Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery

Weixing Zhang,Chandi Witharana,Anna Liljedahl,Mikhail Kanevskiy

doi:10.3390/rs10091487

Weixing Zhang, Chandi Witharana + Show 2 more

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https://doi.org/10.3390/rs10091487

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Abstract

The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km2) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70 and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system.

Highlights

Polygonal topography typical of the Circum-Arctic permafrost region is associated with ice wedges, developed by repeated frost cracking and formation of ice veins as a result of filling cracks with meltwater in the spring time; this process occurs over hundreds to thousands of years and leads to formation of large massive-ice bodies [1,2,3,4,5,6,7,8,9,10,11,12,13,14]
We focus on the two major types—LCP and HCP polygons—widely represented within the Circum-Arctic permafrost region
The main goal of this study is to explore the potential of a state-of-the-art deep learning (DL) Convolution Neural Network (CNN) method (Mask R-CNN) to characterize the tundra ice-wedge polygon landscape

Summary

Introduction

Polygonal topography typical of the Circum-Arctic permafrost region is associated with ice wedges, developed by repeated frost (or thermal contraction) cracking and formation of ice veins as a result of filling cracks with meltwater in the spring time; this process occurs over hundreds to thousands of years and leads to formation of large massive-ice bodies [1,2,3,4,5,6,7,8,9,10,11,12,13,14]. Frost cracking and subsequent development of ice wedges create a network of polygons that forms archetypal polygonal patterned tundra occupying a large portion of the Arctic. Leffingwell [17] described two major types of ice-wedge polygons (IWP): polygons with depressed blocks and polygons with elevated blocks. Later, these types of polygons were renamed to low-centered (LCP) and high-centered (HCP) polygons, correspondingly, and since at least the 1950s, many authors have used these terms to distinguish major types of polygons [2,14,18,19,20,21,22,23,24,25,26,27]. We focus on the two major types—LCP and HCP polygons—widely represented within the Circum-Arctic permafrost region

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