Hyperspectral and multispectral remote sensing for mapping grassland vegetation
Author
Summary, in English
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.
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.
Department/s
Publishing year
2015
Language
English
Full text
Document type
Dissertation
Publisher
Department of Physical Geography and Ecosystem Science, Lund University
Topic
- Physical Geography
Keywords
- Plant diversity Partial least squares Ellenberg indicators Vegetation index Heterogeneity
Status
Published
Supervisor
ISBN/ISSN/Other
- ISBN: 978-91-85793-46-4
Defence date
19 May 2015
Defence time
10:00
Defence place
Pangea
Opponent
- Duccio Rocchini (Dr.)