Comparison of ground- and aerial-based approaches for quantifying polygonal terrain network geometry on Earth and Mars via spatial point pattern analysis
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...
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| Veröffentlicht in: | Planetary and space science Jg. 58; H. 12; S. 1636 - 1649 |
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| Sprache: | Englisch |
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01.10.2010
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| ISSN: | 0032-0633, 1873-5088 |
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| Abstract | 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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| AbstractList | 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. 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 (a140.25m/pixel) aerial images. Images of pixel size a[control][yen1m 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. |
| Author | Haltigin, Tim Pollard, Wayne Dutilleul, Pierre |
| Author_xml | – sequence: 1 givenname: Tim surname: Haltigin fullname: Haltigin, Tim email: timothy.haltigin@asc-csa.gc.ca, timothy.haltigin@mail.mcgill.ca organization: Department of Geography, McGill University, 805 Sherbrooke St. W., Montreal, QC, Canada H3A 2K6 – sequence: 2 givenname: Wayne surname: Pollard fullname: Pollard, Wayne email: wayne.pollard@mcgill.ca organization: Department of Geography, McGill University, 805 Sherbrooke St. W., Montreal, QC, Canada H3A 2K6 – sequence: 3 givenname: Pierre surname: Dutilleul fullname: Dutilleul, Pierre email: pierre.dutilleul@mcgill.ca organization: Department of Plant Science, McGill University, Raymond Building, 21111 Lakeshore Rd., Ste-Anne-de-Bellevue, QC, Canada H9X 3V9 |
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| CitedBy_id | crossref_primary_10_1002_ppp_1810 crossref_primary_10_1016_j_geomorph_2011_07_002 crossref_primary_10_1002_ppp_1741 crossref_primary_10_1007_s12040_024_02337_9 crossref_primary_10_1002_jgre_20111 crossref_primary_10_1016_j_pss_2014_02_013 crossref_primary_10_1002_ppp_2123 crossref_primary_10_1016_j_epsl_2013_11_005 crossref_primary_10_3390_su12041671 crossref_primary_10_1016_j_scitotenv_2017_09_153 |
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| Keywords | Mars Orbiter Camera (MOC) Mars Spatial point pattern analysis Axel Heiberg Island High Resolution Imaging Science Experiment (HiRISE) Polygonal terrain |
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| Title | Comparison of ground- and aerial-based approaches for quantifying polygonal terrain network geometry on Earth and Mars via spatial point pattern analysis |
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