@article{fdi:010061312,
  title = {{C}anopy height model characteristics derived from airbone laser scanning and its effectiveness in discriminating various tropical moist forest types},
  author = {{K}ennel, {P}. and {T}ramon, {M}. and {B}arbier, {N}icolas and {V}incent, {G}r{\'e}goire},
  editor = {},
  language = {{ENG}},
  abstract = {{M}apping tropical forests to a sufficient level of spatial resolution and structural detail is a prerequisite for their rational management, which however remains a largely unmet challenge. {W}e explore the degree to which a forest canopy height model ({CHM}) derived from airborne laser scanning ({ALS}) can discriminate between five forest types of similar height but varying structure or composition. {W}e systematically compare various textural features ({H}aralick, {F}ourier transform-based, and wavelet-based features) and various classification procedures (linear discriminant analysis ({LDA}), random forest({RF}), and support vector machine ({SVM})) applied to two sizes of sampling units (64mx 64 m and 32mx 32 m). {S}imple height distribution statistics achieve at best 70% classification accuracy in our sample set comprising 120 sampling units of 64mx 64 m. {U}sing w avelet-based features, this accuracy increases to 79% but drops by 10% with smaller sampling units (32mx 32 m). {C}lassifier performance depends on the texture feature set used, but {SVM} and {RF} tend to perform better than {LDA}. {H}igh discrimination rates between forests types of similar height indicate that the {ALS}-derived {CHM} provides information suitable for mapping of tropical forest types. {W}avelet-based texture features coupled with a {SVM} classifier was found to be the most promising combination of methods. {A}ncillary data derived from laser scans and notably topography could be used jointly for an improved segmentation scheme.},
  keywords = {{ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {R}emote {S}ensing},
  volume = {34},
  numero = {24},
  pages = {8917--8935},
  ISSN = {0143-1161},
  year = {2013},
  DOI = {10.1080/01431161.2013.858846},
  URL = {https://www.documentation.ird.fr/hor/fdi:010061312},
}