An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery

• Published in: Remote Sensing
• Main Authors: Chandi Witharana, Md Abul Ehsan Bhuiyan, Anna K. Liljedahl, Mikhail Kanevskiy, Torre Jorgenson, Benjamin M. Jones, Ronald Daanen, Howard E. Epstein, Claire G. Griffin, Kelcy Kent, Melissa K. Ward Jones
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: Arctic; permafrost; OBIA; commercial imagery; ice-wedge polygons; troughs; Science; Q; Ice; Tundra; wedge*
• Online Access: https://doi.org/10.3390/rs13040558; https://doaj.org/article/e1abf49d880f420eb9d81a837fc9db1a

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems.