Use this URL to cite or link to this record in EThOS:
Title: Automated dust storm detection using satellite images : development of a computer system for the detection of dust storms from MODIS satellite images and the creation of a new dust storm database
Author: El-Ossta, Esam Elmehde Amar
ISNI:       0000 0004 2748 0032
Awarding Body: University of Bradford
Current Institution: University of Bradford
Date of Award: 2013
Availability of Full Text:
Access from EThOS:
Access from Institution:
Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database.
Supervisor: Ipson, Stanley S.; Qahwaji, Rami S. R. Sponsor: Libyan Centre for Remote Sensing and Space Science
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Brightness ; Temperature difference ; Neural network ; Pixel classification ; Database ; Decision tree ; Dust storm detection ; Automation ; Satellite images ; Dust storm database