Abstract
Detailed maps of vegetation facilitate spatial conservation planning. Such information can be difficult to map from remotely sensed data with the detail (thematic resolution) required for ecological applications.For grass-dominated habitats in the South-East of the UK, it was evaluated which of the following choices improved classification accuracies at various thematic resolutions: 1) Hyperspectral data versus data with a reduced spectral resolution of eight and 13 bands, which were simulated from the hyperspectral data. 2) A vegetation classification system using a detailed description of vegetation (sub)-communities (the British National Vegetation Classification, NVC) versus clustering based on the dominant plant species (Dom-Species). 3) The month of imagery acquisition.Hyperspectral data produced the highest accuracies for vegetation away from edges using the NVC (84–87%). Simulated 13-band data performed also well (83–86% accuracy). Simulated 8-band data performed poorer at finer thematic resolutions (77–78% accuracy), but produced accuracies similar to those from simulated 13-band or hyperspectral data for coarser thematic resolutions (82–86%). Grouping vegetation by NVC (84–87% accuracy for hyperspectral data) usually achieved higher accuracies compared to Dom-Species (81–84% for hyperspectral data). Highest discrimination rates were achieved around the time vegetation was fully developed.The results suggest that using a detailed description of vegetation (sub)-communities instead of one based on the dominating species can result in more accurate mapping. The NVC may reflect differences in site conditions in addition to differences in the composition of dominant species, which may benefit vegetation classification. The results also suggest that using hyperspectral data or the 13-band multispectral data can help to achieve the fine thematic resolutions that are often required in ecological applications. Accurate vegetation maps with a high thematic resolution can benefit a range of applications, such as species and habitat conservation.
Highlights
Biodiversity continues to decline through a variety of anthropogenic drivers (Butchart et al, 2010; Tittensor et al, 2014) and land is under substantial pressure through an increasing human population and rising demand for goods and services (Foresight, 2011; Smith et al, 2010)
The results suggest that using hyperspectral data or the 13-band multispectral data can help to achieve the fine thematic resolutions that are often required in ecological applications
Using hyperspectral airborne data usually achieved higher accuracies and kappa than using simulated data with lower spectral resolution when vegetation was away from edges. This was the case when vegetation was categorized by National Vegetation Classification (NVC) or by the dominant plant species (Dom-Species; accuracy: 83–90%; kappa: 0.81–0.84)
Summary
Biodiversity continues to decline through a variety of anthropogenic drivers (Butchart et al, 2010; Tittensor et al, 2014) and land is under substantial pressure through an increasing human population and rising demand for goods and services (Foresight, 2011; Smith et al, 2010). Managing land better to maintain a high level of biodiversity will be most efficient when planning takes into account the effect of complex landscapes on ecological systems and on species (Benton, 2012). Remote sensing has become an important tool to map and monitor biodiversity, see e.g. Remotely sensed maps often depict broad habitat categories and do not always provide the level of detail (thematic resolution ) needed in ecological applications (Bradley and Fleishman, 2008). A horizon scan in 2014 pointed out the need for better differentiation and monitoring of ‘more difficult’ habitats, such as native grasslands (Sutherland et al, 2014)
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