Evaluation of Vegetation Change Impacts on Dust Storms using Remotely Sensed Data

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Evaluation of Vegetation Change Impacts on Dust Storms using Remotely Sensed Data

ارائه دهنده: Provider: leila safari

اساتید راهنما: Supervisors: Dr. Hossein Torabzadeh

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr. Hassan Khotan Lou and Dr. Morteza Heydari Mozafar

زمان و تاریخ ارائه: Time and date of presentation: 2022

مکان ارائه: Place of presentation: Seminar 2 electricity

چکیده: Abstract: The phenomenon of dust is one of the most important natural hazards that has been on the rise in recent years in our country along with the warming of the earth, the intensification of drought and the subsequent weakness and deterioration of vegetation. Dust is intensifying with the high speed of desertification and the development of internal dust centers. Dust centers in Khuzestan province are increasing with increasing temperature, decreasing rainfall, decreasing vegetation cover and drying up of its wetlands. Therefore, by knowing the areas that are becoming internal dust centers, it is possible to control desertification and reduce dust. In this study, the relationship between satellite data of dust concentration and the number of dust occurrences with satellite data of climatic parameters (amount of plant cover, soil moisture, number of rainy days, number of strong winds per year) in dust centers Khuzestan province has been investigated by linear regression, multivariate regression, SVR and RF methods in the period of 1379-1398. The results of the simple regression method showed that the number of strong winds per year had the highest correlation with the data of the number of dust occurrences. And the multivariate regression method has shown the same results for the number of strong winds per year and the number of rainy days and temperature. Also, the results of the SVR method showed that the parameters have the same effect on the number of dust occurrences, and according to the results of the RF method, the number of strong winds per year had the greatest effect on the number of dust occurrences. While according to the results of linear regression, multivariate regression and RF methods, the highest amount of dust was with the amount of vegetation and soil moisture. According to the results obtained from the RF method, it has performed better in predicting and impacting each point on dust. Also, in order to more closely examine the effect of vegetation on dust, the amount of vegetation (based on the NDVI index) and dust in prevailing conditions, including days when the amount of precipitation per day is less than 0.48 ml, and the wind speed is less than 4.5 (m/s) and temperature was 37 (C), they were highly correlated with each other (Coefficient of Determinatio 0.73).

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