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Position: Home > Articles > Application of near-surface remote sensing in monitoring the dynamics of forest canopy phenology Chinese Journal of Applied Ecology 2018 (6) 1768-1778

近地遥感在森林冠层物候动态监测中的应用

作  者：
刘帆;王传宽;王兴昌
关键词：
近地遥感;冠层物候;辐射表;光谱仪;数码相机;植被指数
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.
译  名：
Application of near-surface remote sensing in monitoring the dynamics of forest canopy phenology
作  者：
LIU Fan;WANG Chuan-kuan;WANG Xing-chang;Center for Ecological Research,Northeast Forestry University;
单  位：
LIU Fan%WANG Chuan-kuan%WANG Xing-chang%Center for Ecological Research,Northeast Forestry University
关键词：
near-surface remote sensing;;canopy phenology;;radiometer;;spectrometer;;digital camera;;vegetation index
摘  要：
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 moderate 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.

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