高分辨率地形图显示了 2024 年 7 月爆发后斯特龙博利火山的形态变化。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-12 DOI:10.1038/s41597-024-04098-y
Riccardo Civico, Tullio Ricci, Alessandro Cecili, Piergiorgio Scarlato
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摘要

2024 年 7 月斯特龙博利火山喷发的特点是以不同的强度表现出斯特龙博利火山所能发生的全部火山活动,而且是迄今为止在过去几十年中对火山口平台和 Sciara del Fuoco 斜坡形态改变最大的一次。我们介绍了 2024 年 7 月 14 日进行的无人驾驶航空器系统(UAS)勘测的结果,包括 4,988 张可见光和热照片,以及结构移动摄影测量,使我们能够制作出高分辨率(0.2 米/像素)的数字地表模型(DSM)。我们记录了 2024 年 7 月 4 日至 11 日喷发造成的斯特龙博利火山的深刻形态变化,并通过差分我们的勘测结果和无人机系统得出的喷发前地表(2024 年 5 月 24 日),获得了海拔高度和体积变化的估计值。
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High-resolution topography reveals morphological changes of Stromboli volcano following the July 2024 eruption.

The July 2024 eruption of Stromboli volcano has been characterised by the manifestation, at variable intensity, of the entire repertoire of volcanic events that Stromboli volcano is capable of, and is by far the one that has most changed the morphology of the crater terrace and of the Sciara del Fuoco slope in the last decades. We present the results of an Unoccupied Aircraft System (UAS) survey performed on 14 July 2024 and consisting of 4,988 visible and thermal photographs coupled with Structure-from-Motion photogrammetry that allowed us to produce a high-resolution (0.2 m/pixel) Digital Surface Model (DSM). We documented the profound morphological changes of the Stromboli volcano resulting from the 4-11 July 2024 eruption and obtained elevation and volume change estimates by differencing our survey and a UAS-derived pre-event surface (24 May 2024).

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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