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Martin Schlerf

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Luxembourg Institute of Science and Technology

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Martin Schlerf

  1. 1. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 MAPPING CROP BIOPHYSICAL VARIABLES FROM UAV-DATA Dr. M. Schlerf Luxemboug Institute of Science and Technology
  2. 2. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 WORLD POPULATION INCREASE Source: Wikipedia Population (logarithm. scale) Year
  3. 3. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 CLIMATE CHANGE Source: National Center for Atmospheric Research (NCAR)
  4. 4. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Change of food production since 1961 (1961=1) Source: H. Charles J. Godfray et al. Science 2010;327:812-818 FOOD: SUPPLY AND DEMAND Change of calorie demand in dependence of standard of living Conclusion: Increase in food demand and increase in risk for crop failure.
  5. 5. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 ND Mueller et al. Nature 490, 254-257 (2013) Average yield gaps for maize, wheat and rice. PRODUCTIVITY IN AGRICULTURE Causes: nutrient and water (maize)
  6. 6. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Photo: CEMA Precision farming can help increase productivity without putting extra pressure onto the environment. PRECISION FARMING Precision farming system Application of fertiliser following the treatment map
  7. 7. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 RapidEye satellite False-color satellite image (6.5 m spatial resolution) EARTH OBSERVATION FROM SPACE
  8. 8. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 CROP DEVELOPMENT Jensen, 2000 Seasonal development (phenology) of crops (top) and how they appear in false colour images (bottom) Map of crop types
  9. 9. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Source: Jensen, 2000 SIGNAL FROM VEGETATION Nitrogen supply Treatment map Source: Cilia et al. 2014 (Rem. Sens.)
  10. 10. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 SENSORS Source: EnMap Science Plan Source: Patenaude et al. 2005 UAV 1 km 30 m 1 m HS1 m LiDAR
  11. 11. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 • a much better resolution (several centimeters) compared to tens or hundreds of meters for spaceborne systems • greater flexibility in selecting suitable sensors • much cheaper and more flexible compared to full-scale manned aircraft remote sensing • high temporal resolution because of the high flexibility of the UAV operation • easily deployable and thus useful for rapid response applications UAV-BASED SYSTEMS Flight time against mass of small (less than 1 kg) drones. Smaller drones have significantly reduced flight times (tens of seconds compared with tens of minutes for larger drones). (from Floreano & Wood 2015, Nature)
  12. 12. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 UAV ASPECTS Zecha’s taxonomy on mobile sensor platforms Taxonomy on UAV-based remote sensing systems operating in vegetated areas. (from Salami et al. 2014) (from Salami et al. 2014)
  13. 13. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 VEGETATION INDEX Reflectance Wavelength (µm) NIR R NIR R NIR RR NIR
  14. 14. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 VEGETATION INDEX •The most commonly used VI is the so-called Normalized Difference Vegetation Index (NDVI) •NDVI = (nIR - red) / (nIR + red) •NDVI values: between +1 and -1 Surface type red nIR NDVI Green vegetation 4 45 0.84 Dark soil 8 13 0.37 Bright soil 25 45 0.29 20 % Vegetation over bright soil 21 45 0.37 Water 2 0.2 - 0.82 field meas.
  15. 15. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 VI IN UAV STUDIES (from Salami et al. 2014)
  16. 16. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Bareth et al. 2015 UAV VS GROUND REFLECTANCE from Bareth et al. 2015 Octocopter with camera Barley fields
  17. 17. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Matese et al. 2015 UAV VS AIRCRAFT VS SATELLITE The low vigor zone in the UAV image in the central zone is progressively less pronounced in the aircraft and in satellite image (from Matese et al. 2015)
  18. 18. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Agüera Vega et al. 2015 EFFECT OF TIMING ON UAV-VI Linear model correlation coefficients between Tetracam NDVI and aboveground biomassSunflower plots 1 cm, 30 cm, 100 cm images
  19. 19. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Zarco Tejada et al. 2013a PHYSIOLOGICAL VS STRUCTURAL VI
  20. 20. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Zarco Tejada et al. 2013b BIOCHEMICALS FROM UAV-VI Carotenoid effect on reflectance Leaf level Carotenoid – R515/R570 UAV level Carotenoid – R515/R570UAV image and carotenoid map in vineyard
  21. 21. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 PHYSICAL MODELS
  22. 22. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Verger et al. 2014 PHYSICAL MODEL WITH UAV DATA Image acquisition times and illumination angles Histogram Morning vs afternoon UAV estimates vs ground truth
  23. 23. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Verrelst et al. 2015 PHYSICAL MODEL TOOLBOX
  24. 24. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 ADVANCED CROP BIOMASS AND YIELD MONITORING Crop Growth Model Optical RTM Brightness temperature RTM Remote sensing Optical (S-2, L8) Radar (S-1) Measured spectra vs Modelled spectra SoilClimate Fertilizer Management Initial Soil water Initial Soil N Initial Soil N Initial Soil N Initial Soil water Initial Soil water LAILAILAI LAI LAILAICAB CAB LAILAISoil M Soil M Biomass Yield weights Initial Soil N Initial Soil water
  25. 25. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 WEB-TOOL FOR THE VISUALISATION OF CROP BIOMASS DEVELOPMENT Source: Machwitz et al. 2015
  26. 26. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Quelle: Machwitz et al. 2015 WEB-TOOL FOR THE VISUALISATION OF CROP BIOMASS DEVELOPMENT
  27. 27. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 WEB-TOOL FOR THE VISUALISATION OF CROP BIOMASS DEVELOPMENT
  28. 28. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 WEB-TOOL FOR THE VISUALISATION OF CROP BIOMASS DEVELOPMENT
  29. 29. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 WEB-TOOL FOR THE VISUALISATION OF CROP BIOMASS DEVELOPMENT
  30. 30. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 WEB-TOOL FOR THE VISUALISATION OF CROP BIOMASS DEVELOPMENT
  31. 31. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 WEB-TOOL FOR THE VISUALISATION OF CROP BIOMASS DEVELOPMENT
  32. 32. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 • Quantitative analysis of multi-/hyperspectral UAV-data requires a calibration with ground spectra; otherwise VI-values may strongly depend on camera type • Vegetation Indices • are a simple and often effective measure of vegetation greenness or biomass • but UAV-based VI-values may often depend on sensor type, spatial resolution, seasonal time and daytime of acquisition • and structural indices (like NDVI) may get misused for physiological phenomena • Physically-based approaches • may transfer UAV-data into crop attributes (LAI, biomass, chlorophyll, nitrogen) in a more robust, generic and accurate way than VIs because • they can handle different illumination conditions and view angles • they can incorporate available information on soil background and leaf angle situations • make use of the entire spectrum instead of few selected bands • but are so far mostly used as a research tool as user friendly Apps for practitioners are rare • Successful quantitative UAV-based crop mapping requires combination of skills • Remote sensing expert can extract reliable information from UAS images • Farmers are familiar with the field and crop conditions and understand the variation of crop condition within and between parcels CONCLUDING REMARKS
  33. 33. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 • Matese, A., Toscano, P., Di Gennaro, S. F., Genesio, L., Vaccari, F. P., Primicerio, J., . . . Gioli, B. (2015). Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture. Remote Sensing, 7(3), 2971-2990. • Floreano, D., & Wood, R. J. (2015). Science, technology and the future of small autonomous drones. Nature, 521(7553), 460-466. • Bareth, G., Aasen, H., Bendig, J., Gnyp, M. L., Bolten, A., Jung, A., . . . Soukkamaki, J. (2015). Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements. Photogrammetrie Fernerkundung Geoinformation(1), 69-79. • Agueera Vega, F., Carvajal Ramirez, F., Perez Saiz, M., & Orgaz Rosua, F. (2015). Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosystems Engineering, 132, 19-27. • Verrelst, J., Rivera, J.P., Moreno, J. (2015). ARTMO's global sensitivity analysis (GSA) toolbox to quantify driving variables of leaf and canopy radiative transfer models. EARSeL eProceedings, Speical Issue 2: 9th EARSeL Imaging Spectroscopy Workshop, 2015. 1-11. • Verger, A., Vigneau, N., Cheron, C., Gilliot, J.-M., Comar, A., & Baret, F. (2014). Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sensing of Environment, 152, 654-664. • Torres-Sanchez, J., Pena, J. M., de Castro, A. I., & Lopez-Granados, F. (2014). Multi-temporal mapping of the vegetation fraction in early- season wheat fields using images from UAV. Computers and Electronics in Agriculture, 103, 104-113. • Salami, E., Barrado, C., & Pastor, E. (2014). UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas. Remote Sensing, 6(11), 11051-11081. • Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G. (2014). Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging. Remote Sensing, 6(11), 10395-10412. • Honkavaara, E., Saari, H., Kaivosoja, J., Polonen, I., Hakala, T., Litkey, P., . . . Pesonen, L. (2013). Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sensing, 5(10), 5006-5039. • Zarco-Tejada, P. J., Morales, A., Testi, L., & Villalobos, F. J. (2013a). Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sensing of Environment, 133, 102-115. • Zarco-Tejada, P. J., Guillen-Climent, M. L., Hernandez-Clemente, R., Catalina, A., Gonzalez, M. R., & Martin, P. (2013b). Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and Forest Meteorology, 171, 281-294. • Berni, J. A. J., Zarco-Tejada, P. J., Suarez, L., & Fereres, E. (2009). Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle. Ieee Transactions on Geoscience and Remote Sensing, 47(3), 722-738. REFERENCES 33
  34. 34. CAPIGI GEOAGRI, Rotterdam, 26 May 2016 Thank you.

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