aliakbar noroozi

Due to the significance of forests in both the natural and human environment, this study aims to investigate the impact of meteorological drought on oak forest dieback in Ilam province. Specifically, the study seeks to determine the relationship between Zagros Forest drought and droughts in this particular region. The analysis utilizes the Standard Precipitation Index (SPI) to identify the frequency of droughts during different time periods. The results indicate that the years 2007, 2008, 2011, 2015, and 2016 experienced the highest occurrence of droughts. Additionally, remote sensing data from MODIS images were employed to examine the trend in tree greenness (NDVI) from 2000 to 2016. The analysis reveals a significant correlation (R2 = 0.9999) between the greenness trend and the drought index (SPI). Moreover, a land survey of oak drying points and simulation using Landsat satellite images, with a 15×15 pixel output from GIS software, indicate that approximately 17,894 hectares of forests in the region experienced drying and destruction between 2000 and 2016. By combining the oak forest drying layer with the output layers derived from drought zoning, visual indicators were created, and statistical analysis was conducted for three 5-year time series. The results demonstrate a correlation coefficient of 96.6% and an explanation coefficient of R2 = 0.985 for the 2002-2006 time series, a correlation coefficient of 95.4% and an explanation coefficient of R2 = 0.980 for the 2007-2011 time series, and a correlation coefficient of 98.8% and an explanation coefficient of R2 = 0.995 for the 2012-2016 time series. These findings illustrate the influence of drought and its variations in terms of intensity and duration on oak forests in the Zagros region of Ilam. Based on the study results, it is predicted that if the drought persists with the same trend, approximately 1,118.4 hectares of oak forests in Ilam province will dry up and be destroyed annually.

In most regions of Iran, including the province of Lorestan, the majority of agricultural activities are conducted in the open air. Climate risks have a significant impact on agricultural productivity. Drought and its effects are among the most significant natural threats to the agriculture industry in that area. The purpose of this study is to investigate the effects of drought on the yield of rain-fed crops in Aleshtar county, Lorestan province. To achieve this goal, a combination of field methods, remote sensing, and statistical methods was employed. During ground surveys, data required for laboratory operations (direct method) and measurements using AccuPAR and MODIS sensor images were collected (indirect method). In addition, precipitation data from synoptic stations in the province of Lorestan over the course of 27 years (1991-2017) were utilized to calculate the drought and its impact on yield. According to the calculated drought indices, in the province of Lorestan and the county of Aleshtar, the trend of increasing drought and the recurrence of long-term cycles of wet and drought are evident. The study of phenology characteristics of rainfed crops (barley) in relation to climate conditions revealed that an increase in thermal and water stress has a direct effect on the performance of rainfed crops. Therefore, an increase of 2.5 °C in the average temperature, combined with a lack of moisture supply during flowering, results in a decrease in the number of seeds per spike (16 seeds per spike) and, consequently, a decrease in the plant's yield. At various growth stages of rain-fed plants, the correlation index between LAI harvested by direct methods and remote sensing methods ranges between 0.57 and 0.96. This value represents the precision of remote sensing techniques. From 1991 to 2017, the correlation index values between the yield of rainfed plants, especially wheat and barley, and the values of various drought indices indicate a positive and direct relationship between yield and drought index values. The correlation index between yield and drought index values reaches its maximum value during 1-6 months, and its value decreases as time scales become longer. The physiological properties of various products are one of the primary causes of this circumstance. On the basis of the obtained results, it can be concluded that the increase in drought and heat stress in the province of Lorestan and the county of Aleshtar has caused a decrease in yield at various stages of plant growth and an increase in water demand for a variety of rainfed crops.

Measuring the moisture content of surface soil, as one of the most important components of the water cycle, plays a significant role in the optimal management of water and soil resources. The aim of this research was to evaluate the accuracy of spectral indices and Sentinel-2 satellite images for estimating soil moisture during the period of 2020-2022 in the Telo region, Tehran Province, Iran. The data used included the daily Sentinel-2 images, 30-Meters Digital Height Model and field measurements using the TDR. The spectral indices used in this study included NDWI, MNDWI, NDVI, NDMI, and SAVI. Analysis of the amount of surface soil moisture recorded showed that the average soil moisture in the area was 14.07%. In terms of time, the maximum amount of soil moisture with a value of 40% was recorded on December 18, 2020, and the minimum value was recorded with a value of 1.3% in early June, 2021. Analysis of the trend of soil moisture values showed that the minimum soil moisture values were recorded in the warm season due to the increase in temperature and evaporation and the decrease in precipitation, while the maximum soil moisture was recorded in the cold season with the beginning of the rainy season in the region and the decrease in temperature. Investigating the relationship between soil moisture values recorded using TDR and spectral indices calculated on Sentinel-2 images showed that there was no clear and significant relationship between NDVI and SAVI indices with TDR soil moisture values; and the maximum correlation was obtained for NDWI indexes with a correlation value of 0.23, NDMI with a value of 0.35 and MNDWI index with a correlation value of 0.59.

Dust event is one of the natural events that occur widely in the world, especially in dry areas. One of the main and effective factors in the occurrence of this phenomenon is the geographical location and climatic conditions of the regions affected by this phenomenon. The purpose of this research is to analyze the cause-and-effect relationships of dust events in Hendijan region with the approach of the Driver-Pressure-Situation-Effect-Response (DPSIR) framework. 

The DPSIR process is an analysis based on the "cause-disability" relationship of factors for policy-making and management planning. The DPSIR framework is a systems thinking framework that assumes cause-and-effect relationships between environmental and socio-economic systems. This conceptual framework uses a cycle of causes and results for the proper integration of basic economic, social and environmental data and information, specifies the relationship between environmental processes and human factors, and also leads to an understanding of the relationship between policy levels and environmental studies. The trend of each component of DPSIR was also evaluated by applying quantitative criteria for the time period of 2007-2019. 

The results of the research showed that the total index of all DPSIR components has an upward trend for the studied period. The slope of the trend related to D, P, S, I and R components was equal to 0.06, 0.03, 0.02, 0.05 and 0.02, respectively. Although some responses were made to reduce the influence of others. The components of DPSIR have been adopted to improve the dust situation, but the research results and the process of changes showed that they were not sufficient and integrated. In this research, a variety of answers related to the components of the driving force, pressure, situation, and effects were identified. The results showed that dust concentration and dusty days in the region increased during the study period and more attention was paid to reactive responses and less focus on preventive responses. Also, paying attention to the response of increasing the efficiency of irrigation due to the high correlation between the state of dust concentration and pressure factors such as the amount of rainfall, soil moisture, and exploitation of water resources in the research area, special attention to the development of water extraction systems as one of the most important responses. Management issues due to the existence of negative and high correlation with the dust situation during the years of research, adequate and sustainable supply of water resources by reducing and minimizing the diversion dams downstream of the Kowsar and Ask dams due to the increase in dust concentration with the increase of flood diversion operations and construction Reservoir and diversion dams since 2009, Considering the water rights of wetlands in the region with the aim of preventing wetlands from drying up and creating a dust center, especially in the west of Hendijan city, and carefully choosing the appropriate plant species, as well as carrying out desertification operations (planting saplings, mulching, building windbreaks). With the ecological conditions of the region, due to the negative correlation between the desertification operation and the reduction of dust concentration since 2014, it can help to improve the dust situation in the research area. 

In this regard, it is recommended to respond to driving forces (D) and pressures (P) so that while improving the conditions and adverse effects caused by it, the drivers and pressures that create the current situation can also be controlled. Based on this, the incomplete implementation of the answers is another reason for not achieving management goals and making the situation and the number of dusty days more unfavorable. It is also suggested to implement the integrated watershed management program in Hendijan city through the development of a joint water, agriculture, and natural resources program upstream of the watershed in order to clarify the effective measures of the water production, distribution, and consumption chain in the upstream lands.

Nowadays, climate change causes abundant worries in different spots in the world. For this reason, a study was conducted to find the trace of this global phenomenon in Khuzestan province. Therefore, we used meteorology data including temperature parameters, rainfall, evapotranspiration and relative humidity for 15 weather stations in a period of 21 years (1996-2016) and Mann Kendal graph to determine the trend. The result illustrates that there is a meaningful downward trend for rainfall parameter in SafiAbad, Hendijan, Masjedsoleiman and Bandar-E-Mahshar with confidence level of 95% and Omidiyeh with confidence level of 99%. Moreover, there is a meaningful upward trend with confidence level of 95% in tempe-rature parameters in most stations, while this level is 99% in Bostan, Hendijan and Izeh. In addition, a meaningful upward trend in evapotranspiration in two station of Bostan and SafiAbad with confidence level of 95% and 99% and a meaningful downward trend with confidence level of 99% for Shushtar, Izeh, Abadan, Masjedsoleiman and Ahwaz as well, is visible. There is a meaningful downward trend in relative humidity in Bostan, SafiAbad, Izeh, Omidiyeh and Masjedsoleiman, which all present the climate change effect in region.Climate change is one of the most important challenges in the 21st century. Global warming has caused very unstable changes in climate parameters such as: changes in rainfall patterns and frequency and intensity of climate changes. The issue of climate change has become one of the main concerns of scientists related to atmospheric sciences due to its impact on various dimensions of human life and the dependence of human activities on it, and many researchers are trying to understand the various dimensions of this important phenomenon. The change in climatic elements, especially temperature and precipitation, is one of the most important signs of this phenomenon. Changes in temperature and precipitation in different parts of the earth do not follow the same trend, and climate change does not necessarily mean simultaneous changes in precipitation and temperature. The amount, distribution and temporal and spatial changes of precipitation and temperature are essential factors for decision-making, management, planning and design, especially in areas such as Iran, which is geographically located in the 28 to 48 degree north latitude and It has a dry and semi-arid climate. In order to detect the trend in the time series of water and meteorological variables, various tests are used, and these tests can be divided into two categories, parametric and non-parametric. Parametric tests are more powerful in detecting trends than non-parametric tests, and when using them, the data should be random and have a normal distribution. On the other hand, non-parametric tests can be used if the data is random and are not sensitive to the normality of the data. Mann-Kendall's test is a non-parametric test that is used in researches to investigate the trends of water and meteorological variables. To carry out this research, climatic data (average rainfall, average evaporation and transpiration, average humidity and temperature parameters) prepared by the meteorological organization of Tehran province for a statistical period of 21 years from 1996 to 2016 have been used. Mann-Kendall test was used to analyze the trend of climatic parameters, which will be explained in detail below.• Mann-Kendall non-parametric testThis test was first presented in 1945 by "Mann" and then developed by "Kendall" in 1966. This test does not require a normal frequency distribution or linear behavior of the data, and it works very strongly compared to the data that deviates from the linear behavior and is used to evaluate the trend (Darabi et al., 2015). In this test, the null hypothesis (H0) and the opposite hypothesis (H1) are respectively equivalent to no trend and the presence of a trend in the time series of observational data. Mann-Kendall diagram test:The steps of the test are briefly as follows:We rank the data and calculate the ti statistic, which is defined as the ratio of rank I to its previous ranks, and then we obtain the cumulative frequency of the statistic ( ). Mathematical expectation, variance and Mann-Kendall index are calculated based on the following formulas (Zahedi et al., 2016):In these relationships, ni is the time order of the data. To check the changes, the index Ui' should also be calculated: rank the data and determine the statistic t'i, which is defined as the ratio of rank I to its next ranks, and then calculate the cumulative frequency of t'i we do The mathematical expectation, variance and index Ui' are as follows:In the above relationships, N is the statistical sample size under study. The intersection of the index Ui and Ui' is a sign of a sudden change in the time behavior of a statistical series. The non-intersection of the curve or their placement within the 95% confidence range does not indicate significant changes in the data, but if the mentioned lines intersect within the critical range of 1.96 and 2.58 and then leave the critical range, it is a sign of a sudden change and a significant trend at the 95% and 99% confidence levels, respectively. If the U curve moves to the positive side, it has a positive trend, otherwise it has a negative trend. Crossings outside the critical range indicate a sudden change in the behavior of the series (Alijani et al., 2009).In general, the results showed that during the studied statistical period, rainfall in all stations had a decreasing trend. Also, the minimum and maximum temperature parameters have been increasing in most of the stations. The parameters of evaporation and transpiration and relative humidity in most of the stations had a non-significant decreasing trend. Based on the results of Mann-Kandall charts, it was found that Safi Abad, Omidiyeh and Handijan stations were more affected by these changes than other stations. It is obvious that dealing with the effects of climate change and its consequences, such as the increase in the temperature of the earth's surface or the decrease in rainfall, requires a detailed investigation of the causes of this phenomenon and the provision of appropriate solutions in order to prevent endangering food security, increasing soil erosion, desertification and other problems.

Performance assessing and economic analysis of rainwater harvesting systems, which is one of the most effective methods to deal with the crisis of water scarcity, will lead to better management of these systems. The purpose of this study was to evaluate the performance of rainwater harvesting systems, Musanejad dormitory and sports club buildings in Tarbiat Modares University, Faculty of Agriculture were selected as study areas. Then, the 22 years old daily precipitation data of the Chitgar Weather Station was prepared. System simulation was performed in Matlab software and reliability and overflow ratio of storage tanks were determined. The Genetic Algorithm was used to determine the optimal volume of the tanks of both buildings and finally, an economic analysis of these systems was carried out using the Net Present Value method and benefit-cost ratio. The results showed that the optimum volume of tanks for the Sports Club and Musanejad dormitory was 2,415 and 1 cubic meters, respectively. These tanks can store up to 82 and 120 cubic meters of water per year, respectively. The results also showed that the performance of rainwater harvesting systems was beneficial for the Musanejad dormitory but had no economic justification for the sports club due to low demand and high overflow.

The purpose of this study was to investigate the oak forest dieback with respect to drought occurrence, soil moisture changes and dust occurrences factors in Ilam, Kermanshah, Lorestan and Chaharmahal and Bakhtiari provinces of Iran. The data used were field surveys collected through GPS, MODIS satellite imagery, GLDAS Soil Moisture, dust and precipitation data of the meteorological stations of the provinces during an 18-years period (2000-2017). The results of the study of greenness values of the forests in the study area showed that the first decline occurred in 2005 and repeated more severely with much wider spatial extent in 2008. Investigation of the relationship between drought events and its spatial and temporal variations with the changes in forests greenness of the study area showed that the reduction in precipitation amount is one of the main reasons for forest greenness reduction in the study area. The increased frequency of periods of rainfall shortage and drought duration, especially at 9 and 12-month time scales, showed a significant relationship between drought occurrences and forests greenness in the study area. The results indicated that by decreasing precipitation drought periods increased, soil moisture decreased, and dust storm occurrences increased. As a result, in most of the years, with decreasing soil moisture and increasing dust storms, the forests greenness of the study area has decreased and vis versa. Therefore, there is a direct relationship between soil moisture and forest greenness while an inverse relationship exists between dust and forest greenness.

Soil is considered to be dynamic and complex both spatially and temporally and thus, many physical, chemical and biological properties should be determined before assessing its quality. To reach this purpose, a large sample must be collected for laboratory tests which is both time-consuming and costly and requires lots of attention and precision. Compared to other components of soil, sand is closely related with the quality of soil and crop growth. Therefore, environmental modeling and digital soil mapping projects should pay special attention to this part of soil texture. However, large-scale detection, mapping and monitoring of sand content using common traditional sampling and usual laboratory analytical procedures are both time-consuming and costly due to the vast spatial variability of sand. Compared to laboratory-based and field spectroscopy, spaceborne and airborne remote sensing have a lower level of accuracy due to atmospheric effects, compositional and structural effects, lower spatial and spectral resolution, geometric distortions and spectral mixing. Hence, an appropriate technology is required to overcome these imperfections and study spatially variable factors. Lab Diffuse reflectance Spectroscopy (LDRS) which utilizes fundamental vibration, overtones and a combination of functional groups has been introduced as a promising tool for soil investigation. The present research uses proximal soil sensing technology to study sand content.

128 samples were collected from a soil depth of 20cm in accordance with stratified randomized sampling method and supplementary data (geology, pedology, land use, etc.). The samples were then divided into two subsets: calibration subset with 96 and validation subset with 32 samples. Afterward, definitive calibration model was developed and reviewed with two & four latent variables in accordance with R, R2, RMSE, RPD and RPIQ indices using multivariate regression analysis-PLSR method, LOOCV cross-validation technique and preprocessing algorithms such as spectral averaging (spectral reduction method), smoothing and 1st derivative (Savitzky-Golay algorithm).
 

Results & Discussion:

The estimating model indicated that out of the seven latent variables, the first two and four variables can provide the best estimate of the volume of sand in 96 calibration samples and the 32 validation subset. Since more than 60% of the variance of sand variable and 95% of the variance of spectral variables can be concentrated in these selected factors, the predicting model was calibrated based on the first four LVs and the full LOOCV procedure. The best model was calibrated with these features: Rc=0.76, R2C=0.57, RMSEc= 9.77 and SEc of about 9.82. The correlation coefficients (R) between sand contents and the effective spectral bands were calculated and equaled UV-390nm= 0.46, Vis-510 to 540nm about 0.53, 680 to 690 about 0.55, NIR- 950 to 970 about 0.67 and 1100nm= 0.70, SWIR- 1410 nm=0.76, 1860 to 1900 about 0.76, 2180 to 2220 about 0.77 indicating that the selected spectral bands (spectral ranges) with the maximum R contents were the most effective independent predictors in the present modeling process. Furthermore, the most influential spectral domains in the modeling process were determined as follows: UV-390 nm, Vis-440-540 nm, NIR- 740-990 nm, SWIR- 1430-1890, 1930, 2190-2240, 2330-2440 nm which was in agreement with previous studies. The quality of the calibrated sand predicting model was evaluated with Hotelling, Adjusted leverage and residual variances tests. The model was validated based on 32 independent samples. General characteristics of the validation process for LV=4 were Rp= 0.82, R2p= 0.67, RMSEp= 8.83, SEp= 8.92 and bias= -0.93 and Rp= 0.83, R2p= 0.68, RMSEp= 8.68, SEp= 8.72 and bias= -1.26 for LV=2.

Results indicate that the final model was capable of predicting sand contents and thus for two factors (LV=2): RPDc= 1.51, RPIQc= 2.44, RPDp= 1.78 and RPIQp= 2.45 were obtained while for four factors (LV=4): RPDc= 1.54, RPIQc= 2.48, RPDp= 1.75 and RPIQp= 2.41 were reached. A RPIQ of more than 2 shows that the model is capable of estimating soil sand content in Mazandaran province using data collected through diffuse reflectance spectroscopy. Since a new generation of hyperspectral remote sensors with high spectral resolution is now available, results of the present study can be the starting point for more accurate mapping of sand particles in soil texture using RS platforms. However, proximal spectroscopy must be more thoroughly investigated. Determining and detecting the key wavelengths in the modeling process can enhance the upscaling operation and the new airborne/satellite hyperspectral sensors and thus result in more precise mapping of the soil texture. Finally, the VNIR-DRS technology was proved to be potentially capable of estimating soil sand content in Mazandaran province. The present model and key spectral domains identified in the present study can make a basis for future studies investigating the sand content in very large-scale samples using airborne/satellite hyperspectral data. This shows the importance of LDRS and its role in identifying optical wavelengths which will be used in space-borne data (upscaling process).

Iran has a large area under cultivation, and reliable and timely crop yield forecasts are critical for making timely food supply decisions. As a result, several methods for estimating crop yield based on remote sensing observations have been published in recent years. One of the first and most critical steps in this study is to estimate real evapotranspiration using the SEBAL algorithm and high spatial and temporal resolution Landsat8 satellite images. The evapotranspiration obtained from satellite images was compared to evaporation data from the Zanjan synoptic meteorological station's evaporation pan, with statistical indicators (MAE=0/36, RMSE=0/45, R2 =0/94) indicating satisfactory results. The AquaCrop model and its four execution steps were used to simulate the product yield, and the necessary coefficients and data from the model were applied in satellite imagery calculations for each of these steps. For comparing CC data determined by satellite imagery and AquaCrop model, the RMSE, MAE, and R2 indices were respectively 11/06, 9/2 percent, and 0/94, and 0.633 and 0/359 mm/day and 0/95 for transpiration. The average yield was calculated to be 1/189 ton/ha in field pixels and its comparison with agricultural statistics and field studies (1-1/2 ton/ha) showed acceptable estimates for wheat yield in the area of this study.

Detailed information about soil moisture and its spatial and temporal distribution provides opportunity for optimized land resources utilization. Our study aimed to estimate soil surface moisture through readily availabile soil parameters and spectral index obtained from Sentinel-2 sensors using two methods, artificial neural networks (ANN) and support vector regression (SVM). There were 124 soil samples collected from three regions of Iran (Tehran, Garmsar, and Lorestan). After normalizing the data, the significance of the correlation between input variables (spectral indices and basic soil properties) and output variables (surface moisture) was evaluated statistically. In the next step, the mentioned methods were used to perform a modeling process, and the results were evaluated. The results showed that the ANN method outperformed the SVM method. Based on ANN technique, the Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), coefficient of determination (R2) and Relative Improvement (RI) in the training step were 0.033, -538, 0.71, 21.25, and in the testing step they were 0.410, -266, 0.69, and 16.06, respectively. Also, RMSE, AIC, R2, and RI in the SVM method in training step were respectively 0.035, -474, 0.71, and 35.16 and in testing step were respectively 0.046, 252, 0.63, and 20.21. Using the ANN method, soil color index (CI) has been shown to estimate soil moisture more accurately than other spectral indices. Therefore, the ANN method constructs a nonlinear relationship between soil surface moisture and input parameters, which enables soil moisture to be estimated with acceptable accuracy in the study area.

The aim of this study was to investigate the pitting and contour rainfall harvesting systems and their role in rainwater collection and forage production in rainfed conditions. This study was performed with five treatments of pitting with seeding (B), contour furrows with seeding (A), pitting without seeding (E), contour furrows without seeding (C) and control treatment (D) in three replications. FAO standard plots measuring 5x40 meters were considered for the project at Tikmehdash Research Station. In seeded treatments, Agropyron elongatum and local sainfoin were cultivated due to the effects of soil protection and forage production. Statistical analysis of the results was performed using SPSS software in the form of randomized complete blocks. The results of statistical analysis showed that the highest to lowest percentage of total canopy cover is related to contour furrows and seeding treatment with an average of 80%, pitting operations with an average of 78% and pitting and seeding with an average of 80%, respectively. Comparison of the canopy cover and forage production in the treatments showed that the canopy cover in A, B, C, D and E were not significantly different and were in the same group. Production of rangeland and succulent plants was higher in E treatment and was significantly different from A, B, C and D treatments. Also, Treatments A, B and C were not significantly different in rangeland production, but were significantly different from D (control). The highest and lowest forage production of 729.17 and 315.33 kg.ha-1 belong to pitting and control treatments, respectively.

Determining the effectiveness of flood spreading schemes, especially from the point of view of effective economic and social indicators, over a quarter century after their implementation in the country, plays an important role in their assessment. In this study, indicators related to the effects of flood spreading plans on Ab-Barik Aquifer of Bam were identified and then ranked based on nonparametric statistical tests from the experts' point of view. For this purpose, first the indicators related to the effects of flood spreading projects on the aquifer based on library studies, expert inquiries as well as site visits and face-to-face interviews with local residents and users were identified and finally the validity of the questionnaire based on expert opinions was identified. Finally, the validity of the questionnaire based on the experts' opinions and their comments was finally confirmed. Also, the reliability of the measuring instrument was tested by calculating Cronbach's alpha component. In this research, based on the multi-response coding method, the variables of the questionnaire were of the type of qualitative and qualitative variables consistent with the Likert scale, so at this stage, a survey was conducted from 30 experts. The range of changes in mean rank values varies from 4.15 to 22.88. The results of the ranking of indices based on Friedman test showed a significant difference in the effectiveness of the effective variables in evaluating flood spreading projects on aquifer, so that the question of “reducing the damage to flood lands and gardens” with the rank mean of 22.88 and also the question of “conflicting among the villagers in the area” with the rank mean of 4.15 have maximum and minimum priority, respectively. In addition, the results of the study indicate the high effectiveness of flood spreading schemes in the Ab-Barik Aquifer of Bam.

Aerosols play an important role in balancing Earth and atmosphere energy. In the last two decades, aerosols have been recognized as one of the key factors in the global climate change assessment. The purpose of this study, Determine the nature of atmospheric aerosols and simulation of the concentration of these particles in order to detect the sources of their emissions to the west and southwest regions of Iran. In order to, two types of information, including horizontal visibility of data from meteorological stations and the optical depth (AOD) MODIS were used Two dust events July 5, 2009 and July 1, 2008.In order to determine the nature of atmospheric aerosols, in the software of the 〖ENVI〗^(@ ) 5.3 , the atmospheric aerosol particle size was calculated using the Deep Blue Aerosol Angstrom Exponent Land algorithm and mapped to the Arc GIS and Then they were analyzed. In order to detect the particle emission sources and Simulation of their concentration was performed using the WRF-Chem coupled numerical model, using the advanced MADE-SORGAM scheme. The results of the remote sensing method showed that the aerosol particles in the western and southwestern regions of Iran have a dusty nature. According to the output of the WRF-Chem numerical model in the both of the dust events, the northwestern regions of Iraq and its center were identified as the main sources of dust emissions.

Engaging people to conserve natural resources is the most rational way. This by reducing the running costs will guarantee the success and implementation of the projects more effectively. For this purpose, the present study was conducted to identify and prioritize the effective indices and sub-indices on the lack of sustainable participation of rural communities in aquifer management projects from the beneficiarychr('39')s viewpoint in Bishezard watershed of Fars province. In this research, 83 stakeholders and residents of the watershed was chosen as statistical population using Cochran formula. To collect data, a questionnaire consisting of four indices and twelve sub-indices with Likert scale was used. The significance of indices and sub-indices was determined by SPSS software (version 23) using t-test and the Friedman nonparametric test was used to prioritize the effective indices and sub-indices. The results showed that in the prioritization of indices from the beneficiarychr('39')s point of view "educational-promotion", "design-executive", "social" and "economic" indices were ranked with an average of 9.75, 9.49, 6.98 and 5.93 as effective indicators, respectively and in sub-indices, "Lack of attention to local force in project implementation (employment)" and "Late-yielding projects" with an average of 11.40 and 5.74 had the highest and lowest priority in non-participation in aquifer management projects, respectively. This finding indicates that if employment is resolved in this watershed, people are willing to take the time accomplish projects. It is recommended that managers of natural resources plan and implement practical training and promotion activities related to natural resources and aquifer management issues.

Iran is facing shortage of water resources due to the climate in which it is located. Since large areas of urban surfaces are Impenetrable streets, the volume of water harvested from these surfaces is significant and can reduce the pressure on water resources and meet a large portion of the non-potable demands, it also prevents the spread of pollution caused by overflows and backlogs in surface water collection facilities and uneconomic increase of their volumes.
In this paper, the design of rainwater harvesting system from asphalt surfaces was carried out with the aim of meeting part of the green space demand of Tarbiat Modares University faculty of agriculture, using drone images. For this purpose, available data of 22 years of daily precipitation and 761 aerial images were obtained using DJI Phantom3 Professional drone. Then, the digital elevation map of the case study was prepared and to determine the runoff direction, the basin of the area was carried out in the GIS. Reliability percentage and overflow ratios of the tanks were estimated for each level for different tank volumes. Then the optimal volume of each tank was determined using genetic algorithm.
The results showed that due to the topographic status of the faculty street surfaces, the runoff from them could not be collected in one tank, so 4 tanks A, B, C and D with optimized volumes of 6.15, 3.46, 1 and 20 cubic meters were considered in different parts of the street surfaces, respectively.
With designing rainwater harvesting systems, a significant amount of rainwater can be stored for non-potable consumption.

Threshold friction speed is an important factor for assessing the soil erodibility, but its measurement is time consuming and costly. Estimating threshold friction velocity by use of soil reflectance increases operating speed and reduces cost. The aim of this study was to compare the efficiency and accuracy of partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) regression models in estimating the threshold friction velocity in dust-prone areas of Khuzestan Province. For this purpose, 91 soil samples were collected from the central and southern parts of dust-prone areas of the province and the threshold friction velocity was determined using wind tunnels. Then, the reflectance spectra of soil samples were obtained with a spectrometer. Pre-processing methods were performed on the main spectrum and modeling was performed using, PLSR, SVR and ANN models. The results showed that the threshold friction velocity in the region was 9.7 m/s and the minimum was 5.25 m/s.  Also, the threshold friction velocity was significantly (p<0.05) correlated with dissolved sodium (r= -0.58) and sodium adsorption ratio (R= -0.48). The ANN model had the best estimation accuracy in the second derivative preprocessing (PRD = 2.52) and the SVR model had the lowest estimation accuracy in the main spectrum (PRD = 0.56). Finally, the key wavelength of the threshold friction velocity was in the range of 1850 and 1930 nm. Because of the soil reflectance correlation with threshold friction velocity (r=0.76), the spectroscopy method can be used to assess the soil erodibility in areas prone to dust production.

Dust storms are one of the atmospheric phenomena which has many negative effects for Hormozgan Province, as one of the most important population and tourism centers in the south and with significant and strategic facilities in the country. For this reason, todays determining the hotspots and areas affected by the storm, as well as identifying important routes of entry and movement is one of the most important needs of relevant organizations in this province. In order to study the dust phenomenon in Hormozgan Province, first, all meteorological data of 12 synoptic stations in the region between 2000 and 2018 were analyzed and 48 dust storm events were identified that their horizontal visibility has decreased to less than 1000 meters and dust mass detection operations were performed using MODIS satellite images and four detection algorithms of Ackerman, TDI, TIIDI and NDDI and areas affected by storm as well as areas of origin were identified. HYSPLIT particle Lagrangian diffusion model was used to route the motion of the dust storm and the entry routes of dust storms into the province also, its routes and areas that have the most impact in this area were identified. Comparing results of four dust detection algorithms indicated better performance of TDI algorithm compared to other algorithms in detecting the focus and mass of dust in the area. According to the results, eastern regions of Hormozgan Province, Jazmourian Wetland, eastern Sistan and Baluchestan, western regions of Afghanistan and Pakistan, as well as central and southern regions of Saudi are one of the most important centers of dust production in the region. Investigating HYSPLIT model maps indicates the existence of three general routes of entry and creation of dust storms in the area which includes the southwestern regions of the country, the south-north route and the north and northwest route. Also, based on model results, about 53.7% of the path of movement and the release of dust after a storm is to the north and northeast direction which causes the spread of pollution and intensification of dust concentration in cities such as Bandar Abbas, Qeshm, Minab, Rudan, Jiroft, Kahnooj, Bam, Iranshahr, Khash, Mirjaveh and Zahedan. Also, about 22.3% of the storms in the region consider the southern route, 14.8% the south-west route and 9.2% the east route to continue their navigation.

Geographical location and proximity to countries such as Saudi Arabia, Pakistan and Afghanistan as well as desert conditions and poor vegetation cover, weather instability and high winds have caused dust storms in different parts of Hormozgan province. This phenomenon is nowadays recognized as one of the environmental challenges in southern Iran. In order to study the dust phenomena in Hormozgan province, were first analyzed the dust data of twelve synoptic stations in the region between 2000 and 2018 Ackerman’s model, Normalized Difference Dust Index (NDDI), Thermal-infrared Dust Index (TDI) and Thermal Infrared Integrated Dust Index (TIIDI) were four Algorithm methods for dust source and plume identification using MODIS data MODIS Level 1B and MODIS Level 2 aerosol data to delineate and compares. Results showed that Qeshm station with 2762 days had the most and Minab station with 356 days had the least frequency of dust occurrence in the province. Also, 2007, 2008 and 2003 have the highest occurrence among the studied years. According to the survey data from 35716 days associated with dust phenomenon, 84% of dust events in the stations were locally originated (code 07) and 16% were of external source (06). The results also showed that the most occurrence of dust occurred in May, April and July, and the least occurrence of dust occurred in November, December and October. The results are shows all of the techniques except NDDI were successful in detecting dust plumes, but the most effective algorithm for plumes identification varied from event to event. In addition, TDI is the best algorithm comparing 3 evidence and eastern regions of Hormozgan, Jazmoorian area, Sistan and Baluchestan, and the western part of Afghanistan and Pakistan and south of Saudi Arabia are most important for dust source in Hormozgan Province.

The face of the earth is always changing due to human activities and natural phenomena. Therefore, to optimize the management of the natural areas, knowledge of the trend, extent and estimation of land cover / use changes is considered necessary. Reviewing these changes through satellite images and evaluating their potential through modeling can help environmental planners and natural resource managers to make more informed decisions. In the present study, quantitative detection and evaluation of changes in vegetation were performed in the areas with combat desertification projects, Shahdad, Bam and Garmsar in Iran, during a 30-year period within 1987, 2002 and 2017. The Normalized Difference Vegetation Index (NDVI) and land use maps were produced using the Enhanced Thematic Mapper Plus (ETM+), Thematic Mapper (TM) and Operational Land Imager (OLI) satellite images in the three corresponding periods for the vegetation/non-vegetation, and agricultural lands. The Kappa coefficient of 0.83 to 0.86, 0.91 to 0.92, and 0.94 to 0.95 was calculated for 1987, 2002, and 2017 respectively, and the total accuracy was between 88 and 97. After providing the land use maps in different years, the monitoring of land use changes was investigated using the Change Detection method. According to the trend of changes during the periods, the results exhibited that the vegetated lands in these three areas had an increasing trend in average 31.33%, and the non-vegetated lands were turned to vegetated lands over time. In other words, they have declined by an average of 35%. Moreover, an increasing trend was found for the agricultural lands during the periods in average 4%. Eventually, the cost-effectiveness of projects implemented in the studied areas was calculated.

Considering that Khuzestan province has a large area of land susceptible to dust generation, novel approaches such as Hyperspectral images could be used in the determination of soil characteristics. One of the main challenges in using hyperspectral images for evaluation of soil properties in these areas is the presence of some compounds such as gypsum which may lead to some errors in estimating other soil properties. This research has mainly been conducted to determine the soil-gypsum key wavelength in the dust center of Khuzestan province. To achieve this goal, at first the original soil spectrum was preprocessed using FieldSpec3 setup via five methods including the Savitzky-Golay filter, the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the Standard Normal Variant (SNV) and the Continuum Removal method (CR). Two Multivariate regression models including Partial Least Squares Regression (PLSR) and Support Vector Machine (SVR) were used and compared in the estimation performance of soil gypsum. The results showed that the SVR model had better performance rather than the PLSR model in estimating soil gypsum. Also, in the SVR model, the continuum removal method (R2cal=0.93, RMSEcal=2.47, RPDcal=3.71) and the main spectra (R2cal=0.76, RMSEcal=6.32, RPDcal=1.59) had the best and weakest performance in estimating soil gypsum, respectively. It is noteworthy that the continuum removal method (R2val=0.88, RMSEval=3.57, RPDval=2.49) and the original spectrum (R2val=0.52, RMSEval=7.81, RPDval=1.12) in the validation group showed the best and the weakest performance, respectively. In the present study, wavelength ranges around 1450, 1550, 1700, 2100, 2200, 2400 nm which had the highest level of correlation with soil-gypsum content, was obtained as the key wavelengths of the soil in sensitive areas to dust production.

Changes in vegetation and land use have been unprincipled on the one hand, and the occurrence of tree decay in the last decade, on the other hand, has put additional pressure on the forests of the Zagros, causing significant significant drying of these forests in particular. It is located in Ilam province. Since awareness of the extent and location of deforestation and spatial distribution of areas at risk of destruction and prevention of this growing trend is the concern of planners and managers of the natural resources sector, the need for sustainable management of these valuable resources is easy access to information and data. The day is high. Therefore, the present study aimed to investigate the distribution of oak tree drying zones in physiographic units (slope, slope direction and elevation classes) as well as land use using geographical information system in this province.

In this study, first, using Alos and Srtm elevation models, in the GIS environment, slope information layers were extracted for slope and elevation classes. Then, based on satellite images and field control, the land use layer of the province was prepared. Also, using the GPS device, the position of the dried oak trees was determined and the relevant dotted layer was prepared. Then, by combining the prepared information layers (slope, slope direction, height floors and land use) with the location layer of dried oak areas, the frequency of tree drought zones of oak forests in physiographic and land use units is determined and examined. Took

According to the results, the drought rate of trees in topographic units (slope, slope direction and elevation classes) extracted from two models is also 12 meters and Srtm is 50 meters. Percentage (30%). 18% is for the northeast slope and 47% is for the 2000-1500 m high floor. Also, according to the Srtm model, the highest frequency of dried oak areas is related to the slope class of 10-20% (30%), 20% is related to the northern slope direction and 47% is related to the altitude of 2000-1500 meters. In terms of land units, most of the dried areas of oak trees are located in mountainous areas, hills, pebbled areas and plateaus, respectively.

In general, there is no difference between the percentage distribution of oak drying with slope classes in the two models studied. However, there are slight differences in the height of the floor and the direction of the slope in both models. In terms of forest use, the highest frequency of oak drying is in low (36.5%), medium (33.9%) and high (4.4%) classes, respectively.

In recent years, a considerable part of the western oak forests of the country has been faced with drought and decline. Initial studies have identified one of the factors affecting oak forest dryness as increasing frequency and severity of dust phenomenon. In this study, the MODIS sensors with a resolution of 1 km and 250 m and meteorological data of Kermanshah Province synoptic stations over a 15-years period (2014-2000) was used to investigate the dust phenomenon and the process of forest vegetation changes in the region. Dust intensity was investigated using visibility data and unsupervised image classification method. Also, in this study, the relationship between the occurrence of dust and vegetation changes in oak forests was investigated. Survey of dust events showed that Kermanshah Province had the highest number of dust events in 2008 and 2009, respectively. The highest incidence of severe dust was in 2012. In addition, satellite imagery and spectral indices also showed that the highest reduction of vegetation in dense forests (>50% canopy cover) by 67% and with an overall accuracy of 62% in the south and southeast of Kermanshah Province and at the border of Lorestan and Ilam provinces has happened. Results also showed that the frequency of dust phenomenon is highly correlated (70%) with the phenomenon of oak forest degradation; and dust has the effect of reducing the greenness of oak forests in Kermanshah province.

Nowadays spectroscopy is used for estimating soil properties. The main objective of this research was to estimate some soil properties of Susceptible Areas of Dust Production using visible and near infrared. Therefore 142 soil sample of Susceptible Areas of Dust Production were Collected and analyzed. Equivalent calcium carbonate, gypsum, organic carbon and nitrogen of soil sample were measured and linear regression models of PCR and PLSR were used to estimate these properties .Three methods of reflection of the main spectra and pre-process methods of first derivative and second derivative were compared in two regression models of PCR and PLSR. The results showed that the PLSR method is more accurate than the PCR model for estimating soil properties. The PLSR model in the pre-processing second derivative with noise reduction, showed the highest accuracy for equivalent calcium carbonate, organic carbon and total nitrogen with the coefficient of determination as 0.95, 0.92 and 0.81, respectively. For gypsum, the highest accuracy in the first derivative with the coefficient of determination was 0.87. The results of this research revealed the use of spectroscopy in estimating soil properties of dust production-prone areas in Khuzestan province have an appropriate accuracy; and due to the extent of these areas and the speed of operation and cheapness of this method, it can be used to predict the amount of soil properties in these areas.

Wind erosion is one of the most important desertification processes in arid and semi-arid regions. This process destroys lands, soil, water resources and more. Dust phenomenon is one of the most important consequences of wind erosion in Iran and worldwide and especially in the west and southwest of Iran. Therefore, the present study is based on a combination of field data, remote sensing, and climatology and physiography data, In order to identify internal dust generating centers in the western part of the country. Based on the statistical results, the maximum dust event was recorded for the study area during 2008, 2009 and 2012. On a monthly basis, May, June and July are the most dusty events. On an hourly scale, the maximum occurrence of dust occurs from 9 am to 6 pm local time. Tracing of inlet dust paths showed that during the warm period of the year most of the input dusts are in the northwest - southeast and west - east direction and in the cold period especially late in the south - north direction of the study area. Land use mapping and surveying changes indicate a decrease in water levels, vegetation cover, and land depletion in 2015 compared to 2000. These features, along with physiographic features such as high erosivity of the formations, presence of saline and marshy soils, low soil moisture, high variability of land use, identified potential areas and potential for dust generation in the target provinces.

In recent years, due to the lack of surface coating and low soil resistance to wind erosion, the large area of Khuzestan province is sensitive to dust production. Among the soil characteristics, the organic matter by collecting soil particles, has important role in soil resistance to wind erosion and dust production. Since these areas are so wide, the use of traditional methods of soil analysis is really costly and time consuming. The spectroscopy approach, due to the advantage of speed and easy movement, can reduce the cost and time of measurement. The aim of this study is to investigate the spectral behavior of soil organic carbon in central and southern regions of Khuzestan province by using tow multivariate regression, Support Vector Regression) SVR( and neural network (PLS-ANN), and key wavelength determination of soil organic matter in these areas.

In this research, the study area was divided into 2 km square grids and systematic and random sampling Methods were performed. The soil organic matter in samples was measured in the laboratory. The Reflectance spectra of soil samples were determined using FildSpect setup in dark room. And spectral measurements were carried out with three types of detectors in range of visible to near infrared (3500-2500 nm). To eliminate the noise in normal reflectance spectra, the main spectra were preprocessed by four methods, including the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variant method (SNV) and the continuum removed method (CR). Next, the performance of SVR and PLS-ANN models in main spectra preprocessed method were compared.

The results showed that the PLS-ANN model had better accuracy compared to SVR model in estimating organic carbon. In SVR models, the continuum removal method (CR) had the best performance (R2CAL=0.84, RMSECAL=0.06 and RPDCAL=1.82) and the main Spectra had the worst performance (R2CAL=0.74, RMSECAL=0.14 and RPDCAL=1.66). In PLS-ANN models, the best performance belonged to the second derivative (SD-SG), (R2CAL=0.92, RMSECAL=0.05 and RPDCAL=2.34) and the worst performance was related to the first derivative (FD-SG), (R2CAL=0.80, RMSECAL=0.1 and RPDCAL=1.86).

In this study, the preprocessing methods improved the overall accuracy of SVR and PLS-ANN models compared to the main spectrum. According to the second derivative method, in PLS-ANN witch had the best accuracy in estimating soil organic carbon, the Wavelength ranges around 800, 1800 and 2000 nm were identified as the key wavelength of the organic carbon in sensitive centers to dust production.