文章基本信息

标题：Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD+
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作者：Martin Herold ; Rosa María Román-Cuesta ; Danilo Mollicone 等
期刊名称：Carbon Balance and Management
印刷版ISSN：1750-0680
电子版ISSN：1750-0680
出版年度：2011
卷号：6
期号：1
页码：1-7
DOI：10.1186/1750-0680-6-13
出版社：BioMed Central
摘要：Measuring forest degradation and related forest carbon stock changes is more challenging than measuring deforestation since degradation implies changes in the structure of the forest and does not entail a change in land use, making it less easily detectable through remote sensing. Although we anticipate the use of the IPCC guidance under the United Framework Convention on Climate Change (UNFCCC), there is no one single method for monitoring forest degradation for the case of REDD+ policy. In this review paper we highlight that the choice depends upon a number of factors including the type of degradation, available historical data, capacities and resources, and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field data (i.e. multi-date national forest inventories and permanent sample plot data, commercial forestry data sets, proxy data from domestic markets) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of techniques providing the best options. Developing countries frequently lack consistent historical field data for assessing past forest degradation, and so must rely more on remote sensing approaches mixed with current field assessments of carbon stock changes. Historical degradation estimates will have larger uncertainties as it will be difficult to determine their accuracy. However improving monitoring capacities for systematic forest degradation estimates today will help reduce uncertainties even for historical estimates.
关键词：REDD+;forest;global change;monitoring;deforestation;degradation;tropical countries;remote sensing