@article{fdi:010086018,
  title = {{A}ssessing the predictive power of {D}emocratic {R}epublic of {C}ongo's national spaceborne biomass map over independent test samples},
  author = {{L}amulamu, {A}. and {P}loton, {P}ierre and {B}irigazzi, {L}. and {X}u, {L}. and {S}aatchi, {S}. and {L}ubamba, {J}. {P}. {K}.},
  editor = {},
  language = {{ENG}},
  abstract = {{R}emotely sensed maps of forest carbon stocks have enormous potential for supporting greenhouse gas ({GHG}) inventory and monitoring in tropical countries. {H}owever, most countries have not used maps as the reference data for {GHG} inventory due to the lack of confidence in the accuracy of maps and of data to perform local validation. {H}ere, we use the first national forest inventory ({NFI}) data of the {D}emocratic {R}epublic of {C}ongo to perform an independent assessment of the country's latest national spaceborne carbon stocks map. {W}e compared plot-to-plot variations and areal estimates of forest aboveground biomass ({AGB}) derived from {NFI} data and from the map across jurisdictional and ecological domains. {A}cross all plots, map predictions were nearly unbiased and captured c. 60% of the variation in {NFI} plots {AGB}. {M}ap performance was not uniform along the {AGB} gradient, and saturated around c. 290 {M}g ha(-1), increasingly underestimating forest {AGB} above this threshold. {S}plitting {NFI} plots by land cover types, we found map predictions unbiased in the dominant terra firme {H}umid forest class, while plot-to-plot variations were poorly captured ({R}-2 of c. 0.33, or c. 0.20 after excluding disturbed plots). {I}n contrast, map predictions underestimated {AGB} by c. 33% in the small {AGB} woodland savanna class but captured a much greater share of plot-to-plot {AGB} variation ({R}-2 of c. 0.41, or 0.58 after excluding disturbed plots). {A}real estimates from the map and {NFI} data depicted a similar trend with a slightly smaller (but statistically indiscernible) mean {AGB} from the map across the entire study area (i.e., 252.7 vs. 280.6 {M}g ha(-1)), owing to the underestimation of mean {AGB} in the woodland savanna domain (31.8 vs. 57.3 {M}g ha(-1)), which was broadly consistent with the results obtained at the provincial level. {T}his study provides insights and outlooks for country-wide {AGB} mapping efforts in the tropics and the computation of emission factors in {D}emocratic {R}epublic of {C}ongo for carbon monitoring initiatives.},
  keywords = {satellite remote sensing ; aboveground biomass ; {UNFCCC} {REDD} plus ; {D}emocratic {R}epublic of {C}ongo ; national forest inventory ; {CONGO} ; {REPUBLIQUE} {DEMOCRATIQUE} {DU} {CONGO}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {14},
  numero = {16},
  pages = {4126 [19 ]},
  year = {2022},
  DOI = {10.3390/rs14164126},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086018},
}