Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high-resolution aerial photographs
Article first published online: 10 APR 2012
© 2012 International Association for Vegetation Science
Applied Vegetation Science
Volume 15, Issue 4, pages 536–547, October 2012
How to Cite
Hantson, W., Kooistra, L., Slim, P. A. (2012), Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high-resolution aerial photographs. Applied Vegetation Science, 15: 536–547. doi: 10.1111/j.1654-109X.2012.01194.x
- Issue published online: 4 SEP 2012
- Article first published online: 10 APR 2012
- Manuscript Accepted: 8 FEB 2012
- Manuscript Received: 17 JUN 2011
- Dutch Ministry of Economic Affairs, Agriculture and Innovation
- Albert Prakken (Rijkswaterstaat, Ministry of Infrastructure and Environment)
- Forestry Commission of Vlieland
- Vegetation mapping;
- alien species;
- digital elevation model;
- pixel-based classification;
- object-based classification;
- R osa rugosa ;
- P runus serotina ;
- H ippophae rhamnoides ;
- grey dunes
Does remote sensing improve classification of invasive woody species in dunes, useful for shrub management? Does additional height information and an object-based classifier increase woody species classification accuracy?
The dunes of Vlieland, one of the Wadden Sea Islands, the Netherlands.
Extensive monitoring using optical remote sensing and LIDAR deliver large amounts of high-quality data to observe and manage coastal dunes as a defence against the sea in the Netherlands. Using these additional data could increase the accuracy of vegetation mapping and monitoring in coastal areas. In this study, a remote sensing approach has been developed to deliver detailed and standardized maps of (invasive) woody species in the dunes of Vlieland using multispectral aerial photographs and vegetation height derived from LIDAR. Three classification methods were used: maximum likelihood (ML) classification using aerial photographs, ML classification combined with vegetation heights derived from LIDAR (ML+) and object-based (OB) classification.
The use of vegetation height from the LIDAR data increased the overall classification accuracy from 39% to 50%, but particularly improved classification of the taller woody species. The object-based classification increased the overall accuracy of the ML+ from 50% to 60%. The object-based results are comparable to human visual analysis while offering automated analysis.
Overall, the object-based classification delivers detailed maps of the woody species that are useful for management and evaluation of alien and invasive species in dune ecosystems.