فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال یازدهم شماره 2 (تابستان 1399)

Land use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other and changing permissible uses into impermissible uses such as changing agricultural lands into residential regions or changing rangelands into eroded and low-yielding dry farming lands are always considered as importand issues in natural resources. Detection of the patterns of the land use changes and prediction of the changes in the future to carry out suitable planning for optimal utilization of uses in natural resource management reveal the need for modeling spatial and temporal changes of LU/LC. This study aims to assess the efficiency of the integrated model of Markov chain automatic cell (CA-Markov model) in simulation and prediction of spatial and temporal changes of Land use/Land cover (LU/LC) in Gorgan-rud river basin by applying three-dimensional Pentius-Melinus analysis in calibration of land use changes by using three assessment indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit as new indices in the assessment of the accuracy of CA-Markov model.

 In this research, the Earth observing sensor images of Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) acquired from the U.S. geographical site dependent on the U.S. Geographical Survey (USGS) were used to predict land use changes by using the integrated model of Markov chain automatic cell in Gorgan-rud river basin. Seven land use classes were separated for Gorgan-rud river basin including forest land class with the use code 1, agricultural land class with the use code 2, rangeland class (a mixture of shrubbery,langeland,agriculture) with the use code 3, water bodies class with the use code 4, barren land class (barren, rangeland, agriculture) with the use code 5, residential and industrial region class with the use code 6, streambed class with the use code 7. In this study, object-oriented classification method and  Support Vector Machine (SVM) algorithm were used to classify Landsat 5 and 8 satellite images and extract the land use classes of Gorgan-rud river basin. Segmentation scale  in this algorithm on a 50 unit scale (SL 50) was selected to classify the satellite images of 1987, 2000, 2009 and 2017. The assessment of the accuracy of Support Vector Machine algorithm in the object-based classification of satellite images was done by representing overall accuracy, Kappa cefficient, user accuracy, producer accuracy, commission error and omission error for four study periods. To understand how the changes in the region were created during the period of the study three decades and which classes had the area expansion and which classes had the area decrease, changes in the limits of the classes were revealed and percent of the changes in each class were obtained by using the classification maps and IDRISI software. CA-Markov model predicts the changes of different groups of LU/LC units based on spatial neighbourhood concept, transition probability matrix. Preparing land suitability maps is necessary to predict land use changes so that spatial changes can be controlled for each use by probability rules via filtering suitability maps. Validation of Markov model was performed by using three-dimensional Pentius-Melinus analysis with three assessment indices of Figure of Merit, Quantity Disagreement and Allocation Disagreement.

 Support Vector Machine algorithm in the classification of the land use based on object-oriented showed that the highest rate of commission error and omission error were observed in rangelands and agricultural lands with 19.12 and 18.55 percent respectively in the land use map of the year 2009. The lowest accuracy of the producer with 71.49 percent belongs to the rangeland use class in the land use map of the year 2009 and the lowest use accuracy with 71.45 percent belongs to agricultural land use class in the land use map of the year 2017. In keeping with the obtained results, the highest positive change belongs to the agricultural land use increase and the highest negative changes belong to rangeland and forest land use decrease during the period of three decades from 1987 to 2017. The highest forest land decrease with 4.8 percent, the highest agricultural land increase with 5.3 percent, the highest rangeland decrease with 9 percent, the highest barren land increase with 4.6 percent and the highest residential and industrial land increase with 0.8 happened during the periods of 2000-2017, 1987-2017, 2009-2017, 2009-2017, and 1987-2017 respectively. After validating the predicted land use chnges in CA-Markov model, based on the analysis of the 5 existing states in three-dimensional Pentius-Melinus analysis, the CA-Markov model with the accurate prediction of simulation of 89.92 percent showed the high efficiency of CA-Markov model in simulation process. After the implementation of the CA-Markov model analysis on the obtained land use map from the classification of the satellite images, one transition probability matrix and one transitioned area matrix were created. In predictions made by using CA-Markov model in 2017 to 2033, the most changes relate to barren and forest land expansion decrease to 16966 and 6961 hectare respectively and in contrast to the use decrease, rangeland, residential and agricultural land expansion increase will be observed to 20397, 3913 and 3825 hectare respectively.

 Detecting land use changes by using LCM tool for the period of three decades 1987-2017 in Gorgan-rud river basin showed that the forest, agricultural and residential use has had significant changes in this region. The obtained results of the prediction of the land use changes during the coming eighteen years by using the integrated model of Markov chain automatic cell following the detected changes by LCM tool show that we will face extreme deforestation phenomenon in this area. Investigation of the obtained results from the implementation of the future use network model by using Markov transition estimator showed that the future use changes can be predicted based on the existing environmental conditions showing that the agriculture will extremely increase in Gorgan-rud river basin during the coming eighteen years. Thus we can protect water and soil resources with comprehensive and long-term management and prevent the degradation of these valuable resources. Three indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit in three-dimensional Pentius-Melinus analysis had an important role in representation of the accuracy rate and calibration of the land use classification and the land use prediction corresponding with the obtained results from the carried out studies concerning the accuracy assessment with indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit. The results of the studied land use changes by using LCM tool and the integrated model of Markov chain automatic cell during the period of 1987 to 2035 show the degradation of more than 24309 hectare of the forest lands and agriculture increase in an area about 62421 hectare indicating human interfernces and deforestation we face in this area.

Rapid development of cities due to extensive changes in land use and land cover has had negative effects on global environmental quality. Land cover and  land use changes, and the development of urban and agricultural regions and deforestation are changing the regional and local temperature regime. Knowing the land surface temperature degrees contribute significantly to a wide range of issues relating to the Earth science such as urban climate, global environmental changes, and the study of the interaction of human and the environment. The lack of sufficient meteorological stations to be aware of temperature values in regions lacking a station is considered as a major flaw in monitoring the land surface temperature. Due to the information limitations, collecting data especially to a large extent,  is associated with many problems and obstacles, and the real-time access is difficult or impossible. Therefore, the need to use remote sensing technology with time conditions along with the feature of continuity and data collection in wide ranges can be very effective. The purpose of this study is to investigate the land surface temperature of Namin county in a period of 28 years and to compare the obtained results with land use and vegetation changes.

The data used in this study included  Landsat 8 satellite image of the OLI sensor in order to extract land use map and  TIRS sensor image to extract land surface temperature for the year 2015. Moreover, Landsat 5 satellite image of the TM sensor were used to extract land use map by using visible and infrared bands, and also to extract land surface temperature by using thermal bands for the year 1987. Images were taken in late spring and early summer due to the lack of high cloudy and snowy covers , as well as the high intensity of sunlight. The eCognition8.9 software was used for object-based classification. Classification in five classes (dry and irrigated farming, rangeland, forest and residential) and six classes (dry and irrigated farming, rangeland, forest, residential and water bodies) were selected  for the years 1987 and 2015 respectively. To assess the accuracy and comparison of the obtained maps, the error matrix, overall accuracy, and kappa statistics were used. Split-Window method was used to extract the land surface temperature of the study area. Finally, in order to analyze the relationship between land surface temperature with vegetation index, the correlation coefficients between land surface temperature and vegetation index were calculated based on land use types in the years 1987 and 2015.

The highest land use area in the years 1987 and 2015 belongs to the rangeland use with 43781 and 34114 hectares  respectively and the second land use area belongs to dry farming use with 23854 and 33277 hectares respectively. Moreover due to the lack of water use , the lowest land use area in 1987 belongs to residential use with 1301 hectares, while in 2015 with the construction of water structures, water use with an area of 86 hectares has the lowest land use area. The highest land use area increase was in the dry farming with 9423 hectares, which is a significant increase compared to 1987. The highest recorded temperature for Namin county in 1987 and 2015 was related to dry farming use (34°C and 27°C, respectively), indicating the concentration of heat in these regions. This type of land use has the highest temperature due to the factors such as the dryness of the products at this time and the harvest of the products. In 1987, dry farming use had the highest temperature (34°C), but in 2015 it experienced a decrease in temperature (27°C), despite the fact that it had the highest land surface temperature compared to other  types of land uses in 2015. The reason can be attributed to the factor of harvesting crops. Due to the fact that the rainfed crops in the study area are mostly wheat, and at this time of the season, most of the wheat is ripe or harvested, so the transpiration of these products is insignificant. The lowest recorded temperatures in Namin county are related to the uses of water bodies (21°C), forest (21°C) and irrigated farming (22°C), respectively.  Since water has a high heat capacity, it has the greatest effect on reducing the temperature. In forest and irrigated farming land uses, due to the higher vegetation density, the land surface temperature has the lowest value (23°C and 24°C in 1987 and 21°C and 22°C in 2015 respectively) compared to the other land use types. Agricultural land use in this area has the lowest land surface temperature (24°C in 1987 and 21°C in 2015) after forest areas. Due to the fact that the crops cultivated in this area are plants such as potatoes and these plants have more water needs, therefore these plants have a high greenness value at June to early July, which has led to more transpiration in the area where they are cultivated than other areas, thus it has been very effective in keeping the land surface temperature cool. The rangeland use has had high land surface temperatures (27°C and 25°C, respectively) in the two study  years, and there is little difference between the two years. According to the study season which was late June to early July, the high temperature of this land use type is due to the increase in the areas lacking canopy cover or areas having low or scattered vegetation. Due to the fact that in August, most of the leaves and brunches of the existing plants are dry and the transpiration is low, high temperatures are also recorded. The relationship between land surface temperature and vegetation index in rangeland use in the two study years had the highest correlation (0.91 in 1987 and 0.83 in 2015), while the correlation coefficient of the forest use was the lowest (0.46 in 1987 and 0.23 in 2015).

Land use type and land use and vegetation changes have a significant effect on land surface temperature changes. However, areas without vegetation have a higher land surface temperature than the areas with vegetation. The results showed that there was no significant correlation between vegetation cover and land surface temperature, which is mainly due to sufficient vegetation. In general, the results showed that in most areas with lower temperatures, there is high density vegetation indicating an inverse relationship between vegetation index and land surface temperature.

Considering the increasing importance of forest ecosystems in climate change mitigation projects, reliable and cost-effective methods are required to estimate the aboveground biomass (AGB). Common  methods used to estimate the aboveground biomass (AGB) include in-situ measurement, the biomass calculation using aalometric equations and using remote sensing techniques. Remote sensing has been widely used to estimate the biomass of forests in recent decades.The used statistical modeling method is one of the most important factors to use remotely-sensed data for estimation of the aboveground biomass. A large number of researches have been carried out about using the modeling methods. However, these studies face the following different challenges: 1) no modeling method has been recommended as the best method 2) the performence of these modeling methods is affected by forest type, the forest structure, and the present disturbance intensity 3) the performance evaluation and the comparion of the results of these methods were done by using goodness-of-fit test and cross-validation methods. The purpose of this study is to considering the role of choosing statistical modeling methods to estimate remotely-sensed aboveground biomass, the current study was conducted to investigate nine statistical modeling methods including linear regression (LR), generalized additive model (GAM), random forest (RF), support vector machine (SVM), boosted regression tree (BRT), k-nearest neighbor (kNN), cubist regression (CR), Gaussian process model (GPR), multivariate adaptive regression spline (MARS) using bootstrap process and 1000-repeated 10-fold cross-validation approach to estimate the aboveground biomass of Zagros forests using Landsat 8 images.

 The cuurent study was conducted in Kermanshah forests which is mostly dominated by oak species trees (Quercus spp.) and is located in western Iran on the Zagros Mountains. Zagros forests are generally sparse and open  and comprise approximately 20% of Iran’s area and 40% forest regions of Iran. In order to conduct this study, two forest regions with different levels of human disturbances were chosen; SarfiruzAbad region with highly degraded (HD) forests, and Gahvareh forest region with minor degradation (MD). Geographical coordinates of SarfiruzAbad and Gahvareh regions are 33º57′-34º04′N / 47º03′-47º17′E & 34º21′- 34º24′N / 46º16′-46º23′ E respectively. The Leaf area index (LAI) map derived from the Landsat images based on a global model was used to collect field-based sample plots  in both regions of the study. Both regions were divided into three  low, moderate and high  Leaf area index (LAI) strata, and the locations of the sample plots were located by using a systematic inventory at the intersections of a 200m×200 m grid in each stratum. 124 georeferenced square plots of field-based sample plots (63 plots in Gahvareh region and 61 plots in SarfiruzAbad region) with 30m×30m dimensions the same size as a Landsat 8 image’s pixel were collected. Allometric equation developed for oak tree in Zagros forests was used to calculate the amount of  the aboveground biomass of each individual tree or sprout-clump. The allometric equation used in this study uses  two vertical tree crown diameters to estimate the amount of the biomass of each individual tree or sprout-clump. The sum of the amount of the biomass  of each individual tree in sample plot was used to calculate the amount of the biomass plot in sample plot level at a ton per hectare. Our study regions were located in a frame of Landsat 8 images (path/row:167/36). A cloud-free Landsat image relating to 19th Mordad 1394 (10th August 2015) relating to the time when the tree canopies are completely closed and near to the date of land inventory was downloaded from earthexplorer.usgs.gov site. Based on the previous studies, the pre-processing of the used image comprising the radiometric and topographic corrections was done.using C method. To estimate the aboveground biomass in the study areas by using remote sensing, 38 spectral variables including band values, simple band ratios, vegetation indices and common linear transformations like tasseled cap and principle component analysis  were extracted from the used Landsat 8 image. Generally, the efficiency of nine different statistical modeling methods including parametric methods (Linear Regression, LR), semiparametric (Generalized Additive Model, GAM), and nonparametric Random Forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Boosted regression trees (BRT), multivariate additive regression splines, cubist regression (CR), and Gaussian processes regression/model) were compared in order to estimate aboveground biomass. To assess the models, two common quality statistics: (i) determination coefficient and (2) root mean square error via 10 fold cross validation repeated 1000 times approach were calculated. This number of repeats helps to ensure an acceptable assessment of robustness of the results.

 The measuredstatistical characteristics of the field sample plots showed that the mean aboveground biomass of SarfiruzAbad and Gahvareh regions were 12.6 ton/ha and 20.5 ton/ha respectively. ANOVA indicated significant differences between modelling methods (treatment effect: p< 0.001) for both R2 and RMSPE calculated in 1000-time repeats using 10-fold cross- validation.The Cubist modeling method with the mean determination coefficient of 0.61 outperformed other methods in SarfiruzAbad region.These resultsfor Gahvareh region showed better efficiency of linear regression (LR), generalized additive model (GAM), and k-nearest neighbor (KNN) with the mean determination coeffieient of 0.87.The multiple comparisons of different models by using Tukey test concerning RMSE showed that in SarfiruzAbad region, cubist method  with the mean of RMSE 3.3 ton/ha and kNN and RF methods with the mean of RMSE 5.8 ton/ha had a significant difference in comparison to the other methods. Totally, the results of the research revealed the suitable efficiency of Landsat 8 image for AGB estimation in Zagros forests. The acceptable results are due to the low AGB in our study regions that did not reached the saturation point as one of challenges of using optical images like Landsat. The other results of this research is the assessment of the effiecieny of modeling method in order to increase the accuracy of the estimation of remotely-sensed aboveground biomass.Unlike the results of the previous studies, linear regression yielded better results compared to nonparametric methods that can be due to the presence of the linear relationship between aboveground biomass and spectral variables derived from Landsat images. Among the used various spectral variables, red, near infrared, and  shortwave infrared 1 and 2  band ratios were selected as the final variable in most modeling methods.

In this study, we evaluated the effieincy of different statistical modeling methods to estimate AGB in Zagros forests by using Landsat images. The biomass estimations were compared by using nine parametric, semi-parametric, and non-parametric methods and using 1000-repeated 10-fold cross-validation. The results illustrated the acceptable potentiality of Landsat images for cost-efficient AGB estimating in Zagros oak forests. The accuracy of AGB estimation in Gahvareh region with low-degraded forest stands was higher than SarfiruzAbad region with highly degraded stands.

Climate, soil characteristics, topography, land use, and biological relationships at various scales are the most important influencing factors on distribution and ecological niches of species. The climate is one of the most important determinants of plant distribution. Therefore throughout the past ecological history, climate change has had profound consequences on the current conditions of the world's ecosystems, including the existing distribution of species. Changes in the distribution of one species in a given geographical area due to the climate change can lead to shifting the presence regions of that species toward higher elevation that leads up to vegetative restriction or even extinction of the species. Shifting, or changing the geographical distribution of species is a strategy to be resistant to the climate change. Therefore, in order to protect the key ecological and valuable plant species, it is necessary to determine suitable habitats via identifying the most important environmental and human factors affecting the species presence in the current and future conditions. Astragalus L. (Fabaceae) is a genus widely distributed throughout the temperate regions. The Astragalus verus Olivier is a small, valuable shrub with many branches. In addition to its protective role from the point of view of the soil, this species has medicinal and industrial values. In recent decades, the geographical range of the A. verus variety has been significantly declined due to factors such as land degradation and over utilization. Despite the national importance of the Astragalus genus, so far little research has been done on the consequences of the climate change on the distribution of species of this genus. The present study was conducted to accomplish the following objectives; 1) To identify suitable habitats and determin the geographical distribution of A. verus in Central Zagros in the current situation; 2) to predict of the consequences of climate change by 2050 and 2070 under different scenarios on geographical distribution of A. verus; 3) to determin the most important factors affecting the distribution of this species. distribution of A. verus; 3) to determin the most important factors affecting the distribution of this species.

 This study was carried out in Chaharmahal and Bakhtiari province in an area about 1.65 million hectare thai is totally located in Central Zagros region. Extensive field studies were integrated to collect geographical coordinates of the presence point (112 points) of this species by using Global Positioning System (GPS) throughout Chaharmahal and Bakhtiari province. Bioclimatic (bio1–bio19), Physiographic variables (elevation, aspect, and slope) and land cover/land variables were used for modeling. Before modeling, two methods of Pearson correlation analysis and Variance Inflation Factor (VIF) were used to check out the correlation between the various environmental variables. In order to model, 19 environmental variables including bioclimatic variables, physiography and land cover / land use were applied to model the distribution based on correlation analysis. Variables with Pearson’s correlation coefficient, r2<±0.8, VIF<3) were selected. Finally and after the omission of the layers having high correlation, nine variables were used for modeling. In order to predict the distribution of the suitable habitats of the Astragalus verus Olivier, Biomad2 software package in R environment (3.1.2 version) was used. In this study, ensemble methods including Maximum Entropy (MaxEnt), Artificial Neural Network (ANN), Generalized Boosting Method (GBM), the Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS) were used to estimate the suitable habitats. We used 80% of the occurrence points as training data for model calibration and 20% of the rest of the data set to evaluate the predition of the models. Prediction of the geographical distribution of the Astragalus verus Olivier in the future (years 2050 and 2070) was made based on four scenarios of the increase in the greenhouse gases (Representative Concentration Pathways; RCPs) RCP2.6, RCP4.5, RCP6, RCP8.5 in general circulation model MRI-CGCM3. Model performance was assessed by using the area under the receiver operating curve (AUC) and the true skill statistic (TSS).

 Our results revealed that the most effective variables in desirability of the study species habitat were the isothermality, mean temperature of the wettest season of the year and seasonal precipitation variables respectively. In keeping with the findings, the Astragalus verus Olivier mostly exists in habitats with Isothermality (bio3) from + 36.8 to + 39.7 °C, Mean Temperature of the Wettest season of the year (bio8) from - 2 to + 3.5 °C, and seasonal precipitation variables (bio15) from 100 to 112 mm and the Annual Precipitation of 280 mm to 490 mm. Based on the results of modeling of current conditions, in comparison to the other regions, northeast and east of the province had the most habitat importance for the Astragalus verus Olivier. Our findings show that about 27.43% of the study area was identified as suitable habitats for the Astragalus verus Olivier. Prediction of the geographical distribution of the Astragalus verus Olivier in the future (years 2050 and 2070) was made based on four scenarios of the increase in the greenhouse gases (Representative Concentration Pathways; RCPs) RCP2.6, RCP4.5, RCP6, RCP8.5 in general circulation model MRI-CGCM3. Based on the future projections were made for the year 2050 and 2070 with four Representative Concentration Pathways (RCPs) scenario (2.6, 4.5, 6 and 8.5) and general circulation model MRI-CGCM3. In keeping with our findings, climate change can have significant consequences for the Astragalus verus Olivier suitable habitats in the study area. Based on various senarios, about 45.70 percent (year 2050, RCP2.6) to 89.88 percent (year 2070, RCP8.5) of the current habitats for the Astragalus verus Olivier will be unsuitable due to the climate change by 2050 and 2070. While in the same period of time, about 1.58 (RCP8.5, 2050) to 13.19 percent (RCP2.6, 2070) may be added to the suitable habitats of this species in areas with higher elevation. According to all scenarios, the suitable habitats of this species will decrease in all habitats, especially in areas with lower elevation. The climate change consequences especially the probability of decling and shifting the geographical range of the plant species in various habitats of Iran especially in the central Zagros and also in Central Iran range are predicted. Assessments showed that the models had acceptable accuracy and Random Forest model was determined as the most reliable model to predict the distribution of this species.

 Generally, this study indicated that ensemble model might predict the potential distribution of the Astragalus verus Olivier with a relatively high accuracy (AUC= 0.92 and TSS= 0.79). The scenarios used in this study predict the probability of the shift of the geographical range of the studied species under climate change scenarios of 2050 and 2070. According to the results, it seems that the suitable habitat extent of the Astragalus verus Olivier in the study area has been decreased and will shit toward the higher elevation. Although land degradation and over utilization may be considered as two important factors in habitat degradation of this species but this study highlights the importance the effects of climate change on the distribution of the Astragalus verus Olivier. As a result of the severe and inappropriate harvest of the Astragalus verus Olivier, the range of its distribution and density has decreased in some areas, which has increased the intensity of phenomena such as soil erosion. This issue requires a double attention of the managers and experts of natural resources to the Astragalus verus Olivier and the other species with similar performance in ecosystems having importance from the view point of economic productivity as well as their ability to conserve the soil.

In addition to tourist attractions, salt domes are one of the most interesting geomorphic phenomena having different mineral resources and can in some cases act as an oil reservoir  and oil trap. It is very important to identify them. Iran is very rich in evaporative deposits and also shows a unique abundance of emerged/outcropped salt domes. Most of the known salt domes are distributed in the south of  Zagros and the Persian Gulf region. But they have also been reported in the other parts of Iran, including the Great Desert, Garmsar, Qom and the Ravar region. So far, no special study has been done on the salt domes of the Ravar region, so that only a few domes in the northern and eastern parts of Ravar have been mentioned. without specifying their location on the map. Therefore, the necessity for further study of this area is specified. The main purpose of this study is to identify the salt domes found/outcropped in the area of Ravar city, Kerman province, by using new remote sensing methods and using  radar and multispectral images.

 There are several ways to process multi-dimensional images that the analysis of the principle components and the false color combination are the most important ones. We will explain how  these methods  have been used in the present study. Aster thermal sensor bands were used to produce the false color combination, so that the mentioned minerals were exposed/highlighted by placing the 12, 11 and 13 bands in the red, green and blue channels respectively, Studies on the use of the main components analysis technique for Sentinel 2 satellite images to detect soil and rock salinity show that the false color combinations of PC7, PC6 and PC2, in red, green and green channels respectively is very suitable for this purpose. This is done in the same way in this study.

 By performing atmospheric corrections on the multi-spectral images of Sentinel 2, the analysis of the main components was performed on it, as a result of which, the corresponding image was divided into 12 components. Using the three main components 2, 6 and 7, a false color combination was prepared. The results show that the different stone units are highlighted with different colors. Meanwhile, according to previous studies and by examining different colors and comparing and matching it with the geological map of the study area, it was specified that the light pink color indicates the salt units in the study area, This has been proven by field studies. It is noteworthy that in addition to determining the salt domes, the pink areas also show the secondary salts caused by weathering and erosion of these domes. Since the composition of the salt domes displayed in the Ravar salt basin varies so that some of these domes are dominated by salt minerals and polyalite, and others by sulfate minerals such as gypsum and Carbonate minerals such as anhydrite form the dominant mineral, different satellite images can be used to highlight the dominant minerals of each group in terms of their characteristics and spectral behavior. Thus Aster images were also used. Therefore, according to the specific spectral behavior of anhydrite and gypsum minerals in the thermal spectrum range, special color combinations can be combined to recognize salt domes by placing bands 12, 11 and 13 in the red, green and blue channels, respectively. As shown in this result, the salt domes having the dominant gypsum and anhydrite mineral are marked by light white. By performing the radar polarimetry technique and applying the CPR index, the relevant images were prepared. As mentioned earlier, CPR image suffering is closely related to the type and spectral behavior of different levels, In order to better analyze the images, the data suffering  were normalized between 0 and 1. The closer these numbers are to the number one, the greater the roughness is due to surface erosion. As a result, the areas that are red in the image are usually very eroded.

 The results of this study show that evaporative minerals and salt domes can be identified by using radar polarimetry method. In this study, with the application of CPR index, salt domes with red color were highlighted. In addition, due to the specific spectral behavior of the anhydrite and gypsum minerals in the thermal spectrum range, with the color combination of bands 12, 11 and 13, ASTER images of light-colored salt domes were identified. Also, the existing salt units in the study area were identified by using the three main components 2, 6 and 7 prepared from Sentinel 2 images,. Based on the obtained results, 27 salt domes were identified in the study area, which are in good agreement with the usual structural mechanism of salt domes creation. In addition, the accuracy of the results were confirmed by field survey.

In recent years, the study of climate changes as well as their effects, has become a constant topic in the scientific fields of many countries. One of the main features of these changes is the increase in air temperature over the last 5 decades compared to the last 500 years. Statistics show an increase of one degree centigrade in air temperature over the last 5 decades. The land surface temperature means the radiant temperature of the earth's crust and the amount of pure energy that is balanced on the earth's surface under climatic conditions and depends on the reached the amount of energy, surface emissivity, humidity and atmospheric airflow. Land surface temperature is considered as one of the key variables in climate and environmental studies of the Earth’s surface. It is also one of the basic parameters in the physical features of  the earth's surface at all scales from local to global. Currently, the most important sources of climatic data are meteorological stations, and these stations provide climatic statistics for certain points, while the temperature may alter at different intervals stations and decrease or increase compared to the desired station. Therefore, it is necessary to have a technology that can eliminate the shortcomings of meteorological stations in calculating the temperature at sampling intervals and in impassable places where it is not possible to build a meteorological station. In recent years, new sciences such as remote sensing have provided new ways to monitor the environment and acquire, evaluate, and analyze environmental data, and can provide a wide range of parameters relating to the environment. This technology is considered as an important and increasing source of information for studying climate change that has a direct impact on global warming. Over the past two decades, 18 algorithms have been developed to calculate the land surface temperature. These algorithms fall into four categories: emissivity-dependent models, two-factor models, complex models, and radio-based models. The results of the comparisons between different algorithms shows that different algorithms perform differently in different situations with different geographical climates. Therefore, the present study aims to compare the types of LST calculation algorithms for MODIS sensor images and determine the best algorithm for East Azarbaijan province.

 Convert digital numbers (DN) to spectral radiation. The following equation was used to convert the numerical values to spectral radiation for thermal bands of MODIS sensor images. Planck's equation was used to convert spectural radation to spectral reflection when the radiant power of thermak data of MODIS sensor is considered to be a maximum of one. In order to estimate the surface emissivity, the Normalized Difference Vegetation Index (NDVI) thresholding method is used. The radiant power is divided into three categories to determine the soil characteristics in each pixel and to calculate the emissivity rate and emissivity difference; 0.2>NDVI, it is considered as dry soil and its radiant power is considered to be equal to 0.978. 0.5 NDVI, it is related to pixels with higher vegetation density and its radiant power is considered 0.985. 0.5>NDVI<0.2, it is based on a combination of pixels relating to vegetation and soil and the radiant power for them can be calculated. The vegetation ratio, that its value can be calculated. The value of each scientific finding depends on its accuracy. To compare the obtained results from the algorithms used to calculate the land surface temperature with the recorded temperature in meteorological station.

The results of the present study show that among the 18 algorithms for the land surface temperature estimation for MODIS sensor images, the Sobrino algorithm with RMSE value of 1.79 has the highest accuracy, Cole Casillas and Prata algorithm with RMSE value of 2.85 is in the second position, and also the Salisbury and Sobrino algorithms with RMSE values of 2.39 have the third place for LST calculation among the other algorithms. The Qin algorithm with a RMSE value of 5.28 has the lowest accuracy for LST estimation.

A review of the data obtained from comparing split-window algorithms shows the overall compliance of the calculated temperatures with the topographic conditions of the region, so that almost the lowest temperature values in all algorithms are related to the parts having more height (mountainous) and green cover of the region and also, temperature values have risen in low-lying areas lacking dense vegetation.