Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery

Abstract

I take this opportunity to express my sincere thanks to my advisor, Dr. Sergio. D. Cabrera, for providing me this opportunity. I appreciate his guidance and support without which this thesis could not be done. I would like to acknowledge the support received from the Texas Instruments Foundation Endowed Scholarship Program. I would extend my heartfelt gratitude to Mr. Vikram Jayaram for sparing his valuable time, guiding me at every stage and for his continuous encouragement and support. I would also like to sincerely acknowledge and thank Dr. Jose Gerardo Rosiles for his assistance and serving my committee. My special thanks to Dr. Thomas E. Gill, from Department of Geosciences, for his timely advice and for serving my committee. I am grateful to Nancy I. Rivera Rivera who has been a great source of help in my venture. Finally, a deep sense of gratitude is due to my family members and friends, who with their valuable suggestions and moral support, helped me in molding this project into a reality.

Dust emission and environmental changes in the dried bottom of the Aral Sea

“…To assure that the selected images demonstrate dust events, the Brightness Temperature Difference (BTD) procedure was applied (Janugani et al, 2009):…”

“…Recent studies have demonstrated the efficiency of using thermal bands for monitoring dust aerosol outbreaks (Hu et al, 2008). An effective method for detecting and differentiating dust from clouds using satellite remote sensing has been the brightness temperature difference (BTD) of NOAA/AVHRR channel 4 and channel 5 (Chavez et al, 2002;Janugani et al, 2009;Tsolmon et al, 2008;Prospero, 1999). The BTD of the two bands can distinguish not only a dust storm but also the density of dust and sand in the moving mass (Tsolmon et al, 2008).…”

“…False color images with an RGB combination of the 1, 2, and 4 bands show the dust plumes in a light yellow-brownish color, and clouds in a bright yellow color (Rivera et al, 2006). Janugani et al (2009) showed that a band combination of 1, 4, and 5 for the colors red, green, and blue of the 5-band NOAA-AVHRR imagery was probably the best one with which to locate the dust sources during visual interpretation. Miller (2003) proposed using the combination of MODIS bands 1, 3, 4 and 26 in the visible spectrum and infrared bands 31-32 to discriminate dust loads from clouds and the desert background.…”

“…Several techniques have been developed to detect dust storms based on visible and thermal infrared satellite data: band math analysis, differencing of radiation temperature or the infrared split-window technique (BTD), single band thresholding and multi-band combinations (Ackerman, 1989(Ackerman, , 1997Janugani et al, 2009). Recent studies have demonstrated the efficiency of using thermal bands for monitoring dust aerosol outbreaks (Hu et al, 2008).…”

Detection of Dust Storms Using MODIS Reflective and Emissive Bands

“…However, in principle, the severity of dust storms, the areas they affect and changes in intensities can be monitored using appropriate remotely sensed images [4]. Many researchers have proposed methods aiming to distinguish dust storms from clouds, ground and water surfaces using images from instrument such as MERIS [5], TOMS [8], SeaWiFS [9], GOES [10] and MODIS (Terra and Aqua) [1], [2], [4], [5], [11] and those on the satellites NOAA-AVHRR [12], Landsat and GMS [13]. In this study we have concentrated on MODIS (Moderate Resolution Imaging Spectroradiometers) data because it includes 36 spectral bands, which potentially provides more opportunities for developing techniques for the detection of dust storms than the data from other instruments.…”