@article{fdi:010065325,
  title = {{S}patial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome},
  author = {{G}uitet, {S}. and {H}erault, {B}. and {M}olto, {Q}. and {B}runaux, {O}. and {C}outeron, {P}ierre},
  editor = {},
  language = {{ENG}},
  abstract = {{P}recise mapping of above-ground biomass ({AGB}) is a major challenge for the success of {REDD}+ processes in tropical rainforest. {T}he usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. {H}owever, there are no studies of the spatial structure of {AGB} at the landscapes scale to support these assumptions. {W}e studied spatial variation in {AGB} at various scales using two large forest inventories conducted in {F}rench {G}uiana. {T}he dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. {A}fter checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. {F}orest inventories provided accurate {AGB} estimates at the plot scale, for a mean of 325 {M}g. ha(-1). {T}hey revealed high local variability combined with a weak autocorrelation up to distances of no more than 10 km. {E}nvironmental variables accounted for a minor part of spatial variation. {A}ccuracy of the best model including spatial effects was 90 {M}g. ha(-1) at plot scale but coarse graining up to 2-km resolution allowed mapping {AGB} with accuracy lower than 50 {M}g. ha(-1). {W}hatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. {W}e concluded that the combined weak autocorrelation and weak environmental effect limit {AGB} maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable {AGB} predictions. {W}aiting for this, using large forest inventories with low sampling rate (< 0.5%) may be an efficient way to increase the global coverage of {AGB} maps with acceptable accuracy at kilometric resolution.},
  keywords = {{GUYANE} {FRANCAISE}},
  booktitle = {},
  journal = {{P}los {O}ne},
  volume = {10},
  numero = {9},
  pages = {art. no e0138456 [22 ]},
  ISSN = {1932-6203},
  year = {2015},
  DOI = {10.1371/journal.pone.0138456},
  URL = {https://www.documentation.ird.fr/hor/fdi:010065325},
}