新一代遥感技术助力生态系统生态学研究

doi:10.17521/cjpe.2019.0206

摘要/Abstract

摘要：

随着气候变化和人类活动的加剧, 生态系统正处于剧烈变化中, 生态学家需要从更大的时空尺度去理解生态系统过程和变化规律, 应对全球变化带来的威胁和挑战。传统地面调查方法主要获取的是样方尺度、离散的数据, 难以满足大尺度生态系统研究对数据时空连续性的要求。相比于传统地面调查方法, 遥感技术具有实时获取、重复监测以及多时空尺度的特点, 弥补了传统地面调查方法空间观测尺度有限的缺点。遥感通过分析电磁波信息从而识别地物属性和特征, 反演生态系统组成、能量流动和物质循环过程中的关键要素, 已逐渐成为生态学研究中必不可少的数据来源。近年来, 随着激光雷达、日光诱导叶绿素荧光等新型遥感技术以及无人机、背包等近地面遥感平台的发展, 个人化、定制化的近地面遥感观测逐渐成熟, 新一代遥感技术正在推动遥感信息“二维向三维”的转变, 为传统样地观测与卫星遥感之间搭建了尺度推绎桥梁, 这也给生态系统生态学带来了新的机遇, 推动生态系统生态学向多尺度、多过程、多学科、多途径发展。因此, 该文从生态系统生态学角度出发, 重点关注陆地生态系统中生物组分, 并分别从生态系统类型、结构、功能和生物多样性等方面, 结合作者在实际研究工作中的主要成果和该领域国际前沿动态, 阐述遥感技术在生态系统生态学中的研究现状并指出我国生态系统遥感监测领域发展方向及亟待解决的问题。

关键词: 近地面遥感, 激光雷达, 无人机, 高光谱, 日光诱导叶绿素荧光

Abstract:

As the increasing pressure caused by climatic changes and human activities, the structure and function of terrestrial ecosystems are undergoing dramatic changes. Understanding how ecosystem processes change at large spatial-temporal scales is crucial for dealing with the threats and challenges posed by global climate change. Traditional field survey method can obtain accurate plot-level ecosystem observations, but it is difficult to be used to address large-scale ecosystem patterns and processes because of spatial and temporal discontinuities. Compared to traditional field survey methods, remote sensing has the advantages of real-time acquisition, repeated monitoring and multi spatial-temporal scales, which can compensate for the shortcomings of traditional field observation methods. Remote sensing can be used to identify the type and characteristic of ground objects, and extract key ecosystem parameters, energy flow and material circulation through retrieving the information contained by electromagnetic signals. Remote sensing data have become an indispensable data source in ecological studies, especially at the ecosystem, landscape, regional or global scales. With the emergence of new remote sensing sensors (e.g., light detection and ranging, and solar-induced chlorophyll fluorescence) and near-surface remote sensing platforms (e.g., unmanned aerial vehicle and backpack), remote sensing is entering the three-dimensional era and the observation platform become more diverse. These three-dimensional, multi-source and time-series remote sensing data bring new opportunities to fully understand ecosystem processes across different spatial scales. This paper reviews the advances of the application of remote sensing in terrestrial ecosystem studies. Specifically, this study focuses on the derivation of biological factors from remote sensing data, including vegetation types, structures, functions and biodiversity of terrestrial ecosystems. We also summarize the current status of the remote sensing technology in ecosystem studies and suggest the future opportunities of ecosystem monitoring in China.

Key words: near-surface remote sensing, lidar, unmanned aerial vehicle (UAV), hyperspectral, solar-induced chlorophyll fluorescence

郭庆华, 胡天宇, 马勤, 徐可心, 杨秋丽, 孙千惠, 李玉美, 苏艳军. 新一代遥感技术助力生态系统生态学研究. 植物生态学报, 2020, 44(4): 418-435. DOI: 10.17521/cjpe.2019.0206

GUO Qing-Hua, HU Tian-Yu, MA Qin, XU Ke-Xin, YANG Qiu-Li, SUN Qian-Hui, LI Yu-Mei, SU Yan-Jun. Advances for the new remote sensing technology in ecosystem ecology research. Chinese Journal of Plant Ecology, 2020, 44(4): 418-435. DOI: 10.17521/cjpe.2019.0206

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2019.0206

https://www.plant-ecology.com/CN/Y2020/V44/I4/418

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传感器 Sensor	技术优势 Advantage	主要应用领域 Application
RGB相机 RGB camera	获取高空间分辨率的颜色和纹理信息 Capturing color and texture at high spatial resolution	生态系统类型、植被状况、物候 Ecosystem type, vegetation monitoring, phenology
多光谱/高光谱成像仪 Multi-spectral/ hyperspectral scanner	获取数个或上百个波段的反射率信息 High spectral resolution; obtaining of several or hundreds spectral bands’ reflectance	生态系统类型、物种分类、生化组分、植被健康、物候 Ecosystem type, species classification, biochemical composition, vegetation health, phenology
微波雷达 Microwave radar	全天候, 对林冠具有一定的穿透性, 能够获取部分地下信息 All-weather; limited penetrating vegetation canopy; capturing belowground information	地形、蓄积量、树高、生物量 Topographic information, stand volume, tree height, biomass
叶绿素荧光扫描仪 Solar-induced chlorophyll fluorescence scanner	获取日光诱导的叶绿素荧光信息 Collecting solar-induced chlorophyll fluorescence	初级生产力、物候、胁迫监测 Gross primary production, phenology, stress monitoring
激光雷达扫描仪 Lidar scanner	对林冠具有穿透性, 获取精细地形、丰富植被三维信息 Penetrating vegetation canopy; obtaining fine terrain and 3D vegetation information	地形、胸径、树高、叶面积指数、生物量 Topographic information, diameter at breast height, tree height, leaf area index, biomass

传感器 Sensor	技术优势 Advantage	主要应用领域 Application
RGB相机 RGB camera	获取高空间分辨率的颜色和纹理信息 Capturing color and texture at high spatial resolution	生态系统类型、植被状况、物候 Ecosystem type, vegetation monitoring, phenology
多光谱/高光谱成像仪 Multi-spectral/ hyperspectral scanner	获取数个或上百个波段的反射率信息 High spectral resolution; obtaining of several or hundreds spectral bands’ reflectance	生态系统类型、物种分类、生化组分、植被健康、物候 Ecosystem type, species classification, biochemical composition, vegetation health, phenology
微波雷达 Microwave radar	全天候, 对林冠具有一定的穿透性, 能够获取部分地下信息 All-weather; limited penetrating vegetation canopy; capturing belowground information	地形、蓄积量、树高、生物量 Topographic information, stand volume, tree height, biomass
叶绿素荧光扫描仪 Solar-induced chlorophyll fluorescence scanner	获取日光诱导的叶绿素荧光信息 Collecting solar-induced chlorophyll fluorescence	初级生产力、物候、胁迫监测 Gross primary production, phenology, stress monitoring
激光雷达扫描仪 Lidar scanner	对林冠具有穿透性, 获取精细地形、丰富植被三维信息 Penetrating vegetation canopy; obtaining fine terrain and 3D vegetation information	地形、胸径、树高、叶面积指数、生物量 Topographic information, diameter at breast height, tree height, leaf area index, biomass