国际森林空间分析技术会议(ForestSAT 2024)学术动向分析OA北大核心CSTPCD
Emerging academic trends at the ForestSAT 2024 Conference
森林空间分析技术会议(ForestSAT)是国际林业遥感领域的一大学术盛事,已成为该领域的学术风向标.ForestSAT 2024于9 月9-13 日在新西兰罗托鲁瓦举行,吸引了来自超过40 个国家的学者,共同探讨38 个议题,包括统计遥感、森林清查、森林生态、遥感算法、遥感产品及未来展望.此次会议呈现 5 大特点:首先,统计遥感备受关注;其次,遥感技术的应用需求旺盛,服务于森林生态和清查;第三,强调数据融合,充分利用多源多模态数据优势;第四,遥感产品为全球"双碳"战略提供支持;最后,把脉林业遥感未来发展路径.会议期间举行了两场主旨报告并深入讨论无人机遥感、尺度上升、小域估计及误差传播等前沿问题.统计遥感作为抽样调查、统计模型与遥感技术的融合,是突破林业遥感瓶颈的重要手段,展现了林业遥感的新发展趋势.
The Forest Spatial Analysis Technology(ForestSAT)Conference is a pivotal assembly in the global domain of forestry remote sensing,functioning as a vital gauge for advancements in this area of research.The ForestSAT 2024 was convented from September 9 to 13 in Rotorua,New Zealand,drawing academics from more than 40 nations to deliberate on 38 subjects,including statistical remote sensing,forest inventory,forest ecology,remote sensing algorithms,remote sensing products,and future trends.The conference underscored five significant aspects:firstly,the prominence of statistical remote sensing;secondly,the robust demand for remote sensing applications within forest ecology and inventory;thirdly,the accentuation on data fusion to capitalize on the strengths of multi-source and multi-modal data;fourthly,the vital support that remote sensing products contribute to the global"double carbon"strategy;lastly,the inquiry into future developmental trajectories for forestry remote sensing.During the event,two keynote speeches were presented,with additional avant-garde topics such as UAV remote sensing,upscaling,small-domain estimation,and error propagation.As an integration of sampling surveys,statistical models,and remote sensing technology,statistical remote sensing is instrumental in addressing the challenges of forestry remote sensing,elucidating the emerging developmental directions in this discipline.
侯正阳;黄华国
北京林业大学森林培育与保护教育部重点实验室,北京 100083
林学
ForestSAT统计遥感尺度上升小域估计误差传播
ForestSATstatistical remote sensingupscalingsmall-area estimationerror propagation
《南京林业大学学报(自然科学版)》 2024 (006)
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国家重点研发计划(2023YFF1304002-05);国家社会科学基金项目(22BTJ005)(统计学部).
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