农业洪涝灾害多源遥感监测研究进展与展望
孙晖 , 姬军红 , 林智韬 , 吴清华 , 梁万杰 , 雷添杰 , 李东伟
水利水电技术(中英文) ›› 2025, Vol. 56 ›› Issue (S2) : 823 -832.
农业洪涝灾害多源遥感监测研究进展与展望
Progress and prospects of multi-source remote sensing monitoring and research on flood disasters
洪涝灾害作为常见的自然灾害,对人类生命安全、基础设施、房屋建筑、农业、自然环境和经济有着广泛影响。在气候变化的大背景下,极端降水事件及其诱发的洪涝灾害日益频繁和严重。因此,科学监测与评估对防灾减灾决策至关重要,提升其时效性和准确性是加强灾害管理的迫切需求。综述了洪涝灾害监测评估的核心技术与流程,将其监测工作细化为四个核心模块:遥感数据获取、灾害信息提取技术、洪涝灾害时空特征分析及风险评估。进一步深入分析了各模块的机制、方法、应用实践,并探讨了其优势与局限。最终,展望了农业洪涝灾害遥感监测的未来发展方向,强调了跨学科研究的重要性,并提倡综合运用遥感、GIS和人工智能技术,以提升农业洪涝灾害监测能力,减轻其对农业生产的影响。
Flood disasters, as common natural disasters, have a wide range of impacts on human life safety, infrastructure, housing construction, agriculture, natural environment, and economy. Against the backdrop of climate change, extreme precipitation events and the flood disasters induced by them are becoming increasingly frequent and severe. Therefore, scientific monitoring and assessment are crucial for disaster prevention and mitigation decisions, and enhancing their timeliness and accuracy is an urgent need for strengthening disaster management. This article summarizes the core technologies and processes of flood disaster monitoring and assessment, refining its monitoring work into four core modules: remote sensing data acquisition, disaster information extraction technology, flood disaster spatiotemporal feature analysis, and risk assessment. It further analyzes the mechanisms, [Methods]and application practices of each module, and explores their advantages and limitations. Finally, the article looks forward to the future development direction of remote sensing monitoring of agricultural flood disasters, emphasizes the importance of interdisciplinary research, and advocates the comprehensive application of remote sensing, GIS, and artificial intelligence technologies to enhance the monitoring capability of agricultural flood disasters and mitigate their impact on agricultural production.
洪涝灾害 / 遥感数据 / 灾害信息提取 / 时空特征 / 农业
flood disaster / remote sensing data / disaster information extraction / spatiotemporal characteristics / agricultural
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