Résumé
Precise mapping of above-ground biomass (AGB) is a major challenge for the
success of REDD+ processes in tropical rainforest. The usual mapping
methods are based on two hypotheses: a large and long-ranged spatial
autocorrelation and a strong environment influence at the regional scale.
However, there are no studies of the spatial structure of AGB at the
landscapes scale to support these assumptions. We studied spatial
variation in AGB at various scales using two large forest inventories
conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5
ha) of undisturbed rainforest distributed over the whole region. After
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. Forest
inventories provided accurate AGB estimates at the plot scale, for a mean
of 325 Mg.ha-1. They revealed high local variability combined with a weak
autocorrelation up to distances of no more than10 km. Environmental
variables accounted for a minor part of spatial variation. Accuracy of the
best model including spatial effects was 90 Mg.ha-1 at plot scale but
coarse graining up to 2-km resolution allowed mapping AGB with accuracy
lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found
with available pan-tropical reference maps at all resolutions. We
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.
Waiting 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.