Framework for Mapping Sublimation Features on Mars’ South Polar Cap Using Object-Based Image Analysis

Mars’ south polar cap hosts dynamic landforms known as Swiss cheese features (SCFs), which form through the sublimation of carbon dioxide (CO<sub>2</sub>) ice driven by the planet’s extreme seasonal and diurnal solar insolation cycles. These shallow, rounded depressions—first identified...

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Bibliographic Details
Main Authors: Racine D. Cleveland, Vincent F. Chevrier, Jason A. Tullis
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
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Online Access:https://www.mdpi.com/2072-4292/17/14/2372
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Summary:Mars’ south polar cap hosts dynamic landforms known as Swiss cheese features (SCFs), which form through the sublimation of carbon dioxide (CO<sub>2</sub>) ice driven by the planet’s extreme seasonal and diurnal solar insolation cycles. These shallow, rounded depressions—first identified by Mars Global Surveyor in 1999 and later monitored by the Mars Reconnaissance Orbiter (MRO)—have been observed to increase in size over time. However, large-scale analysis of SCF formation and growth has been limited by the slow and labor-intensive nature of manual mapping techniques. This study applies object-based image analysis (OBIA) to automate the detection and measurement of SCFs using High-Resolution Imaging Science Experiment (HiRISE) images spanning five Martian years. Results show that SCFs exhibit a near-linear increase in area, suggesting that summer sublimation consistently outpaces winter CO<sub>2</sub> deposition. Validation against manual digitization shows discrepancies of less than 1%, confirming the reliability of the OBIA method. Regression-based extrapolation of growth trends indicates that the current generation of SCFs likely began forming between Martian years 7 and 16, approximately corresponding to Earth years 1976 to 1998. These findings provide a quantitative assessment of SCF evolution and contribute to our understanding of recent climate-driven surface changes on Mars. HiRISE images were processed using the eCognition software to detect, classify, and measure SCFs, demonstrating that OBIA is a scalable and effective tool for analyzing dynamic planetary landforms.
ISSN:2072-4292