Hyperspectral and multispectral remote sensing for mapping grassland vegetation
(2015)- Abstract
- 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
... (More) - 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. (Less) - Abstract (Swedish)
- Popular Abstract in English
European dry extensive grasslands are biodiversity hotspots which are severely
threatened by land use intensification and abandonment. In order to plan efficient
conservation actions it is necessary to collect information on the current status of
grasslands, their species diversity and prevailing environmental conditions. Remote
sensing technology in combination with ground surveys provides an effective tool to
monitor ecosystem properties continuously across the landscape with high spatial
precision in a repeatable way. In this thesis, the potential of hyper-and multispectral
remote sensing imagery to predict grassland ecological... (More) - Popular Abstract in English
European dry extensive grasslands are biodiversity hotspots which are severely
threatened by land use intensification and abandonment. In order to plan efficient
conservation actions it is necessary to collect information on the current status of
grasslands, their species diversity and prevailing environmental conditions. Remote
sensing technology in combination with ground surveys provides an effective tool to
monitor ecosystem properties continuously across the landscape with high spatial
precision in a repeatable way. In this thesis, the potential of hyper-and multispectral
remote sensing imagery to predict grassland ecological parameters, such as grazing
continuity, plant species diversity and habitat environmental conditions was evaluated
studying grassland sites on the Baltic island of Öland, Sweden. Different methods
were compared on the basis of their prediction quality and practical feasibility. The
findings of this thesis provide a useful guidance for the selection of prediction
methods of ecological grassland parameter in future studies. Combined with ground
surveys, remote sensing can serve as time-efficient decision support tool for
prioritising areas of high conservation value for management actions. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5277106
- author
- Möckel, Thomas LU
- supervisor
-
- Karin Hall LU
- Honor C Prentice LU
- opponent
-
- Dr. Rocchini, Duccio, Fondazione Edmund Mach, Italy
- organization
- publishing date
- 2015
- type
- Thesis
- publication status
- published
- subject
- keywords
- Plant diversity Partial least squares Ellenberg indicators Vegetation index Heterogeneity
- pages
- 169 pages
- publisher
- Department of Physical Geography and Ecosystem Science, Lund University
- defense location
- Pangea
- defense date
- 2015-05-19 10:00:00
- ISBN
- 978-91-85793-46-4
- language
- English
- LU publication?
- yes
- id
- 3942a88a-f50e-4be8-98cc-b503eac866bb (old id 5277106)
- date added to LUP
- 2016-04-04 11:33:22
- date last changed
- 2019-03-22 11:42:05
@phdthesis{3942a88a-f50e-4be8-98cc-b503eac866bb,
abstract = {{As a consequence of agricultural intensification, large areas of species-rich grasslands<br/><br>
have been lost and farmland biodiversity has declined. Previous studies have shown<br/><br>
that the continuity of grazing management can have a significant influence on the<br/><br>
environmental conditions and the levels of plant species diversity in grassland<br/><br>
habitats. The preservation of species-rich grasslands has become a high conservation<br/><br>
priority within the European Union and the mapping of grazed grassland vegetation<br/><br>
across wide areas has been identified as a central task for biodiversity conservation in<br/><br>
agricultural landscapes. The fact that detailed field inventories of plant communities<br/><br>
are time-consuming may limit the spatial extent of grassland habitat surveys. If<br/><br>
remote sensing data are able to identify grassland sites characterised by different<br/><br>
environmental conditions and plant species diversity, then field sampling efforts could<br/><br>
be directed towards sites that are of potential conservation interest.<br/><br>
In the thesis, I have examined the potential of hyperspectral and multispectral remote<br/><br>
sensing imagery to map grassland vegetation at detailed scales in dry grazed grassland<br/><br>
habitats. Fieldwork included the recording of vascular plant species and<br/><br>
environmental variables in grasslands plots representing three age-classes within an<br/><br>
arable-to-grassland succession in an agricultural landscape on the Baltic island of<br/><br>
Öland (Sweden). Remotely sensed data were acquired with the help of two airborne<br/><br>
HySpex hyperspectral spectrometers (415–2501 nm) and by the multispectral<br/><br>
WorldView-2 satellite.<br/><br>
The results of the thesis show that the soil nutrient and moisture status within<br/><br>
grassland plots influenced the hyperspectral reflectance. Hyperspectral data had the<br/><br>
ability to classify grassland plots into different age-classes. Hyperspectral reflectance<br/><br>
measurements could be used to predict plant indicator values for nutrient and soil<br/><br>
moisture in grassland plots. Prediction models developed from hyperspectral data<br/><br>
were successfully used to assess levels of plant species diversity (species richness and<br/><br>
Simpsons’s diversity). In addition, between-plot dissimilarities in the satellite spectral<br/><br>
reflectance were shown to be related to between-plot dissimilarities in the species<br/><br>
composition in old grassland sites.<br/><br>
The findings of the thesis demonstrate that remote sensing data are capable of<br/><br>
capturing detailed-scale information that discriminates between grassland plant<br/><br>
communities representing different environmental conditions and levels of plant<br/><br>
species diversity. The results suggest that remote sensing data may have the ability for<br/><br>
use as a decision-support tool to help conservation planners identify grassland habitats<br/><br>
in agricultural landscapes that are of high conservation interest.}},
author = {{Möckel, Thomas}},
isbn = {{978-91-85793-46-4}},
keywords = {{Plant diversity Partial least squares Ellenberg indicators Vegetation index Heterogeneity}},
language = {{eng}},
publisher = {{Department of Physical Geography and Ecosystem Science, Lund University}},
school = {{Lund University}},
title = {{Hyperspectral and multispectral remote sensing for mapping grassland vegetation}},
url = {{https://lup.lub.lu.se/search/files/5800944/5337486.pdf}},
year = {{2015}},
}