@article{fdi:010081465,
  title = {{M}ultisensor data fusion for improved segmentation of individual tree crowns in dense tropical forests},
  author = {{A}ubry-{K}ientz, {M}. and {L}aybros, {A}. and {W}einstein, {B}. and {B}all, {J}. {G}. {C}. and {J}ackson, {T}. and {C}oomes, {D}. and {V}incent, {G}r{\'e}goire},
  editor = {},
  language = {{ENG}},
  abstract = {{A}utomatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. {H}ere, we examine the potential for combining airborne laser scanning ({ALS}) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in {F}rench {G}uiana. {W}e combined an {ALS} point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). {T}his method outperformed a two-step process that involved clustering the {ALS} point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. {W}e used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. {O}ur results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.},
  keywords = {{V}egetation ; {I}mage segmentation ; {H}yperspectral imaging ; {T}hree-dimensional displays ; {F}orestry ; {P}rincipal component analysis ; {B}iomass ; {A}irborne laser scanning ({ALS}) ; data fusion ; deepforest ; high-resolution imagery ; hyperspectral ; 3-{D} adaptive mean-shift ({AMS}3{D}) ; tree crown segmentation ; {GUYANE} {FRANCAISE} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{IEEE} {J}ournal of {S}elected {T}opics in {A}pplied {E}arth {O}bservations and {R}emote {S}ensing},
  volume = {14},
  numero = {},
  pages = {3927--3936},
  ISSN = {1939-1404},
  year = {2021},
  DOI = {10.1109/jstars.2021.3069159},
  URL = {https://www.documentation.ird.fr/hor/fdi:010081465},
}