plant physiological traits from high resolution...
TRANSCRIPT
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Plant physiological traits from high resolution hyperspectral and thermal imagery: models and
indices for early stress detection
European Commission
Joint Research Centre (JRC)
Directorate D – Sustainable Resources
Pablo J. Zarco-Tejada
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Model-retrieved Plant Traits
Plant-trait retrievals for stress detection
Leaf Model
Spectra
Image
Ca+b
Cx+c
Anth
Cw
Cm
LAI LIDF SIF T
Canopy Model
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Challenges ?
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“these guys are playing with toys…”
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What do you do ? “I work in remote sensing using unmanned vehicles”
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Challenges ?
1. Conceptual
2. Technical
3. Self-imposed requirements
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Challenges ?
1. Conceptual
2. Technical
3. Self-imposed requirements
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CASI Hyperspectral Imager
Hyperspectral imager
Inertial navigation system
Storage device
Computer for imagery acquisition
Year 2000
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Year 2011
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Micro-hyperspectral imager on board a 6 kg platform
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Year 2015
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1000 ha flight 260 bands @ 6 nm FWHM
400-1000 nm 50 cm pixel size
It was a flight test during a cloudy day radiometric changes due to changing atmospheric conditions
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Radiometric calibration
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Hyperspectral 45 cm
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Challenges ?
1. Conceptual
2. Technical
3. Self-imposed requirements
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Some dreams … as of 2005
VHR in thermal + multi(hyper)spectral (sub-meter) to identify pure crowns / avoid mixed pixels
Canopy temperature maps with errors below 1 K (absolute, not only relative)
Processing capabilities for 1-day turn-around times decision making
Imagery & products through simpler tools for GIS-unexperienced end-users / technicians
Acceptable cost
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Cameras for stress detection
RGB / CIR cameras pNDVI & DSM generation
Thermal Cameras Water stress detection / irrigation
Multispectral cameras Nutrient stress detection (Cab, Cx+c) Physiological indices (PRI, F) Canopy structure (NDVI, EVI)
Hyperspectral imagers New indices / methods Combined spectral indices
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Remote Sensing Indicators of Vegetation Stress
Pigments-traits Cab / Car – Nutrient deficiencies / effects of diseases less absorption at specific bands
captured by RT model inversion methods & sensitive indices
Structural traits canopy structure / LADF / vegetative growth – Nutrient / water stress & effects of diseases affects canopy growth effects in the near
infrared captured by indices sensitive to canopy structure
Visual
Chlorophyll Fluorescence (CF) F emission Photosynthesis – Excess energy function of the photosynthetic state
– 3% - 4 % of the radiance levels
– Main interest to monitor remotely photosynthesis & stress condition
Xanthophyll cycle pigments (V+A+Z) & Anth rapid changes phot. Efficiency & photoprotective roles PRI: Indicator of the epoxidation state (EPS) of the xanthophyll pigments under stress V+A+Z R530 PRI
Pre-visual
Temperature: Tc Tc-Ta CWSI – Stomata closure Reduction in transpiration and CO2 uptake Decreased
photosynthesis Temperature increase
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OPERATIONAL ?
USEFUL FOR RS ?
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Structure
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Tree height estimation via SfM
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Thermal
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Single-crown temperature for stress detection (40 cm resolution thermal image)
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Gonzalez-Dugo et al. (AgForMet, 2012)
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CWSI map from UAV
Bellvert et al. (2013)
Water potential (bars)
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Al-
ww1
Al-d
Al-
ww2
Or-
ww1
Or-
ww2
Or-
ww3
Ap-d
Ap-ww
Le-d
Le-ww Pe-ww1
Pe-ww2
Pe-d1
Pe-d1
CWSI
0.0
1.0 Gonzalez-Dugo et al. (2013)
Map of CWSI – thermal-based indicator of stress from UAV
42
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SIF & Pigments
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Understanding the retrieval of SIF from broad-band (2-6 nm) hyperspectral imagers on board UAVs
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Assessment of SIF retreival using a 3D model (FluorFLIGHT)
Hernández-Clemente et al. (2017)
Hyperspectral data
FluorFLIGHT simulations
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Assessment of SIF retreival using a 3D model (FluorFLIGHT)
Hernández-Clemente et al. (2017)
6.5 nm FWHM 1 nm FWHM
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Assessment of SIF retrieval using a 3D model (FluorFLIGHT)
6.5 nm FWHM 1 nm FWHM
Hernández-Clemente et al. (2017)
6.5 nm FWHM & oversampling at 1.85 nm / band
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Using SIF for water stress detection in precision agriculture
NDVI MTVI1
SIF SIFn
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Using SIF for water stress detection in precision agriculture
Zarco-Tejada et al. (2017)
Well irrigated
Deficit irrigation
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Chlorosis detection
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Zarco-Tejada et al. (2013)
Chlorophyll & Car content maps nutrient stress
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Chlorophyll & Carotenoid content estimation
FLIGHTy = 0.7077x + 3.7644R2 = 0.46*** (p<0.001)
RMSE=1.28 mg/cm2
SAILHy = 0.9211x + 1.1824R2 = 0.4*** (p<0.001)RMSE=1.18 mg/cm2
3
5
7
9
11
13
15
17
6 7 8 9 10 11 12
Measured Cx+c (mg/cm2)
Est
imat
ed C
x+c (mg/
cm2 )
Ca+b Cx+c
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Disease detection
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VHR hyperspectral & thermal indices for disease detection
Calderon et al. (2013; 2015) Lopez-Lopez et al. (2016)
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VHR hyperspectral & thermal indices for disease detection
Calderon et al. (2013; 2015) Lopez-Lopez et al. (2016)
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Hyperspectral 45 cm
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Hyperspectral 45 cm
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Thermal 60 cm
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Thermal 60 cm
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Index-based Plant Traits
+
Model-retrieved Plant Traits
Linear & non-linear Deep / machine learning
(LDA / SVM / NN)
Tree by tree physiological assessment
Spectral bandset Traits available
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Xanthophyll cycle (Epoxidation state)
Merzlyak et al. (1997)
Chlorophyll degradation (Pheophytinization)
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Sensitivity of Plant Traits to Xf symptoms
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Overall accuracy – 2 year dataset
All traits
w/o F/T
RGBNIR
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Final remarks
Tremendous progress in the past 15 years: from “toys” to science: scientific papers are critical
Proved that we are not collecting just pretty pictures: quantitative RS is possible
Calibration / atm. correction is still a weakness for some RS users / vendors of drones
Progress is needed on hyperspectral use from drones: good quality spectra still hard to get
More studies demonstrating larger scale RS from
drones are needed to convince at other levels
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Do you remember the definition of remote sensing in the 1980s ?
“A solution looking for a problem”
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I have a drone. What can I use it for ?
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Plant physiological traits from high resolution hyperspectral and thermal imagery: models and
indices for early stress detection
European Commission
Joint Research Centre (JRC)
Directorate D – Sustainable Resources
Pablo J. Zarco-Tejada
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HELICOPTER MK-I
PILATUS CROPSIGHT
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Benzin (17’ endurance)
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Benzin (17’ endurance)
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CropSight (1 h endurance)
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Viewer (1.5-3 h endurance)
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CASI hyperspectral imager – 228 spectral bands @ 2 m
spatial resolution
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AVIRIS NASA-JPL hyperspectral sensor - 224 contiguous spectral channels
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MIVIS / AHS / Daedalus – INTA
INTA (Spain) DLR (Germany) NERC (UK)
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Remote Sensing Indicators of Vegetation Stress
Pigments-traits Cab / Car – Nutrient deficiencies / effects of diseases less absorption at specific bands
captured by RT model inversion methods & sensitive indices
Structural traits canopy structure / LADF / vegetative growth – Nutrient / water stress & effects of diseases affects canopy growth effects in the near
infrared captured by indices sensitive to canopy structure
Visual
Chlorophyll Fluorescence (CF) F emission Photosynthesis – Excess energy function of the photosynthetic state
– 3% - 4 % of the radiance levels
– Main interest to monitor remotely photosynthesis & stress condition
Xanthophyll cycle pigments (V+A+Z) & Anth rapid changes phot. Efficiency & photoprotective roles PRI: Indicator of the epoxidation state (EPS) of the xanthophyll pigments under stress V+A+Z R530 PRI
Pre-visual
Temperature: Tc Tc-Ta CWSI – Stomata closure Reduction in transpiration and CO2 uptake Decreased
photosynthesis Temperature increase
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Evaporation
ψ
ABA
Light
Temperature
Fluorescence
Light Reflected
CO2 H2O
Transpiration
Photosynthesis
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Low-cost UAV platforms
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800 ha flight 7 flightlines
260 bands @ 6 nm FWHM 400-1000 nm
40 cm pixel size