数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (2): 60-72.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.02.005

doi: 10.11871/jfdc.issn.2096-742X.2023.02.005

• 专刊：“人工智能&大数据”科研范式变革专刊（上） • 上一篇    下一篇

基于新一代信息技术的温带森林生态系统碳通量精准计量

高添1,2,3,4,5(),朱教君1,2,3,4,5,*(),张金鑫1,2,3,4,孙一荣1,2,3,4,于丰源1,2,3,滕德雄1,2,3,卢德亮1,2,3,4,于立忠1,2,3,4,王宗国6   

  1. 1.中国科学院沈阳应用生态研究所，辽宁清原森林生态系统国家野外科学观测研究站，辽宁 沈阳 110016
    2.中国科学院沈阳应用生态研究所，中国科学院森林生态与管理重点实验室，辽宁 沈阳 110016
    3.科尔森林痕量气体与同位素通量监测研发联合实验室，辽宁 沈阳 110016
    4.中国科学院沈阳应用生态研究所，辽宁省公益林生态与管理重点实验室，辽宁 沈阳 110016
    5.中国科学院沈阳应用生态研究所，辽宁省陆地生态系统碳中和重点实验室，辽宁 沈阳 110016
    6.中国科学院计算机网络信息中心，北京 100083
  • 收稿日期:2023-03-05 出版日期:2023-04-20 发布日期:2023-04-24
  • 通讯作者: 朱教君
  • 作者简介:高添，辽宁省陆地生态系统碳中和重点实验室副主任，中国生态学学会生态遥感专业委员会委员，中国科学院沈阳应用生态研究所研究员。主要从事森林生态系统碳-水通量观测、遥感模拟与遥感模拟与生态系统服务评估等研究。近年来以第一/通讯作者发表SCI收录论文10余篇。主持国家自然基金面上项目，青年基金，国家重点研发项目子课题、中国科学院先导专项专题等。获2019获国家科技进步二等奖1项、2016年中国科学院科技促进发展奖1项。
    本文中负责稿件撰写和数据分析。
    GAO Tian, The deputy director of Key Laboratory of Terrest-rial Ecosystem Carbon Neutrality, Liaoning Province, a member of Ecological Remote Sensing Committee of Chinese Society of Ecology, and a professor at Shenyang Institute of Applied Ecology, Chinese Academy of Sciences. His research interests include forest ecosystem carbon-water flux observation, remote sensing modeling, and ecosystem service assessment. In recent years, he has published more than 10 peer-reviewed papers indexed by SCI as the first/corresponding author. He is in charge of the projects of National Natural Science Foundation of China (NSFC), sub-projects of National Key R&D Program of China and subject of Strategic Priority Research Program of the Chinese Academy of Sciences (XDA). He was awarded the achievement award of Second-Class Prize of State Science and Technology Advancement and a provincial or ministerial achievement awards in science and technology.
    In this paper, he is responsible for developing the manuscript and data analysis of this paper.
    E-mail: tiangao@iae.ac.cn|朱教君，辽宁清原森林生态系统国家野外科学观测研究站/中国科学院清原森林生态系统观测研究站站长，中国科学院沈阳应用生态研究所所长、研究员。长期从事森林生态和林业生态工程研究。中国生态学会副理事长，辽宁省生态学会理事长，SCI期刊Ecological Processes 共同主编。国家杰出青年科学基金获得者，“973”计划项目、国家重点研发项目、国家自然科学基金重大项目首席科学家。第一/通讯作者发表SCI论文125篇、出版专著6部、发明专利7项；第一完成人获国家科技进步二等奖2项、省部级一等奖3项，获国际林联科学成就奖（IUFRO Scientific Achievement Award）等个人奖励20余项。
    本文中负责制定论文框架，论文修改、审定。
    ZHU Jiaojun, The director of Qingyuan Forest of National Observation and Research Station, Liaoning Province & Qing-yuan Forest CERN of Chinese Academy of Sciences, and the director and professor of Institute of Applied Ecology, CAS. His research interests include forest ecology and forestry eco-logical engineering. He has served as Vice President of Ecological Society of China, President of Ecological Society of Liaoning Province and Co-Editor-in-Chief of Ecological Pro-cesses. He was funded by the National Science Fund for Dis-tinguished Young Scholars. He was a project leader and a prin-cipal investigator of the National Basic Research Program of China and the National Key Research and Development Pro-gram of China. He has published more than 125 peerreviewed papers indexed by SCI as the first or corresponding author, 6 monographs, and received 6 patents. As the first accomplisher, he was awarded 2 achievement awards of Second-Class Prize of State Science and Technology Advancement and 3 provincial or ministerial achievement awards in science and technology. He also won more than 20 personal honors, including the IUFRO Scientific Achievement Award.
    In this paper, he is responsible for the development of the framework, revision and validation of this paper.
    E-mail: jiaojunzhu@iae.ac.cn
  • 基金资助:
    中国科学院网络安全和信息化专项应用示范项目(CAS-WX2021PY-0108);辽宁省中央引导地方科技发展专项(2022JH6/100100051);中国科学院野外站网络重点科技基础设施建设项目(KFJ-SW-YW006);中国科学院沈阳应用生态研究所自主部署重大项目(IAEMP202201)

Estimation of Carbon Flux of a Temperate Forest Ecosystem Based on Next-Generation Information Technologies

GAO Tian1,2,3,4,5(),ZHU Jiaojun1,2,3,4,5,*(),ZHANG Jinxin1,2,3,4,SUN Yirong1,2,3,4,YU Fengyuan1,2,3,TENG Dexiong1,2,3,LU Deliang1,2,3,4,YU Lizhong1,2,3,4,WANG Zongguo6   

  1. 1. Qingyuan Forest CERN, National Observation and Research Station, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
    2. CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
    3. CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Shenyang, Liaoning 110016, China
    4. Liaoning Key Laboratory for Management of Non-commercial Forest, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
    5. Key Laboratory of Terrestrial Ecosystem Carbon Neutrality, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
    6. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
  • Received:2023-03-05 Online:2023-04-20 Published:2023-04-24
  • Contact: ZHU Jiaojun

RichHTML

18

PDF

384

摘要：

【目的】森林碳通量估算存在巨大不确定性，其科研应用缺乏信息化技术支撑。本研究旨在改变传统低效的森林信息采集和处理方式，深化森林碳通量研究与信息化技术的融合，探索科研范式变革与实现途径。【方法】融合物联网、近地面遥感、人工智能和大数据等新一代信息化技术，组建多功能数据中心，开展复杂地形下森林碳通量精准计量示范研究。【结果】引入分形维数等指数，提出量化地形复杂度的新参数；建立双季激光雷达点云单木分割方法，提取小流域33万株单木全量结构信息；数字模拟风格局，识别三种风模式；解析CO2浓度信号频域特征，在机器学习框架下计量森林碳通量，不确定性降至15.9%。【局限】仍基于经典涡度协方差法模型，区域尺度森林碳通量计量尚未开展，需边界层气象学、森林生态学、遥感科学等学科及信息化前沿技术的深度融合。【结论】发展了森林碳通量计量与信息化手段融合的技术方法，探索了传统森林生态与林学科研范式变革的实现途径，成功在辽东典型温带森林进行示范。

关键词: 碳中和, 森林生态系统, 复杂地形, 碳通量, 机器学习算法, 物联网, 激光雷达

Abstract:

[Objective] The large uncertainty in forest ecosystem carbon flux estimation challenges the application of information technology in ecology. This study aims to change the traditional inefficient way of collecting and processing forest information and promotes the fusion of research on forest ecosystem carbon flux and the next-generation information technology, which explores paradigm shift in research and implementation of forest ecology and forestry sciences. [Methods] Using information technology such as the Internet of Things, near-ground remote sensing, artificial intelligence, and big data, the carbon flux estimation of a temperate forest ecosystem is demonstrated. [Results] The indices such as fractal dimension are introduced to propose a new parameter for quantifying terrain complexity. A new unmanned aerial vehicle Light Detection And Ranging dataset that fused leaf-off and leaf-on point cloud is introduced to extract the tree structural information for a total of 330,000 trees. The wind regime is numerically simulated, and the three wind patterns are identified. The frequency of CO2 concentration signals is analyzed, and forest ecosystem carbon flux is estimated by a machine learning framework with uncertainty reduced to 15.9%. [Limitations] The estimation of forest ecosystem carbon flux is still based on the classical eddy covariance method model. The estimation of forest ecosystem carbon flux at the regional scale has not been carried out yet. These require the systematic integration of boundary layer meteorology, forest ecology, remote sensing science and other disciplines and cutting-edge information technology. [Conclusions] The integrated framework of forest ecosystem carbon flux estimation with information technology has been developed to realize the shift of the paradigm of traditional studies on forest ecology and forestry sciences. The framework has successfully demonstrated its application in typical temperate forests in the Eastern Mountain of Liaoning Province.

Key words: carbon neutrality, forest ecosystems, complex terrain, carbon fluxes, machine learning algorithms, Internet of Things, Light Detection And Ranging (LiDAR)