Hyperspectral and multispectral remote sensing for mapping grassland vegetation

Sammanfattning: As a consequence of agricultural intensification, large areas of species-rich grasslands have been lost and farmland biodiversity has declined. Previous studies have shown that the continuity of grazing management can have a significant influence on the environmental conditions and the levels of plant species diversity in grassland habitats. The preservation of species-rich grasslands has become a high conservation priority within the European Union and the mapping of grazed grassland vegetation across wide areas has been identified as a central task for biodiversity conservation in agricultural landscapes. The fact that detailed field inventories of plant communities are time-consuming may limit the spatial extent of grassland habitat surveys. If remote sensing data are able to identify grassland sites characterised by different environmental conditions and plant species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the thesis, I have examined the potential of hyperspectral and multispectral remote sensing imagery to map grassland vegetation at detailed scales in dry grazed grassland habitats. Fieldwork included the recording of vascular plant species and environmental variables in grasslands plots representing three age-classes within an arable-to-grassland succession in an agricultural landscape on the Baltic island of Öland (Sweden). Remotely sensed data were acquired with the help of two airborne HySpex hyperspectral spectrometers (415–2501 nm) and by the multispectral WorldView-2 satellite. The results of the thesis show that the soil nutrient and moisture status within grassland plots influenced the hyperspectral reflectance. Hyperspectral data had the ability to classify grassland plots into different age-classes. Hyperspectral reflectance measurements could be used to predict plant indicator values for nutrient and soil moisture in grassland plots. Prediction models developed from hyperspectral data were successfully used to assess levels of plant species diversity (species richness and Simpsons’s diversity). In addition, between-plot dissimilarities in the satellite spectral reflectance were shown to be related to between-plot dissimilarities in the species composition in old grassland sites. The findings of the thesis demonstrate that remote sensing data are capable of capturing detailed-scale information that discriminates between grassland plant communities representing different environmental conditions and levels of plant species diversity. The results suggest that remote sensing data may have the ability for use as a decision-support tool to help conservation planners identify grassland habitats in agricultural landscapes that are of high conservation interest.