The level of detail (of objects and phenomena) represented by an image is often dependent on the pixel (cell) size, or spatial resolution, of the image. The pixel must be small enough to capture the required detail but large enough to facilitate computer storage and analysis efficiencies. More objects on the ground, smaller objects, or greater detail in the extents of ground features can be represented by an image with a smaller pixel size. However, smaller pixel sizes result in larger raster datasets to represent an entire area. This requires greater storage space, which often results in longer processing time.
When choosing an appropriate pixel size, balance the desired spatial resolution—based on the minimum mapping unit of the ground features you need to analyze—with practical requirements for quick display, processing time, and storage. Essentially, in a GIS, the results are only as accurate as the least accurate dataset. If you're using a classified dataset derived from 30-meter resolution satellite imagery, using 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 cover, the larger the cell size can be without affecting accuracy.
Determining an adequate pixel size is as important in planning an application based on remote sensing as determining which datasets to obtain. An image dataset can be resampled to have a larger pixel size; however, resampling an image to have a smaller pixel size does not produce greater detail. You can store a copy of the data at its smallest and most accurate pixel size while resampling it to match that of the largest and least accurate pixel size. This may increase analysis processing speed.
Consider the following when specifying the pixel size:
- The spatial resolution of the input data
- The application and analysis to be performed based on the minimum mapping unit
- The size of the resultant database compared to disk capacity
- The response time
When working with image raster data, there are four types of resolution: spatial resolution, spectral resolution, temporal resolution, and radiometric resolution.
In a GIS, the spatial resolution of an image dataset is important, especially when displaying or comparing raster data with other data types, such as vector. In this case, resolution refers to the pixel size — the area covered on the ground and represented by a single pixel. A higher spatial resolution implies that there are more pixels per unit area. Higher spatial resolution allows you to resolve and analyze smaller ground objects. The first graphic below represents a higher spatial resolution than the third graphic.
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. Other considerations include the number and placement of bands covering an interval of the electromagnetic spectrum. For example, a single-band, grayscale, aerial image records wavelength data extending over much of the visible portion of the electromagnetic spectrum; therefore, it has a low spectral resolution. Conversely, advanced multispectral and hyperspectral sensors collect data from several bands up to hundreds of very narrow spectral bands throughout portions of the electromagnetic spectrum, resulting in data that has a very high spectral resolution. For example, the WorldView-3 satellite sensor collects images at 0.31-meter resolution (panchromatic) and 1.24-meter resolution in the eight visible and near-infrared bands, and 3.7-meter resolution in the eight shortwave infrared bands.
Temporal resolution refers to the frequency at which images are captured over the same location on the earth's surface, otherwise known as the revisit period, which is a term most often used when referring to satellite sensors. For example, a sensor that captures data once a week has a higher temporal resolution than one that captures data twice 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. The more bits an image has, the more differences between objects can be detected and measured. For example, a Landsat-8 shortwave infrared band is typically 12-bit data, and a WorldView-3 (WV-3) shortwave infrared band is 14-bit data; therefore, the WV-3 data has a higher radiometric resolution.
Spatial resolution vs. scale
Spatial resolution refers to the dimension of the pixel size representing the area covered on the ground. For example, if the area covered by a pixel is 5 x 5 meters, the resolution is 5 meters. The higher the resolution of an image, the smaller the pixel size and the greater the detail. This is the opposite of scale. The smaller the scale, the less detail is shown. For example, an orthoimage displayed at a scale of 1:2,000 shows more detail (appears zoomed in) than one displayed at a scale of 1:24,000 (appears zoomed out). However, if this same orthophoto has a pixel size of 5 meters, the resolution remains the same regardless of its display scale, since the physical pixel size (the area covered on the ground and represented by a single pixel) does not change.
The scale of the first image below (1:50,000) is smaller than the scale of the second image (1:2,500); however, the spatial resolution (cell size) of the data is the same.
The spatial resolution of the data used in the first image below is lower than the spatial resolution of the data used in the second image. This means that the pixel size of the data in the first image is larger than that of the data in the second image; however, the scale at which each is displayed is the same.