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Geo Sense - UAV service, unmanned remote sensing


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Geo Sense - UAV service, unmanned remote sensing

  1. 1. Utilizing Hyperspectral Imaging System in unmannedaerial vehicle (UAV) for Agricultural/Palm Oil Analysis Geo  Sense  Sdn.  Bhd.   79A,  Jalan  Seri  Impian  1   T06-­‐03,  Jln  Centry  Square   Taman  Impian  Emas   Block  2320     81300  Johor  Bahru   63000  Cyberjaya  
  2. 2. Geo  Sense  Sdn.  Bhd.  Brief  Background  •  Establish  May  2006  •  MSC  Status  –  in  web  GIS  and  aerial  mapping  •  Pioneering  Civilian  UAV  applicaTons  /   Services  •  Skudai,  Johor  Bahru  Base  Company  &   Cyberjaya  •  CollaboraTon  with  UTM,  IMREC  and  Inst.   Sustainable  Agri  of  Cardoba  Spain  •  Since  2007  -­‐  R&D  in  unmanned  aerial   mapping  and  remote  sensing  •  Recepient  Anugerah  Perdana  Menteri  APICTA   ICT  eGov.  Catergory  in  2007  •  Vision,  to  become  leading  tech  company  in   civilian  UAV  applicaTon  
  3. 3. Sources of Aerial Imagery Light Aircraft Imaging Altitude10k – 30k feet Satellite 500-800 km Cost RC AIrcarft / UAV Altitude 500 – 2000 ft •  erial Camera System A •  adiometric resolution R •  0 cm – 60 cm resolution 2 (avg 30 in resolution) Cloud Issues •  erial mapping survey A • PSAR (SAR) / LIDAR I •  atural color (RGB) N •  - 15 cm resolution 7 •  urveillance / Monitoring S 200 meter90 km 3 km 300 meter 30 km 3 km
  4. 4. Technology  CollaboraTon    
  5. 5. Products  &  Services    Unmanned  Aerial     GIS  /       GPS  –  Geo  Tagging   Mapping   Web  GIS   Mobile  applicaTon  
  6. 6. Experiences  &  Clients  
  8. 8. UAV  Aerial  Mapping  –  Process  Flow  UAV  Image  AcquisiTon   Flight   UAV   Transferring   Launching   Monitoring   Recovering   Planning   InstallaTon   Plan  Image  Processing   Raw   Overlaying   Mosaic  /  STtching   Tiling   Images   RegistraTon  
  9. 9. 27  March  2010   22  July  2010  
  11. 11. Batu  Caves,  Selayang  
  12. 12. Forest  Clearing  &    PlantaTon  Planning  
  13. 13. Highway  Monitoring  (JKR)  
  14. 14. Progress Monitoring  Rumah Rakyat IRDA June  2010   Oct  2010   Feb  2011  
  15. 15. Potential ApplicationsWork Auditing /Verification Feb  2010   June  2010  
  16. 16. Case  Study  1  -­‐  UAV  Mapping  by  Geo  Sense    –  Semporna,  Sabah  (5  sq  km)   Visual  Using  UAV   PopulaTon:  ~  130K   Mail  Volume:  ~  200  per  day   PO:  Semporna  Post  Office   Mail  delivery:  limited     Address:  Using  kampung,  schools  &   PO  Box  (710  units)   Bank:  Maybank  &  BSN  
  17. 17. House  Numbering  and  Address  Assignment  by  Pos  Malaysia   DigiTzing  using  GIS   KAMPUNG  BANGAU-­‐BANGAU   2,436  houses   ADDRESS  SAMPLE   Cikgu  Ahmad     No2,  KampungBangauBangau   PERKAMPUNGAN  AIR  1   91300  Semporna,  Sabah   659  houses   PERKAMPUNGAN  AIR  2   Lat  4.4883  N   Long  118.6050  E   3,313  houses   Approximately  6,000  new   delivery  points  idenTfied    
  18. 18. Base Map Preparation – Using High Res Aerial Image from UAV High  resoluTon  UAV  Images   Image  digitalizing   Asset  Data  in  Base  Map   Base  Map  from  high  res  images  
  19. 19. 3D  /  DTM  –  OrthoracTficaTon  Process  Sojware  upgrade  
  20. 20. Potential ApplicationRiver Monitoring / Flood Management
  21. 21. Project  Monitoring  and  CommunicaTon  (PMCS)  
  22. 22. Scope  of  Work  &  Delivery   UAV  capturing  images   Image  process  &  digitalizing   The  big  mosaic  (sTtched)  image   Image  presentaTon  &  potenTal  uTlizaTon   Big  Poster   Tiles  for  quick  viewing   Online  Tles  visualizaTon   Image  registraTon  (GIS)   IntegraTon  with     -­‐  Project  GIS     Project  Management   -­‐  Project  monitoring  and   System   Online  visualizaTon   Quick  distribuTon  –  images   reporTng  system   -­‐  Structure  modeling   Online  archiving  system   store  in  DVD   -­‐  Decision  support   with  WBS   system  
  24. 24.  Quanta  Lab    Geo  Sense  Geo  Sense,  is  Malaysian  MSC  Status  company,  that  is  using  unmanned  aerial  vehicle  (UAV)  for  aerial  mapping  and  remote  sensing.  Geo  Sense  is  collaboraTng  with  Dr.  Pablo  J.  Zarco  Tejada  the  Director  of  Laboratory  for  Research  in  QuanTtaTve  Remote  Sensing,  under  the  InsTtute  of  Sustainable  Agricultural  in  Cardoba,  Spain  in  uTlizing  UAV  for  advance  remote  sensing  for  agricultural    purposes.      Geo  Sense  
  25. 25. CollaboraTon  of  experts  between  Quantalab  and  Geo  Sense  Sdn.  Bhd.    Profile    Pablo  J.  Zarco  has  been  Course  Director  and   Dr  Pablo  J.  Zarco  Tejada   Teaching  Assistant  within  the  Departments  of   Environmental  Science,  and  Earth  and  Space   Ph.D.  in  Earth  and  Space  Science,  York   Science  (  York  University  ,  Canada  ),  and  Land,  Air,   University  (Canada),  2000     and  Water  Resources  (LAWR),  University  of   M.Sc.  in  Remote  Sensing,  Image   California  Davis  ,  in  courses  related  to   Processing  and  ApplicaTons.  Dept.  of   Environmental  Science  and  Remote  Sensing.  He   Applied  Physics,  Electronic  and   has  also  collaborated  in  other  courses  at  University   Mechanical  Engineering  (APEME),   of  California  ,  Davis  in  Precision  Agriculture  and   University  of  Dundee  (Scotland  ,  UK),   Environmental  Remote  Sensing:     1997     Since  2008,  Dr  Pablo  has  been  uTlizing  UAV  for   B.S.  Agricultural  Engineering  (Cordoba  ,   agri.  remote  sensing.   Spain)      Geo  Sense  
  26. 26. The  collaboraTon  will  offer  Malaysia  users  to  access  to  the  latest  technology  in  agricultural  monitoring  and  analysis  using  UAV  for  quicker  respond  at  lower  cost  compare  with  convenTonal  methods.  The  UAV  with  mulT  spectral  camera  enable  to  meet  any  on  demand  request  for  urgent  requirement  in  any  agricultural  respond  and  analysis,  eg  to  quickly  get  the  assessment  over  agri.  epidemics  in  paddy  field  and  mapping  DOA  IntegraTng  farming  area  without  the  need  to  wait  for  satellite  images  or  convenTonal  airplane.    Geo  Sense  
  27. 27. Sample  of  analysis  from  UAV  hyperspec.  Sensor  operate  by  Quantalab  in  Spain.  
  28. 28. ExisTng  Geo  Sense  UAV  Agricultural  /  Crop  Monitoring  /Precision  farming  Without  hyperspec  sensor  –  limited.   Infrared  Imagery   Crop  Analysis  
  29. 29. Sample  of  large  agricultural  area  (track  record)    –  1000  hectares  olive  farm  in  Cardova,  Spain  .  
  30. 30. Hyperspectral  imagery  acquired  with  an  UAV  plaOorm  over  orchard  crops   Imagery  acquired  at  40  cm  resoluTon  and  260  bands   in  the  400-­‐900  nm  region  @  5  nm  FWHM   Hyperspectral   imagery   acquired   from   an   UAV   plaoorm   and   the  Micro-­‐Hyperspec™  Imaging  Spectrometer  from  Headwall   Photonics.   Imagery   acquired   at   550   m   AGL   over   an   orange   orchard   where   stress   detecTon   experiments   are   conducted   by  QuantaLab  at  the  InsTtute  of  Sustainable  Agriculture  (IAS),   NaTonal  Research  Council  (CSIC),  Spain.  
  31. 31. Hyperspectral  Image  OrthorecQficaQon   AStude  data  acquired  with  an  AHRS  system  onboard  the  UAV  Image   orthorecTficaTon   is   conducted   using   aqtude   data   acquired   with   an   AHRS   instrument  synchronized  with  the  hyperspectral  imager.  Commercial  sojware  and    IAS-­‐CSIC  algorithms  are  applied  in  the  laboratory  ajer  each  flight  campaign.  
  32. 32. Image  CalibraQon  and  Atmospheric  CorrecQon   Spectral   calibraTon   of   the   hyperspectral   instrument   is   conducted   at   IAS-­‐CSIC   using   Hg-­‐Ar   calibraTon   lamps.   Radiometric   calibraTon   coefficients   are   developed   in   the  opTcs  laboratory  at  IAS-­‐CSIC  using  a  radiometrically   calibrated   integraTng   sphere.   Image   calibraTon   and   atmospheric   correcTon   to   obtain   surface   reflectance   are   conducted   from   field-­‐measured   data   and   aerosol   opTcal  depth  measured  at  the  Tme  of  flight.  Radiance   and  reflectance  imagery  are  produced  ajer  calibraTon   algorithms   are   applied   in   QuantaLab   IAS-­‐CSIC   Laboratory.   Imagery  acquired   at  40  cm   resoluTon,  260   bands  in  the     400-­‐900  nm  region   (5  nm  FWHM).  Raw  data   Reflectance  data  
  33. 33. Hyperspectral  Image  SegmentaQon  of  the  crop  canopy   Object  based   image  analysis  for   automaTc  tree   crown   idenTficaTon  and   stress  detecTon   using  spectral   indices   Hyperspectral  reflectance  image   Object  based  analysis  Stress  map  (object-­‐based  analysis)   Interpolated  themaTc   maps  obtained  from  object   based  analysis  conducted   on  hyperspectral  indices  at   the  tree  crown  level.  Stress   maps  are  derived  based  on   photosyntheTc  pigment   concentraTon  and  canopy   density  
  34. 34. Hyperspectral  Reflectance  from  a  water  body  S p e c t r a l   r e fl e c t a n c e  extracted   from   different  areas  of  a  water  body  
  35. 35. Airborne  Hyperspectral  Imaging  for   Palm  Oil  Analysis  
  37. 37. User  Requirements  •  Needs  to  increase  producTvity  by  planTng  more  (new   estate)  &  improve  producTvity  /  yields   –  SoluTon;  Maintain  good  tree  condiTons,  by  having  up  to   date  block  /  sectors  /  trees  informaTon   –  Healthiness  and  nutrient  status   –  Assets,  Land  use,  land  cover  (showing  assets  locaTon,   vegetaTon  and  water  body)  •  Nutrient  checking  (leaves  &  soil)  –  up  to  individual  tree   –  SoluTon;  Soil  Nutrient  &  Foliar  Variability  Mapping  –   showing  the  availability  of  N,P,K,Mg,B(easier  for  detected   less  nutrient  area).  
  38. 38. User  Requirements  •  Healthiness  oil  palm  trees  map  for  detecTng   stress  trees  and  for  esTmaTng  the  yield.   – SoluTon;  Digital  “stressed”  palms  map.  Maps  showing   healthy  trees,  “stressed”  and  dead  palms  and   development  of  spectral  signature  for  all  palms   condiTon.  •  LocaTons  of  the  tree  with  un-­‐healthy  condiTon     – SoluTon;  Tree  status.  Showing  tree  maturity  status (ageing)   – Individual  Oil  Palm  inventory  countswith  precise  GPS   locaTon  Map  (locaTon  each  tree)  
  39. 39. What  aerial  imagery  tells  •  As  evaluaTon  tools  and  diagnosTc  kits,   –  PlantaTon  and  forestry   –  Inventory   –  Healthiness/Stressed/Disease   –  Dead  Trees  (Pest/Disease/Water  Stress,  waterlog,  burnt,  etc)   –  Species/community  types   –  Maturity  •  Marine  and  environmental  features   –  Inventory   –  Community  types   –  Changes  detecTon  and  analysis   –  Coral/sea  weed  mapping   –  Water  quality  (salinity,  turbidity,  pH,  temperature,  etc)  •  Physical  Features   –  Roads/footpath/track/rivers/streams/topo.  etc   –  Area  EsTmaTon=Gross  Area–Vacant  Area=Net  Area   –  Boundaries  
  40. 40. Overview  of  works    proposal  –  design  and  develop  Unmanned  Aerial  Remote  Sensing   Facility  For  Agricultural,  Forestry  and  Palm  Oil  Analysis.    Preparing    UAV  plaoorm   IntegraTng  Micro  Hyperspec  Sensor  for  user  unmanned  remote   Image  capturing  and  image   from  Honeywell  Photonic  (US)  advance   cube.  –  operate  by  Geo  sensing  aerial  vehicle  plaoorm   hyoperspec    sensor  for  UAV  system.  Into  (UAV).  Need  for  stable,  endurance   Sense  &  Quantalab,  Spain   Geo  Sense  UAV  plaoorm.  and  load  (min.  3  kg  load)  system   IntegraTon  work  is  collaboraTon  with  Plaoorm  will  be  provided  by  Geo  Sense   QuantaLab,  Spain  and  Geo  Sense   Online  access  system  –   Design  &  develop  client   web  based  system  for   imaging  database  or  library   mulT  access  via  Internet  /   Image  Analysis  –   system  for  review,  analysis   Intranet   Quantalab,  Spain   and  decision  support   -­‐  Geo  Sense  &  partner   -­‐  Quantalab  &  Geo  Sense  
  41. 41. Grant  Plan   ExisTng   Grant   Spin  off   Hand  launch  glider  UAV   Mid  range  UAV  System     Malaysian  IMU  system   Unmanned  aerial  mapping   (20  kg,  payload  1.5kg)   (autopilot  system)   Per  mission  30  min.  endurance,     Per  mission  90  min   1.5  sq  km  per  mission   Min.  3  sq  km  per  mission   6    sq  km  (600  hectares)  per  day.   10  sq  km  (1000  hectares)  per  day   Remote  sensing  on     RGB  compact  camera   Hyperspectral  Imaging   demand   For  UAV   VisualizaTon   Imaging  analysis   Center  for  unmanned  remote   Sensing  for  tropical  agri.