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

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identification of smoke plume (Chung and Le, 1984). Among various techniques, visible imagery approach is a fast and easy way to visually identify smoke, by assigning three bands (or band combination) as the red, green, and blue channel respectively to generate either true color or false color images (Chung and Le, 1984; Kaufman et al., 1990; Randriambelo et al., 1998; Chrysoulakis and Opie, 2004, Chrysoulakis and Cartalis, 2003). For example, MODIS RGB true color images are generated with bands 1, 4 and 3 jointly. On the other hand, the combination of several bands may act as one band to generate images. Christopher and Chou (1997) used the normalized ratio of Advanced Very High Resolution Radiometer (AVHRR) band 1 and 4 to represent the green channel to produce an image. Then this image was used to compute several textural measures for a 9×9- pixel window (Welch et al., 1988; Trovinkere et al., 1993; Christopher et al., 1996), to visually separate the smoke aerosols from the other scene types. However, these color-based approaches can provide only basic information about smoke and fail to provide automatic identification. Multi-threshold approach is one of effective tools to detect smoke based on the physical property difference between smoke and other scene types, such as cloud, vegetation, water, snow, ice, and soil. Generally, the algorithm employs a set of threshold tests to check all image pixels simultaneously to separate smoke from other scene types step by step. In each test, the particular thresholds, either static or dynamic, are calculated generally by the statistical analysis of training data. Baum and Trepte (1999) proposed a grouped threshold method for scene identification with AVHRR measurements. In their method, the smoke was classified by smoke module consisted with several thresholds test 11
cooperatively: the reflectance at 0.63 µm and 3.7 µm channel, the Bright Temperature (BT) difference between 3.7 µm and 11 µm channel, and the BT difference between anticipated clear-sky value and measured value at 11 µm channel. Li et al. (2001) presented a multi-threshold method for automated smoke plume detection using AVHRR measurements based on the neural networks. The shortcoming of using AVHRR is that AVHRR has only five channels, which is inefficient for smoke detection. Chrysoulakis et al. (2007) proposed a multi-temporal change detection approach using two images at the same target area on the different time. By comparing two images (one is acquired during the fire event and the other is acquired before fire event), the important anomalies in NDVI and infrared radiances were detected hence to detect the core of plume. Then the plume core was enlarged to include the complete smoke area through identifying a pixel as plume pixel if it located spatially and spectrally at the neighborhood of the initial plume core. The new developed one offers a novel ways for smoke monitoring. However, this approach need find a clear day before the occurrence of smoke. The Bidirectional Reflectance Distribution Function (BRDF) issue is also need to be taken into account. The current MODIS aerosol retrieval algorithm uses the dark target approach (Kaufman et al. 1997, Kaufman et al. 1997) with two assumptions: 1) the aerosol is transparent to most aerosol types (except dust) at 2.1 µm so that this channel can be used to detect dark surface targets; 2) the surface reflectance at 0.47 µm and 0.64 µm channel could be retrieved by that at 2.1 µm with the ratio 0.25 and 0.5. With the assumptions, the Aerosol Optical Thickness (AOT) and particle size parameters were retrieved at 0.47 µm 12
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 and 12: VFM Vertical Feature Mask VIS Visib
Page 13 and 14: are validated not only visually wit
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: CHAPTER 2 REVIEW OF APPROACHES FOR
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
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
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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,
Page 95 and 96:
esolutions, the number of smoke pix
Page 97 and 98:
6.1.4 Validation of dust monitoring
Page 99 and 100:
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.,
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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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