The Martian Boulder Automatic Recognition System, MBARS

Don R. Hood1,2, S.F. Sholes3,4, S. Karunatillake5, C.I. Fassett6, R.C. Ewing2, J. Levy7
1Department of Geosciences, Baylor University, Waco, Texas 76706, USA
2Department of Geology and Geophysics, Texas A&M University, College Station, Texas 77843, USA
3Department of Earth and Space Sciences, University of Washington, Seattle, Washington 98195, USA
4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA
5Geology and Geophysics Department, Louisiana State University, Baton Rouge, Louisiana 70803, USA
6NASA Marshall Space Flight Center, Huntsville, AL 35805, USA
7Department of Earth and Environmental Geosciences, Colgate University, Hamilton, New York 13346, USA
Corresponding author: Don Hood (Don_Hood@baylor.edu)

Key Points

Abstract

Boulder-sized clasts are common on the surface of Mars, and many are sufficiently large to be resolved by the High Resolution Imaging Science Experiment (HiRISE) camera aboard the Mars Reconnaissance Orbiter (MRO). The size, number, and location of boulders on the surface and their spatial distribution can reveal the processes that have operated on the surface, including boulder erosion, burial, impact excavation, and other mechanisms of boulder transport and generation. However, quantitative analysis of statistically significant boulder populations which could inform these processes entails prohibitively laborious manual segmentation, granulometry and morphometry measurements over large areas. Here we develop and describe an automated tool to locate and measure boulders on the martian surface: the Martian Boulder Automatic Recognition System (MBARS). The open-source Python-based toolkit autonomously measures boulder diameter and height in HiRISE images enabling rapid and accurate assessments of boulder populations. We compare our algorithm with existing boulder-counting methodologies, manual analyses, and objects of known size to verify accuracy and precision. Additionally, we test MBARS quantitatively characterizing boulders around an impact crater in the martian northern lowlands. We compare this to previous work on rock excavation during impact cratering using manually counted boulders around lunar craters.

Plain Language Summary

Large boulders (>1 m diameter) are widely distributed on the martian surface. They are easily observed from orbit, making them visible with high resolution imaging. Mapping the location, number, and size of boulders is helpful for understanding which geological processes bring boulders to the surface, move them around, and fragment them into smaller rocks and soil. Here, we present and describe the Martian Boulder Automatic Recognition System (MBARS), a set of tools that automatically locates and measures boulders in high-resolution images of the martian surface. We compare results generated by MBARS with results from other automated boulder-measuring tools, as well as with results from manual boulder measurements to ensure accuracy. We also use MBARS to map boulders around an impact crater on Mars and compare the boulder distribution to a similar-sized crater on the Moon.

Keywords

Remote Sensing (5464), Surface Materials and Properties (5470), Instruments and Techniques (5494), Impact phenomena and cratering (5420)

1. Introduction

Images taken by the High Resolution Imaging Science Experiment (HiRISE) camera aboard the Mars Reconnaissance Orbiter (MRO) show that meter-scale boulders, blocks, and other megaregolith observed by landers and rovers are common across the entire surface of Mars (Golombek et al., 2008, 2012). Observations of megaregolith on Mars and other bodies have been used to examine a wide variety of surface processes, including impact cratering (Krishna & Senthil Kumar, 2016; Levy et al., 2018; Watkins et al., 2019), bedrock degradation (Nagle-McNaughton et al., 2020), thermal cracking (Eppes et al., 2015), erosion (Golombek et al., 2006; de Haas et al., 2013), and glacial processes (Levy et al., 2021). Landing site assessments is also an essential step in all missions, for which boulders are a major landing hazard (Golombek et al., 2008, 2012; Wu et al., 2022). However, the fundamentally time-consuming and difficult task of manually identifying, locating, and measuring boulders, blocks, and other megaregolith (henceforth simplyboulders ) is a large burden on these investigations. Tools to automatically locate and measure boulders have been previously developed for martian landing-site analyses (Golombek et al., 2008, 2012, 2016), as well as more general applications (Nagle-McNaughton et al., 2020) and lunar studies (Li & Wu, 2018) to ease this burden. For Mars, the two existing algorithms have substantial challenges to their application. The algorithm developed for landing site analysis (Golombek et al., 2008), henceforth referred to as the Golombek-Huertas (G-H) method, typically requires mission team expertise for accurate use, posing challenges to widespread adoption. The more recent method developed for general use (Nagle-McNaughton et al., 2020), henceforth referred to as the Nagle-McNaughton (N-M) method, does not assess boulder height and results from that method are given as upper and lower bounds on population morphometry, which may be insufficient for some investigations. Here we present an advancement from the existing boulder measuring approaches with an open-source Python-based methodology to automatically identify, locate, and measure boulders in high-resolution satellite images of the martian surface. The core of this methodology is the newly developed Mars Boulder Automatic Recognition System (MBARS) which detects boulders via shadow identification, an established technique (Golombek et al., 2008), and uses common Python libraries and standard Geographic Information System (GIS) files and formats.
In this paper, we describe the MBARS algorithm as well as the complete methodology to determine boulder morphometry from HiRISE images. We then test MBARS against objects of known size, compare MBARS results to other algorithms and manual analysis, and discuss uncertainties and errors within the methodology. As a demonstration of potential applications of MBARS, we examine the boulder population surrounding an unnamed crater in one of the test images, characterizing the ejecta and comparing it to previously analyzed lunar craters of similar size (Watkins et al., 2019).

2. Methods

2.1. The HiRISE Dataset

The primary data for this work are images from the HiRISE camera, a high-resolution camera that provides up to ~25 cm/pixel images of the martian surface. Typical HiRISE footprints are ~3 km x 6 km and images are globally distributed and not contiguous due to their small size. The Point-Spread Function (PSF) of HiRISE is ~1.5 pixels (Kirk et al., 2008; McEwen et al., 2007), so objects ≳1 meter across are resolvable at typical resolutions. The PSF sets a lower bound on measurements made in HiRISE images, as precision below ±1.5 pixels is not achievable without deconvolution or other image-enhancing methods. Unless otherwise specified, we use the map-projected black-and-white JP2 data products available on the Planetary Data System (PDS). All images were spatially corrected, when necessary, using the fix_jp2 protocol available on the PDS (https://pdsimage2.wr.usgs.gov/pub/pigpen/).
The PSF and resolution of HiRISE images limit the diameter of resolvable boulders to ≳1 meter, but the incidence angle of the sun in each observation places further limitations. Lower incidence angles (sun closer to zenith) will cause shadows to shorten, potentially below detection limits, rendering boulders undetectable via their shadows. Using our boulder model (Fig. 1) we can explore if there are incidence angles for which we do not expect to detect boulders within any given size range. An object three pixels across is considered a reasonable lower limit to reliably detect an object, so we use a minimum shadow length of 3 pixels (~0.75 m) to determine the lower incidence angle limit. The minimum detectable boulder height in meters can be calculated as \(h_{\min}=0.75/tan(\theta)\), where θ is the solar incidence angle measured from zenith. On the Moon, boulder height to diameter ratio (h/D) averaged 0.54 across several study sites (Demidov & Basilevsky, 2014) and martian h/D appears comparable or smaller (Golombek et al., 2012). Taking this ratio as a starting assumption for Mars, the minimum boulder diameter that will cast a sufficiently long shadow is \(D_{\min}=1.4/tan(\theta)\). Due to the sun-synchronous orbit of MRO, the incidence angle of most HiRISE images falls between ~40° and ~75°. In these images boulders of diameters above 0.37 m and 1.67 m respectively, will cast detectable shadows. Therefore, boulders that are sufficiently wide to be detected (≳1m) will also cast detectable shadows in most HiRISE images, and boulders > 1.5 m wide are predicted to cast detectable shadows in nearly all HiRISE images if they are present.