Using a Finer Resolution Biomass Map to Assess the Accuracy of a Regional, Map-Based Estimate of Forest Biomass

  • Ronald E. McRobertsEmail author
  • Erik Næsset
  • Greg C. Liknes
  • Qi Chen
  • Brian F. Walters
  • Sassan Saatchi
  • Martin Herold


National greenhouse gas inventories often use variations of the gainloss approach whereby emissions are estimated as the products of estimates of areas of land-use change characterized as activity data and estimates of emissions per unit area characterized as emission factors. Although the term emissions is often intuitively understood to mean release of greenhouse gases from terrestrial sources to the atmosphere, in fact, emission factors can also be negative, meaning removal of the gases from the atmosphere to terrestrial sinks. For remote and inaccessible forests for which ground sampling is difficult if not impossible, emission factors may be based on map-based estimates of biomass or biomass change obtained from regional maps. For the special case of complete deforestation, the emission factor for the aboveground biomass pool is simply mean aboveground, live-tree, biomass per unit area prior to the deforestation. If biomass maps are used for these purposes, estimates must still comply with the first IPCC good practice guideline regarding accuracy relative to the true value and the second guideline regarding uncertainty. Accuracy assessment for a map-based estimate entails comparison of the estimate to a second estimate obtained using independent reference data. Assuming ground sampling is not feasible, a map of greater quality than the regional map may be considered as a source of reference data where greater quality connotes attributes such as finer resolution and/or greater accuracy. For a local, sub-regional study area in Minnesota in the USA, the accuracy of an estimate of mean aboveground, live-tree biomass per unit area (AGB, Mg/ha) obtained from a coarser resolution, regional, MODIS-based biomass map was assessed using reference data sampled from a finer resolution, local, airborne laser scanning (ALS)-based biomass map. The rationale for a local assessment of a regional map is that, although assessment of a regional map would be difficult for the entire extent of the map, it can likely be assessed for multiple local sub-regions in which case expected local regional accuracy for the entire map can perhaps be inferred. For this study, the local assessment was in the form of a test of the hypothesis that the local sub-regional estimate from the regional map did not deviate from the local true value. A hybrid approach to inference was used whereby design-based inferential techniques were used to estimate uncertainty due to sampling from the finer resolution map, and model-based inferential techniques were used to estimate uncertainty resulting from using the finer resolution map unit values which were subject to prediction error as reference data. The test revealed no statistically significant difference between the MODIS-based and ALS-based map estimates, thereby indicating that for the local sub-region, the regional, MODIS-based estimate complied with the first IPCC good practice guideline for accuracy.


Hybrid inference Design-based inference Model-based inference Greenhouse gas inventory IPCC good practice guidelines 



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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Ronald E. McRoberts
    • 1
    Email author
  • Erik Næsset
    • 2
  • Greg C. Liknes
    • 1
  • Qi Chen
    • 3
  • Brian F. Walters
    • 1
  • Sassan Saatchi
    • 4
  • Martin Herold
    • 5
  1. 1.Northern Research StationU.S. Forest ServiceSaint PaulUSA
  2. 2.Faculty of Environmental Sciences and Natural Resource ManagementNorwegian University of Life SciencesÅsNorway
  3. 3.Department of Geography and EnvironmentUniversity of Hawai‘i at MānoaHonoluluUSA
  4. 4.Joint Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  5. 5.Laboratory for Geoinformation Science and Remote SensingWageningen University and ResearchWageningenThe Netherlands

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