Estimating emissions from deforestation and degradation of forests in many developing countries is so uncertain that the effects of changes in forest management could remain within error ranges (i.e. undetectable) for several years. Meanwhile UNFCCC Parties need consistent time series of meaningful performance indicators to set credible benchmarks and allocate REDDC incentives to the countries, programs and activities that actually reduce emissions, while providing social and environmental benefits. Introducing widespread measuring of carbon in forest land (which would be required to estimate more accurately changes in emissions from degradation and forest management) will take time and considerable resources. To ensure the overall credibility and effectiveness of REDDC, parties must
consider the design of cost-effective systems which can provide reliable and comparable data on anthropogenic forest emissions. Remote sensing can provide consistent time series of land cover maps for most non-Annex-I countries, retrospectively. These maps can be analyzed to identify the forests that are intact (i.e. beyond significant human influence), and whose fragmentation could be a proxy for degradation. This binary stratification of forests biomes (intact/non-intact), a transition matrix and the use of default carbon stock change factors can then be used to provide initial estimates of trends in emission changes. A proof-of-concept is provided for one biome of the Democratic Republic of the Congo over a virtual commitment period (2005–2010). This approach could allow assessing the performance of the five REDD+ activities (deforestation, degradation, conservation, management and enhancement of forest carbon stocks) in a spatially explicit, verifiable manner. Incentives could then be tailored to prioritize activities depending on the national context and objectives.