Search published articles

Showing 1 results for Trend of Remotely Sensed Time Series Data

, ,
Volume 4, Issue 4 (1-2018)

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.

Page 1 from 1     

© 2024 CC BY-NC 4.0 | Journal of Spatial Analysis Environmental hazarts

Designed & Developed by : Yektaweb