Abstract
In this study, the Land Use/Cover Area frame statistical Survey (LUCAS) of 2009 was used as a reference dataset for validating a Land Cover Map of Greece for 2007, produced with remote sensing by the Greek Office of the World Wildlife Fund (WWF Hellas). First, all class definitions were decomposed in terms of four vegetation parameters (type, height, density, and composition), considered as critical in indicating unconformities between LUCAS and the WWF Hellas map; their inter-class relations were described in a table of correspondence. Then, a two-tier methodology was applied: an “automated” process, where thematic agreement was based exclusively on the main land cover attribute of LUCAS (LC1); and a “supervised” process, where thematic agreement was based on the reinterpretation of LUCAS ground photos and use of ancillary earth observation imagery; non-square error matrix was deployed in both processes. For the supervised process specifically, a decision-tree was designed, using the critical vegetation parameters (mentioned above) as quantified criteria, thus allowing objective labelling of testing points in both systems. The results show that only a small proportion of the reassessed points verified the WWF Hellas map predictions and that the overall accuracy of the supervised process was reduced compared to that of the automated process. In conclusion, the LUCAS point database was found to be supportive, but not fully efficient, for identifying the various sources of error in country-scale land cover maps derived with remote sensing. Synergy with very high resolution satellite images and air photos, or a dedicated ground truth campaign, seems to be inevitable in order to validate their thematic accuracy, especially in highly heterogeneous environments. In this direction, LUCAS could be used as a verification, rather than a validation, dataset.
Highlights
Validation of land use/cover (LU/LC) maps derived from remote sensing is a challenging and expensive task
Four WWF Hellas classes were excluded from the error matrix, namely “Sclerophyllous vegetation”, “Shrublands”, “Sparsely vegetated areas”, and “Burnt areas”, as they did not correspond clearly to any of the Land Use/Cover Area frame statistical Survey (LUCAS) categories
“automated” process using the main land cover attribute of LUCAS (LC1); and a “supervised” process by reinterpreting all the available ground photos taken by the LUCAS surveyors and ancillary earth observation imagery
Summary
Validation of land use/cover (LU/LC) maps derived from remote sensing is a challenging and expensive task. When large areas have to be mapped, the availability of recent and accurate reference data for an independent and sound validation becomes critical. Statistical observation and use of confusion (or error) matrices, have become the key tool for accuracy assessment of classified images or land cover maps in general [1,2]. In the framework of GMES/Copernicus initiative, strong effort was made to assure high quality of European-wide mapping and monitoring approaches such as the Copernicus Land Monitoring Core. Pre-cursor services such as the Fast Track Services (FTS) already set the frame for validation campaigns on European level (see for instance, [3])
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