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森林生态系统遥感监测技术研究进展
引用本文:何兴元,任春颖,陈琳,王宗明,郑海峰.森林生态系统遥感监测技术研究进展[J].地理科学,2018,38(7):997-1011.
作者姓名:何兴元  任春颖  陈琳  王宗明  郑海峰
作者单位:1.中国科学院东北地理与农业生态研究所湿地生态与环境重点实验室,吉林 长春 130102
2.中国科学院大学,北京 100049
基金项目:国家重点研发计划项目(2016YFC0500300)资助
摘    要:森林是陆地生态系统的主体,森林生态系统监测技术是实现森林可持续利用与全球变化研究的重要支撑与信息保障。从4个方面概述了遥感技术在森林生态系统监测中的应用研究进展:森林遥感分类及变化监测、森林植被参数遥感反演、森林蓄积量与生物量遥感估算、森林干扰遥感监测等。结合遥感技术的发展,总结了森林生态系统监测中使用的多源遥感数据和各类模型,提出集成地面调查数据、高分地-空雷达扫描监测技术,以及多源光学遥感建模技术和生态系统过程模型,构建多维度、多尺度、高时间密度的森林生态系统监测平台的研究展望。

关 键 词:森林分类  植被参数反演  生物量估算  遥感模型耦合  森林干扰  集成监测平台  
收稿时间:2018-06-06
修稿时间:2018-07-19

The Progress of Forest Ecosystems Monitoring with Remote Sensing Techniques
Xingyuan He,Chunying Ren,Lin Chen,Zongming Wang,Haifeng Zheng.The Progress of Forest Ecosystems Monitoring with Remote Sensing Techniques[J].Scientia Geographica Sinica,2018,38(7):997-1011.
Authors:Xingyuan He  Chunying Ren  Lin Chen  Zongming Wang  Haifeng Zheng
Affiliation:1.Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, Jilin, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:Forest is a main component of the terrestrial ecosystem, and its monitoring is the vital support for sustainable utilization of forest and global change researches. The home and aboard progress of remote sensing techniques application on monitoring forest ecosystems was concluded in this paper from four respects: classification and changes detection, the retrieval of vital ecological parameters of forest ecosystems including tree height, leaf area index, canopy density, etc., the estimation of stand volume and biomass, and disturbance monitoring. After summarized remote sensing data and models used in forest ecosystems monitoring, research prospect of establishment of integrated forest ecosystems monitoring platform with multi-dimension, multi-scale and high time density were put forward, which synthesized filed data, land-to-air high-resolution radar scanning techniques, multi-source optical remote sensing modeling and process models.
Keywords:forest classification  vegetation parameters retrieval  biomass estimation  coupling of remote sensing model  forest disturbance  integrated monitoring platform  
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