Figure 5 - uploaded by Louis Scuderi
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Catchment misidentified as a "cirque". Figure outer boundary corresponds to the output bounding box as interpreted by the CNN model. Red dashed line is the approximate outline of the feature contained in that box. Center of feature is at 36.31°N 118.80°W. Highest point on feature is at ~1,735 m while the drainage basin "threshold" is at ~1350 m. Contour interval is 20 m.
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Morphological characteristics of cirques have been studied for decades; however, no repeatable set of metrics has been derived that can consistently identify them. Perhaps more importantly, there is no consensus definition of the form that distinguishes cirques and clusters of cirques from non-cirques. In our approach, we use Shuttle Radar Topograp...
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... lower elevation range matches the approximate Equilibrium Line Altitude (ELA ~ 2,600 m) boundary of glacial ice during the Pleistocene (Burbank, 1991;Clark et al., 1994). The lowest of the six detections >0.20 CL is found at ~1,500 m and is a steep sided enclosed hollow (CL = 0.21, Figure 5). There is a small threshold-like break in slope where the drainage flows into a larger basin and the form is bounded on its southern side by a slightly raised ridge. ...
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Citations
... Evans and Cox, 1995;Federici and Spagnolo, 2004;Seif and Ebrahimi, 2014). In recent years, object-based image classification, deep learning, and automated approaches have been developed to help identify and map cirque outlines (Eisank et al., 2010;Anders et al., 2015;Li and Zhao, 2022;Scuderi and Nagle-McNaughton, 2022). ...
With the availability of improved digital elevation models (DEMs) of global coverage, the morphological analysis of large populations of glacial cirques is possible, and can be used to derive important palaeoclimate and
environmental information related to the distribution and history of former glaciers. In 2017, an ArcGIS toolbox,
ACME (Automated Cirque Metrics Extraction), was developed to derive 16 cirque metrics based on input cirque
outlines, threshold midpoints and DEMs. ACME has been widely used in cirque morphological analysis and
regional comparisons. This paper presents a revised and extended toolbox, ACME2. This extended toolbox includes
new functions to automatically derive cirque threshold midpoints (cirque foci) and 49 morphometric and
locational variables, with attributes related to cirque location, size, shape, altitude, slope, and aspect, including
variables related to the median axis, as well as 3 input metadata attributes. ACME2 also improves the methods
for calculation of the hypsometric maximum and integral, and implements a new method for plan closure to be
more consistent with the original definition. All ACME2 tools are coded in Python and can be imported into
ArcGIS with user-friendly interfaces. Comparisons for 155 cirques in the English Lake District and 51 in the
Shulaps Range, British Columbia, indicate consistency between the ACME2-derived and manually derived
metrics, with most correlations r > 0.90: none <0.70. ACME2 provides more cirque metrics and automates the
whole calculation sequence with cirque outlines and DEMs. Its comprehensive approach facilitates understanding
of cirque form and development in all its variety.
... An interesting application of dimensionality reduction is for testing whether manually extracted features contain meaningful, but disparate, information. A recent paper [191] tested different properties of landslides on Mars such as the slope angle, slide length, and thermal inertia, and similar work has been carried out on Earth-based DTMs, examining the characteristic properties of cirques [192]. Besides this, other Mars researchers have used principal component analysis and sensitivity analyses to identify the most influential features in their data as a preprocessing step, including identifying issues with data transmissions from Mars [193], segmenting Martian dust storms in satellite imagery [124,125], and a rover-based terrain classifier [116]. ...
... For example, cirques normally found in mountainous environments are generally defined as a semicircular feature with a steep head and sidewalls, an over-deepened central area, and a low sill that defines the lower edge of the feature. However, the poorly defined lower edge often grades into a talus slope, and the upper edges may take the form of a sharp arête or a poorly defined break in the slope [192]. Even when the prescribed features are present, the "edges" of the feature may be diffuse/transitional. ...
... Even when the prescribed features are present, the "edges" of the feature may be diffuse/transitional. Measures of cirque size and form can thus vary from observer to observer [192,262]. Nonetheless, even with these uncertainties, a relative novice mountaingoer can recognize a cirque. As such, while robust and verified algorithms can objectively and repeatably identify crisply defined objects, these identifications are currently lacking in the geomorphic realm. ...
Data analysis methods have scarcely kept pace with the rapid increase in Earth observations, spurring the development of novel algorithms, storage methods, and computational techniques. For scientists interested in Mars, the problem is always the same: there is simultaneously never enough of the right data and an overwhelming amount of data in total. Finding sufficient data needles in a haystack to test a hypothesis requires hours of manual data screening, and more needles and hay are added constantly. To date, the vast majority of Martian research has been focused on either one-off local/regional studies or on hugely time-consuming manual global studies. Machine learning in its numerous forms can be helpful for future such work. Machine learning has the potential to help map and classify a large variety of both features and properties on the surface of Mars and to aid in the planning and execution of future missions. Here, we outline the current extent of machine learning as applied to Mars, summarize why machine learning should be an important tool for planetary geomorphology in particular, and suggest numerous research avenues and funding priorities for future efforts. We conclude that: (1) moving toward methods that require less human input (i.e., self- or semi-supervised) is an important paradigm shift for Martian applications, (2) new robust methods using generative adversarial networks to generate synthetic high-resolution digital terrain models represent an exciting new avenue for Martian geomorphologists, (3) more effort and money must be directed toward developing standardized datasets and benchmark tests, and (4) the community needs a large-scale, generalized, and programmatically accessible geographic information system (GIS).
Cirques are typical erosional landforms of glaciers and have been used as bases for paleoclimate and paleoenvironment reconstruction and for understanding the interactions between glacial erosion, climate, and topography. The availability of high-resolution digital elevation models (DEMs) provides the opportunity to map large populations of cirques for regional and global scale analysis. However, cirque outlines are still mainly determined based on manual digitization, which is time consuming and labor intensive. This paper introduces an automated method to recognize and delineate cirques using DEMs based on a series of hydrological and morphological analyses, including delineating stream network, filtering streams, determining potential cirque threshold points, and delineating cirque outlines. A semi-automated tool is also developed based on user-specified threshold points or cross sections. The related tools are coded in python and imported into ArcGIS as a toolbox, AutoCirque, with user friendly interfaces. Comparison in a test area of the eastern Tian Shan, China, indicated that the population statistics are relatively consistent between manually digitized and auto-delineated cirques. Detailed comparisons for 11 selected cirques indicated that the AutoCirque-delineated and manually digitized cirque outlines are similar in shape with an average boundary offset of approximately one DEM cell size (30 m) and a 70–90% overlap-fit percentage. The derived cirque metrics are also similar, especially for elevation, slope, and aspect related metrics. This toolbox can significantly speed up the analytical processes, remove the subjectivity in delineating cirque outlines, and allow for the comparison of cirque morphology and metrics at regional and global scales.
