Automated  analysis  of  digital  cover    videography  for  measuring  LAI  in  urban  street  trees  

Introduc9on:    Leaf  Area  Index  (LAI)  is  defined  as  the  total  one-­‐side  area  of  leaf  6ssue  per  unit  of  ground  surface  area  and  it  is  a  common  parameter  used   to   assess   canopy   growth   and   vigour   of   trees.  Measuring   LAI,   however,   is   a   6me-­‐consuming   process   oBen   requiring   expensive  agroforestry   sensors  with   specific   know-­‐how   to   use   them   that   can   limit   in-­‐situ   con6nuous  monitoring   at   high   temporal   and   spa6al  resolu6on  within  different  landscapes.  Some  studies  have  analysed  con6nuous  s6ll  photographies  over  long  6me  periods  combining  it  with   satellite   imagery   (Fuentes   et   al.   2008;   Ryu,   et   al.   2012).   However,   this   analysis   has   not   yet   been   applied   using  moving   video  cameras.   A   rela6vely   new   methodology   has   been   developed   to   automa6cally   es6mate   LAI   through   computa6onal   analysis   of  videography  from  tree  canopies.  Videos  obtained  from  moving  aerial  and  terrestrial  plaQorms  can  be  analysed  automa6cally  in  order  to  accurately  es6mate  canopy  architecture  parameters  in  a  more  cost-­‐effec6ve  manner  (Fuentes,  et  al.  2014).  This  work  has  evaluated  the  poten6al  applica6on  automated  computa6onal  LAI  es6ma6on  from  videography  of   fully  grown  urban  trees   (Ulmus  procera).  Results  obtained  demonstrated  the  feasibility  of  this  applica6on  and  the  use  of  smartphones  to  es6mate  the  temporal  and  spa6al  varia6ons  of  LAI  from  urban  trees  of  the  city  of  Melbourne  in  Australia.  

Juan  de  Dios  Morales  1,  Pangzhen  Zhang2  and  Sigfredo  Fuentes  2*    1  The  University  of  Melbourne,  Office  of  Environmental  Programs  -­‐  Department  of  Science.  Parkville,  3010.  Victoria.  Australia.  

2  The  University  of  Melbourne,  Faculty  of  Veterinary  and  Agricultural  Sciences.  Parkville,  3010.  Victoria.  Australia.  *Corresponding  author:  sfuentes@unimelb.edu.au    

Orchard'water'status'variability'assessed'using'proximal'and'aerial'infrared'thermography!S. Fuentes1*, Nolan A.P.2, Park S.2, O’Connell M.3, Ryu Dongryeol2

1 The University of Melbourne, Faculty of Veterinary and Agricultural Sciences. Building 142 Royal Parade. Parkville Victoria 3010 Australia.

2 The University of Melbourne, Melbourne School of Engineering. Parkville Victoria 3010 Australia. 3 Department of Economic Development, Jobs, Transport and Resources. Agricultural Research and Development Division.

Tatura. Victoria 3616 Australia

*Corresponding author: sfuentes@unimelb.edu.au INTRODUCTION Projected climate change and its variability predicts increased temperatures, higher evaporative demand and water scarcity over a large portion of Australia. These projections have increased the pressure to obtain high quality fruit production with more efficient water use. Several studies have demonstrated that carefully imposed water stress improves stone fruit quality parameters. However, most of the conventional methods to monitor plant water status are based on manual point measurements, which have low spatial coverage, and are resource expensive and time-consuming. This seriously constraints the efficient assessment of spatial variability of water status from orchards caused by heterogeneous soil characteristics, poor irrigation distribution uniformity and/or variability in canopy structure/architecture. This work has demonstrated the capability of unmanned aerial vehicles (UAVs) in detection of water stress for a stone fruit orchard (peach and nectarine).

MATERIALS AND METHODS The experiment was carried out during the 2014-15 growing season in field-grown grapevines and peach trees from a stone fruit experimental orchard in Tatura, Victoria. Water stress treatments were imposed by restricting irrigation in selected sections for both peach and nectarine orchards. The UAV was equipped with a visible-near/thermal-infrared camera systems (Fig. 1). Water stress indices maps, produced using the UAV-borne images, were evaluated with ground-based measurements of leaf conductance, stem water potential and proximal infrared thermography. During aerial image acquisition all of the camera parameters remained constant while the flight parameters were dynamic. In order to maintain optimal forward overlap between consecutive images, the on-board intervalometer recalculated the forward distance with every new altitude estimate from the UAV’s flight controller (Fig. 2). !!RESULTS Results showed highly comparable and significant agreements between canopy temperatures obtained using the UAV, water deficits imposed to plants and ground truth thermal data (Fig.3 & 4). !!!!!!!!!!!!!!!!!!!!!!!!!!

Figure 3. A comparison between air temperature and the average canopy temperature of treatment areas (left). Comparison between ground truth surface temperature and aerial thermal sensor measurement acquired at 100 m (right).

Fig. 1. The multirotor UAV platform (S900, DJI, Shenzhen, China) carrying a thermal and multispectral payload, IMU, GPS receiver and autopilot.

CONCLUSIONS The presented orchard case study confirmed the effectiveness of the multi-sensor (thermal & multispectral) UAV payload proposed for the estimation of biophysical parameters, such as water stress. The analysis of high spatial resolution thermal imagery confirmed that plants under water stress had a higher averaged canopy temperature (2.65°C) compared to air temperature, enabling the successful detection of water deficit plants.

Figure 4. Thermal images from UAV showing control treatments (right) and water deficit treatments (left). Colour bar represents the temperature scale in oC.

ACKNOWLEDGEMENTS This research was funded by a Horticulture Centre for Excellence Innovation Seed Fund grant and an ARC LIEF grant (LE130100040).

Figure 2. Adaptive intervalometer algorithm used by the UAV.

Materials  and  Methods:  Research   was   conducted   along   the   Royal   Parade   in  Melbourne,  Victoria  Australia.  The  chosen  area  has  been  mostly  planted  with  old-­‐stands  of  Elm  trees  occupying  the  median  strip.  Two  smartphone  devices   (iPhone  5s,  Apple  Inc.   Cuper6no,   CA.   USA)   were   mounted   with   the   back  camera   in   an   upward-­‐looking   orienta6on   (zero   zenith  angle)  and  170  cm  height  from  the  ground  on  the  roof  of  a  small  car.  The  car  was  driven  in  a  straight-­‐line  for  2.5  km  of  distance.  The  iPhones  were  set  in  automa6c  mode  with  a  video  resolu6on  of  1080  x  1920  pixels  HD  at  30  fps  and  recorded  video  along  the  trajectory.  The  GPS  informa6on  was  recorded  using  the  View  Ranger  App  (Augmentra  Ltd,  Cambridge,   UK).     Measurements   were   conducted   once  per  month   during   the   2015-­‐16   growing   season   between  September   (2015)   un6l   February   (2016).   The   automated  data   analysis   of   videos   was   performed   using   the  methodology   proposed   by   Fuentes   et   al.   (2008)   and  Fuentes   et   al.   (2014)   in   order   to   quan6fy   LAI   and   other  canopy  architecture  parameters  from  each  video  frame.  

Results:  The  results  obtained  clearly  showed  the  dynamics  of  tree  growth  with  maximum  values  corresponding  to  each  automa6cally  filtered  tree  (Fig.  2).  Addi6onally,  LAI  values  were  consistent  with  spa6al  and  temporal  varia6on  expected  from  trees  without  leaves  (Fig.  2   up),   fully   grown   canopies   (Fig.   2   boiom)   and   from   the   same   trees   at   different  measurement  periods  (Fig.  3  leB).    Other  parameters,  such  as  the  crown  porosity  was  inversely  propor6onal  to  foliage  cover  (see  Fig.  3  right).  

Conclusions:  Automa6c  video  acquisi6on  and  LAI  computa6on  clearly  demonstrates  the  advantages  and  poten6al  applica6on  to  monitor  deciduous  trees  in  urban  environments  where  unmanned  aerial  vehicles  are  not  allowed.  Addi6onally,  this  study  demonstrated  the  feasibility  of  using  more  accessible   and   cheaper   mul6use   electronic   devices,   such   as   smartphones,   for   the   assessment   of   tree   growth.   More   importantly,   this  systema6c  procedure  can  be  highly  comparable  to  more  expensive  and  less  efficient  in  situ  monitoring  systems.  Further  tes6ng  needs  to  be  performed  using  this  methodology  on  public  transport,  rubbish  trucks  and  council  vehicles.  The  laier  will  effec6vely  transform  vehicles  into  moving  robots  for  automa6c  data  collec6on  and  analysis  helping  in  this  way  offsejng  the  carbon  footprint  of  transport  and  other  services  from  ci6es.  Finally,    this  work  could  effec6vely  contribute  to  the  development  of  smart  resilient  ci6es  that  apply  precision  tools  for  decision-­‐making  prac6ces  related  to  irriga6on,  nutri6on  and  canopy  management  of  urban  trees  in  a  more  efficient  and  cost  effec6ve  manner.  References:  FUENTES,  S.,  et  al.  2008.  An  automated  procedure  for  es6ma6ng  the  leaf  area  index  (LAI)  of  woodland  ecosystems  using  digital  imagery,  MATLAB  programming  and  its  applica6on  to  an  examina6on  of  the  rela6onship  between  remotely   sensed   and   field  measurements   of   LAI.  Func4onal   Plant   Biology,   35,   1070-­‐1079;   FUENTES,   S.   et   al.   2014.   Automated   es6ma6on   of   leaf   area   index   from   grapevine   canopies   using   cover   photography,   video   and  computa6onal  analysis  methods.  Australian  Journal  of  Grape  and  Wine  Research,  20,  465-­‐473;  RYU,  Y.,  et  al.  2012.  Con6nuous  observa6on  of  tree  leaf  area  index  at  ecosystem  scale  using  upward-­‐poin6ng  digital  cameras.  Remote  Sensing  of  Environment,  126,  116-­‐125.    

Figure   3:   (leB)   Example   of   the   seasonal   varia9on  of   LAI   from   three   trees   and   corresponding  Day  of   Year   .   (right)  Canopy   architecture   parameters   including:   Frac9on   of   foliage,   Foliage   cover,   Crown   porosity   and   Clumping   index  from  five  trees.    

Figure   1:   Study   area:   Royal   Parade,   Melbourne,  Australia.  Red   line  denotes   the   trajectory   for  video  acquisi9on   and   sec9on   of   street   analysed.   Source:  Google  Earth,  2015.  

Figure   2:   Automated  LAI   analys is   f rom  videography   of   trees  before   budburst   in  September   2015   or  early  Spring  (Top)  and  f rom   fu l l y   g rown  canopies  in  January  or  mid   Summer   2016  (boWom).   Red   dots  r e p r e s e n t s   t h e  automa9c   filtering   of  the   analysed   data   to  o b t a i n   L A I   f r om  individual   trees   as  peak  values.  

0  1  2  3  4  5  6  7  8  

12-­‐sept-­‐15  11-­‐oct-­‐15   24-­‐dic-­‐15   10-­‐ene-­‐16   7-­‐feb-­‐16  

255   284   358   10   38  

Day  of  Year  

 LAI  

LAI  

 

Video  frames    

LAI  

 

0  

0.2  

0.4  

0.6  

0.8  

1  

1.2  

Sep   Oct  Dec   Jan   Sep   Oct  Dec   Jan   Sep   Oct  Dec   Jan   Sep   Oct  Dec   Jan  

2015   2016   2015   2016   2015   2016   2015   2016  

Frac>on  of  foliage  (Ff)  

Foliage  Cover  (Fc)  Crown  porosity  Φ     Clumping  index  Ω(0)    

Tree  1  

Tree  2  

Tree  3  

Tree  4  

Tree  5