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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.
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.Libyan Centre for Remote Sensing and Space ScienceAppendix A was submitted with extra data files which are not available online
Detection of Dust Storms Using MODIS Reflective and Emissive Bands
YesDust storms are one of the natural phenomena, which
have increased in frequency in recent years in North Africa, Australia
and northern China. Satellite remote sensing is the common
method for monitoring dust storms but its use for identifying dust
storms over sandy ground is still limited as the two share similar
characteristics. In this study, an artificial neural network (ANN)
is used to detect dust storm using 46 sets of data acquired between
2001 and 2010 over North Africa by the Moderate Resolution
Imaging Spectroradiometer (MODIS) instruments aboard the
Terra and Aqua satellites. The ANN uses image data generated
from Brightness Temperature Difference (BTD) between bands
23 and 31 and BTD between bands 31 and 32 with three bands
1, 3, and 4, to classify individual pixels on the basis of their
multiple-band values. In comparison with the manually detection
of dust storms, the ANN approach gave better result than the
Thermal Infrared Integrated Dust Index approach for dust storms
detection over the Sahara. The trained ANN using data from the
Sahara desert gave an accuracy of 0.88 when tested on data from
the Gobi desert and managed to detect 90 out of the 96 dust storm
events captured worldwide by Terra and Aqua satellites in 2011
that were classified as dusty images on NASA Earth Observatory.IEEE Geoscience and Remote Sensing Societ