quality assessment of image & corine land cover...

1

Quality assessment ofImage & Corine Land Cover 2000

Guillermo Villa (1)

ge & Co e d Cove 000database in Spain.Lessons learnt from the project. Future actions

15 July 2005 ICC 2005 A Coruña 1

Antonio Arozarena (1)Isabel del Bosque (1)Ana Porcuna (2)Nuria Valcarcel (1)

(1) IGN Spain(2) Tragsatec

1. Background of I&CLC2000 in Spain

Summary

. Bac g ou d o &C C 000 Spa

2. Quality control

3. Results

4. Lessons learnt

5 Future actions: SIOSE Project


5. Future actions: SIOSE Project

6. Conclusions

2

On 27 June 1985 and by virtue of a decision taken by the Council of Ministers of the European Union (EC/338/85), “an experimental project for the acquisition of data, the coordination and standardization of information on the state of the environment


and natural resources in the Union” was launched. It was called the CORINE Programme (Coordination of Information on the Environment).

The period from 1985 to 1990 saw the creation of an environmental information system, which included the development of nomenclatures and methodologies regarding land cover agreed by the member states of the EU.


member states of the EU.

3

Goals

Homogeneity

Databases comparable between different countries

Allow periodic updating


Principal Objectives I&CLC2000:

Background of I&CLC2000 Spain

• Updating of CLC90 database

• Evaluation of Land Cover changes between 1990 and 2000


990 d 000

4



5

29 Participant countries:

Background of I&CLC2000


Participant organizations:

Background of I&CLC2000 Spain

ETCETC--TETE


A.G.E. CC.AA.

6

CLC in Spain

IGN (Instituto Geográfico Nacional) is Spanish EIONET’s National ReferenceSpanish EIONET’s National Reference Centre for Land Cover

IGN has coordinated CLC90 and I&CLC2000

CLC 90/2000 inSpain: 5th level nomenclature


nomenclature

I&CLC2000 finished (EU’s 3 level and Spanish 5 level databases)


7



8



9

Decentralization in Spain


19 Autonomous regions → 19 Production Teams

Organization of CLC2000 in Spain

AEMA (EEA)Comisión de seguimiento

I&CLC2000 Europa

CTE Medioambiente terrestre

CCI (JRC)

PFN-MIMAM

NRC / CNIG – IGNCoordination

A.G.E. CC AA

Comisión de seguimientoI&CLC2000 España

I&CLC2000 Europa


A.G.E.•Mº Fomento•Mº Medioambiente•Mº Agricultura•Mº Ciencia y Tecnología•Mº Economía•Mº Defensa

CC.AA.-Galicia -Castilla la Mancha-Principado de Asturias -Región de Murcia-Cantabria -Comunidad Valenciana-País Vasco -Extremadura-Comunidad Foral de Navarra -Andalucía-Aragón -Islas Baleares-Cataluña -Canarias-Castilla y León -Ceuta-La Rioja -Melilla-Comunidad de Madrid

10

Financing

CLC Europe (3 Level Nomenclature: 44 clases)

Budget: 1.578.000 € (3,1 €/Km2)g ( , / )

E.U. - 50% (789.000 €)

NA Spain (AGE). - 50% (789.000 €)

CLC Spain (CLC Spain (5 Level Nomenclature: 64 classes 5 Level Nomenclature: 64 classes CLC90 / 85 classes CLC2000CLC90 / 85 classes CLC2000))


))Budget: 2.350.000 Budget: 2.350.000 €€ (4,6 (4,6 €€/km/km22) (included L3)) (included L3)

E.U. E.U. –– 33,56% (789.000 33,56% (789.000 €€))

NA Spain (A.G.E.) NA Spain (A.G.E.) –– 50% (1.175.223 50% (1.175.223 €€))

CC.AA. CC.AA. –– 16,44% (386.223 16,44% (386.223 €€))

Fases Coste EUR/KM2 Coste en EUROSIMAGE2000- Datos satélite + procesado- Mosaico y difusión

0.4 EUR/Km20.1 EUR/Km2

195.447,72 EUROS52.867 EUROS

CLC2000- Fotointerpretación y edición- Dirección nacional- Control e integración datos CCAA- Integración de datos a nivel europeo y difusión

2.9 EUR/Km20.3 EUR/KM20.3 EUR/KM20.3 EUR/km2

1.525.400 EUROS144.182,74 EUROS144.182,74 EUROS144.182,74 EUROS


Gestión y dirección del proyecto 0.3 EUR/km2 144.182,74 EUROS

Coste por Km2 4.6 EUR/Km2 2.350.445,64 EUROS

11

1st Method: 1) correct CLC902) update to CLC2000

IMAGE 2000

Methodologies

IMAGE 1990CLC2000


Old CLC90

New CLC90

BD changes

IMAGEN 1990

2nd Method: 1) Obtain CLC2000 from a regional 2000 Land cover database2) Down-date to CLC90

IMAGEN 2000

IMAGEN 1990 CLC90


CLC2000

BD cambiosBD Ocupación suelo regional

12



13


BD O ió l

3rd Method: 1) CLC90 from regional 1990 database2) CLC2000 from a 2000 regional database

IMAGEN 2000IMAGEN 1990 BD Ocupación suelo regional

BD Ocupación suelo regional


CLC2000CLC90 corregido

BD cambios

14

PRODUCTS Responsible of delivery

Producto 1 – Escenas ortorrectificadas + hojas EEA/JRC

Producto 2 – Mosaico nacional IMAGE2000 EEA/JRC

Producto 3a – CLC2000 NacionalProducto 3b – CLC90 nacional revisado Producto 3aNivel5 – CLC2000 Nacional a nivel 5Producto 3bNivel5 – CLC90 nacional revisado a nivel 5

Países miembros

Producto 4 – Cambios CLC nacionalesProducto 4Nivel5– Cambios CLC nacionales a nivel 5

Países miembros

Producto 5 – Mosaico europeo IMAGE2000 EEA/JRC

Producto 6a – CLC2000 europeo

P d 6b CLC90 i dEEA/JRC


Producto 6b – CLC90 europeo revisado/J

Producto 7 – Cambios CLC90 europeo EEA/JRC

Producto 8 – CLC raster 250 m EEA/JRC

Producto 9 – CLC raster 100 m EEA/JRC

Producto 10 – CLC estadísticas 1 km2 EEA/JRC

Producto 11 – Metadatos nacionales Países miembros

Producto 12 – Metadatos europeos EEA/JRC

Product 1: Landsat 7 ortho-rectified scenes


15

Product 2: National IMAGE 2000 Mosaic


Product 3: CLC90 corrected + CLC2000 national


16

Product 4: National changes database


Product 5: IMAGE 2000 european mosaic


17

Product 6: European CLC2000 and CLC90corrected


Product 7: European changes database


18

Products 8 and 9: Raster format CLC at 250 mand 100 m

Product 10: Estadísticas CLC por km2


Product 11: National Metadata

Product 12: European Metadata


19


Summary


2. Quality control

3. Results

4. Lessons learnt




6. Conclusions

Radiometric and geometric control of Landsat 7 orthorectified images

Quality control

Landsat 7 orthorectified images

Verification of vector database by each production team

Verification of vector database by European Technical Team (2 visits)


( )

Final validation: determination of global database quality level

20


47 complete scenes: 41 Iberian Peninsula and Baleares Islands; 6 Canary Islands

• Check points between porthorectified Landsat 7 images and orthophotos

• 10 x 10 Km grid


21


RMSE xy = 17,97 m

Check points per scene: 21

Rejected images: 2

Radiometric and geometric control of Landsat 7 images

Quality control

Landsat 7 images




( )


22



23



24



Quality control

Landsat 7 images




( )


25



26

Code 111 112 121 122 123 124 131 132 133 141 142 211 212 213 221 222 223 231 241 242 243 244 311 312 313 321 322 323 324 331 332 333 334 335 411 412 421 422 423 511 512 521 522 523 Code111 0 2 2 1 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 111112 1 0 1 1 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 112121 2 1 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 121122 2 1 1 0 1 1 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 122123 2 1 1 1 0 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 2 2 2 2 2 2 123124 2 1 1 1 3 0 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 124131 2 1 1 1 2 2 0 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 2 1 1 1 3 131132 2 1 1 1 2 2 3 0 1 2 2 2 2 2 2 2 2 1 2 2 2 2 1 1 1 1 1 1 1 2 2 2 3 3 3 3 3 3 3 3 1 3 3 3 132133 1 1 1 1 1 1 1 1 0 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 2 3 2 1 1 2 2 133

Probability of changes

141 1 1 1 1 2 2 3 2 1 0 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 141142 1 1 1 1 1 2 2 2 1 2 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 142211 2 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 3 3 3 3 3 3 3 3 3 3 2 1 3 3 3 211212 2 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 3 3 3 3 3 2 3 2 3 3 2 1 3 3 3 212213 2 1 1 1 1 1 1 1 1 1 1 1 1 0 2 2 3 1 1 1 1 2 2 2 2 2 3 1 1 3 3 3 3 3 2 3 3 3 3 2 1 3 3 3 213221 2 1 1 1 1 1 1 1 1 1 1 1 1 2 0 1 1 1 1 1 1 1 1 1 1 1 3 1 1 3 3 2 3 3 2 3 2 3 3 2 1 3 3 3 221222 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 0 1 1 1 1 1 1 1 1 1 1 3 1 1 3 3 2 3 3 2 3 2 3 3 2 1 3 3 3 222223 2 1 1 1 1 1 1 1 1 1 1 1 2 3 1 1 0 1 1 1 1 1 1 1 1 1 3 1 1 3 2 1 3 3 3 3 3 3 3 2 1 3 3 3 223231 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 2 3 1 1 2 3 2 3 3 1 2 2 3 3 2 1 3 3 3 231241 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 2 2 1 1 1 2 3 1 1 2 3 2 3 3 2 3 3 3 3 2 1 3 3 3 241242 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 0 1 2 1 1 1 2 3 1 1 2 3 2 3 3 2 3 3 3 3 2 1 3 3 3 242243 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 0 1 1 1 1 1 2 1 1 2 3 2 3 3 2 3 3 3 3 2 1 3 3 3 243244 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 0 1 1 1 3 3 2 1 2 3 2 3 3 2 3 3 3 3 2 1 3 3 3 244311 2 1 1 1 1 1 1 1 1 1 1 1 2 3 1 1 1 1 1 1 1 1 0 2 1 2 3 3 1 2 3 2 1 3 2 2 2 3 3 2 1 3 3 3 311312 2 1 1 1 1 1 1 1 1 1 1 1 2 3 1 1 1 1 1 1 1 1 2 0 1 2 3 3 1 2 3 2 1 3 2 2 2 3 3 2 1 3 3 3 312313 2 1 1 1 1 1 1 1 1 1 1 1 2 3 1 1 1 1 1 1 1 1 1 1 0 2 3 3 1 2 3 2 1 3 2 2 2 3 3 2 1 3 3 3 313321 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 3 3 1 1 1 2 3 2 2 2 2 3 3 2 1 3 3 3 321322 2 1 1 1 1 1 1 1 1 1 1 1 3 3 3 1 3 1 1 1 1 3 1 1 1 2 0 3 2 1 3 2 1 3 2 2 2 3 3 2 1 3 3 3 322323 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 2 3 0 1 1 1 1 1 3 3 3 2 3 3 2 1 3 3 3 323324 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 0 2 2 1 1 3 2 2 1 3 3 2 1 3 3 3 324331 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 1 1 1 1 1 2 1 0 2 1 3 3 3 3 2 1 3 2 1 3 3 3 331


331 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 1 1 1 1 1 2 1 0 2 1 3 3 3 3 2 1 3 2 1 3 3 3 331332 2 1 1 1 1 1 1 1 1 2 2 3 3 3 2 2 2 3 2 2 2 3 3 3 3 2 3 2 2 2 0 1 3 1 3 3 3 3 3 3 3 3 3 3 332333 2 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 1 2 2 2 2 3 2 2 2 1 2 2 1 1 1 0 2 1 3 3 3 3 3 2 1 3 3 3 333334 3 3 3 3 3 3 3 3 3 3 3 1 1 3 2 2 2 3 3 3 3 1 1 1 1 1 1 1 1 3 3 1 0 3 3 1 3 3 3 2 1 3 3 3 334335 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 1 1 3 0 3 3 3 3 3 2 2 3 3 3 335411 3 2 1 1 1 1 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 3 1 1 1 1 2 3 1 3 3 2 3 3 0 3 3 3 3 2 1 3 3 3 411412 3 2 1 1 1 1 2 2 1 2 2 1 3 1 3 3 3 1 2 2 2 3 1 1 1 1 2 3 1 3 3 2 1 3 1 0 3 3 3 2 1 3 3 3 412421 3 2 1 1 1 1 2 2 1 2 2 2 3 3 3 3 3 2 2 2 2 3 2 2 2 1 2 3 1 2 3 2 1 3 3 3 0 2 2 2 1 1 2 1 421422 3 2 1 1 1 1 2 2 1 3 2 3 3 3 3 3 3 2 2 2 2 3 2 2 2 1 3 2 1 2 3 2 3 3 3 3 1 0 1 2 1 1 2 1 422423 3 2 1 1 1 1 1 2 1 3 2 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 2 2 3 2 3 3 3 3 2 1 0 2 1 3 3 1 423511 3 2 2 2 1 2 1 3 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 1 3 3 2 3 2 2 2 0 1 3 3 3 511512 3 2 2 2 1 2 1 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 1 3 3 1 1 1 2 2 2 0 2 3 2 512521 3 2 2 2 1 1 3 3 1 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 2 2 2 3 1 3 3 3 3 1 1 3 2 2 0 3 3 521522 3 2 2 2 1 1 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 1 1 1 3 3 3 0 3 522523 3 2 2 2 1 1 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 2 2 3 3 0 523

Code 111 112 121 122 123 124 131 132 133 141 142 211 212 213 221 222 223 231 241 242 243 244 311 312 313 321 322 323 324 331 332 333 334 335 411 412 421 422 423 511 512 521 522 523 Code

rows: CLC90 0: not a change 1 : possible (comm on) change 2: rear change (to be confirm ed) 3: unexpected change (explaination is neecolum n CLC2000

COMUNIDADNº de unidades de

verificación

CLC2000 CLC-Cambios

Hojas aceptadas Hojas rechazadas Hojas aceptadas Hojas rechazadas

Andalucía 16 16 0 8 6Andalucía 16 16 0 8 6

Aragón 11 11 0 10 1

Asturias 4 4 0 4 0

Baleares 2 2 0 2 0

Castilla-Leon11 11 0 6 5

Castilla-La Mancha 20 20 0 20 0

Galicia 3 3 0 0 0

M d id 3 3 0 1 2


Madrid 3 3 0 1 2

Melilla 1 1 0 1 0

Murcia 5 5 0 3 2

La Rioja 4 4 0 0 4

País Vasco3 3 0 1 2

Valencia 5 5 0 0 4

Total: 88 88 0 57 26

27


Quality control

Landsat 7 images




( )


Validation Methodology

Standard overall accuracy assessment of entire CLC2000 product, based on a representative sample

Stratification

Determination of the number of sampling points

Sampling methodology: stratified random sampling


points

Random selection of sampling polygons

Check photo-interpretation of polygons

28

Stratification

A) CLC2000: one stratus per class

B) Changes database:


• Non-updated area

• Updated area

Determination of the numberof sampling points

z2 ph (1-ph)

For each stratum h nh =e2

h

nh : Number of sample points

ph: A previously estimated error rate for the stratum h


ph p y

eh: The accepted absolute standard error for the estimationof the percentage of incorrect identified size of area forthe stratum h

z: Abscise of normal curve

29

Thematic accuracy

• Per stratum$

• Overall

Xh

h = nh

$p

: estimation of the overall accuracy of several $p

h: estimation of the percentage of correctly classified area in stratum h

Xh: number of correctly classified points in stratum h

nh: total number of points in stratum h

p


Ah= Σ h

h A$p$p

strata

Ah : Total area of stratum h

A : Total area of the CORINE Land Cover inventory

p

Confidence interval

• It is necessary to have an estimation of error to obtain the

• Moivre theorem

for n Zp P

PQn

=−$

yconfidence interval

P: accuracyQ = 1-P

: accuracy estimation of the sample$p


• For a confidence level = 95% the interval extremes are :

nPQ1.96p̂ ± P is unknown estimation: P ≈ $p

30

Number of validation polygons:


CO

DE

_00

Anda

luci

a

Arag

ón

Astu

rias

Bale

ares

Can

tabr

ia

C-L

eón

C-M

anch

a

Can

aria

s

Cat

aluñ

a

Ceu

ta

Cha

farin

as

Extre

mad

ura

Gal

icia

La R

ioja

Mad

rid

Mel

illa

Mur

cia

Nav

arra

País

Vas

co

Vale

ncia

Tota

l

111 24 8 1 4 1 15 16 7 17 0 0 8 1 2 4 0 5 3 3 13 132112 16 3 3 9 6 9 10 4 25 0 0 2 7 0 12 0 4 1 3 17 131121 18 7 3 2 1 10 8 2 18 0 0 5 5 2 11 0 8 4 8 15 127122 11 6 7 1 2 13 4 1 1 0 0 1 2 0 3 0 1 1 2 9 65123 14 0 3 3 2 0 0 3 15 1 0 0 11 0 0 1 3 0 4 6 66124 7 1 1 3 1 3 2 6 3 0 0 1 3 1 4 1 1 1 2 2 43131 19 9 3 1 2 21 10 3 9 0 0 5 12 0 7 0 5 1 2 8 117132 10 6 6 1 1 9 0 1 3 0 0 1 1 0 9 0 3 2 1 1 55

Number of validation polygons:

132 10 6 6 1 1 9 0 1 3 0 0 1 1 0 9 0 3 2 1 1 55133 26 6 2 3 1 8 3 3 4 0 0 0 2 1 13 0 10 1 2 20 105141 7 2 1 0 1 3 1 1 2 0 0 0 0 1 15 0 1 1 4 3 43142 20 5 2 8 2 5 2 3 15 0 0 1 2 0 11 0 3 1 2 9 91211 24 25 0 3 0 18 29 5 11 0 0 8 0 1 2 0 5 2 1 4 138212 27 13 0 3 0 28 31 2 8 0 0 5 0 1 3 0 5 2 0 5 133213 5 50 0 0 0 0 2 0 2 0 0 26 0 0 0 0 4 1 0 2 92221 11 7 0 0 0 21 36 3 7 0 0 10 3 4 3 0 7 4 0 12 128222 38 9 1 5 0 1 5 4 10 0 0 6 0 1 0 0 24 0 0 27 131223 59 4 0 1 0 2 33 0 3 0 0 23 0 1 2 0 2 0 0 5 135231 0 7 35 2 19 26 1 3 11 0 0 1 1 0 2 0 0 5 18 0 131241 13 1 0 44 0 7 9 0 3 0 0 4 0 1 0 0 1 0 0 5 88242 25 15 2 3 2 13 18 0 10 0 0 4 23 1 1 0 6 1 0 12 136243 15 25 7 3 2 18 16 0 16 0 0 2 10 1 1 0 5 3 1 12 137244 52 0 0 0 0 26 23 0 0 0 0 29 0 0 3 0 0 0 0 0 133311 24 13 12 1 6 32 12 0 8 0 0 10 8 2 2 0 0 3 3 2 138312 16 20 3 4 2 20 14 1 19 0 0 3 5 2 2 0 4 4 6 11 136313 12 19 3 4 2 18 7 0 21 0 0 1 27 1 1 0 1 5 11 3 136321 20 10 3 0 2 37 26 0 5 0 0 19 3 1 4 0 0 2 1 4 137322 0 9 38 0 15 22 0 2 9 0 0 0 24 2 0 0 0 5 7 0 133323 25 22 0 2 0 23 23 1 8 0 0 10 0 1 3 0 5 4 2 8 137324 24 19 5 2 3 20 18 0 10 0 0 8 6 1 3 0 3 2 5 9 138


331 18 18 1 3 3 1 1 8 11 1 0 0 10 0 1 0 7 0 1 10 94332 9 21 6 5 2 33 5 7 19 0 0 3 0 0 1 0 1 2 5 1 120333 14 25 14 0 5 20 8 6 11 0 0 4 0 3 0 0 17 2 0 2 131334 4 0 16 1 1 27 10 0 12 0 0 2 6 0 3 0 0 0 0 5 87335 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5411 5 26 1 0 2 10 11 0 3 0 0 0 0 0 1 0 5 2 1 4 71412 0 3 6 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11421 18 0 5 2 12 0 0 0 10 0 0 0 12 0 0 0 2 0 0 10 71422 31 0 0 4 0 0 1 1 1 0 0 0 0 0 0 0 3 0 0 3 44423 5 0 0 0 2 0 0 0 0 0 0 0 25 0 0 0 1 0 2 0 35511 2 2 0 0 0 5 6 0 5 0 0 4 9 3 8 0 0 5 2 1 52512 23 12 2 0 1 14 19 0 8 0 0 13 6 1 3 0 2 2 2 4 112521 1 0 0 4 2 0 0 1 6 0 0 0 4 0 0 0 1 0 0 7 26522 8 0 4 0 8 0 0 0 0 0 0 0 9 0 0 0 0 0 3 0 32523 1 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 1 6999 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1

701 433 196 132 113 538 420 78 359 2 3 219 237 35 138 2 156 72 104 272 4210

31



Summary


2. Quality control

3. Results

4. Lessons learnt




6. Conclusions

32



33

RESULTS: % changes per Region%

Supe

rfic

ie c

ambi

o /

Supe

rfic

ie C

A

%cambio en España


(*) DATOS PROVISIONALES

RESULTS: changes at level 2



34

RESULTS: % presence of classesup

ació

n / S

uper

ficie

Esp

aña



%O

cu

Urban sprawl


35

Urban sprawl


Urban sprawl


36

Urban sprawl and irrigation



Summary


2. Quality control

3. Results

4. Lessons learnt




6. Conclusions

37

Lessons learnt

1) Land use and Land cover information is a critical issue in Spain:Water supply (quantity and quality)Water supply (quantity and quality)Urban sprawl (especially in the coast)Tourism expansion/sustainabilityInfrastructure planning & ecological impactDesertificationR l E t t b (“b bbl ”?)


Real Estate boom (“bubble”?)Housing problem (huge price increase)Climate change (Kyoto protocol)Very high rate of Land Cover change in Spain

Lessons learnt

2) V bi i i2) Very big impact in:

Communication media (Journals, radio, TV)

Political life


38

Lessons learnt

3) Politicians “sometimes” make promises or

take decisions based on what is published ontake decisions based on what is published on

journals, not on Technical Reports →

We should be careful with the information we

send to journals and other media


Databases should be easy to understand by

non expert people

Lessons learnt

4) Corine Land Cover addresses this information needs but … doesn’t solve them completely, because:

Scale too little

Minimum polygon too big

Updating period too long


Nomenclature not detailed enough

Nomenclature not good enough

39

Lessons learnt

Nomenclature not good enough (1):

It is actually a “thematic map” (classification), not a complete GIS database

It mixes cover and use (→ semantic


problems; erroneous conclusions,…)

Lessons learnt


Mixed classes → information lost

It produces incoherencies when compared with other Land Use and land Cover databases (especially when


analyzed by non expert people)→ political “wars”.

40

Lessons learnt


Only one parameter per polygon (class label). Reality is much more complex !!:

e.g: percentage of trees in a forest, building density,…


e.g. Class 244: Agroforestry areas

The “DEHESA” is an old land-use

i i fsystem consisting of scattered oak trees on pasture or crop land used by livestock.

This system is


yfrequent in the west, south-west and central parts of the Iberian Peninsula

41


Summary


2. Quality control

3. Results

4. Lessons learnt




6. Conclusions

Present situation of Land Use/LandCover information in Spain

Various institutions feel a strong need for more big scale / high detail information

Some institutions of National and regional (Autonomous Communities) levels, keep geographic information databases related with land use/cover at higher level of detail (geometrically –bigger scale- and semantically) than CLC2000.


E.g.: Minister of Agriculture, Minister of Environment, various Autonomous Communities,…

42



43


But this databases are not integrated:But this databases are not integrated:Different scales

Incomplete cover of Spanish territory (regional databases)

Different updating periods


Incompatible nomenclatures (very difficult translations between them)

44

INSPIRE visionData should be collected once and maintained at the level where this can be done most effectivelywhere this can be done most effectivelyit should be possible to combine seamlessly spatial information from different sources across Europe and share it between many users and applicationit should be possible for information collected at one level to be shared between all the different levels, detailed for detailed investigations, general for strategic purposes


geographic information needed for good governance at all levels should be abundant under conditions that do not refrain its extensive use

SIOSE Project (Spanish Land Cover information system)

Nominal Scale: 1:25.000Minimum polygon:

2 Ha general1 Ha artificial coverages


1 Ha artificial coveragesPeriodicity: 5 years

45

SIOSE Project

Base information:SPOT 5 images XS+P 2.5m (2005)Orthophotos 0.5 m (PNOA)

Decentralized production (regional governments)


SIOSE Project

Terrestrial geo-referenced (and oriented) digital h d bphoto database


46

SIOSE Project

A new Land Use / Land Cover Data Model:Multi-criteria (cover + use)M l i ( l i l ibMulti-parameter (multiple attributes possible for 1 polygon)Object oriented (UML description, GML implementation)Extensible (for future and/or specific


needs)Designed in a cooperative effort to fulfill the requirements of all institutions/social agents

SIOSE Conceptual Data Model: Design Guidelines

Separation between Land Cover (biophysical criteria) and Land Use (socio-economic criteria)

There is only one geometric entity class in SIOSE (POLYGON)

Mixed Classes in SIOSE: created by association of


ysingles classes

47

Thematic Working groups are using the first draft version of the SIOSE Conceptual Data model it describes abstract classes and relations, and some characteristic attributes, linked to the existing land cover / uses nomenclatures. So it is not the

SIOSE Conceptual Data Model: Design Guidelines

g /future SIOSE physical data model.SIOSE Conceptual Data model: in UML notation (Unified Modeling Language). It provides normalized notation about classes and relations between them, according to ISO TC211 and OGC recommendations.This standardized notation provides flexibility to the model, so that Thematic Working Groups and future users can


so that Thematic Working Groups and future users can modify and extend it easily.

SIOSE Conceptual Data model has considered previous Spanish Land Cover and Use Nomenclatures and Databases, from national and regional institutions.

SIOSE Data Model: Conceptual Model UML Diagram

ABSTRACT CLASS

InheritanceAssociation

Olive grove Class inheritsproperties and attributes of:


properties and attributes of:•Cover•Agricultural Surfaces•Woody Crops

48

4. SIOSE Conceptual Data Model: UML


SIOSE Conceptual Data Model: Relation Polygon – Land Cover – Use


49

Agricultural Coverages


Land Uses


50

Example: Translation from CLC Nomenclature to SIOSE

SIOSE: Polygon associated to the following cover:Cover 1: 1.4 Infrastructures

(C dS f P t 100%)

Spanish CLC: 1.2.2.2 Class – Railway area

(CoveredSurfacePercentage= 100%)(InfrastructureType = Road Network)(InfrastructureSubtype = railway)


Example 2: Link between CLC - SIOSEMixed Classes

Spanish CLC: 2.4.1.2 class: ‘annual crops associated to permanent irrigated crops’

Non permanent crops associated to permanent irrigated crops in the same plot of land.

SIOSE: polygon associated to 3 covers:Cover 1: 2.1 Herbaceous Crops

CoveredSurfacePercentage = 50%Abandoned= NOHerbaceousType: LabourIrrigated /Non irrigated = Irrigated

Cover 2: 2.2.1 Fruit TreesCoveredSurfacePercentage = 20%

NO

For each polygon, the sum of the covered

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Abandoned = NOFruitTreeType: CitricIrrigated /Non irrigated = Irrigated

Cover 3: 2.2.2 VineyardCoveredSurfacePercentage = 30%Abandoned = NOIrrigated /Non irrigated = Irrigated

surface percentages of its associated covers must be 100%.

51

Example 3: Link between MCA - SIOSE

Crop Map Code = OL [Non irrigated Olive Grove]

SIOSE: polygon associated to 1 cover :Cover 1: 2.2.3. Olive GroveCoveredSurfacePercentage = 100%Irrigated /Non irrigated =Non Irrigated

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Non Irrigated Abandoned = NO


Summary


2. Quality control

3. Results

4. Lessons learnt


10215 July 2005 ICC 2005 A Coruña 102


6. Conclusions

52

Conclusions

Land use / land cover informationLand use / land cover information critical today

This problem is not being adequately addressed by existing databases

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Conclusions

Wh i d d?What is needed?New technological approaches (specially data models)

Much bigger investments (until now: far too low compared with base mapping and

10415 July 2005 ICC 2005 A Coruña 104

too low compared with base mapping and other databases)

53

PRESUPUESTO PROYECTO SIOSE POR COMUNIDADES AUTÓNOMAS

Escala 1:25.000Tamaño mínimo parcela: 2 ha / Urbano: 1 ha

(función perímetro) (función nº polígonos)

50 km / hora 16 min/ pol 60 € / km2

CCAA PERÍMETRO(km)

NUMPOLÍG

PRECIO

(€)PRECIO

(€)

PRECIODIGITALIZACIÓN

(€)

PRECIO FOTOINT+ASIG. COD.

(€)PRECIO

(€)PRECIO

(€)PRECIO

(€)TOTAL

€PRECIO

(€)ANDALUCÍA 249.229 130.110 93.848 45.479 114.645 798.009 41.873 531.266 68.219 1.599.491 187.697ARAGÓN 218.491 98.444 51.120 24.773 100.506 603.789 8.966 289.382 37.159 1.064.575 102.239ASTURIAS 61.311 43.013 11.360 5.505 28.203 263.814 5.612 64.310 8.258 375.702 22.721ISLAS BALEARES 27.458 20.442 5.373 2.604 12.631 125.377 8.206 30.414 3.905 183.137 10.745CANARIAS 33.133 27.660 7.963 3.859 15.241 169.651 10.188 45.078 5.788 249.805 15.926CANTABRIA 24.956 15.110 5.723 2.774 11.480 92.673 3.955 32.399 4.160 147.440 11.446CASTILLA Y LEÓN 382.627 232.528 100.917 48.905 176.008 1.426.172 18.808 571.283 73.357 2.314.534 201.835CASTILLA -LA MANCHA 340.788 220.360 85.052 41.217 156.762 1.351.539 17.000 481.473 61.825 2.109.816 170.105

TOTAL COSTE PRODUCCIÓN

COMPROBACIÓNEN CAMPO

INFORMES YMETADATOS

INTEGRACIÓN DE DATOS

PRODUCCIÓN

ACOPIODATOS

GESTIÓNDATOS MCA ANTIGUO

CORREGIDOS CON MCA NUEVO

DIGITALIZACIÓN FOTOINTERPRETACIÓN + ASIG. COD. INCR.DIGIT.

URBANO

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CATALUÑA 174.713 108.356 34.510 16.724 80.368 664.584 40.766 195.360 25.086 1.022.887 69.021COMUNIDAD VALENCIANA 105.123 52.626 24.924 12.078 48.357 322.772 27.272 141.093 18.118 569.690 49.848EXTREMADURA 133.939 66.071 44.632 21.629 61.612 405.234 6.993 252.657 32.443 780.568 89.264GALICIA 167.996 101.846 31.823 15.422 77.278 624.657 14.644 180.148 23.132 935.281 63.646MADRID 41.692 35.330 8.592 4.164 19.178 216.690 27.061 48.638 6.246 321.978 17.184MURCIA 51.940 32.292 12.259 5.941 23.892 198.058 8.814 69.399 8.911 315.015 24.519NAVARRA 55.680 32.195 11.123 5.390 25.613 197.462 2.966 62.967 8.085 302.483 22.246EUSKADI 38.770 26.055 7.756 3.759 17.834 159.807 6.884 43.909 5.638 237.830 15.513LA RIOJA 22.717 11.865 5.400 2.617 10.450 72.773 1.462 30.567 3.925 121.794 10.799CEUTA 22 10 0 0 200 122 16 348 43MELILLA 15 7 0 0 213 82 11 313 29

TOTAL 2.130.563 1.254.303 542.413 262.856 980.059 7.693.060 251.882 3.070.549 394.283 12.652.688 1.084.827

Thank you for your attention

Guillermo Villa-Alcazar

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Remote Sensing Department Chief

Instituto Geográfico Nacional

Spain

[email protected]

54

Spanish National Plan for Observation of Territory y

(PNOT)


Planes Autonómicos de Ortofotografía

Aérea

0,10 a 0,25 mAlta:

Pancro: 1 a 10 mMultiesp: 4 a 30 m

Media:Pancro: 10 a 15 mMultiesp: 20 a 50 m

5 a 2 años 12 a 3 meses 4 a 1 meses

Baja:Multiespectral:100 a 1.000 m

30 a 1 día

Plan Nacional de Ortofotografía Aérea

(PNOA)

Plan Nacional de Teledetección(PNT)

Frecuencia Temporal

PLAN NACIONAL DE OBSERVACIÓN DEL TERRITORIO (PNOT)

0,5 m

Nombre del Plan

2 años

Resolución espacial

1ª fase:Obt ió t t i t d i á 5 a 2 años 12 a 3 meses 4 a 1 meses

SPOT (HRVIR) Landsat 7 (ETM+)

IRS Landsat 5 (TM)

Futuro sistema Pleiades TERRA (ASTER)

Futuro sistema español deobservación de la Tierra

IRS

Nombre del Sistema Bases de Datos Organismos 2.000 a 5.000 10.000 25.000 25.000 a 50.000 100.000 a200.000 500.000 1.000.000

AGE BCN 25 BCN 200

30 a 1 díaFrecuencia Temporal

Bases Cartográficas (BC)

Escala de la Base de DatosSistema de Información

(Armonización, síntesis y diseminación según los principios INSPIRE entre los distintos organismos que recogen o utilizan esta

información)

Terra (MODIS)

NOAA

Fotografía aérea1: 30.000

Fotografía aérea escalas

1: 10.000 a 1: 18.000

Envisat (MERIS)

SPOT (Vegetation)

2 años

Sensores

2ª Fase: Extracción y

Obtención y tratamiento de imágenes aeroespaciales

Área temática

Sistema CartográficoNacionalCartografía básica

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CC.AA. ♦ ♦

Ocupación del Suelo

Sistema de Informaciónde Ocupación del Sueloen España (SIOSE)

Bases de Datos de Cobertura y Usos del Suelo

AGE /CC.AA. Corine Land Cover

Otros tipos de información

AGE / CC.AA /Universidades

Parámetros Bio-físicos(NDVI, Temp. Suelo, etc…)

SIOSEMCAMFE

t acc ó ydiseminación de la información

Nacional

55

Validación estadística

Método escogido:Aleatorio estratificado se trata de determinar laAleatorio estratificado se trata de determinar la probabilidad p de acierto de cada estrato desde el punto de vista temático

Dos productos a validar:CLC2000

CLCh

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CLChange

Obtención del tamaño de la muestra n

El nivel de confianza ( )2 1 ppzEl nivel de confianza determina el parámetro z

Intervalo de confianza d

Estimación de la probabilidad p de acierto para cada estrato

C ió fi i d > N

( )20

1d

ppzn −=

nnn 11 0

0

−+=

11015 July 2005 ICC 2005 A Coruña 110

Corrección por finitud-> N número total de polígonos en cada estrato

N1+

56

Validación CLC2000

Un estrato por cada clasePara que cada clase tenga representaciónPara que cada clase tenga representación estadística

Determinación del tamaño de la muestra en cada CCAA

Para que en cada CCAA se puedan obtener datos dí i fi bl

11115 July 2005 ICC 2005 A Coruña 111

estadísticos fiables

Validación del CLChanges

Dos estratos:Polígonos de cambioPolígonos de cambio

Validación del tipo de cambios

Polígonos sin cambioValidación de que no se han omitido cambios

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57

Información de referencia:

Para CLC2000Imágenes Landsat IMAGE2000Imágenes Landsat IMAGE2000

Ortofotografía aérea

Mapa de Cultivos y Aprovechamientos

Para CLChangeImágenes Landsat IMAGE2000

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Imágenes Landsat CORINE90

Tamaño de Muestra CLC2000

Nivel de confianza del 95% z=1.96

Intervalo de error del 5% d=0 05Intervalo de error del 5% d=0.05

Probabilidad a priori del 90% p=0.9

Tamaño de la muestra para una población infinita n0=138

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58

Tamaño Muestra CLC2000

Corrección por finitud

Cálculo de n teniendo en cuenta que la población q pno es infinita

Frequency de cada clase en ámbito nacional N

nn 0=

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Nnn 11 0 −+

=

Tamaño de la muestra por CCAA

Para determinar el número de muestras en cada CCAA se necesita un Frequency de q ytodas las clases al nivel 3 para cada CCAA

Reparto proporcional del tamaño de la muestra por CCAA

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59

Selección de muestras por CCAA

Una vez conocido el tamaño de la muestra en cada clase se seleccionan aleatoriamente los n polígonos de entre todos los existentes en esa clase

Con Excel se pueden realizar los siguientes pasos:

=ALEATORIO()*tamaño de la muestra

Y a continuación redondear hacia arriba =REDONDEAR.MAS()

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()

Selección de muestras

Sería necesario comprobar que no existen muestras repetidasp

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quality assessment of image & corine land cover...

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