@incollection{fdi:010067245,
  title = {{I}ndividual tree segmentation over large areas using airborne {L}i{DAR} point cloud and very high resolution optical imagery},
  author = {{Q}in, {Y}. and {F}erraz, {A}. and {M}allet, {C}. and {I}ovan, {C}orina},
  editor = {},
  language = {{ENG}},
  abstract = {{T}imely and accurate measuremen ts of forest parameters are critical for ecosystem studies, sustainable forest resources management, monitoring and planning. {T}his paper presents a processing chain for individual tree segmentation over large areas with airborne {L}i{DAR} 3{D} point cloud and very high resolution ({VHR}) optical imagery. {T}he proposed processing chain consists of fo rest stand level delineation with optical imagery, individual tree segmentation with {C}anopy {H}eight {M}odel ({CHM}) derived from {L}i{DAR} point cloud, rough characterization of trees at forest stand level, and point clustering of individual tree with an {A}daptive {M}ean {S}hift 3{D} ({AMS}3{D}) algorithm. {T}he processing chain is developed with the expectation of supporting operational forest inventory at individual tree level. {E}xperiment is conducted using {L}i{DAR} data acquired in {V}entoux region, {F}rance. {R}esults suggest that the proposed processing chain can be successfully adopted for individual tree characterization over large areas with different forest stands.},
  keywords = {{FRANCE}},
  booktitle = {{M}ultitemp 2015},
  numero = {},
  pages = {800--803},
  address = {},
  publisher = {{IEEE}},
  series = {},
  year = {2015},
  ISBN = {978-1-4673-7119-3},
  URL = {https://www.documentation.ird.fr/hor/fdi:010067245},
}