文献类型：期刊文献

中文题名：中国林草遥感技术研究与应用:最新进展、挑战与对策

英文题名：Research and application of forestry and grassland remote sensing technology in China:Recent progress,challenges and countermeasures

作者：李增元[1,2] 陈尔学[1,2,3] 覃先林[1,2] 郭颖[1,2] 田昕[1,2] 刘清旺[1,2,3] 孙斌[1,2] 赵磊[1,2,3] 蔡尚书[1,2] 杜黎明[1,2] 俞琳锋[1,2] 王藏姣[1,2]

第一作者：李增元

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]国家林业和草原局林业遥感与信息技术重点实验室,北京100091;[3]林木资源高效生产全国重点实验室,北京100091

年份：2025

卷号：29

期号：6

起止页码：1804-1830

中文期刊名：遥感学报

外文期刊名：NATIONAL REMOTE SENSING BULLETIN

收录：;EI(收录号：20252718715909);北大核心:【北大核心2023】；

语种：中文

中文关键词：林草;遥感技术;业务化应用;进展;对策

外文关键词：forestry and grassland;remote sensing technology;operational application;progress;countermeasures

分类号：P2

摘要：过去的5年是人工智能(AI)大模型、通用模型逐渐融入人们日常工作、生活的5年,是遥感+AI技术在地类识别、变化检测等领域迅速发展的5年,也是国家“生态文明”“美丽中国”相关国家战略调整后的具体落实实施的5年,对这5年来林草遥感技术研发和应用取得的进展进行总结,对国家制定未来林草遥感发展规划具有重要指导意义。本文将林草遥感研究归纳为林草地表覆盖类型变化检测与分类、森林参数遥感定量反演/估测、草地等植被参数定量反演/估测、林草灾害预警监测等4个方向,分别总结了近5年内的国内主要技术研发进展。总体来看林草遥感技术研究呈现出从传统的浅层机器学习向深度学习、从“数据”驱动向“数据+机理”双驱动方向快速发展趋势,深度学习方法在变化检测与分类识别上发展快而深入,但在定量参数反演/估测上则相对较慢;全球、全国等大尺度林草专题产品的生产技术也得到了快速发展。对遥感技术纳入林草资源与生态监测、灾害预警监测与自然保护地监测等业务现行技术标准、技术方案的情况进行了分析,结果表明林草地表覆盖类型变化检测/监测与分类识别技术,已广泛、深入应用到林草行业各类资源监管、灾害预警监测业务中,但林草质量参数定量反演/估测技术业务化应用程度还很低。针对目前推动林草遥感技术全面、深入业务化应用存在的挑战,建议林草行业大力整合“天空地”多源对地观测资源,综合应用遥感、AI、统计推断等前沿技术,构建天空地一体化监测技术体系,并大力加强科研投入、技术交流与人才培养。
The past five years are the five years when big model and general model of Artificial Intelligence(AI)are gradually integrated into people’s daily work and life,and the five years when remote sensing+AI technology develops rapidly in the fields of land cover type identification,change detection,etc.It is also the first five-year for the implementation of the national strategy of“ecological civilization”and“beautiful China”.Summarizing the progress made in the research,development and application of forestry and grassland remote sensing technology in these five years is of great significance for the country to formulate the development plan of forestry and grassland remote sensing in the future.The paper summarizes the main progress of the forestry and grassland remote sensing research and development in China in the past five years into four research directions,namely,change detection and classification of forest and grassland cover types,quantitative inversion/estimation of forest parameters by remote sensing,and that of grassland vegetation and early warning and monitoring of forest and grassland disasters.From a general point of view,the research on forestry and grassland remote sensing technology shows a rapid development trend from traditional shallow machine learning to deep learning,and from“data”-driven to“data+mechanism”-doubledriven direction,and the deep learning method develops quickly and deeply in change detection and classification,but not in quantitative parameter inversion/estimation.The production technology of large-scale forest and grassland thematic products,such as global and national products,has also been developed rapidly.An analysis of the integration of remote sensing technology into existing technical standards and technical programs for forestry and grassland resources and ecological monitoring,disaster early warning monitoring and monitoring of nature reserves shows that forest and grassland cover type change detection/monitoring and classification technologies have been widely and deeply applied to various resource supervision and disaster early warning and monitoring operations in the forestry and grassland industry,but the degree of operational application of quantitative inversion/estimation technologies of forest and grassland quality parameter is still very low.In view of the challenges in promoting the comprehensive and in-depth application of forestry and grassland remote sensing technology,it is suggested that the forestry and grassland industry should vigorously integrate the“space-air-ground”multi-source earth observation resources,comprehensively apply remote sensing,artificial intelligence(AI),statistical inference and other cutting-edge technologies to build a“space-air-ground”integrated monitoring technology system,and greatly strengthen the investment in scientific research,technology exchange and talent exchange and cultivation.