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qdOne of the most important limitations of cultivation in the Sistan plain is soil salinity. To investigate the salinity status using remote sensing in Sistan Plain, Landsat 8 (LC08- LOI/TIRS) satellite images were used in April, August and November of 2017 and April 2018. To convert radiance data, the initial correction related to voltage change to digital number (DN) to convert radiance data was done by sensor calibration file as well as data radiometric correction using flat area method in ENVI 5.1 software. To determine the soil sampling points, first draw a 2x2 km grid and then by referring to the area and checking the sampling location, 312 soil samples were taken and the values of Electrical Conductivity (EC), Sodium Adsorption ratio (SAR) and texture in them were measured. By secondary processing, extracting the spectral features of satellite images and using several algorithms and indices, a salinity map was prepared for the surface soils of the region in four periods. The results of the remote sensing investigation showed that surface soil salinity in the region is a dynamic phenomenon and has significant changes with the changes of season, rainfall, irrigation and land management. The results obtained from the interpretation of satellite images showed that time, rainfall events of the planting season and water and soil management have a significant effect on soil salinity and the areas of lands with different salinity. The extent of soils with high salinity increased in the summer season, but in April 2018 (MBE=0.98, NRMSE=17.56%, R²=0.69) which coincided with the sampling due to the occurrence of rains and floods in these areas, this extent decreased. [ABSTRACT FROM AUTHOR]

In this research, a synthesis approach to estimating land surface emissivity (LSE) using remote sensing data and a spectral library that can be used to any optical sensor is proposed. The suggested method not only estimates the LSE more accurately as a function of the reflection of various surface effects, but it also takes into account the spectral response functions (SRF) of the thermal and reflective bands when calculating the LSE. Moreover, by using reflection of all reflectance bands, the proposed method strengthens the prior methods' poor link between LSE and reflectance of only the red band. The suggested approach was applied to a Landsat 8 imagery, and the resulting LSE was compared to and verified using two LSE products from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The findings indicated that the LSE from the proposed methodology in Landsat 8 thermal band 10 has a root- mean-square error of 0.76 % and 0.75 %, respectively, when compared to the equivalent LSE product of the first and second ASTER Images. This error was also calculated in the 11 thermal band, with values of 1.49 and 1.06 %, respectively. This error was also calculated in the 11 thermal band, with values of 1.49 and 1.06 %, respectively. Additional source of uncertainty in thermal band 11 might be the relatively substantial discrepancy in the SRF, spectral range, and effective wavelength between Landsat 8 band 11 and the 14-ASTER band. The results of this study compute the surface emissivity as a function of the reflectance of the reflective bands, and each pixel associated with that reflectance has a unique emissivity value that differs from that of nearby pixels. The prior technique, on the other hand, assigned a constant emissivity coefficient to the value of the group of pixels, and the surface emissivity was computed as a constant discrete value in each part of the image. [ABSTRACT FROM AUTHOR]

Urban heat islands could have destructive effects on the environment and increase energy consumption. The present study aimed to investigate the spatial autocorrelation of land surface temperature and its relationship with land use in Ahvaz city. Daily and Night Land surface temperature maps were extracted from the split window method for the Landsat 8 satellite image and MOD11A1 product, respectively. Land use maps of the study were extracted into five classes: Builtup area, vegetation, bare land, water, and Karun River. Based on the results, Global Moran's index on day and night was equal to 0.94 and 0.9, respectively, according to which Land Surface temperature is distributed in a cluster form. Results of Getis-ord GI statistics showed that the largest area of daily and nightly hot spots in the east of Ahvaz was obtained with 5050.53 and 8055.69 ha, respectively. The results of the Kruskal Wallis test showed that there was a statistically significant difference between the average temperature of land uses (p-value<0.05), so industrial areas and bare land had the highest temperature and the water bodies and vegetation had the lowest temperature. The results also showed that LST has a negative relationship with NDVI (r = -0.47); however, the average NDVI index in the study area was equal to 0.17 which indicated the poor quality of vegetation in Ahvaz. Therefore, it is necessary to take measures to improve the quality of vegetation and expand the urban green space and preserve water resources to adjust the land surface temperature in Ahvaz. [ABSTRACT FROM AUTHOR]

Introduction LST (LST) is one of the important parameters that affect the physical, chemical, and biological processes of the earth as well as environmental science and urban planning. Human activities such as land use changes and the development of urban areas led to an increase in the LST and the appearance of thermal islands. The main source of climate data such as temperature are synoptic stations, however, it is impossible and time-consuming to use traditional methods to estimate the LST for all types of earth conditions, on the other hand, synoptic stations only measure temperature information for specific points, and the obtained values are only related to that specific point; while according to the land cover and other conditions, the temperature in different parts of a region is different compared to the temperature recorded for a specific point and can be several degrees celsius lower or higher, therefore, it is necessary to use scientific methods that provide the possibility of calculating the temperature of any point on the earth's surface. At present, remote sensing images due to features such as wide and continuous coverage, low cost, timeliness, and the ability to obtain information in reflective and thermal ranges, are suitable tools for extracting LST and land use maps. Spatial analysis is one of the important subjects in the temporal and spatial evaluation of land surface data, which can be used to examine the spatial and temporal changes of spatial data in a region. Given that the data that are examined in environmental studies are not independent of each other in most cases and their dependence is due to the location of the observations in the studied space, which are called spatial data; Due to the existence of a spatial correlation between the data, the usual statistical methods are not a suitable method for examining these data, and spatial statistics can be used as a suitable option for analyzing these data. The aim of this research is to extract LST and land use map of Yazd county using a remote sensing technique. In this study, the spatial autocorrelation of LST in Yazd city and the identification of hot thermal clusters have been investigated using the global Moran statistic and the Getis-Ord GI statistic. Materials and MethodsIn this research, Landsat 8 satellite’s multi-spectral and thermal images have been used to extract the land use and LST in the study area, After performing the necessary corrections in the preprocessing stage, the land use map of the study area was prepared in 5 classes (built-up, vegetation cover, water bodies, bare land, and rock) using the support vector machine method and the overall accuracy and kappa coefficient were used to evaluate the classification result. In the next step, LST was extracted by the split window method. The relationship between LST and Soil adjusted vegetation index (SaVI) was investigated using regression analysis. In order to identify the spatial pattern of the LST, the global Moran index was used and hot spots were identified by Getis-Ord GI statistics. Results and DiscussionOur findings show that the kappa coefficient and overall accuracy were equal to 0.96% and 98.99%, respectively, bare lands are the most, and water bodies have the least area, equal to 76.16 and 0.09%, respectively. The average LST was 50.83°C. The result showed that the type of land use had an effect on LST, the water bodies had the lowest, and barren lands had the highest mean LST, equal to 36.91 and 52.13 °C, respectively. Vegetation is one of the factors that regulate the LST, areas without vegetation have a maximum LST and areas with high density vegetation have minimum LST .Based on the results, the vegetation quality of the study area was poor and its average temperature was 45.61°C. The mean of SAVI index was equal to 0.09 and correlation analysis showed a negative correlation between SAVI index and LST (r = -051). The analysis of spatial correlation with global Moron indexes showed that the LST of Yazd has a spatial structure, in other words, LST is distributed in a cluster form, Based on the results of the Getis-Ord GI statistic, the area of hot and cold spots was equal to 66.86% and 27.4%, respectively. In general, parks, cultivated lands, tree and forest cover and water areas, formed the cold spot areas of yazd city, and the hot spot areas of yazd city were located in the industrial areas and surrounding urban lands, hospitals, passenger terminals, gas stations, places near busy roads and bare and uncovered lands. Conclusion The results showed a strong relationship between land use and LST. Based on the results, the LST data of Yazd has a spatial structure pattern, barren lands and industrial areas formed hot thermal islands, and vegetation and water bodies formed cold thermal islands in the study area; the wide area of barren lands, the lack and poor vegetation cover due to the lack of rainfall and drought are factors affecting the LST and the creation of hot thermal islands in the study area. The result showed a negative relationship between LST and SAVI, the vegetation of the study area is weak and its temperature is high. Considering the role of vegetation in adjusting LST, it is recommended to take necessary management measures in order to improve the quality of vegetation and reduce bare land in the study area, and also prevent the conversion of natural land uses into built-up land. The results of this research can be used by managers and planners for better urban management. The results of this research confirm the capability of remote sensing in environmental studies, it is suggested to identify thermal islands in other seasons and at night and compare the results with the results of this research.

In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R2, MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.

Background and objectives: The cultivation and subsequent abandonment of rangelands due to economic non-viability have become prevalent in recent years. Assessing plant productivity in these abandoned rangelands is essential for their restoration. This study aims to compare the effectiveness of vegetation indices in natural and abandoned rangelands, evaluate variations in plant productivity, and provide insights into their ecological conditions. Methodology: Three semi-steppe regions in Chaharmahal and Bakhtiari province were selected as study areas. These regions were previously degraded for agriculture and subsequently abandoned. Field sampling and satellite data were used to assess plant productivity. Plant indices, including NRVI, CTVI, RATIO, TTVI, and TVI, were calculated using Landsat 8 satellite images from 1393. The indices were employed to evaluate plant productivity and compare natural and abandoned rangelands. Results: NRVI, CTVI, RATIO, TTVI, and TVI exhibited a significant correlation with plant productivity, while other indices did not accurately represent it. The NRVI index provided the most accurate estimation of plant productivity in abandoned and cultivated rangelands. Uncultivated rangelands showed higher productivity compared to cultivated ones. The study areas displayed variations in plant productivity, with Marjen Borujen having the highest productivity in uncultivated sections and Sefid Dasht having the lowest. In the cultivated sections, Sefid Dasht had the lowest productivity, while Hoorah had the highest. Shrubs experienced the most substantial reduction in plant productivity among the vegetation forms. Conclusion: Remote sensing, particularly the NRVI index, proved valuable for assessing plant productivity in these rangelands. Utilizing the NRVI index can aid in the management and restoration of abandoned and cultivated rangelands. Conducting similar research and employing remote sensing techniques for assessing and monitoring various rangeland areas are crucial for sustainable land management and conservation efforts. These approaches contribute to the overall health and sustainability of these valuable ecosystems. [ABSTRACT FROM AUTHOR]

Introduction LST (LST) is one of the important parameters that affect the physical, chemical, and biological processes of the earth as well as environmental science and urban planning. Human activities such as land use changes and the development of urban areas led to an increase in the LST and the appearance of thermal islands. The main source of climate data such as temperature are synoptic stations, however, it is impossible and time-consuming to use traditional methods to estimate the LST for all types of earth conditions, on the other hand, synoptic stations only measure temperature information for specific points, and the obtained values are only related to that specific point; while according to the land cover and other conditions, the temperature in different parts of a region is different compared to the temperature recorded for a specific point and can be several degrees celsius lower or higher, therefore, it is necessary to use scientific methods that provide the possibility of calculating the temperature of any point on the earth's surface. At present, remote sensing images due to features such as wide and continuous coverage, low cost, timeliness, and the ability to obtain information in reflective and thermal ranges, are suitable tools for extracting LST and land use maps. Spatial analysis is one of the important subjects in the temporal and spatial evaluation of land surface data, which can be used to examine the spatial and temporal changes of spatial data in a region. Given that the data that are examined in environmental studies are not independent of each other in most cases and their dependence is due to the location of the observations in the studied space, which are called spatial data; Due to the existence of a spatial correlation between the data, the usual statistical methods are not a suitable method for examining these data, and spatial statistics can be used as a suitable option for analyzing these data. The aim of this research is to extract LST and land use map of Yazd county using a remote sensing technique. In this study, the spatial autocorrelation of LST in Yazd city and the identification of hot thermal clusters have been investigated using the global Moran statistic and the Getis-Ord GI statistic. Materials and Methods In this research, Landsat 8 satellite’s multi-spectral and thermal images have been used to extract the land use and LST in the study area, After performing the necessary corrections in the preprocessing stage, the land use map of the study area was prepared in 5 classes (built-up, vegetation cover, water bodies, bare land, and rock) using the support vector machine method and the overall accuracy and kappa coefficient were used to evaluate the classification result. In the next step, LST was extracted by the split window method. The relationship between LST and Soil adjusted vegetation index (SAVI) was investigated using regression analysis. In order to identify the spatial pattern of the LST, the global Moran index was used and hot spots were identified by Getis-Ord GI statistics. Results and Discussion Our findings show that the kappa coefficient and overall accuracy were equal to 0.96% and 98.99%, respectively, bare lands are the most, and water bodies have the least area, equal to 76.16 and 0.09%, respectively. The average LST was 50.83°C. The result showed that the type of land use had an effect on LST, the water bodies had the lowest, and barren lands had the highest mean LST, equal to 36.91 and 52.13 °C, respectively. Vegetation is one of the factors that regulate the LST, areas without vegetation have a maximum LST and areas with high density vegetation have minimum LST .Based on the results, the vegetation quality of the study area was poor and its average temperature was 45.61°C. The mean of SAVI index was equal to 0.09 and correlation analysis showed a negative correlation between SAVI index and LST (r = -051). The analysis of spatial correlation with global Moron indexes showed that the LST of Yazd has a spatial structure, in other words, LST is distributed in a cluster form, Based on the results of the Getis-Ord GI statistic, the area of hot and cold spots was equal to 66.86% and 27.4%, respectively. In general, parks, cultivated lands, tree and forest cover and water areas, formed the cold spot areas of yazd city, and the hot spot areas of yazd city were located in the industrial areas and surrounding urban lands, hospitals, passenger terminals, gas stations, places near busy roads and bare and uncovered lands. Conclusion The results showed a strong relationship between land use and LST. Based on the results, the LST data of Yazd has a spatial structure pattern, barren lands and industrial areas formed hot thermal islands, and vegetation and water bodies formed cold thermal islands in the study area; the wide area of barren lands, the lack and poor vegetation cover due to the lack of rainfall and drought are factors affecting the LST and the creation of hot thermal islands in the study area. The result showed a negative relationship between LST and SAVI, the vegetation of the study area is weak and its temperature is high. Considering the role of vegetation in adjusting LST, it is recommended to take necessary management measures in order to improve the quality of vegetation and reduce bare land in the study area, and also prevent the conversion of natural land uses into built-up land. The results of this research can be used by managers and planners for better urban management. The results of this research confirm the capability of remote sensing in environmental studies, it is suggested to identify thermal islands in other seasons and at night and compare the results with the results of this research. [ABSTRACT FROM AUTHOR]

In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using ArcGIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R², MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective. [ABSTRACT FROM AUTHOR]

One of the most important methods of extracting information from satellite data is various image classification techniques. The current research was conducted to separate and classify plant ecological units by classification tree analysis algorithm on satellite images and also the visual interpretation of Google Earth images in one of the semi-steppe rangelands of Chaharmahal and Bakhtiari province. After applying the classification algorithm in Idrisi TerrSet software, the software generated the error matrix, and then based on the values inside this matrix, the extracted statistics were evaluated. The results of visual interpretation showed that finally, seven types of plant ecological units that were different in terms of structural features were identified and expressed as descriptive statistics, including Astragalus verus, Bromus tomentellus, Scariola orientalis, Astragalus verus-Bromus tomentellus, Astragalus verus -Stipa hohenikeriana, Bromus tomentellus-Stipa hohenikeriana, and Stipa hohenikeriana. The results also showed that the overall accuracy and kappa coefficients of 0.92% and 0.89 for Landsat 8 images and 0.94% and 0.92 for sentinel 2 images were achieved. Based on the obtained results, it was found that satellite images and aerial images have a suitable separation capability to prepare a map of plant ecological units. Since the visual interpretation of Google Earth images is a time-consuming and expensive method, it can be concluded that satellite images, especially Centile 2, can be used as a tool due to their high resolution and highresolution images, practical and usable, provide accurate information and details of the earth's surface phenomena and be used as a suitable source for preparing a map of plant ecological units. [ABSTRACT FROM AUTHOR]

Accurate estimation of dam lake evaporation is of great importance in reservoir water management. This factor has led to the installation of meteorological stations in most dams. At the meteorological station, the rate of evaporation from the lake surface is measured using an evaporation pan. Remote sensing methods are cost effective and can be generalized to the whole area. In this study, the daily evaporation rate from the surface of Suleiman Shah Lake was calculated using a combination of SEBAL algorithm and meteorological data and using five Landsat 8 satellite images. The obtained results were compared with the evaporation rate obtained from different methods. The results showed that among the combined methods, Penman Kimberly 1996 method, among other methods, Blanche-Kridel method had the highest agreement and Hafner method had the least agreement with the measured data indicated with R2, RMSE and MAE equal to 0.96, 2.78 and 5.66, respectively. The results of this study can be used to estimate evaporation in other regions with similar climates.

Awareness of the trend of forest canopy density classification requires an operational exact model for forest crown classification. The preliminary challenge is the separation of the forest crown from other non-warlike vegetation coverings. In the following, previous attempts to improve the performance of the FCD model, in this study, by adding the FCC index and the kernel, improved the average performance of the FCD model. The crown classification of Hyrcanian forests based on images of 1396 Landsat 8 was selected for implementation, evaluation, validation and analysis of the results. Improving the accuracy of the model is entirely sensible and even manual interpretation confirm it. The statistical analysis of the results also indicates a 10% and 24% increase in overall accuracy and kappa coefficient of the improved model compared to the initial model. Specifically, the accuracy of these two classes in the results of the improved model is about 13% and 7%, respectively.

Background and Objectives: Earth monitoring satellites and vegetation indices are very useful to study the plant greenness at different stages of growth. But cloudy sky in plant growth periods leads to remote sensing information in humid areas is less welcomed. The existence of spatialtemporal fusion algorithms use to rebuilding satellite images for use remote sensing data in the period of plant growth. In this study, fusion algorithm were used to rebuilding the images of Landsat 8 and Sentinel 2 satellites to estimate the leaf area index at different stages of growth. Materials and Methods: The present study was conducted in the paddy of Sari Agricultural Sciences and Natural Resources University in the period of June to August 2020. Rice leaf area index was calculated using the non-destructive method in the field and used to relationship with remote sensing indices. To achieve the objectives of this study, after radiometric and atmospheric correction images of Landsat 8 and Sentinel 2 were used to extract the Normalized Difference Vegetation Index (NDVI), Rice Growth Vegetation Index (RGVI) and Soil Adjustment Vegetation Index (SAVI). In cloudy period, STARFM algorithm and MODIS images used to rebuilt the images of Landsat 8 and Sentinel 2. In order to estimate the leaf area index, using satellite images, a suitable relationship was obtained between vegetation indices and rice leaf area index (Oryza sativa) at different stages of growth. Results: Due to the cloudiness in July and the peak of rice greenery, the use of STARFM algorithm led to rebuilt of images in this period and finally with 15 images in the rice growing stages, four linear relationships between NDVI, RGVI and SAVI indices for four stages of growth was created by leaf area index, the highest and lowest correlation coefficients respectively, 0.96 for NDVI index at transplanting and maturing stage and 0.75 for RGVI index at transplanting stage, respectively. Also, the map of changes in leaf area index for both satellites in the period of crop growth showed well the changes in the greenness of rice cover. In the transplanting stage, leaf area index is in the range of 0.5-1 and as the vegetation canopy becomes complete, leaf area index increases and in the stages of tillering, heading and maturing the average leaf area index in the maps, respectively, 2.5, 4 and 3. Conclusion: NDVI index with the highest correlation (0.96) with leaf area index is a good estimator and can be used to obtain maps of leaf area index in large areas. As a result, it seems that by using satellite data and image rebuilding on cloudy days, it is possible to achieve leaf area index with high accuracy and extract various information such as age and growth stage for the rice plant. [ABSTRACT FROM AUTHOR]

Natural hazards are unfortunate accidents that cause a lot of irreparable damage to the natural environment and man-made phenomena every year. Floods are among the hazards that have always been likely to occur as a natural hazard to humans. In the meantime, identifying flood-prone areas is one of the first measures to deal with them. Darab city was not isolated from this matter and this study was conducted to prepare a flood potential map of the city. Because many criteria are involved in flooding in any region. Therefore in this study 12 criteria including Slope, Aspect, Rainfall, Runoff Height, Curve Number status (CN), Flow Accumulation, Soil Hydrological Groups status, Land Use, Vegetation Density, Drainage Network Density, Basin Elevation, and Distance to Drainage Network, selected and maps were prepared for each. In the next step, the prepared maps were weighted using the Analytic Hierarchy Process (AHP) method and using Expert Choice software and their fuzzy process was performed by the fuzzy logic model. Finally, the overlap of the layers was performed using a fuzzy algebraic summation operator (SUM) in Arcmap software and thus maps related to flood risk zoning of Darab city with different return periods and on 5 floors were extracted. In maps obtained with different return periods, the pattern is almost identical and similar. According to the final plans, the risk of floods with return periods of 25, 15, 5, and 50 years, respectively 9.91, 9.39, 8.4, and 9.93% of the area of the city with flood risk Very low fertility and 9.24, 10.64, 11.90 and 8.65% of its area has a very high risk of flooding.

Introduction Water management has always emphasized the need to abandon water storage in reservoirs and pursue a policy of limiting water consumption. Spatial-spatial information on evapotranspiration helps users understand the evacuation and depletion of water due to evaporation and establish the relationship between land use, water allocation, and water consumption. Evapotranspiration is the second element of the water cycle (after precipitation) and its accurate estimation on a regional scale is necessary to design appropriate management strategies. Evapotranspiration is a function of the amount of energy available for vegetation and its exchange. Because of this dependence, it can be estimated using the principle of energy conservation. Due to the limited number of meteorological stations in the country and the high cost of collecting ground data, the cost-effectiveness of the use of satellite data is one of its advantages, and the possibility of retrieving data from all levels of the region at one time is its next advantage. Having timely information makes horizontal monitoring of meteorological and environmental parameters possible. The ability of remote sensing to measure some terrestrial parameters has had an important effect on estimating actual evapotranspiration. The SEBAL model is one of the remote sensing algorithms that calculate plant evapotranspiration based on the momentary energy balance at the level of each pixel of a satellite image. The study area of the current research was the eastern cities of Lake Urmia. The reason for studying this section was the impact of recent droughts on these areas and the reduction of surface and groundwater, which has increased the need to manage water resources in these areas. Methodology In the first step of radiometric corrections, the amount of spectral radiance in the thermal band and at the next step, the reflectance in the visible bands, near-infrared, and short-wavelength infrared bands were calculated. As mentioned above, in the SEBAL model, actual evapotranspiration is calculated through satellite imagery and meteorological data is calculated using the surface energy balance. When satellite imagery provides information for its transit time, SEBAL calculates the instantaneous evapotranspiration flux for that time. Landsat 8 images for 2017-2016-2014-2013 years and meteorological data such as Minimum temperature, maximum temperature, dew point temperature, evaporation pan data, sunny hours, and wind speed were analyzed using ENVI 4.8 - Excel 2013- Arc GIS 10.3 software. Results and Discussion SEBAL is an image processing model that measures evapotranspiration and other energy conversions on the Earth's surface using digital data measured by remote sensing satellites that emit visible, near-infrared, and thermal infrared radiation. This method uses surface temperature, surface reflection, and normalized plant differential index (NDVI) and their internal relationships to estimate surface fluxes for different types of land cover. In this section, using the values ​​obtained from latent heat flux and evaporation heat flux, first, the amount of instantaneous evapotranspiration for each pixel was calculated. Then, using Ref_ET software, the total 24-hour evapotranspiration was calculated and the daily evapotranspiration rate was obtained for the whole image. Conclusion The results showed that there was a good correlation between the values ​​estimated by the remote sensing algorithm (SEBAL) and the FAO-Penman-Monteith method as well as the evaporation pan method. The difference between the amount of SEBAL and the FAO-Penman-Monteith method in the reference plant was less than 4.21 mm/day; the largest difference was related to the 22nd of October. In total, SEBAL and Penman-Monteith methods had an average absolute difference of 4.28 mm/day. According to the results of this study, it can be observed that using the SEBAL model, the actual evapotranspiration and water needs of crops and even orchards and rangelands can be calculated on a large scale. This case could prove the suitability of this model for estimating actual evapotranspiration at different levels of the farm and irrigation networks. Therefore, remote sensing has a very high potential to improve the management of irrigation resources in very large areas using various algorithms and providing an estimate of the amount of ET with minimal use of ground data. Using remote sensing technology and GIS, acceptable results can be obtained in estimating the actual evapotranspiration rate, especially in large areas. If the parameters of the energy balance equations and Penman-Monteith could be calculated from satellite images spatially, with a suitable plant coefficient, the two methods would have similar results in estimating the rate of evapotranspiration. Using this method, the plant coefficient, which is one of the important factors in calculating the evapotranspiration of plants, can be accurately determined.

Introduction One of the emerging environmental hazards caused by the expansion of urbanization is the "thermal island" phenomenon, in which urban areas have a distinct climate compared to rural areas, and the city center has higher temperatures than its surrounding areas. This phenomenon occurs when a large percentage of natural surface coverings are destroyed and replaced by buildings, roads and other installations. The thermal island phenomenon has been studied and recorded in various cities around the world for more than 150 years. And it generally appears as the surface of the earth shifts from natural to non-perishable. Surface temperature is considered one of the most important parameters in identifying a city's climate that directly controls the effect of the city's heat island. And more recently, many regional studies, such as global climate change, hydrological and agricultural processes, urban land use and land cover, and soil moisture assessment, have been identified as important factors. Traditionally, urban heat islands have traditionally been studied using meteorological station data, or vehicle surveys, but today to reduce the weakness of these methods and to study them more closely, Satellite and remote sensing data are used more frequently because of more spatial resolution than terrestrial weather data. Remote sensing images, because of their wide coverage, timeliness and ability to obtain information in the thermal range of the electromagnetic spectrum, are a useful source of heat mapping and estimation of Earth's radiant energy. Methodology Split-Window algorithm is one of the most important methods for estimating surface temperature which is better than other methods for calculating surface temperature. An important feature of this algorithm is the elimination of atmospheric effects. Since this algorithm does not require accurate information on atmospheric profiles during satellite acquisition, it is widely used in several sensors to retrieve Earth's surface propagation capability. The sensors used in this algorithm include the Multi Spectral Sensor and the TIRS Thermal Sensor. The following are the cases: Due to the lack of a database to measure the Earth's surface propagation capability with Landsat 8 satellite images, the C coefficients through various numerical simulations It was obtained from atmospheric and surface conditions.In this study, Landsat 8 images with 7/15/2015 Landsat 8 (OLI and TIRS) images and land use maps were used to analyze the thermal islands. After processing the images, a separate window algorithm was used to calculate the surface temperature and the maximum likelihood method was used to classify the images. Discrete Window Algorithm is a mathematical tool that uses ground information, thermal sensor brightness temperature (TIRS), ground emission capability (LSE) and fractional green vegetation factor (FVC) obtained from OLI and temperature multispectral band. Estimates the surface of the earth. Image analysis was performed in ENVI 5.3 and ArcGIS 10.5 software environments. Result and Discussion Surface temperature is one of the main factors in the study of cities. Because only two or three degrees differs from the air temperature of the lower layers of the urban atmosphere, which is the center of the surface energy balance, which determines the climate between buildings and affects the comfort of urban dwellers. In the present study, preliminary processes such as radiometric, atmospheric and geometric corrections were carried out and then high atmospheric radii were converted to surface radiation and then calculated by vegetation index, vegetation fraction index, radiation power and water vapor column, temperature. Ground level in the study area was obtained using a separate window algorithm. Conclusion The results of thermal extraction showed that maximum temperature was related to low density vegetation, residential, industrial, industrial, asphalt-concrete and brick-iron frameworks. Minimum temperatures are also visible in green, brick-wood and clay-wood. The results of this research for planners and experts at the regional level to obtain information on the status of land surface temperature and their relationship with land use can pave the way for management decisions to conserve natural and agricultural resources. It is suggested that due to the moderating role of vegetation, vacant land and the wilderness be changed to uses such as parks and landscapes, and in addressing other uses, the reasons for residential and industrial and workshop areas should be taken into account, and the surface temperatures of buildings most The city has its own surface area and has the highest amount of radiation reflection can be reduced by planting vegetation on the roofs of buildings known as green roofs. High resolution satellite images are also recommended for land use mapping.

The present study was conducted to investigate the differences between several remote sensing indices, four different plot sizes, and two different sampling methods to estimate the percentage of plant cover and production in three plant communities in 2013. Ground sampling was performed in three communities with dominant vegetation in two forms of six and three quadrats. Four different dimensions of the quadrats were used nested to estimate production and coverage percentage. Sampling was performed in each community within 30 pixels along three transects with different heights. Dominant plant density was measured by counting the bases per plot, plant cover percentage as an estimate, and production in the form of double sampling about cover percentage. The results showed that with increasing plot area, the degree of correlation of plant indexes of Landsat image and their significance about production and percentage of plant cover increased. But this increase in community 2 is more dramatic in most respects with predominantly shrubs. As in this community, most of the studied indicators in 3 X 3 plot have a correlation and a reliable model, and in the three sizes of 1x1, 2x1, and 2x2 plots, the resulting models are not valid enough and have high RMSE. In community one with dominant broadleaf plants, only the model obtained in 1x1 plot, and community two with dominant plants, the model, obtained in 1x1 and 1x2 plots, are not statistically valid, although sometimes they have a significant correlation. The results of the two different models are statistically different in communities 1 and 3, and there was no significant difference between them in community 2. According to the results of the study of indicators in terms of correlation and model validity, the two indicators NDVI and CTVI can be used in community 1, NDVI and TSAVI1 in community 2, and NDVI, NRVI, and TSAVI1 in community 3 recommended for estimating production and percentage of plant cover using satellite images. [ABSTRACT FROM AUTHOR]

Remote sensing-based methods were developed for resolving the limits of direct measurement methods for evapotranspiration. The purpose of this study was to investigate the capability of Landsat 8 satellite imagery and the SEBAL model for estimating actual instantaneous ( ) and daily ( ) evapotranspiration in the forest ecosystem. For this reason, Landsat 8 satellite imagery was utilized at four dates for the forests of the Ramsar city and after image processing and performing necessary calculations the map of NDVI, surface temperature, emissivity and albedo were prepared and all energy components at the surface of evapotranspiration including net radiation, soil heat flux, sensible heat flux and latent heat flux were estimated and amount of instantly ET, and were conducted as a map. Accuracy of estimated and were evaluated based on the three hour data from synoptic Ramsar weather station data using FAO Penman-Monteith method. Estimated average of and were 0.45 and 3.98 and calculated average of these parameters were 0.55 and 4.62 respectively. Results showed that the differences are relatively small and range between 8.38 and 15.17 percent in instantaneous scale and between 11.41 and 18.34 in daily scale. Mean RelativeDifference Percentage for and were 11.45 and 14.27, respectively reflecting high capability of the overall ability of the approach used in this study to estimate evapotranspiration. In conclusion based on results showed that used method in this study has the capability of producing evapotranspiration map in north forests of Iran in spring and summer.

The conventional methods for actual evapotranspiration (ETa) estimating is not sufficiently accurate due to the point based nature of meteorological data and the difficulties related to the plant coefficient (Kc) estimation. The true rate of ET from a basin is an important factor of hydrological cycle and in the water balance equation and accounts for a huge part of out-flow of a basin or any other scale. In this study, the ET values were calculated in the region of Kelestan located in the northwest of Shiraz. The SEBS evapotranspiration model was applied and evaluated by using the Landsat 8 satellite images (nine images) and the meteorological data collected at the Kelestan Station for the period 2015–2017. Results were compared to the FAO Penman-Monteith equation to verify the accuracy of this model in the region of Kelestan. The radiation sources required for the SEBS model were produced by the R.sun module in Grass-GIS. Other remote sensing data were calculated in the Ilwis software environment. The results of evapotranspiration obtained from the SEBS model in comparison with the mentioned equation showed a correlation coefficient of 0.999, which is statistically significant. Variations of the ETa for different landuses were within a reasonable range. The production of reliable data from evapotranspiration is one of the prominent advantages of using this model for application in hydrologic research of the basin.

In the present study, four supervised classification algorithms including Maximum Likelihood, Mahalanobis Distance, Minimum Distance and Neural Network with and without TIR1 were used to mapping land use of southern Khorasan province. Based on the results, the highest of overall accuracy and Kappa coefficient were calculated for the Maximum Likelihood algorithm with and without of TIR1. Using of TIR1 increased classification accuracy by Maximum Likelihood and Mahalanobis Distance algorithms; but using of TIR1 decreased classification accuracy by Minimum Distance and Neural Network algorithms, remarkably. Using of thermal data along with other spectral bands caused facilitation of discriminating classes with similar spectral characteristics. According to the land use map, bare land covered about 60% area of southern Khorasan province, generally more than 90% of the area of the province is involved by sparse land or weak vegetation cover which is prone to wind erosion.

This research aims at investigating the feasibility of Landsat8 images for poplar plantation mapping. Three areas in Talesh and Sumehsara regions, Guilan province, with a total 1025.35 ha with different vegetation mixtures were chosen as study area. Five image-sets were selected from growth and autumn seasons. Studying the images showed no radiometric and geometric distortions. The image enhancements like rationing, PCA and Tasseled-Cap transformation were performed. By preparing training samples in each region and choosing the suitable band-sets, the images were classified using maximum likelihood algorithm. The ground truth was prepared for Hefzabad and Toularoud in Talesh using GPS. It was extracted for Haft-Deghnan and Sheikhneshin from available update-maps. To ensure suitability of the training samples, assessing the accuracy of classification were performed in the training samples. After ensuring this, the accuracy of classification was assessed in whole areas of all images. The results in all areas and dates were almost close to each other. However, the best results were always obtained in growth season, especially in June 2013. Using calculated enhanced bands along with the original bands in classifications in Talesh region showed slight improvement in accuracies, with overall accuracy and Kappa about 95% and 71%, respectively. In Sumehsara region, the best results were obtained in June with 83% overall accuracy and the relatively low Kappa 0.43. While, the results of accuracy assessment in training samples in all cases were about 0.89 to 0.99. It could be stated that the Landsat 8 images had medium capability for poplar plantation mapping. More investigations are needed to create better poplar plantation mapping.

Land use and land cover maps are necessary for planning and natural resources management. In the way, remote sensing data have special place because of providing update data, repetitive covers and low cost images. Therefore Optimum Land Image/ Thermal Infrared Sensor were used to map land-use and land-cover in 1 and 2 level. Because of, this images are new thus radiometric correct was used ERDAS software model maker. Also Normalize Difference Vegetation Index (NDVI), Bare Soil Index (BI) and Principal Component Analyze (PCA) were used as inputs to improve classification accuracy. On the other hand kernels functional and polynomial ranks of Support Vector Machine method evaluated in side others bands and the best result of SVM method compared with Artificial Neural Network (ANN). The results indicated that SVM method has accuracy: 92% with Kappa Coefficient: 0.91 and ANN method has accuracy: 89% with kappa coefficient: 0.87 also SVM method has a good performance in the regions that, classes show similar spectral behavior.