i Detection of Smoke and Dust Aerosols Using Multi-sensor Satellite ...

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ABSTRACT DETECTION OF SMOKE AND DUST AEROSOLS USING MULTI-SENSOR SATELLITE REMOTE SENSING MEASUREMENTS Yong Xie, Ph.D. George Mason University, 2009 Co-Dissertation Director: Dr. John J. Qu ; Dr. Jack Xiong Every year a large amount of aerosols released from wildfires and dust storms into the atmosphere may have potential impacts on the climate, environment, and air quality. Detecting smoke and dust aerosols and monitoring their movements and evolutions in a timely manner is a very meaningful task. Satellite remote sensing has been demonstrated as an effective means for the observation of these two kinds of aerosols. In this dissertation, an algorithm based on the multi-spectral technique for detecting smoke and dust aerosols is developed, by combining measurements of MODoderate resolution Imaging Spectroradiometer (MODIS) reflective solar bands and thermal emissive bands. Data from smoke/dust events occurred during last several years are collected visually as training data for spectral and statistical analyses. According to the spectral curves of various scene types (aerosols, cloud, vegetation, and water et al.), a series of spectral bands is selected jointly or separately and corresponding thresholds are defined for scene classification step by step. The multi-spectral algorithm is applied mainly to detect smoke plumes in the United States and dust storms in Asia. The detection results
are validated not only visually with MODIS true color images, but also quantitatively with products of, Ozone Monitoring Instrument (OMI) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The validations show that this multi-spectral detection algorithm is suitable to monitor smoke and dust in the selected study areas. The accuracy is quite good in most cases. Additionally, this algorithm can be used to detect smoke and dust aerosols at the areas near clouds even mixed with clouds. Detection of dust aerosol with multi-sensor satellite remote sensing measurements, MODIS and CALIPSO, is also performed tentatively in this dissertation. After spatial registration, the dust layers are identified combining CALIPSO Vertical Feature Mask product and measurements of MODIS brightness temperature difference between 12 and 11-µm bands. Based on detecting results, the three-dimension information of dust aerosols is summarized. Additionally, the impacts of the mis-registration on the L1B data and dust aerosol detection results are assessed. The relative errors caused by mis-registration on L1B data are generally less than a few tenths of a percent. The impacts on dust detection results are relative large, usually has the trend as negligible at the homogeneous and semi-homogeneous areas, but large at the non-homogeneous areas.
Page 1 and 2: Detection of Smoke and Dust Aerosol
Page 3 and 4: DEDICATION This is dedicated to my
Page 5 and 6: TABLE OF CONTENTS Page LIST OF TABL
Page 7 and 8: LIST OF TABLES Table Page Table 1.1
Page 9 and 10: Figure 6.2 The UVAI images and the
Page 11: VFM Vertical Feature Mask VIS Visib
Page 15 and 16: 1.1 Importance of Studying Smoke an
Page 17 and 18: 1.3 Objectives and Scopes The main
Page 19 and 20: originalities, and discussions of f
Page 21 and 22: 1.6 Principal Results The principle
Page 23 and 24: CHAPTER 2 REVIEW OF APPROACHES FOR
Page 25 and 26: cooperatively: the reflectance at 0
Page 27 and 28: more information about dust charact
Page 29 and 30: CHAPTER 3 SMOKE AEROSOL DETECTION W
Page 31 and 32: with a two-side scan mirror which r
Page 33 and 34: 3.1.2 MODIS data The 55 0 scan angl
Page 35 and 36: Sun Satellite Figure 3.2: Basic rad
Page 37 and 38: 1 0 /2 b ( 0 0, 0; , ) k1 e
Page 39 and 40: 3.2.3 Training data collection Surf
Page 41 and 42: Reflectance Reflectance Response cu
Page 43 and 44: Figure 3.6: The normalized ratio of
Page 45 and 46: all pixels over both land and ocean
Page 47 and 48: NDDI 1 0.8 0.6 0.4 0.2 0 -0.2 Figur
Page 49 and 50: MODIS L1B measurements Pixels over
Page 51 and 52: is clear that most of smoke plumes
Page 53 and 54: a b c b Figure 3.10: Smoke images o
Page 55: 3.4.2 California 2007 fire The 2007
Page 58 and 59: The multi-spectral approach is test
Page 60 and 61: 4.1 Physical Principle of Dust Aero
Page 62 and 63:
Aqua 2006 102-04/12 04:15 Terra 200
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The dust has a high reflectivity at
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0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2
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7 a Dust pixel 0.5 b 6 Cloud pixel
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4.3 Results Three dust events occur
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c c Figure 4.6: Dust storm over TAK
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4.4 Chapter Summary In this chapter
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operated in the A-train orbit with
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5.1.2 CALIPSO measurements and VFM
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Altitude (km) 20 15 10 5 UTC: 2006-
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After spatial registration, the ove
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Figure 5.4(a) The MODIS true color
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Altitude (km) BTD (K) 20 15 10 15 4
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in the same A-train orbit with simi
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Aqua spacecrafts are operated in th
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the Fig. 6.1a. For easy comparison,
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esolutions, the number of smoke pix
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6.1.4 Validation of dust monitoring
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Figure 6.3 Validation of dust image
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eflectance in red band. Therefore,
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Altitude (km) 20 15 10 5 UTC: 2006-
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image at the same location. The rea
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accuracy could be increased further
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FPAs and cold FPAs (because of diff
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development of future remote sensin
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Figure 7.3: SRCA spatial mode retic
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7.1.3.2 Date Source MODIS L1B data
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characterization. Only the measurem
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these bands, including reflective s
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different spectral bands are slight
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SZA (Degree) SZA (Degree) 65 60 55
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7.2.1.2 Real Impact on L1B Measurem
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7.2.2.1 Theoretical impact analysis
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spatial characterization, the bigge
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Difference NDDI with spatial correc
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8.1 Conclusions CHAPTER 8 CONCLUSIO
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Based on the detection results, the
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3) Validating aerosol detection qua
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REFERENCES Ackerman, S. A., 1989, U
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imagery, Atmospheric Environment, v
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Salomonson, V. V., Privette, J. L.,
Page 145 and 146:
Martinez, A., and Gros, G., 2007-10
Page 147 and 148:
Wang W., Qu, J. J., Liu, Y., Hao, X
Page 149:
CURRICULUM VITAE Yong Xie is a Ph.D
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