Lunar surface roughness based on multiscale morphological method

Cao, Wei; Cai, Zhanchuan; Tang, Zesheng

doi:10.1016/j.pss.2014.09.009

Cited by 18 publications

(5 citation statements)

References 21 publications

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“…This is consistent with Rosenburg et al () who found that while most of the highlands have a fractal nature, the maria seem to have a more complex behavior. This was also noted by Cao et al (, ). Light plains (either cryptomeria or impact basin and crater ejecta deposits) have correlation coefficients similar to the mare values, suggesting that they also tend toward a more complex behavior than the general highlands.…”

Section: The Global Isotropic Roughness Properties Of the Lunar Surfacesupporting

confidence: 82%

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Analysis of the Topographic Roughness of the Moon Using the Wavelet Leaders Method and the Lunar Digital Elevation Model From the Lunar Orbiter Laser Altimeter and SELENE Terrain Camera

2020

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The Wavelet Leaders Method (WLM) is a wavelet-based multifractal formalism that allows the identification of scale breaks (thus scaling regimes), the definition of scaling properties (mono versus multi fractality of the surface), and the calculation of the Hölder exponent that characterizes each pixel, based on the comparison between a theoretical wavelet and topographic values. Here we use the WLM and the SLDEM2015 digital elevation model to provide a near-global and a local isotropic characterization of the lunar roughness. The near-global study of baselines between 330 m and 1,350 km reveals scale breaks at~1.3, 42.2, and 337.6 km. Scaling properties and Hölder exponent values were calculated for the three corresponding scaling regimes: 330-659 m, 1.3-21.1 km, and 42.2-168.8 km. We find that the dichotomy between the highlands and the maria is present at all scales. Between 330 and 659 m, the Hölder exponent map shows the unique signature of Orientale basin, rilles, and a correlation with the age of mare units. Between 1.3 and 21.1 km, it shows the unique signature of the Orientale basin and a relationship with the density of 5-to 20-km-diameter craters. Scaling properties and Hölder exponent values were also calculated locally for complex craters, basins, rilles and light plains, for two scaling regimes: 165-659 m and 1.3-21.1 km. Relationships between the Hölder exponent values at 165-659 m, the density of <500-m-diameter craters and different geologic units were found and a potential scale break near 165 m was identified.Plain Language Summary Measures of topographic roughness are important as they can be used to better understand the fundamental processes shaping planetary surfaces and map regional variations of texture or identify geomorphologic units. Here we use a new approach to characterize the roughness of the Moon, which involves the comparison of a theoretical wavelet to topographic measurements. From this comparison we can identify at which scales the topographic roughness changes in properties and quantify these properties. We find that the roughness has distinct characteristics in three scale ranges: 330-659 m, 1.3-21.1 km, and 42.2-168.8 km. For example, at scales lower than 659 m, mare basalt surfaces appear to become rougher with age as they contain more 330-to 659-m-diameter craters, whereas between 1.3 and 21.1 km, contiguous geological units exhibit different levels of roughness, which allows to delineate them. The new approach to characterize the surface roughness has successfully been applied and validated on the Moon and can thus be confidently used to better understand the processes shaping other less studied planetary surfaces and aid in their geologic mapping.

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Section: The Global Isotropic Roughness Properties Of the Lunar Surfacesupporting

confidence: 82%

“…All of these studies relied on the use of along‐track topographic measurements and thus provided non isotropic assessments of the surface roughness. Cao et al (, ) used fractal‐based approaches and LOLA gridded data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Topographic Roughness of the Moon Using the Wavelet Leaders Method and the Lunar Digital Elevation Model From the Lunar Orbiter Laser Altimeter and SELENE Terrain Camera

2020

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“…Using similar methods, Kreslavsky and Head (2003) found an asymmetry in the Martian slopes, which was interpreted to be due to the asymmetry in the insolation pattern. The roughness maps of the Moon were presented by Rosenburg et al (2011), Kreslavsky et al (2013), and Cao et al (2015). Rosenburg et al (2011) studied roughness and slopes of lunar topography using the Lunar Orbiter Laser Altimeter (LOLA) data and found drastically different roughness properties for lunar maria and highlands.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness and Gravitational Slope Distributions of Vesta and Ceres

et al. 2019

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Heavily cratered terrains dominate the surfaces of asteroid 4 Vesta and dwarf planet 1 Ceres. The data from the Dawn spacecraft allowed reconstruction of high‐resolution shape models of these bodies. We used the stereophotoclinometric shape models to compute gravitational slopes and topographic roughness of Vesta and Ceres. We compute the slope distributions of Vesta and Ceres and compare them to those of the other bodies with heavily cratered terrains. The distribution of slopes of Vesta and Ceres has a kink at ≈34°. The slope distribution is steeper for slopes higher than ≈34°. The Moon and Mars have a similar kink but at a lower (shallower) slope (≈29°–30°). We hypothesize that this kink corresponds to the angle of repose, which is higher on the two minor bodies compared to that of Mars and the Moon. We have also analyzed the topographic roughness of Vesta and Ceres. Vesta's topography is characterized by lower roughness in the southern hemisphere that was resurfaced by giant impacts. The roughness of Ceres is mostly controlled by regional‐scale geology with no significant large‐scale variations. Large, fresh craters have smooth ejecta blankets on both Vesta and Ceres unlike previously observed rough ejecta on the Moon, Mars, and Mercury. On Ceres, the smooth ejecta craters also have smooth floors explained by impact slurry. Lineaments of elevated roughness are abundant on Ceres, whereas are nearly absent on Vesta. The roughness maps we produced provide a synoptic view of the surface texture and could be used to aid geologic mapping.

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“…The roughness extracted from a Digital Elevation Model (DEM) is defined in terms of the variability of the elevation data. The main extraction methods are the root mean square applied to elevation and slope grids (Grohman, 2015), eigenvalue ratios (Cloude, 1999;Cloude et al, 2000), structuring elements (Cao et al, 2015), fractal dimension (Taud and Parrot, 2005), discrete Fourier transform (Bingham and Siegert, 2009), continuous wavelet transform and wavelet lifting scheme (Hani et al, 2011(Hani et al, , 2012, as well as standard deviation in a fit plane (Hobson, 1972;Evans, 1972;Herzfeld et al, 2000;Haneberg et al, 2005,). The results of these measurements depend on the resolution of the DEM and the size of the moving window (Grohmann et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Positive and Negative Roughness According to Local Differences Between Dem Surface and 3d Reference Planes

Parrot¹,

Ramírez-Núñez²

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The irregularities of the earth’s surface are quantified by means of roughness measurements using Digital Elevation Models (DEM’s). This article presents a roughness measurement method that is based on the calculation of the difference of altitude existing between a plane passing through the centre of a moving window and the altitude of the DEM surface inside this window. This method differs from the measure of the standard deviation and best fit plane, in the sense that it considers all difference values, positives or negatives. The measurement is done in a 3 × 3 or a 5 × 5 moving window and contemplates inside this window the plane which passes through the centre of the window and the highest pixel located in the border or perimeter of this window. According to the 3D configuration of the DEM surface inside the moving window, the sum of all the differences is positive or negative, allowing to discriminate the local morphology independently of the global roughness. The roughness variable which distinguishes negative and positive values allows to classify accurately landscape units such as watersheds, riverbeds, volcanic assemblages as well as landforms associated with tectonic structures.

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Lunar surface roughness based on multiscale morphological method

Cited by 18 publications

References 21 publications

Analysis of the Topographic Roughness of the Moon Using the Wavelet Leaders Method and the Lunar Digital Elevation Model From the Lunar Orbiter Laser Altimeter and SELENE Terrain Camera

Analysis of the Topographic Roughness of the Moon Using the Wavelet Leaders Method and the Lunar Digital Elevation Model From the Lunar Orbiter Laser Altimeter and SELENE Terrain Camera

Surface Roughness and Gravitational Slope Distributions of Vesta and Ceres

Positive and Negative Roughness According to Local Differences Between Dem Surface and 3d Reference Planes

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