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Lecture by Dr. Lyndon Estes

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What Can UAV(S) Do That Cheap Satellites (And Field Scientists) Can’t?

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Lecture by Dr. Lyndon Estes

  1. 1. What Can UAV(S) Do That Cheap Satellites (And Field Scientists) Can’t? Lyndon Estes Princeton University
  2. 2. What is a UAS? Large Medium Small Micro Anderson & Gaston, 2013 Fixed Wing Rotary Buoyant
  3. 3. 3
  4. 4. Extent Error/Bias Our Current Observing Methods
  5. 5. The Mountain Bongo Antelope Tragelaphus eurycerus isaaci Why I Got Interested in UAVs
  6. 6. Prob.   occurrenc e Estes  et  al,  2012,  Animal  Conservati
  7. 7.  Bongo  presence  observations  w/o  field   data Bongo  presence  observations  w/field  data Non-­‐presence  observations
  8. 8. Initial analysis of Bongo Habitat Selection Understorey structure – 400 m2 -> Field data Patch structure – 20 ha -> ASTER + Spectral Mixture Analysis ~450 m Estes et al, 2008, Remote Sensing of Environment
  9. 9. 10 20 30 40 50 60 70 80 10 20 30 40 50 60 70 80 90 Observed  SCI  score Predicted  SCI  score X o  Non-­‐presence  obs    (training) +  Bongo  obs  (test)          Mean  of  non-­‐presence  obs        Mean  of  bongo  obs Model  –  5  ASTER-­‐derived  predictors   3  Spectral  Mixture  Analysis   2  texture  variables   R2  =  0.46   RMSE:      Training  =  17%      Test  =  19%   Estes  et  al,  2010  (Remote  Sensing  Environment) Mapping  Structural  Complexity  Indices
  10. 10. Structural  Complexity  Index Canopy “Middlestorey” Herbaceous  understorey ? ✓
  11. 11. ~450  m Hand  launch  a  small  UAV… …And  collect  high  quality  data     in  transects,  rather  than  slogging  it   • Understorey  +  canopy   • 3  dimensions   • Micro  –  landscape  scale   • Every  few  weeks
  12. 12. Estes, Chaney, Herrera-Estrada, Sheffield, Caylor, Wood, 2014, ERL Bringing the Satellite Closer to The Field Beat down error in remotely sensed yield estimates Crop-specific NDVI signature Estes et al, 2013, Glob Ecol Biogeog, Glob Change Biol
  13. 13. Calibrate/Validate Crop Models Generalized Additive Model of Maize Yield
  14. 14. Process-­‐based  Model  
 accuracy  at  400  km2 DSSAT
  15. 15. Accuracy  at  Provincial  Scale
  16. 16. UAVs  over  agricultural  landscapes • Crop  type   • Sowing  date   • Flowering   • NDVI/LAI  –  weekly  to  bi-­‐weekly   • Microtopography   • Surface  soil  moisture?
  17. 17. 18 Crop model -> yield + LAI
  18. 18. MODIS Landsat Crop  Fields  in  Eastern  Province,  Zambia Daily Every  16  days
  19. 19. 21
  20. 20. Extent Error/Bias Environment Observing Methods
  21. 21. For effective use of UAS Need:     1. Geometrically   2. Optically   3. Automated   Rectification  of  collected  data  in  space  &  time  domains 23
  22. 22. Course Goals By end of week: 1. Understand what can (and probably cannot) be done with UAS 2. Learn how to plan and fly mission 3. Successfully post-process imagery, and understand key factors to correct 4. Basic analysis of results

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