Cell size of raster data
Last modified September 22, 2008
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The level of detail (of features/phenomena) represented by a raster is often dependent on the cell size, or spatial resolution, of the raster. The cell must be small enough to capture the required detail but large enough so computer storage and analysis can be performed efficiently. More features, smaller features, or a greater detail in the extents of features can be represented by a raster with a smaller cell size. However, more is not often better. Smaller cell sizes result in larger raster datasets to represent an entire surface; therefore, there is a need for greater storage space, which often results in longer processing time.
Learn about displaying the raster's spatial resolution in ArcMap
Choosing an appropriate cell size is not always simple. You must balance your application's need for spatial resolution with practical requirements for quick display, processing time, and storage. Essentially, in a GIS, your results will only be as accurate as your least accurate dataset. If you're using a classified dataset derived from 30-meter resolution Landsat imagery, then creating a digital elevation model (DEM) or other ancillary data at a higher resolution, such as 10 meters, may be unnecessary. The more homogeneous an area is for critical variables, such as topography and land use, the larger the cell size can be without affecting accuracy.
Determining an adequate cell size is just as important in your GIS application planning stages as determining what datasets to obtain. A raster dataset can always be resampled to have a larger cell size; however, you will not obtain any greater detail by resampling your raster to have a smaller cell size. Depending on your future plans for your data, it may be worthwhile to store a copy of your data at its smallest and most accurate cell size, meanwhile resampling it to match that of your least—this could increase your analysis processing speed.
The following factors should be considered when specifying the cell size:
When working with imaged raster data, there are four types of resolution you may be concerned with: spatial resolution, spectral resolution, temporal resolution, and radiometric resolution.
In a GIS, you are most often concerned with the spatial resolution of a raster dataset, especially when displaying or comparing raster data with other data types, such as vector. In this case, resolution refers to the cell size (the area covered on the ground and represented by a single cell). 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.
Spectral resolution describes the ability of a sensor to distinguish between wavelength intervals in the electromagnetic spectrum. The higher the spectral resolution, the narrower the wavelength range for a particular band. For example, a single-band, grayscale, aerial photograph (image) records wavelength data extending over much of the visible portion of the electromagnetic spectrum; therefore, it has a low spectral resolution. A color image (with three bands) basically 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 very narrow spectral bands throughout portions of the electromagnetic spectrum, resulting in data that has a very high spectral resolution.
Temporal resolution refers to the frequency in which images can be captured over the same place on the earth's surface, otherwise known as the revisit period, which is a term most often used when referring to satellite sensors. Therefore, a sensor that capturesdata 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.
Spatial resolution refers to the dimension of the cell size representing the area covered on the ground. Therefore, if the area covered by a cell is 5 x 5 meters, then the resolution is 5 meters. The higher the resolution of a raster, the smaller the cell size and, thus, the greater the detail. This is the opposite of scale. The smaller the scale, the less detail shown. For example, an orthophotograph displayed at a scale of 1:2,000 shows more details (appears zoomed in) than one displayed at a scale of 1:24,000 (appears zoomed out). However, if this same orthophoto has a cell size of 5 meters, the resolution will remain the same no matter what scale it's displayed at, since the physical cell size (the area covered on the ground and represented by a single cell) does not change.
Below, the scale of the image on the left (1:50,000) is smaller than the scale of the image on the right (1:2,500); however, the spatial resolution (cell size) of the data is the same.
Below, the spatial resolution of the data used in the image on the left is lower than the spatial resolution of the data used in the image on the right. This means the cell size of the data in the left image is larger than that of the data in the right image; however, the scale at which each is displayed s the same.