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NTTS 2011 Brussels February 22, 2011 1
Joint Research Centre (JRC)
Sampling Very High Resolution Images for Area Estimation
Javier.gallego@jrc.ec.europa.eu
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
3Spatial Statistics 2011 Univ. Twente 24 March 2011
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.
4Spatial Statistics 2011 Univ. Twente 24 March 2011
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)
5Spatial Statistics 2011 Univ. Twente 24 March 2011
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
6Spatial Statistics 2011 Univ. Twente 24 March 2011
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…)
?
7Spatial Statistics 2011 Univ. Twente 24 March 2011
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 __
8Spatial Statistics 2011 Univ. Twente 24 March 2011
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
9Spatial Statistics 2011 Univ. Twente 24 March 2011
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
10Spatial Statistics 2011 Univ. Twente 24 March 2011
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
11Spatial Statistics 2011 Univ. Twente 24 March 2011
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?
12Spatial Statistics 2011 Univ. Twente 24 March 2011
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.
13Spatial Statistics 2011 Univ. Twente 24 March 2011
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
14Spatial Statistics 2011 Univ. Twente 24 March 2011
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”
15Spatial Statistics 2011 Univ. Twente 24 March 2011
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
16Spatial Statistics 2011 Univ. Twente 24 March 2011
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
17Spatial Statistics 2011 Univ. Twente 24 March 2011
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
18Spatial Statistics 2011 Univ. Twente 24 March 2011
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
19Spatial Statistics 2011 Univ. Twente 24 March 2011
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.
20Spatial Statistics 2011 Univ. Twente 24 March 2011
Stratification
The equivalent number of points changes with stratification
To which extent?
21Spatial Statistics 2011 Univ. Twente 24 March 2011
Stratification based on GLC2000
22Spatial Statistics 2011 Univ. Twente 24 March 2011
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
23Spatial Statistics 2011 Univ. Twente 24 March 2011
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
24Spatial Statistics 2011 Univ. Twente 24 March 2011
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
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