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4.2   Ground-based observations Previous topic Parent topic Child topic Next topic

Ground-based observations are needed for the estimation of carbon and non-carbon dioxide GHG emissions and removals for REDD+ activities, regardless of the sampling or inferential method used. Ground-based observations are used to estimate emissions and removals factors, establish growth models for different types of forests, to parameterise Tier 3 models and as reference data for estimating activity data. Although availability will differ from country to country, examples relevant ground-based observations include:
  • NFIs, subnational forest inventories, and forest assessments based on plot or transect measurements;
  • growth and yield studies, harvested wood removals, and models for converting these to biomass;
  • auxiliary spatial data on land use, management, disturbance history, soil type which can be used to guide the selection and application of emissions and removals factors ;
  • research data that can be used to estimate emissions and removals in above- and below ground biomass, litter, deadwood and soils;
  • field observations which can be converted to emission/removal factors for non-CO2 GHGs from soils and fire.
For REDD+, emissions and removals estimates can be developed using data from NFIs and related intensive monitoring sites and auxiliary data. In general it will be efficient for the NFMS to collate relevant existing information (Chapter 4) prior to commencing any further sampling, and to conduct a gap analysis to determine the most efficient sampling strategy. Access to original data sets, data collection protocols employed as well as documentation of data quality checks undertaken are important for transparent reporting and assessment of generated estimates. To maintain representativeness, consistency of definitions and protocols, data generally need to be stratified according to forest type, soil and climatic conditions, topography, and the nature of forest disturbances induced by anthropogenic or natural factors (Chapter 2, Section 2.3.2).