Figure 2-1. Concept of remote sensing 34

Figure 2-2. Remote sensing studies in the Earth 35

Figure 2-3. Airborne remote sensing image of KIGAM(left) and space-borne remote sensing image of Seoul(right) 36

Figure 2-4. Active system and an optical image(left) and active system and a microwave image(right) 38

Figure 2-5. Electromagnetic wave 40

Figure 2-6. Long wavelength and short wavelength 41

Figure 2-7. Electromagnetic spectrum 42

Figure 2-8. Refraction and atmospheric scattering 43

Figure 2-9. Rayleigh scattering characteristics 44

Figure 2-10. Absorption of the Sun's incident electromagnetic energy in the region from 0.1 to 30 μm by various atmospheric gases 46

Figure 2-11. Various type of reflector 47

Figure 2-12. Combines effects of atmospheric absorption, scattering, and reflectance 48

Figure 2-13. Digital frame camera area array 49

Figure 2-14. Across-track or scanning mirror and single discrete detectors (whiskbroom) type 49

Figure 2-15. Linear array ("pushbroom", Along-track) type 50

Figure 2-16. Concept of digital image (＝grid system). 51

Figure 2-17. Three method of display 52

Figure 2-18. Spatial resolutions 53

Figure 2-19. Spectral resolution 54

Figure 2-20. Temporal resolutions of MISR and MODI which are both on the TERRA satellite. 55

Figure 2-21. Spectral signatures of water, soil and vegetation (A) and a hypothetical example of how the LANDSAT satellite might record water, green... 56

Figure 2-22. Analytical Spectral Devices (ASD) is widely used field spectrometer 58

Figure 2-23. Including of Analytical Spectral Devices (ASD) 59

Figure 2-24. Measuring, processing and results of the reflectance of the target using ASD 60

Figure 2-25. Measuring optical properties of the surface water in the ocean using ASD 60

Figure 2-26. NOAA-AVHRR (left) and Landsat 7 ETM+(right) image 63

Figure 2-27. Image of the Burj Tower of Dubai, observed by KOMPSAT-3 in the fall of 2012(left) and SAR image of KOMPSAT/Arirang-5 of Sydney, Australia... 67

Figure 2-28. Airborne hyperspectral surveys using 200+ spectral channels from 0.38 to 2.5mm 71

Figure 2-29. Hyperion hyperspectral products from The EO-1 extended mission(NASA) 73

Figure 2-30. 3D mineral map for primary and secondary ore mineralization and alteration by airborne hyperspectral data 74

Figure 2-31. Hyperion instrument aboard EO-1 for geologic field detection(NASA) 75

Figure 3-1. Image processing in remote sensing. 79

Figure 3-2. Atmospheric effect 80

Figure 3-3. Image-to-map method of geometric correction 82

Figure 3-4. Acquiring GCPs in the field 83

Figure 3-5. Geocoding wizard of ER-mapper software for geometric correction of a raw image 84

Figure 3-6. Environmental setup 84

Figure 3-7. Selecting GCP and save the lists 85

Figure 3-8. Perspective projection(left) and polar stereo projection(right) 86

Figure 3-9. Transverse Mercator (TM) projection 86

Figure 3-10. Mosaic each single image into an integrated image 87

Figure 3-11. Image reduction and magnification 88

Figure 3-12. Contrast enhancement 89

Figure 3-13. Left images are the original image and the histogram. Right images are histogram enhanced image and its histogram 90

Figure 3-14. Colour composite images of Landsat 92

Figure 3-15. Image compositions for mineral exploration 92

Figure 3-16. Image pan-sharpening with QuickBird images. 93

Figure 3-17. Extracting tidal channel from pan-sharpened image. 94

Figure 3-18. Correlation matrix among seven Landsat TM bands of Charleston, SC 97

Figure 3-19. Diagrammatic representation of the spatial relationship between the first two principal components 98

Figure 3-20. PCA result for Landsat TM data of Charleston, S.C. obtained on Nov. 9, 1982 99

Figure 3-21. Principal component images for Landsat TM data of Charleston, S.C. obtained on Nov. 9, 1982 100

Figure 3-22. Absorption spectra of chlorophyll a and b 101

Figure 3-23. Typical spectral reflectance characteristics for healthy green grass, dead grass and bare dry soil 102

Figure 3-24. NDVI image of Yeoja Bay tidal flat 103

Figure 3-25. TVI image of Yeoja Bay tidal flat 104

Figure 3-26. MODIS EVI image – degree of green color indicates the biomass 105

Figure 3-27. Supervised classification 107

Figure 3-28. Assign the class to unknown pixel 107

Figure 3-29. Result image of supervised classification 109

Figure 3-30. Unsupervised classification 110

Figure 3-31. Unsupervised classification and determination of the ground cover for each of the clusters 111

Figure 4-1. Landsat spectral band information of seven different bands 115

Figure 4-2. Landsat band combinations with individual seven different bands 116

Figure 4-3. Landsat TM ratio image for geological structure 117

Figure 4-4. Geological structure image 118

Figure 4-5. Combination of Landsat TM ration image and SIR C/X image for interpretation of geological structure 119

Figure 4-6. A case study on the region, Mongolia 126

Figure 4-7. Case study area in Mongolia 126

Figure 4-8. Left is atmospherically uncorrected radiance-at-sensor data of ASTER Level 3A without cloud VNIR(RGB:Band 3, 2, 1) and right is... 127

Figure 4-9. Spectral emissivity curve of Carbonate rock, Quartz, Granite, Diorite, Gabbro and Peridotite 128

Figure 4-10. Left up is VNIR image, left down is RGB image, right down is geological map(1:200,000) and right up is projection image that projects VNIR... 129

Figure 4-11. (a) is VNIR image, (b) is quartz index map, (c) is SO₂ content index map, (d) is Carbonate index map, (e) is geological map, and (f) is... 129

Figure 4-12. Upper figure is IKONOS Image and bottom figure is Aerial photograph of reclaimed coastal land. Red squares represent 38 ground... 132

Figure 4-13. Generation seven good quality images and ten low quality images using eight SAR images from 9/15/02 to 10/28/03 133

Figure 4-14. High quality and low quality interferograms 134

Figure 4-15. Figure (A) shows in situ data by magnetic probe extensometers and Figure (B) represents DInSAR measurements. Figure (C), (D) and (E) show... 135

Figure 4-16. Two examples of (a, g) simulated deformation-only interferograms, (b, h) simulated topographic residual errors of interferograms, (c, i) simulated... 141

Figure 4-17. Simulation of surface deformation on the Seguam Volcano by using Mogi model from June 1993 to July 2007 142

Figure 4-18. Mean LOS velocity map from simulated deformation interferograms between June 1993 and July 2007. (b) Mean LOS velocity map derived from... 143

Figure 4-19. Simulated and SBAS-retrieved LOS time-series deformation at two locations. Crosses represent simulated time-series deformations, and circles show... 144

Figure 4-20. Location of Augustine Island in the southwestern part of Cook Inlet, Alaska 146

Figure 4-21. Geologic map showing the distribution of deposits from recent Augustine Volcano eruptions. Pyroclastic flow deposits emplaced in 1986 are... 147

Figure 4-22. Interferograms from European Remote Sensing Satellite (ERS)-1 and -2, and Environment Satellite (ENVISAT) Synthetic Aperture Radar (SAR)... 150

Figure 4-23. Observed high-coherence interferograms from track 229 ERS-1, ERS-2, and ENVISAT pairs. Each fringe (full color cycle) represents 28.3... 151

Figure 4-24. Averaged InSAR image from 229 tracks created by stacking all the unwrapped 1-year interferograms between 1992 and 2005 152

Figure 4-25. Perpendicular baselines used for small baseline subset(SBAS) interferometric synthetic aperture radar (InSAR) processing at Augustine volcano 154

Figure 4-26. Removal several error effects through SBAS processing. 155

Figure 4-27. Averaging deformation rate map for Augustine Volcano from the refined small baseline subset (SBAS) technique for the period 1992–2005. 156

Figure 4-28. Surface-displacement time-series for selected points on Augustine Volcano for the period 1992–2005. 157

Figure 4-29. (a) The Landsat ETM+ image of the Cheonsu Bay and Hwangdo tidal flat acquired on February 14, 2002. (b) The IKONOS RGB (432 bands)... 165

Figure 4-30. Control factors constructed in this case study. 173

Figure 4-31. (a) Bathymetric map of the case study area overlaid with the sampling stations in March and October 2011. (b) Mosaic of Landsat ETM+... 179

Figure 4-32. (a) RGB composite (R:680 nm, G:555 nm, B:412 nm) after Rayleigh reflectance correction generated from the GOCI image acquired at 12:30 local... 185

Figure 4-33. Match-up comparison of Rrs (660) between GOCI and in situ measurements using the 12 samples acquired on 26 October 2011. GOCI-derived... 186

Figure 4-34. SS algorithm used in the present case study, derived empirically from 34 observations of optical properties and SSC in March and October 2011 188

Figure 4-35. Map of SSC generated by GOCI band 5 based on the SS algorithm in Equation 1. 190

Figure 4-36. Relationship between in situ SSC (g/m3) and SSC (g/m3) derived from reflectance using the GOCI band centered at 660nm using the 12 samples... 191

Figure 4-37. Hourly variation in SSC at locations V1–V9 in Figure 4-32a 192

Figure 4-38. Color scenes derived from the GOCI. 199

Figure 4-39. Ocean colors derived from GOCI. 201

Figure 4-40. (a) A schematic diagram for estimating an ocean surface current vector. This methodology requires two data inputs: a source scene (S) (e.g.,... 203

Figure 4-41. A comparison between the trajectories of ocean surface current results (i.e., arrows) around coastal regions of Gyeonggi Bay using TSM scenes... 205

Figure 4-42. Comparisons between each ocean surface current vector results (i.e., directions (a) and velocities (b)) and in situ data obtained from Gyeonggi Bay on... 206

Figure 4-43. Daily SST data (i.e., colored image) provided by the GHRSST and the trajectories of the estimated ocean surface currents (i.e., arrows) from multiple... 208