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应用生态学报 ›› 2018, Vol. 29 ›› Issue (6): 1768-1778.doi: 10.13287/j.1001-9332.201806.016

• 研究报告 • 上一篇    下一篇

近地遥感在森林冠层物候动态监测中的应用

刘帆,王传宽,王兴昌*   

  1. 东北林业大学生态研究中心, 哈尔滨 150040
  • 收稿日期:2017-09-19 修回日期:2018-03-13 出版日期:2018-06-18 发布日期:2018-06-18
  • 通讯作者: E-mail: xcwang_cer@nefu.edu.cn
  • 作者简介:刘 帆, 女, 1992年生, 博士研究生. 主要从事近地遥感物候与生产力关系研究. E-mail: ecology_liufan@126.com
  • 基金资助:

    本文由黑龙江省自然科学基金项目(QC2017010)、国家科技支撑计划项目(2011BAD37B01)、中央高校基本科研业务费专项资金项目(2572016BA03)和教育部长江学者和创新团队发展计划项目(IRT_15R09)资助

Application of near-surface remote sensing in monitoring the dynamics of forest canopy phenology.

LIU Fan, WANG Chuan-kuan, WANG Xing-chang*   

  1. Center for Ecological Research, Northeast Forestry University, Harbin 150040, China
  • Received:2017-09-19 Revised:2018-03-13 Online:2018-06-18 Published:2018-06-18
  • Supported by:

    This work was supported by the Natural Science Foundation of Heilongjiang Province of China (QC2017010), the National Science and Technology Support Program of China (2011BAD37B01), the Fundamental Research Fund for the Central Universities (2572016BA03), and the Program for Changjiang Scholar and Innovative Research Team in University (IRT_15R09).

摘要: 近地遥感技术是原位观测森林冠层物候的重要手段,具有高时间分辨率的优点,而且空间尺度适中,是实现物候尺度推绎的有力工具.本研究首先评述了利用3种光学传感器(辐射表、光谱仪和数码相机)监测森林物候的近地遥感方法;结合帽儿山通量观测站的实测数据分析识别物候期的不确定性来源,发现最重要的误差来自物候提取方法;剖析近地遥感与其他物候观测方法的衔接以及该技术自身存在的问题.最后提出该领域的重点研究方向: 加强冠层光学(或冠层结构)物候与功能(生理、生态过程)物候的联系;整合各区域冠层物候观测网络,实现冠层尺度的全球物候联网观测与数据共享;充分发挥近地遥感的优势,整合多源多尺度物候数据;发展近地遥感物候模型,改进动态全球植被模型中物候模拟.

Abstract: Near-surface remote sensing is an important technique for in-situ monitoring of forest phenology and a robust tool for scaling of the phenology with a high temporal resolution and mode-rate spatial coverage. Here, we first reviewed the methods of near-surface remote sensing with three major optical sensors (i.e., radiometer, spectrometer, and digital camera) for monitoring forest phenology. Second, we analyzed sources of uncertainties from distinguishing the phenophases by using the data obtained at the Maoershan flux site in the temperate forest. We found that the error was mainly attributed to the extracting method. Third, we analyzed the linkage of near-surface remote sensing with other methods and its intrinsic problems. Finally, we proposed four priorities in the research of this field: 1) linking optical (or canopy structural) phenology with functional phenology (physiological and ecological processes); 2) integrating the regional networks of canopy phenology for global networking observation and data sharing of canopy phenology; 3) integrating multi-source and multi-scale phenological data with the help of near-surface remote sensing; 4) developing phenology models based on near-surface remote sensing in order to improve the phenology simulation in the dynamic global vegetation models.