1. NOTES
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The potential use of a sample of Very High Resolution satellite images for the estimation of land cover area change in the EU
NOTES
1. PLACE, DATE AND EVENT NAME
1.1. Access the slide-set place, date and event name text box beneath the JRC logo from the Slide Master.
1.2. Do not change the size nor the position of that text box.
1.3. Replace the mock-up texts for the place (“Place”), the date (“dd Month YYYY”) and the event name (“Event Name”) with your own texts.
1.4. Set it in MetaPlus Book Roman, if you own the typeface. Otherwise, keep the original typeface – Arial.
1.5. Keep the original flush-left justification.
1.6. Keep the original font colour (white).
1.7. Keep the original font body size (7 pt) and the text on one single line.
2. SLIDE NUMBER
2.1. The slide number on the banner’s lower right-hand side is automatically generated.
3. SLIDES
3.1. Duplicate the first slide as needed.
3.2. Do not change the size nor the position of the slide’s text box.
3.3. Try not to place more text on each slide than will fit in the given text box.
3.4. Replace the mock-up heading text (“Joint Research Centre (JRC)”) with your own text heading.
3.5. Set it in Eurostile Bold Extended Two or in Helvetica Rounded Bold Condensed, if you own one of these typefaces. Otherwise, keep the original typeface – Arial.
3.6. Keep the original flush-left justification.
3.7. Keep the original font colour (100c 80m 0y 0k).
3.8. Keep the original font body size (28 pt) and the heading on one single line whenever possible. Reduce the font body size if needed.
3.9. Replace the mock-up text (“The European Commission’s Research-Based Policy Support Organisation)”) with your own text.
3.10. Set it in MetaPlus Book Roman, if you own the typeface. Otherwise, keep the original typeface – Arial.
3.11. Keep the original flush-left justification.
3.12. Keep the original font colour (100c 80m 0y 0k). Use black if you need a second colour.
3.13. Keep the original font body size (22 pt) or reduce it if unavoidable.
3.14. Replace the EU-27 map mock-up illustration with your own illustration(s).
3.13. Try to keep your illustration(s) right- and top- or bottom-aligned with the main text box whenever possible.

2. Very High Resolution (VHR) satellite images have made a huge progress
Context and target
Very High Resolution (VHR) satellite images have made a huge progress
Part of our everyday’s life (internet)
We see a lot of impressive details
Would a 1% sample of VHR images be a good basis for land cover area estimation?

3. Study area and data
Extent of the area for the simulations: countries for which CORINE Land Cover change is available.
Area ~ 3.5 M km2
Pseudo-truth:
CLC 2000
CLC change

4. Grid of 10 x 10 km
35005 cells of 10 x 10 km.
Cells with less than 50% in the study area eliminated for this simulation

5. Sample of 350 sites of 10 x 10 km (1%)
Simple random sampling to have an idea of the sampling errors
Coefficient of Variation (CV) %
Sample of 350 images
10 x 10 km
LUCAS 2006
(11 countries)
artificial
9.3
1.1
arable
5.2
0.42
perm crops
16.6
0.83
pastures
7.3
0.46
heterogeneous
5.7
n.a.
total agriculture
3.6
0.33
forest and woodland
4.9
0.44
bare land
25.5
1.3
other vegetation
13.6
2.1
glaciers
113.9 90
water
21.1
1.8

6. Equivalent number of points
If a sample of n sites (10 x 10 km each) gives the same sampling error as a sample of m unclustered points, we say that each site is equivalent to m/n points
% area
cv 350
points (%)
cv 350 sites
10 km (%)
equivalent number
of points/site
artificial
4.65
24.2
9.3
6.8
arable
28.64
8.4
5.2
2.6
perm crops
2.92
30.8
16.6
3.5
pastures
12.31
14.3
7.3
3.8
heterogeneous
11.97
14.5
5.7
6.4
total agriculture
43.53
6.1
3.6
2.8
forest and woodland
26.64
8.9
4.9
3.2
bare
1.29
46.8
25.5
3.4
other vegetation
3.46
28.2
13.6
4.3
glaciers
0.04
263
114
5.3
water
1.42
44.6
21.1
4.5

7. Cost threshold
Roughly a site of 10 x 10 km is equivalent to 5 unclustered points
The cost per point in LUCAS 2006 was around 23 €
The cost per site should be at most 115 € (image + analysis) to be competitive
Considering only sampling error
Assuming no stratification (both for unclustered points and for images)
The equivalent number of points depends on the spatial correlation of each land cover type.
Using CLC as pseudo-truth probably over-estimates the spatial correlation
Using a ground-based spatial correlation the equivalent number of points might be around 10-20
The cost threshold might be around 200 – 500 €
At the moment images are too expensive

8. Land cover Change Regrouping main types of land cover change CLC 2000
artif
crops
pasture
heterog
forest &
wood
Natu
ral
artificial 4 1 3
1990 2
forest &wood 5
natural
No change 1
New artificial 2
Agricultural expansion 3
Agricultural abandonment 4
Other changes
Regrouping main types of land cover change

9. Land cover Change in CLC

10. Land cover Change in CLC

11. Land cover Change in CLC

12. Land cover Change in CLC

13. Land cover Change Equivalent number of points
new
artificial
agriculture
agricultural
abandonment
other
changes
% area
0.27
0.15
0.21
1.64
CV 350 sites 10 km
16.4
26.1
19.0
11.7
equiv n points
39.6
28.3
38.0
12.5
CV 350 sites 30 km
10.7
14.5
10.4
8.5
92.6
91.3
126.1
23.5
The cost threshold can be around 2000 – 3000 € per site of 30 x 30 km
Achievable with some satellites (Rapid Eye)

14. Non-sampling errors
For land cover, ground observations are more reliable than satellite image analysis.
Although ground observations are not error-free
Mislocation
Wrong identification of crops
Unsuitable date of visit
Mislocation does not introduce a bias in the estimates if it is independent of the land cover type.
For land cover change, mislocation in ground visits can introduce a major bias
Pseudo-changes
Some changes can be missed, e.g.: expansion of natural vegetation in abandoned pastures
Images can be helpful to understand the changes.

15. Conclusions
For land cover area estimation, VHR images can play a role only when reaching the points is very difficult
Mapping LUCAS points that could not be reached in 2006 and/or 2009
For land cover area change estimation, LUCAS and a sample of VHR (or HR) images may be complementary. Some work needed to define how to use jointly both sources of data.
Landscape indicators: some work already done by LUCAS team.
Main problem: selection of meaningful indicators (better coordination with users?)
Exploiting free images (Google Earth or similar): to be considered but
there is a problem with dates of the images
this would go rather on a perspective of full coverage for ancillary data.