Journal
AQUATIC CONSERVATION-MARINE AND FRESHWATER ECOSYSTEMS
Volume 20, Issue 7, Pages 717-727Publisher
WILEY
DOI: 10.1002/aqc.1144
Keywords
aquatic plants; Habitats Directive; lakes; remote sensing; support vector machines; Water Framework Directive
Funding
- Northumbria, Essex and Suffolk Water
- University of Stirling
- UK Natural Environment Research Council [04/16]
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1. The ecological status of shallow lakes is highly dependent on the abundance and composition of macrophytes. However, large-scale surveys are often confined to a small number of water bodies and undertaken only infrequently owing to logistical and financial constraints. 2. Data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) was used to map the distribution of macrophytes in the Upper Thurne region of the Norfolk Broads, UK. Three different approaches to image classification were evaluated: (i) Euclidean minimum distance, (ii) Gaussian maximum likelihood, and (iii) support vector machines. 3. The results show macrophyte growth-habits (i. e. submerged, floating-leaved, partially-emergent, emergent) and submerged species could be mapped with a maximum overall classification accuracy of 78% and 87%, respectively. The Gaussian maximum likelihood algorithm and support vector machine returned the highest classification accuracies in each instance. 4. This study suggests that remote sensing is a potentially powerful tool for large-scale assessment of the cover and distribution of aquatic vegetation in clear water shallow lakes, particularly with respect to upscaling field survey data to a functionally relevant form, and supporting site-condition monitoring under the European Union Habitats (92/43/EEC) and Water Framework (2000/60/EC) directives. Copyright (C) 2010 John Wiley & Sons, Ltd.
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