Assessing REDD+ performance of countries with low monitoring capacities: the matrix approach

Abstract

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 REDD+ 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 REDD+, 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)(2006)(2007)(2008)(2009)(2010). This approach could allow assessment of 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.

Uncertain Emission Reductions from Forest Conservation: REDD in the Bale Mountains, Ethiopia

“…The measurement of forest area and forest area change is being advanced through better coverage and accessibility of remote sensing imagery (Achard et al 2004, Mayaux et al 2005, Ramankutty et al 2007, Goetz et al 2009, Baker et al 2010. It is increasingly being used to infer forest carbon stocks over large spatial scales, although limitations still exist in linking imagery to on-the-ground data and in the ability to monitor forest degradation and carbon stored in deadwood and litter (Baccini et al 2008, Baker et al 2010, Bucki et al 2012. Discussions are ongoing to develop and agree on methods to establish past and predicted future rates of deforestation from which the emission reductions of an intervention can be estimated (Angelsen 2008, Olander et al 2008, Bond et al 2009, Griscom et al 2009, Huettner et al 2009, Estrada 2011).…”

Implementing conservativeness in REDD+ is realistic and useful to address the most uncertain estimates

“…Below we address these questions, summarizing and further discussing several refinements to the implementation of the conservativeness principle, partly already presented in Bucki et al (2012).…”

Combining Satellite Data and Community-Based Observations for Forest Monitoring

“…Given that forest change is a dynamic process, monitoring needs to be carried out on a regular basis to support national MRV requirements. Establishing such monitoring systems is presumed to be expensive for developing countries [8,[11][12][13]. An activity monitoring system should be based on four broad monitoring objectives related to the location, area, time and drivers of forest change.…”