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Prof. Dr. Steffen Kuntz
1. Department for Remote Sensing and Landscape Information Systems, Faculty for Forestry and Environmental Science, University of Freiburg, Tennenbacherstr. 4, 79085 Freiburg, Germany

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Research Keywords & Expertise

0 Environmental Monitoring
0 Remote Sensing
0 REDD+
0 LULUCF
0 SAR instruments and applications

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Journal article
Published: 23 April 2015 in Remote Sensing
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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.

ACS Style

Christos Karydas; Ioannis Z. Gitas; Steffen Kuntz; Chara Minakou. Use of LUCAS LC Point Database for Validating Country-Scale Land Cover Maps. Remote Sensing 2015, 7, 5012 -5041.

AMA Style

Christos Karydas, Ioannis Z. Gitas, Steffen Kuntz, Chara Minakou. Use of LUCAS LC Point Database for Validating Country-Scale Land Cover Maps. Remote Sensing. 2015; 7 (5):5012-5041.

Chicago/Turabian Style

Christos Karydas; Ioannis Z. Gitas; Steffen Kuntz; Chara Minakou. 2015. "Use of LUCAS LC Point Database for Validating Country-Scale Land Cover Maps." Remote Sensing 7, no. 5: 5012-5041.

Book chapter
Published: 27 January 2014 in Remote Sensing and Digital Image Processing
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The current global economic crisis forces nations to pinch and scrape on all their expenses. At the same time the demand for more and better information on the state of environment is increasing, together with more obligations to report on development and to estimate the impacts of environmental and spatial planning policies. Both needs may motivate a much stronger use of remote sensing, as so-called “free data” covering large regions or even continents become more and more important.

ACS Style

Steffen Kuntz; Elisabeth Schmeer; Markus Jochum; Geoffrey Smith. Towards an European Land Cover Monitoring Service and High-Resolution Layers. Remote Sensing and Digital Image Processing 2014, 18, 43 -52.

AMA Style

Steffen Kuntz, Elisabeth Schmeer, Markus Jochum, Geoffrey Smith. Towards an European Land Cover Monitoring Service and High-Resolution Layers. Remote Sensing and Digital Image Processing. 2014; 18 ():43-52.

Chicago/Turabian Style

Steffen Kuntz; Elisabeth Schmeer; Markus Jochum; Geoffrey Smith. 2014. "Towards an European Land Cover Monitoring Service and High-Resolution Layers." Remote Sensing and Digital Image Processing 18, no. : 43-52.