Hyperspectral Imagery for Environmental Mapping and Monitoring

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Hyperspectral Imagery for Hyperspectral Imagery for Environmental Mapping and Monitoring Environmental Mapping and Monitoring Case Study of Grassland in Belgium Case Study of Grassland in Belgium Biometry, Data management and Agrometeorology Unit [email protected] [email protected] CENTRE WALLON DE RECHERCHES AGRONOMIQUES

description

Hyperspectral Imagery for Environmental Mapping and Monitoring : Case Study of Grassland in Belgium. The objective of this study is to show that hyperspectral imagery can be used to characterise grassland as well as its biophysical and biochemical properties.

Transcript of Hyperspectral Imagery for Environmental Mapping and Monitoring

Page 1: Hyperspectral Imagery for Environmental Mapping and Monitoring

Hyperspectral Imagery for Hyperspectral Imagery for Environmental Mapping and MonitoringEnvironmental Mapping and Monitoring

Case Study of Grassland in BelgiumCase Study of Grassland in Belgium

Biometry, Data management and Agrometeorology Unit

[email protected][email protected]

CENTRE WALLON DE RECHERCHES AGRONOMIQUES

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

ContextContext

The objective of this study is to show that hyperspectral imagery can be used to characterise grassland as well as its biophysical

and biochemical properties.

Inventory of forage production & quality Inventory of forage production & quality Management practicesManagement practices Control of application of agri-environmental measuresControl of application of agri-environmental measures

Grassland is an important component of the agricultural landscape.

Monitoring grassland at the regional level is closely linked to the knowledge of regional:

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

PresentationPresentation

Scientific objectivesScientific objectives

MaterialMaterial Study areaStudy area Field campaign (before flight)Field campaign (before flight) Field campaign (during flight)Field campaign (during flight)

MethodMethod Spectral analysis at Pixel level (intra-parcel)Spectral analysis at Pixel level (intra-parcel) Spectral analysis at parcel level (inter-parcel)Spectral analysis at parcel level (inter-parcel)

ResultsResults

ConclusionsConclusions

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Scientific objectivesScientific objectives

Agri-Environmental Measures (AEM):

• Now compulsory (CAP reform, WR regulations)• Temporal constraints (Specific cutting or grazing periods)• Opportunity of using airborne imaging spectroscopy to trace the

recent history of grasslands in terms of managements

Working hypothesis: cutting or grazing actions

• Can be considered as major stresses, which have an impact on the spectral signatures of grass canopy.

• Changes of spectral signature depend not only on the nature and the intensity of the action, but also on the time spent between the action and the remote sensing data acquisition.

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Scientific objectivesScientific objectives

Several campaigns with CASI/SASI, CASI-ATM airborne Several campaigns with CASI/SASI, CASI-ATM airborne sensorssensors

A continuum and a possible validation of the first campaign:

• CASI-2 (VIS/NIR) and SASI (SWIR) sensor• August 2002• Lorraine test site• Biophysical (wet matter, biomass, grass height…) • Biochemical (protein, VEM, DVE…)

Tries to enlighten how imaging spectroscopy are capable to:

• Determine grassland management practices• Control of application of Agri-Environmental Measures

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

PresentationPresentation

Scientific objectivesScientific objectives MaterialMaterial

Study areaStudy area Field campaign (before flight)Field campaign (before flight) Field campaign (during flight)Field campaign (during flight)

MethodMethod Spectral analysis at Pixel level (intra-parcel)Spectral analysis at Pixel level (intra-parcel) Spectral analysis at parcel level (inter-parcel)Spectral analysis at parcel level (inter-parcel)

ResultsResults ConclusionsConclusions

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Study areasStudy areas

South-east of Belgium.South-east of Belgium. Luxembourg Province Luxembourg Province

(Belgian Lorraine).(Belgian Lorraine). Grassland > 75% of land-Grassland > 75% of land-

use.use.

Study area corners:Study area corners:

Study area = Study area = ±±50 Km²50 Km² 10 flight lines with a rate of 10 flight lines with a rate of

covering of 40%.covering of 40%.

Lat. LongULC 49°43' 23.16 N 5°27' 34.60 ELRC 49°39' 00.43 N 5°31' 12.97 E

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Study areasStudy areas

South-east of Belgium.South-east of Belgium.Luxembourg Province (near Arlon).Luxembourg Province (near Arlon).

Located in Natura 2000.Located in Natura 2000. Grassland > 75% of land-useGrassland > 75% of land-use

Study area = Study area = ±±35 Km² 35 Km²

A subset of 33 parcels was followed:A subset of 33 parcels was followed:

Ground cover was scored visually from the Ground cover was scored visually from the middle of May to the airborne flight.middle of May to the airborne flight.

Biophysical parameters (GH, FMY, DMY…)Biophysical parameters (GH, FMY, DMY…) Field spectroradiometer (ASD)Field spectroradiometer (ASD)

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Field campaignField campaignBiophysical parametersBiophysical parameters

17 parcels17 parcels 4 sampling units / parcel4 sampling units / parcel During airborne flightDuring airborne flight

2 types of samples2 types of samples Mower cutting: total canopyMower cutting: total canopy Scissors cutting: upper canopyScissors cutting: upper canopy

Biophysical parametersBiophysical parameters Grass heightGrass height Grass biomass (wet matter)Grass biomass (wet matter)

Field spectroradiometersField spectroradiometers ASD FieldSpec Pro FrASD FieldSpec Pro Fr GER 1500GER 1500

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Field campaign (before flight)Field campaign (before flight)

PARCEL OBS1 OBS2 OBS3 OBS4 OBS5 OBS6 OBS7 OBS8 OBS9 OBS10 OBS11 MCo LAST CUT1 * P RP P P P RP RP P RP F P 22/06/20042 P P P P P P P P P P F P 22/06/20043 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF F 18/06/20044 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/20045 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF F 18/06/20046 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 15/06/20047 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF8 * P P P P P P P P P P P9 NP NF NP NF NP NF F RF RF RF RF RF RF RF F 28/05/200410 P P R R R P P P P P RP P11 RF RF RF P P P RP P P P P P12 NP NF NP NF NP NF NP NF NP NF NP NF F RF RF RF RF F 09/06/200413 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F F 22/06/200414 NP NF NP NF NP NF F RF RF RF RF RF RF RF F 27/05/200415 NP NF NP NF NP NF F RF RF RF RF RF P RP F 28/05/200416 * P P P P P P P P P P PP17 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/200418 * NP NF NP NF NP NF NP NF NP NF F RF RF RF RF F 07/06/200419 * P P P PR P P RP RP RP RP PP20 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF21 * P P P PR P P P P P RP PP22 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF23 P P RP P P P P P P P P PP24 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/200425 NP NF NP NF NP NF NP NF NP NF NP NF F RF RF RF RF F 07/06/200426 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/200427 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/200428 * NP NF P P P P P P P P P P29 * NP NF NP NF NP NF NP NF NP NF NP NF F RF RF RF F 10/06/200430 * P P P P P P P P P P PP31 * P P P P P P P P RP RP PP32 * NP NF NP NF NP NF NP NF NP NF F RF RF RF RF F 09/06/200433 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF F 18/06/2004

Table 1. Management practices observed on the 33 monitored parcels and their respective observedmanagement class (MCo). (R = Restoration, F = Haying, P = grazing, NP NF = No Grazing and No Haying).

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Spectroradiometric imagersSpectroradiometric imagersCASI – SASI and satellite sensors bandwidth comparison

CASI-2CASI-2 ( (Compact Airborne Spectrographic ImagerCompact Airborne Spectrographic Imager).). VIS-NIR (400-950 nm)VIS-NIR (400-950 nm) # spectral bands: 96 bands# spectral bands: 96 bands Spectral resolution: 5.7 nmSpectral resolution: 5.7 nm Spatial resolution: 2.5 x 2.5 mSpatial resolution: 2.5 x 2.5 m Swath width: 303 pixelsSwath width: 303 pixels

Contiguous narrowbands

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Spectroradiometric imagersSpectroradiometric imagersCASI – SASI and satellite sensors bandwidth comparison

SASISASI ( (Shortwave Infrared Airborne Spectrographic ImagerShortwave Infrared Airborne Spectrographic Imager)) SWIR (850-2450 nm)SWIR (850-2450 nm) # spectral bands: 160 bands # spectral bands: 160 bands Spectral resolution: 10 nmSpectral resolution: 10 nm Spatial resolution: 2 x 2 mSpatial resolution: 2 x 2 m Swath width: 600 pixelsSwath width: 600 pixels

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Hyperspectral data acquisitionHyperspectral data acquisition

End of JuneEnd of June

CASI resolution: CASI resolution: - 2.4 x 2.4 m- 2.4 x 2.4 m- 96 spectral bands96 spectral bands

10 transects by sensors10 transects by sensors

Quality check Quality check (signal & geometric)(signal & geometric)Bad weather conditionsBad weather conditions

• Signal problemsSignal problems• Image correctionImage correction

=> 24 parcels were preserved=> 24 parcels were preserved 0

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Hyperspectral data acquisitionHyperspectral data acquisition

10 transects by sensors10 transects by sensors

Level II bLevel II b

Quality checkQuality check(signal & geometric)(signal & geometric)

SASI = some problemsSASI = some problems

3x3 pixel subset 3x3 pixel subset centred over the centred over the sampling unit locationsampling unit location

SASI – 1345 nm

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Laboratory analysisLaboratory analysisBiochemical parametersBiochemical parameters

Post field campaignPost field campaign

NIR Spectrometry laboratory-based measurementsNIR Spectrometry laboratory-based measurements Samples cut with scissors & mowerSamples cut with scissors & mower Fresh samples & dry samplesFresh samples & dry samples

Biochemical parameters measuredBiochemical parameters measured Dry matter contentDry matter content Proteins contentProteins content Cellulose contentCellulose content Ashes contentAshes content Sugar contentSugar content Energy values which characterised available energy for milk Energy values which characterised available energy for milk

and meat production: (VEM, VEVI, DVE and OEB)and meat production: (VEM, VEVI, DVE and OEB)

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

PresentationPresentation

Scientific objectivesScientific objectives

MaterialMaterial Study areaStudy area Field campaign (before flight)Field campaign (before flight) Field campaign (during flight)Field campaign (during flight)

MethodMethod Spectral analysis at Pixel level (intra-parcel)Spectral analysis at Pixel level (intra-parcel) Spectral analysis at parcel level (inter-parcel)Spectral analysis at parcel level (inter-parcel)

ResultsResults

ConclusionsConclusions

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Methodology : General overviewMethodology : General overview

CASI & SASIImages

Pixel spectral

responses

Moving average+

First derivative

biophysical orbiochemical

variable

Correlogram

Spectralindices

Predictivemodels

Selectednarrowbands

biophysical orbiochemical

variable

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Wavelength (nm)

CASI Sensor SASI Sensor

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1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 2050 2100 2150 2200 2250 2300 2350 2400Dry matter Protein Cellulose Sugar

VEM VEVI DVE OEB

Height Biomass Wet matter Dry matter Protein Cellulose Sugar

VEM VEVI DVE OEB

Height Biomass Wet matter

Reflectances

First derivatives

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Bare soilJust harvested grassland

Not harvested

Identification of best narrow-wavebands Identification of best narrow-wavebands (CASI + SASI)(CASI + SASI)

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Step 2: Spectral indices Step 2: Spectral indices (cont.)(cont.)

Photochemical Reflectance Index (PRI):

Red-edge slope, Red-edge step and Red-edge maximum slope wavelength:

569529

569529

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RRPRI

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This index is related to the green-edge narrowband

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Step 2: Spectral indices Step 2: Spectral indices (cont.)(cont.)

Water Band Index (WBI) :

Normalized Difference Vegetation Index (NDVI):

This index takes advantage of the water absorption features and represents canopy water content.Two reflectance ratios were calculated.

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Spectral analysis at Pixel level Spectral analysis at Pixel level Table 2.Table 2. Observed correlation coefficients between the different indices and Observed correlation coefficients between the different indices and

grassland characteristicsgrassland characteristics . .

Leaf water content

FMY DMY Grass Height

Biomass

λRESL 0.297 0.078 0.023 0.19 0.087

NDVI 0.814 0.753 0.303 0.417 0.585

PRI 0.716 0.567 -0.016 0.243 0.029

RESL 0.579 0.637 0.215 0.149 0.172

REST 0.55 0.595 0.195 0.127 0.152

WBI1 -0.688 -0.587 -0.163 -0.641 -0.335

WBI2 -0.722 -0.504 -0.074 -0.534 -0.123

Leaf water content is more highly correlated with spectral reflectance than Leaf water content is more highly correlated with spectral reflectance than either total wet biomass or total dry biomass.either total wet biomass or total dry biomass.

Results show that ground truth data collection or canopy sampling for remote sensing studies Results show that ground truth data collection or canopy sampling for remote sensing studies of grass canopies should measure the total wet biomass and the total dry biomass. This will of grass canopies should measure the total wet biomass and the total dry biomass. This will allow for the calculation of the leaf water (also Compton conclusions).allow for the calculation of the leaf water (also Compton conclusions).

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Step 3: Multiple regression analysisStep 3: Multiple regression analysis

CASI & SASIImages

Pixel spectral

responses

Moving average+

First derivative

biophysical orbiochemical

variable

Correlogram

Spectralindices

Predictivemodels

Selectednarrowbands

biophysical orbiochemical

variable

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Spectral analysis at Pixel level Spectral analysis at Pixel level This spectral analysis at pixel level is used to investigate and validate This spectral analysis at pixel level is used to investigate and validate

CASI/SASI-2002 results, with regards to the characterisation of grass CASI/SASI-2002 results, with regards to the characterisation of grass canopy with quantitative information regarding biophysical canopy with quantitative information regarding biophysical parameters (FMY, DMY, GH).parameters (FMY, DMY, GH).

CASI-ATM pixel responses were average within 3x3 pixel subset CASI-ATM pixel responses were average within 3x3 pixel subset centered around the sampling unit.centered around the sampling unit.

In addition to the standard channels a number of channel ratios and In addition to the standard channels a number of channel ratios and normalized channel difference indices were developed.normalized channel difference indices were developed.

Photochemical Reflectance Index (Photochemical Reflectance Index (PRIPRI).). Red-edge slope (Red-edge slope (RESLRESL), Red-edge step (), Red-edge step (RESTREST) and Red-edge maximum ) and Red-edge maximum

slope wavelength (slope wavelength (REMSREMS).). Water Band Index (Water Band Index (WBIWBI) ) Normalized Difference Vegetation Index (Normalized Difference Vegetation Index (NDVINDVI).). ……

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Spectral analysis at Pixel level Spectral analysis at Pixel level Table 2.Table 2. Observed correlation coefficients between the different indices and Observed correlation coefficients between the different indices and

grassland characteristicsgrassland characteristics . .

Leaf water content

FMY DMY Grass Height

Biomass

λRESL 0.297 0.078 0.023 0.19 0.087

NDVI 0.814 0.753 0.303 0.417 0.585

PRI 0.716 0.567 -0.016 0.243 0.029

RESL 0.579 0.637 0.215 0.149 0.172

REST 0.55 0.595 0.195 0.127 0.152

WBI1 -0.688 -0.587 -0.163 -0.641 -0.335

WBI2 -0.722 -0.504 -0.074 -0.534 -0.123

Red-Edge indicators (Slope, Step, REMS) have bad correlation coefficients Red-Edge indicators (Slope, Step, REMS) have bad correlation coefficients compared to 2002 campaign. REST had 0.63 for GH and 0.68 for Biomass.compared to 2002 campaign. REST had 0.63 for GH and 0.68 for Biomass.

This difference is probably the consequence of the poor images quality resulting of bad This difference is probably the consequence of the poor images quality resulting of bad meteorological conditions during the flight.meteorological conditions during the flight.

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Spectral IndicesSpectral IndicesRelationship of the grass height and the step value of the

reflectance spectra in the red edge band

y = 0.0469x - 9.2337R2 = 0.63

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Water Balance Index:Water Balance Index: Dry matter content (R²=0.61)Dry matter content (R²=0.61)

Relationship of the water balance indice and the dry matter content of grassland canopy

y = 61.984x - 11.559R2 = 0.615

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Spectral analysis at Pixel level Spectral analysis at Pixel level

Red-edge indicators seems to be too sensible to the meteorological Red-edge indicators seems to be too sensible to the meteorological conditions and can not be considered for an operational system.conditions and can not be considered for an operational system.

Inspection of the regression results obtained in the 2002 and 2003 Inspection of the regression results obtained in the 2002 and 2003 campaigns indicates that NDVI, WBI1 and PRI are the best indicators campaigns indicates that NDVI, WBI1 and PRI are the best indicators to estimate biophysics characteristicsto estimate biophysics characteristics..

Confirm CASI-SASI 2002 results on biophysical parameters (FMY, DMY, GH) Confirm CASI-SASI 2002 results on biophysical parameters (FMY, DMY, GH) directly linked to the age and the management practices supported by directly linked to the age and the management practices supported by grasslands.grasslands.

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Spectral analysis at Parcel level Spectral analysis at Parcel level

In a second step, the project has analysed the possibility to classify In a second step, the project has analysed the possibility to classify grassland in 3 management classes:grassland in 3 management classes:

• Haying grasslands (P)Haying grasslands (P)• Grazing grasslands (F)Grazing grasslands (F)• Neither haying nor grazing grasslands (NP NF)Neither haying nor grazing grasslands (NP NF)

The grassland classification is based on the hypothesis that management The grassland classification is based on the hypothesis that management practices can be identified by the combination of practices can be identified by the combination of

• Vegetation indices (Vegetation indices (quantitative parametersquantitative parameters) selected at the pixel ) selected at the pixel levellevel

• Textural indices (Textural indices (qualitative parametersqualitative parameters))

Different textural indices were calculated:Different textural indices were calculated:

• Global approach (global variance)Global approach (global variance)• Local approach (moving windows of 3x3 pixels)Local approach (moving windows of 3x3 pixels)

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Spectral analysis at Parcel level Spectral analysis at Parcel level Step 1:Step 1: D Discriminant analysis to identify regions of interest in the reflectance iscriminant analysis to identify regions of interest in the reflectance

spectra and to choose the relevant textural indicesspectra and to choose the relevant textural indices

Probability level of the differences between grassland classes

Global texture parameterGlobal texture parameter=> 2 regions of interest=> 2 regions of interest

- Below 500 nm Below 500 nm - Above 725 nm. Above 725 nm.

Local texture Local texture => Quite different

- Around 500 nm - Above 750 nm - BUT observed level are not significant (P<0.05).

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Spectral analysis at Parcel level Spectral analysis at Parcel level Step 1:Step 1: Based on these results, 2 wave bands from the global texture

analysis have been selected (450 nm and 725 nm) together with previously defined vegetation indices.

Step 2:Step 2: Discriminant analysis with all the selected dependent variables (vegetation indicators & textural indicators)

• The stepwise discriminant analysis identified 3 vegetation indices, (PRI, NDVI and WBI1) and one textural global index (450nm) as significant.

• Cross validation procedure show that about 83% of correct classifications may be obtained.

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Results: Cross classificationResults: Cross classification

All the parcels with AEM are well identified MCo = MCi = NP NFAll the parcels with AEM are well identified MCo = MCi = NP NF

Only 4 parcels have been classified in a bad management class Only 4 parcels have been classified in a bad management class (MCo (MCo ≠ ≠ MCi).MCi).

Classification results

Observed management classes

F NP NF P

F 13 0 1

NP NF 2 3 1

P 0 0 4

N total 15 3 6

N correct 13 3 4

Proportion 86,70% 100% 66,70%

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Results: Cross classification Results: Cross classification Only 4 parcels have been classified in a bad management classOnly 4 parcels have been classified in a bad management class

2 parcel with 2 parcel with MCo = FMCo = F classified in classified in MCi = NP NFMCi = NP NF1 parcel with 1 parcel with MCo = P MCo = P classified in classified in MCi = NP NFMCi = NP NF1 parcel with 1 parcel with MCo = PMCo = P with with MCi = FMCi = F

These misclassifications can easily be explained by a regrowth of grass after a long period without pasture or after haying.

These results also show that if the 24 parcels had been declared in These results also show that if the 24 parcels had been declared in AEM, and 18 parcelsAEM, and 18 parcels of them would be irregular (MCo of them would be irregular (MCo ≠ NP NF),≠ NP NF),only 3 parcels would not have been identified as irregular by remote only 3 parcels would not have been identified as irregular by remote sensing (MCi = NP NF).sensing (MCi = NP NF).

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Spectral Indices Spectral Indices (cont.)(cont.)

Red-Edge Step & Water Balance Index can be used toRed-Edge Step & Water Balance Index can be used to discriminate between grassland management types discriminate between grassland management types

Chemical characteristic of grass was not clearly linked to Chemical characteristic of grass was not clearly linked to vegetation indices.vegetation indices.

Except for VEM and VEVI energy values which seem well Except for VEM and VEVI energy values which seem well correlated with NDVI (R²=0.60 )correlated with NDVI (R²=0.60 )

In most of cases results from scissors cutting samples which In most of cases results from scissors cutting samples which represent the upper part of the canopy were best correlated with represent the upper part of the canopy were best correlated with vegetation indices.vegetation indices.

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Multiple regression analysisMultiple regression analysis

CASI sensor = best resultsCASI sensor = best results CASI + SASI = not better predictionCASI + SASI = not better prediction Good predictive quality in generalGood predictive quality in general

Predictive quality of the modelsComparison of the different sensor data

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Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Multiple regression analysisMultiple regression analysisPredictive quality of the models

Comparison of the two cutting systems

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40

50

60

70

80

Drymatter

Protein Cellulose Sugar VEM VEVI DVE OEB

Grass characteristic

Rela

tive r

oo

t m

ean

sq

uare

err

or

Scissors

Mower

RMSE scissors lower than mowerRMSE scissors lower than mower RMSE RMSE ≤≤ 10% => good predictions 10% => good predictions Except for OEB & SugarExcept for OEB & Sugar

Page 35: Hyperspectral Imagery for Environmental Mapping and Monitoring

Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

ConclusionsConclusions

Higher potential

Lower potential

< 1000 kg/ha

> 15 000 kg/ha

Estimation of the wet matter production at regional level.

Question 1:Question 1:Regional monitoring?Regional monitoring?

Page 36: Hyperspectral Imagery for Environmental Mapping and Monitoring

Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

ConclusionsConclusions

Question 1:Question 1:Regional monitoring.Regional monitoring.

Question 2: Question 2: grassland discrimination?grassland discrimination?

Pure ray-grass not yet harvested

Just mowed

Just pastured

Regrown

Page 37: Hyperspectral Imagery for Environmental Mapping and Monitoring

Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

ConclusionsConclusions

Question 1:Question 1:Prediction.Prediction.

Question 2: Question 2: grassland discrimination.grassland discrimination.

Question n°3:Question n°3:Management practices?Management practices?

Just pastured

Not yet pastured

Page 38: Hyperspectral Imagery for Environmental Mapping and Monitoring

Airborne Imaging Spectroscopy Workshop – Bruges, October, 8 2004

Conclusions Conclusions These study assess the ability of Imaging Spectroscopy to be reliable These study assess the ability of Imaging Spectroscopy to be reliable

method for estimating grassland management practices and to control if method for estimating grassland management practices and to control if AEM are correctly applied.AEM are correctly applied.

3 vegetation indices (PRI, NDVI and WBI13 vegetation indices (PRI, NDVI and WBI1 ) are confirmed as good are confirmed as good quantitative parameters quantitative parameters

Textural indices (qualitative parameters) and vegetation indices Textural indices (qualitative parameters) and vegetation indices (quantitative parameters ) are linked to the age and the management (quantitative parameters ) are linked to the age and the management practices supported by grasslandspractices supported by grasslands

Changes of spectral signature depend not only on the nature and the Changes of spectral signature depend not only on the nature and the intensity of the action, but also on the time spent between the action and intensity of the action, but also on the time spent between the action and the remote sensing data acquisition.the remote sensing data acquisition.

Classification results are promising and must be validated with better Classification results are promising and must be validated with better images quality due to the bad meteorological conditions.images quality due to the bad meteorological conditions.