 39.3% of land (equivalent to 13412 hectares) was in good class (II), 24.59% of land (equivalent to 8392 hectares) was in medium class (III) and 1.36% of land (equivalent to 12317 hectares) was placed in poor class (IV); (very good (I) and very poor (V) classes were not observed). In general, the soils of the region were limited in terms of the studied indicators in the parts leading to the lake, and there was a significant correlation between IQITDS and IQIMDS, and between NQITDS and NQIMDS. This shows that the determined MDS set can be a good representative of TDS in soil quality assessment in Urmia Plain.

Attention to Ardabil province is one of the important centers of wheat cultivation, determining the level of cultivation will be of particular importance in economic and political planning and can ensure the country's food security, but in the past, most traditional methods, such as field measurement, etc., have been used to determine the level of crop cultivation, which has many errors, In this study, we tried to use Landsat 8 satellite imagery, Vegetation Indices (NDVI, SAVI, DVI, GVI, IPVI, RVI), Principal components analysis (PCI), Optimum Index Factor (OIF), and classification algorithm to estimate the maximum probability of wheat cultivation area in the study area. Based on the results of main components analysis, more than 99% of data variances were explained in three main components and the best color combination of the OIF index was determined by bands 5, 6, and 7 with a numerical value of 8383.73. Also, the results showed that wheat cultivars under cultivar 94-95 in the studied area with kappa coefficient is 0.87 and the general accuracy of 95.2% were 59203.07 hectares and According to the statistics of agricultural Jahad official in Ardebil province, which is about 62480.21 hectares, In other words, the difference is 5.24% or about 3277.14 hectares which seem to be acceptable. Therefore, it can generally be concluded that Landsat 8 images, Principal components analysis, and Optimum Index Factor are highly effective in determining the level of wheat cultivation.

In this research, the daily data of geopotential height of 500 hectopascals (hPa) with a spatial resolution of 1 degree from the ECMWF database for Southwest Asia and rainfall station data from the National Meteorological Organization (1979 to 2018) have been exerted. The technique used the principal component analysis and cluster analysis. With these analyses, nine circulation patterns were identified. The changes in the patterns were tested at the 95% significance level by the non-parametric Mann-Kendall test, and Sen's slope estimator was exerted to estimate the number of changes. The significance test of the trend for the winter patterns in Iran's rainy season revealed the significant trend of increasing the height of the geopotential, which has led to a decrease in the pressure gradient and a decrease in instability, and finally, a weakening of the winter precipitation patterns. Significant positive trends of geopotential height showed the continuation of these conditions for summer patterns (increasing stability, decreasing rotation, and decreasing precipitation). Of the nine known patterns, only one seasonal pattern showed a significant negative trend in the country. This pattern, with a slight increase in rainfall, indicates the formation of unstable conditions, which can lead to moderate-season rains if moisture is available. The findings showed that a rainy winter pattern had been eliminated in the last two decades, and a summer pattern had appeared instead.

Due to human activities, the face of the earth is always changing. For the optimal management of natural areas, it is necessary to know the trend and amount of changes in vegetation/land use. Estimating these changes is very important. The purpose of the present research is to monitor land cover changes using satellite images in the Gorganrood watershed in Golestan province. The images of TM sensor in 1987, ETM+ sensor in 2000 and OLI sensor in 2019 were processed and analyzed. After performing geometric and atmospheric corrections, monitoring and classification of images using six change monitoring techniques in Gorganrood basin with an area of 8020 square kilometers were analyzed and the changes occurred in this basin in two time periods, from 1987 to 2000 and 2000 to 2019 was investigated. Change monitoring techniques include red band difference, infrared band difference, PCA difference and standardized PCA difference, change vector analysis and comparison after classification. Statistical methods have been used to determine the threshold. After determining the change threshold, the areas with decreasing, increasing and unaffected changes have been identified. In order to evaluate the accuracy of change monitoring techniques, after the ground impressions obtained through field visits, Google Earth satellite images and aerial photos, the manufacturer's accuracy, the user's accuracy, the total accuracy and the Kappa coefficient were used. The results showed that the standardized PCA1 method had the highest total accuracy and kappa coefficient in both periods. The values of these two parameters are equal to 96.5% and 94% for the first period and 91.5% and 86% for the second period respectively. PCA1 method with overall accuracy and Kappa coefficient equal to 84.5% and 74% for the first period and 89% and 82% for the second period after standardized PCA1 method has the highest level of accuracy among other methods. On the other hand, the near-infrared band difference method had the lowest total accuracy and kappa coefficient in both periods compared to other methods. The results also show that between 1987 and 2019, agricultural lands (rainfed) had the most positive changes. The extension of these lands has mostly been at the price of loss of pastures (due to higher fertility). In the space of 32 years, pastures have also undergone changes and transformations, and the decreasing changes in them can be attributed to the plowing of pastures and their allocation to agriculture.

Drought is a complex phenomenon caused by the breaking of water balance and it has always an impact on agricultural, ecological and socio-economic spheres. Although the drought indices deriving from remote sensing data have been used to monitor meteorological or agricultural drought, there are no indices that can suitably reflect the comprehensive information of drought from meteorological to agricultural aspects. In this study, the synthesized drought index (SDI) as a synthesized index from the vegetation condition index (VCI), temperature condition index (TCI) and precipitation condition index (PCI) were used for comprehensive drought monitoring in the Urmia Lake Basin (ULB) based on the Principal Component Analysis (PCA). For this purpose, MOD13A3, MOD11A2 and TRMM 3B43 data series were downloaded y for the period of 2001–2012. After initial processing, drought indicators were calculated using LST NDVI and TRMM data, and monthly drought severity maps were prepared. In order to validate SDI index, the Correlation relationship between SDI and SPI indices was obtained in the 3 month period during the growing season. As well as, SDI correlation relationships were investigated with wheat and barley crop yields. The results indicate that drought occurred in 2008 and 2001 in the ULB. The results of validation show that there is a correlation of 80% between the two SDI and SPI indicators. Also, the results of this study showed that the SDI index, as a comprehensive index of drought monitoring, reflects the effects of drought on agriculture.

Erodibility, which is determined by the soil's intrinsic features, is one of the most important elements in soil erosion. This factor reflects how sensitive the particles of a particular soil are to separation and transmission by erosion causes, both quantitatively and qualitatively. For measuring soil loss, the Universal Soil Loss Equation (USLE) is very useful. Sources reveal that erodibility is influenced by a variety of physical and chemical features of soil. In several soil erosion and sedimentation models, such as USLE, RUSLE, and MUSLE, one of the essential parameters is erodibility, which is represented as K. Particle size distribution, organic matter, structure, and permeability all have a role. The goal of this research was to quantify the amount of erodibility (K) in dry and semi-arid soils, as well as the physicochemical parameters that influence it. Another purpose of this research is to develop a connection that uses principal component analysis (PCA) and linear multivariate regression to estimate the quantity of soil erodibility based on effective physicochemical parameters.

The research location is 20 kilometers from Yazd city, along the Yazd-Ardakan road, on the edge of the dunes facies, which includes bare, mantled, and covered pediments. Using the stratified random sampling approach, soil samples were gathered to a depth of 10 cm within the facies in this study. The size and form of aggregates, as well as water penetration in the soil, were used to calculate soil structure codes using Wischmeier and Smith's tables. In the desert, soil permeability was assessed using double cylinders based on the ultimate infiltration rate. The hydrometer technique was used to determine the spread of soil granulation. Wet sieving and the Walkley and black methods were used to assess the proportion of extremely fine sand and organic matter, respectively. Lime was calculated by multiplying the volume of the hydrochloric acid neutralization reaction by the quantity of neutralizing agents. Statistical indicators such as mean, minimum, maximum, and standard deviation were derived at this step after computing the soil erodibility index. Principal component analysis was performed using SPSS17.0 software, and the linear multivariate regression model was utilized to predict soil erodibility index. After selecting significant components, linear multivariate regression between these components and soil erodibility was conducted concurrently. The coefficient of determination was used to assess the equation's accuracy in this investigation (R2).

The findings of the physical and chemical features of soil study revealed that the texture of the soil is mostly light sandy to loamy, with low organic content and calcareous. In terms of structural form, the analyzed soils were extremely fine granular and spongy, and their structural code was based on USLE (2 and 1). The permeability of the soil profile was high to extremely high (18.4 cm/h), and it was often in Class 1, 2, and in some instances Class 3 according to USLE. In the three naked, mantled, and covered pediments, the estimated erodibility indexes based on Wischmeier and Smith regression relationships were 0.0385, 0.03, and 0.0199 ton.hr/MJ.mm, respectively. According to the particular values acquired from the parameters and the percentage of variance, the top three components may be picked as the major component using principal component analysis. The first, second, and third components have correlation values of 0.88, -0.04, and 0.41, respectively, with the soil erodibility index. As a result, the first component has a stronger relationship with the soil erodibility index than the second and third ones. The percentage of sand and silt, soil permeability, and percentage of clay have a higher correlation with the soil erodibility index, respectively, and the correlation of other factors (organic matter, gravel, fine sand, and lime) is low in this component, according to the values for the given loading period. The amount of sand in the soil and its permeability are negatively correlated; whereas, the percentage of silt and clay in the soil is positively correlated. The maximum load is connected to the variables of gravel and lime in the second component, and it is related to organic matter and extremely fine sand in the third component. The effect of characteristics on soil erodibility is significant (0.001>p) and its coefficient of determination (R2) is 0.88 percent, according to an investigation of the relationship between soil erodibility and principal component values obtained from PC1, PC2, and PC3 using a linear multivariate regression model.

 The quantity of erodibility in dry and semi-arid soils, as well as the physicochemical parameters that impact it, were investigated in this research. Using principal component analysis and linear multivariate regression, a link was found to estimate the quantity of soil erodibility based on the effective physicochemical parameters. Because of the high amount of sand in the region's soils, these soils are readily separated due to poor adhesion, but because they contain bigger particles, they resist runoff and hence create less sediment. This barrier to transfer reduces as the quantity of clay and silt in the soil increases, and consequently more sediment is transported. Furthermore, a considerable quantity of sand improves soil permeability and reduces runoff. However, when the amount of silt and clay in the soil increases as a result of surface sealing, permeability reduces and greater runoff occurs. Soil erodibility is additionally influenced by organic content, lime, gravel, and permeability. Lime has a negligible influence on soil erodibility since it contains calcium cation, which increases particle homogeneity and hence increases soil resilience to rain drops. Organic matter has a negative relationship with soil erodibility as well. The breakdown of aggregates is slowed by increasing the quantity of organic matter in the soil. As a result, as organic matter levels rise, the rate of aggregate decomposition in a particular soil falls by one-third. Similar research studies in other semi-arid and arid soils in Iran are required to provide a more reliable connection for forecasting erodibility of soils in semi-arid and arid locations.

Water pollution has always been one of the environmental issues around the globe. Among the surface and underground waters, running water and rivers are constantly exposed to more pollution due to the crossing of different areas. The purpose of this study was to evaluate the water quality of Kor River in south of Iran and determine the source of contamination by different factories located nearby. Maternal sample in 2016 on 14 physical and chemical parameters, December 3, April and September). 29 stations completed. Principal Component Analysis (PCA) determined the most important parameters affecting water pollution, and WQI and HEI indices determined the contamination status of each site according to the permissible limits in the river water, and finally, using Karjing method. The spatial distribution of elements in adjacent factories was investigated. The results of the measurements using Principal Component Analysis (PCA) showed that PCA introduces 13 components. The average of the lowest WQI index at station 17 was 0.9 ± 5.0 with non-food quality and highest value at station 6 with mean 87.3 ± 2.0 with good drinking status. Results from HEI showed that stations 16 and 17 had the highest values of HEI index indicating the most polluted stations. The results of the HEI showed that the values of As, Cd were much higher than the permissible limit in drinking water. Finally, the results of heavy metal zoning maps using Kriging method showed that the highest concentration accumulation was related to the central and southern parts adjacent to the factories in the study area.

Explanation of the subject: The annual drought phenomenon, by affecting economic, social and environmental issues, leads to the vulnerability of urban and rural households and the instability of their livelihoods. Yazd is one of the provinces with drought. Consecutive droughts in the province necessitate integrated management and community adaptation in times of drought.

Taking into account the length of the statistical period of 20 years and to obtain the results with a high level of confidence, the main data of the census documents that have been compiled for the development of cities and villages have been used. By analyzing the main components of several factors, it was selected as the main components. By calculating the standard precipitation index in the arid region, the driest year was determined and by calculating the weighted average of their correlation index with the main components of socio-economic and ecological environment based on appropriate statistical inference. At the end of the year, the effect of drought on different dimensions was presented by step-by-step linear regression, analysis and communication between them to adapt and resilience of individuals in society.
 According to the general results, one of the most important economic and dry economic losses is the annual income of the villagers, which can be due to the decrease in the area under cultivation and production of the main agricultural products. In the social sector, people with knowledge and awareness should increase their adaptive capacity to the occurrence of drought, in order to reduce the vulnerability of social issues to the phenomenon of drought. The results show that unemployment insurance has increased following the drought. The main reason for this is the unemployment of farmers affected by drought, so changing jobs along with temporary migration or the production of handicrafts, etc. can increase the relative income of households at the time of occurrence and prevent unemployment in these conditions. Increasing unemployment will cause other social harms such as poverty, declining health, increasing disease, and reducing judicial and social security. According to the results, one of the components that has established a high standard of rainfall during the drought year is the theft of livestock, which shows a decrease in the social security of the community. People in the study community increase their adaptability to the annual drought by increasing breeding work, such as rangeland improvement, rainfall collection, biological improvement, afforestation, and irrigation reform.

The main sources of albedo change are variations in snow cover, variations in soil moisture, droughts, and variations in vegetation phenology, forest fires, and land use/ cover changes directly related to human activities, such as deforestation, irrigation, and urbanization. Forests obtain lower albedo values than shrubs, dry crops, grasslands, and bare soils. As a result, the conversion of forests to these land cover types leads to increases in surface albedo. This potentially has local and regional feedback, since an increase in surface albedo leads to a reduction in net radiation, turbulent heat fluxes, convective clouds, and precipitation, leading to a drier atmosphere Furthermore, black carbon decreases the surface albedo when deposited on snow and glaciers because it is incorporated in snowflakes, darkening snow and ice surfaces and increasing surface melt. Aerosols like dust transferred into the atmosphere and transported by the wind into the mountains where it settles on snow and glaciers, reducing albedo and leading to enhanced warming at higher elevations. It is noted that even though precipitation is the main driver of variations in soil moisture, its impact on albedo is controlled by evaporation, soil type, irradiation, vegetation, and topography. The present paper aims to evaluate the spatiotemporal variations of white sky albedo in Iran. For this, daily Albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied from 2000 to 2019 with a spatial resolution of 500 × 500 m. MODIS provides black-sky albedo for direct and white-sky albedo for isotropic diffuse radiation at local solar noontime. For this, daily white sky albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied. One of the main applications of the principal component analysis (PCA) is climatic zoning which is a method of determining environmental changes in temporal dimensions. A plethora of studies have been conducted using principal component analysis in the field of climatology but little has been done in relation to the albedo variation. To the best of the authors’ knowledge, this study uses a technique that has not been applied in scientific texts related to Modis albedo data. The questions that we will address in this study include: what is the temporal-spatial behavior of white sky albedo in Iran? How many components explain the variation of white Sky Albedo? What factors will distinguish white sky albedo in Iran?

In this investigation, daily white sky albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied for the period of 2000-02-24 to 2019-06-03 (7040 days) with a spatial resolution of 500 × 500 m. Among the various MODIS datasets, white sky albedo datasets were extracted. The daily white sky albedo was averaged over the 19-year period for each pixel inside the border of Iran. The size of this array was 7541502 pixel *12month. Long-term monthly and seasonal means were also calculated by the available time series data. In the next step, the PCA method was applied to analyze the spatio-temporal variations of albedo in Iran. PCA is a method to reduce the number of the data and convert them into several finite components so that these few components explain the largest amount of the variance. This procedure is searching for the variable with the largest amount of the variance in space (PCA was invented in 1901 by Karl Pearson , and it was later developed by Harold Hotelling in the 1930s. In this method, initial variables are converted into n principal components each being a linear combination of the variables. In this way, the first principal component has the largest possible variance, and the components afterward explain a smaller percentage of the variance. Principal component analysis leads to the analysis of space-time array into two time-array and space-array. In this case, it is possible to identify what important spatial patterns the primary data have and at what time periods each of these patterns has been active or inactive. Because the principal components are finite, the temporal and spatial patterns introduced by the first component are more important than the temporal and spatial patterns of subsequent components.

The long-term average of Iran's white sky albedo was calculated; The results showed that the average albedo of spring, summer, autumn and winter in Iran 14.99%, 16.06% , 15.53%, and 19.58%, respectively. The evaluated long-term mean white sky albedo for each season showed that the highest value had occurred in winter. The dramatic increase in this value was placed along the Zagros, Alborz, Sahand, and Sabalan Mountains which exceeds 90 to 100 percent in some places. In the next step, the temporal-spatial variations of white sky albedo values in Iran were analyzed using principal component analysis, and the results showed that the three main components are able to explain 97.7% of the data variation. The first component explains more than 73%, of the total changes, the second component more than 20.8% and finally the third component explains more than 3.9% of the changes.

Spatial analysis revealed that the values which are higher than the mean are places in highlands and mountainous regions of Iran, such as the Zagros and Alborz Mountains, Sabalan, Sahand mountains and Zard Kuh-e Bakhtiari, which are associated with snow cover Therefore, the first component was named as snow cover as the maximum variance of albedo was explained by snow cover. The spatial analysis of the second component revealed that higher values were placed in small areas across Iran including, Hajaligholi desert Gavkhuni wetland, Qom salt lake, Sirjan salt lake parts of Loot desert. In the second component, most of the cell's scores upper of average in Iran corresponded to areas covered with salt. As the maximum variance is explained with salt cover, therefore, it can be named as the salt land. Spatial analysis indicated that in very limited parts of Alborz, Zagros, Alam-Kuh Mountain , Sahand, and Sabalan mountains Kino Mountain values are mostly positive which is related to the glaciers (regions with appropriate conditions to keep the snow cover in most of the year) and is the origin of the seasonal or permanent rivers Therefore, according to the cell scores (upper of average in Iran) in the third component, it was found that these cells corresponded to the , so it was named as the glacier component.

Today, economic challenges is the most important obstacles to achieving sustainability in the cities of developing countries. Therefore, recognition and geographical analysis of the factors affecting the economic sustainability of cities are among the important goals and priorities of urban and regional planning.

This research has been done by quantitative and survey method and using secondary data. In this study, with using the content validity index and content validity ratio, 17 objective indicators of economic sustainability were selected and economic sustainability of Chaharmahal and Bakhtiari cities measured and analyzed by using principal component analysis, spatial autocorrelation (Morans I) and inverse distance weighting interpolation methods.

Findings of the research in the principal component analysis show that the three principal components (economic welfare, economic dynamism and economic diversity) with a total variance of 60.7%, explain the correlation between research variables to measure and evaluate the urban economic sustainability of the province. Also, spatial autocorrelation Moranchr('39')s Index with a value of 0.031 indicates the random spatial pattern of urban economic sustainability in the province and the significance of spatial relationships of 7 urban centers in economic welfare, 8 urban centers in economic dynamism, 6 urban centers in economic diversity and 9 urban centers in the composite index of economic sustainability.

The results of the study, while showing the unfavorable situation of the cities of the province in the economic welfare component, show the concentration of the better situation of the cities located in the eastern half, especially the cities located in Saman, Borujen and Shahrekord county compared to other regions of the province in all three principal components and combined economic sustainability index.

Precipitable water (PW) is highly variable in space and time and is one of the most important abundant greenhouse gases that play crucial role in the study of climate change, hydrological cycle, energy budget and numerical weather prediction. The knowledge about the spatial and temporal variability of PW is important in understanding climatic processes and in order to monitor drought conditions and desertification processes (Kaufman & Gao,1992). It is therefore necessary to obtain the distribution condition of water vapor in the atmosphere and to understand the effects of spatial–temporal variation of PW on regional, meso-micro scales and on global climate change (Wang, 2013). PW has a very short life cycle in atmosphere and this rapid turnover, joined to temperature variations with altitude and geography, distance to sea, evapotranspiration and moisture advection causes an irregular PW distribution in atmosphere, both horizontally and vertically. The purpose of this study was to identify the distribution patterns of PW in Iran and relationship these patterns with elevation and distance to sea.

In the present research, MODIS Aqua data (MYD05_L2. A V06) were used. The data with spatial resolution of 1 km (Near Infrared) have been selected. The selected study period covers since 2002/07/04 to 2017/07/25 (5501 days) that was exploited from NASA web site. These data are errors in the range between 5% and 10% (Kaufman & Gao,2003). The spatial resolution of the PW data are 1 km and temporal resolution is twice per day. Then using functions, these data converted from Level_2(swath data) to Level_3(grid data) and PW values interpolated on sinusoidal grid in 1800×2700 matrix with 1 km spatial resolution and daily temporal resolution. These data have been extracted for pixels within the political boundary of Iran and obtained a matrix with 1884080 rows (locations) and 200 columns (PW classes). Then on the base this matrix, calculated frequency distribution in 1 mm intervals from 0-199 mm for each of pixels (1884040×200). Finally, Principal Component Analysis (PCA) performed and frequency distribution patterns in Iran identified. The effects of altitude and distance to sea on these patterns, analyzed. The special program was developed and employed in MATLAB software for analysis the data.

The spatial distribution of atmospheric humidity in Iran is controlled by the height above the sea level, distance to sea and moisture advection. Based on the results, the mean annual PW of the country is about 12 mm. PW is maximum near the southern and northern coasts of the country. The highest and lowest amount of PW near the Oman sea coast (31 mm) and the peak of Damavand (3 mm), respectively. The results of PCA showed that 95% of spatial variation of PW can be explained through 4 components. Based on the results, local factors like distance to sea and altitude are the most important in spatial distribution of PW. The study of the relationship between distance from the sea and frequency distribution patterns of PW shows the effect of distance and proximity to the sea in the frequency distribution patterns. This fact is more evident in the first and second components. Up to the distance of approximately 250 kilometers in the first component and 150 kilometers in the second component, as expected, the amount of PW will gradually decrease. From now on, the spatial pattern of PW is affected by altitude and morphology rather than by distance from sea and sealand breeze. In the third component, due to the formation of a moisture convergence belt at approximately 11 and 4 km, respectively, on the south and north coasts, the amount of atmospheric moisture is maximum. Then from 11 to 66 kilometers due to the Alborz Range, which is a short distance from the Caspian Sea, the amount of PW is minimal. Minimal atmospheric humidity on the southern coast occurs approximately at 250 kilometers away from the sea. In the South Coast, moisture penetrates the country further away from the coast, as it is smoother than the North Coast; so that sea moisture enters through the straits of Kahnouj area into the Jazmourian plain and distinguishes this area from its surrounding areas in terms of moisture. Moisture in the Caspian Sea enters the Tarom Valley through the Manjil Strait. The spatial distribution of moisture in the western, middle and eastern Persian Gulf coasts does not have a similar pattern; this difference is due to factors such as the dominance of the sea-land breeze in the eastern areas of Bushehr and the presence of small firth and bays in the area that increase the atmospheric moisture of these areas. than the environment around them. The amount of moisture in the coast of the Oman Sea is clearly different from PW of the Persian Gulf; PW MODIS is also overestimated in places such as near beaches with high temperatures and humidity.In addition to the height above the sea level and distance to sea, the role of moisture advection should not be ignored. In the coastal region the variability caused by the high temperature and moisture advection and in areas far from coastline, height above the sea level causes many spatial differences in moisture distribution.

Although Iran is bounded from the north and south to the sea, atmospheric moisture is very low in the country. Based on the result, minimum and maximum difference of PW is about 27 mm, so that, in the region with more than 3000 m elevation PW is less than 6 mm, and the coast of the Oman Sea 60% of the time is above 26 mm. This result means, in spite of the great source of water in south and north, atmosphere of Iran suffers from poor moisture. Topography is a barrier to the entry of moisture north and south seas to the inland. In the inland region, altitude and in the coastal region, moisture advection and temperature, play crucial role in frequency distribution of PW. In this way, moisture advection is the important factor that well justified spatiotemporal variations of PW in Iran and through this, affected on water budget.

The purpose of this study is to investigate and analyze turbulence, fluctuations and jumps of Iranian regions. For this purpose, environmental data has been gathered in two parts. In the first part of the data, the results of the interpolation of the daily precipitation observations of 1434 stations of climate and climate were used from the beginning of 1340 to 1383. After the formation of a database to identify the Iranian regions, a cluster analysis was used on average data and annual and monthly rainfall variation coefficients. Silhouette analysis has been used to validate the Iranian rainy areas. In order to investigate disturbances, mutations and fluctuations in Iranchr('39')s rainy areas, this study was carried out. The results of cluster analysis indicate that Iranchr('39')s peripheral areas are divided into six classes. In the meantime, the Caspian region (area 4) has the highest rainfall and the lowest coefficient of variation. The distribution of rainfall regime in each of the six areas shows that Iranchr('39')s precipitation regime is more frequent in winter and spring and sometimes in autumn. Investigation and analysis of rainfall turmoil has shown that rainfall, except in the 4th district (Caspian region), in other areas of distribution of rainfall occurred along with disturbance. Although most disturbances occur in the zagros area, the highest sequence of disturbances is related to the fifth load region. The least sequence of disturbances occurred in the central and eastern part of the country. The results of mutation analysis and fluctuations indicate that rainfall disturbances, except in the early years of precipitation regions two and five in other regions of the other regions, have no significant mutation, while short-term fluctuations of 3-5 years on rainfall Each of the six domains has dominated.

Introduction:

 Soil degradation by water is the most serious form of land degradation in many parts of the world, especially in arid and semi-arid areas, where soil formation rates are usually less than soil degradation due to rapid soil erosion, the impact of human abuses And incorrect use of soils. For this reason, crushing land control strategies such as agricultural agriculture, mulch, environmental improvement or land expansion are necessary to avoid drought in agricultural land. Awareness of the process of soil erosion and sediment transport as an effective factor in reducing soil fertility and soil loss, filling dams, catching and blocking irrigation channels, polluting water from rivers, and reducing water quality have long been considered by geoscience experts. Understanding the factors affecting sediment production plays an important role in determining the amount of sediment yield of a basin and understanding the phenomenon of erosion and its consequences and can be used to prioritize sub basins in a watershed. Areal characteristics encompass morphological characteristics such as drainage density, stream frequency and watershed shape parameters. Ease access to Digital Elevation Models, remote sensing data as sediment yield predictors, simplify the calculation of the watershed geomorphic characteristics. The purpose of this study was to use the geographic information system to extract the watershed geomorphic characteristics and determine their relationship with sedimentation in the Gharanghoo basin.

Methodology:

This study was conducted in 19 subwatersheds in Gharanghoo basin. In order to select appropriate subwatersheds, the hydrometric and rainfall data for hydrometric and meteorological stations were obtained from East Azarbaijan Regional Water authority for the selected watersheds. Annual sediment load was calculated using sediment rating curve method. Physiographic and geomorphic characteristics including 25 geomorphic parameters were calculated for each sub watershed using digital elevation model with spatial resolution of 30 m. In order to determine the relationship between geomorphic characteristics and sediment yield of the subwatersheds, a multivariate regression stepwise analysis was used. In the multivariate regression, the important geomorphic characteristics which affect watershed sedimentation are identified and based on those parameters, the best annual sediment yield and geomorphic characteristics equation were presented.

 Results:

 The annual amount of sediment varies from 63500 tons per year in the Kalghan sub basin (Kalghan dam) to 4636762.6 tons per year in the gharanghoo area at the intersection with Ghezel Ozan. Basin sedimentation weight as dependent variable and other parameters were considered as independent variables. The variables of flow volume, area, environment, equivalent rectangular length, equivalent rectangular width, drainage density, branching index, minimum height, coefficient of elongation and roughness of the basin were compared. Other variables have higher correlation with sediment yield. The result of the study of the relationship between geomorphic characteristics and sediment of sub-basins showed that the amount of sediment produced with flow volume and basin coefficient was positively correlated and was significant at 5% level. The principal components analysis (PCA) method was used to identify the factors affecting sediment yield of the existing variables. The results show that the four factors of area, area, length and coefficient of form of basin are 50, 20.9, 13.6 and 7.7 percent of the variance of all variables, respectively. In total, the four finalized factors have been able to explain 92.2% of the variance of all research variables.The results show that the four factors of area, area, length and coefficient of form of basin are 50, 20.9, 13.6 and 7.7 percent of the variance of all variables, respectively. In total, the four finalized factors have been able to explain 92.2% of the variance of all research variables.

Discussion & Conclusions:

 The results of this study indicate that there is a significant relationship between the geomorphic characteristics of the studied watersheds and annual sediment yield. Watershed Form factor is a dimensionless index for flood flow and movement, erosion severity and sediment transport capacity of watersheds. This factor is a function of watershed area and length. Run off and amount of flood peak in bigger watersheds will increase sediment yield. Many researches have reported high correlation between rainfall and sediment yield. Arid climate and poor vegetation cover in selected watersheds is the main reason for high correlation of rainfall and sediment yield. Soil erosion and sediment yield will increase due to high intensity and low duration of rainfall along with scarcity of vegetation cover and erodible soils in this region. Overall, study results indicated that with the development of new technologies and the possibility of extracting different physiographic and geomorphic parameters of watersheds from a digital elevation model, it is possible to present regional equations for prediction of sediment yield using geomorphic characteristics that can be used in sediment control and Watershed Management Programs.

Oak is a common species in Iran and the most important one in Zagros forests. Zagros forests play a crucial and effective role in water supply, soil conservation and climate modification in Iran. Unfortunately, a significant part of those forests suffer from oak decline. Oak decline (or oak mortality) is a widespread phenomenon in oak forests around the world, which has gained the attention of many researchers in forestry over the past decade. In Iran, this phenomenon was first observed in Zagros forests in 2013. Factors affecting oak decline and their mutual interactions are not clearly identified, which makes understanding and modeling of these processes challenging. Only a few studies have been performed in relation to this phenomenon in Iran. Thus, we chose to determine the most effective parameters and find the best modeling method for oak decline in Iran and especially in Lorestan province.

In order to find effective environmental variables, related literature review was thoroughly investigated. Environmental parameters including temperature, precipitation, elevation, slope, direction, soil type, and amount of aerosols were selected as basic influencing parameters. All parameters were then interpolated to produce raster data with 30-meter cell resolution. To find the optimal combination of the parameters, four operators including multiplication, logarithm, hyperbolic transformations, and principal component analysis (PCA) were used. A total 385 different combinations of the influencing parameters were produced using the above mentioned operators. The relation and weight of each parameter are unknown, thus Artificial Neural Networks were used to model oak decline process. Three feed forward artificial neural network, including Back-propagation Neural Network (BP), Probabilistic neural network (PNN) and Support Vector Neural Network (SVNN) were selected as modeling methods. Then, 385 different combinations of the influencing parameters were used in the above mentioned models. To train and evaluate each neural network, a total number of 10000 samples were randomly selected from the study area. 70 percent of these random samples were used to train, 15 percent to evaluate and 15 percent to validate the models. Also, cross-validation method was used to avoid over fitting of neural networks. Finally, 1155 created NN models were compared using R parameter to find the best configuration for modeling oak decline and identifying the most influential environmental parameters in oak decline.

Evaluating 1155 different networks indicated that Probabilistic neural network (R=0.87) with 6 inputs, including 1) elevation, 2) slope, 3) direction, 4) aerosols, 5) soil type and 6) principal component of temperature and precipitation, performed better than SVNN and BP in modeling oak decline. Moreover, using different combinations of influencing factors improved the results and increased correlation coefficient (R) of optimal inputs by 0.05 as compared to initial inputs. Thus, it can be concluded that increased number of inputs does not necessarily guarantee a better performance. Furthermore, two principle parameters of temperature and perception have a more significant role in modelling drought stress as compared to other parameters.

Oak decline is a complicated phenomenon and different factors contribute to its occurrence. The present study investigates all environmental parameters affecting oak decline through a comprehensive literature review. Results indicate appropriate performance of probabilistic neural networks in modeling oak decline. Moreover, principal component analysis is considered to be a useful tool for modeling of drought stress in oak trees. Due to different accuracy and precision of these neural networks, it is necessary to evaluate different configurations. For further researches, it is suggested to use other parameters, such as distance from population centers, water table, age of oak trees, oak tree height and characteristics of other nearby trees.

Rainfall is the most significant phenomenon or feature of the environment. The factors causing rainfall have been the subject of numerous studies. Both the rainy or very dry years have considerable impact on the quality of humans’ living environments. Rainfall occurs when there is enough moisture as an effective factor. Both of these conditions are provided through circulation patterns of the atmosphere. The purpose of the present study is to identify the circulation patterns and factors causing rain showers in the mountainous regions of Iran (i.e. Southern Alborz and Eastern Zagros mountain range); moreover, it is attempted to explain how they are formed, during which period of the year they could evolve more significantly, and at what times they occur in the region, along with their similarities and differences. Given the breadth and considerable socioeconomic significance of the region being studied (i.e., Southern Alborz and Eastern Zagros mountain range) it is necessary to identify the circulation patterns causing such damaging rain showers. To this end, the occurrence of this phenomenon could be predicted by observing the commencement of the sequences of patterns leading to torrential rain showers at least one or two days beforehand. In this case, there would be enough time to make necessary preparations. In this study, the circulatory synoptic approach to environment was used; in this regard, first the statistics and data on the climate codes of past and present in stations including codes 80 to 99 related to showers or heavy rainfalls accompanied by lightning were extracted from 50 synoptic stations in the region of the study during a 30-year period (1985-2014). Next, the days of occurrence (code 80-99) were identified followed by the days with rain showers among the entire statistical data which had been observed in at least 50% of the stations of the region; ultimately, 80 days of rain showers were identified. Then, clustering approach was used on data in order to identify circulation patterns causing such showers based on Euclidean distances and integrate it with Ward’s method in SPSS. It was carried out according to the elevation data at 700 hectopascal level of these days, which is the best level for demonstrating how rain showers are formed. Furthermore, data on the vertical speed (omega) and components of orbital, meridional and vorticity winds obtained from clustering patterns at 10-80 E and 10-70 N with a spatial resolution of 2.5 2.5 degrees were provided. In the end, the related maps were drawn and analyzed in Surfer and GrADS softwares. According to the findings obtained from principal components analysis, seven primary components constituting 80% of total data diffraction were selected so as to indicate climate circulation patterns. Given 80 days representing the entire days being studied (19333 days) in 50 synoptic stations during a 30-year period (1985-2014), 7 clusters (circulation patterns) causing rain showers were identified through the aforementioned method as well as conducting cluster analysis with Euclidean distances based on data at the level of 700 hectopascal using Ward’s integration method. Drawing conclusions on the analysis of seven clusters at the region of the study showed the presence of drought in the western part of the region as well as blocking and cut-off low at the level of 700 hectopascals, low-pressure systems in Saudi Arabia and Eastern Iran and a high-pressure system at the northern part of the region, and dominant synoptic phenomena during days of rain showers at the region. Examining the patterns of climate’s upper levels in the region studied shows that the highest volume of rainfall occurs when systems are formed as blocking, while the high-altitude subtropical center moves toward more southern latitudes. Given the analysis of drawn synoptic patterns, according to the data of level 700 hectopascals, in general, it must be pointed out that among the obtained patterns from clustering the aforementioned level data, the highest number of days with rain shower occurrences in the region follows the cut-off low pattern of the Black Sea as well as the deep drought of North-Western Iran; these patterns have been formed mainly during Spring (March, April, and May). The highest amount of rainfall in this pattern has occurred in the Western and North Western of the region. The examination of this pattern demonstrated the presence of a deep and strong closed low-altitude center on the Black Sea. While this low altitude contour gradient center in the region moves, it paves the way for the formation and expansion of baroclinic waves in the eastern regions of the trough. As the low altitude center (cut-off low) is closed, the movement of Western systems over the Iranian mountainous region is slowed down and rainfall continues. On the other hand, the western and north-eastern parts of the region under investigation are influenced by the 1025 hectopascal high pressure of Western Europe in this pattern while the eastern parts of the region are affected by the low-pressure center. The presence of a strong ridge in the Northern Mediterranean Sea as well as a deep scot-off low in the Eastern Black Sea have strengthened baroclinic waves at the lower level of the atmosphere; consequently, considering the proper convergence in low levels, suitable conditions for ascension and positive vorticity, divergence at upper levels of the atmosphere, and desirable rain showers (more than 40 mm) have been observed, particularly, in the north-eastern parts of the region. In this pattern, humidity transfer has occurred in the north-eastern and eastern parts of the region over the Caspian Sea, through high-pressure tabs. In addition, the role of the cyclonic movement of south-eastern systems in transferring and enhancing the humidity of the Arabian Sea and the Persian Gulf cannot be overlooked. According to the 7 synoptic patterns formed in this clustering, the southern and south-western low-pressure systems are of substantial importance in offering convergence among low levels and rain showers. Rainfalls occur in high volumes when systems appear as cut-off low or deep and strong low-altitude centers in the upper layers of the atmosphere. Furthermore, the presence of a strong ridge in Western Europe and the subsidence of cold weather from high latitudes have enforced pass pression center at low levels while its tabs have led to the northern streams and the injection of Caspian humidity to the western parts of the region under investigation.