PostGIS Raster : Extending PostgreSQL for The Support of ... - CoDE

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Appendix E Raster Resolution When working with raster data, there are four types of resolution often encountered: spatial resolution, spectral resolution, temporal resolution and radiometric resolution. Spatial resolution refers to the size of the smallest object that can be resolved on the ground. In a digital image, the resolution is limited by the pixel size, i.e. the smallest resolvable object cannot be smaller than the pixel size. The spatial resolution of raster data refers to the cell size. A higher spatial resolution implies that there are more pixels per unit area. Therefore, the graphic on the left represents a higher spatial resolution than the graphic on the right. Figure E.1: Raster data represented at different resolution or cell sizes [11]. Spectral resolution describes the ability of a sensor to distinguish different wavelength intervals in the electromagnetic spectrum. The higher the spectral resolution, the narrower the wavelength range for a particular band. For example, a grayscale image (a single band) records wavelength data over the visible portion of the electromagnetic spectrum. However, it has a low spectral resolution. A color image (with three bands) collects wavelength data from three smaller parts of the visible portion of the electromagnetic spectrum: the red, green, and blue parts. Therefore, each band in the color image has a higher spectral resolution than the single band in the grayscale image. Advanced multispectral and hyperspectral sensors collect data from up to hundreds of spectral bands, resulting a very high spectral resolution data. Temporal resolution refers to the frequency with which images can be captured over the same place on the earth’s surface. It is also known as the revisit period, which is a term most often used for satellite sensors. Therefore, a sensor that captures data once every week has a higher temporal resolution than one that captures data once a month. Radiometric resolution describes the ability of a sensor to distinguish objects viewed in the same part of the electromagnetic spectrum. This is synonymous with the number of possible data values in each band. For example, a Landsat band is typically 8-bit data and an IKONOS band is typically 11-bit data. Therefore, the IKONOS data has a higher radiometric resolution. 101
Appendix F Continuous Surfaces One type of continuous surface, that is derived from those characteristics that define a surface, is a surface where each point in the surface is its measured from a fixed reference point. These include elevation (the fixed reference point is a sea level and the measure at each point is its height above the sea level) and aspect (the fixed reference point is one of four directions: north, east, south and west and the measure at each point is the direction of a normal at that point). Figure F.1: Elevation represented in raster format [33]. Another type of continuous surface includes phenomena that progressively vary across a surface from a source. Examples of such phenomena include fluid and air movement. These surfaces are characterized by the way in which the phenomenon moves. The first type of movement is through diffusion in which the phenomenon moves from areas with high concentration to those with less concentration until the concentration level evens out. Surface characteristics of this movement type include salt concentration moving through the ground or water, contamination moving away from a hazardous spill or a nuclear reactor and heat from a forest fire. In this type of continuous surface, there has to be a source, so-called source-concentration surface. The concentration is always greater near the source and decreases following a function of distance and the intermediate substance. In the source-concentration surface above, the concentration at any position is a function of the capability of the phenomenon to move through the environment. Another type of concentration surface is governed by the inherent characteristics of the moving phenomenon. For example, the movement of the noise from a bomb blast is governed by the inherent characteristics of noise and the environment that it moves through. Mode of movement can also limit and directly affect the surface concentration of a feature, as is the case with seed dispersal from a plant. The means of movement, whether it be bees, man, wind, or water, all affect the surface concentration of seed dispersal for the plant. Other concentration surfaces include dispersal of animal populations, potential customers of a store (cars being the means of locomotion and time being the limiting factor) and the spreading of a disease. 102
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Faculté de Sciences Appliquées Se
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Abstract Nowadays, in many applicat
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3.2.4 Overlapping Raster and Vector
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D.5 Geology, Geophysics and Geochem
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4.9 Raster cell [16]. . . . . . . .
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Chapter 1 Introduction 1.1 Context
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Chapter 2 Geographic Information Sy
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2.2.3 Imagery Figure 2.3: Polygon f
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Figure 2.8: Surface expressed by co
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4-foot logo. The file size is the s
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2.4.2 Data Theme Figure 2.14: Multi
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measures of latitude. Longitude mea
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2.6 GIS Applications 2.6.1 Crime Ma
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2.6.3 Traditional Knowledge GIS Tra
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These additional functionalities ma
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Figure 3.3: Physical storage of Geo
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DROP INDEX spain_images_idx; CREATE
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In summary, the intersection is muc
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Attribute information Table 4.1: Po
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Figure 4.2: Information representat
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for discrete data while floating-po
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the matrix are parallel to the x-ax
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Lines Figure 4.15: Raster point rep
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Choosing an appropriate cell size i
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The multi-band conception is result
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"32BUI" = 32-bit unsigned integer "
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Figure 4.30: Pyramids at different
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ectangular because there might be s
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Figure 4.39: Vector to raster conve
Page 59 and 60: Figure 4.42: Web application with o
Page 61 and 62: In this example, the dummy_rast tab
Page 63 and 64: One approach to make this intersect
Page 65 and 66: 4.19 Intersection The fact that Pos
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Page 69 and 70: ST_Present and ST_Passes predicates
Page 71 and 72: Chapter 5 User Guide Sometimes stor
Page 73 and 74: SELECT rast As origin, ST_Resample(
Page 75 and 76: • gvSIG plugin • MapServer thro
Page 77 and 78: SELECT ST_AsJPEG(rast) As rastjpg F
Page 79 and 80: FROM buff_temp GROUP BY id ORDER BY
Page 81 and 82: Chapter 6 Application Most of GIS a
Page 83 and 84: 6.2 Tools Figure 6.3: PostGIS conne
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Page 87 and 88: Figure 6.10: The loading map parall
Page 89 and 90: Figure 6.14: Maps representing worl
Page 91 and 92: Figure 6.17: Maps with a color lege
Page 93 and 94: their levels of awareness of geogra
Page 95 and 96: [15] Jorge Arevalo. PostGIS Raster
Page 97 and 98: Appendix A PostGIS Raster Utilisati
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Page 101 and 102: Appendix C Code Implementation C.1
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Page 109: Figure D.4: Age of the sea floor. M
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