Precision Viticulture Ampelos 2013
-
Upload
zachariaskandylakis -
Category
Technology
-
view
912 -
download
2
description
Transcript of Precision Viticulture Ampelos 2013
Advanced remote sensing techniques Advanced remote sensing techniques && high spatial and spectral resolutionhigh spatial and spectral resolution data data
for Precision Viticulturefor Precision Viticulture
National Technical University of AthensSchool of Rural and Surveying Engineering
Department of Topography
Authors:Karantzalos, Karakizi, Kandylakis, Oikonomou, Makris, Georgopoulos
Precision ViticulturePrecision Viticulture
2
Precision?
Estimate the within field variability of
various vine/grape.. ..must/wine quality properties
Precision ViticulturePrecision Viticulture
3
Estimate the within field variability of Vines
Grapes
canopy, vigor, foliar pigments (chlorophylls, carotenoids, anthoc.), water stress, health, etc.
Brix, Total acidity, pH, malic acid, polyphenols, color index, ripeness, etc.
Precision ViticulturePrecision Viticulture
4
Estimate 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, Expert Organoleptic
Field/Lab Analytical Measurements
Earth Observation/ Remote Sensing
Vines
Grapes
Remote SensingRemote Sensing
5
A supplement to vine grower’s / wine maker’s skills, experience & knowledge
Remote SensingRemote Sensing
6
Spectral Analysis/ Remote Sensors sensitive to:
Optical Near Infrared Thermal Microwave etc
Remote SensingRemote Sensing
7
Spectral Signatures - Vegetation
8
During veraison: Field Work, Satellite Images
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
9
Based on advanced remote sensing techniques & high spatial and spectral resolution satellite data
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, ripeness
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Where are the vineyards?Where do we cultivate vines?
data pre-processing, image fusion, etc classification (spectral, geometric & texture)
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Where are the vineyards?Where do we cultivate vines?
data pre-processing, image fusion, etc classification (spectral, geometric & texture)
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Where are the vineyards?Where do we cultivate vines?
data pre-processing, image fusion, etc classification (spectral, geometric & texture)
Trapeza Megaplatanos
Quality Indices
Multispectral
DataFused Data
Multispectral
DataFused Data
Completeness 86% 86% 94% 96%
Correctness 89% 92% 81% 92%
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Where are the vineyards?Where do we cultivate vines?
data pre-processing, image fusion, etc classification (spectral, geometric & texture)
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 BandsSpatial
ResolutionVarieties
Lacar et al. (2001)
CASI400-900 nm
12 1 m
1. Cabernet Sauvignon 2. Syrah
Ferreiro-Armán et al. (2006)
CASI400-950 nm
1443 m
1.Cabernet Sauvignon 2. Merlot Noir
Ferreiro-Armán et al. (2007)
CASI-2400-950 nm
144 3 m
1. Cabernet Sauvignon 2. Merlot Noir
CASI-2407,8-942,2 nm
483 m
1. Cabernet Sauvignon 2. Merlot Noir 3. Cabernet Frank , per 2
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Apprx. 20 dif. varieties 300 samples
330 spectral bands 90.000 ground observ.
+ 23 million remote observ.
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
What are the spectral differences of each vine variety ?Can we detect or discriminate them remotely ?
detected vineyards supervised classification (spectral)
COMPLETENESS Ground Truth
Classification Results
Syrah Ι MerlotSauvignon
Blanc IISauvignon
Blanc I
Syrah Ι 82,49% 2,11% 14,88% 12,66%
Merlot 0,49% 96,97% 0,44% 0,00%
Sauvignon Blanc II
5,50% 0,92% 83,66% 0,70%
Sauvignon Blanc I
11,52% 0,00% 1,03% 86,64%
Overall Accuracy 85,21%
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
What are the spectral differences of each vine variety ?Can we detect or discriminate them remotely ?
Karakizi et al., 2013. Vineyard detection and vine variety discrimination from high resolution satellite data, European Conference on Precision Agriculture
COMPLETENESS Ground Truth
Classification ResultsCabernet
SauvignonSyrah Robola Merlot
Sauvignon Blanc
Cabernet 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 detection and vine variety discrimination from high resolution satellite data, European Conference on Precision Agriculture
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
What are the spectral differences of each vine variety ?Can we detect or discriminate them remotely ?
Estimate Vine properties - Within Field Variability
Canopy, Vigor, Foliar pigments (chlorophyll, carotenoids, anthoc..), Water stress, Health, etc
Johnson et al., 2003. Mapping vineyard leaf area with multispectral satellite imagery. Computers and Electronics in Agriculture.
Haboudane et al. 2004. Hyperspectral Vegetation indices.. ..for predicting green LAI, Remote Sensing of Environment.
Zarco-Tejada et al., 2005. Assessing vineyard condition with hyperspectral indices, Remote Sensing of Environment.
Meggio et al. 2010. Grape quality assessment in vineyards.. ..using narrow-band physiological remote sensing indices. Remote Sensing of Environment.
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Estimate Vine properties - Within Field Variability
Canopy, Vigor, Foliar pigments (chlorophyll, carotenoids, anthoc..), Water stress, Health, etc
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Estimate Vine properties - Within Field Variability
Canopy, Vigor, Foliar pigments (chlorophyll, carotenoids, anthoc..), Water stress, Health, etc
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Estimate Vine properties - Within Field Variability
Canopy, Vigor, Foliar pigments (chlorophyll, carotenoids, anthoc..), Water stress, Health, etc
Estimate Grape (Must/Wine) properties
Brix, pH, Total Acidity, Malic Acid Polyphenols, Color Index Ripeness, etc.
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Estimate Grape (Must/Wine) properties
Brix, pH, Total Acidity, Malic Acid Polyphenols, Color Index Ripeness, etc.
Kandylakis et al., 2013. Evaluating spectral indices from WorldView-2 satellite data for selective harvesting in vineyards, European Conference on Precision Agriculture
GIS
GeographicInformation
System
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Remote SensingRemote Sensing for for Precision ViticulturePrecision Viticulture
Thank you !!!Thank you !!!
Authors:Karantzalos, Karakizi, Kandylakis, Oikonomou, Makris, Georgopoulos
National Technical University of AthensSchool of Rural and Surveying Engineering
Department of Topography