% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Montzka:997380,
      author       = {Montzka, Carsten and Donat, Marco and Raj, Rahul and
                      Welter, Philipp and Bates, Jordan Steven},
      title        = {{S}ensitivity of {L}i{DAR} {P}arameters to {A}boveground
                      {B}iomass in {W}inter {S}pelt},
      journal      = {Drones},
      volume       = {7},
      number       = {2},
      issn         = {2504-446X},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2023-01182},
      pages        = {121 -},
      year         = {2023},
      abstract     = {Information about the current biomass state of crops is
                      important to evaluate whether the growth conditions are
                      adequate in terms of water and nutrient supply to determine
                      if there is need to react to diseases and to predict the
                      expected yield. Passive optical Unmanned Aerial Vehicle
                      (UAV)-based sensors such as RGB or multispectral cameras are
                      able to sense the canopy surface and record, e.g.,
                      chlorophyll-related plant characteristics, which are often
                      indirectly correlated to aboveground biomass. However,
                      direct measurements of the plant structure can be provided
                      by LiDAR systems. In this study, different LiDAR-based
                      parameters are evaluated according to their relationship to
                      aboveground fresh and dry biomass (AGB) for a winter spelt
                      experimental field in Dahmsdorf, Brandenburg, Germany. The
                      parameters crop height, gap fraction, and LiDAR intensity
                      are analyzed according to their individual correlation with
                      AGB, and also a multiparameter analysis using the Ordinary
                      Least Squares Regression (OLS) is performed. Results
                      indicate high absolute correlations of AGB with gap fraction
                      and crop height (−0.82 and 0.77 for wet and −0.70 and
                      0.66 for dry AGB, respectively), whereas intensity needs
                      further calibration or processing before it can be
                      adequately used to estimate AGB (−0.27 and 0.22 for wet
                      and dry AGB, respectively). An important outcome of this
                      study is that the combined utilization of all LiDAR
                      parameters via an OLS analysis results in less accurate AGB
                      estimation than with gap fraction or crop height alone.
                      Moreover, future AGB states in June and July were able to be
                      estimated from May LiDAR parameters with high accuracy,
                      indicating stable spatial patterns in crop characteristics
                      over time.},
      cin          = {IBG-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {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)POF4-2173 / G:(GEPRIS)390732324},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000945084800001},
      doi          = {10.3390/drones7020121},
      url          = {https://juser.fz-juelich.de/record/997380},
}