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Gembloux Agro-Bio Tech (GxABT)
Gembloux Agro-Bio Tech (GxABT)
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Differentiation of ecosystems in the outback of Australia using airborne LiDAR

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Wagelmans, Isabelle ULiège
Promotor(s) : Bastin, Jean-François ULiège ; Sparrow, Ben
Date of defense : 29-Aug-2023 • Permalink : http://hdl.handle.net/2268.2/18217
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Title : Differentiation of ecosystems in the outback of Australia using airborne LiDAR
Translated title : [fr] Différenciation des écosystèmes de l'outback australien à l'aide du LiDAR aérien
Author : Wagelmans, Isabelle ULiège
Date of defense  : 29-Aug-2023
Advisor(s) : Bastin, Jean-François ULiège
Sparrow, Ben 
Committee's member(s) : Meersmans, Jeroen ULiège
Lejeune, Philippe ULiège
Language : English
Keywords : [en] LiDAR, remote sensing, ecosystem, classification, drylands
Discipline(s) : Life sciences > Multidisciplinary, general & others
Funders : Erasmus Plus, TERN
Research unit : Terrestrial Ecosystem Research Network (TERN)
Target public : Researchers
Professionals of domain
Student
General public
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en bioingénieur : sciences et technologies de l'environnement, à finalité spécialisée
Faculty: Master thesis of the Gembloux Agro-Bio Tech (GxABT)

Abstract

[en] Monitoring ecosystems plays a key role in facing climate change impacts. A better understanding of
ecosystem functioning is needed to take appropriate actions toward their conservation. Because of their
endemism, their vital services to local populations, and their fragility caused by human pressures and climate change, ecosystems of the outback of Australia are of major importance. Remote sensing is widely
used for large-scale ecosystem monitoring however, drylands remote sensing faces unique challenges
not typically encountered in other regions. Their high heterogeneity and the soil background reflectance
are just a few of the difficulties encountered in these lands. The innovative LiDAR technology has the
advantage of detecting the three-dimensional structure of the vegetation and could potentially overcome
the uncertainty generated by optical imaging in arid and semi-arid areas.
The general goal of this work is to study the potential use of high resolution airborne LiDAR data
to classify different ecosystems found in the outback of South Australia. In order to characterize the
ecosystems, various structural components were calculated from LiDAR point clouds. The relevance
of these metrics in the discrimination of our ecosystems was assessed through a PCA coupled with an
analysis of their descriptive statistics. Three classification models were build (a hierarchical clustering,
a decision trees and a LDA) with different numbers of input variables. Their performance was compared
with the accuracy related to the confusion matrix. The two-variable, top of canopy height and number of
trees, models offer the best compromise between parsimony and accuracy. The LDA is the best predictor
with an accuracy of 84%. However, the decision tree, whose overall accuracy is only 1% less, is easier
to interpret and to relate to the ecological reality.
Then, in order to evaluate the ability of the models to be upscaled, discriminant models were tested
on larger plots, but the results were not satisfactory. This is due to the number of trees, used as input
variable, being dependent on the size of the plot. The use of GEDI data was also explored to assess
the potential of models to be extrapolated to the global scale. However, the comparison of airborne and
spaceborne LiDAR metrics revealed a significant difference between the two datasets.
The results confirm the hypothesis that metrics derived from high resolution airborne LiDAR are
capable of discriminating some Australian ecosystems. Nonetheless, this study is a first approach and
further research are required to improve the large-scale characterization of drylands ecosystems.Monitoring ecosystems plays a key role in facing climate change impacts. A better understanding of
ecosystem functioning is needed to take appropriate actions toward their conservation. Because of their
endemism, their vital services to local populations, and their fragility caused by human pressures and climate change, ecosystems of the outback of Australia are of major importance. Remote sensing is widely
used for large-scale ecosystem monitoring however, drylands remote sensing faces unique challenges
not typically encountered in other regions. Their high heterogeneity and the soil background reflectance
are just a few of the difficulties encountered in these lands. The innovative LiDAR technology has the
advantage of detecting the three-dimensional structure of the vegetation and could potentially overcome
the uncertainty generated by optical imaging in arid and semi-arid areas.
The general goal of this work is to study the potential use of high resolution airborne LiDAR data
to classify different ecosystems found in the outback of South Australia. In order to characterize the
ecosystems, various structural components were calculated from LiDAR point clouds. The relevance
of these metrics in the discrimination of our ecosystems was assessed through a PCA coupled with an
analysis of their descriptive statistics. Three classification models were build (a hierarchical clustering,
a decision trees and a LDA) with different numbers of input variables. Their performance was compared
with the accuracy related to the confusion matrix. The two-variable, top of canopy height and number of
trees, models offer the best compromise between parsimony and accuracy. The LDA is the best predictor
with an accuracy of 84%. However, the decision tree, whose overall accuracy is only 1% less, is easier
to interpret and to relate to the ecological reality.
Then, in order to evaluate the ability of the models to be upscaled, discriminant models were tested
on larger plots, but the results were not satisfactory. This is due to the number of trees, used as input
variable, being dependent on the size of the plot. The use of GEDI data was also explored to assess
the potential of models to be extrapolated to the global scale. However, the comparison of airborne and
spaceborne LiDAR metrics revealed a significant difference between the two datasets.
The results confirm the hypothesis that metrics derived from high resolution airborne LiDAR are
capable of discriminating some Australian ecosystems. Nonetheless, this study is a first approach and
further research are required to improve the large-scale characterization of drylands ecosystems.


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