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@ARTICLE{Bates:890511,
      author       = {Bates, Jordan and Montzka, Carsten and Schmidt, Marius and
                      Jonard, François},
      title        = {{E}stimating {C}anopy {D}ensity {P}arameters
                      {T}ime-{S}eries for {W}inter {W}heat {U}sing {UAS} {M}ounted
                      {L}i{DAR}},
      journal      = {Remote sensing},
      volume       = {13},
      number       = {4},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2021-01002},
      pages        = {710 -},
      year         = {2021},
      abstract     = {Monitoring of canopy density with related metrics such as
                      leaf area index (LAI) makes a significant contribution to
                      understanding and predicting processes in the
                      soil–plant–atmosphere system and to indicating crop
                      health and potential yield for farm management. Remote
                      sensing methods using optical sensors that rely on spectral
                      reflectance to calculate LAI have become more mainstream due
                      to easy entry and availability. Methods with vegetation
                      indices (VI) based on multispectral reflectance data
                      essentially measure the green area index (GAI) or response
                      to chlorophyll content of the canopy surface and not the
                      entire aboveground biomass that may be present from
                      non-green elements that are key to fully assessing the
                      carbon budget. Methods with light detection and ranging
                      (LiDAR) have started to emerge using gap fraction (GF) to
                      estimate the plant area index (PAI) based on canopy density.
                      These LiDAR methods have the main advantage of being
                      sensitive to both green and non-green plant elements. They
                      have primarily been applied to forest cover with manned
                      airborne LiDAR systems (ALS) and have yet to be used
                      extensively with crops such as winter wheat using LiDAR on
                      unmanned aircraft systems (UAS). This study contributes to a
                      better understanding of the potential of LiDAR as a tool to
                      estimate canopy structure in precision farming. The LiDAR
                      method proved to have a high to moderate correlation in
                      spatial variation to the multispectral method. The
                      LiDAR-derived PAI values closely resemble the SunScan
                      Ceptometer GAI ground measurements taken early in the
                      growing season before major stages of senescence. Later in
                      the growing season, when the canopy density was at its
                      highest, a possible overestimation may have occurred. This
                      was most likely due to the chosen flight parameters not
                      providing the best depictions of canopy density with
                      consideration of the LiDAR’s perspective, as the
                      ground-based destructive measurements provided lower values
                      of PAI. Additionally, a distinction between total
                      LiDAR-derived PAI, multispectral-derived GAI, and brown area
                      index (BAI) is made to show how the active and passive
                      optical sensor methods used in this study can complement
                      each other throughout the growing season.},
      cin          = {IBG-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {217 - Für eine nachhaltige Bio-Ökonomie – von
                      Ressourcen zu Produkten (POF4-217) / 2173 -
                      Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / DFG project 390732324 - EXC 2070: PhenoRob -
                      Robotik und Phänotypisierung für Nachhaltige
                      Nutzpflanzenproduktion},
      pid          = {G:(DE-HGF)POF4-217 / G:(DE-HGF)POF4-2173 /
                      G:(GEPRIS)390732324},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000624431000001},
      doi          = {10.3390/rs13040710},
      url          = {https://juser.fz-juelich.de/record/890511},
}