Use of LUCAS LC Point Database for Validating Country-Scale Land Cover Maps

Abstract

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 OPEN ACCESS Remote Sens. 2015, 7 5013 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.

Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium

“…The European Land Use/Cover Area frame statistical Survey (LUCAS) point database was used as a training sample and reference data. The database provides detailed attributes for LC and LU accompanied by a set of ground photographs covering all European countries [15]. The dataset is based on a systematic 2 × 2 km grid of points on which each sample is assigned to one of the eight LC categories through a two-phase sampling.…”

“…Then, all the points were updated and validated by means of the interpretation of the Sentinel-2 mosaic. Numerous studies have highlighted a class imbalance problem [15,16]. To obviate this problem and avoid having points located in the boundary of two different land categories, the location of some points were modified.…”

“…problem [15,16]. To obviate this problem and avoid having points located in the boundary of two different land categories, the location of some points were modified.…”

“…Several studies have compared the performance of different classifiers for LULC classification (e.g., [19,20]). Their results have shown that it is difficult to produce a general statement to advise on the classifier ranking [15]. Four popular classifiers-the maximum likelihood classification (MLC), random forest (RF), k-nearest neighbor (KNN), and the minimum distance classification (MDC)-were selected and implemented in SNAP.…”

Validation of the Global High-Resolution Globeland30 Land Cover Map in Europe Using Land Cover Field Survey Database - Lucas

“…Moreover, certain classes did not contain a sufficient number of sample points, so per-class accuracy indexes (Omission and Commission error) could not be confidently estimated. In another study, Karydas et al (2015) used LUCAS 2009 to validate the 2007 WWF Hellas LC map of Greece, with 30m resolution. A two-tier methodology was employed: first, a so called, "automated" process based validation directly on exploiting information about LC of LUCAS data; and second, a "supervised process" reinterpreted photos collected in the LUCAS campaign.…”