MONITORING OF VEGETATION MONITORING OF VEGETATION DYNAMICS: PERSPECTIVES, METHODS, DYNAMICS: PERSPECTIVES, METHODS,

AND SENSOR TECHNOLOGYAND SENSOR TECHNOLOGY

1st GMES WorkshopKyiv, 26th – 27th November 2008

Vitaliy YatsenkoSpace Research Institute of NASU and NSAU

Kyiv, Ukrainvyatsenko@gmail.comhttp://my.elvisti.com/fond/

OUTLINE

Problems (order parameters, vegetation monitoring)Experimental spectral data

• Methods (chlorophyll, nitrogen, water, anthocyan)• Risk analysis (CVaR, crop yield forecasting)

Sensor technologies (optical spectrometers)Perspectives (aircraft hyperspectrometer, nano-sensors,

intelligence sensors)Summary

IntroductionIntroduction

• Agriculture: Supporting sustainable agriculture and combating desertification

• Strengthening observation, data sharing and prediction

• New methods to obtain various informative parameters of reflectance spectra

EXPERIMENTAL DATAEXPERIMENTAL DATA

Two kind data sets were used for estimation of chlorophyll, nitrogen, and water content in vegetation:

simulation dataexperimental data

The spectral curves of eight samples The spectral curves of eight samples obtained from the spectrometerobtained from the spectrometer

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WAVELENGTH, nm

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Fig. 1

Chlorophyll concentration:

a) 9.18 mg/dc^2; b) 7.65; c) 6.82 ; d) 6.15; e)5.29 ; f) 4.62; g) 3.75;i)2.76.

Typical reflectance spectrum of winter wheat leaveTypical reflectance spectrum of winter wheat leaveunder normal and under normal and defficientdefficient mineral mineral nutrationnutration

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Typical Reflectance Spectrum of Winter Wheat leave under normal mineral nutrition (RP37.dat)

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Indicator chlorophyll presents

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Typical Reflectance Spectrum of Winter Wheat leave under deficient mineral nutrition (RP67.dat)

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Fig. 2

Fig. 3 Here are the leaves from plants grown under various condFig. 3 Here are the leaves from plants grown under various conditionsitionsleaf from plant under optimal environment conditions. It is normleaf from plant under optimal environment conditions. It is normal green coloredal green coloredleaf from plant under nitrogen deficient conditions. It is yelloleaf from plant under nitrogen deficient conditions. It is yelloww--green coloredgreen coloredleaf from stressed plant (unfavorable ecological conditions). Itleaf from stressed plant (unfavorable ecological conditions). It is red colored, but its RS (Reflectance Spectrum) is red colored, but its RS (Reflectance Spectrum) fingerprints (see next page) allows to reveal the chlorophyll prfingerprints (see next page) allows to reveal the chlorophyll presence at the leaf. Induction fluorescence test esence at the leaf. Induction fluorescence test displays its photosynthetic activity. Therefore this leaf is alidisplays its photosynthetic activity. Therefore this leaf is aliveveleaf from stressed plant by other type of stress (leaf from stressed plant by other type of stress (technogenictechnogenic pollution)pollution)

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Reflectance Spectrum of the leaf from plant under optimal environment conditions (Rvv257.dat)

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Reflectance Spectrum of the leaf from plant under nitrogen deficient conditions (Rvv258.dat)

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Reflectance Spectrum of the leaf from stressed plant (unfavorable ecological conditions) (Rvv259.dat)

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Reflectance Spectrum of the leaf from stressed plant by other type of stress (technogenic pollution) (Rvv260.dat)

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METHODSMETHODS

Derivative method (ratio of two maxima of the 1-st derivative from reflectance spectral curves in the region 680-750 nm)

Global optimization approachPrincipal component analysisNonlinear component analysisSynergetics approachSVM

Fig. 4. Estimation of the 1Fig. 4. Estimation of the 1--st derivative from reflectancest derivative from reflectancespectral curves using a spectral curves using a SavitzkySavitzky--GolayGolay smoothing filtersmoothing filter

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Fig. 5. The 1Fig. 5. The 1--st derivative plots from reflectance st derivative plots from reflectance spectral curves of winter wheat leaves with various spectral curves of winter wheat leaves with various

chlorophyll concentration, 1, 2, 3 and 4 are chlorophyll concentration, 1, 2, 3 and 4 are corresponded to 9.2; 6.6; 4.1 and 1.5 mg/dm^2corresponded to 9.2; 6.6; 4.1 and 1.5 mg/dm^2

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Estimate of the chlorophyll concentration in vegetation using the ratio of two maxima of spectral curves

Chlorophyll concentration surfaceChlorophyll concentration surface

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The dependence of the chlorophyll concentration in the winter wheat from two extremaof the spectral curves

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Estimate of the chlorophyll Estimate of the chlorophyll concentration in vegetation concentration in vegetation using principal compousing principal compo--nentsnents of the spectral of the spectral curvescurves

Nitrogen concentration Nitrogen concentration –– Fig. 9Fig. 9

SOFTWARE

Graphical user interface

Graphical results visualization subsystem

Algorithmlibrary

Web siteWeb site

Sensors

Matlab Tools

ControlSubsystem

Database

Fig. 10

SENSOR TECHNOLOGYSENSOR TECHNOLOGY

Optical componnentsNoise compensationModeling Pre-processing

Fig. 11

•The device consists of two blocks – optical and computing.

•The optical block is a two-cannel spectrometer with high resolution, adjusted to measurements under field conditions.

•Results of measurements in a digital form are recorded on a flash card or can be transferred to the computing block for accumulation in a database.

•The device is supplied with detectors of temperature, irradianceand the GPS-receiver for determining of geographical coordinates of a site where measurements are carried out.••A developed software has two channels for processing of the datatransferred from optical block – user's (subsystem USWHL) and for scientific purpose.

HYPERSPECTRAL DEVICEHYPERSPECTRAL DEVICE

Spectral range -500 ÷ 800 нм;Spectral resolution - ≤ 2,0 нм; Field of view - 16° х 16°(0,4 х 0.4 m, h= 1,5 m);Chlorophyll content - -8 мг/dм2.

Fig. 12

TESTINGTESTINGwww.vegetation.kiev.ua

RESULTS

• Projective covering 71 - 29 %

PERSPECTIVES PERSPECTIVES

• Hyperspectral Monitoring• Calibration and Validation Technologies• Nano-sensors

IMPLEMENTATION OF HYPERSPECTRAL MONITORING

VALIDATION TECHNOLOGY

PAPERS PAPERS

• V.A.Yatsenko, S.M. Kochubey, P.M.Pardalos, L.Zhan. Estimation of chlorophyll concentration in vegetation using global optimization approach. ‘Technologies, Systems, and Architectures for Transnational Defence II’, SPIE Conference ‘AeroSence. Technologies and Systems for Defence & Security’, 21-25 April 2003, Orlando USA, Proc. of SPIE, V. 5071.

• S.M.Kochubey, V.A. Yatsenko. Monitoring system for agricultural crops on chlorophyll basis. SPIE 10th International Symposium Remote Sensing 8-12 September 2003, Barcelona, Spain, Vol. 5232-07

• Advances in Modeling Agricultural Systems. Series: Springer-Optimization and Its Applications , Vol. 25, 2008.

PROJECTS PROJECTS

• STCU Project #2416: Development of the Method and the Device for Remote Sensing of Vegetation (DRSV) (2002-2004)

• STCU project U039k: Development of the Cryogenic-Optical Sensor for the Highly Sensitive Gravity Meters (2002-2004)

• NATO project SST.GLS.975052: Information Technology for Analysisof Nuclear Objects on the Basis of Modern Mathermatical Methods(2002-2003)

• STCU project Gr.33(J): Information Technology for Analysis ofEcological Dangerous Objects on the Basis of Modern MathematicalModeling and Optimization Methods (2002-2003)

• NATO project SST.GLS.975032: Calculating Robust BayesianEstimates Using Optimization Approaches (2000-2001)STCU projects (US, EU)

• NASU projects

COLLABORATIONS COLLABORATIONS

• GMES• CEOS• SPIE• Max Plank Institute• University of Florida• Cennedy Space Center

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GEOSS: A Global, Coordinated, Comprehensive and Sustained System of Observing Systems

GEOSS

CONCLUSIONCONCLUSION

The ratio of intensities in two maxima of the 1-st derivative plots from reflectance spectral curves of winter wheat leaves is a chlorophyll index high resistant to interference of the soil reflection. The maximal distortion is equal to 11% at the 25% level of projective covering.

CONCLUSIONCONCLUSION

• We shown that two principal components of spectral curves contain essential Shannon's information. These components can be used as independent variables of a nonlinear regression model.

• We proposed new methods and derivative indexes(chlorophyll, nitrogen, water, anthocyan, crop forecasting, estimation of projective covering, detection of illness)

SUMMARY

Follow Reccomendations from GMESBuild upon 2009 – 2012 Work PlanDevelopment and Implementation of Data Sharing PrinciplesImplementation Web-based Portal, Clearinghouse for Searching Spectral Data, Information and ServicesGlobal Agriculture MonitoringRisk Analysis (Use of Sattellites for Risk Management, Systematic Monitoring for Earthquake Risk Assessment)Special Sessions on GMES and Integral Risk Management Moving Forward with the Implementation of GEO-UA