稿件编号:20120604
黄从红,张志永,张文娟,杨军.国外森林地上部分碳汇遥感监测方法综述[J].世界林业研究,2012,25(6):20~26
国外森林地上部分碳汇遥感监测方法综述
A Review of Overseas Remote Sensing Monitoring Methods for Aboveground Forest Carbon Sink
投稿时间:2012-05-28  
DOI:
中文关键词:  森林碳汇  遥感监测  光学传感器  微波雷达  激光雷达
英文关键词:forest carbon sink  remote sensing monitoring  optical sensor  Radar  Lidar
基金项目:国家林业局公益面上项目(2011473和201104035)
作者单位E-mail
黄从红 北京林业大学森林培育与保护重点实验室, 北京 100083  
张志永 北京林业大学森林培育与保护重点实验室, 北京 100083  
张文娟 北京林业大学森林培育与保护重点实验室, 北京 100083  
杨军 北京林业大学森林培育与保护重点实验室, 北京 100083 larix001@gmail.com 
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中文摘要:
      森林的碳汇功能对缓解气候变化具有重要作用,森林碳汇的计量和监测方法备受关注,其中应用遥感方法对森林地上部分碳汇进行监测计量已经成为目前林业遥感的热点。文中基于光学遥感、微波雷达和激光雷达3种常用的遥感数据源综述了国外森林地上部分碳汇遥感监测的主要方法,并讨论了这些监测方法的精度和不确定性。得出:1)基于光学遥感数据的多元回归分析法在森林地上部分碳汇估算中应用最为广泛,人工神经网络法具有更高的估算精度;2)微波雷达系统能够穿透云层,可用于多云地区森林地上部分碳汇的估算;3)基于激光雷达数据的估算结果是三者中精度最高的,可用于高生物量地区森林地上部分碳汇的监测。
英文摘要:
      Forest carbon sink is important for mitigating the climate change. Therefore the methods for quantifying and monitoring of forest carbon sink have attracted great attentions. Among them, monitoring the aboveground forest carbon sink with remote sensing has become a hotspot in the research of forest remote sensing. This article reviewed the main methods that foreign countries adopt to monitor the aboveground forest carbon sink with remote sensing based on three types of remote sensing data (i.e., optical sensor data, Radar data and Lidar data). Then we discussed the accuracy and uncertainty of these monitoring methods with remote sensing techniques. We reached the following conclusions: 1) The multiple regression analysis method with optical remote sensing data is the most common method in estimating the aboveground forest carbon sink, while the artificial neural network method tends to produce more accurate results than the multiple regression analysis method; 2) Radar system has the ability to penetrate cloud cover, so it can be used to estimate the aboveground forest carbon sink in cloudy areas; and 3) The accuracy of estimating results based on Lidar data is the highest among three types of remote sensing data, and Lidar data can be used for monitoring the aboveground forest carbon sink in high biomass areas.
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