日本地球惑星科学連合2024年大会
セッション概要| 地球人間圏科学 (H) | ||||
|---|---|---|---|---|
| セッション小記号 | 計測技術・研究手法 (TT) |  |
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| セッション ID | H-TT14 | |||
| タイトル | 高精細地形表層情報と人新世におけるコネクティビティ | |||
| タイトル短縮名 | 人新世高精細地形情報連結 | |||
| 開催日時 | 口頭セッション | 5/28(火) PM2 | ||
| 現地 ポスター コアタイム |
5/28(火) PM3 | |||
| 代表コンビーナ | 氏名 | 早川 裕弌 | ||
| 所属 | 北海道大学地球環境科学研究院 | |||
| 共同コンビーナ１ | 氏名 | Gomez Christopher | ||
| 所属 | 神戸大学 海事科学部 海域火山リスク科学研究室 | |||
| 共同コンビーナ２ | 氏名 | 笠井 美青 | ||
| 所属 | 北海道大学大学院農学研究院 | |||
| 共同コンビーナ３ | 氏名 | 小倉 拓郎 | ||
| 所属 | 兵庫教育大学学校教育研究科 | |||
| セッション言語 | E | |||
| スコープ |
Recent technical developments have enabled us to acquire high-definition topographic and geophysical data for geoscientific research, including land surface processes, subsurface structures, submarine/aerial environments, and geo-ecological interactions. Such high-definition or high-resolution data of the Earth's surface, hereby referred to as HiMESD (High-definition Multilayered Earth Surface Data), are particularly useful for studies on landscape developments over relatively short-term (decadal to millennial time scales), which are often assessed with the concept of connectivity in spatial and temporal contexts. HiMESD from ground and airborne platforms has become ubiquitous in everyday life, from car navigation systems to automated vacuum cleaners at home, playing a central role in quantifying and understanding "connectivity" in transportation networks, as well as in the sediment cascade from steep mountain tops to shallow and slow coastal rivers. HiMESD takes center stage, from the "micro-scale" of robot navigation to identifying the connectivity of active and more quiescent fault lines spanning several tens of kilometers.
In this session, we invite submissions on topics that challenge the issues of connectivity in the Anthropocene, the most recent geological era of the Earth affected by human activities. A range of topics would fit the session framework, including theoretical work, data acquisition, pre- and post-processing, extensive data preservation and archiving, statistical analysis, physical modeling, machine learning, and numerical simulation. The methodological approaches may include, but are not limited to, laser scanning (Lidar), photogrammetry (SfM), GNSS precise positioning, SAR interferometry, multi-beam sonar, ground-penetrating radar, geomagnetic/electromagnetic sensors, and multi/hyperspectral sensors, based on terrestrial (fixed or mobile), aerial (UAS/UAV or manned airborne), or satellite platforms. |
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| 発表方法 | 口頭およびポスターセッション | |||
| 共催情報 | 学協会 | 日本地形学連合 | ||
| ジョイント | EGU | |||