@incollection{fdi:010072206,
  title = {{R}egional scale rain-forest height mapping using regression-kriging of spaceborne and airborne lidar data : application on {F}rench {G}uiana},
  author = {{F}ayad, {I}. and {B}aghdadi, {N}. and {B}ailly, {J}.{S}. and {B}arbier, {N}icolas and {G}ond, {V}. and {H}erault, {B}. and {E}l {H}ajj, {M}. and {L}ochard, {J}. and {P}errin, {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{L}i{DAR} remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. {W}hilst airborne {L}i{DAR} data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne {L}i{DAR} data acquired from the {G}eoscience {L}aser {A}ltimeter {S}ystem ({GLAS}) have a coarser acquisition density associated with a global cover. {I}t is therefore valuable to analyze the integration relevance of canopy heights estimated from {L}i{DAR} sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution. {I}n this study, canopy heights extracted from both airborne and spaceborne {L}i{DAR}, were first extrapolated from available environmental data. {T}he estimated canopy height maps using random forest ({RF}) regression from the airborne or {GLAS} calibration datasets showed similar precisions ({RMSE} better than 6.5 m). {I}n order to improve the precision of the canopy height estimates regression-kriging (kriging of {RF} regression residuals) was used. {R}esults indicated an improvement in the {RMSE} (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the {GLAS} dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne {L}i{DAR} dataset.},
  keywords = {{GUYANE} {FRANCAISE}},
  booktitle = {2015 {IEEE} international geoscience and remote sensing symposium ({IGARSS}) proceedings},
  numero = {},
  pages = {4109--4112},
  address = {{P}iscataway},
  publisher = {{IEEE}},
  series = {},
  year = {2015},
  DOI = {10.1109/{IGARSS}.2015.7326729},
  ISBN = {978-1-4799-7929-5},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072206},
}