google earth engine

Today, air pollution has become a serious problem in human societies. One of the appropriate methods for better management in dealing with this destructive environmental phenomenon is to monitor and investigate the pollution trend using modern technologies, including satellite images. In this study, the production of a seasonal map and the investigation of pollution trends of carbon monoxide (CO), nitrogen dioxide (NO2), methane (CH4), and sulfur dioxide (SO2) are targeted. The study area FOR producing a pollution map is Iran in Southwest Asia. Also, the data used are related to Sentinel 5 satellite images. By creating a model and using special bands, the seasonal pollution map of Iran is produced. In the following, a point in the city of Tehran was considered to analyze the pollution trend. By creating a function in Google Earth Engine, the graph of 1402 related to CO, NO2, CH4, and pollutants is drawn. The results of these two parts show that the amount of pollution in winter is higher than in other seasons. Some areas, especially on the coasts of the Persian Gulf and the Caspian Sea, are dangerous in terms of the presence of CO in all 4 seasons of the year. NO2 reporting is hazardous in metropolitan areas throughout the year. Ch4 maps are almost neutral. On the other hand, partial SO2 and NO2 are seen more in industrial and densely populated areas. The graphs also show that the trend of CO, NO2, and SO2 pollution increases drastically in the second half of the year.

Sand dunes are one of the complications of hot and dry regions, which are associated with many risks, including the creation of dust centers and the destruction of agricultural lands and human constructions. Among the sand dunes that have played an important role in the human activities nearby is the sand dunes of Khuzestan in the southwest of the country. Considering the importance of the subject, in this research, the condition of sand in Khuzestan and the risks caused by it have been investigated. In this research, Google Earth images, Landsat 9 satellite images, MODIS satellite images, as well as digital layers of information are used as the most important research data. The most important research tools include Google Earth, Google Earth Engine and ArcGIS. In this research, firstly, the range of Khuzestan's reservoirs has been drawn and then its changes have been analyzed. In the following, using the AOD index and MODIS images, the dust concentration map of the region has been prepared and its relationship with the region's sensors and human activity has been evaluated and analyzed. The results of this research have shown that the sand of Khuzestan has an area of 2602 square kilometers, and under the influence of the speed and direction of the winds in the region, about 43% of it has an active state, and this problem has caused sand piles in some areas. Move towards agricultural lands.

Due to its proximity to major sources of dust (the deserts of Iraq, Syria and Saudi Arabia) in the west, Iran has always faced dust storms and their sequences. Moreover, the Tigris and Euphrates basin (TEB) is considered one of the main centers of dust in the region. Hence, it can lead to adverse environmental, social and economic consequences, especially in the dry areas of southwestern Iran, and cause problems in human activities. Thus, identifying and evaluating different dimensions of this phenomenon is of vital importance. Accordingly, the aim of the current study is to investigate the temporal and spatial patterns of dust distribution in the southwest of Iran and to identify its relationship with the trend of surface water changes in the TEB. In this regard, the JRC Landsat dataset was used to monitor surface water changes and an integrated approach based on visual interpretation and assessment of aerosol optical depth (AOD) of MODIS images was used, and the processing was done on the platform of Google Earth Engine (GEE). The findings have shown that the occurrence of dust was largely correlated with the temporal pattern of surface water, and there was a negative correlation of 0.412. Accordingly, with the beginning of the decreasing trend of water in 2008, which has reduced its area to about 8199 km2, the dust storm has intensified and the highest annual frequency of dust has been observed with 2612 cases. Therefore, the role of water changes has been highlighted in this context.

Optimal classification of satellite images enhances the extraction of spatial-temporal information for land use and land cover classification, enabling the examination of various socio-economic and environmental impacts. Initially, three supervised classification algorithms—K-Nearest Neighbors, Support Vector Machines, and Random Forests—were utilized with default parameters. The results indicated that the Random Forest algorithm outperformed the others in accuracy. Consequently, the Random Forest classification algorithm on the Google Earth Engine platform, using Landsat 8 images as input for 2013 to 2023, was employed. The impact of different image combinations based on several spectral indices on the final classification accuracy was assessed. The findings show that using Landsat products (TOA) and the GEE platform provides faster and more accurate classification, with optimal hyperparameter values for 2013, 2018, and 2023 being 280, 180, and 80, respectively. The findings indicate an increase in vegetation cover alongside gradual urban expansion, reflecting stable development policies. However, this growth is primarily attributed to the establishment of large leisure areas and tree planting on the city outskirts. Despite initiatives aimed at reducing air pollution and promoting community well-being, vegetation in central urban areas has either remained stagnant or declined in certain areas. Considering the implications of climate change and increased heavy rainfall, the lack of vegetation in central zones may lead to the formation of impermeable surfaces, exacerbating the risk of flash floods. This underscores the critical need for enhanced research focus and proactive measures in this field.

One of the challenges that many societies face is the occurrence of natural hazards. There are different types of natural hazards, and flood is one of the most important natural hazards, which is always associated with a lot of human and financial losses. In fact, floods are considered as one of the most destructive hazards that cause many human and financial losses every year, so that according to the report of the international database of hazards, floods along with earthquakes and droughts, have had the highest human and financial losses. Also, according to the statistics published in 2010, more than 40% of the natural disasters in the world are caused by floods. In recent years, due to the increasing trend of population and increase in human activities and misplaced human interventions in nature, the possibility of flood occurrence has increased and the risks caused by it have intensified. According to geomorphology, hydro climatic, land cover, etc., different areas have different potentials in terms of flood risk occurrence. Among the regions that have a high potential against flood risk are the southeastern regions of the country. These areas have a high potential against flood risk due to torrential rains, being located at high altitudes and low slopes, as well as poor vegetation, and this issue has caused many floods to occur in recent years. Let's be in this area. Considering the importance of the issue and the occurrence of floods in Esfand 1402 in this region, in this research, the identification of flooded areas and also the analysis of factors affecting its occurrence have been done.

In this research, Sentinel 1 and 2 radar images, Landsat 9 images, and a digital model at a height of 30 meters have been used as the most important research data. The most important research tools are the Google Earth Engine system (to prepare maps of flooded areas and land cover maps) and ArcGIS (to prepare the desired maps). Also, in this research, fuzzy logic model has been used to identify vulnerable areas against flood risk. According to the topic and objectives, this research has been done in several stages. In the first stage, using radar images before the flood and images after the flood, a map of the flooded areas has been prepared. In the second stage, the role of natural factors (elevation, slope, distance from the river, and ranking of the river and land cover) in the flood has been analyzed. In the third stage, based on the results obtained from the previous stages and according to the extent of the flooded areas in different floors, various parameters have been valued and standardized, and finally, by combining the desired parameters based on the fuzzy gamma operator, a map of the damage areas is created. It has been prepared against flood risk.

In this research, the Google Earth Engine system was used to identify the flooded areas in the study area. After preparing the desired images and also applying filters and specifying the threshold limit in Google Earth Engine system, a map of the flooded areas in the southeast of Sistan and Baluchistan province was prepared in March 1402. According to the prepared map, a large part of the southeastern counties of Sistan and Baluchistan province have faced floods, but Dashtiari county and especially its southeastern regions along the Pakistan border have faced the highest amount of flooding. After Dashtiari city, Chabahar city has faced the highest amount of flooding. The analysis of the effective factors in the floods has shown that the height, slope and type of land cover have the highest influence because in a general trend, with the decrease in height, decrease in slope and also decrease in vegetation density, the extent of flooded areas increased.

The results of the identification of flooded areas using radar images have shown that in March 1402, a large part of the region, especially the south-eastern areas of Dashtiari city, faced the risk of flooding. The results of the analysis of the effective factors in the floods have shown that a large part of the flooded areas were related to low altitude and low slope areas, which indicates the direct role of altitude and slope in the floods of the region. Also, the areas that were in the category of desert and barren lands in terms of land cover also had the highest percentage of flooding, so the type of land cover was also one of the effective factors. But the interesting point is that there was no strong relationship between the parameters of the distance from the river and the ranking of the river with the floods that occurred. That is, some areas that were far from the main rivers also faced floods, the main reason of which was the morphological condition of the rivers in the region. According to the mentioned cases, among the effective factors, the height and slope have the highest influence and the distance from the river has the least influence (among the studied parameters). Also, in this research, based on the condition of flood-affected areas and the influence of parameters, vulnerable areas against flood risk have been identified. Based on the results, the southern regions of the study area, including the middle and southern parts of Dashtiari city, the eastern and southern regions of Chabahar city, as well as the southern regions of Konarak city, have the highest flood potential.

In addition to natural disasters such as floods, which cause great damage to the environment and human societies, the phenomenon of dust also causes irreparable damage to urban environments, transportation systems, respiratory systems, etc. Identifying Potential Dust Source Areas is considered the first step to control and prevent this phenomenon. Various researches have been conducted to identify dust sources, but most studies have relied on small-scale images. This research aims to use medium-scale satellite images to identify local areas prone to dust production. Google Earth Engine was used to analyze factors influencing dust generation, including slope, Digital Elevation Model (DEM), land use, soil salinity, Normalized Difference Vegetation Index (NDVI), soil moisture, wind speed, precipitation, and Land Surface Temperature (LST). Analytical Hierarchy Process (AHP) was used to assign weights to these elements. The resulting weights were: soil moisture (0.264), NDVI (0.208), wind speed (0.153), precipitation (0.107), land use/soil salinity (0.081), LST (0.064), DEM (0.024), and slope (0.020). The inconsistency index (0.015) indicated a high degree of consistency between the assigned weights, which is below the acceptable threshold (1). Due to the lack of ground-based air quality measurements, the Aerosol Optical Depth product (a satellite-derived measurement of airborne particles) was used to validate the resulting dust source maps. The final map showed that the potential for dust generation decreased closer to the heights of Sahand and increased closer to the lake.

Environmental changes are one of the most critical challenges to achieving sustainable development. Wetlands are part of the earth's structure and as one of the important ecosystems consisting of water, vegetation, soil and microorganisms. Monitoring, management and assistance in decision-making and policy-making of surface water changes can be done according to the availability of satellite data. The availability of Landsat data helps a lot in preparing a high-quality map to show the land surface changes. Although remote sensing is superior to traditional methods in terms of time, speed, and cost, these methods require the use of powerful and practical systems that include complex analysis. The use of data and images on the web is a solution that can be used to solve the mentioned problem, which studies can be done with high accuracy and speed without the need for a strong hardware and software system. The Google Earth Engine system creates suitable conditions for processing satellite images for environmental monitoring and analysis. The purpose of this research is to monitor the dynamic changes in the Miangaran wetland sub-basin in the period (2013-2022).

Miangaran wetland with an average area of 2500 hectares is located at a distance of one and a half kilometers from Izeh city, in the northeast of Khuzestan province. Time series analysis is one of the most common operations in remote sensing that helps to understand and model seasonal patterns as well as monitor changes. In this research, 421 images from the ee.ImageCollection ("LANDSAT/LC08/C02/T1_L2") data set were used for the period from 2013 to 2022. The construction of a harmonic model was used in this research due to its flexibility in cyclic calculation with simple and repeatable forms. The normalized differential water index is an index for drawing and monitoring content changes in surface waters. Also, the Normalized Difference Vegetation Index (NDVI) is one of the most common remote sensing indices. Harmonic time series of water body and vegetation cover were extracted using NDWI and NDVI indices in Google Earth Engine platform, and Mann-Kendall's non-parametric test was performed using time series data output with XLSTAT extension in Excel software. Finally, global water data was used to confirm and complete the results of time series analysis.

The results of the harmonic time series of the water body showed a decreasing and negative trend and more changes in the sub-basin. Kendall's statistical test confirmed the decreasing and negative trend of the water body. Accordingly, since the calculated p-value (<0.0001) is lower than the alpha significance level (0.05), the null hypothesis should be rejected and its alternative hypothesis, the existence of a trend in the time series, should be accepted. The value of Kendall's tau also confirmed a negative value (-0.245) and a decrease. Due to the negative sen's slope statistic for the water area (-0.002), changes are more in the Miangaran Wetland sub-basin. The results of the Mann-Kendall test for the observed vegetation data showed the absence of a trend in the harmonic time series. Since the calculated p-value (0.064) is higher than the significance level of alpha (0.05), the null hypothesis (absence of trend) cannot be rejected. The risk of rejecting the null hypothesis (while true) is 43.6%. Kendall's tau statistic showed a negative value (-0.060) and a non-significant decrease. Therefore, accepting the null hypothesis (absence of trend) indicates that vegetation changes in the harmonic time series were not significantly different from each other. Also, the negative sen's slope statistic for vegetation (-0.026) indicates more changes in the sub-basin of Miangaran Wetland. By comparing with the results and analysis of other researches, it seems that human intervention and change of land use can be the cause of the lack of trend in the Miangaran Wetland sub-basin. Also, according to the negative value of Man-Kendall's vegetation cover which showed a non-significant decreasing trend, it seems that climate change and drought have also played a major role in the changes under the Miangaran wetland basin. The study of the global water data also showed that the water occurrence in terms of space-time is decreasing and the intensity of the change of water occurrence is critical under the basin of Miangaran wetland. The marginal parts of Miangaran Wetland show seasonal water loss, most of these changes occur during the period. This research confirmed the use of harmonic time series in monitoring wetland dynamic changes. Finally, the allocation of water rights, the establishment of laws and the determination of the limit of the ecological bed, and the use of Google Earth Engine capabilities to monitor environmental changes (use, temperature, precipitation, evaporation, etc.) of the Miangaran Wetland sub-basin were suggested.

In this study, using images of Landsat-8, Landsat-7 and Sentinel-2 satellites in the coding environment of Google Earth Engine, their uses and changes during the two periods before and after urbanization (from 2000 to 2008 and from 2008 to 2019) will be categorized and then the next five-year development forecast of Sahand city (until 2025) will be made. Perceptron multilayer artificial neural network (MLP) method has been used as a method for predicting spatial multi-criteria decision making (MCDM). The independent variables used in the present study in predicting the physical development of the city are land price, type of use, slope, slope direction, altitude, distance from urban areas, distance from waterway network, distance from fault, distance from network Passages (main and secondary). The results of classification of satellite images showed that the physical development of Sahand new city has been done in order to turn barren lands into urban land. In addition, physical development was built to turn cheaper land into areas. The built lands have been greatly developed and from 64,155 square meters in 2000 to 682,192 square meters in 2019. Among the image classification methods for land use extraction, the SVM method was the best method and also the Sentinel-2 satellite images had the highest accuracy. The multilayer perceptron artificial neural network was used to predict the future physical development of the new city of Sahand, which according to studies, the development is predicted in directions that are based on the cheapness of the land and the limitations. Geomorphological is like slope and altitude.

Surface water is the most important source of water from planetary water resources, which covers 71% of the earth's surface and is vital for the survival of human and ecological systems. However, during recent decades, due to the combined effects of climate change and human activities, the water balance of lakes has changed. In this regard, surface water changes as one of the most important resources of the earth are very important in local, regional and global planning. Accordingly, one of the prime regions within the Middle East is the Tigris and Euphrates Basin (TEB) which has undergone tremendous changes in the past decades due to matters of global warming and climate change, drought, desertification, and population growth or human-related activities. Nevertheless, much attention has not been paid to the pattern of changes in water areas and understanding the potential effects of various environmental factors. Therefore, in the present research, the time series of surface water changes in this basin has been evaluated in relation to the impact of various environmental factors.*

The conventional methods of monitoring water bodies are very expensive and time-consuming due to their special characteristics. Hence, remote sensing (RS) techniques have facilitated water bodies change analysis, where the required maps can be retrieved at any time and spatial scales at unprecedented speed and extent. In this regard, the time series of environmental parameters have been extracted using various GEE products. The time period studied in the research is a long-term period from 2001 to 2021 and the analyses have been done in two phases and in total using 252 months of data in the form of a composite time series. Our data are extracted from products such as FLDAS, TerraClimate, MOD13Q1.061, MYD17A3HGF.061, WorldPop and JRC 1.4. The main goal of this framework was to analyze the trend of TEB surface water changes and determine its correlation with the temporal changes of various environmental factors.*

The results showed that lakes and wetlands have witnessed various changes during the period of 2001-2021. In this regard, the area of Hamrin and Razzazah lakes has gradually decreased. In other words, the area of Lake Hamrin in northeastern Iraq has decreased from 157.98 square kilometers in 2001 to 107.63 square kilometers in 2011 and 37.05 square kilometers in 2021. In addition, the area of Razzazah lake in Karbala province has decreased from 1260 square kilometers in 2001 to 440.04 square kilometers in 2021. Furthermore, Qadisiyah, Tarthar, Habbaniyah lakes and Horul-Azim wetlands have had different trends in different years. The results show that the average surface water area of the region in the period of 2001-2021 was about 9287.58 square kilometers and in 9 years of this period, the values were higher than the average and in the other 13 years, the values were lower than the long-term average. According to the analysis, the surface water area of the region was at its lowest level (8199.52 square kilometers) in 2010 and at its highest level (11424.76 square kilometers) in 2021. In addition, the time-varying trend of water changes in the region has been observed. In general, the linear trend model has indicated a general increasing trend in this time period. Our findings show that due to the ever-increasing expansion of urbanization and the growth of urban areas, the population has an exponential trend that has always increased from the beginning to the end of the period. The evaluation of temperature values has indicated that the air temperature suddenly became abnormal after a relatively stable trend. The beginning of this trend was in 2010, when the maximum temperature reached 28.55 degrees Celsius and the minimum temperature reached 14.11 degrees Celsius, and the average temperature increased to 21.4 degrees Celsius. In the meantime, according to the trend governing the topography of the basin, precipitation did not have a significant effect on the mentioned changes, and instead, evapotranspiration played an important role. In other words, water areas and evapotranspiration have a negative correlation, and the increase in evapotranspiration is associated with the decrease in water area. Moreover, the results have shown that other environmental parameters such as vegetation, soil texture and wind speed did not have a significant effect on the water level changes in the region and did not have a significant relationship in this regard. Therefore, it should be said that climate changes considered as the most important factors in the process of increasing or decreasing the water level in the TEB.*

Based on the research methodology and the results obtained, it can be said that the Google Earth Engine (GEE) platform can provide researchers and decision makers with valuable tools in the field of water level monitoring. In the case of time series, the volume of data is very large, which increases the data processing time and its computational load and from this point of view, continuous monitoring of water bodies in the TEB in the context of this platform is very important, taking into account the annual changes and environmental conditions. On the other hand, the efficiency of the various products of GEE has made the desired processes to be performed with maximum accuracy and speed without the need for special hardware and software prerequisites. In general, the GEE platform enables the proposed research framework to be used to monitor and analyze surface water changes at different spatial and temporal scales. Nevertheless, due to the large scale of the study in the current research, it is suggested that in the next research, a case study of each of the water bodies in the TEB will be conducted and monthly and seasonal time series data should be used to analyze the trends of changes and factors involved in this field.*Keywords: Surface water, change assessment, Tigris and Euphrates, time-series data, GEE.**

One of the major global environmental concerns in recent years is the phenomenon of global warming. Understanding the relationships between the phenomena affecting this phenomenon can be a way to reduce and control the surface temperature and prevent the formation of thermal islands.The aim of this study was to determine the relationship between surface temperature intensity with variables So2, CH4, Co, O3, NO2, HCHO, H2O, PM, LST-Day as well as temporal and spatial changes of thermal islands in Tehran province. In this study, 8-day productions of Aqua satellite and Sentinel-5 data along with coding method in JavaScript language in Google Earth engine system, path analysis and statistical clustering method have been used. According to the research findings, the trend and direction of spatial changes in the thermal islands of Tehran province during the years 2022-2000 from Tehran to the surrounding cities and satellite towns have been moving and relocating.Based on the temperature similarity between the 22 urban areas, six clusters were formed by K-mean clustering method. Based on the results of the final table of path analysis using AMOS software, among the studied variables, the highest coefficient of total impact on the increase in night surface temperature was related to the daily surface temperature, water vapor and carbon monoxide, respectively. PM particles also have the highest overall impact factor in reducing the night temperature.

Soil erosion is very obvious and noticeable in Iran, where a large part of it is a desert and the soil does not have a suitable cover. In order to reduce soil erosion and control it, it is necessary to identify factors affecting soil erosion. Land exploitation methods, forest and pasture exploitation, creation of residential and urban areas, geological conditions, precipitation, weather factors, etc. are some of the factors that affect the intensity of erosion in the region. Therefore, knowing the quantitative values of soil erosion is effective in accurately estimating the adverse, hidden, and intangible effects of erosion

In this research, the Revised Universal Soil Loss Equation (RUSLE) Model has been used. The amount of soil erosion in the basin was calculated in two 20-year periods (1980-2000 and 2000-2020). This soil erosion model is calculated through six factorsA=R×K×L×S×C×P      (R) the Rainfall erosivity factor, (k) soil erodibility factor, (LS) are slope length and slope degree factors respectively, (c) vegetation management factor, and (P) soil protection factor. The data in this research for the implementation of this model including the rainfall erosivity factor (R), topography including slope length and slope degree (LS), and vegetation management (C) were prepared from the Google Earth Engine system. The soil erodibility factor (K) was obtained from the natural resources report. The BLM model was also used to estimate the evaluation of the RUSLE model.  The Google Earth Engine system was used to prepare the land use maps of each of the 20-year periods. The land use map of the first period was prepared using the supervised classification method and calling Landsat 5 images, and The land use map of the second period was done using the CGLS-LC100 product, which is produced and updated at the Copernicus Global Earth Center and using Sentinel satellite images

By analyzing the obtained maps, the rain erosion factor for the first period (1980-2000) has an average of 2.02, the minimum value is 1.80 and the maximum is 2.29, and the second period (2000-2020) is from 1.55 to 2.07 is variable, its average is 1.783. The erodibility factor of the region's soils varies from 0 to 0.349 and its average value is 0.0264. The topography factor of the studied basin varies from 0 to 250 and its average value is 8.77. The vegetation management factor varies between 0.181 and 0.505 and its average is 0.353. This factor varies between 0.315 and 0.494 in the second period, and its average is 0.429 and has an increasing trend compared to the first period, which indicates the decrease of vegetation in the study area compared to the first period. The ground protection factor is also considered to be 1. To calculate the annual average soil erosion rate of the Award Basin in the first period (1980-2000), the various factors of the RUSLE model were converted to the same raster format and cell size, and coordinate system. To determine the risk of soil erosion, the produced layers including the rainfall erosivity layers, soil erodibility, topography, vegetation and soil protection factor were multiplied with the help of the Spatial Analyst extension of the ArcGIS program. The amount of annual loss of soil in terms of (tons per hectare per year) was obtained on a cell-by-cell basis. According to the results, the amount of erosion in the first period varies from 0 to 59.62 tons per hectare and its average is 1.64. The highest amount of soil erosion is in the middle and eastern regions with high slopes. In the second period, the layers of the soil erodibility factor, topography and soil protection factor are the same as in the first period because they are considered constant. The amount of erosion in the area in the second 20-year period varies from 0 to 63.38 tons per hectare, and its average is 1.75 and shows an increasing trend compared to the first period. The RMSE, MAE and MSE statistical indices were used to evaluate the accuracy of the RUSLE model and the degree of agreement of its erosion classes with the output of the BLM based on the map of sampling points. Examination of the values of the mentioned indicators showed that the root mean square error, mean absolute error and mean square error statistics in the RUSLE model have low values of 1.23, 0.96 and 1.52, respectively, as a result this model has It is a small error. After preparing the land use map for the first period, it was determined that in this period, the studied basin includes four land uses: forest, agriculture, pastures and woodlands, and residential. In the second period, there have been forest uses, destroyed forests, agriculture, pastures and woodlands, and residential areas. Then the land use maps prepared in the Google Earth Engine in two periods were transferred to the GIS environment system and the area of each of the above uses was obtained. The area of forests in the studied basin has decreased by 1590.956 hectares and 2014.827 hectares have been added to the area of degraded forests. Also, 2128/368 hectares have been added to the area of agriculture in this basin, and 2557/865 hectares have also decreased from the area of pastures in this area. It should be noted that 5.6266 hectares have been added to the area of residential areas of this basin.Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text.

The findings of this research in two 20-year periods (1980 2000) and (2000 2020) in the studied area show that the amount of precipitation in the second period has decreased compared to the first period. The amount of Rainfall erosivity in the first period had a minimum value of 1.80 and a maximum value of 2.29. In the second period, its value varied between 1.55 and 2.07. Thus, the amount of precipitation in the second period did not greatly affect the process of increasing soil erosion. However, the examination of the pictures and maps prepared on the amount of land use changes shows that the amount of forest and pasture area in this region has decreased and the area of destroyed forests and agricultural and residential lands has been added. Therefore, the role of land use changes in increasing soil erosion is significant. The research results of Teimouri (2018), Mayahi (2021) and Arkhi (2022) also confirm that increasing rainfall, changing land use and reducing vegetation are effective in increasing soil erosion.

One of the factors influencing the changes in the morphology of desert areas is the frequency of wind blowing due to the relative poverty of wind blowing and the surface and covering cover in these areas. Wind transport of sand is a complex process that depends on factors such as wind speed, amount of bare soil, dry air conditions, soil grain size, vegetation, local and external air system, short-term discharge, extent of deforestation, in an area. Based on this, the activity of winds in carrying out erosion is significantly related to the climatic conditions of each region. Wind erosion in desert areas is seen in the form of sand dunes.The importance of sand dunes studies is due to their impacts on water and soil resources, flora and fauna, human infrastructure, and roads. Therefore, for the purpose of basic planning, monitoring the speed and direction of movement or the expansion of sand dunes, due to the damages caused by it and especially the preservation of natural resources and human projects and facilities, is of special importance in the management of desert areas.

In this research, wind characteristics, dust characteristics, soil moisture and its effects on Zabol region are investigated. To check wind speed and direction, the data of Zabol anemometer station were evaluated in Wrplot software. Then, the sand graph was drawn in SandRose-Graph software in order to check the indicators of sand movement. The changes of dust and AOD index of the region were extracted in the Erthengine system and MODIS satellite images in the period from 2012 to 2023. Changes in soil moisture index in the region were also monitored using SMAP satellite images in Arth Engin.

In this research, the predominant wind direction of the region was determined as northwest-southeast by examining the wind direction of Zabol station. On the other hand, examining the trend of wind speed changes at Zabol station shows that the average wind speed at Zabol station has been increasing over the past 56 years; So that the average wind speed has reached from 2.5 m/s per month in 1962 to about 4.8 m/s per month in 2018, which shows an increase of 2.3 m/s during this period. The amount of sand carrying potential (DPt) at this station is 2079/8, which is estimated to be high based on Freiburger's and Dean's classification of wind energy at this station. The variability index (UDI) of wind directions for Zabol station is in the low variability group with the classification of unidirectional and channelized winds, which greatly increases the intensity of erosion and dust production. The highest amount of displaced sand (DSF) in Zabol station indicates the amount of sand particles transported in the region.
In addition to examining the characteristics of the wind in the region, the changes in the dust index (AOD) and aerosol were also carried out using MODIS sensor images in the environment of Erth Engin from 2012 to 2023, and the results showed that the highest percentage of dust with a size greater than 55 microns and less than 47 microns in the summer of every year and with the decrease in surface humidity and the increase in temperature and the occurrence of 120-day winds in Sistan, also the examination of the percentage of dust changes in the air in the 5-year period from 2018 to 2023 showed a significant increase. which indicates an increase in the percentage of airborne particles due to the expansion of wind erosion in the region.
Next, the soil moisture of the region was monitored using the Smap satellite image series, including changes in the percentage of surface and subsurface soil moisture in the Zabol region in the Google Earth Engine environment and the time period from 2015 to 2023, and the results showed that the highest percentage of soil moisture is from February to April. And the lowest percentage of soil moisture is from July to September, which corresponds to the occurrence of 120-day winds in Zabol region.

The results obtained from this research show that the main cause of dust occurrence in Zabol region is the increase in the wind speed in the region in the long term and the increase in dust supply centers around the region, which, along with climate change and the decrease in rainfall and the occurrence of drought, in the scale Locally, the drying up of Lake Hamon as a result of the Afghan government's non-compliance with the 1351 Kabul agreement regarding the introduction of 850 million cubic meters of water per year into Lake Hamon has had a significant impact on this issue. Considering the location of the dried Hamon lake in the northwest and west of Zabol city, and the direction of the prevailing wind, it can be said that most of the dust particles transported to Zabol originate from this area. Therefore, the only solution to the current situation of Zabol can be the restoration of Lake Hamon and stabilization of the sediments on the bottom of Lake Hamon. On the other hand, the presence of channelized and dominant northwest-southeast in the region has provided the conditions for the construction of wind power plants in this region, considering the criticality of the country's energy, which is used with proper planning and investment for this issue.

The Central Plateau of Iran, due to climate changes and the reduction of available water resources on one hand, and the increase in population and the consequent increase in demand on the other hand, is facing a severe water crisis. The science of remote sensing and the availability of numerous satellite products have made it possible to monitor the process of changes in various environmental parameters, especially surface and underground water sources, with appropriate accuracy. For this purpose, using the Google Earth Engine system, 16 different satellite products including different environmental parameters such as precipitation, temperature, evaporation and transpiration, soil moisture, runoff, total water storage (GRACE), vegetation cover index and water surface area were received and prepared for the time period 2000-2022. Then, using the non-parametric Mann-Kendall test and the Sen’s slope estimator, the change trend of these parameters was investigated. According to the results, the changes in earth's gravity, which indicates the level of underground water, as well as the area of water surfaces, which indicates surface water resources, and soil moisture, showed a significant decreasing trend. On the other hand, maximum temperature, minimum temperature, potential evaporation and transpiration and NDVI index have a significant increasing trend. Despite the decrease in water surface area, the vegetation cover index has increased, which indicates the increase in the area under cultivation of agricultural products and excessive harvesting of underground water resources, which is also confirmed by the decreasing trend of the GRACE satellite product. The correlation coefficients between parameters with significant trends also showed that there is a significant correlation between GRACE and NDVI parameters, minimum temperature, maximum temperature, soil moisture and area of surface water bodies and and in future researches, satellite products related to these parameters can be used to evaluate the condition of surface and underground water resources in other regions and basins of Iran.

Nitrogen dioxide is considered one of the important indicators for evaluating the air quality of cities, therefore, identifying the areas contaminated with this pollutant is very important. One of the sciences and technologies that can help urban environmental experts to identify these areas in the shortest time and at a lower cost is satellite remote sensing. Currently, satellite remote sensing is a useful technology for measuring atmospheric pollutants at the global, regional, and urban levels. Therefore, the present research has identified and investigated the temporal-spatial distribution of nitrogen dioxide gas during the spread of Covid-19 using Sentinel-5P satellite data in the Shiraz metropolis for 24 months (2019 and 2020). Based on the results of this research, the highest monthly average values of nitrogen dioxide gas in 2019 and 2020 are related to the autumn season. In addition, district 2 of Shiraz was identified as the most polluted area in the two years studied. In addition, after analyzing the parameters of wind, precipitation, and temperature, it was found that the wind factor with a correlation coefficient of -0.638 at a significance level of 1% compared to other climate factors had the greatest impact on the change in nitrogen dioxide gas values. The Covid-19 crisis also played a role in changing the amount of nitrogen dioxide gas in 2020 compared to 2019 every month with its impact on the reduction of traffic and urban traffic. The pattern of changes in the average nitrogen dioxide gas related to satellite data in the studied years had a decreasing trend, which was consistent with the pattern of changes in the values obtained from the pollution monitoring station. The results of this research can be useful in urban livability and crisis management.

Flood is one of the most important natural disasters in Iran. Floods carry adverse effects such as life and financial risk in the years to come, as floods are more likely to occur and also because population growth is likely to result in more people settling in flood-vulnerable areas. Monitoring and revealing flooded areas has a lot of function in managing the crisis and reducing the damages of the areas in case of the possibility of future floods. Based on this, the purpose of this study is to monitor and detect the flood event of 1398 in Lorestan province using satellite data in Google Earth Engine system. This system can be used to process and analyze flood maps without the need to download data or use high computing hardware. In this article, the time series data of TRMM and GPM satellites were extracted for the period of one year and one month of Farvardin 1398 and showed that the peak of heavy rainfall was on the 5th and 12th of Farvardin. Images related to the detection of flooded areas were also produced and analyzed using the data of Sentinel 1 and Landsat 8 satellites. The spatial survey of the flood areas in the relevant images shows that the cities of Noorabad, Al-Shatar, Borujerd, Durood, Azna and Khorramabad were the most and most prone to flood areas in Lorestan province and were more affected by floods than other cities. Also, the surface area of flooded areas for April 2018 in Lorestan province was estimated as 673.82 square kilometers. Finally, the results of land cover studies showed that the advance of flood was more in areas with grasslands, agricultural lands and urban and built areas.

The aim of this study was to analyze the temporal and spatial nature of dust storms during the period 2016 to 2018 in Kermanshah Using the HYSPLIT routing model and the MCD19A2 product, the Modis sensor was performed in the Google Earth web engine.In order to route the origin of dust particles, the Lagrangian method of HYSPLIT model was used in 48 hours before the occurrence of dust phenomenon in Kermanshah at three altitude levels of 200, 1000 and 1500 meters.Findings from HYSPLIT model tracking maps indicate that the general route for dust transfer to the study area is the north-west-southeast route with the origin of the deserts of Iraq and Syria at three altitudes of 200, 1000 and 1500 meters. On June 17, 2016 and October 27, 2018, as well as the southwest-west route originating in Kuwait, Northern Saudi Arabia and part of Iraq on November 2, 2017.The results of the maps obtained from the MCD19A2 product of the Modis sensor, especially the maps of periodicity, cumulative concentration, spatial variation and the highest AOD map, show a high correlation with the routed maps extracted from the HYSPLIT model. In general, based on the findings of the maps extracted from the product MCD19A2, Modis sensor during the period 2016 to 2018 in Kermanshah, the central and eastern regions have always been more affected by dust storms than in other parts of the city. On average, they were more exposed to dust pollution than other parts of the city. In this regard, the final results show a high correlation between the actual PM10 data and the AOD values derived from the MODIS sensor.

Earthquake is one of the most important natural disasters that annually causes a lot of damage to human and natural communities. These damages have always led researchers to look for a way to predict this phenomenon. These include using satellite images and analyzing them. Since technology has not yet been invented to accurately predict the location and time of an earthquake, it is very useful to examine the information and analysis of past earthquakes to better understand earthquake-related phenomena. In this study, thermal investigation of Bam fault, using ASTER and MODIS images in December 2003 earthquake Singel-channel algorithm and web coding in Google Earth engine, the heat trend was calculated for six months before and after the earthquake. This process was also monitored using hourly weather data from the Meteorological Organization near the study site to obtain a better view of the thermal status of the area. The results showed that contrary to existing beliefs, the earthquake had no detectable thermal effect with the ASTER and MODIS satellite images on the Earthchr('39')s surface.

Nowadays, the creation of a spatial distribution map for assessing fire risk is widely recognized as a crucial management tool at various levels. This tool helps monitoring natural resource sustainability and the effective control of this environmental hazard. Through the integration of field operations, remote sensing data, geographic information system techniques, and diverse statistical methods, it becomes feasible to develop a dependable spatial prediction of fire hazard potential for different regions. In this research, nine factors were identified to be effective in fire risk modeling, including altitude, slope, aspect, distance from the road, NDVI, LST, TWI, and TPI. Fuzzy Analytic Hierarchy Process (FAHP) and Logistic Regression (LR) were employed to identify risk areas and determine the most significant factors influencing the occurrence and spread of fire. Historical fire areas were identified using Google Earth Engine and MODIS images. The initial results showed that both models assign the highest coefficients to NDVI, LST, and distance from the road. However, during the verification phase, the performance characteristic curve of both models was relatively similar (0.847 for FAHP method and 0.837 for LR method). Upon examining historical fire pixels, it was found that FAHP method correctly identified approximately 87% of the pixels belonging to classes with high and very high risk, whereas LR method only overlapped with 22% of these pixels. This suggests that FAHP method is better at identifying areas with a high risk potential compared to LR method. While it is important to acknowledge that the creation of risk prediction maps using various models cannot completely eliminate all fires, it can greatly diminish their frequency and help their management by offering effective solutions.

Vegetation plays an important role in protecting water and soil resources, stabilizing carbon and improving air quality. In Middle Zagros, forest and pasture vegetation is very important in terms of protecting soil and water resources and sustaining economic activities. In this research, using the Google Earth Engine platform and Landsat 7 satellite images, the drought of Middle Zagros (Lorestan province) was monitored with vegetation indices NDVI, SAVI and VCI, as well as meteorological drought index SPI for the statistical period of 2020-2000. To calculate the SPI index, the precipitation data of 9 synoptic meteorological stations with appropriate spatial distribution and the length of the statistical period (2020-2000) were used, and the processing was done in DPI software. In order to calculate the plant indices, first, all the geometrically corrected satellite images of the ETM+ sensor of the Landsat satellite were called for Lorestan province for each year. At this stage, an average of 52 images were called for each year. Then the images with less than 5% cloud cover were selected and processed. The results of the VCI index showed that mainly the studied area was affected by mild drought during the statistical period of 2020-2000. The year 2008 had the highest amount of drought related to the middle class with 5880.6 hectares among the studied years. The results of the SPI index showed that there was a moderate drought in 2010, a severe drought in 2008 and 2017, a mild drought in 2006, and a severe drought in 2019. The results of NDVI and SAVI indices also show the increase of thin vegetation classes and areas without vegetation by 1.331679 and 115164 hectares, respectively, and the decrease of normal and dense vegetation by 446160.7 and 682.4 hectares respectively per year. 2008 was compared to 2006 and 2007. Based on the results of all three investigated indicators, the favorable conditions of vegetation cover and ecological threat were obtained in 2016, 2019 and 2020. The highest level of this coordination between SPI meteorological drought and vegetation indices was observed in 2008 and 2010 and to some extent in 2019. In general, the results show that the increase or decrease of vegetation can be caused by the occurrence or absence of drought, while other factors such as land use changes should also be considered.