  Mapping forest AGB (Above Ground Biomass) is of crucial importance to
estimate the carbon emissions associated with tropical deforestation. This
study proposes a method to overcome the saturation at high AGB values of
existing AGB map (Vieilledent's AGB map) by using a map of correction factors
generated from GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data.
The Vieilledent's AGB map of Madagascar was established using optical images,
with parameters calculated from the SRTM Digital Elevation Model, climatic
variables, and field inventories. In the present study, first, GLAS LiDAR data
were used to obtain a spatially distributed (GLAS footprints geolocation)
estimation of AGB (GLAS AGB) covering Madagascar forested areas, with a density
of 0.52 footprint/km 2. Second, the difference between the AGB from the
Vieilledent's AGB map and GLAS AGB at each GLAS footprint location was
calculated, and additional spatially distributed correction factors were
obtained. Third, an ordinary kriging interpolation was thus performed by taking
into account the spatial structure of these additional correction factors to
provide a continuous correction factor map. Finally, the existing and the
correction factor maps were summed to improve the Vieilledent's AGB map. The
results showed that the integration of GLAS data improves the precision of
Vieilledent's AGB map by approximately 7 t/ha. By integrating GLAS data, the
RMSE on AGB estimates decreases from 81 t/ha (R 2 = 0.62) to 74.1 t/ha (R 2 =
0.71). Most importantly, we showed that this approach using LiDAR data avoids
underestimating high biomass values (new maximum AGB of 650 t/ha compared to
550 t/ha with the first approach).
