Mapping macrophytic vegetation in shallow lakes using the Compact Airborne Spectrographic Imager (CASI)

Abstract

ABSTRACT1. 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.

Retrospective assessment of macrophytic communities in southern Lake Garda (Italy) from in situ and MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data

“…They proposed a quantitative relationship between the Normalized Difference Vegetation Index (NDVI) and the dry weight of leaves in order to obtain a biomass distribution map. Lastly, Hunter et al (2010) classified macrophyte growth habits and discriminated submerged species in clear-water shallow lakes with airborne hyperspectral data. The lakes studied are situated in a large British protected wetland and the study provides an evaluation of the potential use of remote sensing data in support of ecological and conservation waterbody assessment status.…”

Numerical study of hydrodynamic and salinity transport process in Pink Beach wetlands of Liao River Estuary, China

“…Remote sensing is an effective and powerful way to monitor vegetation status, growth and biophysical parameters (Hunter et al, 2010;DeFries 2008;Ustin and Gamon 2010) and allow frequent acquisitions for multi temporal studies and reconstruction of historical time series in a cost-effective way (Coppin and Bauer 1994;Munyati 2000). The objective of the present research is to adopt remote sensing to obtain the information of vegetation in wetland of Liao River estuary.…”

Mapping Submerged Aquatic Vegetation Using RapidEye Satellite Data: The Example of Lake Kummerow (Germany)

“…Several studies therefore evaluate mappings qualitatively [16,19,27,59]. Studies which determined discrete classes (e.g., less dense SAV, bare substrate, submerged, floating vegetation) collected field data by boat or ancillary maps to tabulate error matrices and associated accuracy measures [38,39,[60][61][62]. Dekker et al [26] recommend a minimum patch size of at least three times the pixel size covering homogenous coverage.…”