Day 3 Question 1: The significance of image resolution in remote sensing

Question 1:

A working knowledge of image resolution is critical for understanding the practical and conceptual aspects of remote sensing. Write an essay to discuss the significance of image resolution as a concept that extends across some major aspects of remote sensing. Whenever possible, you are encouraged to use your own examples to make the case.

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One of the most critical aspects of any remote sensing application is the spatial resolution of the sensor platform. Satellite sensors vary in pixel resolution from sub-meter to many kilometers. Each remote sensing application must carefully match an adequate spatial resolution to the scale of the phenomenon being studied. Typically, the satellite sensor resolution should be less than half the size of the smallest feature that must be resolved (Jensen, 1996).

The issues of scale in remote sensing share similarities and differences with those of ground-based studies. For example, in most ecological studies, the challenge is to apply conclusions drawn from detailed studies of small patches to broader scales (Turner et al., 2001). In fact, the size of the study area used in ground-based studies often affects the conclusions drawn from the study. This scaling issue also applies to remote sensing applications (Lillesand et al., 2004), however, in remote sensing the challenge is often to apply what is learned over a broad area (the satellite scene) to a smaller, more detailed patch, which can range from a large group of pixels to sub-pixel areas.

Remote measurements in mountainous terrain require similar spatial resolution to that of the topographic features. In most montane areas, this is on the order of tens of meters (Dozier et al., 1987). Satellite data of this resolution are available from Landsat Thematic Mapper, Enhanced Thematic Mapper, SPOT, and several newer commercial satellites.

In some applications, lower spatial resolution can be offset by higher spectral resolution, or the number and size of wavelength bands that the sensor measures. Hyperspectral data, data which have higher spectral resolution than required by the given application, have been employed to detect features much smaller than the spatial resolution of the sensor. For example, Nolin and Dozier (2000) have employed hyperspectral data to detect the grain size of snow. Their technique was based on the unique spectral reflectances of snow of different grain sizes. Painter et al. (2003) also determined snow-covered area from hyperspectral imagery. Similar applications have been used by geologists to locate rare minerals.

Other scale issues haunt the application of remote sensing. Although remotely-sensed data offer broad coverage, for any given study a balance must be struck between spatial coverage and spatial resolution. The surface of the Earth is too large to sense with high resolution imagery. The resulting dataset would be too large to manage. Furthermore, ground-truth data are usually collected from relatively small areas, which raises issues when relating those data to the broader spatial scales of the remotely-sensed imagery. Turner et al. (2001) warn that any conclusions drawn from studies at one spatial scale may not apply to other scales. Finally, there is great trouble when combining datasets with different spatial resolutions. This issue is covered in more detail in question 2.

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Works Cited:
 
Dozier, J., et al. (1987), Prospects and Concerns for Remote Sensing of Snow and Ice, DRAFT, 31 pp, Ad Hoc Panel on Remote Sensing of Snow and Ice, Committee on Glaciology, Polar Research Board, National Academy of Sciences.

Jensen, J. R. (1996), Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd edition ed., 316 pp., Prentice Hall, New Jersey.

Lillesand, T. M., et al. (2004), Remote Sensing and Image Interpretation, 5th ed., 763 pp., John Wiley & Sons, Inc., New York.

Nolin, A. W., and J. Dozier (2000), A hyperspectral method for remotely sensing the grain size of snow, Remote Sensing of Environment, 74, 207-216.

Painter, T. H., et al. (2003), Retrieval of subpixel snow-covered area and grain size from imageing spectrometer data, Remote Sensing of Environement, 85, 64-77.

Turner, M. G., et al. (2001), The critical concept of scale, in Landscape Ecology and Theory in Practice: Patterns and Processes, Springer, New York.