Analogous to manually drawn hardcopy maps, digitized maps (hardcopies transferred to computers) or computer-designed maps most often consist of three types of geometrical features expressed as vectors (Fig. 1): zero-dimensional points, one-dimensional lines, and two-dimensional polygons. For instance, a point could represent a tethered Nori platform in a bay, linked to a database containing quantitative fields (temperature, nutrients, salinity, biomass, number of active harvesting boats), Boolean fields (presence/absence of several species), and categorical fields (owner's name, quality level label). In turn, polygons encompassing several of these points may depict farms, regions, or jurisdictions. Lines could either intersect these polygons (in case of isobaths) or border them (in case of coastal structures). Vector maps and their associated databases are easy to edit, scale, reproject, and query while maintaining a limited file size.
The raster data type (Fig. 1), also called grid or image data in which all remote sensing data come, differs greatly from vector map data. Each image (whether analogously acquired and subsequently scanned, directly digitally acquired, or computer-generated) is composed of x-columns times y-rows with square pixels (or cells) as the smallest unit. Each pixel is characterized by a certain spatial resolution (the spatial extent of a pixel side), typically ranging from 1 to 1,000 m, and an intensity (z-value). The radiometric resolution refers to the number of different intensities distinguished by a sensor, typically ranging from 8 bits (256) to 32 bits (4.3 x 109). In modern remote sensing platforms, different parts (called bands) of the incident electromagnetic spectrum are often recorded by different sensors in an array. In this case, a given scene (an image with a given length and width, the latter also termed swath, determined by the focal length and flight altitude) consists of several raster layers with the same resolution and extent, each resulting from a different sensor. The amount of sensors thus determines the spectral resolution. A "vertical" profile of a pixel or group of pixels through the different bands superimposed as layers results in a spectral signature for the given pixel(s). The spectral signature can thus be visualized as a graph plotting radiometric intensity or pixel value against band number (Fig. 1). The term multispectral is used for up to ten sensors (bands), whereas hyperspectral means the presence of ten to hundreds of sensors. Some authors propose the term superspectral, referring to the presence of 10-100 sensors, and reserve hyperspec-tral for more than 100 sensors. Temporal resolution indicates the coverage of a given site by a satellite in time, i.e., the time between two overpasses. In the Nori farm example, one or more satellite images might be used as background layers in GIS (Fig. 1) to digitize farms and the surroundings (based on large-scale imagery in a geographic sense, i.e., with a high spatial resolution) or to detect correlations with sites and oceanographical conditions (based on small-scale imagery in a geographic sense, i.e., with a low spatial resolution).
An important aspect in GIS and remote sensing is georeferencing. By indicating a limited number of tie points or ground control points (GCPs) for which geographical coordinates have been measured in the field or for which coordinates are known by the use of maps, coordinates for any location on a computer-loaded map can be calculated in seconds and subsequently instantly displayed. Almost coincidentally with GIS evolution, portable satellite-based navigation devices (Global Positioning System, GPS) have greatly facilitated accurate measurements and storage of geographical coordinates of points of interest. In the current example, a nautical chart overlaid with the satellite images covering the Nori farms might be used as the source to select GCPs (master-slave georeferencing), or alternatively, field-measured coordinates of rocky outcrops, roads, and human constructions along the coast, serving as GCPs recognizable on the (large-scale) satellite images, might be used for direct georeferencing (Fig. 1).
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