جستجوی مقالات مرتبط با کلیدواژه « satellite images » در نشریات گروه « کشاورزی »

PirBazar River is one of the most important rivers leading to Anzali Wetland. It is considered as one of the most polluted rivers in Iran due to the entry of a huge volume of industrial and urban sewage and leachate. Accordingly, about 50% of the pollution and sediment load leading to the Anzali Wetland comes from this river. The aim of this article is to investigate the trend of land use changes in the PirBazar watershed and its effects on the levels of pollution in the rivers of this area (Zardjob and Gohroud). In this study, the process of changes from 1990 to 2021 (30 years) was investigated using Landsat series satellite images (Landsat 5 and 8). 5 categories including urban, industrial, forest, agricultural and water land use were classified through vector machine algorithm and neural network. Also, the changes of each land use type in the area were examined through the LCM model. Results of the changes showed that the urban land use has been increasing in the study period. Moreover, the water quality index (NSFWQI) was directly related to the increasing trend of the residential land use and decreasing trend of the agricultural land use. According to the study results, the control of wastewater from residential areas can be an effective solution to reduce the pollution load of PirBazar River.

Snow is an important source for water supply in the agricultural sector, electricity production in factories, groundwater reserves, and rivers. This natural resource is important since it stores water in winter with low demand and releases it in hot seasons with high demand. Melted snow flow can be very useful in low-water seasons. In arid and semi-arid regions, snow is considered a basic source of fresh water. Significant spatiotemporal changes in distribution of snow on the scale of a basin can be important in determining the time and amount of snow melting in spring. The lack of sufficient and correct information about snow reserves can lead to inappropriate use of water resulting from snow melting and, as a result, irreparable damages. Therefore, measuring the surface covered by snow and its water equivalent, along with other information such as snow density, especially in areas where snow accounts for a large share of precipitation, is essential for resource planning and management. However, it is not possible to measure it in many areas due to harsh environmental conditions. Also, the data measured at one point cannot be generalized to a wide area of a basin. Thus, the use of satellite images can be considered as one of the methods of investigating spatial and temporal changes in distribution of snow in a region.

To obtain the monthly values of snow cover (SC), snow water equivalent (SWE) and snow depth (SDepth) from January 1982 to December 2023, the FLDAS (Famine Early Warning Systems Network—FEWS NET—Land Data Assimilation System) product from the Noah 3.6.1 Land Surface model was used. This product is at latitude 90° to -60° and longitude 180° to -180° with a spatial resolution of 0.1°×0.1° in netCDF format and its images are monthly available from January 1982 until now.The non-parametric Mann-Kendall test was used to investigate the increasing, decreasing or constant trend of the data during specific time intervals in the period. The Theil-Sen slope was used to calculate the slope of the trends. The correlation of the temporal distribution of snow cover with the two meteorological parameters of temperature and precipitation was determined using the Pearson's method.

The results showed that the year 1982 with a total of 41043.04 km2 had the highest and the year 2021 with a total of 4048.12 had the lowest SC. These data indicate a 90.13 percent decrease in SC in 42 years, which is understandable considering the 35.08 percent increase in the mean temperature (from 9.49 to 12.82 ºC) in this period. The monthly average of SC in this period showed that there was no snow cover from May to October, and in April and November it was very insignificant and negligible compared to in January, February, March and December. Among these four months, January had the highest average SC.According to the Mann-Kendall test, the trends of SC, SWE and SDepth was decreasing in all the four months of January, February, March and December. Temperature and precipitation values had increasing and decreasing trends, respectively.According to the Teil-Sen slope test, SC had the steepest and gentlest decreasing slope in January and December, respectively. In the case of SWE and SDepth, the steepest decreasing slope was related to February, and the gentlest decreasing slope occurred in December. The increase in temperature had the gentlest slope in the two consecutive months of December and January and the steepest slope in the two consecutive months of February and March. The steepest and gentlest slope of decrease in precipitation occurred in March and February, respectively.The Pearson's correlation coefficient values indicated that SC, SWE and SDepth have inverse correlation with temperature and direct correlation with precipitation. Overall, SC, SWE, and SDepth were more correlated with temperature than precipitation, especially in February and March when temperature was higher than in December and January.

In this study, using the satellite images obtained from the FLDAS product, the data of snow cover, snow water equivalent and snow depth were obtained in the province of Hamedan for the 42-year period of 1982-2023. The results showed a significant decrease in the snow cover during this period, which was expected due to climate change and temperature increase. The snow cover, snow water equivalent and snow depth have had decreasing trends. The trends of the two climatic parameters, temperature and precipitation, have been increasing and decreasing, respectively. The steepest and gentlest slope of the decrease in snow cover occurred in January and December, respectively. There was an inverse correlation of snow cover, snow water equivalent and snow depth with temperature, and a direct correlation with precipitation. In general, the correlation of snowfall parameters with temperature, especially in warmer months, was more than their correlation with precipitation.

Fresh water availability and its shortage is one of the most important issues in the world today that some countries are facing. In the last two decades, Kabul province, Afghanistan, has seen a decrease in water resources under the influence of natural and human factors. The purpose of this research is to use satellite data and remote sensing techniques to investigate changes in surface and underground water resources in Kabul province. For this purpose, the satellite data and products available in the Google Earth Engine in the period 2000 to 2022 and the climate data of ground stations in the period 2006 to 2021 have been used, which include: evaporation data- Transpiration, Enhanced Vegetation Index (EVI), Global Product of Surface Water Areas (JRC), GRACE data, OLS Satellite Night Images, Sentinel 2 Satellite Image, Landsat 7 Satellite Image, Temperature, Humidity and Rainfall Data. The change process of the used data was analyzed through the Mann-Kendall test and the significance level of these data was checked. The supervised classification method was used on the two mentioned images to calculate the area of vegetation, water area, residential areas, and barren lands. The obtained results show the reduction of underground water resources (significant reduction trend of GRACE satellite data) and the reduction of the surface water area changes in Kabul province, under the influence of natural and human factors, which among these factors can reduce He pointed out the amount of rainfall, increase in temperature, increase in evaporation-transpiration, increase in the level of vegetation and physical development of Kabul city and increase in the population using water resources.

Wetlands are highly beneficial to human societies due to their positive environmental functions, direct and indirect functions, as well as their value as an asset. It has led to an increase in the attention given to their restoration and maintenance in different societies. Shadgan Wetland contains fresh-salty, and brackish water, and approximately 900 million cubic meters of Jarahi water resources enter Shadgean every year. The wetland is at risk of serious harm as a result of the developmental activities occurring around it, which are causing pollutants to enter the wetland and reducing the quantity of fresh water coming in. A number of factors contribute to the destruction of this wetland, including over-exploitation from its water resources, the discharge of urban waste within its limits, the fragmentation of the wetland as a result of road construction, the construction of stations to increase the pressure on electricity, gas and oil pipelines, as well as effluents from industries such as bread making, alcohol production, sugarcane cultivation and industry. The economic value of wetlands and climate regulation, flood prevention, protection of plant and animal diversity, beauty and inherent visual attractions of wetlands, tourist attractions, as well as creating an opportunity for migratory birds to nest and a place for scientific research are among the most important considerations in the design of a wetlands. The development of water resources schemes and the regulation of river flows are often recognized as the most serious threats to the ecological sustainability of rivers and wetlands.

This study attempts to determine the change in water salinity of Shadegan wetland in the last five decades using the electrical conductivity index due to the importance of Shadegan wetland in various ways. Finally, a relationship has been established between the area and the salinity of this significant wetland. Also, by using electrical conductivity data from 23 stations in the lagoon and with the help of satellite images and remote sensing techniques and interpolation methods (IDW), the changes of this index in the mentioned period were investigated, leading to a mathematical relationship.

According to the research results, upstream human activities, especially dam constructions and agricultural development projects, have had a great impact on the quantity and quality of the wetland. With climate change and drought, these effects have intensified, resulting in a reduction of the wetland level as well as an increase in the salinity of the wetland water. These changes can be observed both in terms of their temporal and spatial dimensions. Consequently, the results show the trend of increasing salinity from the southern parts to the north and also the greater manifestation of the increase in salinity in the southern parts due to the decrease in the incoming fresh water flow (more than twice). According to the results, there are three salinity levels in the wetland: saline, brackish water, and super salinity, and a salinity increase is observed in all three zones The present results and equations are used as an achievement by water and environment managers and they can estimate the EC of water in key and indicator stations and finally at the level of the wetland by measuring the size of the wetland using different technologies. So over time, the levels and zones of saline and super salinity have expanded, and the levels of brackish water have decreased. It is expected that this process will continue over time, resulting in the sea salt water advancing towards the wetland and increasing the amount of salinity within it.

According to this study, based on the relationship between salinity and the level of the wetland, as well as the water area of the wetland, it is possible to estimate its salinity in three zones. By measuring the salinity of water at several key stations within each of the three zones, the wetland's water level can be estimated. In monitoring, managing, and qualitatively protecting the wetland and consequently its species, this equation and its relationships can play an important role.

Evapotranspiration (ET) is one of the most important factors in the hydrological cycle and is a key determinant of energy equations on the earth’s surface. evapotranspiration estimates are important for hydrology, irrigation, forest and rangeland, and water resources management. The evapotranspiration drives the soil water-energy balance which is largely used in general circulation models and climate modelling. Consequently, river water flow forecasting, crop yield forecasting, irrigation management systems, river/lake water quality are all dependent on evapotranspiration levels. For this reason, it is essential to accurately estimate the water budget. Numerous models have been developed to estimate evapotranspiration using remote sensing methods. The review of recent research shows that remote sensing and the use of satellite images have a high ability to estimate the amount of actual evapotranspiration.

The aim of this study is calibrating the METRIC algorithm in estimating evapotranspiration in the Sohrin-Qaracheryan Plain, which is affected by flood spreading. This method has been used by many researchers around the world to estimate evapotranspiration. On the other hand, estimating the actual evapotranspiration is of great importance in the plains affected by the flood, especially in the Sohrin-Qaracherian Plain’s flood spreading. Therefore, in this research was conducted to estimate evapotranspiration using the metric algorithm in the Sohrin-Qaracherian Plain, for the optimization management of water resources in the region and regions with similar conditions. In this research, were used of the daily and hourly meteorological data of Zanjan Airport synoptic station from 2020 to 2021. These the data included minimum and maximum temperature, minimum and maximum humidity, wind speed average, sunshine hours and air pressure. To check the application of metric algorithm, were downloaded Landsat 8 images for 2020-2021 years and were done necessary corrections and preprocessing on them. Landsat images are available at 16-day intervals with a spatial resolution of 30 m and were obtained from the United States Geological Survey website (http://glovis.usgs.gov). After the images processing, is obtained the albedo, surface emissivity, land surface temperature, plant indicators, incoming-outgoing radiation fluxes, net radiation flux and the soil heat flux. Next, the sensible heat flux is calculated by determining the hot and cold pixels. Finally, evapotranspiration maps are plotted. In addition, for a better comparison of the results, were compared of the layers related to vegetation index include soil heat flux and land surface temperature in the different stages of the growth period. After extracting these indices, the evapotranspiration map was extracted using ENVI software.

Results show that daily evapotranspiration increases is directly related with increase in vegetation density. at the initial of the growth period, the range of evapotranspiration is estimated between 0.08 and 4.97 mm.d-1, while this value in the middle and late of the growing season is estimated in the range of 0.086 to 5.56 and 0.59 to 9.57 mm.d-1 respectively. Based on the results of this research evapotranspiration obtained from the soil water balance model and METRIC model were estimated as 24115 and 25648 m3, respectively. The results validation of evapotranspiration obtained from the metric model was compared with the actual evaporation and transpiration obtained from the soil water balance model, and the error coefficient was obtained equal to 5.97%.

According to the results of this research, it was determined that the use of energy balance models using the science of remote sensing provides the possibility of estimating evaporation and transpiration regionally. On the other hand, the calculation error percentage shows that the metric algorithm is accurate enough to estimate ET in the studied area.

Evapotranspiration govern the water and energy balance of the soil, primarily used in general circulation models and hydrological modeling of weather conditions. Consequently, predicting river flow, forecasting crop performance, water management systems, and the quality of rivers or lakes all depend on evapotranspiration levels. Therefore, accurate estimation of water bilan is essential. Improved and precise estimations of evapotranspiration enable effective irrigation planning and optimal water usage for other agricultural purposes. The goal of this research is to evaluate and validate the surface energy balance model, SEBS, in determining evaporation-transpiration in the Sohrin-Qareh Chiran Plain, located 30 kilometers northwest of Zanjan city.

In this study, the actual evapotranspiration were determined using the SEBS energy balance model and Landsat imagery in the Sohrin-Qareh Chiran Plain. A total of 17 Landsat-8 images, along with weather data from the Zanjan airport station, were examined. Additionally, with the help of the SEBS model, the evapotranspiration of a five-hectare wheat field was calculated and validated for the time period from September 22, 2020, to September 22, 2021. The required inputs for the SEBS model, including albedo, land surface temperature, emissivity, NDVI, vegetation cover index, leaf area index, vegetation height, and canopy density, were prepared in the ENVI software environment. The data on precipitation and water consumption volume were also determined using the soil water balance model.

The study found that the evapotranspiration estimated by the soil water balance model was 24115 m3, while the evapotranspiration estimated by the SEBS model was 28750 m3, with a 16.12% error. Additionally, the SEBS model was calibrated using four indices - R2, RMSE, MAE, and MBE - with values of 0.844, 1.06, 1.12, and 0.25, respectively. These results indicate that the SEBS algorithm is accurate enough to estimate ETa in the study area.

Conclusion and Suggestions:

Therefore, based on the results of this research, it can be said that the use of satellite images in estimating actual evapotranspiration (ET) is reliable compared to field measurement methods. Considering the limitations of traditional moisture balance methods (such as being point-specific, time-consuming, costly, requiring precise instruments, and almost impractical with the use of lysimeters) and the advantages of utilizing satellite images (covering a wide area of farms or orchards, being cost-effective and fast), acceptable results can be achieved in estimating evapotranspiration while accepting a low margin of error.

Evapotranspiration (ET) as a major component of the hydrological cycle and an important variable in the calculation of the earth's surface water and energy balance has a key role in agricultural water management. Under limited meteorological data, ET estimations using empirical models are less accurate and require adjustments based on water balance approach or lysimetric measurements. Combining ground observations with remote sensing information (such as satellite images) can improve the accuracy of these estimations. In this study, SENTINEL-2 satellite products were used to estimate actual evapotranspiration values in 8 stations of the Gorganrud-Gharehsoo basin, north of Iran, during the period 2016 to 2018. Considering the number of required images and significate variations of ET during warm season, a four months period of April to June was selected for comparisons. The Penman Monteith equation estimations using observed data of study stations was chosen as an evaluation metric of satellite products.Comparisons were evaluated using statistical indices of R2, RMSE. The coefficient of determination (R2) values between Penman-Monteith equation (PM) estimations and SENTINEL-2 outputs for the study stations of Rezvan, Bandar Turkman, Aliabad-e-katol, Gonbadkavos, Kalaleh, Gorgan, Gorgan Hashem-Abad, and Minoodasht, were 0.95, 0.76, 0.86, 0.87, 0.80, 0.86 and 0.84, respectively. The highest correlation of SENTINEL-2 and PM equation was obtained in Rezvan station (RMSE: 0.21, R2 = 0.95), while the lowest correlation was observed in Ghonbad kavoos stations, (RMSE = 0.37, R2 = 0.87). According to results, the SENTINEL-2 evapotranspiration estimations can be used in regions with in adequate observed data. The finer spatial resoulution of SENTINEL-2 improves the ET estimations accuracy. This satellite products may be recommended for regions with inadequate weather stations especially in semi arid regions

Arjan wetland is one of the internationally important wetlands in Fars province, Iran. Despite its ecological and geological values as a typical polje, this wetland has lost its ecological services in the last two decades, except for short seasons in other dry seasons. The purpose of this study was to estimate the ecological water requirement of Arjan wetland. The amount of ecological (environmental) water requirement of the wetland was estimated based on the changes in the total population of migratory birds and also the selected species. Using satellite images, monthly and annual changes in the surface of the wetland as well as permanent and temporary water were estimated during the years 1990-2019.The selected species included one species of goose (gray goose) and 4 species of ducks (including shelduck, mallard, widgeon and teal). Satellite images show that in all the years studied, this wetland has been a temporary wetland, although the water level of the wetland has decreased more than 4 times in the last decade compared to the first studied decade. The abundance of gray-goose population showed the highest correlation with changes in the water level of the wetland. According to calculations, under normal conditions, the water level required to support more than 20,000 birds is between 200 and 400 hectares. Reducing the level of the wetland to less than 200 hectares creates critical conditions and the level of more than 500 hectares is desirable. Therefore, taking into account the hydrological and ecological conditions of the wetland basin, the water requirement of Arjan wetland to provide ecological services during different years  in terms of climatic conditions can be estimated as follows: Drought years: at least 3 Million cubic meters, normal years: 5.25 million  and wet years: 7.5 million cubic meters. Since the watershed has no permanent river to feed the wetland, protection of withdeawal from runoffs and aquifers are required to ensure the water requirement supply of wetland.

Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting.

In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods.

This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods.

In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.

This study aims to determine the forest distribution and area over the period 1993-2013 and model the possibility of changes in forest. For this purpose, the relationship of forest changes with physiographic factors and many human factors using logistic regression were studied.

After geo-correction of the images and their classification using maximum likelihood algorithm, forest land use map related to 1993-2013 period was prepared. The map of forest changes was derived from intersection of the two maps.

To investigate the spatial relationship between forest changes and physiographic and human factors, logistic regression was used with slope and elevation as topographic variables, and distance from roads and village as human variables. Forest area has been 13250 ha which has decreased about 528 ha (equal to 9.8%) during the 20 years. Relative agreement between obtained model and the map of forest changes map by Pseudo R2 and ROC coefficient was equal to 0.22 and 0.73, respectively.

Distance from village, elevation and slope variables had negative relationship with the rate of destruction. However, the rate of destruction increases with increasing distance from the roads. Since the population influences the process of deforestation, it is recommended to use this factor in destruction evaluation and modeling.

Prediction of land use changes in explaining the interactions between ecosystems and human activities is important for helping decision makers. A Land use map is considered an information resource in natural resource management. Optimal resource management needs to be investigated, as recognition of changes and resource degradation in the past, and proper and principled planning in order to control and control possible future degradation. The purpose of this study was to evaluate the performance of the Markov chain model (CA Markov) in determining and predicting land use changes for the future in the Ghaleh Jogh area located in the city of Torbat-e-Heydarieh.

Satellite imagery, remote sensing, and the Markov chain model were used for modeling and detecting land use change. The study area of the Ghaleh Jogh Watershed is from Torbat-e-Heydarieh province. A topographic map with a scale of 1: 25,000 and OLI, ETM +, and Landsat MSS images of 1987, 2002, and 2015 were used to prepare land use maps and the changes process. Satellite imagery with first order polynomial equation was corrected by the method of closest neighboring geometric correction and corrected using linear and histogram explanations. For the categorization of images, the controlled classification method and the maximum probability algorithm with acceptable accuracy are used. A Land use map was prepared in 4 ranges of pasture land, Bayer lands, crops, and gardens. In this research, using the CA Markov model, the 2015 forecast map was compared with the monitored map. The percentage of land use, garden, rangeland, lawn, and arable land use varies from 5, 54, 24, and 17 percent in 1987 to 7, 4, 7, 36, 51, and 7.6 percent in 2015, and the user's map Lands for 2025 and 2040 are foreseen. Model calibration was used for predicting the 2015 model.

The results showed that according to the Kappa coefficient, the prepared map has high accuracy. Based on the results of the detection and simulation of the pasture and cultivating lands, they are among the most unstable classes. Garden lands due to more attention and economic significance, there have not been any significant changes in their extent. In general, the largest changes occurred in pasture and agricultural land. The corrective work done in the region has reduced the area of arable land and added to the rangelands.

Soil salinity is one of the most important environmental problems, especially in arid and semi-arid regions. Soil salinity is caused naturally and/or by humans. High soil salinity negatively affects crop growth and productivity and ultimately leads to land degradation. Monitoring and mapping of soil salinity Due to the serious problems of spreading this issue to regional ecology, food security and agricultural development in the early stages, it is necessary to implement an effective soil rehabilitation program to prevent and reduce soil salinity. Remote sensing science performs better than traditional methods for assessing soil salinity and offers fast and cost-effective techniques for monitoring and mapping soil salinity. Soil salinity can be identified using direct indices that are related to the properties of surface soil salts as well as indirect indices. The aim of this research is to review the challenges of selecting appropriate indices in soil salinity studies through the investigation of researches conducted in the field of soil salinity and spectral indices used in soil salinity cases, which are helpful in land management at the regional scale. It is worth noting that in this regard, the most common vegetation and salinity indices used to detect and mapping of soil salinity were discussed. Many researchers have used different remote sensing indicators to map soil salinity. Among these, BI Brightness index, SI salinity index, NDVI normalized differential vegetation index and NDSI normalized differential salinity index showed the highest correlation between data obtained from satellite images in salinity-affected soils. Choosing the most appropriate band or indices depends on the soil conditions, geographical area, climatic conditions, satellite data, physiography of the area and the type of land use.

The occurrence of fires is one of the important factors that determine the different characteristics of many terrestrial ecosystems. For a long time, fires have severely affected forest areas, and sometimes their negative effects remain for several years after the occurrence of the fire, so that the state of vegetation does not return to its previous state. The aim of this study is to model the restoration of vegetation in Zagros forests (Ilam province) following fire.

We used various climatic and environmental data as independent variables (vegetation at the time of fire (NDVI+1), burn severity index, temperature and precipitation anomaly, average temperature, annual precipitation, slope, aspect, and elevation) and NDVI +5 as dependent variable for the modeling (using random forest, decision tree and gradient boosting) the vegetation recovery following fire. Landsat satellite images were used to prepare indices indicating vegetation density status and burn severity, and after preprocessing the images, these indices were prepared by spectral ratio. Climatic variables (precipitation, average temperature, minimum temperature and maximum temperature) were also estimated according to the regression relationships between these variables and the elevation in the study area. Finally, three machine learning algorithms, including decision tree, random forest, and gradient boosting, were used for modeling, and also the accuracy of these models were evaluated.

The results showed that among the various variables investigated, the annual precipitation, average annual temperature, normalized vegetation difference index (NDVI) and burn intensity index at the time of fire were the most important factors affecting the vegetation restoration post fire in these forests. The precipitation and temperature were the most important factors affecting the restoration among the mentioned factors. Also, the results showed that among the different models, the gradient boosting algorithm with R2 = 0.66 models vegetation restoration better than other models. In this model, the climatic factors were the most important in the vegetation recovery.

According the relationships between the NDVI and other studied factors and the results of the modeling; it is possible to explain the effective role of climate factors in the vegetation restoration in the study area.

In Iran, as in other countries, social and economic factors have led to land use changes depending on regional conditions. The Taleghan watershed, due to its proximity to population centers such as Tehran and Karaj, construction of the Taleghan Dam in 2001, and becoming a tourist center, has undergone significant changes. Many studies have been conducted to evaluate land use changes in this area, but these studies have investigated changes only until the time of dam water withdrawal in 2005 or until 2016 at most. Thus, this research investigates and analyzes land use changes in the Taleghan watershed over a period of thirty years (1987-2017).

The study focuses on land use changes in the middle Taleghan watershed. A land use map was prepared for the last thirty years (1987, 1998, 2008, and 2017) using Landsat satellite imagery. The hybrid classification method (a combination of supervised and unsupervised methods) was used, and accuracy was measured using Kappa and overall accuracy, yielding acceptable results in percentages. The T index was used to calculate land use changes.

The study shows that the abandoned dryland area (12.08%) is the largest after pasture land use (71.06%). The area of villa construction increased, while the garden area decreased over the thirty-year period. The T index calculation reveals that barelands experienced the most changes during the thirty-year period.

Spatial patterns of land use changes were estimated using spatial statistics and the calculation of the share of changes in various land uses. Location quotients were used to analyze the data, and it was calculated for the land uses that experienced the most changes in each sub-watershed in 1987, 1998, 2008, and 2017. The results of the LQ show that the rangeland area was almost constant during the period of 1987-2017. The areas of irrigated and rainfed agriculture fluctuated due to emigration and immigration during this period. The area of residential land also increased significantly due to the construction of villas and promenades around the lake by indigenous and non-indigenous people. Investigating the variations of the spatial pattern of different land uses revealed that the most changes occurred in the sub-watershed around the dam lake and the main river.

 According to Article 45 of the Constitution of the Islamic Republic of Iran and Article 2 of the Law on Fair Water Distribution, rivers are a national asset. They are in possession of the Islamic State. Therefore, the government is obliged to study and determine the bed and river boundaries, and if it recognizes the aristocracy in the bed and their area for disturbing water or electricity issues, to evacuate or tin and suppress them. Today, due to the increase in the economic value of land and the demand for land construction in lands along rivers and waterways, unfortunately, the process of using the riverbed has increased, which is a threat to access to safe water and its protection for future generations. Occupying rivers is associated with reduced land use and land use change. This disrupts the natural flow of the river, resulting in flooding and social, economic, and environmental damage. It is not possible to manage water resources, especially flood management, without knowing and analyzing the flow of rivers, flood zoning, and determining their bed boundaries and boundaries. At present, a land survey is being conducted to determine the extent of the floods and to determine the extent of the riverbed. This method is very time-consuming and expensive to perform. In this regard, using satellite imagery and aerial photographs instead of terrestrial mapping can be helpful in speeding up studies and reducing costs. Much research has been done in our beloved country of Iran and the world on the use of satellite images in various fields. In particular, several studies have used satellite imagery to study the changes in land use in watersheds and to study the morphological changes of the river. As noted, research has been conducted on the use of satellite imagery in hydrological studies and watersheds, but for the first time in this study, it is possible to use satellite imagery to map the river and extract its cross-sectional areas for flooding and riverbed delimitation. Has been studied. In recent years, the bed of the Ardak River above the Eradak Dam has been extensively occupied and altered. This has led to an increase in the number of floods and a decrease in the quantity and quality of water in the Ardak Dam, which supplies part of Mashhad's drinking water. For flood management and quantitative and qualitative protection of the Ardak dam, flood zoning and determination of the Ardak riverbed is necessary. At present, the ground mapping must be done first. Land surveying to map the river and extract its cross sections requires a lot of time and money. Therefore, the aim of this study was to investigate the possibility of using satellite images with a resolution of 28.28 m instead of terrestrial mapping to increase the speed of work and reduce the cost of studies of water projects and projects and river engineering.

ASTER satellite imagery and HEC-GeoHMS software were used to draw the catchment area and extract its physical parameters. The existing riverbed map and its margin were prepared and added to the land use map of the basin as a new layer. The HEC-HMS hydrological model was used to simulate precipitation and runoff. First, a metering and validation model was used for five rainfall and runoff events. The precipitation histogram for different return periods was then introduced to the HEC-HMS model based on the basin concentration time. The execution and flood model were simulated with different return periods. The river's geometric information was extracted in transverse sections from both terrestrial mapping and satellite imagery using the HEC-GeoRAS appendix. Information from river flow modeling in HEC-RAS software was transferred to the GIS environment through HEC-GeoRAS extension and in the mentioned environment, flood zoning and riverbed boundary determination were determined by two methods of using land mapping information and using satellite images.

 The results indicate that the flooding area and the determination of the riverbed can be done by using satellite images with a resolution of 28 × 28 m. In this case, the statistical indicators of the mean relative error and regression correlation coefficient were 13.2 and 92%, respectively. If cross-sectional crossings are taken at several points along the river route and replaced by cross-sections obtained by satellite imagery, the accuracy of flood zoning and riverbed delimitation will be enhanced by the use of satellite imagery. If at a distance of 150 meters and at a distance of 8 km, 47 cross-sections are located and grounded and replaced in the HEC-RAS model by cross-sections obtained from satellite images, the error of using the satellite imagery method for flood zoning and riverbed delimitation Will be reduced to 8.1%.

It is possible to use satellite images with a quality of 28 × 28 m to determine the river bed limit. This method is associated with the average relative error and regression correlation coefficient of 13.2% and 92%, respectively, which can be reduced by 8% with ground cutting.

Among the applications of remote sensing in agriculture, we can mention the estimation of crop yield, the preparation of the cultivation map, the factors affecting the crop yield. The models presented to estimate the crop yield are generally based on the calculation of vegetation indicators, which are used to estimate the amount of production using these indicators and with a specific algorithm. Researchers have used other methods (in addition to the direct use of vegetation indices) to estimate crop yield. In this regard, we can refer to Bastianssen and Ali's research (Bastianssen and Ali, 2003). This model (Bastiansen model) is a combination of the Monteith model to calculate the absorbed photosynthetic active radiation, the Stanford model to determine the absorbed energy efficiency, and the SEBAL model to describe the spatial-temporal changes of evapotranspiration.

This research was carried out in rapeseed fields in the cultivated lands of Qazvin plain irrigation network. In this research, the fields were selected to cover soil texture, soil salinity, different crop management, irrigation water salinity and different irrigation methods. In order to be able to analyze the leaf area index in the process of crop performance modeling, rapeseed cultivars were the same in all the selected fields. In this research, a hybrid model was used to estimate crop yield, including the Monteith model to calculate the absorbed photosynthetically active radiation (APAR), the Stanford model to determine the light consumption efficiency (LUE), and the surface energy balance algorithm (SEBAL). In order to evaluate the crop yield prediction model, Pearson's correlation coefficient was used between the data to analyze the correlation of yield and leaf area index in different stages of growth.

The analysis of the leaf area index in the studied fields showed that the date of cultivation was one of the most important factors influencing the process of plant phenological growth and consequently the difference in crop yield in the fields. Considering that the potential yield of rapeseed in the Qazvin Plain irrigation network is estimated at 4000 kg/ha, none of the farms have reached the maximum leaf area index, and considering the direct effect of the leaf area index in the flowering stage on the crop yield, the maximum yield potential in the selected farms is not available. Therefore, the leaf area index in the flowering stage is considered a suitable criterion for estimating the yield reduction of rapeseed. The results of Pearson's correlation coefficient analysis showed that crop yield had a significant correlation with leaf area index in development and middle stages of rapeseed growth, and the highest correlation was related to the middle stage of crop growth.The results of Pearson's correlation coefficient analysis showed that there was a significant correlation at the 1% probability level between the field recorded data and yield estimation values. Also, the values of explanation coefficient (R2), root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE) were equal to 0.91, 444.06, 41.23, 433.03 kg/ha respectively. Is. Also, the results of the correlation coefficient analysis of yield values and calculated evapotranspiration based on the SEBAL method showed that there was no significant correlation.

Several factors are effective in product performance, but modeling by simplifying the relationships related to a phenomenon, justifies the mutual relationships between independent and dependent variables by spending the least amount of time and money. The results of the research regarding yield estimation using vegetation indices, evapotranspiration algorithms and hybrid models show that it is possible to make an acceptable estimate of crop yield by using Remote Sensing techniques. For example, the results of present research showed that by preparing the selected image of Landsat 8 satellite (OLI and TIRS) related to the beginning of rapeseed flowering period in the following years and extracting the leaf area index in the middle period of growth, the yield of field can be predicted with reasonable accuracy. Also, the leaf area index in the rapeseed flowering stage is a suitable measure to estimate the yield gap of the rapeseed crop. The important point is that the accuracy of predicting crop performance by satellite images is still reported was average. The accuracy of field measurements, the low spatial resolution of satellite images, as well as the presence of clouds, fog, gas, and suspended particles, along with the complexities related to plant growth modeling, have an effect on reducing the accuracy of yield prediction and the validity of models. Although these researches are expected to improve and expand with the variety of satellite images and the entry of cloud computing into the field of complex computing.

Remote sensing methods for mapping farms and crops have been widely used in the last three decades. This method is applied to identify irrigated areas around the world (Alipour et al., 2014), although most of these studies are in areas with semi-arid climates and low rainfall or lack of rainfall which has a significant effect on the spectral characteristics of plants. In this study, Landsat 8 and MODIS satellite images were used to identify and separate two irrigated and rain-fed wheat farms in Hamadan province. Two algorithms of support vector machine (SVM) and minimum distance (MD) were used simultaneously to classify irrigated and rain-fed farms. In the next step, the area under cultivation of rain-fed and irrigated wheat was predicted in the whole cultivated area of Hamadan province. Finally, the cultivation area of rain-fed and irrigated crops was calculated in the province using Sentinel 3 satellite images based on the random forest algorithm in 2016.

The study area is Hamedan province, which is located between 59◦ 33′ and 49◦ 35′ north latitude and also from 34◦ 47′ to 34◦ 49′ east longitude of the Greenwich meridian. A 50-hectare rain-fed wheat farm in Amzajerd was used as a sample to extract the properties of rain-fed wheat. Also, irrigated indices were extracted from a 100-hectare irrigated wheat farm located in Kaboudrahang. Satellite images were applied to separate irrigated and rain-fed wheat in Hamadan province. NDVI, EVI and NDWI indices were extracted from 16-day images of Landsat, MODIS, and Sentinel 3 sensors in the five-year period (2015-2019). Google Earth Engine (GEE) system was the environment for performing image processing calculations and extracting indices and maps.

The NDVI and EVI of irrigated and rain-fed wheat farms were calculated in 2015-2019. A small peak was observed in the rain-fed and irrigated NDVI trend in November due to the early germination of wheat leaves in winter, and the larger peak in May and June showed the maximum greenness of irrigated and rain-fed wheat, respectively. The ascending or descending trend of NDVI / EVI had no constant slope. This can be due to changes in meteorological parameters, which sometimes cause a sudden increase or decrease in the values of these indices. Despite the non-linearity of the NDVI / EVI trend over time, the maximum greenness was recorded just a month before the wheat harvest, which was seen in the third decade of May to the first decade of June. One of the cases is the sharp drop of NDVI / EVI after its final peak, which was definitely due to yellowing wheat and harvesting. Since the distinction between rain-fed and irrigated crops was difficult only based on NDVI, NDWI was also used to determine the water content of wheat so that irrigated wheat could be identified. However, the difference between rain-fed and irrigated wheat in terms of NDWI spectral density was insignificant; the maximum and minimum occurrence times of NDWI and NDVI of rain-fed and irrigated wheat were chosen for their separation. In order to map the cultivation area, in addition to the MODIS sensor, Sentinel 3 was used due to its ability to detect chlorophyll accurately. Due to the fact that the imaging of the Sentinel 3 satellite started since 2016, the map of rain-fed and irrigated cultivation as well as the cultivation area and their separation was done based on the random forest algorithm in 2016.

The results of this study showed that the appropriate method for distinguishing between rain-fed and irrigated wheat is the simultaneous use of several indices. Also, the greatest difference is in the maximum greenness, which happened almost one month before harvest. MD and SVM classification algorithms could distinguish irrigated and rain-fed wheat from other crops with 90% and 80% accuracy, respectively. Distinguished maps of irrigated and rain-fed crops based on the random forest algorithm were obtained using Sentinel 3 satellite imagery which can show the fertility of agricultural lands in the province.

The purpose of this research was to calculate the environmental water requirement and ecological water level of Parishan wetland to transfer water from Nargesi Dam. Based on hydrological information, there is no relationship between the volume of water in the wetland, the amount of rainfall and runoff input. Therefore, the main factor of wetland death is the improper use of water in agriculture in addition to drought. To determine the hydrological water right, the balance formula of lakes was used. Accordingly, the environmental water requirement is 15.05 million cubic meters per year in the case of stop-cultivation plan, and 35.95 in case of limiting 50% of agricultural wells. If the first scenario is implemented, without drought and water transfer after 3.5 years, the groundwater will reach to zero level and then the restoration process will begin, but with the implementation of the second scenario, this time is 5 years and in case of water transfer from the dam, it will be accelerated. Flagship species include Dalmatian pelican, Golden barb and common reed. The year of 2007 was the last year that the entire area had water. Multi Species Indicators (MSI) was 80 and desirable. In 2007, the amount of fishing was 64 tons and it was desirable. The area of ​​reeds in the same year was 220 hectares. Therefore, the minimum ecological level, based on the volume of wetlands in 2007, is 39.3 million cubic meters, but the ecological balance level based on the average volume of wetlands in watery years is 51.6 million cubic meters. Thus, using hydrological and ecological indicators determine the minimum and optimal volume for the restoration of the wetland to its natural functions, and the role of accurate statistics of indicator organisms is very important.

Land subsidence is a vertical movement downwards of the Earth's surface. It is difficult to identify or measure because of rapidly depleting ground water. This hazard, as a hydrogeomorphology phenomenon, is revealed in many areas of the country because of the uncontrolled depletion of the groundwater. There are a number of methods for identifying and determining the rate and range of subsidence. Radar interferometry (D-INSAR) technique is known as a robust technique for land subsidence measurement. This method has superior features in terms of cost, accuracy, scale of the study area and time, in comparison with other detection methods, and can provide a precise estimation of the phenomenon. In this study, the Abarkouh plain was studied from January 2016 until February 2018. Sentinel 1 sensor images using radar interferometry technique were used to estimate subsidence. To identify the impact of groundwater extraction in land subsidence, groundwater fluctuations data from 44 piezometric wells, and also land subsidence data were analyzed using regression models. Results showed that the highest rate of groundwater depletion occurred in the town of Mehrdasht and around the agricultural fields, which led to the maximum rate of subsidence up to 12 cm/year. Ccorrelations coefficient between aquifer fluctuations and land subsidence rate is 0.79 that indicated significant relation between these parameters.

Soil salinity has sharply been increasing in recent decades due to improper use of basic resources. This issue has had severe harmful effects in different parts of Iran, especially in arid and semi-arid regions where the accumulation of soluble salts in soil surface has reduced crop yields and destroyed arable lands. Given the significance of this issue, the present research investigated the trend of temporal and spatial variations of soil salinity in Minab Plain for which the satellite images of 2001, 2011, and 2021 were used. The Envi5.1 software package was used to develop the soil salinity maps, and the hybrid CA-Markov model in the TerrSet software package was employed to study the soil salinity changes and predict it for the future period. The results showed that the land salinity would increase in these regions over time so that the area of very high salinity class has been 39.46, 45.26, and 63.09 km2 in 2001, 2011, and 2021, respectively. This increase was even greater in southern and southwestern parts of the plain. Furthermore, the prediction map showed the expansion of salinity in the studied region so that the highest area of salinity change rate in 2021 was found to be related to the very high salinity class (20.24%) and the area of very highly saline lands has increased from 12.20% to 29.62% from 2001 to 2021 whereas the area of moderately saline lands has decreased from 60.47% in 2001 to 13.88% in 2021. In general, an approach for preventing soil salinity aggravation in this region is to change the irrigation system to prevent severe water use and the loss of water quality, which would finally influence the soil to a lesser extent.