Precision Viticulture Ampelos 2013

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This slideshow was presented at the Ampelos 2013 International Symposium in Santorini.

It's goal is to inform about recent developments in the field of Remote Sensing, that can be used as a supplement to vine grower's/wine maker's experience and knowledge, to aid him/her in achieving better results.

The complete title of the study is: Advanced remote sensing techniques & high spatial and spectral resolution data for Precision Viticulture.

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Precision Viticulture Ampelos 2013

  1. 1. Advanced remote sensing techniquesAdvanced remote sensing techniques &&high spatial and spectral resolutionhigh spatial and spectral resolution datadatafor Precision Viticulturefor Precision ViticultureNational Technical University of AthensSchool of Rural and Surveying EngineeringDepartment of TopographyAuthors:Karantzalos, Karakizi, Kandylakis, Oikonomou, Makris, Georgopoulos
  2. 2. Precision ViticulturePrecision Viticulture2Precision?Estimate the within field variability ofvarious vine/grape.. ..must/wine quality properties
  3. 3. Precision ViticulturePrecision Viticulture3Estimate the within field variability of Vines Grapescanopy, vigor, foliar pigments (chlorophylls,carotenoids, anthoc.), water stress, health, etc.Brix, Total acidity, pH, malic acid, polyphenols,color index, ripeness, etc.
  4. 4. Precision ViticulturePrecision Viticulture4Estimate the within field variability ofcanopy, vigor, foliar pigments (chlorophylls,carotenoids, anthoc.), water stress, health, etc.Brix, Total acidity, pH, malic acid, polyphenols,color index, ripeness, etc.Experience, ExpertOrganolepticField/Lab AnalyticalMeasurementsEarth Observation/Remote Sensing Vines Grapes
  5. 5. Remote SensingRemote Sensing5A supplement to vine grower’s / wine maker’sskills, experience & knowledge
  6. 6. Remote SensingRemote Sensing6Spectral Analysis/ Remote Sensors sensitive to: Optical Near Infrared Thermal Microwave etc
  7. 7. Remote SensingRemote Sensing7Spectral Signatures - Vegetation
  8. 8. 8During veraison: Field Work, Satellite ImagesRemote SensingRemote Sensing forforPrecision ViticulturePrecision Viticulture
  9. 9. 9Based on advanced remote sensing techniques & high spatial and spectral resolution satellitedata Detect where the vineyards are Estimate the spectral difference of various vine varieties Calculate within field vine properties- canopy, Vigor, foliar pigments Estimate grape (must/wine) properties- Brix, Total acidity, pH, malic acid,polyphenols, color index, ripenessRemote SensingRemote Sensing forforPrecision ViticulturePrecision Viticulture
  10. 10. Where are the vineyards?Where do we cultivate vines? data pre-processing, image fusion, etc classification (spectral, geometric & texture)Remote SensingRemote Sensing forforPrecision ViticulturePrecision Viticulture
  11. 11. Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureWhere are the vineyards?Where do we cultivate vines? data pre-processing, image fusion, etc classification (spectral, geometric & texture)
  12. 12. Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureWhere are the vineyards?Where do we cultivate vines? data pre-processing, image fusion, etc classification (spectral, geometric & texture)
  13. 13. Trapeza MegaplatanosQualityIndicesMultispectralDataFusedDataMultispectralDataFused DataCompleteness 86% 86% 94% 96%Correctness 89% 92% 81% 92%Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureWhere are the vineyards?Where do we cultivate vines? data pre-processing, image fusion, etc classification (spectral, geometric & texture)
  14. 14. What are the spectral differences of each vine variety ?Can we detect or discriminate them remotely ? detected vineyards supervised classification (spectral)Authors /Year SensorBand Width &Number of BandsSpatialResolutionVarietiesLacar et al.(2001)CASI400-900 nm121 m1. Cabernet Sauvignon2. SyrahFerreiro-Armánet al. (2006)CASI400-950 nm1443 m1.Cabernet Sauvignon2. Merlot NoirFerreiro-Armánet al. (2007)CASI-2400-950 nm1443 m1. Cabernet Sauvignon2. Merlot NoirCASI-2407,8-942,2 nm483 m1. Cabernet Sauvignon2. Merlot Noir3. Cabernet Frank , per 2Remote SensingRemote Sensing forforPrecision ViticulturePrecision Viticulture
  15. 15. Apprx. 20 dif. varieties300 samples330 spectral bands90.000 ground observ.+23 million remote observ.Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureWhat are the spectral differences of each vine variety ?Can we detect or discriminate them remotely ? detected vineyards supervised classification (spectral)
  16. 16. COMPLETENESS Ground TruthClassificationResultsSyrah Ι MerlotSauvignonBlanc IISauvignonBlanc ISyrah Ι 82,49% 2,11% 14,88% 12,66%Merlot 0,49% 96,97% 0,44% 0,00%SauvignonBlanc II5,50% 0,92% 83,66% 0,70%SauvignonBlanc I11,52% 0,00% 1,03% 86,64%Overall Accuracy 85,21%Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureWhat are the spectral differences of each vine variety ?Can we detect or discriminate them remotely ?Karakizi et al., 2013. Vineyard detectionand vine variety discrimination from highresolution satellite data, EuropeanConference on Precision Agriculture
  17. 17. COMPLETENESS Ground TruthClassification ResultsCabernetSauvignonSyrah Robola MerlotSauvignonBlancCabernet Sauvignon 68,35% 20,30% 17,28% 0,76% 27,92%Syrah 4,60% 45,79% 7,09% 3,55% 7,01%Robola 16,59% 19,40% 67,27% 2,24% 15,97%Merlot 0,05% 1,62% 1,12% 91,37% 2,78%Sauvignon Blanc 10,39% 12,89% 7,22% 2,08% 46,27%Overall Accuracy 63,59%Karakizi et al., 2013. Vineyard detectionand vine variety discrimination from highresolution satellite data, EuropeanConference on Precision AgricultureRemote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureWhat are the spectral differences of each vine variety ?Can we detect or discriminate them remotely ?
  18. 18. Estimate Vine properties - Within Field Variability Canopy, Vigor, Foliar pigments (chlorophyll, carotenoids, anthoc..), Water stress, Health, etcJohnson et al., 2003. Mapping vineyard leaf area with multispectral satellite imagery. Computers andElectronics in Agriculture.Haboudane et al. 2004. Hyperspectral Vegetation indices.. ..for predicting green LAI, Remote Sensing ofEnvironment.Zarco-Tejada et al., 2005. Assessing vineyard condition with hyperspectral indices, Remote Sensing ofEnvironment.Meggio et al. 2010. Grape quality assessment in vineyards.. ..using narrow-band physiological remotesensing indices. Remote Sensing of Environment.Remote SensingRemote Sensing forforPrecision ViticulturePrecision Viticulture
  19. 19. Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureEstimate Vine properties - Within Field Variability Canopy, Vigor, Foliar pigments (chlorophyll, carotenoids, anthoc..), Water stress, Health, etc
  20. 20. Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureEstimate Vine properties - Within Field Variability Canopy, Vigor, Foliar pigments (chlorophyll, carotenoids, anthoc..), Water stress, Health, etc
  21. 21. Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureEstimate Vine properties - Within Field Variability Canopy, Vigor, Foliar pigments (chlorophyll, carotenoids, anthoc..), Water stress, Health, etc
  22. 22. Estimate Grape (Must/Wine) properties Brix, pH, Total Acidity, Malic Acid Polyphenols, Color Index Ripeness, etc.Remote SensingRemote Sensing forforPrecision ViticulturePrecision Viticulture
  23. 23. Remote SensingRemote Sensing forforPrecision ViticulturePrecision ViticultureEstimate Grape (Must/Wine) properties Brix, pH, Total Acidity, Malic Acid Polyphenols, Color Index Ripeness, etc.Kandylakis et al., 2013. Evaluatingspectral indices from WorldView-2satellite data for selectiveharvesting in vineyards, EuropeanConference on Precision Agriculture
  24. 24. GISGeographicInformationSystemRemote SensingRemote Sensing forforPrecision ViticulturePrecision Viticulture
  25. 25. Remote SensingRemote Sensing forforPrecision ViticulturePrecision Viticulture
  26. 26. Thank you !!!Thank you !!!Authors:Karantzalos, Karakizi, Kandylakis, Oikonomou, Makris, GeorgopoulosNational Technical University of AthensSchool of Rural and Surveying EngineeringDepartment of Topography

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