An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery
An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery
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DOI：
10.3390/rs13040558
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发表时间：
2021-02
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通讯作者：
C. Witharana;Md Abul Ehsan Bhuiyan;A. Liljedahl;M. Kanevskiy;T. Jorgenson;B. Jones;R. Daanen;H. Epstein;C. Griffin;K. Kent;Melissa K. Ward Jones
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作者：
C. Witharana;Md Abul Ehsan Bhuiyan;A. Liljedahl;M. Kanevskiy;T. Jorgenson;B. Jones;R. Daanen;H. Epstein;C. Griffin;K. Kent;Melissa K. Ward Jones
非常高的空间分辨率商业卫星图像可以为大型苔原地区的微地形过渡的观察、制图和记录提供信息。迄今为止，由于空间分辨率和地理覆盖面缺乏重叠，将精细实地研究与泛北极系统评估相结合的工作受到限制。这可能会在气候对北极地区的影响以及北极地区对全球气候系统的反馈方面带来偏差。这项探索性研究的中心目标是开发一种基于对象的图像分析工作流程，以便从空间分辨率非常高的商业卫星图像中自动提取冰楔多边形槽。我们采用了一个系统的实验，以了解不同的苔原植被单位，莎草苔原和草丛苔原，专注于相同的语义类的基于知识的工作流程的互操作性的程度。在我们的多尺度槽建模工作流程中，我们将数学形态滤波与分割过程相结合，以提高图像对象候选的质量和分类精度。雇用的主rulingum莎草苔原报告的分类准确性的正确性为0.99，完整性为0.87，和F1得分为0.92。当主规则适用于草丛苔原没有任何适应，分类精度仍然有希望，而报告的正确性为0.87，完整性为0.77，和F1得分为0.81。总体而言，结果表明，基于对象的图像分析为基础的槽建模工作流程具有很大的互操作性，在整个地形，同时产生有前途的分类精度。从北极地球科学的角度来看，绘制的槽与ArcticDEM相结合，可以允许水文评估的横向连接的迅速变化的北极苔原景观，重复测绘可以让我们跟踪大区域的精细尺度变化，并有潜在的重大影响较大的河流系统。
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.