JRC Place on dd Month YYYY – Event Name 1 Land cover change Objective: estimate land cover changes, in particular between agriculture and non-agriculture.

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Presentation transcript:

JRC Place on dd Month YYYY – Event Name 1 Land cover change Objective: estimate land cover changes, in particular between agriculture and non-agriculture classes The best data source at the moment is probably CORINE Land Cover: changes from photo-interpretation but bias of direct estimates from CLC-change is insufficiently known Scale effect (minimum mapping unit) Identification inaccuracy in photo-interpretation. When comparing loss of UAA in France between CLC and TERUTI: difference of 1 to 5 !! Ideal data source: point survey (LUCAS) Two years needed with the same sample (2006 and 2009?) Critical issue: avoiding “pseudo-changes” from location errors or nomenclature interpretation LUCAS failed producing reasonable land cover change matrices because surveyors in 2003 did not have the information on the 2001 observations. LUCAS 2006 changed sampling plan; it is better than LUCAS for land cover area estimation, but not for changes.

JRC Place on dd Month YYYY – Event Name 2 Land cover change and CLC LUCAS can be used to calibrate the changes derived from CORINE Land Cover (change layer) (used as co-variable) Problems to be tackled: –How to integrate National Statistics in change matrix estimation from LUCAS? –Which grouping of classes is meaningful (ex: soil sealing, abandonment, aforestation, etc.

JRC Place on dd Month YYYY – Event Name 3 Land cover change from a sample of images GEOLAND2 Photo-interpretation by point would eliminate the bias generated by the scale First analysis by simulation using CLC-change ( ) as pseudo-truth Sampling errors for units with different size 10 km20 km30 km40 km50 km60 km n new artificial new agriculture Agric. Abandonment other changes Large sites (~50 km) would be more efficient than small sites (~10 km) But this means lower resolution  non sampling errors (photo-interpretation) still to be assessed Sampling errors at equal cost (CV %) Person to contact: