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
- The Martian Boulder Automatic Recognition System is a new tool to
detect and measure boulders on the martian surface.
- MBARS is comparably or more accurate than prior published algorithms
that measure boulders.
- MBARS can readily reproduce results originally acquired through manual
measurement
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.