@article{fdi:010065281,
  title = {{A}boveground-biomass estimation of a complex tropical forest in {I}ndia using {L}idar},
  author = {{V}ega, {C}. and {V}epakomma, {U}. and {M}orel, {J}. and {B}ader, {J}. {L}. and {R}ajashekar, {G}. and {J}ha, {C}. {S}. and {F}eret, {J}. and {P}roisy, {C}hristophe and {P}{\'e}lissier, {R}apha{\¨e}l and {D}adhwal, {V}. {K}.},
  editor = {},
  language = {{ENG}},
  abstract = {{L}ight {D}etection and {R}anging ({L}idar) is a state of the art technology to assess forest aboveground biomass ({AGB}). {T}o date, methods developed to relate {L}idar metrics with forest parameters were built upon the vertical component of the data. {I}n multi-layered tropical forests, signal penetration might be restricted, limiting the efficiency of these methods. {A} potential way for improving {AGB} models in such forests would be to combine traditional approaches by descriptors of the horizontal canopy structure. {W}e assessed the capability and complementarity of three recently proposed methods for assessing {AGB} at the plot level using point distributional approach ({DM}), canopy volume profile approach ({CVP}), 2{D} canopy grain approach ({FOTO}), and further evaluated the potential of a topographical complexity index ({TCI}) to explain part of the variability of {AGB} with slope. {T}his research has been conducted in a mountainous wet evergreen tropical forest of {W}estern {G}hats in {I}ndia. {AGB} biomass models were developed using a best subset regression approach, and model performance was assessed through cross-validation. {R}esults demonstrated that the variability in {AGB} could be efficiently captured when variables describing both the vertical ({DM} or {CVP}) and horizontal ({FOTO}) structure were combined. {I}ntegrating {FOTO} metrics with those of either {DM} or {CVP} decreased the root mean squared error of the models by 4.42% and 6.01%, respectively. {T}hese results are of high interest for {AGB} mapping in the tropics and could significantly contribute to the {REDD}+ program. {M}odel quality could be further enhanced by improving the robustness of field-based biomass models and influence of topography on area-based {L}idar descriptors of the forest structure.},
  keywords = {{INDE}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {7},
  numero = {8},
  pages = {10607--10625},
  ISSN = {2072-4292},
  year = {2015},
  DOI = {10.3390/rs70810607},
  URL = {https://www.documentation.ird.fr/hor/fdi:010065281},
}