Morphological processes often induce meter-scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote-sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross-section, we estimate meter-scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high-quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR-X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co-registered TerraSAR-X high-resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter-scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.

中文翻译：

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使用 SAR 幅度图像量化大规模地表变化：2019-2020 年希沙尔丁火山喷发期间的火山口形态变化

形态过程通常会引起米级的海拔变化。当火山喷发时，跟踪此类过程可以深入了解喷发活动和相关危害的样式和演变。与光学遥感产品相比，合成孔径雷达（SAR）在恶劣天气和夜间观测地表变化。差分 SAR 干涉法估计 SAR 采集之间的相位变化，通常用于量化变形。然而，大变形或其他相干性损失会限制其使用。当无法以其他方式检索重复的数字高程模型 (DEM) 时，我们开发了一种适用的新方法。假设各向同性雷达横截面，我们通过利用高质量 DEM 的优化方法直接从 SAR 幅度图像估计米级垂直形态变化。我们通过模拟我们在地形上调制的塌陷特征来验证我们的实现。我们模拟雷达效应并恢复模拟的坍塌。为了验证我们的方法，我们估计了 2011-2012 年克利夫兰山喷发的 TerraSAR-X 带状图图像的海拔变化。我们的结果重现了之前两项研究的结果；一个使用相同的数据集，另一个基于热卫星数据。通过将该方法应用于 2019-2020 年阿拉斯加 Shishaldin 火山的喷发，我们从数十个共同配准的 TerraSAR-X 高分辨率聚光灯图像中生成高程变化时间序列。我们的结果量化了 2019 年 11 月之前未解决的锥体生长、12 月至 1 月与爆炸相关的塌陷以及 2020 年春季火山口高度的进一步变化。