@article{fdi:010072532,
  title = {{R}educing uncertainty in mapping of mangrove aboveground biomass using airborne discrete return {L}idar data},
  author = {{R}ocha de {S}ouza {P}ereira, {F}. and {K}ampel, {M}. and {G}omez {S}oares, {M}.{L}. and {C}alderucio {D}uque {E}strada, {G}. and {B}entz, {C}. and {V}incent, {G}r{\'e}goire},
  editor = {},
  language = {{ENG}},
  abstract = {{R}emote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. {T}he main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass ({AGB}) maps of mangrove forests. {AGB} was estimated in 34 small plots scatted over a 50 km2 mangrove forest in {R}io de {J}aneiro, {B}razil. {P}lot {AGB} was computed using either species-specific or non-species-specific allometric models. {A} total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors ({A}uto-{PLS})) were tested to predict the recorded {AGB}. {T}he models developed using species-specific allometric models were distinctly more accurate ({R}2(calibration) = 0.89, {R}2(validation) = 0.80, root-mean-square error ({RMSE}, calibration) = 11.20 t·ha−1, and {RMSE}(validation) = 14.80 t·ha−1). {T}he use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. {I}t was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the {AGB} in mangrove forests. {C}onversely this study underlines the potential bias affecting the estimates of {AGB} in other forested landscapes where only non-species-specific allometric equations are available.},
  keywords = {{BRESIL} ; {AMAZONIE}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {10},
  numero = {},
  pages = {art. no 637 [21  en ligne]},
  ISSN = {2072-4292},
  year = {2018},
  DOI = {10.3390/rs10040637},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072532},
}