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

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Appendix D Raster Sources Raster data is collected and used by a variety of geographic information technologies, including remote sensing, airborne photogrammetry, cartography, and global positioning systems. The collected data is then analyzed by digital image processing systems, computer graphics applications, and computer vision technologies. These technologies use several data formats and create a variety of products. This section briefly describes some of the main raster data sources and their applications. D.1 Remote Sensing D.1.1 Introduction Remote sensing obtains information about an area or object through a device that is not physically connected to the area or object. For example, the sensor might be in a satellite, balloon, airplane, boat, or ground station. There are two main types of remote sensing: passive remote sensing and active remote sensing. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding areas. Reflected sunlight is the most common source of radiation measured by passive sensors. Examples of passive remote sensors include film photography, infrared,charge-coupled devices, and radiometers. Active collection, on the other hand, emits energy in order to scan objects and areas whereupon a sensor then detects and measures the radiation that is reflected or backscattered from the target. RADAR and LiDAR are examples of active remote sensing where the time delay between emission and return is measured, establishing the location, height, speed and direction of an object. D.1.2 Applications Remote sensing applications include environmental assessment and monitoring, global change detection and monitoring, and natural resource surveying. Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Remote sensing applications include monitoring deforestation in areas such as the Amazon Basin, glacial features in Arctic and Antarctic regions, and depth sounding of coastal and ocean depths. Military collection during the Cold War made use of standoff collection of data about dangerous border areas. Remote sensing also replaces costly and slow data collection on the ground, ensuring in the process that areas or objects are not disturbed. Orbital platforms collect and transmit data from different parts of the electromagnetic spectrum, which in conjunction with larger scale aerial or ground-based sensing and analysis, provides researchers with enough information to monitor trends such as El Niño and other natural long and short term phenomena. Other uses include different areas of the earth sciences such as natural resource management, agricultural fields such as land usage and conservation, and national security and overhead, ground-based and stand-off collection on border areas. By satellite, aircraft, spacecraft, buoy, ship, and helicopter images, data is created to analyze and compare things like vegetation rates, erosion, pollution, forestry, weather, and land use. These things 95
Figure D.1: Synthetic aperture radar image of Death Valley [37]. can be mapped, imaged, tracked and observed. The process of remote sensing is also helpful for city planning, archaeological investigations, military observation and geomorphological surveying. The data collected by remote sensing is often called geo imagery. The wavelength, number of bands, and other factors determine the radiometric characteristics of the geo images. The geo images can be single-band, multiband, or hyperspectral, all of which can be managed by PostGIS Raster. These geo images can cover any area of the Earth (especially for images sensed by satellite). Generally, remote sensing works on the principle of the inverse problem. While the object or phenomenon of interest (the state) may not be directly measured, there exists some other variable that can be detected and measured (the observation), which may be related to the object of interest through the use of a data-derived computer model. The common analogy given to describe this is trying to determine the type of animal from its footprints. For example, while it is impossible to directly measure temperatures in the upper atmosphere, it is possible to measure the spectral emissions from a known chemical species (such as carbon dioxide) in that region. The frequency of the emission may then be related to the temperature in that region via various thermodynamic relations. The quality of remote sensing data consists of its spatial, spectral, radiometric and temporal resolutions. The temporal resolution can be high, such as with meteorological satellites, making it easier to detect changes. For remote sensing applications, various types of resolution (temporal, spatial, spectral, and radiometric) are often important. D.2 Photogrammetry D.2.1 Introduction Photogrammetry is used to determine the geometric properties of objects from measurements made on photographic images. In the simplest example, the distance between two points that lie on a plane parallel to the photographic image plane can be determined by measuring their distance on the image, if the scale (s) of the image is known. This is done by multiplying the measured distance by 1/s. Most photogrammetry applications use airborne photos or high-resolution images collected by satellite remote sensing. In traditional photogrammetry, the main data includes images such as black and white photographs, color photographs, and stereo photograph pairs. Photogrammetry rigorously establishes the geometric relationship between the image and the object as it existed at the time of the imaging event, and enables you to derive information about the object from its imagery. The relationship between image and object can be established by several 96
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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 "
Page 53 and 54: Figure 4.30: Pyramids at different
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Page 65 and 66: 4.19 Intersection The fact that Pos
Page 67 and 68: Figure 4.51: Raster and raster inte
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
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Page 91 and 92: Figure 6.17: Maps with a color lege
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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 and 110: Figure D.4: Age of the sea floor. M
Page 111 and 112: Appendix F Continuous Surfaces One
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