First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension: The 2018 Map for Wallonia

First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension: The 2018 Map for Wallonia

Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as observed in the Walloon region (Belgium), ver...

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Bibliographic Details
Published in:Data (Basel) Vol. 5; no. 4; p. 117
Main Authors: Bassine, Céline, Radoux, Julien, Beaumont, Benjamin, Grippa, Taïs, Lennert, Moritz, Champagne, Céline, De Vroey, Mathilde, Martinet, Augustin, Bouchez, Olivier, Deffense, Nicolas, Hallot, Eric, Wolff, Eléonore, Defourny, Pierre
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-12-2020
Subjects:
Accuracy
Biodiversity
Climate models
Decision analysis
Estimates
Flow mapping
Grasslands
Hydrology
Image analysis
Land cover
Land management
Land use management
Landscape
Remote sensing
Spatial resolution
Structural members
Trees
User needs
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Summary:Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as observed in the Walloon region (Belgium), very high spatial resolution is required to depict all the infrastructures, buildings and most of the structural elements of the semi-natural landscapes (like hedges and small water bodies). Because of the resolution, the vertical dimension needs explicit handling to avoid discontinuities incompatible with many applications. For example, how to map a river flowing under a bridge? The particularity of our data is to provide a two-digit land cover code to label all the overlapping items. The identification of all the overlaps resulted from the combination of remote sensing image analysis and decision rules involving ancillary data. The final product is therefore semantically precise and accurate in terms of land cover description thanks to the addition of 24 classes on top of the 11 pure land cover classes. The quality of the map has been assessed using a state-of-the-art validation scheme. Its overall accuracy is as high as 91.5%, with an average producer’s accuracy of 86% and an average user’s accuracy of 91%.
ISSN:2306-5729
2306-5729
DOI:10.3390/data5040117