List of Articles Google Earth

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  1 - Comparative evaluation of Landsat8 and Sentinel2 images to prepare a fire occurrence map in Arasbaran
  Omid Rafieyan Khalil Valizadeh Kamran Mohammad Ebrahim Ramazani Sajjad Moshiri
  10.30495/jrnr.2022.69827.10260
  The valuable Arasbaran forest is a complex and dynamic ecosystem that has always been subject to extensive fires. The purpose of the present research is to utilize the technology of remote sensing and geographic information system and the technical abilities of the Goog More

  The valuable Arasbaran forest is a complex and dynamic ecosystem that has always been subject to extensive fires. The purpose of the present research is to utilize the technology of remote sensing and geographic information system and the technical abilities of the Google Earth Engine system in order to prepare a fire occurrence map in the rangelands and forests of Arasbaran. In order to choose the appropriate method and type of satellite between Sentinel2 and Landsat8, the separability index was used. Accordingly, the RdNBR differential index was selected among the different indicators of fire detection to prepare the final map of the last 9 years and the cumulative fire map. Based on the accuracy assessment of the resulting map quantitatively, 84% of the actual fire points recorded by the General Directorate of Natural Resources and Watershed of East Azarbayjan province were placed at a distance of 200 meters from fire polygons extracted from satellite images, which showed the high accuracy of the fire map. The field visit also showed a good match among the fire areas resulting from the processing of satellite images with the existing situation of the region. The current research showed the high potential of these two satellites as well as the extraordinary ability and facilities of the Google Earth Engine system in providing a huge amount of remotely sensed data and advanced processing on them to prepare fire occurrence maps. The advantages of Landsat8 compared to Sentinel2 are having thermal bands and more time series. The spatial and radiometric resolution of both are almost similar, and the low-temporal resolution of Landsat8 will be compensated by combining it with Landsat 9 images. Therefore, in line with the results of similar studies, Landsat8 is generally preferable to Sentinel2. For the correct and scaled spatial data from Arasbaran region, it is suggested to create an integrated and large-scale Geo-database due to the lack of accuracy. Manuscript profile
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  2 - Evaluation of Daily, Decade and Monthly Data Satellite Images to Estimate of Precipitation Using Google Earth engine in Khuzestan Province
  Arash Tafteh Sina Mallah Niazali Ebrahimipak
  Since synoptic metrological stations have non-uniformed scattering pattern in Iran and on the other hand precipitation determination and forecasting is essential for irrigation planning, a method precisely determine precipitation of agricultural lands in farm level has More

  Since synoptic metrological stations have non-uniformed scattering pattern in Iran and on the other hand precipitation determination and forecasting is essential for irrigation planning, a method precisely determine precipitation of agricultural lands in farm level has great importance. This study was carried out in Google Earth Engine Code programming environment using GPM, TRMM and CHIRPS satellite data which is daily, decade and monthly, respectively in Ahwaz and Izeh metrological stations for calibration and 9 meteorological stations for validation during 2015-2016 and 2017 - 2018 Growing season. Results showed that monthly interval could obtain better accuracy with R2 of 0.99 and NRMSE = 0.36, respectively. The validation results of the rest 9 meteoroidal station indicated that precipitation prediction had 51% and 3.1 mm error and under estimation on average, respectively. The efficiency was reasonable and F-Test showed no significant difference between observed and prediction samples. The standard error value was 14.2 mm which is a significant error and need to work on updated better functions. It can be concluded that this method can be a useful tool for monthly precipitation prediction of areas with no climatic data if integrated with Kriging, co-Kriging and Inverse Distance Weighted (IDW) geostatistical models for interpolation. Manuscript profile
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  3 - A scalable physical model based on remote sensing in paddy yield estimation
  Ehsan Asmar Mohammad H. Vahidnia Mojtaba Rezaei Ebrahim Amiri
  Background and Objective: Rice is one of the most strategic plants in Iran. On the other hand, agriculture makes a wide variety of environmental amenities and problems. Thus researches that help the production and sustainable development in this area are significant. Th More

  Background and Objective: Rice is one of the most strategic plants in Iran. On the other hand, agriculture makes a wide variety of environmental amenities and problems. Thus researches that help the production and sustainable development in this area are significant. The main purpose of this research is the design and development of a scalable remote sensing-based paddy yield model.Material and Methodology: In this study, we used several different images available in Google Earth Engine (GEE) to estimate paddy yield at various temporal (growing seasons) and spatial scales (from 30 m resolution to regional scales). Then, a remote sensing-based light use efficiency (LUE) model integrated with inanimate environmental stressors, was implemented. This operational model was assessed against actual field-level yield data in 2016, 2017, and 2019 growing seasons across more than 691 paddy fields in Gilan province.The efficiency of the current model was evaluated through different statistical measures. The results showed a positive correlation and a signed agreement between the estimated and measured values so that in the studied growing seasons, the average correlation coefficient (R) and agreement index (d) was equal to 0.55. The average RMSE equal to 500 kg/ha, the average MAE equal to 440 kg/ha, and the average NRMSE equal to 0.12, all indicate that the accuracy of the model in estimating crop yield in these locations and years is satisfactory. Also, the submitted model showed the appropriate variability of yield values at the farm scale.Discussion and conclusion: In general, this new approach has confirmed that the use of remote sensing in the GEE is appropriate for estimating crop yield at various temporal and spatial scales, as the current model can be utilized in a wide range of applications such as agricultural management and insurance.   Manuscript profile
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  4 - Efficiency of Google Earth Engine (GEE) system in land use change assessment and predicting it using CA-Markov model (Case study of Urmia plain)
  Naser Soltani Vahid Mohammadnejad
  10.30495/girs.2021.679276
  Background and ObjectiveLand use reflects the interactive features between humans and the environment and describes how humans are exploited for one or more purposes on earth. Land use is usually defined based on human use of land, with an emphasis on the functional rol More

  Background and ObjectiveLand use reflects the interactive features between humans and the environment and describes how humans are exploited for one or more purposes on earth. Land use is usually defined based on human use of land, with an emphasis on the functional role of land in economic activities. Land use map is one of the main factors in the study of natural resources and environmental management. Knowing the changes in land use and examining their causes and factors in a period of time can be of interest to planners and managers. The use of satellite data is a good tool for land use mapping, especially in large geographical areas, due to the provision of a wide and integrated view of an area, reproducibility, easy access, high accuracy of information obtained, and high-speed analysis. One of the most widely used methods of extracting information from satellite images is image classification, which allows users to generate different information. Google Earth Engine (GEE) is a web, cloud-based system developed by Google to store and analyze large amounts of data at the petabyte scale (including various satellite imagery, digital models, climatic and vector data). Speed in processing and access to diverse data is one of the issues and problems of land use change studies. The purpose of this paper is to classify satellite images using the support vector machine learning method in the two periods of 2000 and 2020 and to produce a land use map of these two periods in the Google Earth engine system. Materials and Methods In this paper, Urmia city and its surrounding areas (Urmia plain) have been evaluated. In order to prepare land use maps and study its changes, Landsat 7 ETM+ sensor for 2000 and Landsat OLI 8 for 2020 have been used. Images from June were used, when vegetation reached its maximum vegetative growth. Various methods have been developed to monitor and measure land cover and land use changes. In this paper, the efficiency of the Google Earth Engine system for collecting, managing, and processing remote sensing data has been evaluated in order to prove and introduce the speed and accuracy of this system. In order to produce the land use map, the Support Vector Machine classification method has been used. The main difference between this paper and other research is that the management and processing of images have been done in the Google Earth Engine system, which means that the researcher does not need expensive and licensed software such as ENVI and only by access to the Internet can do the processing. By developing the code for image classification using the support vector machine method, the images of 2000 and 2020 were classified. Six land use classes were identified, including barren lands, man-made lands, orchards, irrigated agriculture, rainfed agriculture, and irrigated areas. After classifying images, the results were stored in Google Drive and prepared for further analysis. The classification results were entered into ArcGIS software and the classification accuracy was evaluated using control points obtained from Google Earth images as well as data related to the land use management plan of West Azerbaijan province. In this paper, in addition to preparing a land use map in the Google Earth Engine system, it was used to forecast and model land uses for 2040 using the CA-Markov transfer estimator. Results and Discussion After calling and classification of images in the Google Earth engine environment using the SVM method, land use map for 2000 and 2020 was produced. The prepared maps include man-made lands, orchards, irrigated agriculture, rainfed agriculture, and barren lands. A comparison of different land use in 2000 and 2020 shows that extensive changes have taken place in them. Some of these changes are positive and some are negative. The area of barren lands in 2020 compared to 2000 has increased by about 10 square kilometers, man-made lands, 42.62 square kilometers, orchards 67 square kilometers, and water bodies 0.39 square kilometers. In contrast, rainfed agriculture has lost 39.45 and irrigated agriculture has lost 80 square kilometers. The reason for the increase in orchards can be seen in the change of irrigated agricultural uses to orchards, as well as urban development and the creation of various human infrastructures, which is very evident in recent years. Most of the changes are related to the use of orchards with a positive trend during which many irrigated agricultural lands have become garden lands. These changes have increased the production of horticultural products in Urmia and become one of the hubs of horticultural production, especially apples. the area of man-made land has almost doubled, which usually happens in other parts of the country and is normal. Usually, with the increase in the population of cities as well as villages and the need to build new buildings and infrastructure facilities such as factories, sports fields, roads, entertainment spaces, etc., man-made uses have increased. According to the forecast for 2040 using the CA-Markov method in Idrisi software, the highest growth is related to rainfed agricultural use. It is predicted that during this period, the area of rainfed lands will reach 73.40 square kilometers. The man-made land will increase to 90.9 square kilometers. While its value in 2020 was 76.38 square kilometers. On the other hand, the area of orchards will increase from 31.61 square kilometers in 2020 to 72.15 square kilometers. Irrigated agriculture will increase to 27.38 square kilometers with an increasing trend. Conclusion Studies show that the growth of man-made lands in Urmia city and its surroundings is not commensurate with other land uses and this has led to the growth of land use area of ​​the man-made lands compared to other uses and this issue has caused the phenomenon of expansion has become in Urmia city. On the other hand, the results show that the study of land use using the time series of satellite images is a time saver and cost, and as mentioned in the paper. different land uses for the years 2000 and 2020, prepared using the Google Earth system, and their changes were identified. Another important result of this paper is the high efficiency of the GEE system in processing large volumes of satellite images. Using this system does not require any specialized remote sensing software and the user can easily process various data using a computer browser or even a smartphone. Another important point is that in this system, there is no need to download different images, but the user can only download the processing result. This is very useful in terms of time and processing speed. The GEE system is able to process large volumes of time series data (here satellite imagery), different regions of the world with very high speed and very low time, and present the results in the form of various maps and graphs. Manuscript profile
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  5 - Monitoring of chlorophyll-A, organic carbon, salinity and water surface temperature off the coast of Sistan and Baluchestan using remote sensing data
  Elham Shahri Mohammad Hossein Sayadi Elham Yousefi
  10.30495/girs.2021.684236
  Background and Objective The seas and oceans play an important role in climate conditions as well as climate change. In addition, physical and biological phenomena are among the most important factors affecting the chemistry and environment of the sea. Therefore, it is More

  Background and Objective The seas and oceans play an important role in climate conditions as well as climate change. In addition, physical and biological phenomena are among the most important factors affecting the chemistry and environment of the sea. Therefore, it is important to know the physical processes that govern the seas and oceans, as well as the correlation between these properties and biological properties. Remote sensing algorithms use a close range of blue, green, yellow, red, and infrared, so monitoring of chlorophyll-A, the phytoplankton pigment of oceanic and coastal waters, can be measured and evaluated using state-of-the-art remote sensing technology.Materials and Methods In this study, the capability of remote sensing methods has been used to investigate the status of coastal water quality characteristics of Sistan and Baluchestan provinces. For this purpose, the status of chlorophyll-A has been used using OC3 bio-optical algorithms in ENVI as well as the predecessors of the Google Earth Engine platform. Google Earth Engine is an open-source spatial analysis platform that enables users to visualize and analyze planetary satellite images. Using this system, various spectral processes can be performed on different surface phenomena with different satellite data. It is also possible to perform calculations on large volumes of data without the need for high-power systems. The salinity parameter of MIRAS's SMOS satellite was used in SNAP software to investigate the parameters of chlorophyll, temperature, and organic carbon using Terra's MODIS satellite images. The time to be studied in the images used and field sampling is May 2020. In order to extract the concentration of chlorophyll-A, bio-optical algorithms based on blue and green bands (OC3) were used in ENVI software. Bio-optical models combine optical measurements of reflection or radiation with biological parameters such as chlorophyll concentration, water quality, and more. Water temperature is one of the most important factors in the life of the sea, so those marine animals can survive and reproduce only in a certain range of water temperatures. Therefore, phytoplankton is very sensitive to changes in water temperature and react to temperature. Water level can determine their frequency and distribution. In this study, the product MIR_OSUDP2 of the SMOS satellite of MIRAS on 3rd of May 2020, for the study area from  https://smos-diss.eo.esa.int/ was used.Results and Discussion The results showed that the amount of chlorophyll-A is higher along the shores and the stations near Joud and the estuary has a higher concentration of chlorophyll-A. The results showed the outputs of two different methods for estimating chlorophyll-A in the study area are similar. Also, according to the results, it is clear that the amount of chlorophyll-A has increased in Chabahar, Konarak, Jude, and Goater stations in recent years. In Chabahar and Konarak regions, this increase has been significant for ten years, and the sudden increase in chlorophyll in recent years in field stations requires more studies to identify the causes and should be considered. The chart below shows the rate of change in chlorophyll-A from 2019 to 2020. According to the results, the amount of organic carbon follows the amount of chlorophyll-A and in areas such as Chabahar and Konarak we see higher levels of organic carbon. Also, the highest increase in temperature in all three periods studied was in Chabahar and Konarak ports, of which human activities are one of the main factors. Also, by examining the ten-year trend, increasing temperature changes can be seen in the ports of Maidan and Jude. The general trend of temperature is decreasing to the east as expected because it is closer to open waters. Seasons when water temperatures are lower, chlorophyll-A levels are higher. Chlorophyll-A map output results by ENVI software and Google Earth Engine platform, chlorophyll-A concentrations were higher in autumn and winter than in spring and summer, high chlorophyll-A-concentrations are common in cold tropical and subtropical seasons. Also, the concentration of chlorophyll-A in the study areas along the coast is higher than the offshore areas, which is related to the chlorophyll-A harvesting algorithm in type 1 waters; In other words, coastal areas have more value than offshore areas due to shallow depth, high turbidity and suspended sediments. Because there is no river discharge in this area, these areas are mostly affected by hydrodynamic processes such as wind direction and sea currents. The lowest chlorophyll-A concentrations were observed in the region from May to September, which was contrary to fluctuations in water surface temperature, which could be due to rising currents. The amount of organic carbon is one of the most important factors for evaluating the performance of aquatic ecosystems, which determines the potential of ecosystems for fishery products; The results of the study of organic carbon showed that the amount of organic carbon as chlorophyll-A in the two seasons of autumn and winter was higher than spring and summer so that the trend of changes in organic carbon also followed the trend of changes in chlorophyll-A. There is a correlation between temperature fluctuations and chlorophyll-A, this correlation indicates the importance of water surface temperature in changes in the growth rate of phytoplankton as one of the climatic factors and has made the most important parameter affecting chlorophyll-A, water surface temperature. According to the obtained results, the trend of temperature changes in the last ten years is increasing and the hottest stations are Chabahar and Konarak stations. In terms of salinity, areas with lower salinity had higher chlorophyll-A levels. Comparison of the data obtained from this study with the above indicates that the range of recorded fluctuations of the quality parameters studied in the natural waters of the region and is consistent with similar studies in the study area by other experts.Conclusion The results of this study show the acceptable accuracy of the results compared to the data of similar researchers in addition to the speed and ease of the method. Therefore, with the help of remote sensing science, timely monitoring of the quality parameters of water areas can prevent major crises and save time and money, problems that may be irreversible if they occur. Manuscript profile
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  6 - Monitoring Bakhtegan wetland using a time series of satellite data on the Google Earth Engine platform and predicting parameters with Facebook’s Prophet model
  Mohsen Dastaran Shahin Jafari Hossein Moslemi Sara Attarchi Seyed Kazem Alavipanah
  10.30495/girs.2022.685454
  Background and Objective Wetlands are habitats for vegetation and wildlife and because of this, they have a high environmental value. Also, wetlands reduce soil erosion, restore aquifers, store rainwater in a flood event, and provide water for agriculture or livestock. More

  Background and Objective Wetlands are habitats for vegetation and wildlife and because of this, they have a high environmental value. Also, wetlands reduce soil erosion, restore aquifers, store rainwater in a flood event, and provide water for agriculture or livestock. Wetlands are vulnerable to human interventions and changes such as drainage, urban sprawl, infrastructure development, and over-exploitation of groundwater resources. Prediction of the condition of wetlands in the future requires a correct understanding of the evolution of wetlands and identifying their trend of change. Nowadays, Remote Sensing technology is widely used for mapping wetlands, and its ability to monitor the changes in wetlands regardless of the diversity of wetlands has significantly increased the value of this science in this field. Remote Sensing can be an effective means of simulating and predicting wetland degradation processes by providing images at different times and through dynamic spatial modeling. In this study, the changes in the Bakhtegan wetland have been monitored. This wetland has high environmental and tourism importance and its drying affects negatively the living conditions and health of local people as well as tourism in the region. In addition, predictions of precipitation parameters, groundwater level, and temperature have been conducted. For this purpose, the Google Earth Engine platform was used to capture and process images. Google Earth Engine is a platform that can capture and process images in the shortest time and at high speed. In this regard, using Google Earth Engine, changes in the lake water area along with changes in temperature, groundwater level, and precipitation were extracted and monitored. Moreover, a comparison took place between these parameters to determine the changes that have taken place in the lake over the past two decades. To predict the parameters, the changing pattern was predicted and analyzed using the Prophet model. The most important advantage of the Prophet model is its ability to convert discrete data to continuous data to make the best predictions. This method automatically detects the trend of seasonal data and displays the trend of seasonal changes.Materials and Methods Satellite images were acquired from the Google Earth Engine platform to monitor the wetland. Landsat 7 and 8 images were used for water body extraction, GRACE Data were used for extraction of groundwater level changes, MODIS product was used for extraction of vegetation and wetland surface temperature, and TRMM image product was used to extract precipitation values. An automated water extraction index was used to extract the wetland body water. The groundwater level was extracted from the GRACE sensor. MODIS sensor product was used to obtain the surface temperature time series for the study area. For the extraction of precipitation time series, the monthly cumulative data of the TRMM (3B43V7) satellite with a spatial resolution of 0.25°C was extracted using Google Earth Engine and the trend of changes was evaluated and analyzed. The Mann-Kendall test is one of the most widely used non-parametric tests for detecting meteorological and environmental data trends, which is used to detect a monotonic trend line since this test is a non-parametric method, it does not need that the data follow a normal distribution. The Prophet predictive model is a predictive library developed by Facebook and is available in R and Python programming languages. This library supports additive modeling methods and can properly predict discrete values continuously. This feature is called "Holiday". Another feature of this library is the automatic detection of daily, weekly, seasonal and annual trends. The mean absolute error (MAE), by default, exists in the Prophet library. This error represents a more natural standard than the mean error and unlike the RMSE error, it is unambiguous.Results and Discussion In the present study, we monitored the Bakhtegan wetland using the Google Earth Engine platform to observe the trend of water level changes in this wetland from 2000 to 2020. In addition, Parameters were also predicted using the Prophet Prediction method which is developed and published by Facebook. By examining this trend, it can be observed that the water level of the wetland has been significantly reduced during two decades. In this regard, the trend of groundwater level, temperature, and precipitation in the area was investigated. Examining these factors, it was found that along with a 58.3% decrease in the water level of the wetland, there was a 260% decrease in the groundwater level of the region, although the amount of rainfall in the region has been less compared to other factors and has been decreased about 29%. Using Mann-Kendall statistical test, the trend of this decline was proved. To predict the parameters, the Prophet model has been able to make predictions for 1500 days as continuous data using discrete data. The output of the model has shown that for rainfall parameters and groundwater level a downward trend is predictable over the next 1500 days which is low intensity for precipitation but with high intensity for groundwater level. Temperature prediction indicated that it has a seasonal trend, and has a high amount of fluctuation within a year, but its annual trend indicates stability in the coming years. The results of the model for the water level of the wetland also show a relatively low upward trend that has a probability of change of ±12.5 Square kilometers. Also, the error of the parameters at the 95% significant level has acceptable accuracy, which indicates the validity of the prediction. An automated water extraction index was used in this study to extract the time series of the water body of the wetland. Using the mean time series extracted, the maximum and minimum wetland’s water body area belongs to 2006 with 629.23 square kilometers and 2014 with 156.82 square kilometers, respectively. The time series of changes in this wetland indicates that the water volume of the wetland has been declining in the last two decades. According to this study, it can be concluded that the trend of changes in the water level of the wetland has been decreasing. The descending changes in the lake based on the trend of changes in groundwater levels indicates a decrease in water volume in the area. Considering that the trend of precipitation changes has been stable, it can have assumed that improper management and excessive use of groundwater may be a reason for lowering the water level of the wetland. Due to the same decrease in the water level of the lake, the temperature has also decreased by about 3°C.Conclusion According to this study, it can be concluded that groundwater levels and precipitation will have a downward trend in the future, which will lead to a decrease in the water level of the wetland, which itself has the potential to fluctuate in the future, and the downward trend continues. With the current trend, the only solution is to plan properly to preserve the wetland. If this trend continues, we will face the destruction of the wetland. Given the monthly trend of the wetland surface, it is suggested not to over-exploit groundwater resources, especially in the summer. For further research, the Google Earth Engine platform can be used without the need to download the images and spend a lot of time and money, to obtain the time series of images. Regarding the prediction, in future studies, the Prophet model can be applied, since it uses discrete data and at the same time provides the desired accuracy. Manuscript profile
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  7 - Application of landsat imageries for mapping post-earthquake landslide, case study: 2012 Ahar-Varzegan earthquake, NW Iran
  Leila Khodaei Geshlag Shahram Roostaei Davood Mokhtari Kalil Valizadeh
  10.30495/ijes.2020.677468
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  8 - Monitoring and detection of Lorestan 1398 flood using satellite data in Google Earth Engine
  Seyed Hossein Mirmousavi zahra taran
  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-vu More

  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. Manuscript profile
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  9 - Identification of Patriot MIM-104 missile defense systems around Iran by Sentinel-1 radar images
  Ahmad Ardakani Mohammad Hossein Fathi
  Iran is located in one of the most critical regions of the world called the Middle East. Due to its convenient location and abundant energy reserves, this region has always been of interest to foreign countries, led by the United States. This has led to numerous US inte More

  Iran is located in one of the most critical regions of the world called the Middle East. Due to its convenient location and abundant energy reserves, this region has always been of interest to foreign countries, led by the United States. This has led to numerous US interventions and the establishment of numerous military bases around Iran, especially after September 11th incident. Continuous monitoring of these bases and related facilities is one of the most important deterrents that can be done by remote sensing knowledge. One of the remote sensing techniques is the synthetic aperture radar, which has been considered in recent years, while the Sentinel-1 satellite launched in 2014 by the European Space Agency uses the same technique. One of the applications of this satellite, which is introduced in this article, is the detection of Patriot MIM-104 systems. This is done by combining the VV and VH polarizations with two images taken during the satellite's day and night transitions from a single point, which are represented by X-shaped lines where the Patriot systems are located. The images related to this satellite are also processed by Google Earth Engine system, which saves considerable time in analysis and increases the accuracy of the results due to the minimization of human error. The results showed that in the regions around Iran and especially in the Arab countries of the Persian Gulf, there are at least 16 active Patriot systems, which indicates its great development in recent years and shows the need for continuous monitoring of these systems. Manuscript profile
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  10 - Evaluation and prediction of future vegetation status in Dasht-e Fahleh region of Fars province using Markov model
  Mohammad Javad Behi Mohammad Hossein Mokhtari Gholamhossein Moradi Mohammad Ali Saremi Naeini
  The purpose of this study is to evaluate the temporal and spatial changes of vegetation in Mullah Fahleh of Firoozabad region of Fars province. The standard vegetation index data of Landsat 5, 7 and 8 satellites, the precipitation data and Palmer index, which are availa More

  The purpose of this study is to evaluate the temporal and spatial changes of vegetation in Mullah Fahleh of Firoozabad region of Fars province. The standard vegetation index data of Landsat 5, 7 and 8 satellites, the precipitation data and Palmer index, which are available as a remote sensing product, were extracted for 1992 to 2020 from the Google Earth Engine. Markov chain method was used to predict future vegetation changes. Using data from 1992 and 2002, the model was first run for 2020 and based on its acceptable performance (the kappa index of 75%), prediction was extended to 2030. The results showed an improving trend in vegetation status, so that the dense cover will increase from 4% in the initial period to 19% in 2030. The results of this study can help rangeland managers in the region to better exploit the region's natural resources and prevent the destruction of this ecosystem. Manuscript profile
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  11 - Extraction of surface water zones of seasonal lake Jazmourian using remote sensing indicators.
  Mojtaba Solimani Sardo Zohreh Ebrahimi Mehdi Zarei
  Water resources are one of the most important components of land life and sustainable development. In the present study, the study of time series changes in the area of ​​Jazmourian Playa water areas, using the surface reflection data of Landsat 8 satellite from 2013 to More

  Water resources are one of the most important components of land life and sustainable development. In the present study, the study of time series changes in the area of ​​Jazmourian Playa water areas, using the surface reflection data of Landsat 8 satellite from 2013 to 2019, was considered and the Modified Normalized Water Difference Index (MNDWI) was used to separate the water areas on the images. Then the trend of changes in the area of ​​water areas was estimated. All of these processes and satellite image analysis were performed in the Google Earth Engine software environment, which is a web and open source system for performing spectral and radiometric analyzes on satellite images. ArcGIS 10.5 software was also used to prepare spatial maps. . Findings showed that the area of ​​the seasonal lake of Jazmourian Lake is estimated to be 21426 Km based on MNDWI index, approximately 1515 Km based on automatic water extraction index (AWEI) and 1610 Km2 based on water absorption ratio (WRI) index. On the other hand, the analysis of surface water production of Landsat images showed that the highest rate of change was related to temporary seasonal and new seasonal water zones, so that changes in temporary seasonal zones occupied an area of ​​about 11245 km2 and new seasonal surface zones about 35355 km2. Is. Jazmourian plain water intake is related to the occurrence of seasonal floods and increased rainfall in the basin, so that a high correlation (R2 = 0.89) between the annual rainfall of the basin and the increase in the area of ​​water areas resulting from the MNDWI index has been observed. Manuscript profile
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  12 - Comparison of different methods with common method of producing land use/cover maps of natural resources studies (Case Study, Ghoshchi watershed, Urmia)
  ardavan ghorbani Azad Kakehmami Mahmood Mohamad hasanpoor Farnoosh Aslami sahar ghafari Arash Raufi masole
  Private companies as consulting engineers play important role to the study of natural resources. Land use map is one of the information generated in the studies by the consulting engineer that the accuracy of this information is effective on the final results, expenditu More

  Private companies as consulting engineers play important role to the study of natural resources. Land use map is one of the information generated in the studies by the consulting engineer that the accuracy of this information is effective on the final results, expenditures spending on the natural resources and future projects. The purpose of this study was to evaluate the capability of the visual interpretation of images from Google Earth (GE) in comparison with the map, which was produced by consulting engineers and object-based interpretation of Landsat images as a new and low-cost method for land use /cover mapping in the natural resources studies of Iran. For this purpose, the land use/cover map provided by consulting engineers (2007) was compared with land use maps, which was produced using object-based method of TM Image (2007) with eCognition software and GE images (visual interpretation) with ArcGIS software (2009) in terms of accuracy assessment results. Overall accuracy and Kappa of the land use/cover maps using GE images were 0.99 % and 0.99, and Overall accuracy and Kappa of consultant engineers and object-based method based on Landsat image were calculated 59% and 0.32, and 89% and 86%, respectively. Result of the study demonstrated the high capability of the GE images in land use/cover mapping. Overall, the map generated from the GE image has higher accuracy in comparison with the other two maps, and the map produced by the consultant engineers with the low Kappa coefficient was unacceptable. Manuscript profile
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  13 - Analysis of meander evolution of Dez River in agricultural and mountainous areas by Google Earth Engine (GEE) and GIS
  Ladan Khedri Gharibvand
  10.30495/jonass.2022.1955561.1037
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