seyed kazem alavipanah

Soil moisture is one of the most important environmental parameters for water resources management and irrigation planning systems in agricultural areas. In agricultural areas, most soil moisture retrieval models are unstable in terms of their accuracy and performance during crop growth season. As a result, there is no consensus on which model performs optimally during the agricultural season. This is because of the uncertainties associated with model physics, input data, vegetation attenuation and soil characteristics. To deal with these practical concerns, in this research, a simple but effective soil moisture retrieval method for using combination of multiple models based on machine learning has been introduced. Firstly, the WCM with different vegetation descriptors were calibrated and validated in sugarcane fields for Sentinel-1 backscattering coefficients . For this purpose, soil moisture measurements of sugarcane fields (400 samples in total) during the plant growing season in 2020 were used. Then optimization of calculations was done using the GRNN. The results showed that WCMNDWI retrieves soil moisture more accurately than other models in the early stages of sugarcane growth, while WCMVWC and WCMLAI were more accurate in the late sugarcane growth. Time-series soil moisture retrieval accuracy using the combined method based on GRNN was higher than that of single WCM models. According to the results of the in situ validation for sugarcane fields, with the optimal combination of models, the minimum MAE) is less than 0.02 m3m-3, the RMSE is approximately 0.085 and the R it was equal to 0.7 for the growing season.

Land cover maps are essential elements in geographical analysis and spatial planning. The accuracy and effectiveness of these maps rely on three factors: Satellite imagery, classification algorithms and training samples. The quality of the training dataset significantly impacts the accuracy of classification results. This study aims to generate reliable training samples using the training sample migration method to monitor land cover changes in northwestern Iran from 2002 to 2022.

The study area covers 7653 square kilometers in northwestern Iran, situated between 44°35′59′′ to 46°01′25′′ longitude and 38°38′46′′ to 38°47′48′′ latitude. Data utilized in this research include satellite images and ground truth data, specifically Landsat images. The research methodology comprises five main steps. Initially, satellite images were obtained, followed by pre-processing steps involving radiometric and geometric corrections. Subsequently, training samples were prepared using high-resolution satellite images (Google Earth images) and ground surveys. The third step involved training sample migration, where spectral similarity between training samples from reference and target years was assessed using two parameters: Euclidean distance (ED) and spectral angle distance (SAD). After determining a suitable threshold, migrated training samples were distinguished from non-migrated samples. Evaluation of the accuracy of migrated training samples was conducted using reference data derived from Google Earth. In the fourth step, classification of satellite images from different years was performed using the migrated training samples. Finally, the accuracy of the classified images was assessed through the calculation of a confusion matrix in the fifth step.

The results indicate that a threshold of 0.9 to 1.1 is optimal for distinguishing migrated training samples from non-migrated training samples across different years. It can be observed that there is an inverse relationship between the accuracy of migrated training samples and the percentage of migrated training samples, with an increase in the percentage leading to decreased accuracy. Evaluation of the accuracy of migrated training samples based on each parameter (SAD and ED) reveals that migrated training samples based on the SAD parameter exhibit higher accuracy than those based on the ED parameter. Furthermore, the use of migrated samples based on both parameters has resulted in a 10.45% increase in accuracy compared to using the ED parameter alone, and a 5% increase compared to using the SAD parameter alone. Analysis of the percentage of migrated training samples in different land cover classes demonstrates that, on average, 80.6% of water class training samples, 75.4% of bare land class samples, 71.2% of built-up class samples, 64.6% of grassland class samples, 60.2% of cropland class samples, and 54.4% of wetland class samples were migrated from the reference year (2022) to each of the target years (2002, 2007, 2012, and 2017). The accuracy assessment of migrated training samples in different land cover classes also reveals that the water, built-up, bare land, grassland, cropland, and wetland classes had the highest accuracy in the migrated training samples, in that order. Analysis of land cover changes between 2002 and 2022 indicates a decrease in the area of bare land, water, and wetland classes from 2002 to 2022, while the area of the built-up class has increased during this period. Additionally, the grassland and cropland classes did not exhibit a consistent trend of change during this period, with their trends differing in different years. However, overall, the area of these two classes increased in 2022 compared to 2002. 

Future studies should consider using other satellite images (including Sentinel-2) for migrating training samples to evaluate the impact of different spectral bands and satellite images on the migration process. Furthermore, investigating the effectiveness of the training sample migration method for migrating training samples of other land covers could be a potential research topic for future studies.The study area is located in northwestern Iran with an area of 7653 square kilometers. The study area lies between 44°35′59′′ to 46°01′25′′ longitude and 38°38′46′′ to 38°47′48′′ latitude. This study used satellite images and ground truth data.The research methodology consists of five main steps. The initial step involved obtaining satellite images and performing pre-processing steps (radiometric and geometric correction). In the second step, training samples were collected using high-resolution satellite images (Google Earth images) and ground surveys. The third step involved the migration of training samples. To do this, the spectral similarity of the training samples from the reference and target years was first calculated using two parameters: Euclidean distance (ED) and spectral angle distance (SAD). Then, adopting the specified threshold, the migrated training samples were separated from the non-migrated samples. Furthermore, the accuracy of the migrated training samples was evaluated using reference data prepared from the Google Earth. In the fourth step, using the migrated training samples, the classification of satellite images in different years was performed. Finally, using the indices obtained from the confusion matrix, the accuracy of the classified images was evaluated.The results showed that the threshold of 0.9 to 1.1 is the optimal threshold for separating migrated training samples from non-migrated training samples in different years. We also found a reverse correlation between the accuracy of the migrated training samples and the percentage of the migrated training samples, with higher percentages resulting in lower accuracy.The accuracy assessment of the migrated training samples based on each parameter (SAD and ED) showed that the migrated training samples using the SAD parameter have higher accuracy than the migrated training samples using the ED parameter. Moreover, employing the migrated samples considering both parameters has increased the accuracy by 10.45% compared to using the ED parameter to migrate the training samples, and by 5% compared to using the SAD parameter to migrate the training samples.The analysis of the percentage of migrated training samples in different land cover classes showed that, on average, 80.6% of the training samples of the water class, 75.4% of the bare land, 71.2% of the built-up, 64.6% of the grassland, 60.2% of the cropland, and 54.4% of the wetland were migrated from the reference year (2022) to each of the target years (2002, 2007, 2012, and 2017). The accuracy assessment of the migrated training samples in different land cover classes also showed that the water, built-up, bare land, grassland, cropland, and wetland classes had the highest accuracy in the migrated training samples, in that order.The classification of satellite images was performed using Landsat images between 2002 and 2022. Accordingly, satellite images were classified into six different land cover classes. The accuracy assessment results showed that the overall accuracy of the classified images in 2022, 2017, 2012, 2007, and 2002 was 94.95%, 91.93%, 90.74%, 89.45%, and 88.94%, respectively. The accuracy assessment of different land cover classes based on two parameters, producer accuracy and user accuracy, showed that the water class has the highest producer and user accuracy among different classes (98.2% and 99.34%, respectively in 2022). In contrast, the wetland class had the lowest producer and user accuracy (90.1% and 91.25%, respectively in 2022).The analysis of land cover changes between 2002 and 2022 showed that the area of bare land, water, and wetland classes decreased from 2002 to 2022, while the area of built-up class increased during this period. Furthermore, the grassland and cropland classes did not exhibit a constant trend of change during this time period, and their trends varied by year. However, the area of these two classes increased in 2022 compared to 2002. The analysis of the changes in the area of built-up class throughout this period shows a significant increase in the area of this land cover, which has increased from 20.38 square kilometers in 2002 to 123.98 square kilometers in 2022.It is suggested that in future studies, other satellite images, like Sentinel-2, be used to migrate training samples in order to evaluate the effect of different spectral bands and satellite images on the migration of training samples. In addition, investigating the effectiveness of the training sample migration method in migration training samples of other land covers can be one of the research topics in future studies.

Given the significant impact of expanding impervious surfaces on the urban environment, obtaining accurate and up-to-date information about impervious surfaces (IS) is important in urban planning and sustainable management. Remote sensing data, especially aerial photos, have a high potential to provide the mentioned information and have been successfully used in recent years. Despite the widespread use of these data in urban impervious surfaces (UIS) mapping, the reliability and accuracy of the final map still need further investigation. Therefore, in this study, using a detailed and precise classification scheme, drone, and Sentinel satellite data, the impact of three parameters including utilized data, urban structure, and vegetation canopy on the output maps accuracy was evaluated. The results showed that all three factors are of great importance and may cause significant uncertainty in the output maps. Vegetation cover can lead to up to 10% underestimate in the IS. Additionally, changes in urban structure in different areas and changes in the utilized data can also result in a 20% change in the overall accuracy. Results from this work can be used to provide a proper understanding of the reliability of remote sensing products and depict directions for future methodological development

Application of unmanned aerial vehicles (UAVs) is widespread in measurement of quantitative characteristics of single trees such as crown area. However, the efficiency of different types of data collected by UAVs are less compared. Therefore, the aim of this study was comparison of UAV RGB imagery and point clouds in crown area estimation of individual pine trees within a man-made forest in Pardisan Park, Northern Khorasan province, Iran. Both datatypes were analyzed by similar segmentation method (Multiresolution segmentation on the RGB images and Marker-Controlled Watershed algorithm in the point clouds) to estimate crown area of 324 sample pine trees. The results showed that the crown area measured on the point clouds had higher accuracy and precision compared to RGB imager (Spearman correlation of 0.95 and 0.81, coefficient of determination of 0.97 and 0.59, p-value of 0.97 and 0.001, respectively). Additionally, crown area of pine trees with large crown (> 18 m2) delineated on the point clouds showed the highest accuracy in comparison to trees with small and medium crowns. In general, it was concluded that segmentation of UAV point clouds was more efficient than RGB imagery segmentation in quantification of crown area of pine trees within the study area.

The effect of urban thermal islands due to intersections with major environmental challenges of the 21st century is one of the most important studies on environmental phenomena, and in this regard, the study of the land surface temperature gives a clear perspective of the thermal islands in cities, which, according to the warm and dry climate of Yazd, examines the status and factors affecting the land surface temperature in this city seem to be necessary. This research, using the spectrally and spatially fused image of Landsat-8, for August 2020, and using machine learning algorithms, tries to model the changes in land surface temperature by calculating different parameters related to urban land perspective. Based on the results of this study, the spectral-spatial fusion of Landsat-8 with Sentinel-2 by Pan sharpening, increased 10.7% of the overall accuracy and 16.5% of the Kappa coefficient in the classification of this image. The study also showed that most neighboring parameters associated with land cover are ranked 1 to 11 of influencing the land surface temperature of Yazd city. In this area, the proximity to bare lands in the radius of 100, 50, and 150 meters ranked 1 to 3 of the most important parameters affecting the land surface temperature respectively. This study showed that the change in land cover arrangement could affect the land surface temperature and changing the bare lands to the built-up areas, up to 1.1°C, to vegetation, up to 2.1°C, and changing 30% of bare land to vegetation, up to 1.6°C can reduce the average land surface temperature in Yazd. Also, this study showed that two different models of vegetation simulation in Yazd city showed that the "land-sparing " model could reduce the average land surface temperature in Yazd by 1.3° and the "land-sharing" model by 1.4°C.

 Wetlands are habitats for vegetation and wildlife and because of this, they have a high environmental value. Also, wetlands reduce soil erosion, restore aquifers, store rainwater in a flood event, and provide water for agriculture or livestock. Wetlands are vulnerable to human interventions and changes such as drainage, urban sprawl, infrastructure development, and over-exploitation of groundwater resources. Prediction of the condition of wetlands in the future requires a correct understanding of the evolution of wetlands and identifying their trend of change. Nowadays, Remote Sensing technology is widely used for mapping wetlands, and its ability to monitor the changes in wetlands regardless of the diversity of wetlands has significantly increased the value of this science in this field. Remote Sensing can be an effective means of simulating and predicting wetland degradation processes by providing images at different times and through dynamic spatial modeling. In this study, the changes in the Bakhtegan wetland have been monitored. This wetland has high environmental and tourism importance and its drying affects negatively the living conditions and health of local people as well as tourism in the region. In addition, predictions of precipitation parameters, groundwater level, and temperature have been conducted. For this purpose, the Google Earth Engine platform was used to capture and process images. Google Earth Engine is a platform that can capture and process images in the shortest time and at high speed. In this regard, using Google Earth Engine, changes in the lake water area along with changes in temperature, groundwater level, and precipitation were extracted and monitored. Moreover, a comparison took place between these parameters to determine the changes that have taken place in the lake over the past two decades. To predict the parameters, the changing pattern was predicted and analyzed using the Prophet model. The most important advantage of the Prophet model is its ability to convert discrete data to continuous data to make the best predictions. This method automatically detects the trend of seasonal data and displays the trend of seasonal changes.

 Satellite images were acquired from the Google Earth Engine platform to monitor the wetland. Landsat 7 and 8 images were used for water body extraction, GRACE Data were used for extraction of groundwater level changes, MODIS product was used for extraction of vegetation and wetland surface temperature, and TRMM image product was used to extract precipitation values. An automated water extraction index was used to extract the wetland body water. The groundwater level was extracted from the GRACE sensor. MODIS sensor product was used to obtain the surface temperature time series for the study area. For the extraction of precipitation time series, the monthly cumulative data of the TRMM (3B43V7) satellite with a spatial resolution of 0.25°C was extracted using Google Earth Engine and the trend of changes was evaluated and analyzed. The Mann-Kendall test is one of the most widely used non-parametric tests for detecting meteorological and environmental data trends, which is used to detect a monotonic trend line since this test is a non-parametric method, it does not need that the data follow a normal distribution. The Prophet predictive model is a predictive library developed by Facebook and is available in R and Python programming languages. This library supports additive modeling methods and can properly predict discrete values continuously. This feature is called "Holiday". Another feature of this library is the automatic detection of daily, weekly, seasonal and annual trends. The mean absolute error (MAE), by default, exists in the Prophet library. This error represents a more natural standard than the mean error and unlike the RMSE error, it is unambiguous.

 In the present study, we monitored the Bakhtegan wetland using the Google Earth Engine platform to observe the trend of water level changes in this wetland from 2000 to 2020. In addition, Parameters were also predicted using the Prophet Prediction method which is developed and published by Facebook. By examining this trend, it can be observed that the water level of the wetland has been significantly reduced during two decades. In this regard, the trend of groundwater level, temperature, and precipitation in the area was investigated. Examining these factors, it was found that along with a 58.3% decrease in the water level of the wetland, there was a 260% decrease in the groundwater level of the region, although the amount of rainfall in the region has been less compared to other factors and has been decreased about 29%. Using Mann-Kendall statistical test, the trend of this decline was proved. To predict the parameters, the Prophet model has been able to make predictions for 1500 days as continuous data using discrete data. The output of the model has shown that for rainfall parameters and groundwater level a downward trend is predictable over the next 1500 days which is low intensity for precipitation but with high intensity for groundwater level. Temperature prediction indicated that it has a seasonal trend, and has a high amount of fluctuation within a year, but its annual trend indicates stability in the coming years. The results of the model for the water level of the wetland also show a relatively low upward trend that has a probability of change of ±12.5 Square kilometers. Also, the error of the parameters at the 95% significant level has acceptable accuracy, which indicates the validity of the prediction. An automated water extraction index was used in this study to extract the time series of the water body of the wetland. Using the mean time series extracted, the maximum and minimum wetland’s water body area belongs to 2006 with 629.23 square kilometers and 2014 with 156.82 square kilometers, respectively. The time series of changes in this wetland indicates that the water volume of the wetland has been declining in the last two decades. According to this study, it can be concluded that the trend of changes in the water level of the wetland has been decreasing. The descending changes in the lake based on the trend of changes in groundwater levels indicates a decrease in water volume in the area. Considering that the trend of precipitation changes has been stable, it can have assumed that improper management and excessive use of groundwater may be a reason for lowering the water level of the wetland. Due to the same decrease in the water level of the lake, the temperature has also decreased by about 3°C.

 According to this study, it can be concluded that groundwater levels and precipitation will have a downward trend in the future, which will lead to a decrease in the water level of the wetland, which itself has the potential to fluctuate in the future, and the downward trend continues. With the current trend, the only solution is to plan properly to preserve the wetland. If this trend continues, we will face the destruction of the wetland. Given the monthly trend of the wetland surface, it is suggested not to over-exploit groundwater resources, especially in the summer. For further research, the Google Earth Engine platform can be used without the need to download the images and spend a lot of time and money, to obtain the time series of images. Regarding the prediction, in future studies, the Prophet model can be applied, since it uses discrete data and at the same time provides the desired accuracy.

Previous studies have demonstrated a significant correlation between happiness and physical and spiritual health. Not only does happiness expedite patient recovery, but it also bolsters the health of non-ill individuals. This suggests a novel strategy for promoting public health by enhancing people's happiness. Depressive disorders, which are characterized by a lack of life satisfaction and happiness, are among the leading causes of disability worldwide. Evidence indicates that modern industrial lifestyles may contribute to a higher likelihood of depression, underscoring the importance of identifying factors that influence happiness and depression. The factors underlying happiness can be divided into two primary categories: internal and external. Happiness, as an emotion, arises from the interplay of these factors. Genetics and genes contributing significantly influence individual happiness levels. Internal factors impacting happiness include the interaction of happiness-related neurotransmitters such as serotonin, dopamine, norepinephrine, and endorphin with the emotional regulation centers in the brain. The functions of certain hormones like cortisol, adrenaline, and oxytocin, the activity of the immune system, inflammation, and the level of oxidative stress also play a role. Researchers posit that the biological pathways associated with happiness are modulated by environmental and social factors. In other words, the biochemical pathways leading to happiness can be managed through lifestyle and environmental adjustments. Elements such as a balanced diet, adequate sleep, regular exercise, prayer and meditation, calm music, exposure to nature and sunlight, positive attitude to social relationships are good lifestyle factors that can enhance happiness and life satisfaction.

Leaf Area Index (LAI) plays a critical role in the mass and energy exchanges between the earth and the atmosphere. Like of other plants, LAI of sugarcane is a good indicator of the health status and growth of this crop which is of great economic importance due to its role in the food and energy industries. Launched in 2019, the PRISMA satellite provides one of the most recent hyperspectral data sources which are applicable especially for mapping plant variables. In this study, a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Networkk (BRANN) which applies Bayes' theorem to overcome the overfitting problem of neural networks is used. The model was implemented on a data set consisting of spectrum obtained by PRISMA satellite as an independent variable and sugarcane LAI measurements as a dependent variable. The ground measurements of sugarcane LAI were carried out in 118 elementary sampling units on the fields of Amir Kabir sugarcane cultivation and industry in Khuzestan province and on seven different dates during a sugarcane growth period in 2020. Comparing the performance of BRANN in retrieving sugarcane LAI from PRISMA spectra with that of a conventional ANN trained with the Levenberg-Marquardt algorithm (LMANN) indicates that the retrieval RMSE is reduced from 2.26 m2/m2 applying LMANN to 0.67 m2/m2 applying the BRANN method. In this study, the principle component analysis was also used dimensionality reduction. Retrieving LAI from the first 20  principle components, RMSE was also reduced from 1.41 m2/m2 applying LMANN to 0.71 m2/m2 applying BRANN. Exploiting principal components significantly reduced computational time. By implementing the calibrated BRANN model over the PRISMA image pixel by pixel, the sugarcane LAI map was generated. Evaluating this map showed that this map represents the spatial variations of sugarcane LAI well. The results of this study indicate the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.

Background and Objective:

 Land surface temperature is a vital indicator for studying environmental changes, hydrological conditions and the energy balance of the earth, which can also be used to monitor the temperature changes of cities. The lack of meteorological stations in most parts of the country, including the study area, has created information limitations in the field of surface temperature data. There are also a large number of non-remote sensing users who need LST maps, and most of them are not familiar enough with LST computing software and inevitably have to spend a lot of time mapping to prepare their maps. This process can be time-consuming even for remote sensing professionals if the number of images is high. The use of valid data for validation that has the least time difference with the satellite passes time is very important in estimating the accuracy of the results. By reviewing internal research similar to the one under study, most internal studies used only meteorological station data to validate the results, the data recording time at these stations is different from the satellite passes time. In this study, due to the large area of the study area and the insufficient number of meteorological stations, in addition to the surface temperature data measured in synoptic stations, the land surface temperature in two ground stations was recorded simultaneously with the satellite. Creating a graphical user interface (GUI) to automatically calculate the surface temperature of Ardabil city with two single-channel and RTE algorithms and use the results to evaluate the temperature changes of land uses

Materials and Methods:

 In this study, in order to automatically calculate the land surface temperature of Ardabil city from three types of data: Landsat 5 and 8 satellite images, land surface temperature data recorded at two meteorological stations in the study area and also due to an insufficient number of stations Meteorological data land surface temperature data measured with digital thermometers are also used as the satellite passes. After preparing thermal and multispectral images, first MODTRAN web computing software was used to model the atmospheric transferability and atmospheric coefficients were extracted. Then, to create graphical user interfaces and automatic calculation of LST, land surface temperature with two algorithms single-channel and RTE method with Landsat 5 and Landsat 8 satellite images for two dates: 31/07/2000 and 21/08/2019 in MATLAB software were coded and using these codes, graphical user interfaces were created for each algorithm and finally, an automatic land surface temperature calculator application was produced. Also, the land use map of Ardabil city for both mentioned dates was classified and extracted using a random forest algorithm in the Google Earth engine system environment with 7 classes. This algorithm has a much better performance compared to traditional methods such as maximum likelihood due to its hierarchical structure in selecting each pixel to the appropriate class. To validate surface temperature maps from two types of surface temperature data recorded in two meteorological stations and surface temperature recorded by a digital thermometer that simultaneously passes the satellite in two points of the homogeneous non-urban environment with agricultural use (alfalfa) and Bayer that product It was harvested, used. To evaluate the accuracy of land use maps, using Google Earth, which has a better spatial resolution than the image used, 248 ground control points were obtained from pure pixels of different land uses and used in the validation process. Also, statistical parameters such as error matrix, overall accuracy and kappa coefficient were applied to the output of both land use maps.

Results and Discussion :

Using the codes written in MATLAB software, graphical user interfaces (GUI) were created and then the automatic LST calculator application was produced. The output of the application was surface temperature maps with single channel algorithms and radiation transfer equation (RTE) for 31/07/2000 using thermal image (band 6) of Landsat 5 satellite TM and 21/08 / 2019 was created by the 10 TIRS sensor band of Landsat 8 satellite. After comparing the output maps with the meteorological station and ground station data, the results showed that the single-channel method had the lowest temperature deviations compared to the stations in both years. After preparing LST maps and selecting the optimal algorithm (single channel), land use maps of Ardabil city were prepared using a random forest algorithm in the GEE platform. Statistical evaluations of the classification results showed that for 2000, the highest pixel interference was related to the middle and poor rangeland class, which has a 16-pixel displacement with residential and rainfed agricultural classes. Due to the improved spatial resolution of the Landsat 8 satellite compared to the Landsat 5, followed by better class separation, this pixel displacement in the 2019 user map shows a smaller value. The most common error was related to the aquaculture class, which had a displacement of 10 pixels with rich rangeland and rainfed agriculture classes. Finally, using the LST map and land use map, the temperature changes of the land uses over a period of 19 years were evaluated. By entering the input images and atmospheric parameters in the application, the land surface temperature was calculated with two one-channel algorithms and the RTE method. Evaluation of output maps with meteorological and terrestrial data showed that the single-channel algorithm with a difference of +2.5 and -2 with stations 1 and 2 for the year 2000 and with a temperature difference of +1.3, +0.9, -1 and -0.9 with stations 1, 2, 3 and 4 in 2019, respectively, had higher accuracy than the RTE method. Also, the results of validation of land use maps showed an overall accuracy of 0.95 and a kappa coefficient of 0.94 for 2000 and overall accuracy of 0.96 and a kappa coefficient of 0.95 for 2019.

Conclusion :

Assessing the relationship between land surface temperature and land use maps showed that despite the significant physical growth of the urban sector over a period of 19 years, except for residential areas, all land uses in 2019 compared to 2000 with an increase in average surface temperature. It seems that factors such as the expansion of agricultural lands with irrigated cultivation around the urban area up to a radius of 10 km and the entanglement of these farms with the urban sector have a great impact on the temperature adjustment of the urban sector. In 2000, these lands were mainly under cultivation of rain-fed crops, and by solving the water problem (digging deep wells and water transfer projects), they became orchards and irrigated farms such as potatoes. Due to the high water requirement, these products also have high greenery, and this factor has increased the rate of evapotranspiration, followed by cooling of the cultivation area and the urban sector. Among other classes, in both years of water use, the lowest and the use of barren lands had the highest average surface temperature. The generated application can be run on any operating system that supports the exe format, and the user by specifying atmospheric parameters can automatically estimate the LST. This application can also be used in various sectors such as agricultural systems, and climate and water resources management.

Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard method to compute this parameter is to utilize the climate data and experimental equations. The problem with classic methods is that the meteorological station isn’t available in the agricultural lands and usually, we have data limitations. The optimized solution is to utilize remote sensing data. So with the combination of different datasets then the reference evapotranspiration and actual evapotranspiration will be estimated. The goal of the study is to an evaluation of open-source WaPOR and ERA5 to compute daily reference evapotranspiration based on the FAO-Penman Monthis equation at the meteorological stations of Sistan and Baluchestan province. The result has shown that the open-source dataset estimated the reference evapotranspiration as more than 80 percent accurate at the place of the meteorological station and in all of the stations RMSE was less than 2 mm per day. The accuracy assessment of results shown at different crop seasons that ET0 in the autumn season is better than in the spring season. So that the ERA5 combined with the GLDAS Wind data has a better correlation with in situ measurement of ET0 than to the WaPOR. All of the results shown that this dataset can be used in each place in the province to estimate ET0. Therefore, the present study is to investigate the possibility of using the products of WaPOR and ERA5 systems to calculate the amount of daily reference evapotranspiration based on the experimental method of Penman-Monteith and to evaluate and validate its outputs in Sistan and Baluchestan Province of Iran.The results showed that remote sensing systems with an accuracy of over 80% at meteorological stations estimated the amount of reference evapotranspiration and an error of less than 2 mm was reported in all stations. Also, studies during the growing season (June 15 to November 6) compared to the growing season (1 November to 15 May) showed that the reference evapotranspiration obtained from satellite data in the first growing season has a higher (R2). Also, the results of NRMSE index evaluation indicate that the reference evapotranspiration obtained from ERA-GLDAS2.1 data is appropriate.Therefore, since the estimated and validated values had acceptable accuracy, in the next step, these systems can be used anywhere in the province.

The accuracy of retrieved LST from satellites is of great importance. Among different LST validation methods, a cross-calibration procedure is highly cost-effective and applicable. The Indian National Satellite-3D series (INSAT-3D) and Meteosat Second Generation (MSG) are two geostationary satellites that which provide LST products with high temporal resolution. Considering MODIS as the reference (polar orbit that is onboard Aqua and Terra satellites), the comparison of the LST products of these geostationary satellites was evaluated from 4th March to 1st September 2015. For this purpose mean LST ratios were calculated for both MODIS-Imager (from INSAT-D) and MODIS-SEVIRI. Then the behavior of their mean LST ratio was analyzed for the exciting four major land covers and five elevation classes in the study area. The results showed that Imager data underestimated and overestimated the LST in comparison to MODIS data during the day and night time respectively. The SEVIRI LSTs underestimated the LST in both day and night time in comparison with MODIS products. In order to model the discrepancies between MODIS-Imager and MODIS-SEVIRI, for each land cover a multilinear regression model was fitted based on slope, aspect, azimuth, and View Zenith Angle (VZA). The results showed that barren, Shrub, grass, and cereal crops had low RMSEs in model fitting, respectively.

Nowadays, coronavirus or the so-called Covid19 has spread all over the globe endangering humans' lives. On the other hand, it has also been a lesson of life for all human beings to take, which has exerted some beneficial impacts on their lives; hence, this immensely little invisible creature has to be treated wisely enough. Thus, one has to refer to old sayings and pieces of advice given by late wise man and philosophers so as to come to a good realization of the situation. In this regard, the late great Iranian bard called Saadi Shirazi, the poet of all ages and eras, is a world-known poet and philosopher whose clear guidelines can be an effective solution to humans' problems these days. Saadi wrote the following triple rhyming couplets about 800 years before universal declaration of human rights. These couplets are still being caroled in different languages as melodies, Anthems and global music. It is, of course, worth mentioning that Achaemenid Cyrus Charter of Human Rights Cylinder was the very first and the oldest human rights charter referring back to 600 BC.

The expansion of urbanization has increased the scale and intensity of thermal islands in cities. Investigating how cities are affected by these thermal islands plays an important role in the future planning of cities. For this purpose, this study examines and predicts the effect of land cover (LC) changes in the three classes of LC including urban areas, barren lands, and vegetation on land surface temperature (LST) in the city of Yazd during the last 30 years using Landsat 5 and 8 images. This study also examines the effect of the ratio of proximity to the barren land and vegetation classes during this period to examine how the recorded LST is affected by the mentioned ratio.

The LC maps of Yazd city were extracted using a supervised Artificial Neural Network classifier for 1990, 2000, 2010, and 2020. Terrestrial data, google earth, and ground truth maps were used to derive training data. The LST of Yazd was obtained from the thermal band of Landsat 5 and Landsat 8. After that, the LST was classified into six available classes, including 16-20, 21-25, 26-30, 31-35, 36-40, and 41-46°C which has shown that the four last classes play an important role in LST changes in Yazd city during last 30 years. To evaluate the effects of the proximity of barren land and vegetation LC classes on the LST recorded by the sensor, firstly the proximity ratio was calculated in 5×5 kernels for all image pixels. Then the mean of LST was derived based on this ratio of barren and vegetation lands.

The results of this study showed that in Yazd city, from 1990 to 2020, the area of the urban area has grown 91.5 % (33.6 km2) over the last 30 years. Barren and vegetation land, have negative growth in the area over the same period. From 1990 to 2020, barren lands in Yazd experienced a growth -79.4% (21.3 km2), which the sharp growth of urban areas justifies this negative growth in barren lands. Vegetation classes in Yazd from 1990 to 2020, have experienced a growth -68.5% (12.2 km2). The average ground temperature of this city has been constantly increasing during these 30 years. By 2020, the city of Yazd, reaching an average of 38.1°C compared to 29.2°C in 1990, has experienced a 30.4% increase in its average LST. The temperature classes of this city have also moved towards warmer temperature classes in these 30 years. As the main part of the LST area of Yazd, in 1990, in the first place, the class of 26-30 °C with 47 km2 and at the second place the class of 31-35 °C with 26.4 km2 are classified. In 2000, in a reverse trend, the main LST class was 31-35°C with 52.8 km2 as the first place and the 26-30°C class with 20 km2 as the second place. With an increased class, the LST class of 36-40 °C for both 2010 and 2020 with 40.2 and 63 km2 respectively has been recorded as the largest LST class. The LST class of 31-35 °C has been recorded as the second LST class of both years with 33.2 and 9.7 km2, respectively. The difference between these two years is in the growth -70.7% (23.5 km2) of the class area of 31-35°C and the increase of 10.3% (0.8 km2) of the hottest class of the statistical period, 41-46°C, in 2020, compared to 2010. The results of this study also showed that the highest average temperature in all year was recorded for barren lands at 37.3°C. Also, a positive correlation (mean correlation 0.95) was shown between the proximity to barren land cover and the mean LST. However, the sharp upward trend of urban areas in the whole statistical period (91.5% with 33.6 km2) as the second class with the highest average LST after the barren lands with an average of 34.1 °C versus a downward trend of 79.4% (21.3 km2) of barren lands has increased the average LST over a statistical period of 30 years. It is because the decrease of 68.5% (12.2 km2) of vegetation areas as an LC class with the lowest average LST (32.2°C) in the same period, neutralized the effect of decreasing barren lands and intensified the trend of increasing the LST. Meanwhile, a negative correlation (mean correlation -0.97) was established between the ratio of proximity to vegetation and the average LST. The results of forecasting land cover changes in 2030 in the city of Yazd indicate that in a process similar to previous periods, the class of urban areas will increase. This growth will not be significant compared to 2020, with 1.6% (1.1 km2). However, a significant decrease in green areas (vegetation) by -19.6% (1.1 km2) in the same period, along with a slight decrease in barren lands -1.8% (0.1 km2) will cause the earth’s surface to become warmer, and the area of LST classes will be increased by the year. Accordingly, the main area of the LST class in 2030 for the city of Yazd, as in 2020, is forecasted 36-40°C with 58.2 km2 (-7.6% growth compared to 2020). But the dramatic growth of the hottest class of LST over the statistical period (41-46°C) with 166.3% (14.3 km2) growth as the second major class of LST in this year (2030), as well as the negative and dramatic growth of the relatively cooler class 31-35°C with -97.9 % (9.5 km2) in this year indicates the warmer ground surface temperature in 2030.

The results of this study indicate that in 30 years in Yazd city, the decrease in vegetation in the first place, along with the increase in urban areas in the second place, has caused an increase in LST. Thus, the vegetation class reduces the LST due to its cooling effect considering its water content. In this study, it was shown that by taking all factors into account, the reduction of barren lands will lead to a decrease in LST, and also increasing urban areas with a lower impact factor than barren lands will increase the LST. However, the decrease in the area of green lands (vegetation) in recent years, along with the sharp increase in the area of urban areas has caused an increase in LST. Increasing the proximity to vegetation by creating green areas by increasing the ratio of vegetation in the vicinity of different LC and also reducing the area of barren lands, can be a good solution to deal with the impact of urbanization in recent years on ground surface temperature.

Anzali Wetland in Iran as one of the most valuable wetlands registered in the Ramsar Convention is being destroyed by environmental factors and human activities. In the last two decades, among various satellite images, radar images have played a special role in wetland monitoring. Radar is an all-weather sensor and it is sensitive to surface roughness and moisture, they serve as a valuable source for quick and accurate monitoring of wetlands. However, similarities in backscattering coefficients of different wetland classes and relatively difficult processing – in comparison to optical images- are the most important factors that limit their application. In this study, the capabilities of SAR images in the classification of Anzali wetland and the three main land use classes around the wetland (i.e. agricultural lands, reeds, and built-up areas) were evaluated. Two radar images; Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) and Sentinel 1 captured in 2018 were used. The texture parameters of the two images have been extracted. The images and their extracted texture layers have been fused by the feature-level method and further classified by the random forest method. The overall accuracy of feature-level fusion is equal to 75% and the kappa coefficient is equal to 0.62. The evaluation results related to producer and user accuracy are 100% and 83.33%, respectively, show the high capability of radar images in the classification and detection of wetlands. However, some errors have been observed in the separation of agricultural lands, reeds, and built-up areas.

The Cypress of Abarkouh (Persian: ‎ Sarv-e Abarkouh), is a Cypresses sempervirens tree in Abarkouh in Yazd Province of Iran. This ancient tree with a height of 25 meters which is estimated to be over four thousand years, considered as one of the tree in Iran, was recognized as a national natural monument in 2003 and has been a major tourist attraction. This individual tree is located in central parts of Iran next to an old Qanat in the city of Abarkouh. This genetic reserve is unique not only from the aspect of age, botanical forms and beauty, but also from historical and ancient point of view. It has witnessed many events over the history both environmental and social, such as wars, global warming, and climate change. As a matter of fact, there are many more aspects to the study of this tree that reveal its value; namely ecological memory which provides us with valuable information on environmental factors of the past, and geological and evolutionary information. Anthecologists are investigating the hypothesis of having many Mediterranean cypress trees planted by Elamites around 5000 years ago which strongly admits the value of this tree in revealing historical information from the past.

The accuracy of retrieved LST from satellites is of great importance. Among different LST validation methods, a cross-calibration procedure is highly cost-effective and applicable. The Indian National Satellite-3D series (INSAT-3D) and Meteosat Second Generation (MSG) are two geostationary satellites that which provide LST products with high temporal resolution. Considering MODIS as the reference (polar orbit that is onboard Aqua and Terra satellites), the comparison of the LST products of these geostationary satellites was evaluated from 4th March to 1st September 2015. For this purpose mean LST ratios were calculated for both MODIS-Imager (from INSAT-D) and MODIS-SEVIRI. Then the behavior of their mean LST ratio was analyzed for the exciting four major land covers and five elevation classes in the study area. The results showed that Imager data underestimated and overestimated the LST in comparison to MODIS data during the day and night time respectively. The SEVIRI LSTs underestimated the LST in both day and night time in comparison with MODIS products. In order to model the discrepancies between MODIS-Imager and MODIS-SEVIRI, for each land cover a multilinear regression model was fitted based on slope, aspect, azimuth, and View Zenith Angle (VZA). The results showed that barren, Shrub, grass, and cereal crops had low RMSEs in model fitting, respectively.

Over the past two decades, the intense need for land surface temperature information for environmental studies and management and planning activities has made estimating the land surface temperature one of the most important scientific topics. On the other hand, different methods have been proposed to estimate the land surface temperature, each of which has resulted in different results for different regions. In this study, the algorithms that have had acceptable results in different studies have been selected and evaluated. In the field of thermal studies, what is considered as a major defect in monitoring the land surface temperature is the lack of sufficient meteorological stations to know the temperature values in places without stations and information limitations in preparing temperature data, especially for large areas. The study area is also facing this shortage, and this limitation further highlights the importance of the topic selected for this study to estimate the surface temperature using remote sensing technology. Verification and validation of results obtained from estimating the land surface temperature are other basic and discussed topics in thermal studies. The purpose of this study is an estimation of temperature in Ardabil city and evaluate the accuracy of the four single-channel algorithms, the improved mono-window, the Planck's inversion function method and the radiative transfer equation (RTE) method, to compare the accuracy of the two Landsat 5 and Landsat 8 satellites in estimating the land surface temperature.

Three types of data have been used in this study; Landsat 5 and 8 satellite images, data of two meteorological stations and ground data harvested with a digital thermometer. The images used are from the two satellites Landsat 5 and Landsat 8 with a time interval of 19 years. The meteorological data used were obtained from two synoptic stations in the study area. In addition to land surface temperature, relative humidity, minimum temperature and maximum temperature data of 24 hours were also obtained on two dates. Also, two points of the study area were selected and land surface temperature in the position of these two stations simultaneously with the satellite Recorded from two digital thermometers. MODTRAN web version calculator software version 6 has been used to model the radiation and the amount of atmospheric transmission. Emissivity with two methods of LSE methods based on NDVI and LSE NDVI Thresholds Method and land surface temperature with four algorithms: single-channel algorithms, An Improved mono-window, inversion of Planck’s function and radiative transfer equation using band 6 Landsat 5 and band 10 Landsat 8 bands. It was coded in MATLAB software for 2000 and 2019. Finally, the accuracy of the algorithms was evaluated using synoptic station surface temperature data and field sampling.

The collected data and results are analyzed and while presenting the output maps, the accuracy of the methods with terrestrial and meteorological data as well as the accuracy of Landsat 5 and Landsat 8 satellites in estimating the land surface temperature has been compared and evaluated. The results showed that for the three single-channel algorithms, the inversion of Planck’s function and RTE, the first method of emission and for the An Improved Mono-Window algorithm, the second method of emission had a higher accuracy. Land surface temperature data obtained from meteorological stations in 2000 differ by 12 minutes in terms of time and by 2019 differ by 4 minutes in terms of satellite transit time. The first meteorological station is located somewhat within the city limits and according to the results, it seems that the most important factor is the greater difference between the data of the first station and the estimated LST compared to the second station is the same factor because the heterogeneity of pixels and large changes in levels in urban areas interfere with a pixel value. And subsequently increases the likelihood of errors in estimating surface temperature within the urban anthropogenic range. For the ground station, two points with a homogeneous environment and outside the urban area with agricultural use (alfalfa) and barren use of the harvested product were selected and their surface temperature was measured at the same time as the satellite. The output results of land surface temperature estimation were compared and evaluated with two synoptic stations and two ground stations. In both histories, the single-channel algorithm showed the least difference with the temperature recording stations.

In this research, using Landsat 5 and Landsat 8 satellite images, four algorithms for estimating the land surface temperature of the earth, including single-channel algorithms, An Improved mono-window, inversion of Planck’s function and radiative transfer equation and land surface temperature maps of Ardabil city for two 2000 and 2019 were coded and extracted in MATLAB software environment. The band 6 Landsat 5 satellite was used for 2000 and the band 10 Landsat 8 satellite was used for 2019 due to less noise than the 11th band and the proximity of 9.66 (which is the highest radiation in this range). Comparison of land surface temperature maps obtained by the algorithms with synoptic and ground stations showed that in both 2000 and 2019, the single-channel algorithm was more accurate than the other methods. Comparison of the results of the single-channel method with the stations shows a difference of  +2.5 and 2- with stations 1 and 2 for the year 2000 and a temperature difference of  +3.3, +0.9, 1- and -0.9. Shows stations 1, 2, 3 and 4 for 2019, respectively. It seems that the direct use of atmospheric transmittance coefficients in the single-channel method process has been effective in the high accuracy of this method. In terms of accuracy, after the single-channel algorithm, the An Improved Mono-Window method, the RTE algorithm, and finally the Planck function inverse correlation algorithm were placed, respectively. The results of comparing the output of all four algorithms with the data of stations 1, 2, 3 and 4, show that the ground stations harvested with a digital thermometer are more accurate than the data of meteorological stations. One of the reasons for this is the location of meteorological stations (especially, Station_1) in the urban area due to the heterogeneity of the urban environment and the possibility of pixel interference and temperature interference of land uses, while ground stations from the out-of-town area. And was selected from an environment with homogeneous pixels (barren and agricultural). Also, the results of all four algorithms extracted from the Landsat 8 image show more accuracy compared to the results of the four algorithms obtained from the Landsat 5 image, and due to the improved spatial resolution of the TIRS sensor compared to the TM, the TIRS sensor output is more accurate, It was predictable.

Modeling and providing accurate information on the spatial distribution of soil properties is a key factor in many environmental and agricultural applications. Therefore, the purpose of the present study was to model and prepare a digital map of soil organic carbon using remote sensing indices in the Balikhli Chay watershed. At first, topographic and spectral characteristics affecting soil organic carbon content were extracted from digital elevation model and Landsat 8 satellite image. Then the performance of soil organic carbon modeling for different states was evaluated and compared based on random forest models. The states including 1) terrain covariates, 2) spectral indices, and 3) combination of terrain and spectral covariates, were evaluated and compared together. To this end, the correlation coefficient (R2) between the estimated and measured soil organic carbon and root mean square error (RMSE) were calculated for the different states. The results showed that the amount of organic carbon in the study area varied from 0.32 to 6.98 and the mean value was 3.04%. Carbon changes in the study area mostly dependent on changes in spectral indices. Elevation and Emissivity were respectively the most important terrain and spectral covariates in soil organic carbon modeling. The R2 values in the three models were 0.61, 0.62 and 0.75 and the RMSE values were 0.88, 0.67 and 0.57, respectively, which indicates the better performance of the third model. The use of a combination of terrestrial and spectral variables significantly increases the accuracy of soil organic carbon modeling.

The use of satellite data for rapid estimation of soil moisture (SM) and determination of environmental factors affecting it has been developed in recent years. The aim of this study was to investigate the effect of biophysical and topographic characteristics on spatial distribution of SM. For this purpose, SM was measured at 148 points in Balikhli-Chay watershed in Ardabil province and triangular method based on ASTER digital elevation model, land cover map and climatic data was applied for SM modeling. Surface biophysical properties including wetness, greenness, brightness, and land surface temperature and topographic variables (solar local incidence angle, elevation, slope, and aspect) were calculated. Model error in different months was determined using error statistics. According to the results, the average SM content in the region in July, August and September were 4.67, 6.22 and 4.66%, respectively. The lowest coefficient of determination (R2) and root mean square error (RMSE) of estimated and measured SM were related to September (0.78 and 1.44, respectively). The strongest linear relationship between SM and biophysical variables (topography) was related to July (with R2 and RMSE equal to 0.53 and 0.29, respectively). SM decreased with increasing land surface temperature and brightness, however increasing greenness, wetness, elevation and solar local incidence angle increased SM content. This study showed that the triangular model can be used to investigate the spatial distribution of SM using biophysical and surface topographic properties.

Many scientists and experts have tried to provide better conditions for human comfort with the help of science and technology.From the following two paths, such as existence and non-existence, wisdom and love, matter and meaning, color and colorlessness, medicine and placebo, and in short, we are too far and too close. Since many of the ancients, including sages, poets, and philosophers, are examples that have watered down their inner pleasures and love, here we have also used their poems and sayings to prove our claim. Some of the functions of nature and living things that have evolved in the realm of nature have been examined as a model in this discussion. To do so, the functioning of many organisms, not just living organisms but also non-living organisms such as viruses, including the coronavirus, can be the basis for new patterns and definitions of future scientific and technological algorithms. Paying attention to the subject of the biomimetic shows us brilliant ways that we did not imagine before.

Soil moisture is one of the key parameters in watershed and water resources studies. Field measurement of this parameter is extremely difficult, time-consuming and costly. Hence, in recent years, numerous satellite-based methods for estimating and modeling soil moisture have been developed and presented. Among proposed methods, surface energy models performed better and have a higher degree of accuracy because of their physical nature. But, due to their particular complexity, they have been used rarely. Therefore, this research was carried out to estimate soil moisture using Landsat 8 Satellite imagery and Surface Energy Balance System (SEBS) near the Shadegan Wetland, located in the south-west of Iran. For this purpose, volumetric soil moisture content was measured at 39 points on 27 June 2016, simultaneous with the overpass of Landsat 8 Satellite over the study area. After necessary image processing, the was calculated using the applying the SEBS on satellite image. Then, the evaporation fraction was used as the main input in an experimental model (saturation ratio model) for estimating the soil moisture. Results showed the good ability of the model for estimating soil moisture with the coefficient of determination of 0.69 and the RMSE error value of 0.03 . It can be concluded that combination of remote sensing data, surface energy balance system and the experimental model of soil moisture can be used for modeling soil moisture in a large scale.

The present article pays to some Alpine-Himalayan ophiolites of Late Cretaceous age from Darepahn area in the southern part of Nain-Baft Ophiolite belt. Whole rock processed data clearly shows that the metaperidotite rocks are silica-poor with low CaO, Al2O3, and TiO2 that reflect the predominance of olivine over calcic pyroxene in the protolithes. The considerable content of Co (85.6–124.6 ppm) and very low contents of Sc (4.3–15.3 ppm) and Sr (0.7-14 ppm) provided more evidence for olivine enrichment in the rock parentages. Calculating numbers of cation per formula unites based on EMPA results for distinguished Serpentine polymorphs (classified structurally via Raman spectroscopy) display that Tschermak substitution of trivalent cations for lizardite is tetrahedrally, and for chrysotile and antigorite, it is octahedrally. These chemical differentiations, lead to identifying chemical field plots in separator diagrams between SiO2, MgO, H2O, and Al2O3. Lizardite polymorphs can consider as primarily metasomatism results thru an iron-rich fluid occurrence. It has a considerable occupation of trivalent cations (especially Fe3+) and simultaneous less than 4 Si atoms in formula structure that reflected tetrahedral Tschermak substitutions. Antigorites have some deformed recrystallized textures along with high Σoct (Total Octahedral Cations) and low H2O content that could be considered as thermal originated polymorph. Chrysotiles have bimodal MgO and FeO contents that return to magnetite formations in groundmass and octahedral Tschermak substitutions on late-stage veins. The results exemplify compositional variations of Darepahn Serpentine polymorphs as a function of textural behaviors, structural position and thermodynamic condition of the formation process.

Introduction A major issue has been the role of large floods in comparison to more frequent floods with lower magnitude. This work addresses the geomorphic response of Mountain Rivers to extreme floods, exploring the relationships between morphological changes and controlling factors. In October 2015, following the occurrence of extreme and sudden rainfall, a large and devastating flood occurred in Ilam province. The floods caused major changes in the morphology of Ilam's rivers and it was accompanied by a considerable extension. The rate of channel expansion in different sections of the studied rivers varied, which led us to examine the influential and controlling factors that led to the diversity of river behavior.The working hypothesis was that explanation of geomorphic effects requires models that include other variables (e.g: lateral confinement, degree of sediment) besides hydraulic related variables (cross-sectional or unit stream power). The main aim was thus to explore the relationship between channel widening and arrange of controlling factors.Other specific aim addressed were which channel width (i.e: pre- or post-flood width) should be considered to calculate unit stream power in order to have a better explanation of channel response? Since few studies have been done in this field, this research was conducted with the aim of investigating the factors controlling the response of Mountain Rivers to extreme flood events in upstream of Ilam dam. Materials and methods The research was conducted on three tributaries of the Konjancham River (upstream of Ilam dam) whose catchments were affected by an extreme flood on 7th October 2015. An integrated approach was deployed to study this flood, including (i) Analysis of channel width changes by comparing aerial photographs taken before and after the flood, (ii) Estimate of peak discharges in studied reaches, (iii) Determine the degree of sedimentation in studied reaches. Delineation of spatial units was carried out according to the approach proposed by Rinaldi et al. (2013), which is a modification of the approach by Brierley and Fryirs (2005).According to that approach, stream sectors were defined as macro reaches having similar characteristics in terms of lateral confinement, while reaches are homogeneous also in terms of channel morphology (channel pattern, width, slope) and hydrology. We used the reach scale (reach length was commonly from 200 to 1300 m) for an overall assessment of magnitude of channel changes and for a preliminary investigation of controlling factors. The dominant process observed in the study reaches was channel widening, which was analyzed in detail by comparing aerial photographs taken before and after the flood. To assess changes in channel width, channel banks, and islands, these features were digitized on pre- and post-flood orthophotos. Channel width was calculated dividing channel area by the length of the reach, and changes in channel width were expressed as a width ratio (channel width after/channel width before the flood).The estimation of peak discharges used to calculate cross-sectional stream power and unit stream power. The last part of the methodological section deals with statistical analysis carried out to explain channel response to the flood event, by exploring the relationships between changes in channel width and controlling factors. Results and Discussion The relationships between the degree of channel widening and possible controlling factors were explored using multiple regression analysis. The analysis was carried out for the widening (width ratio) at reach scale.The whole data set includes 38 reaches. Seven controlling variables were considered (confinement index, percentage of reach length with artificial structures, degree of sedimentation, channel slope, cross-sectional stream power, and unit stream power calculated using pre-flood and post-flood channel width), but each regression model incorporated only three to four variables. Each model included only one of the variables expressing potential or flood flow energy (channel slope, cross-sectional stream power, unit stream power). All four multiple regression models turned out to be significant (p< 0.001) and gave high coefficients of multiple determination (R2and adjusted R2 ranged between 0.73 and 0.8 and between 0.69 and 0.77, respectively). The best model was the one including unit stream power calculated based on pre flood channel width and confinement indexas explanatory variables. Conclusion Results confirmed the main hypothesis of this work that hydraulic variables alone are not sufficient to explain channel response to an extreme flood event. The inclusion of other factors, specifically lateral confinement, degree of sedimentation, and percentage of reach length with artificial structures, led to satisfactory models explaining the observed variability in the degree of channel widening. These results suggest that the widening process is essentially controlled by two factors: flood power and valley confinement. Notably, flood duration above a critical threshold was not included in our analysis, but it is a variable that very likely would increase the robustness of regression models in these reaches. The analysis carried out in the three subcatchments of the Konjancham River basin showed that unit stream power calculated based on pre-flood channel width has stronger relations with channel widening in comparison to unit stream power calculated based on post-flood channel width and to cross-sectional stream power. Because peak discharge was used for stream power calculation, we are aware that neither pre-flood nor post-flood channel width is actually appropriate for the estimation of unit stream power, as the most appropriate would be the (unknown) width at the flood-peak time. The fact that using the pre-flood width gives better relations with the degree of channel widening (width ratio) could suggest that most width changes occurred after the flood peak.

Much has been said about what and how the ideas of prominent scientists in the world come from different specialties and from different geographical areas. But the nature of science is such that research results and scientific achievements are not so dependent on internal factors and social and political environment conditions. Just as the intellectual and emotional outpourings of sages, mystics, artists, and poets can be a function of the inner and outer conditions, so much they can converge in feeling, understanding, and knowing inside and out, providing many questions. In this article we have tried to interpretations, the explanation and vocabulary of the words in the poems since the original order poet and reality of what has been spoken, and the literal meaning of what is desirable poet, a bridge to be struck. Along with that the convergence of scientific, literary and spiritual concepts with new ways has been considered. The spiritual connection between two thoughtful poets, Goethe from Germany and Hafiz from Iran, who have lived in two different languages and cultures for more than 500 years, demonstrates the possibility of intellectual, cultural and social connection beyond contractual boundaries. Although we were not satisfied with the two, we cite examples of Mowlana, Attar, and Sa'di to show that all the elders in the cupola had a photograph of the scene. We interpret the visage of the mistress as a shared love that knows neither the geographical boundary nor the political or cultural boundary; Not afraid of time or place. Although science also creates such convergence and connection, it brings people together in love, spirit, spirituality, and thought, and avoids disagreements and creates empathy. Today, in spite of the achievement of rationalism, not only the problems of mankind have not been resolved, but also an increase. It may be that everyone thinks of himself as common sense, while everyone is the only one who knows everything and everyone has not yet been born! So, we suggested empathy by speaking it with the language of love, perhaps with this human mechanism achieving promised peace and inner satisfaction.In this soil in this soil, in this clean farmExcept for love and affection, we should not sow any other seed (Mowlana)

In the last decades, the earth’s surface has experienced various changes due to some obscure reason being caused by human activities consisting of deforestation and cities expansion. These widespread human changes pose several adverse problems. For instance, an environmental qualitative decrease which culminates in the reduction of living quality is the result of these adverse changes. Warming of the urban environment owing to oblivious effects of unstable urban expansion, replacing of natural land cover with urbanization phenomena, inter alia, pavements, buildings, concrete and other urban constructions, are discerned as the main factors of creating heat island, which cause the vanishing of land surface cooling effects. Moreover, skyscrapers and narrow streets diminish the airflow and give rise to an increase in the environment temperature. The remote sensing images are known as an appropriate information source for preparing heat maps and also benefiting from widespread applications for the precise investigation of climate changes and urban and non-urban land use changes, due to the continuous and extensive coverage, timeliness and the ability to acquire information in the reflective and thermal range of electromagnetic waves. The population of Babol city steadily increase as a result of population growth and villagers’ emigration and bring about excessive and unplanned constructions, alteration in the physical model of the city and finally expansion of the city in various directions. Physical expansion leads to numerous changes in urban land use and suburbs agricultural uses. Consequently, several serious problems occur including adversity in uses, the urban environment disorder as well as the vanishing of suburbs agricultural lands and their land use change into urban uses (residential, industrial and etc.). One of the adverse effects of urban physical expansion, declining of green space and changing of agricultural land use into the urban land use is the rise in the surface temperature. The aim of this study was to investigate the effects of Babol city expansion on changes in temperature classes and the thermal effects of built-up and non-built-up lands on each other during the period of 1985-2015. For this purpose, multi-temporal Landsat images were used in this study. For calculating the land surface temperature, ingle channel algorithm were used, and Maximum likelihood algorithm was also applied to classify images. Therefore, land use changes and land surface temperatures (LST) were examined, and thereby the relationship between land-use changes was analyzed with the land surface temperature. Surface temperature changes map for the period of 1985-2015 was prepared and analyzed regarding land use changes map for the study area to investigate the effects of land use changes on surface temperatures changes. By using the mean and standard deviation of normalized thermal images, the area was divided into three thermal classes. The status of each land use in the specified thermal classes and the impact of surface temperature in built-up and non-built-up lands on each other were investigated. The results indicate that most land use changes in the studied area belong to the change of agricultural and green space uses into built-up use in suburbs, which are 740.52 and 472.14 hectares, respectively. As it was shown through the findings, 92% rise was observed for the built-up use area. These changes are more significant in the periphery of the city. The use of green space has risen from 1656.55 hectares in 1985 to 2036.52 hectares in 2015, which shows an increase of 23 percent. The trend of growing the use of green space on the periphery of the city is clearly characterized by the conversion of agricultural land to citrus gardens. The growth of the use of green space is less than the growth rate of built-up use. The built-up use has experienced a significant growth trend over the study period, as area of built-up use has risen from 19% in 1985 to 52.52% of the area in the studied area in 2015. The results of the LST mean survey of land use types for the study area show that the built-up lands than the other lands have the highest LST for all years. Water lands have the lowest LST owing to the high water heat capacity. In most of the years, arable land has a lower LST mean than green space land, which is mainly due to the high moisture of the arable land and the greater activity of evapotranspiration. Most changes in surface temperature of the area are related to the distance of 0-800 meters of built-up area. The main reason could be the conversion of the agricultural and green space lands into the built-up lands in the area. The most prevalent temperature class in all years is the medium temperature class which covers the suburb lands. The hot temperature class is more highlighted in the center of the city, streets and ways out of the city. Although the adjacent of the city is covered by medium temperature class, cold temperature class are located far from the built-up urban area. Cold temperature class which follows a decreasing trend, is related to lands which are far away from the city. Also, hot temperature class at which the area increases annually, is adjacent to the city core and exit ways of the town. The highest temperature changes belong to areas which transformed from the other uses into built-up use during the past 30 years. Due to human activities which produce heat, the area which has remained in the form of built-up land use during this time period has had a noticeable temperature rise. Green space and agricultural areas which have not transformed into other land uses benefit from the least temperature changes during this time period. On account of growing of built-up land use, an increase has occurred in the area of hot temperature classes and a decrease in the area of cold temperature categories. Built-up lands have direct effect on their adjacent land surface temperature. The results of the survey with regard to arable lands and green space in different temperature classes indicate that the areas of green space and arable lands, located above the upper temperature, are proportional to areas of the land that are located in lower temperature classes and they are located in the average distance closer to the built-up lands. In other words, the green space and arable lands that are located closer to the built-up lands have higher temperature relative to the green space and arable lands which are far from the built-up lands. Also, green space lands which are located in urban environments have a higher temperature in proportion to the area of the green space lands adjacent the city owing to the high temperature of their surrounding areas. Green space lands in the urban environment, which have no high area, are more affected and classified into hot temperature classes. Built-up lands, which are located in the urban environment and adjacent to the green space, also has a lower average surface temperature than the green space, and sometimes located in the middle temperature class. This refers to the effect of moderating surface temperatures in built-up lands by green space lands. As a result, non-built-up lands with higher temperature classes are in a lower average distance from built-up lands compared to those with lower temperature classes. Built-up lands in the adjacent agricultural and green space lands have lower surface temperature compared to other built-up lands. As a result, these lands are considered to be medium temperature class. The results of this study showed the importance of planning and management for preserving agricultural and green space lands and preventing them from being transformed into built-up lands which increases the surface temperature and negative environmental impacts.