Mapping and satellite data
A map can provide an overview of a large geographical area. However, it contains only a general and limited level of detail about the mapped area and the level depends on the compiler's point of view and/or the purpose of the mapping.
A thematic map is often limited to illustrating the spatial distribution of a single feature such as temperature or population density.
A choroplethic map is a special thematic map which has many similarities with digital images.
Chorological mapping methods are used today in digital image processing. A simple example of settlement mapping can illustrate this - settlement density can be mapped chorologically by spreading a grid over a topographical map. The number of houses is then counted in each square.
The result of the count is a chorological matrix consisting of numbers placed in a coordinate system. The geographical distribution of the settlement has now, in a sense, been digitised, that is converted into digits (numbers), so that the computer may be used to handle the data. By compiling statistics on the distribution of houses it is now possible to get an overview.
The histogram shows the spread of data in the chorological matrix. On the basis of the histogram, the image data can be divided into different classes.
The illustration shows two examples of classifications based on the histogram shown. One classification has four classes (agriculture, village, town and other) and the other classification has two (rural and town).
As the basis of the chorological matrix, a regular grid can be used to plot the number of houses in each square (0,1,2,...etc.). Such a grid can be based on geographical coordinates or on the UTM coordinate system. The grid size has to be defined. In a UTM grid it might be 100 m or 10 km, depending on the scale of the map and the distribution of the features to be mapped. The histogram overview provides the basis for defining meaningful clusters (classes): squares with 0 houses might, for example, be defined as forest and recreational areas, squares with 1 to 7 houses as agricultural areas, squares with 8 to 11 houses might be defined as villages, and squares with more than 11 houses as towns.
It is up to the cartographer to decide upon a suitable classification depending on the purpose of the map. Classifications are always open to debate and the same chorological matrix may form the basis of many different maps. The selected classification is placed in the histogram, and each class is given hatching, grey shading or colours. The squares in the grid are hatched according to their classification (number of houses) and this produces a thematic map.
A digital image is a chorological matrix. The size of the squares in the grid is equal to the spatial resolution of the satellite image and depends upon the instrument providing the data. Similarly, the numbers in the grid are determined by the ability of the equipment to distinguish variations. Digital images often contain values between 0 and 255, which are matched exactly by the capacity of 1 byte in the computer.
The chorological matrix is loaded into the computer and the individual squares in the grid are represented by a dot on the screen (a pixel). The numerical value in each matrix square refers to the corresponding pixel-position with 'x,y' coordinates. Every pixel is given a grey shading corresponding to the pixel value. The matrix then appears on the screen as an image or a thematic map.
Each scanned unit area is given a number corresponding to the amount of radiation. If the geographical coordinates of each unit area are also known, a chorological matrix is produced. Such a matrix may be subject to calculations to have it displayed as a map. In such an operation, new row and column positions have to be calculated, and the corresponding pixel values assigned. At this point, these values have to be interpolated, thus slightly changed.
Such a matrix may be manipulated endlessly, with other data sources/maps added, subtracted, multiplied and divided. These techniques refer to what is known as digital image processing, and are employed in handling the large chorological matrices resulting from remote sensing via satellites.
Today this type of data is an essential source of mapping. Remote sensing and digital image processing are quick and inexpensive techniques which enable us to constantly get updated maps, and they are necessary tools in the search for real-time local and global mapping of environmental changes.
Last update: 26 November 2009