4.7 Article

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

期刊

REMOTE SENSING
卷 13, 期 4, 页码 -

出版社

MDPI
DOI: 10.3390/rs13040558

关键词

Arctic; permafrost; OBIA; commercial imagery; ice-wedge polygons; troughs

资金

  1. U.S. National Science Foundation's Office of Polar Programs (NSF-OPP) [1720875, 1722572, 1721030, 1820883, 1927723, 1927872]
  2. Office of Integrative Activities (NSF-OIA) [1929170]
  3. Polar Geospatial Center, University of Minnesota, under NSF-OPP [1043681, 1559691]
  4. Directorate For Geosciences
  5. Office of Polar Programs (OPP) [1720875] Funding Source: National Science Foundation
  6. Office of Polar Programs (OPP)
  7. Directorate For Geosciences [1721030, 1820883, 1722572] Funding Source: National Science Foundation
  8. Office Of The Director
  9. Office of Integrative Activities [1929170] Funding Source: National Science Foundation

向作者/读者索取更多资源

The research focuses on developing an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. Through systematic experiments, the workflow demonstrates good interoperability and classification accuracy across different tundra vegetation units. Overall, the results suggest substantial interoperability of the workflow across the terrain, potentially influencing climate impacts and geological modeling in the Arctic region.
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.

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