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Volume 4, Issue 4 (1-2018)
Volume 4, Issue 4 (1-2018)
Abstract
Dust is one of the common processes of arid and semiarid regions that its occurrence frequencies has increased in recent years in Iran. The proper identification of sand and dust storms (SDS) is particular importance due to its impact on the environment and human health. So far, several methods for identifying these sources have been proposed such as methods based on field studies and geomorphologic studies, as well as methods on the basis of a numerical model of air flow simulation. Therefore, identifying the process of land cover changes and changes in suspended particles in the air can help to identify the correct sources of sand and dust. Also, to manage the reduction of dust, it will be very useful to analyze the trend of changes in sand and dust sources. This data can provide some useful information to the decision makers about the future occurrence of sand and dust storm and control it. Satellite-based remote sensing is an appropriate tool for examining changes in the surface conditions of the earth over time. Satellite sensors are well suited for this purpose because of the fact that constant measurements can be repeated on a fix spatial scale. Therefore, in this research, we have tried to test different remotely sensed data time series for validation of the identified SDS sources using the latest remote sensing techniques and their integration with other information.
The aim of this study is to validate the identified dust generation sources in Alborz province using time series of satellite data and meteorological stations data. In first step, OLI data of Landsat 8 during the years 2013 through 2015 were used to make maps of vegetation cover, soil moisture and land cover sensibility to wind erosion. These maps were combined with geology and roughness indices by multi-criteria evaluation method to obtain a map of sand & dust source potential areas. Also, based on the location of the intersection of the air flow with the surface of the earth and the application of masks of non-wind erodible areas on them, probable sand and dust sources were identified. These regions were integrated with the map of sand & dust source potential areas using the MCE method (WLC) and based on a stratified random sampling plan, susceptible sites of sand & dust sources were identified. Then in this research, the time series of satellite data and weather stations data were used and the trend of vegetation, soil moisture and surface temperature at the location of identified areas during a 15-year period were monitored. Product of LPRM_TMI_DY_SOILM3 from TMI sensor, data of 16-day vegetation, 8-day land surface temperature and data of aerosol optical depth from MODIS sensor were received. Also ground- based data of dust from synoptic and air pollution monitoring stations were received. Changes Trend analysis of soil moisture, temperature and vegetation cover was done during the period. Also aerosol optical depth in dust events with high concentration was evaluated for possible sources. In addition, the areas with higher dust optical depth than other areas were identified during the period. Finally, identified sources was validated using ground- based data of dust.
The result of trend analysis showed a significant decrease in vegetation, soil moisture and land surface temperature at the place of possible dust sources during the studied period. Decreasing temperature in the southern part of Alborz Province and west of Tehran province was associated with higher frequency of dust in the area that shows why dust events has high frequency. Study of time series of aerosol optical depth data showed that concentration of dust is at or near the detected sources and the high concentration in this area is indicating identified areas are accurate. Checking optical depth in the event of high concentration and checking concurrent of air direction showed the detected sources has been correctly identified. Also Integration of dust information of synoptic and air pollution monitoring stations with the wind direction confirmed the high accuracy of identified dust sources.
Overall, findings showed the ability time series of remote sensing data to validate dust storm sources. The results of the analysis of the time series of the satellite remote sensing data showed that the surface temperature as an important climatic parameter can be well used in the identification and validation of sand & dust sources. Based on the results of this analysis in areas where the frequency of sand & dust storm events is high, there is a significant decrease in the surface temperature. This is particularly evident in the annual maximum surface temperature in the southwestern part of Iran, an area that is considered to be the predominant trajectory of sand & dust storm.
The aim of this study is to validate the identified dust generation sources in Alborz province using time series of satellite data and meteorological stations data. In first step, OLI data of Landsat 8 during the years 2013 through 2015 were used to make maps of vegetation cover, soil moisture and land cover sensibility to wind erosion. These maps were combined with geology and roughness indices by multi-criteria evaluation method to obtain a map of sand & dust source potential areas. Also, based on the location of the intersection of the air flow with the surface of the earth and the application of masks of non-wind erodible areas on them, probable sand and dust sources were identified. These regions were integrated with the map of sand & dust source potential areas using the MCE method (WLC) and based on a stratified random sampling plan, susceptible sites of sand & dust sources were identified. Then in this research, the time series of satellite data and weather stations data were used and the trend of vegetation, soil moisture and surface temperature at the location of identified areas during a 15-year period were monitored. Product of LPRM_TMI_DY_SOILM3 from TMI sensor, data of 16-day vegetation, 8-day land surface temperature and data of aerosol optical depth from MODIS sensor were received. Also ground- based data of dust from synoptic and air pollution monitoring stations were received. Changes Trend analysis of soil moisture, temperature and vegetation cover was done during the period. Also aerosol optical depth in dust events with high concentration was evaluated for possible sources. In addition, the areas with higher dust optical depth than other areas were identified during the period. Finally, identified sources was validated using ground- based data of dust.
The result of trend analysis showed a significant decrease in vegetation, soil moisture and land surface temperature at the place of possible dust sources during the studied period. Decreasing temperature in the southern part of Alborz Province and west of Tehran province was associated with higher frequency of dust in the area that shows why dust events has high frequency. Study of time series of aerosol optical depth data showed that concentration of dust is at or near the detected sources and the high concentration in this area is indicating identified areas are accurate. Checking optical depth in the event of high concentration and checking concurrent of air direction showed the detected sources has been correctly identified. Also Integration of dust information of synoptic and air pollution monitoring stations with the wind direction confirmed the high accuracy of identified dust sources.
Overall, findings showed the ability time series of remote sensing data to validate dust storm sources. The results of the analysis of the time series of the satellite remote sensing data showed that the surface temperature as an important climatic parameter can be well used in the identification and validation of sand & dust sources. Based on the results of this analysis in areas where the frequency of sand & dust storm events is high, there is a significant decrease in the surface temperature. This is particularly evident in the annual maximum surface temperature in the southwestern part of Iran, an area that is considered to be the predominant trajectory of sand & dust storm.
Dr Amir Saffari, Dr Ali Ahmadabadi, Mr Amieali Abbaszadeh,
Volume 8, Issue 4 (1-2021)
Volume 8, Issue 4 (1-2021)
Abstract
Subsidence is one of the most important natural hazards that has affected many plains of the country in recent years. Eyvanakey plain in Semnan province is among the plains that have faced this danger. Due to the importance of the subject, in this research, the evaluation of the subsidence risk and the estimation of the subsidence rate in this plain have been done. In this research, Sentinel 1 radar images, Landsat satellite images and SRTM 30 meters high digital layer are used as the most important research data. The most important research tools are GMT, ArcGIS and Super Decisions. Also, Fuzzy-ANP logic and SBAS time series models have been used in this research. This research has been done in two stages, in the first stage, the assessment of the subsidence risk and in the second stage, the estimation of the rate of subsidence in Eyvanakey Plain. Based on the results, 251 square kilometers of the study area (equivalent to 58.5% of the area) has a high and very high risk of subsidence, which mainly corresponds to the southern areas of the Eyvanakey Plain. Also, the results of the SBAS time series method have shown that the Eyvanakey plain has subsided between 28 and 533 mm during a period of 6 years. Considering that, the high risk class has the highest amount of subsidence in the study area, so it can be said that there is a strong relationship between the subsidence risk classes with radar images and the accuracy of the results of the subsidence risk classes is confirmed.
Negar Hamedi, Ali Esmaeily, Hassan Faramarzi, Saeid Shabani, Behrooz Mohseni,
Volume 11, Issue 2 (8-2024)
Volume 11, Issue 2 (8-2024)
Abstract
Forest fire in many ecosystems is a natural phenomenon, but also a serious and dangerous threat with environmental, ecological, and physical effects. Therefore, this research investigated the risk areas of fire in Zagros forests identification to evaluate the changes in the time series of deals with a potential fire hazard. To achieve this goal fuzzy layers of analysis network process and order weighted average method were used regularly. For this purpose, fire Zagros forests using satellite images Landsat and MODIS Lordegan city in the period between 2000, 2007, and 2014 and the factors affecting fire are examined. The high-risk areas based on classification utility area and the number of zones were identified as fire-prone areas. In the analytical network process procedure, the largest weighs were assigned to the distance from residential areas and roads, GVMI index, and maximum daily air temperature factors which were 0.209, 0.198, 0.09, and 0.0716, respectively. Time series analysis map showing the extent of critical areas from 2000 to 2014 decreased by investigating the factors affecting fire occurrence in critical areas, distance for roads and residential areas, slope, aspect, GVMI index, and NDVI and maximum temperatures have the greatest impact were on fire. The low-risk scenario and a small amount of compensation with the ROC higher than 0.7 as the best model was the estimated risk of forest fires. The preparation of a map of areas susceptible to fire, as well as analyzing and analyzing the time series of factors affecting the fire in different years, is an effective step in helping forest managers to plan and implement preventive operations in high-risk areas.
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