Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems
Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems
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使用多传感器无人航空系统对火山沉积物进行测绘和分类
DOI：
10.1016/j.rse.2021.112581
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发表时间：
2021
影响因子：
13.5
通讯作者：
Marliyani, Gayatri Indah
中科院分区：
文献类型：
--
作者：
Carr, Brett B.;Lev, Einat;Sawi, Theresa;Bennett, Kristen A.;Edwards, Christopher S.;Soule, S. Adam;Vallejo Vargas, Silvia;Marliyani, Gayatri Indah
火山爆发产生的沉积物是火山活动的记录。这些矿床的识别、分类和解释对于了解火山过程和评估灾害至关重要。然而，矿床往往覆盖很大的面积，可能难以进入或存在危险，使得实地测绘既危险又耗时。遥感技术通常用于绘制和识别火山爆发的沉积物，尽管这些技术在图像分辨率、波长和观测频率方面有其自身的利弊。在这里，我们提出了一种使用多传感器无人机系统（UAS）对火山沉积物进行测绘和分类的新方法，并展示了其在与2018年Sierra Negra火山（厄瓜多尔加拉帕戈斯群岛）喷发相关的熔岩和火山碎屑沉积物上的应用。我们调查了研究区域，并收集了可见光和热红外（TIR）图像。我们使用结构从运动摄影测量创建一个数字高程模型（DEM）的视觉图像和计算太阳能加热率的表面温度地图的基础上TIR图像。我们发现，太阳加热率是最高的火山灰沉积物和最低的火山灰熔岩，与pāhoehoe熔岩具有中间值。这与太阳能加热速率与表面的密度和颗粒大小相关是一致的。熔岩流的太阳能加热率也随着距离火山口的增加而降低，这与熔岩脱气时密度的增加相一致。我们结合表面粗糙度（根据DEM计算）和表面的太阳加热率来对火山灰沉积物和不同的熔岩形态进行远程分类。我们应用了监督和无监督机器学习算法。监督分类方法可以复制手动分类，而非监督方法可以识别没有地面实况信息的主要表面单元。这些方法可以对各种火山沉积物进行高空间分辨率（<1米）的远程制图和分类，并有可能应用于其他过程（例如，河流，冰川）和其他行星上的沉积物。
The deposits from volcanic eruptions represent the record of activity at a volcano. Identification, classification, and interpretation of these deposits are crucial to the understanding of volcanic processes and assessing hazards. However, deposits often cover large areas and can be difficult or dangerous to access, making field mapping hazardous and time-consuming. Remote sensing techniques are often used to map and identify the deposits of volcanic eruptions, though these techniques present their own trade-offs in terms of image resolution, wavelength, and observation frequency. Here, we present a new approach for mapping and classifying volcanic deposits using a multi-sensor unoccupied aerial system (UAS) and demonstrate its application on lava and tephra deposits associated with the 2018 eruption of Sierra Negra volcano (Galápagos Archipelago, Ecuador). We surveyed the study area and collected visible and thermal infrared (TIR) images. We used structure-from-motion photogrammetry to create a digital elevation model (DEM) from the visual images and calculated the solar heating rate of the surface from temperature maps based on the TIR images. We find that the solar heating rate is highest for tephra deposits and lowest for ʻaʻā lava, with pāhoehoe lava having intermediate values. This is consistent with the solar heating rate correlating to the density and particle size of the surface. The solar heating rate for the lava flow also decreases with increasing distance from the vent, consistent with an increase in density as the lava degasses. We combined the surface roughness (calculated from the DEM) and the solar heating rate of the surface to remotely classify tephra deposits and different lava morphologies. We applied both supervised and unsupervised machine learning algorithms. A supervised classification method can replicate the manual classification while the unsupervised method can identify major surface units with no ground truth information. These methods allow for remote mapping and classification at high spatial resolution (< 1 m) of a variety of volcanic deposits, with potential for application to deposits from other processes (e.g., fluvial, glacial) and deposits on other planetary bodies.
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