数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (1): 65-73.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.01.006

doi: 10.11871/jfdc.issn.2096-742X.2023.01.006

• 专刊:科学数据资源、技术与政策联合专刊 • 上一篇    下一篇

森林每木生物量数据质控方法和技术研究

郭学兵1,2,*(),张黎1,2,3,何洪林1,2,3   

  1. 1.中国科学院地理科学与资源研究所，生态系统网络观测与模拟重点实验室，北京 100101
    2.国家生态科学数据中心，北京 100101
    3.中国科学院大学，资源与环境学院，北京 100190
  • 收稿日期:2022-03-23 出版日期:2023-02-20 发布日期:2023-02-20
  • 通讯作者: 郭学兵
  • 作者简介:郭学兵，中国科学院地理科学与资源研究所，生态系统网络观测与模拟重点实验室，高级工程师，研究方向为生态信息学。
    负责本文撰写、生物量数据质量控制总体管理工作及软件工具研制。
    GUO Xuebing is an Associate Professor at the Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Res-ources Research, Chinese Academy of Sciences. Her res-earch interest covers Eco-Informatics.
    She is responsible for the paper writing, study, and development of TBW data quality control software tools.
    E-mail: guoxb@igsnrr.ac.cn
  • 基金资助:
    国家重点研发计划“政府间国际科技创新合作/港澳台科技创新合作”重点专项项目“中国及中亚“一带一路”区域典型陆地生态系统综合监测与应用”(2019YFE0126500)

Study on Quality Control Method and Technology about Forest Tree Biomass Weight Data

GUO Xuebing1,2,*(),ZHANG Li1,2,3,HE Honglin1,2,3   

  1. 1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101,China
    2. National Ecosystem Science Data Center, Beijing 100101, China
    3. Resources and Environment Academy, University of Chinese Academy of Sciences, Beijing 100190,China
  • Received:2022-03-23 Online:2023-02-20 Published:2023-02-20
  • Contact: GUO Xuebing

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摘要：

【目的】野外森林生态站乔木生物量长期动态观测数据，对于支撑中国典型森林碳储量、森林生产力等分析研究具有重要价值。每木生物量（Tree biomass weight,TBW）数据是估算森林生物量的基础。TBW数据质量是其长期观测的生命线，也是生物量数据相关科学研究获得正确结论的基础。【方法】TBW数据质控方法的研究、数据质控标准的建立和数据质控信息技术手段的使用，是提升TBW数据质量的三个方面。本文首先介绍中国生态系统研究网络（CERN）森林生态站TBW数据生产过程、TBW数据模式和数据项标准化内容，然后介绍TBW数据质控总体框架及质控方法，最后介绍信息技术（基于OLE编程、Python多进程计算）在TBW基础与实测数据质控及在派生数据自动核验方面的应用。【结果】本文实现了TBW数据一致性、准确性的快速检查，有助于形成标准化的数据质控业务流程，对于海量的森林生态站多样地TBW数据的高效质控和快速计算具有重要参考价值。

关键词: 森林乔木, 每木生物量, 数据质控, OLE, 中国生态系统研究网络

Abstract:

[Objective] Forest field stations dynamically carry on forest biomass observation during a long period, the obtained data possess important values to support calculation of forest carbon storage amount and estimation of forest productivity, and so on. Tree biomass weight (TBW) is the basis for calculating forest biomass. Thus, good TBW data quality is essential to TBW long-term observation, and is also the foundation for obtaining correct conclusions via scientific research. [Methods] Method, standardization, and advanced information technology for TBW data quality control (QC) are three aspects to promote data quality. Firstly, the paper introduces the flow of TBW data production, TBW data schema, and standardization of data items in forest field stations of the Chinese Ecosystem Research Network (CERN). Secondly, the main framework, standards, and methods of data QC are described. Lastly, the paper illustrates how information technologies (program by OLE technology, multi-processing computation by Python) are applied to the procedure of checking the validity of field observed data and derived data. [Results] The paper presents a way for quick validation check of data consistency and accuracy, which is helpful in formulating standardized QC flow utilized and referred by forest field stations to process their multiple plots of TBW data efficiently.

Key words: forest tree, tree biomass weight, data quality control, Object Linking and Embedding, Chinese Ecosystem Research Network