remote sensing of the canadian arctic

151 Pages·2017·5.61 MB·English

REMOTE SENSING OF THE CANADIAN ARCTIC:
MODELLING BIOPHYSICAL VARIABLES
by
Nanfeng Liu

A thesis submitted to the Department of Geography and Planning
In conformity with the requirements for
the degree of Doctor of Philosophy

Queen’s University
Kingston, Ontario, Canada
(June, 2017)

Copyright © Nanfeng Liu, 2017
Abstract
It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and
precipitation patterns expected with climate change, including changes in phenology, productivity, biomass,
cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for
monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the
potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two
important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed
Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC
and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty
Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW),
Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were
examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial
resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs
exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling
fine spectral features. The optimal hyperspectral bands were located at important spectral features observed
in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge,
leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared.
At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the
NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data.
Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68),
with a systematic overestimation of 0.08, which was attributed to PAR absorption by soil that could not be
excluded from the fAPAR calculation. This research clearly demonstrates that high spectral and spatial
resolution remote sensing VIs can be used to successfully model Arctic biophysical variables. The methods
and results presented in this research provided a guide for future studies aiming to model other Arctic
biophysical variables through remote sensing data.
Co-Authorship
This dissertation is based on the following three manuscripts:
Chapter 2: Liu, N., Budkewitsch, P., and Treitz, P. 2017. Examining spectral reflectance features related
to Arctic percent vegetation cover: Implications for hyperspectral remote sensing of Arctic tundra. Remote
Sensing of Environment 192, 58-72. doi: 10.1016/j.rse.2017.02.002
Chapter 3: Liu, N. and Treitz, P. 2016. Modelling high arctic percent vegetation cover using field digital
images and high resolution satellite data. International Journal of Applied Earth Observation and
Geoinformation 52, 445-456. doi: 10.1016/j.jag.2016.06.023
Chapter 4: Liu, N. and Treitz, P. 2017. Multi-scale remote sensing of Arctic percent vegetation cover and
fAPAR. Submitted to International Journal of Applied Earth Observation and Geoinformation (in review).

For manuscript 1, Sarah Allux, Paul Treitz and Paul Budkewitsch developed the field sampling design and
Sarah Allux and Paul Budkewitsch collected the field data. I developed the research goals and objectives,
and developed and implemented the analysis design. For manuscripts 2 and 3, I was responsible for the
design and implementation of the study. I was also responsible for the writing of all manuscripts. My
supervisor, Dr. Paul Treitz provided helpful guidance on the study and was consulted on the sampling
designs and analysis methods. He also reviewed and edited the manuscripts.

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Acknowledgements
First, I would like to thank my supervisor Dr. Paul Treitz for your excellent supervision. Thank you for the
invaluable patience, advice and guidance on my PhD study and research. Thank you for sharing your
experiences of Arctic field work with me. Thank you for your detailed and critical comments which highly
improved my manuscripts. Most importantly, your continuous encouragement has always boosted my
confidence when I got stuck. I am very thankful for the opportunity to work with you.
I would also like to thank the committee members of my qualifying examination: Dr. Ryan Danby, Dr. Paul
Martin, Dr. Dongmei Chen and Dr. Neal Scott. Your insightful comments and suggestions helped narrow
down my research questions and shape this final thesis. Special thanks to Dr. Dongmei Chen and Dr. Neal
Scott for their field sampling design advice. I would also like to thank Dr. Greg Henry for being my external
reviewer.
Many thanks to my colleagues in the Laboratory for Remote Sensing of Earth and Environmental Systems
(LaRSEES). Especially, Amy Blaser and Rebecca Edwards: thank you for all your help during my Arctic
field work; Karin van Ewijk: thank you for sharing the remote sensing lecture materials with me, which
helped my teaching a lot. Many thanks to the staff of the Geography and Planning Department. John Bond,
thank you for your IT support and help. Sheila-Rae MacDonald, Joan Knox, Sharon Mohammed and Kathy
Hoover, thank you for your patience and help. Joan, wish you get well soon. I would also like to thank my
Chinese friends at Queen’s University. Chen Shang, Pengpeng Ni, Tikang Li, Mengqi Yang and Yao Feng,
thank you for your help and encouragement when I needed it. I enjoyed the great time spent with you at
Queens.
Financial support for this work was provided by NCE ArcticNet, the Norther Science Training Program
(NSTP), Polar Knowledge Canada, Polar Continental Shelf Project (PCSP), the Natural Sciences and
Engineering Research Council (NSERC), and Queen’s University. I would like to thank Dr. Scott
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Lamoureux and Dr. Melissa Lafreniere for their support of this research. I would also like to thank Sarah
Allux and Dr. Paul Budkewitsch for collecting the field data for Chapter 2.
Finally, I would like to thank my family – my parents and girlfriend, Zhihui Wang. I am most grateful to
my parent’s support and unconditional love. Zhihui, I am sincerely grateful to you for patience, tolerance
and most importantly your love. I love you all so much.

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Statement of Originality
I hereby certify that all the work described within this thesis is the original work of the author. Any
published (or unpublished) ideas and/or techniques from the work of others are fully acknowledged in
accordance with the standard referencing practices.

(Nanfeng Liu)
(June, 2017)

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Table of Contents
REMOTE SENSING OF THE CANADIAN ARCTIC: MODELLING BIOPHYSICAL VARIABLES .... i
Abstract ......................................................................................................................................................... ii
Co-Authorship.............................................................................................................................................. iii
Acknowledgements ...................................................................................................................................... iv
Statement of Originality ............................................................................................................................... vi
Table of Contents ........................................................................................................................................ vii
List of Figures ............................................................................................................................................... x
List of Tables ............................................................................................................................................. xiii
List of Abbreviations .................................................................................................................................. xv
Chapter 1 Introduction .................................................................................................................................. 1
1.1. Arctic Vegetation and Environmental Change ............................................................................. 1
1.2. Remote Sensing of Arctic Vegetation ........................................................................................... 2
1.2.1. Arctic Vegetation Classification ........................................................................................... 2
1.2.2. Spatial and Temporal Dynamics of Arctic Vegetation ......................................................... 4
1.2.3. Arctic Biophysical Variable Estimation ................................................................................ 4
1.3. Research Issues ............................................................................................................................. 5
1.3.1. Research Objective 1 ............................................................................................................ 6
1.3.2. Research Objective 2 ............................................................................................................ 8
1.3.3. Research Objective 3 ............................................................................................................ 9
1.4. References ................................................................................................................................... 10
Chapter 2 Examining spectral reflectance related to Arctic percent vegetation cover: Implications for
hyperspectral remote sensing of Arctic tundra............................................................................................ 16
2.1. Abstract ....................................................................................................................................... 16
2.2. Introduction ................................................................................................................................. 17
2.3. Study Area .................................................................................................................................. 20
2.4. Methods ....................................................................................................................................... 21
2.4.1. Field Sampling Design ........................................................................................................ 21
2.4.2. Percent Vegetation Cover ................................................................................................... 23
2.4.3. Field Spectra ....................................................................................................................... 24
2.4.4. Regression ........................................................................................................................... 32
2.5. Results and Discussion................................................................................................................ 33
2.5.1. Percent Vegetation Cover ................................................................................................... 33
2.5.2. Spectral Characteristics of Arctic Vegetation ..................................................................... 37
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2.5.3. Broadband VIs .................................................................................................................... 42
2.5.4. Narrowband VIs .................................................................................................................. 43
2.5.5. Conclusion .......................................................................................................................... 48
2.6. Acknowledgement ...................................................................................................................... 49
2.7. References ................................................................................................................................... 49
Chapter 3 Modelling High Arctic Percent Vegetation Cover Using Field Digital Images and High
Resolution Satellite Data ............................................................................................................................. 57
3.1. Abstract ....................................................................................................................................... 57
3.2. Introduction ................................................................................................................................. 58
3.3. Study Area .................................................................................................................................. 61
3.4. Methods ....................................................................................................................................... 63
3.4.1. Field Sampling Design ........................................................................................................ 63
3.4.2. Percent Vegetation Cover ................................................................................................... 66
3.4.3. Vegetation Indices............................................................................................................... 70
3.4.4. PVC-VI Regression Models ................................................................................................ 70
3.5. Results and Discussion................................................................................................................ 71
3.5.1. Percent Vegetation Cover Estimation – A Comparison of Methods .................................. 71
3.5.2. Temporal Patterns of Percent Cover ................................................................................... 73
3.5.3. PVC-NDSI relationships ..................................................................................................... 74
3.6. Conclusion .................................................................................................................................. 77
3.7. Acknowledgement ...................................................................................................................... 79
3.8. References ................................................................................................................................... 79
Chapter 4 Remote Sensing of Percent Vegetation Cover and fAPAR on Baffin Island, Nunavut, Canada 85
4.1. Abstract ............................................................................................................................................ 85
4.2. Introduction ...................................................................................................................................... 86
4.3. Study Area ....................................................................................................................................... 89
4.4. Methods ............................................................................................................................................ 90
4.4.1. Field Sampling Design .............................................................................................................. 90
4.4.2. Percent Vegetation Cover ......................................................................................................... 92
4.4.3. The Fraction of Absorbed Photosynthetically Active Radiation .............................................. 94
4.4.4. Normalized Difference Vegetation Index ................................................................................. 95
4.4.5. Satellite Image Processing ........................................................................................................ 95
4.5. Results and Discussion .................................................................................................................... 96
4.5.1. Temporal Patterns of PVC and fAPAR .................................................................................... 96
4.5.2. Comparison of PVC between the point-frame and image classification methods .................... 98
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4.5.3. PVC/fAPAR-VI relationships ................................................................................................. 100
4.5.4. Comparison with MODIS LAI/fAPAR product ..................................................................... 103
4.6. Conclusion ..................................................................................................................................... 105
4.7. Acknowledgement ......................................................................................................................... 106
4.8. References ...................................................................................................................................... 106
Chapter 5 Synthesis and Future Directions ............................................................................................... 112
5.1. Objective 1: To assess the utility of hyperspectral remote sensing data for modelling Arctic PVC
using field spectra. ................................................................................................................................ 112
5.2. Objective 2: To model Arctic PVC at landscape scales using field digital images and high-
resolution satellite data. ........................................................................................................................ 114
5.3. Objective 3: To develop models of fAPAR based on relationships between fAPAR and high spatial
resolution satellite derived VIs. ............................................................................................................ 115
5.4. Conclusion ..................................................................................................................................... 116
5.5. References ...................................................................................................................................... 117
Appendix A Sabine Peninsula Site ........................................................................................................... 119
Appendix B Cape Bounty Arctic Watershed Observatory (CBAWO) Site .............................................. 123
Appendix C Apex River Watershed (ARW) Site ..................................................................................... 131

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List of Figures
Figure 1-1. Three study sites located in the Canadian Arctic: (1) Sabine Peninsula (7627’N, 10833’W),
Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (7524’N, 10930’W), Melville
Island, NU; and (3) Apex River Watershed (63°45’N, 68°30’W), Baffin Island, NU. ................................ 6

Figure 2-1. Study area on the Sabine Peninsula, Melville Island, Nunavut, Canada (A-B). The WorldView-
2 unsupervised classification in panel C was adapted from Allux et al., (2012). Sampling regions (i.e.,
ellipses: ~2.5 km × 1 km) spanning at least two geological units were identified. Plots (i.e., dots: 20 m × 20
m) exhibiting homogeneous vegetation types were randomly located within each sampling region and
validated in the field. ................................................................................................................................... 21

Figure 2-2. PVC and spectral reflectance sampling design. For each homogeneous plot (~ 20 m x 20 m, A),
PVC and spectra were measured within a 1 m x 1 m quadrat located at the center of the plot (i.e., green
square in A). For PVC measurements, a 1 m x 1 m quadrat with 10 cm grids was used (B). For spectral
reflectance measurements, fifteen measurements were collected at five different locations within the 1 m x
1 m quadrat: three in the center of the quadrat and three in each corner of the quadrat (C). The circles in C
represent the field of view (FOV) of the ASD spectroradiometer. ............................................................. 24

Figure 2-3. PVC by plant functional group for each vegetation type. Vegetation types are organized along
a moisture gradient (i.e., polar semi-desert to wet sedge/moss). A) PVC results (i.e., PVC calculated using
3D
all contact points); B) PVC results (i.e., PVC calculated using first contact points only). N represents the
2D
number of plots sampled for each vegetation type...................................................................................... 34

Figure 2-4. Comparison between PVC (y-axis) and PVC (x-axis) for each plant functional group (i.e.,
3D 2D
Green Moss, Green Forb, Green Graminoid/Sedge) and Total Green Vegetation (i.e., aggregated PVC).
These plots include the 1:1 line................................................................................................................... 36

Figure 2-5. The regression for the difference in total PVC between the two methods (i.e., total PVC -
3D
PVC ) and canopy height. Polar Semi-Desert (PD); Dry Mesic Tundra (DMT); Mesic Tundra (MT); Wet
2D
Mesic Tundra (WMT); Wet Sedge/Moss (WSM). ..................................................................................... 37

Figure 2-6. Mean (± 1 std. dev.) field spectra for each vegetation type: Polar Semi-Desert (PD); Dry Mesic
Tundra (DMT); Mesic Tundra (MT); Wet Mesic Tundra (WMT); Wet Sedge/Moss (WSM), as measured
at Sabine Peninsula, Melville Island. .......................................................................................................... 41

Figure 2-7. Correlations (R2) between green PVC and WorldView-3 derived VIs: NDSI (A) and RDSI (B).
The x- and y-axes are the bands of WorldView-3, respectively. For each map, the lower-right diagonal area
represents the R2 between PVC and VIs; the upper-left diagonal area represents the R2 between PVC
2D 3D
and VIs. The R2 > 0.70 values were labelled directly on each map. ........................................................... 43

Figure 2-8. Correlations (R2) between PVC and Hyperion first-order derivatives (A: correlations with
PVC ; B: correlations with PVC ). Green dots represent the correlation between PVC and the first
2D 3D
derivatives of reflectance (i.e., dR) while red dots represent the correlation between PVC and the first
derivatives of the reciprocal of reflectance (i.e., d(1/R)). Thresholds are identified for R2 = 0.6 and R2 = 0.7.
.................................................................................................................................................................... 44
x