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

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UVAI image; (b) MODIS true color image; (c) Smoke image. According to the procedure of aerosol selection presented in OMAERUN README file (http://daac.gsfc.nasa.gov/documents/OMAERUV_README_File_v3.doc), the aerosol associated with biomass burning usually has the UVAI values larger than 0.7. Therefore, the OMI UVAI images with the UVAI values larger than 0.7, 1.0, and 1.2 are displayed respectively in Fig. 6.2, for multiple comparisons with smoke image derived with multi-spectral algorithm using MODIS measurements. Some obviously misclassifications (bad or low quality data) in OMI UVAI product, are filtered out to improve the accuracy of comparisons. The distributions of identified, unidentified, and misidentified pixels in three comparisons are given in Fig. 6.2. In the first comparison (UVAI > 0.7; Figs. 6.2a and 6.2b), there are 513,582 pixels are identified correctly (labeled as smoke in both images), listed in Table 6.2. About 365,853 pixels are unidentified (detected by OMI only) and 1,563 pixels are misidentified (detected by MODIS only) in MODIS smoke image. The percentage of identified smoke pixels increases to 73.78% with acceptable 4.34% misidentification in second comparison (UVAI > 1.0; Figs. 6.2c and 6.2d). In the third comparison (UVAI > 1.2; Figs. 6.2e and 6.2f), the unidentified smoke pixels decreases further to 11.18% but around 19.05% pixels are labeled as smoke in the smoke image which is labeled as nonsmoker pixel in OMI UVAI image. Overall, the core part of smoke is explicitly detected by MODIS smoke image in all three comparisons. The major difference exists at the edge of plume, which may attribute to the different spatial resolutions between two sensors. Because of different spatial 81
esolutions, the number of smoke pixels in MODIS smoke image (1 km) is not enough to aggregate a corresponding pixel in OMI UVAI image (10 km). In another word, some smoke pixels in OMI UVAI image are not filled with smoke pixels in MODIS smoke image. a b c e d 82
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Detection of Smoke and Dust Aerosol
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DEDICATION This is dedicated to my
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TABLE OF CONTENTS Page LIST OF TABL
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LIST OF TABLES Table Page Table 1.1
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Figure 6.2 The UVAI images and the
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VFM Vertical Feature Mask VIS Visib
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are validated not only visually wit
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1.1 Importance of Studying Smoke an
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1.3 Objectives and Scopes The main
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originalities, and discussions of f
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1.6 Principal Results The principle
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CHAPTER 2 REVIEW OF APPROACHES FOR
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cooperatively: the reflectance at 0
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more information about dust charact
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CHAPTER 3 SMOKE AEROSOL DETECTION W
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with a two-side scan mirror which r
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3.1.2 MODIS data The 55 0 scan angl
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Sun Satellite Figure 3.2: Basic rad
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1 0 /2 b ( 0 0, 0; , ) k1 e
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3.2.3 Training data collection Surf
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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
Page 64 and 65: The dust has a high reflectivity at
Page 66 and 67: 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2
Page 68 and 69: 7 a Dust pixel 0.5 b 6 Cloud pixel
Page 70 and 71: 4.3 Results Three dust events occur
Page 72 and 73: c c Figure 4.6: Dust storm over TAK
Page 74: 4.4 Chapter Summary In this chapter
Page 77 and 78: operated in the A-train orbit with
Page 79 and 80: 5.1.2 CALIPSO measurements and VFM
Page 81 and 82: Altitude (km) 20 15 10 5 UTC: 2006-
Page 83 and 84: After spatial registration, the ove
Page 85 and 86: Figure 5.4(a) The MODIS true color
Page 87 and 88: Altitude (km) BTD (K) 20 15 10 15 4
Page 89 and 90: in the same A-train orbit with simi
Page 91 and 92: Aqua spacecrafts are operated in th
Page 93: the Fig. 6.1a. For easy comparison,
Page 97 and 98: 6.1.4 Validation of dust monitoring
Page 99 and 100: Figure 6.3 Validation of dust image
Page 101 and 102: eflectance in red band. Therefore,
Page 103 and 104: Altitude (km) 20 15 10 5 UTC: 2006-
Page 105 and 106: image at the same location. The rea
Page 107 and 108: accuracy could be increased further
Page 109 and 110: FPAs and cold FPAs (because of diff
Page 111 and 112: development of future remote sensin
Page 113 and 114: Figure 7.3: SRCA spatial mode retic
Page 115 and 116: 7.1.3.2 Date Source MODIS L1B data
Page 117 and 118: characterization. Only the measurem
Page 119 and 120: these bands, including reflective s
Page 121 and 122: different spectral bands are slight
Page 123 and 124: SZA (Degree) SZA (Degree) 65 60 55
Page 125 and 126: 7.2.1.2 Real Impact on L1B Measurem
Page 127 and 128: 7.2.2.1 Theoretical impact analysis
Page 129 and 130: spatial characterization, the bigge
Page 131 and 132: Difference NDDI with spatial correc
Page 133 and 134: 8.1 Conclusions CHAPTER 8 CONCLUSIO
Page 135 and 136: Based on the detection results, the
Page 137 and 138: 3) Validating aerosol detection qua
Page 139 and 140: REFERENCES Ackerman, S. A., 1989, U
Page 141 and 142: imagery, Atmospheric Environment, v
Page 143 and 144: Salomonson, V. V., Privette, J. L.,
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Martinez, A., and Gros, G., 2007-10
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Wang W., Qu, J. J., Liu, Y., Hao, X
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CURRICULUM VITAE Yong Xie is a Ph.D
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