To qualify for future REDD+ financial incentives, both for deforestation and forest degradation reductions, countries need to assess historical forest-cover changes and establish forest reference emission levels, i.e. CO2 emissions resulting from changes in forest cover. The establishment of a national forest definition is essential to monitor changes in forest areas and a prerequisite to develop a consistent system to monitor forest reference emission levels.
The establishment of such a monitoring system requires choices on many variables (e.g. mapping unit, forest thresholds, remote sensing techniques). The choices made will have technical, political and economic implications that are difficult to predict and that will have impacts on, among others, the type of forests monitored, the methods and data required to provide accurate and reliable information, and possibly, the people deriving their livelihoods from forest land.
The minimum mapping unit selected by countries has to be adapted to the spatial resolution of the remote sensing data employed, and selected as a balance between the ease of visual interpretation (processing and quality control) and the feasibility of the work load. The forest definition has to be adapted to every country and each ecoregion; otherwise, forest maps will not reflect the natural conditions and will derive misleading deforestation and degradation area estimates.
Current attempts at mapping forest cover and forest-cover changes globally are seen by countries as potential baseline data for assessing and monitoring forest-cover changes on their national territory. However, there are important differences in the reported forest cover and forest-cover change estimates depending on sources.
The Tanzania case study shows that i) global data sets on forest cover and forest-cover change misrepresent several land-cover classes; they need to be carefully assessed for accuracy and integrated with locally relevant data before being used for national statistics or baseline forest change scenarios, and ii) the commonly used “30 metres (m)” Landsat data are unsuitable for mapping areas with fragmented and degraded forests, as in many areas with forest cover of 20%-90% is difficult to accurately quantify; we suggest that 5-m resolution data (e.g. from the RapidEye sensor) demonstrates a viable potential for current and future land-cover estimates.
Carbon stock estimates for degradation monitoring remain difficult to retrieve directly with optical remote sensing data, while field surveys at the national level are very expensive and time consuming. Estimation of carbon stock at national levels could be feasible using a combination of high resolution optical satellite and limited field data. For this, satellite data need to be affordable for countries and field data collected with remote sensing imagery in mind; this would facilitate the correlation of forest biophysical variables and remote sensing parameters. However, more work is needed in developing adequate and reliable methods with needs adapted according to the ecoregion.
The reflectance and textural properties of high resolution imagery can be correlated to forest biophysical variables that are related to biomass; therefore, biomass prediction models can be developed from remote sensing parameters. Country forest biomass maps can be obtained by extrapolating adequate models, which have to be adapted according to the ecoregion.
In order to exploit field data for monitoring forest degradation, the field survey has to be specifically designed for linking with satellite images. This is needed to avoid geolocation problems and sampling bias. Moreover, the field survey has to include a balanced data set from all the ecoregions in the country, as information collected in one vegetation type cannot be exploited in others.
In this Infobrief, we outline the rules and choices to be addressed by participatory countries in REDD+ activities, and show some technical problems they can face, and some options they can adopt.