Data Resolution and Extent

Remotely sensed data provide a synoptic or regional view of the Earth's surface as well as the opportunity to identify particular features of interest. Analysis techniques frequently relate particular data values in an image to certain ground features, or to parameters which identify those features. However, the data acquisition methods of remote sensing implicitly involve at least one level of indirection. For example, a particular study may aim to determine vegetative cover and condition. Since such parameters are not directly measurable using remote sensing, they must be related to a property of vegetation which can be 'measured' remotely, namely reflectance. A further limitation which must be considered is that the data we collect using remote sensing only sample the potential range of measurements in the selected 'measurement space'. Figure 33 shows the indirect relationship between the data and measurement spaces.

Figure 33: Relationship between the measurement and analysis of image features.

To relate earth surface features or parameters to remotely sensed data the intrinsic ability of the parameters to be resolved in the type of measurements being made must be considered together with the effectiveness of the models which relate physical processes to these measurements. The question of the structural model is too broad to consider here, but the issue of measurement is both fundamental and vital to planning remotely sensed data acquisition and its subsequent processing.
The usual measurement space for remotely sensed data can have a variety of measurement 'dimensions', such as intensity, wavelength and position. These can be considered as providing a co-ordinate framework in space and time. A data set is usually a sample in this co-ordinate frame. The objective of any data analysis exercise is to distinguish effects and/or events in the data. To achieve this objective, a data set must be sufficiently resolved and cover a large enough extent.

Resolution refers to the intensity or rate of sampling, and extent refers to the overall coverage of a data set. Extent can be seen as relating to the largest feature, or range of features, which can be observed, while resolution relates to the smallest. For a feature to be distinguishable in the data, the resolution and extent of the measurement dimensions of the data set need to be appropriate to the measurable properties of the feature. For a feature to be separable from other features, these measurements must also be able to discriminate between the differences in reflectance from the features.

Resolution and extent can be seen to operate in four 'dimensions' of remotely sensed data acquisition:

a. Spectral p; resolution relates to the width of wavelength channels, extent describes the number and spectral range of channels in the image
b. Spatial p; resolution relates to pixel size, extent to the overall image coverage
c. Radiometric p; resolution relates to the energy difference which determines different radiation (or brightness) levels in an image, extent to the number of levels detected
d. Temporal p; resolution relates to the repeat cycle or interval between successive acquisitions, extent to the total period over which imagery is available.

Section 4 related remotely sensed measurements to interpretative parameters. This Section is concerned with the measurement model which is implicit in remotely sensed data and the way in which these four dimensions of data acquisition can affect its interpretative value. The suitability of a particular remotely sensed data source to a specific application will depend on the resolution and extent of all data dimensions. While the final selection of a data set is usually a compromise involving other factors such as cost and project timing, these aspects need to be carefully considered to ensure that the features to be identified can be adequately discriminated in the chosen data set. This concept is discussed in more detail in Chapter 1 of Colwell (1983).