@article{fdi:010076069,
  title = {{A} comparative assessment of the performance of individual tree crowns delineation algorithms from {ALS} data in tropical forests},
  author = {{A}ubry-{K}ientz, {M}. and {D}utrieux, {R}. and {F}erraz, {A}. and {S}aatchi, {S}. and {H}amraz, {H}. and {W}illiams, {J}. and {C}oomes, {D}. and {P}iboule, {A}. and {V}incent, {G}r{\'e}goire},
  editor = {},
  language = {{ENG}},
  abstract = {{T}ropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. {A}irborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. {T}herefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in {F}rench {G}uiana. {W}e measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. {W}e also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. {T}he different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. {S}egmentation methods based on the point cloud ({AMS}3{D} and {G}raph-{C}ut) globally outperformed methods based on the {C}anopy {H}eight {M}odels, especially for small crowns; the {AMS}3{D} method outperformed the other methods tested for the overlap analysis, and {AMS}3{D} and {G}raph-{C}ut performed the best for the automatic matching validation. {N}evertheless, other methods based on the {C}anopy {H}eight {M}odel performed better for very large emergent crowns. {T}he dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method.},
  keywords = {individual tree crown segmentation ; airborne laser scanning ; tropical forest ; benchmark ; {GUYANE} {FRANCAISE}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {11},
  numero = {9},
  pages = {art. 1086 [21 p.]},
  ISSN = {2072-4292},
  year = {2019},
  DOI = {10.3390/rs11091086},
  URL = {https://www.documentation.ird.fr/hor/fdi:010076069},
}