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Forest and Grassland Resources Research ›› 2025›› Issue (3): 1-8.doi: 10.13466/j.cnki.lczyyj.2025.03.001

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Research advances in space-air-ground integrated monitoring for smart forestry and grassland

XIE Ningbo1(), XU Xinqiao1(), WANG Qianpeng2, CHEN Zhibo2, YAN Ruihua1, FENG Ge1, ZHOU Qingyu1, HUANG Yuxuan1   

  1. 1. Information Center,National Forestry and Grass Administration,Beijing 100714,China
    2. School of Information Science and Technology,Beijing Forestry University,Beijing 100083,China
  • Received:2025-03-21 Revised:2025-06-10 Online:2025-06-28 Published:2026-01-07

Abstract:

Through the new generation of information technologies such as cloud computing,Internet of Things,mobile internet,big data and artificial intelligence,smart forestry and grassland can realize the comprehensive perception,accurate identification,dynamic monitoring,intelligent analysis and assistant decision-making of forestry and grassland resources and ecological environment.The high-quality development of smart forestry and grassland needs to be supported by high-quality monitoring data.In recent years,space-air-ground integrated monitoring technologies have been continuously innovated and developed,and monitoring capabilities have been continuously improved,which provide important opportunities for the development of smart forestry and grassland.In view of the integration of three monitoring paradigms:space-based monitoring,air-based monitoring,and ground-based monitoring,this paper focuses on the monitoring of forestry and grassland resources,wildfire monitoring,and pest and disease monitoring.We reviewedthe latest research progress of integrated space-air-ground monitoring technologies in the field of forestry and grassland in the world,and prospectsthe future research direction,expecting to provide a reference for smart forestry and grassland monitoring technology.

Key words: smart forestry and grassland, space-air-ground integrated monitoring, forestry and grassland resources monitoring, wildfire monitoring, pest and disease monitoring

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