Comparison of ground- and aerial-based approaches for quantifying polygonal terrain network geometry on Earth and Mars via spatial point pattern analysis

Details Comparison of ground- and aerial-based approaches for quantifying polygonal terrain network geometry on Earth and Mars via spatial point pattern analysis Comparison of ground- and aerial-based approaches for quantifying polygonal terrain network geometry on Earth and Mars via spatial point pattern analysis

Oct 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010p%26ss...58.1636h&link_type=abstract

Planetary and Space Science, Volume 58, Issue 12, p. 1636-1649.

Physics

3

Scientific paper

Recently, a particular statistical method - spatial point pattern analysis (SPPA) - has been introduced as an effective means by which qualitative, observable variations in polygonal terrain network arrangements on Earth and Mars can be quantified. A number of ground- and aerial-based techniques are available from which to derive the required input data: the spatial (x-y) coordinates of all polygon trough intersections within the site. However, each of the data collection methods may contain some level of error. Thus, the overarching question addressed by this research is: “how are the results of SPPA affected by the method by which the input data were generated?” At two polygonal terrain sites in the Canadian High Arctic, we performed ground-based surveys using differential and non-differential Global Positioning Systems (GPS) as well as photogrammetric analysis of aerial and satellite images of varying resolution to determine the trough intersection coordinates. It was found that the most robust statistical results were produced when using data from a combination of differential GPS surveys and high-resolution (˜0.25 m/pixel) aerial images. Images of pixel size ≥1 m were found to be unsuitable for this type of analysis. With respect to the investigation of similar Martian landforms, HiRISE and MOC images of polygonal terrain sites in southwestern Utopia Planitia were analyzed. Our results show that it is strongly preferable to perform SPPA using HiRISE images, though an empirical model is outlined that could be used to correct for errors arising from the reduced resolution inherent to MOC images.

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