{"id":974,"date":"2024-08-30T03:46:50","date_gmt":"2024-08-29T20:46:50","guid":{"rendered":"http:\/\/www.tneutron.net\/sipil\/?p=974"},"modified":"2024-08-28T08:07:54","modified_gmt":"2024-08-28T01:07:54","slug":"understanding-raster-data","status":"publish","type":"post","link":"https:\/\/www.tneutron.net\/sipil\/understanding-raster-data\/","title":{"rendered":"Understanding Raster Data"},"content":{"rendered":"<p><span class=\"notranslate\">Excellent raster data to represent the boundaries are changed gradually, such as soil type, soil moisture, vegetation, soil temperature and so on.<\/span> <span class=\"notranslate\"> The main limitation of raster data is the large size of the file;<\/span> <span class=\"notranslate\"> the higher the resolution of its grid-the larger the file size and is highly dependent on the available hardware capacity.<\/span> <span class=\"notranslate\"> Each data format has advantages and disadvantages.<\/span><\/p>\n<p><span class=\"notranslate\"> Raster data (also called grid cells) is data generated from remote sensing systems.<\/span> <span class=\"notranslate\"> In the raster data, geographic object is represented as a grid cell structure called a pixel (picture element).<\/span> <span class=\"notranslate\"> In the raster data, resolution (visual definition) depends on the size of its pixels.<\/span> <span class=\"notranslate\"> In other words, a pixel resolution depict actual size on the earth&#8217;s surface represented by each pixel in the image.<\/span> <span class=\"notranslate\"> The smaller the size of the Earth&#8217;s surface are represented by a single cell, the higher the resolution.<\/span><\/p>\n<p><span class=\"notranslate\"> Selection of data format used depends on the intended use, the available data, the volume of data generated, the desired accuracy, and ease of analysis.<\/span> <span class=\"notranslate\"> Matrix raster has a geometrically regular shape and has ordered automatically, therefore each pixel cell position or positions do not have to be recorded one by one.<\/span> <span class=\"notranslate\"> If everything is recorded even be a waste of memory unnecessary.<\/span><\/p>\n<p><span class=\"notranslate\"> This is what distinguishes it from vector data.<\/span> <span class=\"notranslate\"> Nevertheless, some local coordinates [in units of column lines] (which serve as ground control points or GCPs) in the raster data set is needed to bind (me-register) this grid system to a desired coordinate system.<\/span> <span class=\"notranslate\"> Meanwhile, to read (content) raster data file correctly, the data recording sequence must be observed.<\/span><\/p>\n<p><span class=\"notranslate\"> Raster has characteristics that can distinguish them from each other.<\/span> <span class=\"notranslate\"> These characteristics can include spatial resolution, temporal, spectral, orientation (to the north), the existence of zone (area where the pixel-pixel have the same identification number), the domain of pixel values \u200b\u200b(intensity), and the coordinates of the pixel.<\/span><br \/>\n<a href=\"http:\/\/www.tneutron.net\/sipil\/wp-content\/uploads\/sites\/8\/2015\/12\/image-19.png\"><img loading=\"lazy\" decoding=\"async\" title=\"image\" src=\"http:\/\/www.tneutron.net\/sipil\/wp-content\/uploads\/sites\/8\/2015\/12\/image_thumb-20.png\" alt=\"image\" width=\"335\" height=\"436\" border=\"0\" \/><\/a><br \/>\n<span class=\"notranslate\"> Figure 2. Example of Data Display Coordinate System Raster After Transformation<\/span><\/p>\n<p><span class=\"notranslate\"> In the coordinate system like this (post-transformation), the origin of coordinates (X0, Y0), the raster data is located in the lower left corner point.<\/span> <span class=\"notranslate\"> In addition, there are a number of M column (abscissa) and the N line (ordinate) in accordance with the direction of each coordinate axis.<\/span> <span class=\"notranslate\"> Each pixel or grid cell has a width and height values \u200b\u200bb (according to the spatial resolution).<\/span> <span class=\"notranslate\"> So by paying attention to these values, the coordinates of the point of the other corners are left-over (Xo, Yo + N * b), the bottom-right (Xo + M * a, Yo), and right-upper (Xo + M * a, Yo + N * b).<\/span><\/p>\n<p><span class=\"notranslate\"> By utilizing the principle of the same count, it can be seen that the coordinates of the center point pixel row i and column j is (Xo + (j-0.5) * a, Yo + (i-0.5) * b. The boundaries of pixel row i and column j is (Xo + (j-1) * a &lt;X &lt;Xo + j * a) for X, and (Yo + (i-1) * b &lt;Y &lt;Yo + i * b) for Y.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Excellent raster data to represent the boundaries are changed gradually, such as soil type, soil moisture, vegetation, soil temperature and<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"colormag_page_container_layout":"default_layout","colormag_page_sidebar_layout":"default_layout","footnotes":""},"categories":[1865],"tags":[1487,1488,1493,1492,1491,1489,1495,1496,1494,1490],"class_list":["post-974","post","type-post","status-publish","format-standard","hentry","category-english","tag-raster-data","tag-raster-data-definition","tag-raster-data-download","tag-raster-data-formats","tag-raster-data-gis","tag-raster-data-model","tag-raster-data-structure","tag-raster-data-types","tag-raster-data-vs-vector-data","tag-raster-dataset"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - 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