Lidar-derived digital surface model of Stromboli island updated to August 2023.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Marina Bisson, Roberto Gianardi, Riccardo Civico, Paolo Madonia, Tullio Ricci, Claudia Spinetti
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引用次数: 0

Abstract

Digital surface models reproduce the 3D topography of a territory at different spatial resolutions depending on the acquisition technique of source data. In active and densely populated volcanic areas, updated digital topographies are fundamental for mapping and quantifying the morphological changes generated by the eruptive events and play a key role in modelling volcanic phenomena and related hazards. This work presents the high-resolution Digital Surface Model of Stromboli Island, Italy, updated to 4th August 2023. The model, obtained by elaborating more than 109 × 106 Airborne Lidar points (x,y,z), reconstructs the volcano's surface through an elevation matrix at a spatial resolution of 50 cm, reproducing both natural and anthropic elements. The model has been validated by using Ground Control Points and the vertical accuracy results in 8 cm. Nowadays, this model represents the most updated and accurate digital 3D topography of the entire island and, for this reason, can be considered a relevant data not only for multi-temporal morphological and volcanological analyses but also for hazard assessment studies.

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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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