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：Li, Zengyuan[1,2] Chen, Erxue[1,2,3] Qin, Xianlin[1,2] Guo, Ying[1,2] Tian, Xin[1,2] Liu, Qingwang[1,2,3] Sun, Bin[1,2] Zhao, Lei[1,2,3] Cai, Shangshu[1,2] Du, Liming[1,2] Yu, Linfeng[1,2] Wang, Cangjiao[1,2]

第一作者：李增元;Li, Zengyuan

机构：[1] Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China; [2] Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing, 100091, China; [3] National Key Laboratory of Efficient Production of forest resources, Beijing, 100091, China

年份：2025

卷号：29

期号：6

起止页码：1804-1830

外文期刊名：National Remote Sensing Bulletin

收录：EI(收录号：20252718715909)

语种：中文

外文关键词：Classification (of information) - Deep learning - Earth (planet) - Ecology - Environmental monitoring - Forest ecology - Forestry - Learning systems - Remote sensing - Space optics - Timber - Vegetation

摘要：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"-double-driven 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. ? 2025 Science Press. All rights reserved.