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@ARTICLE{Neuville:889716,
      author       = {Neuville, Romain and Bates, Jordan and Jonard, Francois},
      title        = {{E}stimating {F}orest {S}tructure from {UAV}-mounted
                      {L}i{DAR} {P}oint {C}loud{U}sing {M}achine {L}earning},
      journal      = {Remote sensing},
      volume       = {13},
      number       = {3},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2021-00338},
      pages        = {352},
      year         = {2021},
      abstract     = {Monitoring the structure of forest stands is of high
                      importance for forest managers to help them in maintaining
                      ecosystem services. For that purpose, Unmanned Aerial
                      Vehicles (UAVs) open new prospects, especially in
                      combination with Light Detection and Ranging (LiDAR)
                      technology. Indeed, the shorter distance from the Earth’s
                      surface significantly increases the point density beneath
                      the canopy, thus offering new possibilities for the
                      extraction of the underlying semantics. For example, tree
                      stems can now be captured with sufficient detail, which is a
                      gateway to accurately locating trees and directly retrieving
                      metrics—e.g., the Diameter at Breast Height (DBH). Current
                      practices usually require numerous site-specific parameters,
                      which may preclude their use when applied beyond their
                      initial application context. To overcome this shortcoming,
                      the machine learning Hierarchical Density-Based Spatial
                      Clustering of Application of Noise (HDBSCAN) clustering
                      algorithm was further improved and implemented to segment
                      tree stems. Afterwards, Principal Component Analysis (PCA)
                      was applied to extract tree stem orientation for subsequent
                      DBH estimation. This workflow was then validated using LiDAR
                      point clouds collected in a temperate deciduous
                      closed-canopy forest stand during the leaf-on and leaf-off
                      seasons, along with multiple scanning angle ranges. The
                      results show that the proposed methodology can correctly
                      detect up to $82\%$ of tree stems (with a precision of
                      $98\%)$ during the leaf-off season and have a Maximum
                      Scanning Angle Range (MSAR) of 75 degrees, without having to
                      set up any site-specific parameters for the segmentation
                      procedure. In the future, our method could then minimize the
                      omission and commission errors when initially detecting
                      trees, along with assisting further tree metrics retrieval.
                      Finally, this research shows that, under the study
                      conditions, the point density within an approximately
                      1.3-meter height above the ground remains low within
                      closed-canopy forest stands even during the leaf-off season,
                      thus restricting the accurate estimation of the DBH. As a
                      result, autonomous UAVs that can both fly above and under
                      the canopy provide a clear opportunity to achieve this
                      purpose.},
      cin          = {IBG-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000615480700001},
      doi          = {10.3390/rs13030352},
      url          = {https://juser.fz-juelich.de/record/889716},
}