Una veloce presentazioneUna veloce presentazione((Silvio GRIGUOLO - DAEST/IUAV))

• Qualche slide iniziale è tratta da una presentazione diQualche slide iniziale è tratta da una presentazione di

Sander Mucher, Wageningen, coordinatore del progettoSander Mucher, Wageningen, coordinatore del progetto

• Le ultime slides sull’applicazione alla biodiversità sonoLe ultime slides sull’applicazione alla biodiversità sono

di Camiel Heunks, del National Institute for publicdi Camiel Heunks, del National Institute for public

Health and the Environment (RIVM)Health and the Environment (RIVM)

• La presentazione usa sia l’italiano che l’ingleseLa presentazione usa sia l’italiano che l’inglese

Land Cover Classification at the Land Cover Classification at the continental scalecontinental scale

the PELCOM Projectthe PELCOM Project

A shared Cost Action, EU 4th framework Environment & Climate

Start September ‘96 - End November ‘99

• Wageningen UR ( formerly DLO)• Austrian Research Centre Seibersdorf (ARCS)• Centre National de Recherches Meteorologiques

(CNRM) - Toulouse• National Institute for Public Health and the

Environment (RIVM)• Swedish Space Corporation (SSC)• Instituto Universitario di Architettura (IUAV)• Space Applications Institute (SAI-JRC)• Geodan

PELCOMPELCOMPan European Land COver Monitoring(Research funded by the European Commission)

PELCOM Homepage

www.geodan.nl/ec_lu/

Perché il Progetto Perché il Progetto PELCOM?PELCOM?

Stato dell’arteStato dell’arte

• CORINE-LC procede a rilento• Altri database raster esistenti sono

largamente basati su statistiche amministrative

• La classificazione DISCover (IGBP) ha un livello di dettaglio insufficiente per l’Europa

• Il progetto FIRS si concentra soprattutto sulle foreste

CORINECORINE land cover database land cover database

Detail of IGBP global land cover Detail of IGBP global land cover databasedatabase

In definitiva...In definitiva...

Manca un’informazione aggiornata del Land Cover per l’intera Europa

Dunque…Dunque…investigare l’applicabilità delle immagini satellitari NOAA-AVHRR per:

• Mapping• Monitoring

Gli obiettivi del ProgettoGli obiettivi del Progetto• definizione di una metodologia di classificazione consistente

• Costruzione di una database raster delle coperture del suolo (Land Cover) ad 1 km dell’intera Europa, da utilizzare come input per modelli dinamici ambientali alla scala continentale

• Applicazioni sperimentali della mappa

Fasi (1996-99)Fasi (1996-99)

• 1st year sviluppo della metodologia di

classificazione e monitoraggio• 2nd year esperimenti regionali di

classificazione

• 3rd year Studio di casi su ambiente & clima

Primi esperimenti di Primi esperimenti di modellisticamodellistica

1 Modello dinamico della bio-diversità, basatoModello dinamico della bio-diversità, basato sulla definizione di sulla definizione di Natural Capital IndexNatural Capital Index (pressione sulla bio-diversità dovute a (pressione sulla bio-diversità dovute a cambiamenti climatici, densità di insediamentocambiamenti climatici, densità di insediamento umano, consumo e produzione, acidificazione,umano, consumo e produzione, acidificazione, eutrofizzazione, ozono, ecc.)eutrofizzazione, ozono, ecc.)

RIVM - Indice di RIVM - Indice di pressione sulla bio-pressione sulla bio-diversità nelle aree diversità nelle aree naturali naturali

(proiezione al 2010)(proiezione al 2010)

2 MeteoFrance (Toulouse)MeteoFrance (Toulouse):: applicazione a modelli metereologiciapplicazione a modelli metereologici.. Gli scambi vegetazione-atmosfera sono Gli scambi vegetazione-atmosfera sono unauna componente importante nei modelli componente importante nei modelli numerici dinumerici di previsione del tempo.previsione del tempo. La mappa PELCOM della vegetazione è La mappa PELCOM della vegetazione è statastata assunta come input per il modello assunta come input per il modello metereolo-metereolo- gico ARPEGE-ALADDIN. gico ARPEGE-ALADDIN.

VOC emission dalle foreste usando PELCOMVOC emission dalle foreste usando PELCOM (ARCS) (ARCS)

Le piante sono una delle maggiori fonti di Composti OrganiciVolatili, a loro volta precursori della formazione di ozono.L’emissione di composti organici è ritenuta una risposta fisiologica delle piante ai fattori di stree ambientale, comel’alta temperatura o la scarsità idrica.

I datiI dati

MaterialiMateriali• AVHRR NDVI

monthly MVC’s 1997

• AVHRR Daily Multi-spectral mosaics ‘95/’96/’97

• Ancillary geographic data:

• CORINE land cover data• FIRS regions and strata• Digital Chart of the World

Il Digital Terrain Model (raster) per la nostra area (1430x1700)

The The digital Forest Mapdigital Forest Map looks reasonable…. looks reasonable….

…….yet some details are quite strange..yet some details are quite strange.

Regions in

Colours

Strata in Yellow lines

Regional approach - FIRS regions and Regional approach - FIRS regions and stratastrata

Similar NDVISimilar NDVI

profiles may haveprofiles may have

a differenta different

meaning in meaning in

different regionsdifferent regions

Black lines FIRS regionsRed lines FIRS strata

AOI per partner:•Yellow: SSC•Green: SC-DLO•Blue: CNRM•Purple: ARCS•Orange: IUAV

Regional approach

Source: EMAP unit (SAI-JRC)

Defined Classification MethodologyMulti-spectral

AVHRR dataMasksNDVI ‘97 CORINE

Pure Pixels

MaskedNDVI

Classification

SignaturesDistances

1st - best2nd - bestclasses

CompilationEnhancements

Post-classification

Regions and strata

Data

base

PELCOM 1km land cover database Conif. Forest

Decid. ForestMixed Forest

Arable landIrrigated landPermanent cropsShrublandBarren landIce and SnowWetlandsWater

Grassland

Urban

PELCOM land coverPELCOM land cover - Pixel: 1.1 kmPixel: 1.1 km

Our AreasAreas of Interest of Interest, each including one or more FIRS strata. The parts pointed at by arrows show where strata extend into regions for which no CORINE info exists.

IUAV - South-East Europe - Final classified imageIUAV - South-East Europe - Final classified image

Scelta delle immagini e problemi connessiScelta delle immagini e problemi connessi

• Archivio MARS/JRC : immagini NOAA-AVHRR,Archivio MARS/JRC : immagini NOAA-AVHRR, 1995, multispettrali, diverse date;1995, multispettrali, diverse date; cattiva registrazione, nuvolecattiva registrazione, nuvole

• Archivio MARS/JRC: immagini decadali NDVI, MVCArchivio MARS/JRC: immagini decadali NDVI, MVC cattiva registrazione, nuvolecattiva registrazione, nuvole

• Archivio MARS/JRC: immagini processate SMARTArchivio MARS/JRC: immagini processate SMART problemi di registrazioneproblemi di registrazione

• Archivio EROS (IGBP) immagini 1995Archivio EROS (IGBP) immagini 1995 migliori, ma forse è solo apparentemigliori, ma forse è solo apparente

• DLR Berlino: MVC NDVI, mensili e decadaliDLR Berlino: MVC NDVI, mensili e decadali

Le immagini EROS sembrano buone? Chissà…Le immagini EROS sembrano buone? Chissà…

E’ stata applicata una maschera all’acqua, non è dato sapereE’ stata applicata una maschera all’acqua, non è dato sapere

quanto ben registrate fossero le immagini daily usate perquanto ben registrate fossero le immagini daily usate per

costruire le MVC.costruire le MVC.

Anche quelle MARS Anche quelle MARS

diventerebbero accettabilidiventerebbero accettabili

(almeno in apparenza) se (almeno in apparenza) se

si mascherasse l’acqua.si mascherasse l’acqua.

Ma il “blurring” rimane.Ma il “blurring” rimane.

Immagini SMART 1991: la Immagini SMART 1991: la

registrazione sembra buona...registrazione sembra buona...

……ma quelle relative al 1997, non mascherate, sono meno convincentima quelle relative al 1997, non mascherate, sono meno convincenti

Esempio di “blurring” causato dalla cattiva registrazione delle immaginigiornaliere usate come input

But first, some basic But first, some basic

terms and concepts...terms and concepts...

Which type of Classifier to use?

Some open questions...Some open questions...Statistical

orneural?

Which clustering method?

But first of all...But first of all...

Is the satellitary information alone sufficient?Is the satellitary information alone sufficient?

supervisedor

unsupervised?

Generally NOGenerally NO, it is necessary to have recourse to

some auxiliary information to solve dubious cases.

(insufficience of the description)insufficience of the description).

Image pixelsImage pixels: geo-referenced statistic units, described by a

suitable set of characters (spectral bands, images observed

at different dates, synthetic images, ecc.).

(the description is multidimensional)

Pixels can be grouped (or clustered clustered) in classes:

• according to their similarity with some pre-defined themes, represented by a set of suitably selected

pixels supervised methodssupervised methods;

oror• according to their similarity with one another

un-supervised (constructive-exploratory) un-supervised (constructive-exploratory) methodsmethods;

Principal Components Analysis (PCA)Principal Components Analysis (PCA)

• Channel values used as variables are often higly

correlated.A PCAPCA is used to convert the initial variables to new latent variables, called Principal Components, that are uncorrelated. This operation:

• reduces the dimensionality of the description

• filters out noise (i.e., casual, unimportant, non- structural aspects).A PCA is often applied before clustering, to savecomputation time.

AssumptionAssumption

•two pixels are two pixels are similar when they lie when they lie closeclose to each to each otherother

in the feature space (this depends on the way thein the feature space (this depends on the way thedistancedistance is defined). is defined).

To cluster

Create groups (classes) ascompact as possible in thefeature space

Now, a short description Now, a short description

of the main approachesof the main approaches

for clustering...for clustering...

Supervised methodsSupervised methods

• A suitable training set must be defined.

• The target-themes (water, built-up, vegetation, etc.)The target-themes (water, built-up, vegetation, etc.) are given a priori;are given a priori;

• The theme to which any pixel included in the training set belongs must be explicitly stated.

The classifier learns from the training set to recognize the themes’ features, and to assign all new pixels to the most appropriate theme.

ProblemsProblems

• Pixels are generally mix of different themes

• The construction of the training settraining set is a hard and error-prone job.

Statistical supervised ClassifiersStatistical supervised Classifiers

• maximum likelihood

• minimum Mahalanobis distance

Neural supervised ClassifiersNeural supervised Classifiers

•BackPropagation

Unsupervised methodsUnsupervised methods (esploratory)

• themes are not defined a priori;

• pixels are grouped according to the similarity of the patterns that describe them;

•the meaning of the groups thus obtained must be

assessed ex-post (interpretation).

Problems with unsupervised methodsProblems with unsupervised methods

• The classification is optimal from the viewpoint of the algorithm,

but...but...

• classes are built upon the most frequent patterns;

• therefore, the meaning of the groups is often not neat;

• rare patterns, albeit well characterized, are unable to get a specific class allocated for them.

Statistical unsupervised ClassifiersStatistical unsupervised Classifiers

• numerousnumerous methods (mostly variants of the k-meansk-means)

• minimum Internal Inertia (or Variance within groups)

Neural Unsupervised ClassifiersNeural Unsupervised Classifiers

• Kohonen’s Self Organizing Maps (SOM)

• gli esempi presentati alla rete consistono solo di un insieme di pattern (vettori di attributi), classificati per similarità.

• Ad ogni nodo (i, j) della mappa è associato un vettore di riferimento m i,j (codebookcodebook)

Kohonen’s MapKohonen’s Map The pixel with pattern xx is assigned to class (1,1)

Reti SOM - procedura di istruzione

• Ciascun pattern xx viene confrontato con tutti i vettori mmijij ed assegnato al nodo associato al

vettore mmijij più somigliante.

• Il nodo vincitore e quelli vicini vengono modificati in modo da accentuare la loro somiglianza con il pattern xx.

• Una SOM sviluppa in modo autonomo durante la fase di apprendimento un’organizzazione interna: pattern di ingresso simili attivano lo stesso nodo della mappa.

Le applicazioni sono di carattere esplorativo

Primi esperimenti di classificazione...Primi esperimenti di classificazione...

• Classificazione non supervisionataClassificazione non supervisionata

• Classificazione semi-supervisionataClassificazione semi-supervisionata

• Reti NeuraliReti Neurali

• Classificazione supervisionata (Mahalanobis)Classificazione supervisionata (Mahalanobis)

Example of a semi-supervised classification.

The windows chosen to train the classifier….

Details from the classified image issued by a classifier

trained on selected ares. Built-up areas are split

into two density zones.

Calcolo sistematicoCalcolo sistematico

di distribuzionidi distribuzioni

statistiche spazialistatistiche spaziali

• StatisticheStatistiche sulla distribuzione dei temi calcolate da sulla distribuzione dei temi calcolate da CORINE e dal DTM (quote).CORINE e dal DTM (quote).

• SistematicamenteSistematicamente, per ogni tema ed ogni area., per ogni tema ed ogni area.

• ObiettivoObiettivo: aiutare l’interpretazione delle classi, formulare : aiutare l’interpretazione delle classi, formulare

opportune regole di post-classificaziome opportune regole di post-classificaziome

CORINE ha 43 temi PELCOM ha 11 temi, da suddividere in sub- temi quando possibile

North Italy (ITN) - Distribuzione di temi sulla quotaNorth Italy (ITN) - Distribuzione di temi sulla quota

North Italy - Distribuzione di temi sulla quotaNorth Italy - Distribuzione di temi sulla quota

Italia Centrale - Distribuzione di temi sulla quotaItalia Centrale - Distribuzione di temi sulla quota

VignetiVigneti

OlivetiOliveti

ITC ITC Distribuzione Distribuzione spazialespaziale

dei temi dei temi Vigneti, OlivetiVigneti, Oliveti

ITSITSDistribuzione Distribuzione spazialespaziale

dei temi dei temi Aree irrigateAree irrigate

Puglia (ITP) Puglia (ITP) Distribuzione deiDistribuzione deitemi temi sull’elevazionesull’elevazione Aree irrigateAree irrigate

Altre statistiche per area

e tema:

serie storica dell’ Indice

di Vegetazione (NDVI)

Non molto confortante ..Non molto confortante ..

Determinazione di regole di decisione (post-classificazione)Determinazione di regole di decisione (post-classificazione)

Altre statisticheAltre statistiche per area e temaper area e tema calcolate durante leclassificazioni: mappe raster della distanza dei pixeldai temi (rappresentati dalla firma spettrale calcolataa partire dai pixel di addestramento)

Clustering MethodologyClustering Methodology

•Selection of training pixels

•Knowledge-based ClassificationKnowledge-based Classification

•Validation

Strongly methodological, the work concerned mainly:

• the optimal territorial stratification;

• the choice of the images to be used;

• extended experimental comparison of the available clusering methods

Eventually, a supervised supervised method was chosen that included:

• an original way of choosing the training pixels;

• the direct utilization of available ancillary information within the clustering procedure itself.

The ClassificationThe Classification

•Variables: 20 images NDVI during year 1997, produced by DLR, Berlin;

•Training pixels: for each theme, a set of pixels were chosen in each region, that were sufficiently pure in CORINE.

•Ancillary information: CORINE, Digital Terrain Model

at 1 km., Digital Chart of Forests, Bartholomew Maps,

FIRS strata, administrative statistics, etc.

1. Choice of training pixel1. Choice of training pixel

(the method can be easily extended to Neural(the method can be easily extended to Neural

Network Classifiers)Network Classifiers)

Distribution of training pixelsDistribution of training pixels

used for Italy in PELCOM.used for Italy in PELCOM.

For each area of interest,

a certain number of “pure”

pixels (at least 87% belonging

to the same theme in CORINE)

have been selected,

and used to train the classifier.

All other pixels are then

assigned to the most similar

group.

ITN - Projection of candidate training pixelscandidate training pixels onto the first factorplane. For most themes the level of confusion is high.

Confusion matrix for initial training pixelsColumns: pixels theme in CORINE.Rows: theme to which pixels are assigned by the classifier, after computing the themes’ signatures.

Selection of the most reliable training pixelsSelection of the most reliable training pixels(repeated for each region)

Initial selection of all candidates (14/16 pure CORINE)

Computation of the signatures for all relevant themes

Assignment of each training pixel to the closest signature

according to the Mahalanobis distance, thus keeping into

account the dispersion of each theme in the reference space

Exclusion of ill-assigned pixels

Too many ill-classified pixels?

yes

noEND

ITN - Training pixels retained after several iterations. Confusion is much reduced.

Confusion matrix for the finally selected training pixelsColumns: pixels theme in CORINE.Rows: theme to which pixels are assigned by the classifier, aftercomputing the themes signatures.

2. Clustering Method that makes use of 2. Clustering Method that makes use of the available ancillary informationthe available ancillary information

(Knowledge-based Classification)(Knowledge-based Classification)

(Can it be applied to a Kohonen’s SOM NN?)

The ancillary info usedThe ancillary info used while clustering:while clustering:

• For each AoI, For each AoI, the the distribution of each distribution of each theme over altitudetheme over altitude

Problems with clusteringProblems with clustering

•At the 1.1 km scale pixels are mix of themes, their spectral signaturesspectral signatures are averages;

•The profiles of some themes are very similar;

A classification purely based upon radiometric data can sometimes lead to unreliable or absurd results:

Example: arable land pixels detected at high elevation, owing to their similarity with bare soil.

How should pixel be assigned?How should pixel be assigned?

• In two steps:1. Purely according to the similarity of their

profiles (with much confusion)… 2. … then applying some suitable

decision rules to correct the results.• Or Knowledge-based Knowledge-based

ClassificationClassification

…making use of some relevant ancillary information during the very clustering process.

The Knowledge-based Classification The Knowledge-based Classification AlgorithmAlgorithm1. Selection of a set of reliable training pixels

2. For each activeactive theme:•computation of the average signature•computation of the specific variance-covariance

matrix and of its inverse (needed to compute Mahalanobis distances)

3. The elevation is divided in user-defined intervals and the table TargetTarget that cross-tabulates themes vs. elevation ranges is computed from CORINE.

4. TargetTarget is corrected by taking into account also contributions to active themes coming from pixels that belong in CORINE to non-active themes.

Current:Current:themes versus elevation as computed from clustering

5.Pixels are assigned to active themes according to the Mahalanobis distance. This is based purely on pixels’ signature.For each pixel, the Mahalanobis distances from all active themes are saved to a temporary file.The table CurrentCurrent of themes’ frequency vs. elevation is computed.

6. At this point we have two matrices:

Target:Target:themes versus elevation as in CORINE

• The classification is changed iteratively so as to have the computed table CurrentCurrent better match the knowledge base (CORINE) represented by TargetTarget;

• at the same time, also TargetTarget is slightly modified so as to keep somehow into account info in input data.

Common structure of matrices TargetTarget and CurrentCurrent:•n user-defined elevation ranges;

•p active themes;

•Cij is the frequency of themej at elevation range i in the CurrentCurrent table;

•on each iteration, assignment to cell (i,j)

- is encouraged if Cij < Tij

- is dis-couraged if Cij > Tij

7. A table of correction factors CFij (also sized n x p) is defined. Initially they are all equal to 1.

Each correction factor refers to one cell. CFij is used to correct the Mahalanobis distance from theme j of pixels at elevation range i.

8. A cicle of iterationscicle of iterations is started, aimed at orienting the assignments (no hard constraints), so as to encourage CurrentCurrent to adhere to TargetTarget.At the same time also TargetTarget is corrected (though less), moving it towards CurrentCurrent.

One iterationOne iteration (cycle 9-10 is repeated)

• re-read its Mahalanobis distances from file, and

correct each with the correction factor CFij

appropriate to the theme and to the pixel’s elevation;

• assign the pixel to the theme for which the so-corrected Mahalanobis distance is minimum.

9. For each pixel::

10. Update the CurrentCurrent distribution Update the correction factors:

Update TargetTarget

All AOIs - 12/16 pure pixels (not used for training)Columns: theme of pixel in CORINE.Rows: theme to which pixel is assigned by the classifier.

V A L I D A T I O N ( 1 )

All AOIs - 12/16 pure pixels (not used for training)Columns: theme of pixel in CORINE.Rows: theme to which pixel is assigned by the classifier.

V A L I D A T I O N ( 2 )

Forests are aggregated, as well as grassland/pastures.

RemarksRemarks

• frequent themes, like arable or deciduous, are well classified;

• less frequent themes, like permanent crops, coniferous or grassland, are better classified in AOIs for which enough training pixels can be found;

• when the classification was attempted with only few training pixels (pastures or permanent crops in some areas), results were unreliable;

An excerpt of Validation tables- summary per themeNon-irrigated arable land

Irrigated arable land + Rice

ITS - Spatial distribution of irrigated land

A3 - Spatial distribution of irrigated land

ConclusionsConclusions• When the target themes are pre-defined, a supervised classifier is convenient.

• Statistical and neural methods lead to results of comparable accuracy.

• The methods used with statistic classifiers in order to select optimally the training pixels, as well as the knowledge-based classification, should be extended to neural classifiers.

The Natural Capital Index (NCI)

enables to monitor and describe

the current and future

State of the Environment

100%

100%0%

quality

quantity

Principle Natural Capital Index:

Quantity: % area

Quality : % baseline state

NCI = quantity x qualityNCI = quantity x quality

•self-regenerating current / natural baseline • man-made current / cultural baseline

} “gap-analysis”

Self-regenerating areas are defined as:

virgin land, nature reserves; all forests except virgin land, nature reserves; all forests except wood plantations with exotic species; areas wood plantations with exotic species; areas with shifting cultivation; all fresh water areas; with shifting cultivation; all fresh water areas; and extensive grasslands (marginal land used and extensive grasslands (marginal land used for grazing by nomadic livestock).for grazing by nomadic livestock).

“ “all not human-dominated land, all not human-dominated land, irrespective of wether it is pristine or irrespective of wether it is pristine or

degraded”degraded”

Man-made Areas are defined as:

arable land; permanent cropland; wood arable land; permanent cropland; wood plantations with exotic species; pasture for plantations with exotic species; pasture for permanent livestock; urban areas; permanent livestock; urban areas; infrastructure; and industrial areas. Most infrastructure; and industrial areas. Most domesticated land is in fact agricultural domesticated land is in fact agricultural land.land.

““all human-dominated landall human-dominated land””

Self regenerating land in PELCOM database

Pressures to biodiversity with respective standardizing values

Pressures High chance on highecosystem quality

Low chance on highecosystem quality

1. Rate of climate(temperature) change

< 0.2°C change in 20years

> 2.0°C change in 20years

2. Human population density < 10 persons/km2 > 150 persons/km2

3. Consumption andproduction (GDP)

US$ 0 per km2 > US$ 6,000,000 perkm2

4. Fragmentation/isolation % natural area > 64% % natural area < 1%

5. SO2 deposition Deposition < criticalloads

Deposition > 5 xcritical loads

6. NH3 deposition Deposition < criticalload

Deposition > 5 xcritical load

7. Exposure to high ozoneconc.

AOT40 < critical level AOT40 > 5 x criticallevel

Pressure index in remaining self-regenerating land (1990)

Based on:

• Ozone

• Isolation

• Temperature

• GDP

• Acidification

• Eutrofication

NCI statistics (1990) on Eu-country level

Country ozon

eiso

lation

popu

lation

tempe

rature

gdp

acidificatio

n

eutro

fication

total p

ressu

re

quali

ty

area(%

)

NCI country Country ozon

eiso

lation

popu

lation

temperature

gdp

acidi

ficati

on

eutro

ficati

on

total

pressure

quali

ty

area(%

)

Finland 0 1 52 233 40 442 105 871 65 97 63Sweden 34 12 45 190 34 528 64 906 64 88 56Greece 381 111 220 35 40 0 15 794 68 48 32Spain 559 84 244 119 91 9 55 1157 54 54 29Portugal 528 90 382 77 79 0 67 1224 51 57 29UK 92 133 258 94 147 579 28 1384 50 33 17Ireland 84 238 131 51 47 95 51 698 72 22 16Austria 731 48 334 135 155 546 495 2443 14 68 9Italy 884 96 514 98 279 153 387 2412 16 42 7France 952 161 301 175 162 164 410 2327 18 35 6Denmark 445 644 451 194 268 207 224 2398 16 13 2Germany 902 239 610 187 382 898 880 4098 0 33 0Netherlands 702 400 582 203 435 992 986 4301 0 12 0Belgium-Lux. 986 174 443 202 202 996 900 3903 0 21 0

Pressure index in remaining self-regenerating land (baseline 2010)

Based on:

• Ozone

• Isolation

• Temperature

• GDP

• Acidification

• Eutrofication

Country ozon

eiso

lation

popu

lation

tempe

rature

gdp

acidificatio

n

eutro

fication

total p

ressu

re

quali

ty

area(%

)

NCIFinland 0 1 57 378 56 11 29 531 78 98 77Sweden 8 12 49 317 50 141 18 593 76 91 69Greece 271 111 226 104 58 0 9 714 71 65 46Spain 366 84 247 225 137 0 29 1069 57 52 30Portugal 386 90 383 165 135 0 49 1195 52 54 28Austria 421 48 408 240 273 58 307 1749 34 71 24UK 53 133 263 181 198 273 3 1131 60 36 21Ireland 30 238 143 121 111 0 28 639 74 21 16France 622 161 319 299 220 36 245 1879 30 40 12Italy 647 96 514 192 374 55 290 2131 23 39 9Denmark 201 644 447 324 362 101 90 2079 24 20 5Netherlands 485 400 586 339 527 405 617 3327 20 16 3Germany 542 239 617 313 429 412 444 2974 7 36 2Belgium-Lux. 849 174 459 334 282 428 641 3141 6 23 1

NCI statistics (2010) on Eu-country level

Conclusions II

• NOAA derived land cover data are an important and

accurate source of input data for current NCI calculation

• The consistent classification for the entire pan-European

area is the most important added value of PELCOM