|Appears in Collections:||Biological and Environmental Sciences Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Mapping macrophytic vegetation in shallow lakes using the Compact Airborne Spectrographic Imager (CASI)|
support vector machines
Water Framework Directive
|Citation:||Hunter P, Gilvear D, Tyler A, Willby N & Kelly A (2010) Mapping macrophytic vegetation in shallow lakes using the Compact Airborne Spectrographic Imager (CASI), Aquatic Conservation: Marine and Freshwater Ecosystems, 20 (7), pp. 717- 727.|
|Abstract:||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.|
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