Establishment and Application of Natural Hazards Database Based on Remote Sensing and GIS : From Hazard Modeling to Risk‐Informed Decision Making
リモートセンシングとGISに基づく自然災害データベースの構築と応用：ハザードモデリングからリスクに基づく意思決定へ (AI 翻訳)
Chong Xu, Xiangli He
🤖 gxceed AI 要約
日本語
本論説は、リモートセンシングとGISに基づく自然災害データベースの構築と応用に焦点を当て、ハザードメカニズム、脆弱性評価、曝露分析、レジリエンス評価、システム影響評価、予測への利用を論じる。地すべりダム湖決壊洪水、雪氷災害、地震降雨複合地すべり、土石流、洪水、ヒートアイランド、水質悪化、鉄道網混乱、鉄砲洪水予測など多様な災害と手法を扱う。将来の応用では、曝露統合、モデル堅牢性、不確実性の伝達、解釈可能性、転移可能性、実用的有用性を重視すべきと主張する。
English
This editorial focuses on the establishment and application of natural hazards databases based on remote sensing and GIS, shifting attention to their use in hazard mechanisms, susceptibility assessment, exposure analysis, resilience evaluation, systemic impact assessment, and operational prediction. It covers diverse hazards and methods, including landslide-dammed lake outburst floods, snow- and ice-related hazards, earthquake-rainfall coupled landslides, debris flows, floods, urban heat islands, water quality degradation, railway network disruption, and flash flood prediction. The editorial argues that future applications should emphasize exposure integration, model robustness, uncertainty communication, interpretability, transferability, and practical usability.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX文脈では、自然災害データベースは気候変動適応策の基盤となる。特に国土強靱化やBCP策定において、リスク評価の精緻化に貢献し得るが、本稿は直接的な脱炭素には言及しておらず、間接的影響に留まる。
In the global GX context
In the global GX context, natural hazards databases support climate adaptation and resilience building, which are key components of the green transition. The paper provides a methodological overview applicable to risk-informed decision making under climate change, but does not directly address decarbonization or climate disclosure.
👥 読者別の含意
🔬研究者:This editorial provides a comprehensive overview of how natural hazards databases can be used for modeling and risk assessment, offering a research agenda for future work.
🏢実務担当者:Organizations involved in disaster risk management can use the methods described to improve exposure analysis, resilience evaluation, and operational prediction for infrastructure and community planning.
🏛政策担当者:Policymakers can draw on the paper's emphasis on uncertainty communication and model robustness to inform evidence-based disaster risk reduction and climate adaptation policies.
📄 Abstract（原文）
Natural hazards databases are valuable not only because they record past events, but also because they provide the foundation for modeling, assessment, prediction, and risk‐informed decision‐making. This editorial focuses on the application of natural hazards databases based on remote sensing and Geographic Information Systems (GIS), shifting attention from database establishment to their use in hazard mechanisms, susceptibility assessment, exposure analysis, resilience evaluation, systemic impact assessment, and operational prediction. The papers discussed in this virtual issue cover diverse hazards and methods, including landslide‐dammed lake outburst floods, snow‐ and ice‐related hazards, earthquake–rainfall coupled landslides, debris flows, floods, urban heat islands, water quality degradation, railway network disruption, and flash flood prediction. Together, these studies demonstrate how geospatial databases can support process reconstruction, scenario analysis, socioeconomic loss assessment, infrastructure network analysis, environmental monitoring, and uncertainty‐aware forecasting. This editorial argues that future applications should place greater emphasis on exposure integration, model robustness, uncertainty communication, interpretability, transferability, and practical usability. As climate change, urbanization, infrastructure expansion, and social interdependence continue to reshape disaster risks, natural hazards databases will become increasingly important bridges between disaster observation, scientific interpretation, and risk‐informed action.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1111/tgis.70355first seen 2026-07-18 07:13:44
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