NTTS 2011 Brussels February 22, 20111 Joint Research Centre (JRC) Sampling Very High Resolution Images for Area Estimation [email protected]

NTTS 2011 Brussels February 22, 2011 1

Joint Research Centre (JRC)

Sampling Very High Resolution Images for Area Estimation

[email protected]

2Spatial Statistics 2011 Univ. Twente 24 March 2011

Why sampling satellite images

Very large areas Global studies

Tropical deforestation

Continents

Small imagesCovering a medium-size country with Very High

Resolution Images


Geoland2-SATCHMO

Geoland2: large FP7-GMES project >50 partnersCovers a wide range of topics in terrestrial monitoring, mainly in EuropeThe target is mainly building pre-operational tools

SATCHMO: One of the Geoland2 “Core Mapping Services”More research-oriented than the rest of Geoland2

Aim of SATCHMO-AFS (Area Frame Sample): One of the components of SATCHMOassessing the use of a sample of Very High Resolution (1- 4 m) images for

land cover (or change) area estimation

Sampling units10 x 10 km to 50 x 50 were assessed, but at the end 10 x 10 km units were

imposed by image availability.


SATCHMO-AFS Stratification

Strata1: Cyprus and Malta. N=942: above 1200 m (>50%).

N=13763: Euroland “transects”. N=11654: coastal areas (buffer 10km) .

N=39545: Urban atlas. N=46760: all the rest. N=31613

Most strata are determined by commitments with Euroland and LUCAS. Not a proper statistical criterion

(priorities insufficiently clear)


SATCHMO-AFS Sample

Systematic on blocks of 200x200 km

Replicates selected with distance constraintsTo avoid that two replicates are

too close to each other

Number of replicates depends on the stratum


Land cover map vs sample

CORINE Land Cover (no sampling)

LUCAS: sample of points (field survey)

Sample of VHR images

Sampling error

Non-sampling error

None Medium High

High Medium Low

Expected errors (to be checked…)

?


Comparing sampling errors

Usual criterion: comparing variances of two different sampling schemes for the same cost. But the cost of samples of VHR images has a too wide variability.

Alternative indicator: “equivalent number of points”:

Example: if a sample of 4000 unclustered points gives the same variance as 200 sites (clusters) of 10x10 km we say that a site is equivalent to 20 points.

yVyVQ clusnptn __


Using CORINE Land Cover as pseudo-truth

% areacv 200

points (%)cv 200 sites10 km (%)

equivalent number of points/site

artificial 4.65 32.0 12.3 6.8

arable 28.64 11.2 6.9 2.6

perm crops 2.92 40.8 21.9 3.5

pastures 12.31 18.9 9.7 3.8

heterogeneous 11.97 19.2 7.6 6.4

total agriculture 43.53 8.1 4.8 2.8

forest and woodland 26.64 11.7 6.5 3.2

bare 1.29 62.0 33.8 3.4

other vegetation 3.46 37.4 18.1 4.3


Using a land cover map as pseudo-truth

Is the comparison fair when we use a (coarse resolution) land cover map as pseudo-truth?

Coarse resolution

Lower within-site variance

Points in the site appear more redundant than they are

Smaller equivalent number of points than using fine scale information


Variance in single-stage cluster sampling

For a sample of n clusters out of N, with M elementary units in each cluster.

Mclus MSnNM

nNyV 11)( 2

intra-cluster correlation

i kj

ikijM yyyySnMM

))(()1)(1(

1ˆ

2

MM

MQ

11

if n is large and n/N is small

MQ 1 if M (cluster size) is also large

True in our case


Equivalent number of points

The “equivalent number of points” can be approximated from the intra-cluster correlationthat quantifies the link between nearby points (in the same cluster)

Also the correlogram measures the link between nearby points

Any link?


Correlogram and Intra-cluster correlation

ds n

y y y yd

jn

kd

12

The correlogram at distance d is estimated by:

The intra-cluster correlation is a weighted average of the correlogram:

0ˆˆ

dT

dM d

n

n

Thus we can approximately compute the “equivalent number of points” from the correlogram.


Correlogram Arable land

Arable land

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50

distance (km)

co

rre

log

ram

Arable coarse scale

Arable fine scale

Arable adjusted


Correlogram interpolation

bd daexp~

An exponential model

gives a good adjustment to the behaviour of most correlograms

(other models might be better)The adjusted correlogram is used to estimate the

Intra-cluster correlation and the “equivalent number of points”


Wheat and Sunflower

Wheat and sunflower

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50

Distance (km)

co

rre

log

ram

wheat

sunflower

wheat adjusted

sunflower adjusted


Intracluster correlations

Intracluster correlation

Site size Arable CLC Arable LUCAS Wheat Sunflower

5km 0.54 0.42 0.20 0.14

10km 0.46 0.36 0.17 0.12

20km 0.39 0.30 0.13 0.09

30km 0.35 0.27 0.12 0.08

equivalent number of points

5km 1.9 2.4 5.0 7.0

10km 2.2 2.7 6.0 8.6

20km 2.6 3.3 7.5 11.1

30km 2.8 3.8 8.6 13.0


Some cost considerations

The average field survey cost per point in LUCAS ranges between 20 € and 25 €.

The equivalent number of points per site of 10x10 km ranges between 2 and 10 for major land cover types.

The cost per VHR image (including processing) should be at most 250 € to be cost-efficient in the EU from the point of view of sampling error.

Bad news for the use of VHR images in the for area estimation in the EU, at least from a marketing perspective


Better news

Land cover change is more scattered than land cover statusLower spatial correlationHigher equivalent number of points per siteBetter chances to be cost-efficient

For sites of 10x10 km (coarse resolution)

% areacv 200 points (%)

cv 200 sites 10 km (%)

equivalent number of points/site

New artificial 0.27 136.7 21.8 39.6

New agriculture 0.15 183.5 34.7 28.3

Agricultural abandonment 0.21 154.6 25.2 38

Other changes 1.64 54.8 15.6 12.5


Remote areas

Tropical rainforest SiberiaCountries with restricted access (North Korea…)

The cost of a point survey has nothing to do with the cost of LUCAS.

Correlograms?

The assessment can change a lot from case to case.


Stratification

The equivalent number of points changes with stratification

To which extent?


Stratification based on GLC2000


Stratification

The correlogram in each stratum is lower than the non-stratified correlogram Higher equivalent number of points

Wheat

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 5 10 15 20 25 30 35 40

Distance in km

Co

rrel

og

ram

stratum 0

stratum 1

stratum 2

stratum 3

stratum 4

stratum 5

stratum 6

No strat


Stratification

But not always uniformly lower than the correlogram in each stratum

Forest fine scale (LUCAS)

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 10 15 20 25 30 35 40

Distance in km

Co

rrel

og

ram

stratum 0

stratum 1

stratum 2

stratum 3

stratum 4

stratum 5

stratum 6

No strata


Equivalent number of points

stratum

equiv n. points 0 1 2 3 4 5 6No

strata

Arable coarse 5.7 4.0 3.6 3.3 4.3 3.4 2.9 2.0

Arable 10.1 5.3 6.5 5.1 7.1 5.6 5.0 2.5

Forest coarse 3.1 2.4 2.9 3.3 3.3 3.3 4.4 2.8

Forest 2.9 2.6 3.1 3.5 3.7 3.9 5.7 2.7

Vineyard coarse 194.0 12.6 3.1 2.7 2.8 6.4 2.3 2.5

Vineyard 52.0 13.4 10.6 6.6 6.0 10.2 3.7 3.7

Wheat 26.4 9.6 11.4 11.9 12.0 9.2 6.2 4.7

Sunflower >100 >100 68.4 >100 9.6 16.4 10.1 5.8

Documents

NTTS 2011 Brussels February 22, 20111 Joint Research Centre (JRC) Sampling Very High Resolution Images for Area Estimation [email protected]