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LEAF AREA INDEX, CARBON CYCLING DYNAMICS AND ECOSYSTEM RESILIENCE IN MOUNTAIN
PINE BEETLE AFFECTED AREAS OF BRITISH COLUMBIA FROM 1999 TO 2008
by
Peter Czurylowicz
A thesis submitted in conformity with the requirements for the degree of Master of Science
Graduate Department of Geography University of Toronto
© Copyright by Peter Czurylowicz 2010
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LEAF AREA INDEX, CARBON CYCLING DYNAMICS AND ECOSYSTEM RESILIENCE IN MOUNTAIN
PINE BEETLE AFFECTED AREAS OF BRITISH COLUMBIA FROM 1999 TO 2008
Master of Science, 2010
Peter Czurylowicz
Graduate Department of Geography, University of Toronto
ABSTRACT
The current unprecedented mountain pine beetle (Dendroctonus ponderosae) (MPB)
outbreak in British Columbia affecting lodgepole pine (Pinus contorta var. latifolia) forests was
examined from 1999 to 2008 in order to determine the consequence to leaf area index (LAI) and
net ecosystem production (NEP). Annual LAI maps for the outbreak duration were produced
using SPOT VEGETATION reflectance data and were used as inputs to a spatially distributed,
process-based carbon (C) cycle model – Boreal Ecosystem Productivity Simulator (BEPS). Both
LAI and NEP were validated using field measurements. LAI was found to decrease on average
by 20% when compared to pre-outbreak conditions, while NEP decreased on average 90%. The
cumulative annual NEP values ranged from 2.43 to -8.03 MtC between 1999 and 2008, with the
ecosystem converting from a sink to source of C in 2000. The annual average NEP was -2.90
MtC while the cumulative total over the ten year period was -28.97 MtC. The inter-annual
variability for both LAI and NEP was characterized by substantial initial decreases followed by a
steadily increasing trend from 2006 to 2008 with NEP returning to near C neutrality in 2008 (-
1.84 MtC). The apparent resistance of LAI and NEP to MPB attack was examined under the
framework of ecosystem resilience which was manifested in the form of secondary overstory
growth and increased production of non-attacked host trees. Based on previous research the
ecosystem resilience was attributed to reduced competition for resources particularly light and
nutrients. It was inferred that ecosystem resilience strongly mitigated the effects of MPB on
BC’s pine forests and initiated recovery of both NEP and LAI earlier than previously anticipated.
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ACKNOWLEDGMENTS
My most sincere thanks are primarily directed to Professor Jing Chen for seeing in me the
potential to succeed in the realm of research even when my marks may have said otherwise and
for giving me the opportunity to pursue my master’s degree when the odds of being accepted into
the program were heavily stacked against me. He encouraged me to pursue my research
independently in turn teaching me to solve problems on my own, think in a dynamic and
innovative manner and that progress is only made on the backs of ones mistakes. Yet during
times of confusion and frustration when I needed guidance he was always available to help even
via email from China or some other distant place where he could have easily overlooked my
requests. I sincerely value all that he has taught me and once again offer my most heartfelt
gratitude.
I would like to thank all the members of the Remote Sensing and Climate Modelling
group at the University of Toronto, both past and present for their continued support and
understanding of the hardships faced during the pursuit of graduate study. Particular thanks go to
Mr Daniel Pierre for his efforts in acquiring and processing the remote sensing data needed for
leaf area index map production; to Mr Gang Mo for his support with the modelling component of
my research and his patience towards my lack of programming skills; to Dr. Mustapha El
Maayar for teaching me the intricacies of C code and constant willingness to help with model
problems; to Dr. Michael Sprintsin for engaging me in thought provoking conversation and
providing much needed help with remote sensing tasks; to Ms Ting Zhang for her willingness to
drop everything and write IDL programs to help me process data more efficiently and to Dr. Jan
Pisek for assisting with the field work and teaching me how to operate the LAI measurement
devises.
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My sincere thanks go to all the people from other universities and organizations for their
contribution to my research. Andy Black and Mathew Brown from UBC for providing me with
meteorological and flux tower data; Dr. David Spittlehouse, Dr Rita Winkler and Ms. Vanessa
Foord from the BC Ministry of Forests and Range for their invaluable guidance during the field
campaign.
I would like to thank the Department of Geography at the University of Toronto for
providing me with the opportunity to study and all the staff who assisted me in the process.
Finally, I would like to thank my family for constantly supporting me during the last and
at times very painful two years of my life. My most sincere and heartfelt gratitude goes to my
mom for instilling in me a passion for learning. Her unwavering determination to achieve
academic excellence and to overcome significant life challenges has given me the strength and
motivation to achieve greatness in my life. It is to her that I dedicate this thesis.
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TABLE OF CONTENTS
ABSTRACT ....................................................................................................................................... II ACKNOWLEDGMENTS .................................................................................................................... III TABLE OF CONTENTS ..................................................................................................................... V LIST OF FIGURES ......................................................................................................................... VIII LIST OF TABLES .......................................................................................................................... XIII CHAPTER 1.0: INTRODUCTION ....................................................................................................... 1
1.1 Research Objectives ............................................................................................................ 5
1.2 Ecosystem resilience framework ....................................................................................... 6
CHAPTER 2.0: MOUNTAIN PINE BEETLE DYNAMICS .................................................................... 9 2.1 MPB biology ........................................................................................................................ 9
2.2 Factors influencing the MPB outbreak ........................................................................... 11
CHAPTER 3.0: METHODOLOGY FOR LAI MAPPING .................................................................... 15 3.1 Satellite data acquisition and processing ........................................................................ 17
3.2 Reduced simple ratio vegetation index ........................................................................... 18
3.2.1 Overview of vegetation indices used in LAI calculation .............................................. 18 3.2.2 Principles and advantages of the RSR vegetation index .............................................. 20 3.2.3 Methodology for RSR application to SPOT VEGETATION reflectance data .............. 22
3.3 Algorithms for LAI retrievals .......................................................................................... 23
3.3.1 Overview of LAI algorithm development methodologies ............................................. 23 3.3.2 Empirically based method for LAI derivation based on the RSR vegetation index ...... 24
3.4 Seasonal LAI trajectory smoothing by the locally adjusted cubic spline capping
method ...................................................................................................................................... 26
3.4.1 Overview of techniques for correcting seasonal LAI patterns ..................................... 27 3.4.2 Locally adjusted cubic-spline capping method for correcting seasonal LAI patterns . 28 3.4.3 Results of LACC smoothing on VEGETATION LAI ..................................................... 30
3.5 LAI map production ......................................................................................................... 32
CHAPTER 4.0: FIELD VALIDATION PROCEDURES FOR LAI. ........................................................ 34 4.1 Site selection and description ........................................................................................... 35
4.2 Measurement theory, techniques and procedures ......................................................... 38
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4.2.1 LAI measurement theory ............................................................................................... 38 4.2.2 Determination of parameters required for ground based LAI retrievals using TRAC 42 4.2.3 LAI measurement procedure ........................................................................................ 44
4.3 Fine resolution LAI map acquisition and processing .................................................... 47
4.4 Fine to course resolution scaling and accuracy assessment .......................................... 49
CHAPTER: 5.0 MEASUREMENT RESULTS AND DISCUSSION FOR OVERSTORY AND UNDERSTORY LAI ............................................................................................................................................... 51
5.1 Comparison between fine resolution RSR and coarse resolution VEGETATION LAI
.................................................................................................................................................. 54
5.2 RSR verses SR and NDVI vegetation indices as predictors of LAI ............................. 56
5.3 Multiple scattering effects on LAI retrieval using LAI-2000 instrument .................... 58
5.4 Discussion .......................................................................................................................... 60
CHAPTER 6.0: LAI VARIATION AS A FUNCTION OF MPB ATTACK ............................................. 63 6.1 Annual LAImax mapping ................................................................................................... 63
6.2 Annual LAI change from pre-outbreak conditions (1999) ........................................... 68
6.3 Discussion .......................................................................................................................... 73
CHAPTER 7.0: METHODOLOGY FOR NEP CARBON CYCLE MODELLING .................................... 74 7.1 BEPS model descriptions and input parameters ........................................................... 76
7.1.1 Key ecological processes simulated by BEPS .............................................................. 80 7.1.2 Spatially explicit input data .......................................................................................... 83
7.1.2.1 Meteorological inputs ........................................................................................... 83
7.1.2.2 LAI and biomass inputs ........................................................................................ 85
7.1.2.3 Soil and litter carbon pool inputs .......................................................................... 85
7.1.2.4 Soil specific inputs ................................................................................................ 88
7.1.2.5 Land cover ............................................................................................................ 88
7.1.2.6 Other input ............................................................................................................ 90
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7.1.3 Biological and ecosystem specific input parameters .................................................... 91 CHAPTER 8.0: SITE LEVEL NEP VALIDATION USING FLUX TOWER MEASUREMENTS ................ 93
8.1 Site descriptions ................................................................................................................ 94
8.2 Site specific BEPS model inputs ...................................................................................... 95
8.3 NEP validation results ...................................................................................................... 96
8.3.1 Measured versus modelled NEP ................................................................................... 96 8.3.2 Secondary structure and healthy tree growth response to MPB attack ....................... 99 8.3.3 Assessment of BEPS performance .............................................................................. 101
CHAPTER 9.0: CARBON VARIATION AS A FUNCTION OF MPB ATTACK .................................... 104 9.1 Annual NEP mapping and NPP and Rh results ........................................................... 104
9.2 Controlling factors of NPP and Rh ................................................................................ 111
9.3 Site level LAI and NEP recovery ................................................................................... 113
9.4 Comparison with previous results ................................................................................. 117
CHAPTER 10.0: CONCLUSIONS AND SUMMARY ......................................................................... 121 10.1 Summary ........................................................................................................................ 121
10.2 Research limitations ..................................................................................................... 123
10.3 Future work ................................................................................................................... 124
10.4 Conclusion ..................................................................................................................... 126
REFERENCES ............................................................................................................................... 127
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LIST OF FIGURES
Figure 1. The cumulative extent of the mountain pine beetle (MPB) outbreak in British
Columbia (BC) from 1999 to 2008. The severity, from moderate to very severe is characterized
by the percentage of killed trees in a single square kilometre pixel ranging from 30 to 90 percent.
......................................................................................................................................................... 2
Figure 2. General procedure for remotely sensed leaf area index (LAI) production and mapping
applied to SPOT VEGETATION reflectance data. ...................................................................... 16
Figure 3. Comparison between original (raw) VEGETATION leaf area index (LAI) and
smoothed LAI by the locally adjusted cubic spline capping (LACC) method at the MPB-03 flux
tower site north of Prince George British Columbia for (a) 1999 and (b) 2007, before and after
the mountain pine beetle (MPB) attack respectively. ................................................................... 31
Figure 4. General procedure for the validation of VEGETATION leaf area index (LAI) products
using ground based measurements and fine resolution satellite imagery. .................................... 34
Figure 5. The locations of leaf area index (LAI) measurement sites including the two flux tower
sites MPB-06 and MPB-03 (LAI not measured). Orange areas represent the cumulative extent of
mountain pine beetle affected areas from 1999 to 2008. .............................................................. 36
Figure 6. The element clumping (ΩE) index becomes asymptotic at high values of mean element
width (w). The w value for lodgepole pine was 50 mm. .............................................................. 43
Figure 7. The relationship between measured leaf area index (LAI) of the overstory and
measured LAI of the understory (LAIu) exhibits a decreasing exponential relationship.
Measurements used in this figure include those by this study and those taken during the
BOREAS study in Saskatchewan and Manitoba by Liu et al. (2003). ......................................... 52
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Figure 8. Measured understory leaf area index (LAIu) as a function of overstory leaf area index
from this study and Liu et al. (2003). The y-axis is displayed in logarithmic form in order to
linearize the data which are related exponentially. No significant difference between the LAIu to
overstory LAI relationship was found between both studies (p = 0.16). ...................................... 53
Figure 9. Fine resolution (ASTER, SPOT 5) to coarse VEGETATION image comparison for
leaf area index (LAI) measurement sites. The image acquisition month and year is displayed in
the brackets. (a) Kennedy Siding, (b) Mackenzie Lakes, (c) Penticton, (d) McKay Lake and Kay
Kay, (e) Kelowna. ......................................................................................................................... 55
Figure 10. The relationship between leaf area index (LAI) derived from a combination of
LAI2000 and TRAC optical instruments and several vegetation indices – (a) simple ratio (SR),
(b) normalized difference vegetation index (NDVI) and (c) the reduced simple ratio (RSR). .... 57
Figure 11. Effective leaf area index (Le) computed using LAI-2000 instrument rings 1 – 3 and
rings 1 – 5. The exclusion of low zenith angles (1 – 3) reveals an 8% underestimation in Le to
that computed using all 5 rings. This is due to the multiple scattering effect of light within the
canopy. .......................................................................................................................................... 60
Figure 12. Annual time series of mountain pine beetle (MPB) affected area, partitioned by
severity classification. ................................................................................................................... 64
Figure 13. Leaf area index (LAI) dynamics over time. The bars represented on the primary axis
display the percentage change of LAI as compared with the unaffected state 1999. The line
represented by the secondary axis displays the annual change in July average LAI (maximum
seasonal LAImax). .......................................................................................................................... 64
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Figure 14. Spatial and temporal leaf area index (LAI) dynamics for mountain pine beetle (MPB)
affected areas from 1999 to 2008. The small inlay maps show the severity classification of
affected areas of each year (red to yellow = most to least severe). .............................................. 66
Figure 15. Average maximum leaf area index (LAImax) change with mountain pine beetle (MPB)
attack severity. The error bars represent the range in LAImax values over the ten year period of
the study. ....................................................................................................................................... 67
Figure 16. The annual percent change of leaf area index (LAImax) as compared with non-
disturbed conditions of 1999 for areas exhibiting LAI values greater than in the non-disturbed
year (line). The bars represent the percentage of the total affected area of each year exhibiting
better than non-disturbed values of LAI. ...................................................................................... 68
Figure 17. Scatter plots of maximum annual leaf area index (LAImax) for the maximum extent of
mountain pine beetle (MPB) affected area against the unaffected baseline (1999). The steady
increase in the percentage of total area showing improvements in baseline LAI underlines the
increasing influence of secondary overstory. Highlighted areas indicate the rapid secondary
overstory recovery signal. ............................................................................................................. 72
Figure 18. General procedure for carbon (C) cycle modelling of net ecosystem production (NEP)
using spatially explicit, process-based models driven by remote sensing inputs. ........................ 75
Figure 19. Mountain pine beetle (MPB) cumulative affected areas used to create land cover (all
conifer) input mask for Boreal Ecosystem Productivity Simulator (BEPS) modelling. Red boxes
delineate the 6 sub-regions used for net ecosystem productivity (NEP) calculations in order to
allow for multiple processor use for a single year. ....................................................................... 89
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Figure 20. Measured versus modelled annual net ecosystem production (NEP) time series
comparisons for MPB-03 and MPB-06 validation areas. Total monthly measured NEP in gC/m2
derived form eddy covariance (EC) flux tower data is used for the comparison. ........................ 97
Figure 21. Scatter plots of measured versus modelled net ecosystem production (NEP) from
2007 and 2008 for flux tower validation sites. Cumulative measured wintertime fluxes were
plotted as the five month (November to March) average. ............................................................ 98
Figure 22. Net ecosystem production (NEP), net primary production (NPP) and heterotrophic
respiration (Rh) for mountain pine beetle (MPB) affected areas from 1999 to 2008. ................ 105
Figure 23. Annual net ecosystem production (NEP) for the cumulative mountain pine beetle
(MPB) affected area from 1999 to 2008. .................................................................................... 108
Figure 24. Cumulative net ecosystem production (NEP) for mountain pine beetle (MPB)
affected areas in British Columbia’s Interior pine forests from 1999 to 2008. .......................... 110
Figure 25. Annual net primary production (NPP) response to changes in leaf area index (LAI) as
a result of mountain pine beetle (MPB) outbreak. NPP is plotted on the primary axis, while LAI
is on the secondary axis. ............................................................................................................. 112
Figure 26. The response of heterotrophic respiration (Rh) to average growing season air
temperature. ................................................................................................................................ 114
Figure 27. The response of heterotrophic respiration (Rh) to average total growing season
precipitation. ............................................................................................................................... 114
Figure 28. Net ecosystem production (NEP) and leaf area index (LAI) change as a function of
mountain pine beetle (MPB) increasing severity for flux measurement site MPB-03 with late
attack history. Value in brackets under the x axis denote the percentage of trees killed that year.
..................................................................................................................................................... 115
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Figure 29. Net ecosystem production (NEP) and leaf area index (LAI) change as a function of
mountain pine beetle (MPB) increasing severity at a mature pine site with early attack history.
Value in brackets under the x axis denote the percentage of trees killed that year. ................... 115
Figure 30. Net ecosystem production (NEP) and leaf area index (LAI) change as a function of
mountain pine beetle (MPB) increasing severity at a mid age pine site with late attack history.
Value in brackets under the x axis denote the percentage of trees killed that year. ................... 115
Figure 31. Carbon (C) balance of British Columbia’s pine forests undergoing mountain pine
beetle (MPB) outbreak over the last decade from 2 studies: (1) Carbon Budget Model of the
Canadian Forest Sector (Kurz et al., 2008) based on forest inventory data and changes in age
structure due to disturbance. Results from 2006 and 2007 are projections based on probable MPB
spread patterns. (2) Spatially explicit, process-based modelling accounting for changes in
meteorology, forest health – leaf area index, and soil moisture conditions (This study). (3) The
results of this study with the annual harvested lumber subtracted from net ecosystem production
(NEP). ......................................................................................................................................... 120
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LIST OF TABLES
Table 1. British Columbia Ministry of Forests and Range (BCMoFR) mountain pine beetle
(MPB) severity classification scheme. .......................................................................................... 33
Table 2. Summary of leaf area index (LAI) measurement site characteristics for British
Columbia Interior sites measured in June of 2009. ....................................................................... 37
Table 3. Mean leaf area index (LAI) values for British Columbia Interior sites measured in June
of 2009. ......................................................................................................................................... 45
Table 4. The location and brief description of fine resolution imagery used in this study to
validate leaf area index (LAI) maps. ............................................................................................. 48
Table 5. Summary of statistics for fine resolution ASTER and SPOT to coarse resolution
VEGETATION reduced simple ratio (RSR) vegetation index comparison over the same region.
....................................................................................................................................................... 53
Table 6. Critical parameters for net ecosystem production (NEP) calculations in the Boreal
Ecosystem Productivity Simulator (BEPS) model, particularly for photosynthesis and stomatal
conductance. .................................................................................................................................. 92
Table 7. Annual total and average carbon (C) flux (net ecosystem production) comparison
between measured and modelled site level validation procedures. .............................................. 98
Table 8. Results of statistical analysis for measured versus modelled site level validation of net
ecosystem production (NEP). ....................................................................................................... 98
Table 9. Annual values of modelled net ecosystem production (NEP), net primary production
(NPP) and heterotrophic respiration (Rh) for the cumulative mountain pine beetle affected area in
British Columbia. ........................................................................................................................ 105
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Table 10. Annual carbon (C) source and sink distribution for mountain pine beetle affected areas
displayed as total area (km2) and percentage of total area. ......................................................... 109
Table 11. Comparison between annual values of net biome production (NBP) from Kurz et al.
(2008) and this study and the annual amounts of harvested carbon. .......................................... 119
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CHAPTER 1.0: INTRODUCTION
There is little doubt amongst the scientific community that the global climate is changing
(Charlson et al., 1992). A predominate consequence of this climate change is the warming of
major terrestrial ecosystems (Charlson et al., 1992; Friedlingstein et al., 2003). The magnitude of
this global warming is tightly coupled to carbon (C) cycle feedbacks such as the increased
frequency of forests fires accelerating warming or increased carbon dioxide (CO2) fertilization in
forests decelerating warming (Myneni et al., 1997a; Melillo et al., 2002; Gregory et al., 2009;
Moss et al., 2010; Zaehle et al., 2010). The current understanding of the terrestrial C cycle relies
on the basic assumption that northern forests reduce the magnitude of climate change by
sequestering large amounts of CO2 (Myneni et al., 1997a). Under static conditions such a
statement may be true, but positive feedbacks associated with warming climates may limit the
capacity for C sequestration. These feedbacks may result in the conversion of forests from sinks
to sources of C. Therefore, the potential for the terrestrial C sink to switch to a C source is
critically dependant on the interaction between global warming and C cycle feedbacks. A
fundamental component of this relationship is the sensitivity, and response of disturbance
regimes particularly insect infestations to global warming. The potential for increased insect
disturbance with climate warming can substantially limit the strength of the terrestrial C sink due
to a decrease in photosynthetic uptake and large CO2 emissions from decaying tree biomass as a
function of insect kill (Berryman, 1988). But the magnitude of this response is highly uncertain
due to a fundamental lack of understanding of the processes of terrestrial ecosystems particularly
in their response to insect disturbance.
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Figure 1. The cumulative extent of the mountain pine beetle (MPB) outbreak in British Columbia (BC) from 1999 to 2008. The severity, from moderate to very severe is characterized by the percentage of killed trees in a single square kilometre pixel ranging from 30 to 90 percent.
A recent and unprecedented large scale outbreak of mountain pine beetle (Dendroctonus
ponderosae) (MPB) in British Columbia’s (BC) Interior forest as a result of warming climates
provides evidence for the notion of increased disturbance associated with global warming. MPB
outbreaks are a naturally occurring phenomenon affecting predominately lodgepole pine (Pinus
contorta var. latifolia) forests ranging from Mexico to Central BC along North Americas western
half. The current outbreak of MPB in BC began in 1999 and has expanded exponentially to 2007
affecting more than 50% of BC’s total forest area equal to over 100 000 km2 (Figure 1). The
outbreak is projected to stabilize by 2009 and rapidly recover to pre-outbreak levels (< 5000
km2) by 2020 (Westfall and Ebata, 2008). The estimated timber losses due to lodgepole pine
mortality total 435 million m3 and average 21 million m3 per year which equates to 31% of the
total annual harvest in BC (Kurz et al., 2008).
The implications of this large scale disturbance with respect to C cycling are not well
understood. Of particular concern is the physical response of the ecosystem to MPB attack which
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has received little attention with respect to large scale modelling studies or site-based
measurements of C dynamics. The research that does exist is conflicted in its characterization of
ecosystem response to MPB and thus differs in its conclusions regarding the magnitude of source
and sink distribution following the outbreak.
Kurz et al. (2008) conducted an empirically-based modelling study driven by disturbance
but lacking factors of climate change and physical representation of ecosystem process as
affecting the C exchange between the biosphere and atmosphere for MPB affected areas in BC
from 2000 to 2006 and projected future implications to 2020. Results suggested a dramatic and
consistently decreasing net ecosystem production (NEP) with recovery beginning in 2009
according to the model. The ecosystem was converted to a source of C in 2003 and did not
recover to sink status until 2020. The cumulative C losses as a result of disturbance and forest
harvest totalled -106 MtC with an average of -11MtC/yr from 2000 to 2008. The large C release
was attributed to a sudden increase in respiration due to large inputs of killed biomass and the
reduction of production associated with tree mortality.
Brown et al. (2010) conducted C flux measurements at two MPB affected sites in BC’s
Northern Interior region for 2007 and 2008 utilizing eddy covariance (EC) techniques. The
results suggested that NEP at both sites declined due to MPB attack in the first year of
measurements but with increasing beetle severity a rapid recovery of NEP occurred. One of the
sites showed that with over 90% of the pine overstory killed, NEP returned to sink levels. This
recovery was attributed to reduced plant respiration and more significantly the improved
production of unaffected healthy trees. Furthermore, rapid developments of secondary overstory
trees particularly subalpine fir (Abies lasiocarpa), white spruce (Picea glauca), birch (Betula
papyrifera), trembling aspen (Populus tremuloides) and willow (Salix alpina) as a result of
4
reduced competition for radiation and nutrients strongly mitigated the effects of MPB attack.
This secondary overstory growth release was documented by Coates et al. (2009) who measured
increases in tree seedling and sapling density bellow the canopy and increases in growth
efficiency in canopy trees that survived the MPB attack.
The inconsistencies between site measurements and empirically driven, regional C cycle
modelling outlined above, underline the importance of quantifying the C cycle with methods
driven by process-based representations of ecosystem dynamics. This is stressed by the inability
of empirical models to simulate the measured responses of ecosystems to MPB attack, in
particular the rapid expansion of secondary structure which may mitigate the effect of reduced
production due to overstory mortality. It must be noted that site based observations cannot be
applied to regional scales but the process-based relationships governing ecosystem function
determined at a site can be assumed to exist throughout a particular ecosystem.
In order to accurately quantify the response of BC’s forests to the large scale MPB
outbreak during the last decade with respect to C cycle dynamics, a process-based modelling
approach should be used. The net balance between C uptake and release from the biosphere to
the atmosphere is termed net ecosystem production (NEP) and is the primary measure used to
quantify C dynamics. In order for such an approach to be implemented on regional scales an
accurate source of spatially distributed inputs is required. Such a source is found in the
application of satellite remote sensing which has been shown to accurately quantify critical land
surface parameters of which most important is leaf area index (LAI) (Chen and Black, 1992;
Chen et al., 2002; Chen et al., 2003). LAI is defined as one half the total green leaf area per unit
ground surface area (Chen and Black, 1992). It is a measure of the total amount of foliage layers
within a canopy and reflects the total health of an ecosystem by yielding higher values for areas
5
showing higher levels of greenness. Furthermore, LAI captures the changes in foliage
composition with respect to MPB induced losses and vegetation regrowth gains. Thus it can be
used as an ideal indicator of the total function of an ecosystem.
The following dissertation attempts to accurately quantify the C source and sink
distribution for BC’s MPB affected areas from 1999 to 2008 by utilizing a process-based
modelling approach driven by remote sensing inputs. The relevance of such work is driven by
the need to accurately establish government policy and mediation practices concerning Canada’s
C budget. Such results will increase understanding of terrestrial ecosystem function with respect
to changing climate. The primary benefit of this research is to contribute to the development of a
framework for reducing the uncertainties in the estimation of Canada’s C budgets, by integrating
disturbance. Further benefits are evident in that the process-based understanding of C cycling
will aid in the preservation of natural ecosystems, forest health and sustainable environments for
future generations.
1.1 Research Objectives
Because of the importance of accurate C cycle quantification for the purposes of climate
change feedback understanding and the inconsistency of previously published research on the
impact of MPB outbreak on BC’s forests, the major objectives of the research are: (1) to produce
satellite-based LAI maps for MPB affected areas in BC from 1999 to 2008 and validate these
maps through field measurements of LAI using optical instruments, (2) to use the LAI maps as
major inputs to spatially distributed, process-based C cycle modelling of C dynamics for MPB
affected areas in BC from 1999 to 2008 and validate the results with measured C flux data, (3) to
investigate the potential of ecosystem resilience as a mitigating force combating the presumed
negative effect of MPB on C cycling.
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1.2 Ecosystem resilience framework
The ecosystem response to MPB of increased healthy tree production and stimulated
secondary overstory growth as evident through past measurement studies (Coates et al., 2009;
Brown et al., 2010) can be characterized under the framework of an ecosystem resilience
concept. Such resilience is defined by the capacity of an ecosystem to resist or tolerate
disturbance and not collapse into a chaotic state controlled by different processes. Ecosystems
build this resilience allowing them to withstand shocks and rehabilitate themselves (Holling,
1973). This resilience has also been characterized as the amount of time required for the return to
stable state conditions following disturbance. More recent interpretations of the resilience
concept have introduced an adaptive capacity component. Gunderson et al, (2000) defined this
capacity as the control on the strength of ecosystem resilience. In other words the adaptive
capacity is the process that modifies resilience, while resilience mediates ecosystem transition
between stable states.
The framework of ecosystem resilience can be characterized as a function of ecosystem
response to abrupt changes based on the following dynamics: (1) adaptive cycles, (2) panarchy,
(3) adaptability and (4) transformability (Walker et al., 2006).
The adaptive cycle characterizes the succession of an ecosystem post-disturbance. The
first stage is characterized by increased growth in response to readily available resources. The
second stage is characterized by increased susceptibility to disturbance as the system develops
complex connections between its components and resource limitation increase. Following
disturbance the ecosystem experiences a release of bound-up nutrients which leads to a
reorganization phase where non-disturbed species can take hold leading to the initiation of a new
growth phase (Gunderson et al., 2000).
7
Panarchy refers to the nonlinear response of ecosystems to disturbance through adaptive
cycles and regime and threshold alterations at multiple scales and with cross-scale interactions
(Gunderson and Holling, 2002). This implies that ecosystems function based on both top-down
and bottom-up cross-scale interactions. Observed shifts in ecosystem dynamics due to
disturbance occur due to cross-scale interactions ranging from individual plants to entire
ecosystem (Walker et al., 2006). This concept is fundamentally the opposite of a hierarchy which
employs a rigid top-down structure not allowing for adaptive change.
Adaptability is the capacity of ecosystem components to manage their resilience to
disturbance while transformability is the capacity of an ecosystem to transform into a
fundamentally new state governed by the same processes as the previous one (Walker et al.,
2006). Adaptability can be related to the amount of biodiversity within a system. Even
ecosystems dominated by a single species may contain sufficient biodiversity to maintain
resilience against disturbance (Peterson et al., 1998). A large scale disturbance directed at the
dominant ecosystem species may effectively transform the system to one dominated by non-
disturbed species. Walker et al. (2006) argued that such transformations may result in the
creation of new ecosystems which may be more resilient than those prior to the disturbance.
A process-based modeling approach, driven by remote sensing inputs (LAI) for C cycle
quantification as a function of disturbance may at least partially capture some components of
ecosystem resilience. Washington-Allen et al. (2008) argued that ecosystems changes based on
vegetation indices observed by satellites can partially quantify ecosystem resilience as a function
of the amplitude and malleability of the changes. Amplitude was defined as the magnitude of
recovery post disturbance while malleability was the degree of recovery as quantified by
vegetation indices. As such, process-based models driven by LAI calculated from vegetation
8
indices may be able to partially quantify ecosystem resilience. Furthermore, a process-based
approach may provide a more robust representation of C cycling dynamics post-disturbance than
an empirical approach. Empirical models typically function with a high degree of accuracy under
a constrained set of parameters. This resembles a hierarchical framework in that such models
impose a set of rules which govern ecosystem function and are not flexible in the applicability of
these rules under changing conditions. Conversely, a process-based approach functions with
greater accuracy outside the constrained parameter range somewhat resembling a panarchical
framework. Such an approach may be advantages in the examination of severely disturbed
ecosystems since it imposes rules which govern ecosystem function, but those functions are
allowed to change based on environmental circumstances as a result of disturbance.
9
CHAPTER 2.0: MOUNTAIN PINE BEETLE DYNAMICS
The mountain pine beetle has been called the “most damaging biotic disturbance in
mature lodgepole pine forests in western Canada” (Safranyik and Wilson, 2006). In order to
examine the effects of MPB on C cycling some brief description of MPB dynamics will be
provided. MPB physiology, life cycle and host interaction will be examined in the subsequent
sections. Although the species is native to BC’s forests, the recent outbreak is an order of
magnitude larger in both spatial extent and severity to any previously recorded (Safranyik and
Wilson, 2006). The factors influencing this unprecedented outbreak will also be examined.
2.1 MPB biology
The MPB, like other taxa in the bark beetle (Scolytidae) family, reproduces in the inner
bark of the host tree. It typically ranges in size from 0.2 to 0.8 cm and exhibits a one year life
cycle (Safranyik and Wilson, 2006). This cycle is characterized by four life stages: egg, larva,
pupa and adult, of which all except the dispersal phase of adulthood occur within the host tree. In
late July to mid August female beetles emerge from their host trees and disperse. Typically this
is done in short below canopy flights to adjacent trees. Some evidence for long range dispersal
above the canopy exists but the fraction of beetles observed to initiate such action is usually less
than 3% for any given population (Cole and Amman, 1980). Some beetles may be caught in
warm convective winds, which can carry them distances of more than 20 km (Furniss and
Furniss, 1972).
The pioneer beetles (first to emerge and disperse) have been shown to prefer mature trees
due to their relative insulating capacity during winter months, greater food availability (phloem
tissue) and relative lack of resistance as compared with young trees. Such a selection functions
on the basis of visual cues. The beetle chooses trees whose projected silhouettes average about
10
80 cm in diameter (Shepherd, 1966). Furthermore, it has been shown that beetles prefer injured
or diseased trees presumably based on odours given off by the tree (Gara et al., 1984). The
dispersal duration usually last between 7 to 10 days, with the majority of beetles finding suitable
hosts within 2 days. Optimum temperatures for emergence and dispersal typically range between
22 to 32oC (Safranyik, 2004).
After the beetle lands on the host tree it begins to bore a hole into the soft phloem tissue
constructing galleries parallel to the direction of the stem in which eggs will be laid (Safranyik
and Wilson, 2006). At this point the female beetle does two things critical to the successful
colonization of the new host. The first is the introduction of a blue stain fungus (Ophiostoma
clavigerum or Ophiostoma montium) into the host tree tissue. This fungus has evolved a
symbiotic relationship with the MPB. The fungus prevents the tree from releasing resins to the
newly bored hole in an effort to expel the invading beetle. Furthermore, it disturbs the transport
of water and nutrients within the tree (Safranyik and Wilson, 2006). The second is the release of
a pheromone called trans-verbenol which attracts male beetles and initiates a mass attack
(Boeden et al., 1987). These two strategies effectively eliminate the natural tree response nearly
ensuring successful colonization and subsequent tree mortality. The newly arrived male beetles
mate with the females and the females lay their eggs in the constructed galleries. The females
continue to lay eggs until the fall at which time temperatures drop and end the process.
Larvae hatch from the eggs within approximately 14 days and begin to feed on the soft
phloem tissue of the tree. This is done in a horizontal direction effectively cutting of water and
nutrient flow to the tree crown and inducing tree mortality (Safranyik and Wilson, 2006). In late
October and November triggered by temperature declines the larvae stop feeding. Cold tolerance
of up to -40oC in the winter has been shown in MPB populations because the accumulation of
11
glycerol in the blood stream acts as an antifreeze (Bentz and Mullins, 1999). In early spring the
larvae resume feeding and triggered by warming temperatures transform into pupae by early
June. By late June to mid July new adults are hatched and depending on temperature emerge and
restart the cycle of infestation (Safranyik and Wilson, 2006).
Three distinct stages of an MPB attack are reflected in the resulting colour changes of
affected tree stands. The first stage is the green attack stage at which the trees have become
infected with beetles but have not yet shown significant signs such as foliage discoloration.
During this stage which lasts until the following spring the tree shows signs of decreased foliage
moisture but does not exhibit a dramatic drop in production (photosynthesis) (Brown et al.,
2010). The second stage is the red attack stage typically occurring during the following summer
after the initial attack and lasting for one to two years. This stage is characterized by tree
mortality and the subsequent change of foliage to a yellow brown then red colour. The final stage
is the grey attack stage during which time the tree foliage turns grey brown and begins to drop
rapidly. Trees in the grey attack stage are termed standing dead and can remain standing for up to
ten years following the attack (Cole and Amman, 1980).
The spatial pattern of MPB attack in a tree stand is characterized by a patchy infestation
with gradual outward spread. It is rare for beetles to infect entire tree stands in a given year due
to the nature of their dispersion. Stands become devastated (90% killed trees) after successive
years of infestation (Taylor and Carroll, 2004).
2.2 Factors influencing the MPB outbreak
Outbreaks are typically initiated by periods of decline in host resistance which may be
induced by prolonged drought conditions and after tree diseases which reduce the vigour of host
populations. Favourable conditions for rapid beetle spread include several consecutive years of
12
warm and dry weather with particularly mild winter temperatures (Safranyik and Wilson, 2006).
Outbreaks begin in stands where average tree diameter is large exhibiting a clumped distribution
which expands outward. The initial characteristic of an outbreak is the appearance of large
infestation centres and new attack areas well beyond those centres, suggesting long range
dispersal of beetles. The initial factors controlling outbreaks are density dependant – clusters of
acceptable host trees and proper tree age characteristics. Such density dependant factors have
been shown to control beetle populations during non-outbreak years (Safranyik, 2004). On an
ecosystem scale density independent factors of climate dictate the distribution of the outbreak
causing a synchronicity of beetle outbreaks amongst disjointed areas (Safranyik, 2004).
Outbreaks typically display annual population increases 2 to 4 times those of non-outbreak years
(Safranyik, 2004).
Outbreaks typically last 5 to 10 years and then subside when conditions for further
spreads become unfavourable. The occurrence of severely cold weather in the spring or late fall
can interrupt the natural beetle cycle and induce wide spread mortality. The depletion of
available host trees caused by the rate of beetle mortality exceeding the amount of new beetle
production due to the colonization of fewer trees will ultimately end the outbreak (Safranyik and
Wilson, 2006). Some evidence exists for an increase in surviving host tree resistance to MPB
attack by increased growth efficiency beginning 3 – 5 years after the initial attack. The typical
period of intense outbreak prior to a collapse of 5 – 10 years may be controlled by the increase in
resistance of surviving host trees (Waring and Pitman, 1985).
The current MPB outbreak in BC is characterized by exponential increases in attack area
from 1999 to 2007. Three critical explanatory factors have been established for the
unprecedented nature of the current MPB attack: (1) gradual climate warming allowing for beetle
13
spread into previously unsuitable environments; (2) insufficient winter minimum temperatures to
induce larvae die back; (3) the widespread abundance of mature host trees (Taylor and Carroll,
2004).
Climate change, particularly warming over the last century in BC was characterized by
average annual temperature increase of 0.75oC and average winter time temperature increases of
2.5oC according to the Pacific Climate Impacts Consortium, an independent climate analysis and
monitoring network located in Victoria BC. Such changes have influenced MPB dynamics
significantly. The rapid expansion of MPB over the last century is directly a result of insufficient
winter temperatures required to kill larvae hibernating within the trees. A sustained period of
several days of < -40oC temperatures is required to achieve significant larvae mortality. Such
temperatures have not been prevalent in BC as a result of substantial winter time warming
(Taylor and Carroll, 2004). Furthermore, according to (Carroll et al. 2004) who used historic
climate data and digital terrain models of BC, the latter half of the 20th century saw dramatic
increases in previously unsuitable MPB habitats further north and at higher elevation. Such shifts
in climate have allowed the MPB to spread at its current rate and encompass an area of
unprecedented size. If current climate trends persist the potential for MPB spread eastward into
the vast boreal expanse of Canada may increase. This threat is largely dependant on the capacity
of the MPB to infect non-favourable pine species such as jack pine (Pinus banksiana).
Lodgepole pine trees are considered fire-dependent in that during the heat of crown fire
seeds are released from the tree cones and relatively even aged stands are established replacing
those that burned. The 60 years recurrence interval of stand replacing fire events has traditionally
dictated the life cycle of BC’s lodgepole pine forests. In this way forests age structure reached a
steady state following a negative exponential distribution resulting in stand age approximately
14
equal to the fire cycle duration. This mechanism effectively controlled the susceptibility of
forests to MPB attack by preventing large areas of mature or overly mature pine trees (Safranyik
and Wilson, 2006). Due to the implementation of strict fire suppression policies in an effort to
protect property interests in BC since the start of the last century a steady decline in area burned
has occurred. The occurrence of stand replacing events decreased from 1% to 0.3% from the
1920’s to the 1980’s (Taylor and Carroll, 2004). This has resulted in an increase in the
availability of host trees in terms of both amount and density. (Taylor and Carroll, 2004)
estimated that the amount of mature (host) trees at the start of the century reached 50% of the
total population rising from on average 18% during historical fire cycle regimes.
15
CHAPTER 3.0: METHODOLOGY FOR LAI MAPPING
The first objective of this study was to quantify the impact of MPB on LAI derived from
remote sensing sources. LAI is the primary parameter for estimating the effect of ecosystem
changes on vegetation status. It is primarily a measure of ecosystem health, capturing the spatial
and temporal dynamics of changes to said health as a function of large scale disturbances. On a
regional scale, LAI can be derived quite accurately from satellite based data. Furthermore, in
order to investigate ecosystem resilience impacts on LAI satellite based investigations can
provide regional scale insight. With overstory mortality due to MPB, opportunities for secondary
species and understory components become enhanced. Secondary structure may begin to thrive
under such conditions in effect mitigating the overall beetle impact on the ecosystem. Both the
overstory and secondary overstory are captured within satellite derived LAI, in effect producing
a measure of net beetle impact. As such LAI is considered a critical parameter and is a primary
driver of ecosystem modelling as part of the second study objective.
The following sections outline in some detail the steps required for producing LAI maps based
on satellite imagery data for MPB affected areas of BC. The basic step by step methodology is as
follows: (1) satellite image reflectance data are first downloaded and processed to fit the study
area; (2) the reflectance data are converted to a vegetation index which estimates the ‘greenness’
or vigour of the observed vegetation; (3) based on empirically derived relationships the
vegetation index is converted to LAI; (4) the LAI maps are statistically smoothed in order to
eliminate residual atmospheric effects; (5) annual LAI maps are complied for analysis and
presentation purposes and for their subsequent function as key inputs for ecosystem modelling of
the C cycle. A visual representation of the LAI map production methodology is outlined
in Figure 2.
16
Figure 2. General procedure for remotely sensed leaf area index (LAI) production and mapping applied to SPOT VEGETATION reflectance data.
17
3.1 Satellite data acquisition and processing
The French-led satellite sensor VEGETATION on board the Satellite Pour l'Observation
de la Terre (SPOT) acquired the raw satellite imagery data used in this study. SPOT was
developed by the French Space agency in conjunction with the Belgian Scientific and Technical
Service and the Swedish National Space Board. The images were downloaded from the Flemish
Institute for Technological research free VEGETAITON products website
(http://www.vgt.vito.be). The VGT sensor functions on a 1 km resolution with a field of view of
2400 km and covers the earth on an almost daily basis. The VEGETATION reflectance data are
acquired in 4 bands – red (B2, 610 – 680 nm), green (B0, 430 – 470 nm), near infrared (B3, 780
– 890 nm) and middle infrared (MIR, 1580 – 1750 nm) making it ideal for determining regional
scale changes to vegetation health (level of greenness through vegetation indices). Daily Images
of North America are synthesized (based on maximum NDVI) from east to west ground swaths
every 10 days in order to reduce the effect of cloud cover. The images are acquired with the S10
processing level which includes: (1) the application of the simplified method for atmospheric
correction (SMAC) which produces estimates of surface reflectance (Rahman and Dedieu,
1994); (2) maximum projected instantaneous filed of view (PIFOV) across track growth of less
than 1.35; (3) the application of a cubic re-sampling algorithm which generates a uniform 1 km
grid distribution with geometric registration better than 540 m at the 95% confidence interval; (4)
the application of both geometric and radiometric correction (Anonymous, 1999).
Using remote sensing software, PCI Geomatica the North American images were cropped
to show only the maximum extent of MPB affected areas in BC with the coordinates for the top
left (TL) and bottom right (BR) corners as follows: TL = 57.0135 N, -127.9530 W and BR =
48.9977 N, -114.7244 W. The four separate reflectance images for each 10 day synthesis were
18
compiled to a single image with three layers – red, near infrared and middle infrared. 36 annual
multi layer reflectance images were produced in this manner spanning the 10 year study period
from 1999 to 2008. A geographic LAT/LONG coordinate system with WGS83 datum was
applied to the images. For mapping and analysis of the final LAI maps the North American
Datum 1983 British Columbia Environmental Albers projection (NAD83_BC_env_albers) was
used.
3.2 Reduced simple ratio vegetation index
The process of determining land surface parameters from spectral reflectance data
involves the calculation of a vegetation index. Such indices are based on semi-empirical
relationships between the reflectance of vegetation and the desired land surface parameters. Such
an approach is rather simplistic but methods utilizing process-based relationships inverted
against observations in order to determine the value of land surface parameters are still in the
early developmental stages (Verstraete and Pinty, 1996; Deng et al., 2006). The fundamental
basis of all vegetation indices is derived from an attempt to isolate the spectral contribution of
vegetation overstory from the undesired influence of the background. This is achieved by
exploiting the fact that green vegetation absorbs solar radiation in the visible wavelengths –
particularly the red band (RED) (630 – 690 nm) and scatters solar radiation in the near infrared
band (NIR) (750 – 900 nm) (Verstraete and Pinty, 1996; Stenberg et al., 2004; Brown et al.,
2000). The interface between the RED and NIR band (700 nm) i.e. the ‘red edge’ at which a
sharp increase in scattering occurs is not present in non-vegetated spectra particularly bare soil. It
is therefore possible to determine the level of greenness (the amount of green vegetation) by
simply taking the ratio of some combination of RED to NIR reflectance (Stenberg et al., 2004).
3.2.1 Overview of vegetation indices used in LAI calculation
19
A large amount of vegetation indices have been developed for the quantification of
ecosystem characteristics. In the interest of this study focus will be given to reviewing only two
of the most commonly used: (1) normalized difference vegetation index (NDVI) (Deering,
1978); (2) simple ratio vegetation index (SR) (Jordan, 1969). The effectiveness of these indices
in determining LAI will be examined in the subsequent paragraphs.
The NDVI index was first developed as a means to investigate biophysical characteristics
of rangeland vegetation of the Great Plains region in the central United States. A major issue for
correlating satellite reflectance data to biophysical parameters was the wide expanse of the Great
Plains region which encompassed several latitudes and consequently solar zenith angles. The
NDVI index was the solution which effectively normalized the influence of multiple zenith
angles on land surface parameter retrieval (Deering, 1978). The NDVI index is presented as
follows:
(1)
where ρNIR and ρRED correspond to near infrared and red reflectance respectively. NDVI values
range from -1 to 1 with high values around 0.6 signifying very healthy green vegetation with
near complete crown closure.
In response to the need for rapid LAI measurements in order to determine productivity
and chemical element cycling Jordan (1969) developed the SR vegetation index. Prior methods
included basic destructive sampling measurements and their correlation with optical density
measurements. Such procedures were very labour intensive and prone to error in that the optical
density measurements were required to be taken simultaneously both above and below the
canopy. The SR index is presented as follows:
20
(2)
where the variables correspond to those of the NDVI index. For vegetation, SR values are always
positive as NIR reflectance is typically larger than RED reflectance and values range from 1 to 8
corresponding with LAI of .5 to 6 for conifer species.
Both NDVI and SR have been shown to correlate well with LAI at a very site specific
level and with a strong cover type dependency (Jakubauskas and Price, 1997; Chen et al., 1999a;
Nilson et al., 1999; Hasegawa et al., 2010). In order to utilize NDVI or SR on a regional scale,
vegetation-cover-type specific relationships with LAI need to be determined. This results in the
need for NDVI and SR to be sensitive to small changes in LAI throughout its natural range of
values. This is not the case with NDVI or SR and as such they tend to saturate at high levels of
LAI resulting in inaccurate LAI retrieval in this range. Furthermore, due to the varying levels of
canopy closure with cover type and within cover type, NDVI and SR to LAI relationships need
to be adjusted for varying degrees of background reflectance interference. Attempts to scale the
background effect include the soil adjusted vegetation index (SAVI) witch is NDVI modified by
a coefficient representing vegetation density (Qi et al., 1994). Such processes require site specific
forest density information and limit the usefulness of the index on a regional scale. Therefore
both the SR and NDVI indices are sensitive to cover type, require fairly site specific background
reflection correction and tend to saturate at high LAI values. Particularly for regional scale
coarse resolution remote sensing studies an index which alleviates the above mentioned
problems is required.
3.2.2 Principles and advantages of the RSR vegetation index
The reduced simple ratio (RSR) vegetation index was created with the primary purpose
of reducing background vegetation effects on LAI retrieval. The foundation for such research
21
was laid by Nemani et al. (1993) who investigated the inaccuracy of NDVI-based LAI retrieval
at the watershed scale and attempted to remedy the problems with a middle infrared band (MIR)
(1550 – 1750 nm) scalar correction. The RSR index is presented as follows:
1 (3)
where ρMIRmin and ρMIRmax correspond to the minimum and maximum observed middle infrared
reflectance values of vegetation. These values correspond to areas with canopy closure and areas
with no overstory vegetation respectively. Chen (1996a) investigated the vegetation index to LAI
relationship for Canada’s Boreal region and found highest correlations between the SR to LAI
relationship. Furthermore, it was acknowledged that a MIR modification may improve the
quality of the SR to LAI relationship. Following Nemani et al. (1993), Brown et al. (2000)
applied a MIR modifier to the SR index in order to reduce background effects. The principle of a
MIR modifier stems from the fact that increases in liquid water contained in vegetation in the
background result in decreases in MIR reflectance. By applying a MIR scalar the SR is reduced
by the difference in observed MIR reflectance and the MIRmin reflectance (value for an area with
canopy closure).
Brown et al. (2000) used a canopy reflectance model (4-scale geometric-optical
bidirectional reflectance model) (Chen and Leblanc, 1997; Leblanc et al., 1999) to quantify the
effect of background reflectance on total canopy reflectance for various conifer and deciduous
dominated sites. The results showed that the MIR band reflectance was inversely and
curvilinearly proportional to LAI. Furthermore, due to the similarity of MIR reflectance for
varied backgrounds and the large sensitivity of MIR to LAI, the RSR index performed better
than the SR index alone. This resulted in increased correlation of 30% for RSR to LAI
relationship as compared with the SR to LAI relationship. Furthermore, RSR showed larger
22
increases than did SR for small increases in LAI resulting in the elimination of saturation at high
values. A major advantage of the RSR index particularly for coarse resolution LAI retrieval for
which the percentage of vegetation cover types per pixel is unknown is its ability to unify cover
types. The RSR index is not as specific to cover type as are the SR and NDVI indices
respectively. Brown et al. (2000) underlined this point by merging deciduous and coniferous data
during RSR to LAI correlation. It was shown that the RSR to LAI relationship increased by 76%
in comparison to the SR to LAI relationship for the mixed cover type data. This is of particular
importance for LAI retrievals in MPB affected areas in that secondary structure regrowth
following overstory desiccation is typically characterized by significant amounts of deciduous
species. Consequently the temporal dynamics of vegetation cover in MPB affected areas
gradually change from conifer dominated to nearly 50% deciduous.
The three main advantages of the RSR vegetation index include: (1) ability to reduce the
influence of background vegetation on LAI retrieval by the MIR scalar; (2) relative insensitivity
to cover type; (3) and the lack of saturation at high LAI values. Further investigation into the
relationship between LAI and RSR, SR and NDVI is provided in the subsequent LAI
measurement results chapter.
3.2.3 Methodology for RSR application to SPOT VEGETATION reflectance data
The 36 annual 4 band reflectance images compiled from the VEGETATION sensor were
converted to the RSR vegetation index. The choice of MIRmin and MIRmax values can
dramatically affect the resulting RSR values. It is recommended that the chosen values be based
on field measured reflectance or some cover type specific values rather than simply selecting the
minimum and maximum value from the MIR histogram for the entire image (Brown et al.,
2000). The presence of water, snow and damaged pixels can dramatically distort the MIRmin and
23
MIRmax values. Since the study area was too large for obtaining measured MIRmin and MIRmax
values a 1% cut off point was used in the MIR histogram to determine said values on a scene by
scene basis. Furthermore, to isolate the MIR effect on coniferous vegetation the MIR histogram
was computed for the cumulative extent of the MPB outbreak. In other words the MIRmin and
MIRmax values were chosen only from the pixels used in this study under the assumption that the
affected areas are predominantly conifer vegetation. This procedure ensured the elimination of
contained pixels and the influence of water and snow on MIR reflectance while isolating the
MIRmin and MIRmax values for coniferous vegetation alone.
3.3 Algorithms for LAI retrievals
Several methodologies have been used to convert between a vegetation index and LAI;
they include: (1) empirically derived relationships with some dependence on radiation transfer
models which simulate the relationship between the various spectral components of a vegetation
index and LAI (Chen et al., 2002; Sellers et al., 1996; Myneni et al., 1997b; Cihlar et al., 1997;
Liu et al., 1997); (2) the use of radiation transfer models to invert physically based parameters
and their relationships with vegetation index components to LAI (Bicheron and Leroy, 1999;
Bacour et al., 2002); (3) and model-derived lookup tables which provide lists of parameters of
interest coupled with measured spectral reflectance (Deng et al., 2006; Pisek et al., 2010). This
study employs the empirical approach, which is the simplest, yet relatively effective on a
regional scale when field data are available for validating the approach. Prior to discussing the
specifics of the empirical approach some brief description of methods 2 and 3 will be discussed.
3.3.1 Overview of LAI algorithm development methodologies
A completely physically based approach which functions on the basis of relationships
between LAI and satellite derived data such as individual spectral band reflectance and
24
vegetation indices is viewed as the future for satellite based LAI retrieval. Such an approach is
based on the inversion of canopy radiation models which are based on physical processes
theoretically implying much greater accuracy in LAI retrieval than simple empirical approaches
(Deng et al., 2006). As such this type of method requires large amounts of data inputs resulting
in significant computation resource requirements. Furthermore, the interaction between radiation
and the canopy is an inherently complex process and the number of iterations of the inversion
necessary to provide an appropriate solution is often extremely large particularly at regional to
global scales.
A more popular method is that of coupling physically based inversion methods with
simple look up tables which provide some of the necessary variables which would normally be
calculated by a completely physically based approach. This type of approach is particularly
effective for global applications which exhibit a large number of cover types for which site
specific relationships do not apply. Deng et al. (2006) utilized such an approach for global
applications with the consideration of background reflectance through the use of the RSR index
in the inversion process. Pisek et al. (2010) further developed this approach by incorporating a
seasonally variable background vegetation spectrum function. This was necessary due to the
conclusion that understory effect was not completely removed by the RSR index alone
(Rautiainen, 2005; Pisek and Chen, 2007).
3.3.2 Empirically based method for LAI derivation based on the RSR vegetation index
The empirically based methodology for LAI retrieval is primarily based on measured
relationships between LAI and vegetation indices. The algorithms used for converting between
RSR and LAI are adapted from Chen et al. (2002). The algorithms are presented as follows:
25
Coniferous 1.242 (4)
Deciduous 3.86 1 9.5 (5)
Mixed 2.93 1 9.3 (6)
Crop, Grass 1.3 (7)
Since the MPB affected areas are by definition conifer dominated and due to the lack of within
pixel cover type distribution information only the coniferous type algorithm was used. These
algorithms are based on the relationship between RSR and LAI derived form a large number of
measurements both during the Boreal Ecosystem – Atmosphere Study (BOREAS) and
subsequent Canada-wide work by Chen et al. (2002). The original BOREAS algorithms (Brown
et al., 2000; Chen, 1996a) were limited to Manitoba and Saskatchewan and employed published
cover type relationships without the use of actual regional measurements. Thus, Chen et al.
(2002) modified these existing algorithms by incorporating cover type specific relationships
between RSR and LAI and by expanding the area of measurements to geographically diverse
study sites spanning the entire Canadian landmass. Furthermore, the simple empirical
relationships were improved by a 4 scale bidirectional reflectance model to incorporate the
effects of solar zenith angle and view angle on LAI retrievals (Chen and Leblanc, 1997). This
process corrects the satellite input data to a common 45 degree solar zenith angle and sensor
view angle (NADIR). Such a normalization functions adequately particularly on regional scales
in incorporating the angular effects on the RSR – LAI relationship without inducing severe
normalization errors (Chen et al., 2002). The accuracy of the new LAI algorithm was tested by
comparing coarse resolution (1 km) LAI maps with fine resolution (30 m) maps derived using
the same procedure and downscaled to match the coarse resolution. The total per pixel error was
26
found to be between 25 and 50% (calculated as the ratio between RMSE to the average LAI of
the scene) for all cover types which coniferous pixels exhibited the smallest levels of error
(<25%). Furthermore, the coniferous sites exhibited the largest correlation coefficients R2 = 0.63
to 0.78. These errors were attributed primarily to residual atmospheric effects which are
effectively removed by applying a smoothing methodology to the LAI maps described in the
next section.
The accuracy of this empirical approach for correlating LAI to RSR is discussed in
subsequent chapters, but the following is important to note at this point. The field measurements
taken at BC sites by this study were incorporated into the conifer algorithm outlined in equation
4 and no significant variation was found. Therefore, the algorithm for conifer LAI retrieval based
on RSR was utilized in the above outlined form. Due to the presence of damaged pixels, snow
cover and open bodies of water, a small percentage of LAI values (<1%) were unrealistically
small (negative values) or large (values >10). For this reason a constraint function was applied to
all LAI maps which limited LAI values to a range between 0 and 9.9.
3.4 Seasonal LAI trajectory smoothing by the locally adjusted cubic spline capping method
A major problem with satellite based retrieval of land surface parameters - particularly
LAI is the influence of atmospheric effects on reflectance. Components of the atmosphere such
as aerosols, water vapour, ozone, dust and clouds distort the reflectance signal particularly in the
red and NIR bands (key components in calculating LAI) (Vermote et al., 1997). The
VEGETATION datasets used in this study have been subject to atmospheric correction
procedures by utilizing real-time atmospheric observations during the retrieval of land surface
parameters. Measurements of pollutants, particularly aerosols, are used to scale the retrieved
reflectance values in order to remove their effects. Additionally, the VEGETATION sensor
27
reduces the effect of clouds by utilizing a ten day composite period. Such a technique attempts to
minimize cloud effects by utilizing the best value over a ten day period. Regardless of these
attempts at accuracy it has been shown that relatively long composite periods of 1 month can
produce significantly overestimated red band results as a function of the lingering aerosol bias
(Vermote et al., 1997). Red band overestimation results in underestimation of LAI by virtue of
underestimated RSR values. These effects manifest themselves in the form of underestimated
LAI values, dramatic inter-monthly LAI increases or decreases and inconsistencies in seasonal
trajectories (Cihlar et al., 1997). Pisek and Chen (2007) found abrupt changes in LAI of up to 6
over a period of 10 days while this study notes monthly LAI variability of up to 90%. The LAI
maps produced by this study represent only coniferous forests, which maintain their foliage year
round. Regardless of this a distinct seasonal pattern of LAI is evident with decreasing values
around a summer season maximum. This is due to the nature of the reduced simple ratio
vegetation index used to compute LAI. The index is sensitive to the chlorophyll content (level of
greenness) of the needles which decreases as a function of decreasing temperature due to the
reduction of plant functions in colder months (Brown et al., 2000). Furthermore, snow cover
inhibits the accuracy of spaceborne LAI detection. Due to the erratic variation in inter-monthly
LAI values as a function of residual atmospheric effects and the well defined seasonal trajectory
of LAI values for conifer forests a post processing correction technique is needed to establish
accurate LAI maps.
3.4.1 Overview of techniques for correcting seasonal LAI patterns
Different techniques have been used as a form of quality control in the derivation of land
surface parameters. The simplest methods utilize the identification of erroneous data and
correction of such data based on temporal interpolation (Henderson-Sellers, 1982; Simpson and
28
Gobat, 1996; Viovy et al., 1992). A second technique involves the use of Fourier analysis to
apply a fast Fourier transform (FST) algorithm in order to limit extremely high and low values
(Verhoef et al., 1996; Roerink et al., 2000). Such an approach requires temporally equidistant
and relatively cloud free observations. Since cloud free days are relatively rare at any given pixel
only two harmonic frequencies are used, representing the annual and semi-annual LAI cycles
respectively. The resulting curves do not allow for sufficient variability in seasonal LAI and are
often too rigid to simulate realistic seasonal changes. Such changes include the rapid vegetation
change at the beginning and end of the growing season (Chen et al., 2006a). With respect to this
study such a technique is insufficient in capturing rapid secondary overstory and understory
development as a result of overstory mortality. In order to alleviate the need for relatively cloud
free observations at a pixel Viovy et al. (1992) proposed a curve fitting technique by which data
smoothed according to a curve fitted by utilizing capped values from a moving window rather
than a single pixel. This technique relies upon several good observations within a moving
window and thus is highly sensitive to the size of a chosen window. This technique results in a
mathematically unsmooth curve which is incapable of producing accurate values of first and
second derivatives needed to predict land surface parameters (Chen et al., 2006a).
3.4.2 Locally adjusted cubic-spline capping method for correcting seasonal LAI patterns
Due to the inadequacies of the above mentioned curve fitting techniques Chen et al.
(2006a) proposed a locally adjusted cubic spline capping (LACC) method for correcting the
seasonal trajectories of land surface parameters. The LACC method is based on a locally
weighted regression method called LOESS which was developed by Cleveland and Devlin
(1988). Rather than utilizing polynomial functions to fit curves to the desired data the LOESS
method employs multivariate functions in order to smooth the dependant variable as a function
29
of the independent variable based on a moving average window. The application of the LOESS
method is as follows: A curve of desired shape is fitted to the time series to be smoothed. Such a
curve is chosen based on the nature of the data set, for example if the time series exhibits
substantial curvature a locally quadratic function is applied. Based on the chosen window size
the departure of the data points from the fitted curve is calculated and each point is assigned a
weight. The final curve is fitted based on the above calculated weight departures of each data
point from the originally fitted curve. Weights are the smallest for points exhibiting the largest
departure from the originally fitted curve and the weight is symmetric both above and below the
curve (Cleveland and Devlin, 1988).
The LOESS method in its original form cannot be applied directly to remotely sensed
image data due to two fundamental problems; (1) the symmetric weight distribution does not
correctly alleviate the problem of atmospheric contamination found in remotely sensed data due
to the fact that such errors typically result in negative biases (i.e. underestimation) in the
retrieved seasonal trajectory of vegetation indices; (2) the initial curve fitting procedure requires
control over the smoothness of the curve. A uniform global smoothing parameter such as a linear
or quadratic function is inadequate since land surface parameters typically do not follow a
uniform seasonal trajectory.
The LACC method proposed by Chen et al. (2006a) remedies the above mentioned
inadequacies and allows for accurate smoothing of remotely sensed surface parameters
particularly LAI. In order to eliminate the problem of equal weight distribution the LACC
method employs a cubic spline curve for the calculation of weights at every data point. A cubic
spline does not require an initial control of curve smoothness and can therefore be fitted exactly
to the observed data points resulting in no departure from the curve and the data points. A
30
smoothing parameter is then applied to the curve in order to identify abnormally low values,
which are replaced by values found on the smoothing curve. Abnormally high values are left
unchanged at this stage in the process. An iterative procedure is repeated three times in order to
determine an effective capping or upper boundary for the original data series. Since the cubic
spline curve can fit even abrupt changes in the seasonal trajectory an erratic control coefficient is
used to identify problematic data points and smooth them accordingly. Such a control allows for
the removal of abnormally large LAI retrievals while maintaining the systematic changes in LAI
associated with increases and decreases in plant function near the beginning and end of the
growing season respectively.
The LACC method is a superior curve fitting technique for land surface parameter
correction, in that it can produce mathematically smooth curves without the omission of rapid
seasonal changes such as leaf on and leaf off events, while accurately correcting atmospherically
contaminated data points by temporal interpolation (Chen et al., 2006a).
3.4.3 Results of LACC smoothing on VEGETATION LAI
The LACC smoothing technique was applied to the seasonal trajectories (36 images) of
LAI for all years from 1999 to 2008. Figure 3 shows the results of smoothing for the Crooked
River Flux tower site (MPB-03) for years 1999 and 2007 before and after the MPB attack
respectively. The rapid changes in LAI, at both the beginning and end of the growing season are
31
Figure 3. Comparison between original (raw) VEGETATION leaf area index (LAI) and smoothed LAI by the locally adjusted cubic spline capping (LACC) method at the MPB-03 flux tower site north of Prince George British Columbia for (a) 1999 and (b) 2007, before and after the mountain pine beetle (MPB) attack respectively.
well captured by the smoothed LAI. Such changes are attributed to the rapid response of semi
dormant coniferous vegetation and the rapid growth of secondary overstory particularly
deciduous species (Aspen, Birch and Willow). Of particular interest is day 241 (Late August) in
2007 which exhibits an 86% LAI drop for a ten day period (from 1.4 to .2). Such a dramatic
decrease is unrealistic and can be attributed mainly to an erratic value not filtered out by the
initial VEGETATION atmospheric correction. The smoothed curve reduces this dramatic LAI
decrease to a more acceptable 25% decrease.
It was beyond the scope of this study to evaluate the effectiveness of the LACC
smoothing method but it has been shown to produce accurate LAI trajectories based on
comparisons with field measurements and various numerical experiments. By introducing
random LAI decreases of up to 100% to 55% of an originally smooth curve and then
reconstructing the series using the LACC method Chen et al. (2006a) concluded that 92% of the
artificial error can be successfully removed with three iterations of LACC. Furthermore, Pisek et
0
0.5
1
1.5
2
2.5
3
3.5
1 51 101 151 201 251 301 351
LA
I
DOY
(a) 1999 rawsmoothed
00.20.40.60.8
11.21.41.61.8
2
1 51 101 151 201 251 301 351
LA
I
DOY
(b) 2007 rawsmoothed
32
al. (2007) introduced isolated erratic values (5 times LAI increase) at the beginning and end of
the annual LAI trajectory and found that in all cases the isolated outliers were completely
removed by the LACC method. In all cases the corrected value for the erratic data points
corresponded almost exactly with the originally retrieved non-biased data point from
VEGETATION.
3.5 LAI map production
36 LAI maps per year were created based on the above described procedure. The maps
were compiled to text format for subsequent use as inputs to C cycle investigation through
process-based, spatially distributed modelling. For the purposes of investigation (annual change
detection) and display (mapping for presentation) the July average LAI (LAImax) was calculated
under the assumption that this value is the maximum annual LAI. The utilization of LAImax
rather than average annual LAI was considered more representative of the MPB induced changes
to vegetation distribution and overstory mortality.
In order to determine the special extent of the beetle outbreak and investigate the
temporal patterns of MPB spread, aerial overview survey data were obtained from by the British
Columbia Ministry of Forests and Range (BCMoFR) (Westfall and Ebata, 2008). These datasets
contain annual images of affected area polygons derived from visual survey. Each polygon is
assigned a severity classification based on the percentage of affected trees found within it. Such
survey is made possible by the red coloration of affected trees during the first summer post
attack. The severity classification is provided in Table 1. Utilizing ArcGIS software the annual
affected area polygons were converted to raster images and overlain onto the LAI maps of the
corresponding year. The raster resolution of affected areas was then downscaled to match the 1
km pixel size of the LAI maps. LAI values were extracted for only the affected areas of each
33
year in order to assess MPB induced consequences to LAI on an annual basis. The first year
(1999) of the study was used as a baseline or unaffected LAI since levels of attack in that year
were consistent with naturally occurring historical magnitudes of attack for the past century
(<5000 km2 affected area). Annual difference images were then produced comparing all years to
the pre-attack baseline in 1999. The results of such analysis are presented in Chapter 6.
Table 1. British Columbia Ministry of Forests and Range (BCMoFR) mountain pine beetle (MPB) severity classification scheme.
Severity Class Percent Affected Area Trace (1)* <1% Light (2) 1-10%
Moderate (3) 11-30% Severe (4) 31-50%
Very Severe (5)* >50% *Classes added in 2004 due to the scale and rate of the MPB outbreak. Prior to 2004 classes 1 and 5 are contained within classes 2 and 4 respectively.
34
CHAPTER 4.0: FIELD VALIDATION PROCEDURES FOR LAI.
Figure 4. General procedure for the validation of VEGETATION leaf area index (LAI) products using ground based measurements and fine resolution satellite imagery.
In order to asses the accuracy and reliability of the annual VEGETATION LAI products
ground based measurements are required. Inaccuracies in space-born LAI retrieval stem from the
lack of surface homogeneity of the Earth’s surface. Particularly at coarse resolutions (>= 1km)
within-pixel cover type deviations can be substantial (Chen et al., 2002). A key focus of the
Canadian Carbon Program (CCP) formerly Fluxnet Canada is the regular measurement of LAI at
all flux tower sites with the intention of validating satellite derived LAI maps. The basic
procedure for LAI map validation is as follows: (1) spectral instruments are used to collect
35
ground based measurements of LAI; (2) fine resolution satellite imagery covering the same
spatial and temporal extent of the measured areas are acquired and converted to the RSR
vegetation index and correlated with measured LAI; (3) fine resolution imagery is downscaled to
1 km resolution matching the coarse VEGETATION RSR products produced by the procedure
described in the previous chapter; (4) the fine resolution images are co-registered to the
VEGETATION RSR products in order to assess the validity of the coarse resolution products;
(5) functionality of the RSR vegetation index in determining LAI is evaluated against other
commonly used indices; (6) measured LAI data are analysed and relevant relationships are
investigated. The validation procedure was carried out on the RSR vegetation index maps rather
than on LAI since the LAI algorithms require land cover information in order to produce fine
resolution LAI maps. It is inherently understandable that good correlation between fine and
coarse resolution RSR will by extension result in good correlation between fine and coarse
resolution LAI. This procedure is represented graphically in Figure 4 and is outlined in detail in
the following sections.
4.1 Site selection and description
An intensive LAI validation campaign was carried out in the summer of 2009 from June
2nd to 15th. The intention of the validation effort was to acquire as many LAI measurements in
the widest range of forest age categories for the largest geographical extent as possible.
Measurements were taken at predominantly lodgepole pine dominated sites with little to no MPB
infestation. The purpose of acquiring LAI from only healthy stands was to validate the baseline
or control LAI – the unaffected state. Measuring LAI in infected sites would yield exaggerated
values as the radiation transmitted through the canopy (the fraction which is seen by spectral
instruments) would be affected by the standing dead vegetation which retains its needles up to
36
several years following mortality. Furthermore, due to the function of spectral instruments
(discussed in detail in the subsequent section) vegetation canopies are seen as being black thus
causing the instrument to be indiscriminate to live versus dead object (foliage) in the canopy.
Figure 5. The locations of leaf area index (LAI) measurement sites including the two flux tower sites MPB-06 and MPB-03 (LAI not measured). Orange areas represent the cumulative extent of mountain pine beetle affected areas from 1999 to 2008.
37
Table 2. Summary of leaf area index (LAI) measurement site characteristics for British Columbia Interior sites measured in June of 2009.
With the aid of members of the BCMoFR research sites were selected. Efforts were
focused predominantly on the heart of MPB territory near Prince George BC in the northern
extent of MPB range. In the interest of adding robustness to the LAI measurements several sites
were selected in southern areas of the MPB extent near Penticton and Kelowna. The Prince
Site Location Site Code
Latitude Longitude Age
Mckay Lake A 53.8209 -123.7857 >100 Mckay Lake B 53.8306 -123.7951 10 - 20 Mckay Lake C 53.8302 -123.7957 30 - 60 Mckay Lake D 53.8312 -123.7970 60 - 100 Mackenzie Lakes E 53.5578 -122.9540 10 - 20 Mackenzie Lakes F 53.5721 -122.9562 10 - 30 Mackenzie Lakes G 53.5483 -122.9612 60 - 100 Mackenzie Lakes H 53.5229 -122.9818 60 - 100 Kennedy Siding I 55.1127 -122.8078 30 - 60 Kennedy Siding J 55.1356 -122.8079 10 - 20 Kennedy Siding K 55.1108 -122.8768 10 - 20 Kennedy Siding L 55.1111 -122.8158 <10 Kennedy Siding M 55.1145 -122.8066 <10 Kay Kay N 54.0933 -123.3942 10 – 30 Kay Kay O 54.0928 -123.3941 10 – 30 Kay Kay P 54.0552 -123.3364 10 – 20 Kay Kay Q 54.0478 -123.3248 >100 Kay Kay R 54.0556 -123.3636 60 - 100 Mackenzie Lakes S 53.4165 -123.0372 30 - 60 Mackenzie Lakes T 53.4449 -123.0373 60 - 100 Mackenzie Lakes U 53.4537 -123.0325 60 - 100 Mackenzie Lakes V 53.4534 -123.0316 >100 Mackenzie Lakes W 53.4387 -123.0390 <10 Kelowna X 49.9101 -120.0737 30 - 60 Kelowna Y 49.9188 -120.1729 30 - 60 Kelowna Z 49.9172 -120.1709 60 - 100 Kelowna AA 49.9049 -120.1582 60 - 100 Kelowna AB 49.8804 -120.3040 >100 Upper Penticton AC 49.6554 -119.3146 10 – 30 Upper Penticton AD 49.6390 -119.2760 >100 Upper Penticton AE 49.6504 -119.4055 >100
38
George study sites consisted of four general areas - Mackenzie Lakes, Kennedy Siding (UBC
Flux tower site, MPB-06), Kay Kay, McKay Lake and Crooked River (UBC Flux tower site,
MPB-03). The southern study area consisted of 2 general sites those near Kelowna and sites near
Penticton (Upper Penticton Creek Experimental Watershed) (Figure 5). On average one day was
spent at each site (two days at Mackenzie Lakes) during which approximately 5 measurements
were made totalling 31 measurement sites encompassing the full extent of studied areas. Effort
was made to capture the distribution of forest age categories and stand density distributions from
very young to very old and sparse to dense sites respectively. Site characteristics such as location
and stand age are displayed in Table 2.
4.2 Measurement theory, techniques and procedures
LAI measurements were made using two optical instruments: (1) LAI-2000 Plant Canopy
Analyzer (Li-COR Biosciences, Lincoln Nebraska) (Welles and Norman, 1991); (2) Tracing
Radiation and Architecture of Canopies (TRAC) developed at the Canada Centre for remote
sensing (Third-Wave Engineering, Ottawa Canada) (Chen and Cihlar, 1995).
4.2.1 LAI measurement theory
The theory behind optically based methods for LAI measurement can be presented as a
function of 2 major problems: (1) quantification of leaf angle distribution; (2) quantification of
leaf spatial distribution (Chen, 1996b).
The first problem of quantifying leaf angle distribution is largely resolved by the use of
the LAI-2000 instrument. LAI-2000 measures the gap fraction of the canopy by utilizing diffuse
radiation transmission through the canopy simultaneously for five hemispherical rings ranging in
zenith angle from 0 – 75 degrees. The radiation is seen in the blue spectrum (400 – 490 nm)
where vegetation is the darkest. This is done since blue wavelength scatter within a canopy is
39
minimal and the potential for multiple scatter effects is reduced. Nevertheless the multiple scatter
effect remains a factor and is examined in Chapter 5, section 3. The gap fraction data is then
inverted to obtain the effective LAI (Le) under the assumption of random leaf spatial distribution.
The term Le refers to the LAI incorporating leaf angle distribution, while assuming the spatial
distribution is random (mathematically outlined below) (Chen, 1996b). The angular canopy
distribution as a function of zenith angle - P(θ), is described as follows (Nilson, 1971):
Ωcos (8)
where G(θ) is the projection coefficient, Lt is the plant area index and Ω is the total foliage
clumping index. This approach follows Markov chain theory based on modifications to Beer’s
law quantifying the probability of a solar beam penetrating multiple layers of a forest canopy
(Chen, 1996a). G(θ) quantifies the leaf angular distribution defined as the ratio of the area
projected on a surface perpendicular to the radiation direction to the leaf area. If the leaves cover
the sphere uniformly this is considered to be random (G(θ) = 0.5). For a canopy with leaves at
various angles, G(θ) can be quantified by moving the leaves onto a sphere while preserving the
angular position. The assumption of random distribution is made during LAI-2000 calculations.
The consideration of leaf spatial distribution is made by incorporating the Ω term as follows:
Ω (9)
As Ω decreases canopies become increasingly clumped, in effect reducing LAI. Ω occurs in
canopies at various scales. The within shoot clumping particularly in conifer vegetation is a
substantial component of total Ω (Chen et al., 1997).
The effectiveness of TRAC is its capability to indirectly quantify the effects of various
components of Ω, in tern producing estimates of LAI that can be considered to be the ‘true’
40
value. The TRAC instrument is built using 3 photosynthetically active radiation (PAR) sensors –
two facing up for LAI measurements, one facing down to compute the fraction of PAR absorbed
by the canopy. A data logger (Campbell Scientific, Logan Utah, model CR10) and storage
module (model SM 716) make up the remaining components. The instrument takes a
measurement at about 10 mm intervals along a transect (sampling frequency of 32 Hz and
walking pace of 1m/3s). The sensor measures sunflecks on the forest floor and as such requires
direct sun light perpendicular to the direction of the transect. Based on the observed sunflecks or
the radiation transmission through the canopy TRAC determines a gap size distribution by
incorporating the mean element width (w) which is the typical size of shadows cast by the
canopy. The gap size represents the physical dimension of gaps in the canopy. This is different
from the gap fraction (percentage of gaps in a canopy) measured by the LAI-2000 instrument in
that the gap size distribution can very within a given gap fraction. The gap size distribution
contains information on canopy architecture which is a function of tree crowns, branches and
shoots and dictates the spatial distribution of leaves. Due to this relationship between canopy
architecture and leaf distribution the effect of foliage clumping is detected by TRAC. A gap size
distribution curve is produced by plotting the accumulated gap fraction from the largest to the
smallest gap. The total gap fraction of the canopy is represented by the value at gap size of zero.
Such a curve not only contains the gap fraction but also the gap size distribution of a canopy.
The gap size distribution function contains many gaps attributed to non-randomness in the
canopy. By applying a know curve for a random canopy, the gaps from non-randomness can be
excluded. Based on the difference between the measured gap fraction and the gap fraction after
non-random gap removal, the canopy clumping effect is quantified (Chen and Cihlar, 1995).
41
The basic principle for LAI retrieval by TRAC is demonstrated in the following equation
(Chen, 1996b):
1Ω (10)
where α is the woody to total area ratio, Le is the effective LAI (measured by TRAC but
substituted with LAI-2000 Le for reasons discussed in previous paragraphs), and Ω is the total
clumping index. Due to the nature of measurement acquisition (near the ground surface) Le
reflects not just canopy foliage elements but all above ground materials (dead leaves, branches,
tree trunks, moss and lichens growing on the branches). In order to remove the non-leafy
materials the term (1-α) is used. The Ω value is expressed in the following way (Chen, 1996b):
ΩΩ
(11)
where ΩE is the clumping index at scales larger than the foliage element and γE is the needle to
shoot area ratio quantifying clumping at scales smaller than the foliage element. Conifer needles
are grouped according to shoots, branches and tree crowns. The shoot is the basic spatial unit that
can be separated using optical instruments and therefore is considered to be the foliage element.
It is difficult to measure this amount of needle area due to the penumbral effect causing small
gaps to disappear in shadows (Chen and Cihlar, 1995; Chen and Black, 1992). This results in the
need to separate Ω into its two components as shown in Eq. (11). The total clumping is then a
factor of the ratio between foliage clumping within shoots (γE) and at scales larger than the shoot
(ΩE). The shoot clumping component is a fundamental difference between TRAC and LAI-2000
measurement methodologies. This results in the final form of the equation to derive LAI from
TRAC as follows:
42
1ΩE
(12)
4.2.2 Determination of parameters required for ground based LAI retrievals using TRAC
In order to calculate LAI using TRAC the following parameters are necessary - γE (needle
to shoot area ratio, α (woody to total area ratio) and w (mean element width). Due to the time
constraints and need to maximize LAI measurement sites, direct measurements of the above
mentioned parameters were not carried out as dictated by the LAI measurement protocols
outlined by (Chen et al., 2002). Literature values and analysis of photographs in the lab were the
source of the required parameters.
The γE values based on literature review typically do not change much within species and
between age classes (Chen et al., 2002; Gower et al., 1999; Chen et al., 2006b; Hall et al., 2003).
Hall et al. (2003) conducted LAI measurements at various sites near Kananaskis Alberta
including 8 homogeneous mature lodgepole pine plots. They employed a destructive sampling
technique of Chen et al. (1997) to measure the γE values = 2.08. This value was assumed by this
study and remained a constant for all measurement plots.
The α values were determined based on literature and visual analysis. A value of zero
indicates no wood was seen by the instrument. From literature, a range of values was chosen for
young (0.04) mid-age (0.18) and mature (0.28) age classes. The young and mid-age values
corresponded to those for species similar to lodgepole pine namely jack pine (Chen et al.,
2006b), while the mature class value was derived through allometric measurements on lodgepole
pine trees (Hall et al., 2003). These values were used as a guide (minimum and maximum range)
during the analysis of photographs from which site specific α values were derived.
43
Figure 6. The element clumping (ΩE) index becomes asymptotic at high values of mean element width (w). The w value for lodgepole pine was 50 mm.
The w parameter was chosen based on values of similar a species (jack pine) reported in the
literature by Chen and Cihlar (1995). This value was set at 50 mm for all measurement plots.
Hall et al. (2003) used jack pine as a reference for determining the w parameter for lodgepole
pine stands. They concluded that such an inter-species comparison may not be ideal due to slight
morphological differences between the species, particularly the relative needle length of jack
pine being shorter than that of lodgepole pine. According to Chen and Cihlar (1995) the choice
of w value in calculating ΩE with the gap size distribution technique of TRAC does not
significantly change the resulting ΩE within a reasonable range of w. The ΩE value increases
with increasing w and becomes asymptotic at high values of w. Figure 6 displays a plot of ΩE
outputs for a range of artificially selected values of w. It is clear that ΩE remains constant at
about 0.89 at w values greater than 75 mm. Furthermore, for a range in w values from 0 to 75 the
ΩE values display a range of 0.65 to 0.89(only a 20% increase). If w values are chosen based on
appropriate and reasonable literature values the resulting ΩE calculation can be regarded as
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100 150 200
Ele
men
t Clu
mpi
ng (Ω
E)
Mean Element Width (w) (mm)
44
highly accurate based on the relative lack of sensitivity between the two parameters. Parameters
for LAI calculation using TRAC are displayed in Table 3.
4.2.3 LAI measurement procedure
The procedure for measuring LAI follows protocols recommended by Chen et al. (2002)
which are outlined as follows: (1) use LAI-2000 to measure Le; (2) use TRAC to measure ΩE; (3)
measure γs where possible otherwise use default values or those presented in literature for
particular tree species at different ages; (4) measure α where possible, otherwise use allometric
equations or estimates. For each site LAI-2000 measured Le was substituted for TRAC measured
Le to produce the final LAI values. This is done due to the advantage of hemispherical exposure
of the LAI-2000 instrument. This provides better angular coverage of the measurement stand
than does TRAC thus producing more reliable Le estimates. Furthermore, LAI-2000 is less
restricted by sky conditions due to its use of diffuse radiation transmission in calculating gap
fraction, whereas TRAC requires cloud free conditions near the sun’s direction during
measurements. Nevertheless, the TRAC component of LAI measurement is invaluable since leaf
spatial distribution greater than the shoot (foliage clumping) changes among different stands
even of the same tree species (Chen et al., 2002).
Table 3. Mean leaf area index (LAI) values for British Columbia Interior sites measured in June of 2009.
Site Location Site Code Le LAI-2000 Corrected Le
Understory Le
Le TRAC α γE ΩE TRAC Ω LAI TRAC + LAI-2000
1-5 1-3a Mckay Lake A 2.49 2.57 2.66 0.10 2.2 0.28 2.08 0.82 0.55 4.54 Mckay Lake B 1.03 1.00 1.03 0.24 1.62 0.07 2.08 0.88 0.45 2.26 Mckay Lake C 1.65 1.74 1.76 0.10 2.44 0.28 2.08 0.97 0.65 2.55 Mckay Lake D 1.46 1.51 1.56 0.31 1.98 0.28 2.08 0.97 0.65 2.25
Mackenzie Lakes E 0.44 0.38 0.44 0.49 0.48 0.07 2.08 0.89 0.46 0.96 Mackenzie Lakes F 1.69 1.96 1.80 0.27 2.73 0.18 2.08 0.94 0.55 3.06 Mackenzie Lakes G 2.74 2.96 2.94 0.27 3.66 0.28 2.08 0.84 0.56 4.89 Mackenzie Lakes H 1.83 1.82 1.96 0.27 2.33 0.2 2.08 0.84 0.50 3.63 Kennedy Siding I 1.32 1.38 1.41 0.04 1.58 0.18 2.08 0.89 0.52 2.53 Kennedy Siding J 0.96 0.75 0.96 0.21 1.78 0.1 2.08 0.94 0.50 1.90 Kennedy Siding K 1.21 1.19 1.29 0.17 1.61 0.07 2.08 0.88 0.45 2.66 Kennedy Siding L 0.19 0.25 0.19 - 0.98 0.02 2.08 0.99 0.49 0.38 Kennedy Siding M 0.24 0.20 0.24 - 1.12 0.02 2.08 0.99 0.49 0.49
Kay Kay N 1.21 1.26 1.29 0.37 1.44 0.18 2.08 0.97 0.57 2.13 Kay Kay O 1.13 1.20 1.20 0.34 1.48 0.1 2.08 0.98 0.52 2.15 Kay Kay P 1.59 1.64 1.70 0.34 2.09 0.07 2.08 0.91 0.47 3.37 Kay Kay Q 2.09 2.17 2.23 0.04 2.97 0.25 2.08 0.95 0.61 3.43 Kay Kay R 2.04 2.34 2.18 0.08 2.53 0.26 2.08 0.84 0.55 3.73
Mackenzie Lakes S 2.52 2.65 2.70 0.11 2.64 0.26 2.08 0.85 0.55 4.57 Mackenzie Lakes T 2.71 3.09 2.90 0.05 3.31 0.2 2.08 0.89 0.53 5.07 Mackenzie Lakes U 2.87 3.03 3.07 0.09 3.12 0.18 2.08 0.89 0.52 5.50 Mackenzie Lakes V 1.94 1.78 2.08 0.11 1.95 0.28 2.08 0.82 0.55 3.54 Mackenzie Lakes W 0.10 0.07 0.10 - 0.28 0.02 2.08 0.99 0.49 0.20
Kelowna X 3.02 3.49 3.23 0.16 2.99 0.25 2.08 0.96 0.62 4.90 Kelowna Y 2.97 3.38 3.18 0.05 3.16 0.18 2.08 0.98 0.57 5.17 Kelowna Z 2.46 2.86 2.63 0.11 2.65 0.28 2.08 0.88 0.59 4.18 Kelowna AA 2.73 2.87 2.92 0.15 3.1 0.18 2.08 0.98 0.57 4.74 Kelowna AB 2.20 2.76 2.36 0.13 3.01 0.28 2.08 0.96 0.64 3.43
Upper Penticton AC 1.27 1.44 1.36 0.15 1.41 0.12 2.08 0.99 0.54 2.36 Upper Penticton AD 2.73 2.99 2.92 0.29 3.18 0.22 2.08 0.85 0.52 5.21 Upper Penticton AE 2.50 2.83 2.68 0.16 2.73 0.28 2.08 0.85 0.55 4.41
Also shown are all parameters needed to calculate LAI using Eq. (12). Two techniques are used: LAI-2000 and TRAC. The combination of LAI-2000 and TRAC provides the best estimates (in bold). a Rings 4 and 5 in LAI-2000 data are blocked in processing.
45
46
Individual measurement plots for all sites were set up in an identical manner. Each plot
consisted of two parallel transects spaced approximately 20 m apart in a west to east direction.
The transects were measured to be 40 m long and marker flags were placed at 10 m intervals.
Effort was made to locate sites with minimal to no slope. Geographic coordinates were taken
near the centre of each plot using a handheld Garmin GPS devise. Furthermore, plots were
generally chosen near roads or other obvious markers in order to simplify the process of locating
them on remote sensing images. In order to eliminate edge effects, all measurements were taken
at least 50 m away from open areas such as roads.
TRAC measurements were made along both transects moving from east to west in order
to maintain the sun in a perpendicular position to the transects. The TRAC instrument was
operated during clear sky conditions at times of day when the difference between the transect
direction and the solar azimuth angle was greater than 30o. This was typically achieved during
late afternoon when the solar azimuth angle was approximately 57o. A reference measurement
was made in an open area with direct view of the sun either directly before or after the transect
measurements. TRAC data was downloaded to a PC after every measurement sequence and
processed using software distributed with the devise (TRACwin) (Leblanc et al., 2002). The LAI
value measured by TRAC for each plot was taken as the average between both transects. LAI-
2000 measurements were made along both transects at 10 m intervals. These were done only
during cloudy conditions (which were rare) and during the brief 45 minute time period during
which the sun was near the horizon (> 75o solar zenith angle) prior to sun set in order to
minimize the effect of direct solar radiation. The instrument was operated with a 90o view cap to
block any remaining direct radiation and to minimize the effect of the operator on the sensor.
Furthermore, the measurements were taken with the operators back towards the sun. Prior to
47
utilizing LAI-2000 in individual plots a reference measurement was made in an open area large
enough for only sky to be observed by the sensor. Two measurements were taken at each flag
marker: (1) above knee level, approximately 1 m above the ground surface; (2) at the forest floor
to capture the LAI of understory. This measurement may also be influenced by topographic
effects. In order to minimize this, sites with little to no topographic variability were chosen. The
understory component contains grass and shrubs typically less than 1 meter in height, while tree
saplings taller than 1 m are considered as overstory. All LAI-2000 data are processed using the
provided FV2000 software under the assumption of horizontally distributed canopies. This
means that all the sensor’s rings see through the top of a flat canopy. In other words tree height is
assumed to be uniform. LAI-2000 computes overstory LAI by taking the difference between the
ground and 1 m measurements. The ground surface measurement represents the total LAI – both
overstory and understory components. In other words, the understory contribution is removed in
order to isolate overstory LAI. In order to retrieve the understory LAI value the sequence of
measurements is artificially inverted within the FV2000 software. In other words, the overstory
component is subtracted from the total LAI, isolating the understory LAI. (LI-COR, 2002).
4.3 Fine resolution LAI map acquisition and processing
For the purpose of validating VEGETATION LAI maps fine resolution images are
needed. These images are correlated with measurements and downscaled to match coarse
resolution VEGETATION maps. The accuracy of coarse resolution maps is assessed as a
function of the fine resolution images. Fine resolution images were selected based on the image
acquisition date, amount of cloud cover, geographic extent capturing the measurement sites and
availability of visual near infrared and middle infrared bands for vegetation index calculations.
Images were chosen to match as closely as possible their acquisition date with the date of
48
Table 4. The location and brief description of fine resolution imagery used in this study to validate leaf area index (LAI) maps.
Sensor Image code Measurement sites Scene date Longitude range
(west) Latitude range
(north) Resolution
(m)
ASTER A-1 McKay Lake, Kay Kay 07 Jul 07 123° 49'60" – 122° 51'16"
53° 43'8" – 54° 17'4" 15
ASTER A-2 Kelowna 04 Jul 07 120° 34'16" – 119° 41'14"
49° 54'23" – 50° 27'51" 15
SPOT 5 S-1 Kennedy Siding 02 Jun 09 123° 8'32" – 122° 29'31"
54° 56'26" – 55° 19'45" 10
SPOT 5 S-2 Mackenzie Lakes 13 Jun 09 123° 19'41" – 122° 37'37"
53° 19'1" – 53° 39'0" 10
SPOT 5 S-3 Penticton 13 Sep 07 119° 33'47" – 118° 55'11"
49° 26'33" – 49° 53'57" 10
measurements. Since it was difficult to optimize all the above mentioned criteria for every image
multiple sensors were used and the image acquisition dates included the summer of 2007 and
2009. Sensor specific information is shown in Table 4.The first set of fine resolution images was
acquired from the United States Geological Survey (USGS) Global Visualization Viewer
(GloVis) online search and order tool. These images came from the Advanced Spaceborne
Thermal Emission and Reflectance Radiometer (ASTER) onboard NASA’s Terra satellite
launched in December of 1999 as part of the Earth Observing System (EOS). ASTER geo and
atmosphere corrected surface reflectance images were received separated by band number. A
total of 9 bands were acquired – three VNIR and six MIR with 15 and 30 m resolution
respectively. The necessary bands (B2 – RED, B3 – NIR, B4 – MIR) were mosaiced to form a
single 3 band file and the MIR band was bilinearlly down scaled to 15 m resolution matching the
VNIR spectra using PCI Geomatica – GCPWorks software.
The second set of fine resolution imagery was acquired from the Alberta Terrestrial
Imaging Centre in Lethbridge, Alberta (ATIC). The images came from the multispectral sensor
onboard SPOT 5 satellite launched in May of 2002 by the French Space agency. SPOT images
49
were acquired with both a geo and atmospheric correction containing reflectance values in the
same spectral bands as ASTER at 10 m resolution.
In order to accurately locate measurement sites on the fine resolution imagery and to
position the fine resolution scenes within coarse VEGETATION maps further geographic
correction was necessary. Both sets of fine resolution imagery were registered using ground
control points (GCP) obtained from 1:50,000 topographic maps of the same region. Each image
contained approximately 30 GCPs which included obvious features such as road intersections,
bridges, river bends, and well-defined lake features. This type of registration was accurate to
within ± 1 pixel (Chen et al., 2002). The registered images were then used to compute fine
resolution RSR vegetation index maps. Measurement sites were located within the fine
resolution scenes and an LAI to RSR comparison was made.
4.4 Fine to course resolution scaling and accuracy assessment
The fine resolution ASTER and SPOT RSR images discussed in the previous section
were compared with coarse resolution LAI in order to asses the accuracy of the coarse
VEGETATION maps. Fine resolution images were first downscaled to mach the VEGETATION
resolution of 1 km using a bilinear interpolation scheme. The five downscaled fine resolution
RSR maps were then paired with a VEGETATION LAI scene closest to the date of fine
resolution image acquisition. The fine resolution maps were overlaid onto the VEGETATION
scenes by simply linking the images based on the geographic coordinate system. The fine
resolution scenes were then manually adjusted to match obvious GCP between both scenes.
Correlations between fine resolution RSR and VEGETATION LAI where computed based on
the scatter plots produced from each image pair. Pixels in the fine resolution images
50
contaminated by clouds were excluded from the analysis by the application of a NIR threshold
mask for values greater than 48% (the minimum range of cloud NIR reflectance).
51
CHAPTER: 5.0 MEASUREMENT RESULTS AND DISCUSSION FOR OVERSTORY AND UNDERSTORY
LAI
The results of LAI measurement are listed in Table 3 along with key input parameters
required for measurement processing. Measurement sites included a range in tree age of
approximately 5 to 110 years old. The range in measured LAI values was 0.19 to 5.50. In general
it was observed that LAI increased with age and density. Furthermore, typically lodgepole pine
stands grow to mid-age in quite a dense configuration. As light and resource limitations become
more pronounced due to the greater tree height and crown size, natural thinning occurs. Smaller
weaker trees die-off and the surviving trees begin to lose the majority of their foliage near the
ground level. As the trees reach maturity they exhibit distinct crowns in the top 25% of the tree
height. As such stands of middle age were found to have LAI values similar to and in some cases
greater than mature stands. It is difficult to provide a distinct age – LAI relationship since the
number of influencing factors (density, resource limitations, and soil conditions) is too large and
the variability between stands is high.
Understory LAI (LAIu) measurements were in the range of 0.04 to 0.49. Since the
understory component is thought to be a critical mitigating agent against the effects of overstory
mortality due to MPB attack investigation was carried out in order to determine a relationship
between LAI and LAIu. Furthermore, for subsequent C cycle modelling efforts, such a
relationship is used to define the understory contribution to LAI in the modelling scheme.
52
Figure 7. The relationship between measured leaf area index (LAI) of the overstory and measured LAI of the understory (LAIu) exhibits a decreasing exponential relationship. Measurements used in this figure include those by this study and those taken during the BOREAS study in Saskatchewan and Manitoba by Liu et al. (2003).
Previous work done by Liu et al. (2003) found that LAIu decreased exponentially as a function of
increasing LAI (LAIu = 1.1749e-0.9909LAI , R2 = 0.68) for conifer forests in the BOREAS
ecosystem study. Through the combination of LAI and LAIu measurements from this study and
from that of Liu et al. (2003) a similar relationship was developed and is presented as follows:
1.18 . (13)
The coefficient of determination was found to be 0.66 (Figure 7). The combination of
measurements by this study and those of Liu et al. (2003) was necessary since few measurements
were made at sites with small values of overstory LAI. Nevertheless, no significant difference
was found between the two sets of measurements as evident by a p-value of 0.16. This value was
calculated by fitting an exponential function to both datasets and calculating the expected value
of LAIu as a function of overstory LAI along the regression line. The expected values were then
tested for covariance (Figure 8).
y = 1.18e-0.858x
R² = 0.5746
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00
Mea
sure
d L
AI u
LAI
53
Figure 8. Measured understory leaf area index (LAIu) as a function of overstory leaf area index from this study and Liu et al. (2003). The y-axis is displayed in logarithmic form in order to linearize the data which are related exponentially. No significant difference between the LAIu to overstory LAI relationship was found between both studies (p = 0.16).
Table 5. Summary of statistics for fine resolution ASTER and SPOT to coarse resolution VEGETATION reduced simple ratio (RSR) vegetation index comparison over the same region.
Image Resolution
Statistic Kelowna McKay Lake, Kay
Mackenzie Lakes
Kennedy Siding
Penticton
ASTER ASTER SPOT 5 SPOT 5 SPOT5 Fine Resolution scaled to 1 km
Min 0 0.21 0.45 0 0 Max 5.8 6.75 6.55 2.53 7.12 Mean 1.09 3.27 2.58 1.01 3.06 SD 1.2 1.4 0.71 0.55 1.34
Coarse Resolution VGT 1 km
Min 0 0.42 0.22 0 0 Max 7.7 6.95 6.68 2.01 6.49 Mean 1.34 3.49 2.89 0.62 2.93 SD 1.1 2.27 0.74 0.26 1.33
R2 0.71 0.55 0.51 0.34 0.63 RMSE 0.25 0.22 0.31 0.39 0.13
0.01
0.1
1
10
0 2 4 6 8
Und
erst
ory
LA
I
Overstory LAI
This Study
Liu et al. (2003)
54
5.1 Comparison between fine resolution RSR and coarse resolution VEGETATION LAI
In general the fine to coarse resolution comparison of RSR yielded fairly good correlation
ranging from R2 = 0.34 to 0.71 with an average of R2 = 0.55 (Figure 9). The RMSE of the
comparisons ranged from 0.13 to 0.39 (Table 5). The error of individual pixel RSR values in the
fine resolution and VEGETATION images was in the range of 4 – 60% with an average value of
20%. This was determined by taking the ratio of RMSE to the average RSR of the scene. The
largest error was found in the Kennedy Siding scene most likely due to the comparison of SPOT
5 imagery from June of 2009 and a VEGETATION image from June of 2008, because the forest
might have changed due to MPB effects and natural forest growth.
The observed deviations between fine and coarse resolution RSR maps can be attributed
to 3 factors. (1) Potential error exists in the co-registration of the ASTER and SPOT images onto
the VEGETATION image. The process of up-scaling from fine to coarse resolution was found to
be accurate to ± 1 pixel. Although such a small deviation may not seem significant in affecting
the correlation between fine and coarse resolution imagery the large pixel size (1 km2) may
impart errors due to vegetation heterogeneity; (2) the time difference between the acquisition of
the coarse and fine imagery can impart errors in vegetation composition differences. The
VEGETATION data are compiled on ten day intervals but the exact day of the image used is not
known. A deviation of several days can produce subtle changes in vegetation composition which
add errors to the resulting correlation.
55
0
0.75
1.5
2.25
3
0 0.75 1.5 2.25 3
VE
GE
TA
TIO
N R
SR (J
une
2008
)
SPOT 5 RSR (June 2009)
0
1.75
3.5
5.25
7
0 1.75 3.5 5.25 7
VE
GE
TA
TIO
N R
SR (J
une
2008
)
SPOT 5 RSR (June 2009)
0
2.25
4.5
6.75
9
0 2.25 4.5 6.75 9
VE
GE
TA
TIO
N R
SR (S
spt.
2007
)
SPOT 5 RSR (Sept. 2007)
0
1.75
3.5
5.25
7
0 1.75 3.5 5.25 7
VE
GE
TA
TIO
N R
SR (J
uly
2008
)
ASTER RSR (July 2009)
0
1.5
3
4.5
6
0 1.5 3 4.5 6
VE
GE
TA
TIO
N R
SR (J
uly
2007
)
ASTER RSR (July 2007)
(a) (b)
(c) (d)
(e)
Figure 9. Fine resolution (ASTER, SPOT 5) to coarse VEGETATION image comparison for leaf area index (LAI) measurement sites. The image acquisition month and year is displayed in the brackets. (a) Kennedy Siding, (b) Mackenzie Lakes, (c) Penticton, (d) McKay Lake and Kay Kay, (e) Kelowna.
56
A rather more significant example of such error exists in the comparison of fine
resolution imagery from the summer of 2009 with VEGETATION imagery from the summer of
2008. This was most evident in the two SPOT images covering Kennedy Siding and Mackenzie
Lakes which were retrieved in the summer of 2009 and compared with VEGETATION images
from the summer of 2008; (3) the process of determining RSRmin and RSRmax values although the
same for both fine and coarse resolution imagery further adds to the correlation error. Since the
fine resolution imagery encompasses only a fraction of the total VEGETATION scene the range
of MIR values may be significantly smaller than that observed in the entire VEGETATION
scene. Attempts to correct for this were made by isolating only the conifer MIR values in the
vegetation scene by virtue of producing histograms for only affected areas (which are assumed to
be predominantly conifer). The total estimated error due to co-registration, date inconsistency,
atmospheric correction, resolution scaling and RSR computation variability between the fine and
coarse resolution imagery was estimated to be on average 20%, as calculated by the ratio of
RMSE to the average RSR of each scene.
5.2 RSR verses SR and NDVI vegetation indices as predictors of LAI
Measured LAI (LAI2000 + TRAC) was correlated against the RSR, SR and NDVI
vegetation indices in order to evaluate the potential of those indices to determine LAI through a
remote sensing approach.
RSR was found to correlate most closely with LAI (R2 =0.68) while NDVI (R2 =0.34) and
SR (R2 =0.24) were significantly less reliable when using a linear regression technique (Figure
10). The coefficients of determination for NDVI and SR increased to R2 = 0.41 and R2 = 0.31
57
Figure 10. The relationship between leaf area index (LAI) derived from a combination of LAI2000 and TRAC optical instruments and several vegetation indices – (a) simple ratio (SR), (b) normalized difference vegetation index (NDVI) and (c) the reduced simple ratio (RSR).
R² = 0.2404
0
1
2
3
4
5
6
0 1 2 3 4 5 6
LA
I200
0 +
TR
AC
LA
I
SR
R² = 0.338
0
1
2
3
4
5
6
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
LA
I200
0 +
TR
AC
LA
I
NDVI
R² = 0.631
0
1
2
3
4
5
6
7
0 1 2 3 4 5
LA
I200
0 +
TR
AC
LA
I
RSR
(a)
(b)
(c)
58
respectively when a logarithmic regression was applied. Both the SR and NDVI showed
significant increased variability in predicted LAI values at high values of the index. This can be
attributed to increased NIR scatter due to increasing crown shadows as crown closure develops
(Chen et al., 2002). Furthermore, calculation of the SEE for all indices revealed the values of
0.85, 1.23 and 1.33 for RSR, NDVI and SR respectively. This implies that the RSR to LAI
relationship exhibits the least amount of variability around the expected values from the
regression relationship. Secondly, the effectiveness of the MIR background scalar is underlined
by the low SEE value for RSR in that a major cause of variability is in fact the background
influence. The RSR to LAI relationship shows no saturation at high values of RSR. This may be
due to the lack of measurements at high LAI but is consistent with past studies. One explanation
for the relatively weak performance of SR and NDVI is the heterogeneity of the measurement
sites. Effort was made to measure only healthy homogeneous lodgepole pine stands but with
widespread mortality associated with the MPB this was not always possible. Approximately 50%
of the measurement sites had between 10 – 50% inclusion of non-lodgepole pine species,
particularly black spruce, subalpine fir, birch and aspen. The good performance of RSR at these
sites underlines its lack of sensitivity to cover type as discussed in previous sections.
5.3 Multiple scattering effects on LAI retrieval using LAI-2000 instrument
In general the Le retrieved by LAI-2000 is more robust due to the utilization of a much
larger angular domain than that of the TRAC instrument. A significant error in Le can occur in
LAI-2000 measurements due to the multiple scattering effect of light within the canopy. The
blue wavelengths (400 – 490 nm) reflected by vegetation canopies are assumed to be black by
the LAI-2000 instrument. The resulting gap fraction calculation may be overestimated due to
59
significant blue scattering albedos of leaves which occur in reality. This effect is most
pronounced at high zenith angles where the gap fraction is smallest (Chen et al., 2006b). In order
to evaluate this multiple scattering effect, LAI-2000 Le computed utilizing all 5 hemispherical
rings was compared to Le computed with rings 4 (45o – 60o) and 5 (60o – 74o) excluded in the
analysis. Such an approach assumes a spherical leaf angle distribution with constant projection
coefficient within the canopy (Chen and Black, 1991). LAI measurements were carried out
exclusively on lodgepole pine dominated stands which typically exhibit vertical tree crowns and
horizontal branches, not the typical spherical distribution resulting in such investigation being
potentially invalid. Nevertheless, Chen et al. (2006b) concluded that the influence of non-
spherical leaf distribution reduced the difference between Le derived from rings 1-5 and rings 1-3
only to a small degree. The resulting underestimation of Le calculated by all 5 rings for
coniferous (douglas fir (Pseudotsuga menziesii), balsam fir (Abies balsamea), black spruce
(Picea mariana), jack pine and white pine (Pinus strobus)) and deciduous sites (aspen and mixed
woods) was 16%, the majority of which was attributed to the multiple scattering effect.
Le calculated by this study from rings 1 – 3 for Le greater than 1 was larger than that
computed from rings 1 – 5 on average by 8%. This was similar to the findings of Chen et al.
(2006b) signalling the existence of a multiple scattering effect. Curiously, for sites with Le less
than 1 the average difference between the two computation methods revealed a 13% decrease in
Le. This is attributed to the site morphology. Sites with values less than 1 were predominantly
very young plantations less than 2 meters in height. The elimination of rings at high zenith
angles actually reduced the amount of vegetation seen by the sensor effectively reducing
Le. Figure 11 shows a scatter plot of Le computed using rings 1 – 3 versus rings 1 – 5 with clear
underestimation at Le greater than 1 and overestimation at Le less than 1.
60
Figure 11. Effective leaf area index (Le) computed using LAI-2000 instrument rings 1 – 3 and rings 1 – 5. The exclusion of low zenith angles (1 – 3) reveals an 8% underestimation in Le to that computed using all 5 rings. This is due to the multiple scattering effect of light within the canopy.
5.4 Discussion
Several studies have investigated the relationship between vegetation indices particularly
and LAI and found that the RSR to LAI relationship is superior to that produced by SR or NDVI
for all cover types with particularly emphasis on conifer and mixed species ( Brown et al., 2000;
Chen et al., 2002; Stenberg et al., 2004; Rautiainen, 2005; Deng et al., 2006; Pisek et al., 2007;
Pisek et al., 2010;). The original application of the RSR index produced coniferous LAI results
with 30% greater accuracy than did the SR – LAI relationship (Brown et al., 2000). Further
investigation by Chen et al. (2002) which produced Canada wide LAI maps at 1 km resolution
and validated these maps against extensive ground measurements found that the RSR and SR
relationships with LAI could explain about 75% of the variability in LAI. For coniferous species
both the SR-LAI and RSR-LAI relationships were essentially linear with the latter exhibiting less
saturation at high values. Furthermore, Chen et al. (2002) found that the RSR index performed
better (R2=0.73) in comparison with the SR index (R2=0.66) for coniferous vegetation cover
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
0.00 1.00 2.00 3.00 4.00
Le
Rin
gs 1
-5
Le Rings 1-3
61
types. The RMSE values for the RSR-LAI relationship were lower than those for the SR-LAI
relationship implying less variability (lower variance around the regression line) in the RSR-LAI
relationship. For mixed sites the RSR index performed significantly better than did the SR index
as evident in R2 values of 0.63 and 0.26 respectively. The authors concluded that due to the good
performance of the RSR index in mixed forest sites the RSR index was relatively insensitive to
cover type, underlining its usefulness for LAI retrieval at coarse resolutions where cover type
percentages within pixels are unknown.
Work done by Stenberg et al. (2004) in coniferous forests of Finland found similar
advantages to the RSR index as compared with both SR and NDVI. All three indices correlated
well with LAI but showed strong difference in their predictive power by varied degrees of
sensitivity to LAI changes. RSR was most sensitive to small changes in LAI, had the strongest
correlation with LAI (R2 =0.63) and had the smallest standard error of estimates (SEE) (0.43).
NDVI correlated well with LAI (R2 =0.52) with an SEE of 0.48 but had a very narrow range
(0.64 to 0.89) across the values of LAI (0.36 to 3.72). SR exhibited similar characteristics as
NDVI with R2 = 0.52 and SEE = 0.49 respectively.
The multiple scattering effect observed by this study was smaller than that reported by
Chen et al. (2006b). Reasons for the observed difference between the 16% deviation found by
Chen et al. (2006b) and the 8% deviation reported here can be attributed to differences in
vegetation cover type and its corresponding projection coefficient (G(θ)). Very early work by
Monsi and Saeki (1953) revealed the relationship between G(θ) and the zenith angle. G(θ) of
horizontally distributed leaves decreases with increasing zenith angle while the opposite is true
for canopies with vertical distributions. A horizontal distribution is termed planophile, since the
projection of leaf angles onto a sphere results in a flattened ellipsoid due to more leaves at angles
62
close to the horizontal direction than in a spherically distributed case. A vertical leaf distribution
exhibits more leaves near vertical angles when compared with a spherical distribution and thus is
termed erectophile (Ross, 1981). Douglas-fir canopies typically exhibit a planophile branch
structure resulting in decreasing G(θ) with zenith angle. The Le at the vertical direction is larger
than in the horizontal direction resulting in a reduction in the difference between rings 1 – 3 and
1 – 5 and an underestimation of the multiple scattering effect. Spruce canopies, on the other
hand, typically exhibit an erectophile leaf distribution resulting in an increasing G(θ) with zenith
angle and an overestimation of the multiple scattering effect (Chen and Black, 1991). Chen et al.
(2006b) argued that due to the diversity of sites (wide range of species) at which measurements
were taken the foliage distribution effects cancel out resulting in the near isolation of the
multiple scattering effect. The measurements conducted by this study were almost exclusively at
lodgepole pine sites. This tree species exhibits planophile branch structures somewhat similar to
Douglas-fir. As a result, the percent difference (rings 1 – 3 and 1 – 5) reported by this study (8%)
is lower than that reported by Chen et al. (2006b) of 16%. In other words the difference is partly
offset by the structural effect resulting in lower calculated values of the multiple scattering effect
than expected.
63
CHAPTER 6.0: LAI VARIATION AS A FUNCTION OF MPB ATTACK
The following chapter outlines the results of LAI mapping for MPB affected areas in BC
from 1999 to 2008. Ten maps were produced in total each displaying the average July LAI
(LAImax) for every pixel at a 1 km resolution. For the purposes of displaying LAI and the spread
pattern of MPB maps were created using the affected areas of each year as a mask. For the
purposes of quantitative analysis, LAI data from the cumulative outbreak area are used for each
year.
6.1 Annual LAImax mapping
The spatial distribution of MPB during the study period is primarily defined by the
extremely rapid rate of outbreak spread. Figure 12 outlines the annual affected area size in
square km according to severity class. The first two years of attack (1999, 2000) were on the
same order magnitude (5000 km2) as historical outbreak levels. A rapid MPB range expansion
began in 2001 and peaked in 2007 (100 thousand km2). 2003 and 2004 exhibited very large and
sudden increases in outbreak area, each nearly doubling the size of the previous years outbreak
(30 000 km2 from 2002 to 2003 and 2003 to 2004). The average annual affected area increase
was 13 000 km2. Furthermore, severe (30-50% attack) and very severe (>50% attack) attack
intensity classes show a more than doubling of area from 2004 to 2005 but remain steady until
2007. These two most severely infested classes make up approximately 20% of the affected area
from 2004 to 2005. The steady increase in lightly (1-10% attack) affected areas signals an ever
increasing MPB spread into areas not dominated by lodgepole pine species. As mature living
host stands become less readily available beetles spread to less favourable fringe areas with
patchy host distributions. These areas include young lodgepole pine stands, deciduous dominated
stands with some lodgepole pine interspersed and stands of other pine species typically not
64
Figure 12. Annual time series of mountain pine beetle (MPB) affected area, partitioned by severity classification.
Figure 13. Leaf area index (LAI) dynamics over time. The bars represented on the primary axis display the percentage change of LAI as compared with the unaffected state 1999. The line represented by the secondary axis displays the annual change in July average LAI (maximum seasonal LAImax).
0
10
20
30
40
50
60
70
80
90
100
110
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Aff
ecte
d A
rea
(tho
usan
ds K
m2 )
Year
Very SevereSevereModerateLightTrace
0
0.5
1
1.5
2
2.5
3
3.5
4
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
99 00-99 01-99 02-99 03-99 04-99 05-99 06-99 07-99 08-99
Ave
rage
Max
imum
Lea
f Are
a In
dex
% L
eaf A
rea
Inde
x C
hang
e
Year
65
affected by MPB. In 2008 the affected areas show a substantial decrease by about 20,000 km2
from 2007. According to the BCMoFR, this is the beginning of a beetle saturation stage which
will remain steady at 2008 levels until 2011 at which time the dieback phase will begin,
characterized by exponential area decreases until 2020 at which time pre-outbreak conditions
will be restored (Westfall and Ebata, 2008).
The annual changes in LAImax are shown graphically in Figure 13 and the corresponding
maps are displayed Figure 14. LAImax changes did not correlate well with increases in affected
areas. An initial LAImax drop of 20% is evident from 1999 to 2001 (LAImax = 2.23) during which
newly affected areas became shocked by the sudden influx of beetles and the drop lasted for
more than a year because of the 1 to 2 year lag associated with tree mortality post initial attack.
During the rapid range expansion of MPB in 2002 and 2003 (first major attack) LAImax
recovered to nearly pre-outbreak levels (LAImax 1999 = 3.64 and 2003 = 3.51). This was
followed by a second dramatic LAImax decrease in 2005 (LAImax = 2.45) again associated with
MPB lag effect. Following 2005 LAImax increases and stabilizes near 3.1. The response of
LAImax to MPB attack may provide an initial signal for some form of ecosystem resilience
towards MPB attack. Such a response will be examined in the following section. Spatial patterns
of MPB spread as a function of LAImax indicate the development of severe outbreak hot spots
(evident through extremely low LAImax values). Outbreaks typically spread outward from these
hotspots with decreasing LAImax damage moving away from the epicentre. Furthermore, higher
than pre-attack values of LAImax are evident particularly on the fringes of attack areas where the
outbreak is not so severe most likely due to vegetation recovery and increased secondary
structure growth.
66
Figure 14. Spatial and temporal leaf area index (LAI) dynamics for mountain pine beetle (MPB) affected areas from 1999 to 2008. The small inlay maps show the severity classification of affected areas of each year (red to yellow = most to least severe).
67
Figure 15. Average maximum leaf area index (LAImax) change with mountain pine beetle (MPB) attack severity. The error bars represent the range in LAImax values over the ten year period of the study.
By plotting the average ten year LAImax per severity class (Figure 15) a distinct
decreasing pattern is evident. As severity increases LAImax decreases. An apparent LAI
stabilization occurs at high severity classes (severe and very severe) at around LAImax = 2.8. This
stabilization most likely reflects the relatively small contribution of the severe and very severe
classifications to the total outbreak size. Such stabilization may also have been attributed to
averaging artefacts and increased stand heterogeneity with increasing attack severity. The inter-
annual variability between years at each severity class was quite large: on average ± 17% of the
ten year average value. This indicates that although severity does influence LAI the magnitude of
the influence is highly variable and to some extent dictated by other factors (ecological
characteristics, tree species composition and resource availability). These factors limit the
response of LAI with increasing severity further suggesting some form of ecosystem resilience
acting as a mitigating force against the impact of MPB outbreak.
2
2.5
3
3.5
4
4.5
Unaffected Trace Light Moderate Severe V.severe
Ave
rage
Max
imum
Lea
f Are
a In
dex
Severity
68
6.2 Annual LAI change from pre-outbreak conditions (1999)
Figure 16. The annual percent change of leaf area index (LAImax) as compared with non-disturbed conditions of 1999 for areas exhibiting LAI values greater than in the non-disturbed year (line). The bars represent the percentage of the total affected area of each year exhibiting better than non-disturbed values of LAI.
The intention of this investigation was to identify potential reasons for the lack of
dramatic LAI decreases in affected areas. Furthermore, through annual LAI comparison with the
control scenario or an unaffected baseline – 1999 LAI values, the relative impact of MPB on LAI
was examined. The percentage change of LAI over time as compared to 1999 was found to be a
series of two sudden dramatic decreases followed by periods of recovery (Figure 13) (discussed
in the previous section with respect to average annual LAI data).
The average ten year LAI decrease from control was 21% with a minimum and maximum
decrease of 3 and 38%. Such changes have the potential to dramatically impact C cycling since
the amount of C taken up by vegetation strongly depends on LAI. The affected areas of each year
were partitioned into those showing an increase (better than control conditions) and decrease in
LAI (Figure 16). Interestingly, it was found that the total area performing better than 1999
0
5
10
15
20
25
30
0
20000
40000
60000
80000
100000
120000
99 00-99 01-99 02-99 03-99 04-99 05-99 06-99 07-99 08-99
LA
I Cha
nge
(%)
MPB
Aff
ecte
d A
rea
(km
2 )
Year
area with decreasing LAIarea with increasing LAI% increase from unaffected condition
69
conditions was steadily increasing from 3% in 2000 to 42% in 2008. Furthermore, the
corresponding average LAI increase in these areas was also on the rise moving from 10 to 14%
between 2000 and 2004 and to 26% in 2008. This may be attributed to some form of ecosystem
resilience mitigating the MPB effect on LAI.
During the process of examining scatter plots of LAI for all years compared to the control
year 1999 a potential signal for ecosystem resilience in the form of secondary structure
regeneration was observed (Figure 17). This signal manifested itself in the scatter plots as sharp
deviations in the direction of the dependant variable from the general plot trajectory. In other
words these ‘spikes’ corresponded to annual LAI values exceptionally greater than those in the
non-disturbed year 1999. Areas under MPB attack at varying severity levels were exhibiting
substantially larger LAI values than when they were under healthy conditions. Furthermore, this
signal was most pronounced during the most severe outbreak years – 2003 and 2005. The year
2003 saw the largest increase in severe and very severe outbreak of all years yet the LAI change
from 1999 was the lowest – only a 3% decrease. This suggests that as a result of large scale,
rapid and very severe disturbance directed at the dominant species of an ecosystem secondary
species may react extremely rapidly. This may have been a function of increased resource
availability. During years 2006 to 2008 the signal was no longer very apparent. Due to nearly
half the affected areas showing better than control LAI values in these years, the rapid
regeneration signal has been incorporated into the total area response. During these years
secondary vegetation may have been established as the dominant factor in the ecosystem
potentially resulting in the return of pre-outbreak conditions with respect to LAI.
70
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
00)
Maximum LAI (1999)
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
01)
Maximum LAI (1999)
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
02)
Maximum LAI (1999)
Avg. LAImax change -39% % area with increasing LAImax 3% Avg. LAImax of increasing area 10%
Avg. LAImax change -30% % area with increasing LAImax 19% Avg. LAImax of increasing area 10%
Avg. LAImax change -22% % area with increasing LAImax 3% Avg. LAImax of increasing area 13%
71
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
03)
Maximum LAI (1999)
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
04)
Maximum LAI (1999)
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
05)
Maximum LAI (1999)
Avg. LAImax change -4% % area with increasing LAImax 27% Avg. LAImax of increasing area 14%
Avg. LAImax change -11% % area with increasing LAImax 23% Avg. LAImax of increasing area 13%
Avg. LAImax change -33% % area with increasing LAImax 14% Avg. LAImax of increasing area 19%
72
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
06)
Maximum LAI (1999)
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
07)
Maximum LAI (1999)
0
2.5
5
7.5
10
0 2.5 5 7.5 10
Max
imum
LA
I (20
08)
Maximum LAI (1999)
Figure 17. Scatter plots of maximum annual leaf area index (LAImax) for the maximum extent of mountain pine beetle (MPB) affected area against the unaffected baseline (1999). The steady increase in the percentage of total area showing improvements in baseline LAI underlines the increasing influence of secondary overstory. Highlighted areas indicate the rapid secondary overstory recovery signal.
Avg. LAImax change -23% % area with increasing LAImax 37% Avg. LAImax of increasing area 18%
Avg. LAImax change -14% % area with increasing LAImax 42% Avg. LAImax of increasing area 26%
Avg. LAImax change -19% % area with increasing LAImax 39% Avg. LAImax of increasing area 21%
73
6.3 Discussion
From field observations and data collected by Brown et al. (2010) ecosystem resilience
manifests itself in the form of rapid secondary overstory development following overstory
mortality. Secondary structure is comprised of mid age to mature tree species particularly
subalpine fir, white spruce, aspen, birch and pine species not affected by MPB. Some of these
trees live in the shadows of the much taller and dominant lodgepole pines. With the elimination
of large percentages of overstory new opportunities arise for the secondary trees. Competition for
resources (light, nutrients and water) decreases, potentially resulting in a rapid expansion of
secondary trees. A further contribution to the total ecosystem resilience may be the increased
vitality of lodgepole pine trees not attacked by the beetle. Even during the most severe outbreaks
it is rare for 100% of the lodgepole pine overstory of a given stand in a given year to be affected
by MPB. Furthermore, MPB rarely attack young lodgepole pine trees due to their lack of
sufficient insulating capacity for the beetle eggs and larvae. These trees may respond similarly to
secondary species adding to the recovery observed in LAI values, post attack, particularly in
homogeneous stands with little to no secondary overstory present. It must be said that of the
total percentage of area exceeding control conditions in any given year an unknown proportion
may be attributed to natural tree growth and regeneration of forests not disturbed by MPB but by
harvesting or fire. Therefore it cannot be said with total certainty that secondary overstory
regeneration is 100% responsible for the increases in LAI but due to the large scale of the MPB
attack in comparison with harvest and fire activities the majority of this increase is most likely a
result of MPB.
74
CHAPTER 7.0: METHODOLOGY FOR NEP CARBON CYCLE MODELLING
The second major objective of this study was to asses the role of MPB on C cycling
dynamics in BC. The intention was to investigate the changes in the C source and sink
distribution in order to discover the impact of insect feedbacks on climate change and update
Canada’s C budget. Furthermore, the concept of ecosystem resilience as a mitigating factor
controlling the impact of MPB on C was investigated. This ecosystem resilience concept is
rooted in the ability of ecosystems to adapt and even flourish as a function of dramatic
disturbance events (described in Chapter 1, section 2).
The methodology utilized to investigate NEP dynamics in MPB affected areas was
centered on a process-based, spatially distributed modelling effort with remote sensing inputs.
This type of modelling attempts to quantify key ecosystem processes by applying mathematical
relations derived from a physical understanding of these processes. On a regional scale the most
robust method for determining the critical inputs necessary for modelling efforts is the use of
remotely sensed parameters of which LAI is most important because it quantifies the the
ecosystem health both spatially and temporally. The basic methodology for modelling efforts to
be described in the following sections is presented as follows: (1) modelling input files were
prepared for the cumulative extent of MPB affected areas; (2) the Boreal Ecosystem Productivity
Simulator (BEPS) (Liu et al., 1997) was used to model net primary production (NPP) (the net
balance between photosynthesis and plant respiration) for the control year 1999; (3) the resulting
NPP map was used as an input into the Integrated Terrestrial Ecosystem Carbon Cycle Model
(InTEC) (Chen et al., 2000) which modelled the composition of litter and soil C pools used for
calculating heterotrophic respiration; (4) these pools were then entered as inputs back into the
BEPS model and heterotrophic respiration (Rh) and net ecosystem production (NEP) (the net
75
balance between NPP and soil respiration) were modelled for all study years at 1 km resolution
at hourly time steps; (5) annual NEP maps were produced and the spatial and temporal dynamics
of NEP were assessed; (6) NEP maps were validated at two flux measurement stations (2 pixels)
for 2007 and 2008 (described in the next chapter). A graphical representation of this process is
shown in Figure 18.
Figure 18. General procedure for carbon (C) cycle modelling of net ecosystem production (NEP) using spatially explicit, process-based models driven by remote sensing inputs.
76
7.1 BEPS model descriptions and input parameters
BEPS is a spatially distributed, process-based biogeochemical model which simulates C
dynamics on site, regional and global scales. BEPS was originally created at the Canadian Centre
for Remote Sensing with the intention of mapping NPP for the Canadian landmass at 1km
resolution on a daily time step (Liu et al., 1997; Liu et al., 2002). Since then the model has
undergone numerous revisions and additions yielding the version used in this study and outlined
as follows. Photosynthesis calculations are based on a modified version of Farquhar et al. (1980)
which calculates C sequestration rates based on light and nutrient limitation at the leaf scale.
Heterotrophic respiration (Rh) is calculated based on a modified version of the CENTURY
model which simulates biogeochemical relations between C and nitrogen (N) (Parton et al.,
1993; Ju and Chen, 2005). Energy balance, sensible and latent heat fluxes, soil water and
temperature are computed based on the ecosystem atmosphere simulation scheme developed by
Chen et al. (2007). This method partitions the soil zone into seven distinct layers for which water
and temperature dynamics are calculated. Functions of the root distribution with depth are used
to calculate the amount of water extracted by plants, while soil respiration is quantified
according to temperature at each soil layer. Stomatal conductance is characterized by the Ball-
Woodrow-Berry (BWB) model which relates stomatal conductance to photosynthesis and
relative humidity according to a linear relationship (Ball et al., 1987). Such an approach is based
on physical principles rather than empirical relations, thus it requires less tuneable coefficients
than other approaches such as that proposed by Jarvis (1976). The Jarvis method quantifies
stomatal conductance as the reduction of some maximum value based on environmental scalars
of temperature, radiation, soil water and vapour pressure deficit. However, the BWB method has
been shown to lack the ability to force stomatal closure due to water deficit conditions
77
(Baldocchi, 2003). Rh is quantified as a function of the amount of C in the various pools, their
decomposition rates and respiration efficiency based on stress factors of soil water and
temperature (Lloyd and Taylor, 1994; Potter, 1997).
The BEPS model used in this study functions at a 1 km resolution on a hourly time step.
The forest is partitioned into overstory and understory components for which all computations
are made separately. Spatial scaling of stomatal conductance from leaf to canopy follows a two
leaf approach based on sunlit and shaded canopy portioning for both understory and overstory
through the determination of sunlit and shaded LAI (dePury and Farquhar, 1997; Chen et al.,
1999b). The most widely used alternative approach for such scaling is the big leaf approximation
which simply multiplies the stomatal conductance for a single leaf by the LAI to achieve canopy
conductance. Such an approach has been shown to underestimate annual C fluxes and
inadequately capture the daily and inter-annual variability of C fluxes. Chen et al. (1999b)
compared the big leaf and sunlit shaded approaches with measured C fluxes during the BOREAS
study and found that the big leaf model did not correlate well with measurement (R2=0.09) and
did not capture the day to day variability in NPP. The sunlit-shaded approach displayed a
relationship with measured values much closer to the 1:1 line but the data scatter was fairly large
resulting in R2 values of 0.46. 50% of the scatter was attributed to sub-daily variability in
climatic conditions not captured by the daily time step of the input data. The remaining scatter
was attributed to measurement error mostly due to soil respiration measurements using chamber
methods which under represent the average conditions of the site. BEPS was modified to
function on an hourly time step after this study was completed to account for sub-daily
meteorological variability and its effect on NPP. BEPS is one of a number of process-based
models which unlike big-leaf models contain information on canopy structure and as such can be
78
used to estimate processes such as NEP. Other such models include Biome Biogeochemical
Cycle (BIOME-BGC) model (Thornton et al., 2002), Soil Plant Atmosphere (SPA) model
(Williams et al., 1996) and Carbon-Nitrogen Canadian Land Surface Scheme (CN-CLASS)
model (Arain et al., 2006).
Several inadequacies of the big leaf approach have been documented of which most
important is the inaccurate representation of the CO2 pathway from the atmosphere to the plant
intercellular space by replacing stomatal conductance with canopy conductance. The dominant
control on CO2 assimilation is the internal resistance (inverse of conductance) found within the
inter-cellular space of the leaf. The big leaf model overestimates this control on a canopy scale
and in tern underestimates NPP. The sunlit-shaded approach eliminates this underestimation by
more accurately quantifying the CO2 pathway. Stomatal conductance is divided by the LAI or
the number of leaf layers effectively reducing the total resistance to CO2 flow within the leaf. In
other words if CO2 is being taken up by a canopy with two leaf layers the total resistance of the
canopy is divided by two. The inability of the big leaf model to capture daily NPP variability is
mostly due to the fact that the canopy is never light limited in the model. In other words, the one
single big leaf always has sufficient light for photosynthesis, making the radiation control on
photosynthesis irrelevant. In this formulation, temperature and nutrient limitations dominate
photosynthesis. The sunlit-shaded approach considers the fact that the majority of a canopy is
often shaded and in turn light limited producing a distinct daily variability in NPP calculations.
Big leaf models persist in current practice since underestimation can frequently be solved by
unrealistically representing the major controlling parameters of photosynthesis such as the
maximum rate at which plants can photosynthesize and the maximum stomatal conductance
(Chen et al., 1999b).
79
BEPS has been successfully used and validated in a diverse range of applications at site,
regional, and global scales. Furthermore, it has been shown to function effectively in ecosystems
beyond the boreal landscapes for which it was originally created. Examples include Canada-wide
NPP maps produced using BEPS and validated for boreal forest regions (Liu et al., 2002);
evapotranspiration mapping for Canada’s land mass in 1994 (Liu et al., 2003); examination of N
controls on NPP in Saskatchewan (Liu et al., 2005); the development of algorithms used for
spatial scaling of NPP based on sub-pixel information at research sites near Frazerdale Ontario
(Simic et al., 2004); investigation of NPP recovery following forest fires in various Canadian
locations prone to fire (Amiro et al., 2000); simulation and validation of water and C fluxes in an
aspen forest in Saskatchewan (Ju et al., 2006); NPP mapping for the landmass of China (Feng et
al., 2007); a study of the influence of topographic variability on NPP in Shaanxi Province
northwest China (X. Chen et al., 2007); NPP mapping in the Qilian Mt region of western China
for conifer dominated forests (Zhou et al., 2007); and the study of urbanization effects on NPP in
the Yangtze Delta of China (Xu et al., 2007).
Recent use of the BEPS model includes work done by Deng et al. (2007) who used
modelled NPP results as a constraint to atmospheric inversions to determine the C source and
sink distribution at the global scale. Furthermore, BEPS has been coupled to a hydrological
model called TerrainLab and used to investigate the effects of hydrological flow conditions on C
cycling and water dynamics for both boreal and wetland environments (Govind et al., 2009b;
Govind et al., 2009a; Sonnentag et al., 2008). A current study utilizing BEPS focuses on
validating model outputs for southern latitudes particularly in the United States. Such
information will be used to map NPP for the entire United States in 2004 (Sprintsin et al., in
preparation).
80
7.1.1 Key ecological processes simulated by BEPS
BEPS simulates NEP as the difference between photosynthesis and respiration for
understory and overstory components separately as follows:
, ,
, (14)
where GPP is the gross primary production or amount of photosynthesis, Ra and Rh are
autotrophic and heterotrophic respiration and subscript (o) and (u) denote overstory and
understory components respectively. NEP is therefore the net exchange of C between the
atmosphere and ecosystem.
Photosynthesis is quantified in BEPS by an instantaneous leaf-level biophysical model of
Farquhar et al. (1980) which solves for the rate of photosynthesis (Anet) by choosing the
minimum value of nutrient (Rubisco) and light limitations on photosynthesis denoted as Ac and
Aj (µmol m-2 s-1) respectively. The model is mathematically represented as follows:
, , , ,
0.015 (15)
Rd represents a correction for daytime leaf dark respiration calculated as a function of the
maximum carboxylation rate (Vmax) according to Collatz et al. (1991). The subscript (i) denotes
the sunlit and shaded leaf fractions which require separate calculation of Anet (Liu et al., 2002;
Chen et al., 1999b). Ac and Aj are calculated as follows:
,Γ
1 (16)
,
Γ4 2Γ (17)
81
where J is the electron transport rate (µmol m-2 s-1); C and O are the intercellular CO2 and
oxygen concentration (mol mol-1) respectively; Γ* is the CO2 compensation point without dark
respiration (mol mol-1) accounting for the amount CO2 concentration within the leaf; Kc and Ko
are Michaelis-Menten constants for CO2 and O2 (mol mol-1) respectively and quantify the
temperature dependant molecular activity (reaction rate) of these gases within the leaf (Chen et
al., 1999b; Ju et al., 2006). Total canopy photosynthesis (Atot) is calculated as the sum of sunlit
and shaded photosynthesis denoted as Asun and Ashade scaled by the amount of sunlit and shaded
LAI denoted as LAIsun and LAIshade respectively:
(18)
LAIsun is determined based on the method of Norman (1982) modified by Chen et al. (1999b) to
include the effect of foliage clumping. The approach is based on a somewhat linear relationship
between decreasing solar zenith angle and increasing LAIsun. LAIshade is taken as the difference
between LAI and LAIsun.
Stomatal conductance (gs) is calculated using the BWB methodology which
simultaneously accounts for C assimilation (photosynthesis) and stomatal conductance response
by using the results of photosynthesis calculations described above at every time step. Iterative
calculations are made to update current stomatal conductance by equilibrating photosynthesis at
the previous time step against the stomatal conductance for CO2 (Ball et al., 1987). The basic
formulation is as follows:
(19)
where k is the slope of the relationship between gs and (Anet hs cs-1); hs is the relative humidity at
the leaf surface; cs is the CO2 concentration (mol mol-1) at the leaf surface; is the intercept
which accounts for gs during times of stomatal closure. The physical basis for such a formulation
82
is that in order for Anet to increase gs must increase. This relationship is essentially linear and the
effect of Anet is controlled by hs and cs (Ball et al., 1987). Such an approach is employed since it
must function on at least an hourly time step. As such diurnal variations in meteorological
conditions and their effect on photosynthesis are captured. The majority of this diurnal variation
is a result of gs variability during the day (Chen et al., 1999b; Ju et al., 2006).
Respiration calculations are conducted separately for Ra and Rh. Ra is partitioned further
into growth and maintenance components denoted as Rg and Rm, respectively. Rg is computed
based on its relationship to GPP which is Rg = 0 .20(GPP) (Liu et al., 2002). Rm is the sum of
respiration values for various plant parts (leaf, stem, coarse root, fine root) each calculated as the
percentage of biomass respired as a function of respiration rate at a base temperature and its
sensitivity to temperature as quantified by the Q10 function. Rh is computed as the sum of
released C from 9 pools (5 litter pools and 4 soil pools – described in Chapter 7, section 1.2.3) as
follows:
(20)
where the subscript (j) denotes the C pool being calculated; τ is the respiration efficiency defined
as the percentage of decomposed C released from the pool to the atmosphere; k is the
decomposition rate of the pool and C is the size of the C pool. The decomposition rate (k) is
calculated as the product of soil temperature and soil water stress factors. The soil temperature
dependence of Rh is calculated based on an Arhenius-type equation which dictates an
exponential increase in respiration with increasing temperature based on the respiration at 10o
(Lloyd and Taylor, 1994). The stress factor of soil water (f(θ)) is calculated based on Potter
(1997):
83
Coarse textured soils 5.44 5.03 0.492
5.63 4.64 0.745 (21)
Fine and medium textured soils
where θ is the soil water content and P is the soil porosity which is a function of soil texture.
Such parabolic relationships are based on empirical measurements. At near saturation and dry
conditions, the soil moisture scalar takes on a value of 0.2 (Potter, 1997; Ju et al., 2006).
The soil zone is partitioned into seven layers based on the EASS simulation scheme (B.
Chen et al., 2007). As such the decomposition rate of C pools is determined by taking the
weighted average of soil moisture and temperature stress factors at each soil layer. The relative
weight of each stress factor decreases with depth from the surface following the distribution
pattern of soil C content (Ju and Chen, 2005).
7.1.2 Spatially explicit input data
BEPS requires spatially distributed input parameters that drive the various biological and
ecological processes outlined in the previous section. The major drivers of BEPS are the
meteorology and LAI inputs. Secondary inputs include soil texture, soil moisture, soil
temperature, land cover, snow depth, biomass, 9 C pools, clumping index and pixel coordinates.
The acquisition and processing of these inputs is outlined below.
7.1.2.1 Meteorological inputs
Meteorological data including downward shortwave radiation, air temperature, specific
humidity, precipitation and wind speed were acquired from the United States National Weather
service operated by the National Centre of Environmental Prediction (NCEP). The data were part
of a 40 year reanalysis project which produced standardized climate data sets based on satellite
and field measurements and parameter modelling (Kalnay et al., 1996). NCEP global data were
acquired formatted in a T62 Gaussian grid at 2.5 degree resolution at a 6 hour time step. The data
84
were first interpolated to 1 degree grid resolution for global modelling applications. NCEP data
were interpolated from 6 hourly to hourly formats in several different ways depending on the
parameter. Hourly values of specific humidity and wind speed were taken as the same value for
each NCEP 6 hour time step. Radiation data were scaled as a function of solar zenith angle at
each time of day and location on Earth. Hourly temperature was determined by scaling the six
hour values based on the assumption that minimum and maximum temperatures occurred at 2pm
and 2am local solar time respectively. All parameters were then extracted to encompass only the
modelling domain (MPB affected areas) and were downscaled to 1 km resolution using a bilinear
interpolation scheme.
NCEP datasets have been shown to overestimate radiation data due to the lack of
accounting for the effect of atmospheric aerosols in reducing the downward radiation flux by
approximately 20 – 40% (Liu et al., 1997; Liu et al., 2002; Liu et al., 2003). NCEP radiation data
were compared with measurements at five meteorological stations (including two flux tower
sites) across BC in 2007 and 2008. Station data were acquired from the Environment Canada
National Information and Climate Archive. NCEP data were found to overestimate
measurements by an average of 25% on a daily time step. A reduction coefficient of 0.75 was
incorporated in BEPS to account for this overestimation. Furthermore, NCEP temperature and
precipitation were also compared to measurements on a daily time step. NCEP was found to
overestimate precipitation by approximately 13%. This overestimation was partly attributed to
measurement underestimation of snow amounts during winter months and therefore a correction
factor was not introduced in BEPS. Temperature data comparison yielded good agreement
between NCEP and measurements. The average correlation coefficient between data sets was
85
found to be 0.92 with absolute average difference between the data sets of 1.8oC on a daily time
step.
7.1.2.2 LAI and biomass inputs
BEPS requires daily overstory LAI inputs for each pixel. Ten day LAI files were created
on the basis of remotely sensed SPOT VEGETATION data described in Chapter 3 totalling 36
files per year. BEPS uses the same daily LAI value for every ten day period corresponding to the
day of year range in the input files.
A single file for each year containing the maximum annual LAI (defined as July average
LAI) for that year was created in order to calculate biomass within BEPS. BEPS first calculates
total biomass as a function of maximum LAI according to cover type specific quadratic
relationships derived by Liu et al. (2002) between measured biomass and LAI. From this value
biomass is partitioned into foliage, stem and root components for both overstory and understory
separately according to relationships derived by Kurz et al. (1996). The decreases in LAI
attributed to MPB attack result in annual losses in total biomass. These biomass components are
critical in calculating the total amount of C stored in vegetation components for the calculation
of autotrophic respiration and subsequent NPP.
7.1.2.3 Soil and litter carbon pool inputs
In order to calculate NEP, Rh must be accounted for. In order to calculate Rh BEPS
requires information on the amount of C stored within various C pools. These pools include: 5
litter pools - coarse structural detritus (Ccd), surface structural detritus (Cssd), surface metabolic
detritus (Csmd), fine root structural detritus (Cfsd), fine root metabolic detritus (Cfmd), and 4
soil pools - surface microbe (Csm), soil microbe (Cs), slow carbon (Cs) and passive carbon (Cp).
The amount of C released from these pools is calculated based on soil temperature and water and
86
the rate of C release is determined by coefficients specific to each pool (i.e. some pools such as
structural detritus release C at a much slower rate than others such as microbial pools).
BEPS cannot accurately simulate the size of the C pools and thus requires C pool inputs
derived by other techniques. Furthermore, BEPS does not simulate the transfer between pools or
the annual variability between pools. Pool sizes remained constant for the duration of the
modelling period. This may have produced errors in the representation of Rh but they were not
expected to be significant in that the size of C pools particularly coarse elements such as tree
stems, roots and branches, changes at a rate larger than a decadal scale. The C efflux from fine
roots and foliage elements which decompose at a relatively fast rate was underrepresented by the
model. Further work is necessary to at least partially quantify these fast C transfers by
developing an NEP scaling coefficient related to MPB attack severity and the rate at which
needles fall from attacked trees. C pools for the initial year of study (1999) were calculated by
the InTEC V.3 model using a 100 year spin up procedure briefly described below.
InTEC is a spatially distributed, process-based C cycle model which operates at a 1 km
spatial resolution producing NPP and C pool outputs at an annual time step. Major inputs include
meteorology, LAI, soil texture, NPP of reference year, nitrogen deposition, atmospheric CO2
concentration and forest age. The model accounts for both disturbance (harvest, fire) and non-
disturbance (nitrogen deposition, CO2 fertilization and climate warming) factors in determining
the historical annual C balance for the study region. A reference year for which an NPP map and
LAI are provided is selected and C dynamics are modelled for a period of 100 years prior to that
year. The changes in C pools for the 100 year historical period are then used to calculate the
pools for the reference year (Ju et al., 2006). The mechanisms for calculating photosynthesis are
the same in InTEC as in BEPS but InTEC possesses a dynamic method for calculating soil C
87
pool variation with time. The size of C pools including above ground biomass components and
below ground root and soil components are dynamically calculated at each model time step.
Furthermore, the transfers between individual pools are calculated unlike in BEPS. The transfer
between pools is dictated by disturbance events, for example during fire some C is transferred to
the atmosphere while the remaining quantity moves to soil C pools. The release of C from these
pools is dictated by soil temperature and water dynamics (Chen et al., 2000). Chen et al. (2003)
compared Canada-wide modelling results by InTEC with results of Kurz and Apps (1999) who
utilized the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS) accounting for
only disturbance factors and driven by forest inventory data. It was found that due to the lack of
the positive effects of increased nitrogen deposition, CO2 fertilization and climate warming on
plant growth and the large scale magnitudes of forest disturbance the CBM-CFS model produced
a sink to source conversion of Canada’s forest during the early 1980s. With these positive effects
considered, InTEC showed Canada’s forest remained a weak sink until 1998 (the final year of
study). Furthermore, InTEC was able to capture the inter-annual variability of C dynamic, due to
the consideration of climate which was a critical control on C uptake and release.
BEPS was run for the reference year 1999 in order to simulate NPP (no need for soil C
pools). The NPP map was then used in InTEC as the reference value. InTEC simulated the
annual C dynamics retrospectively to 1901, where a dynamic equilibrium between NPP and Rh is
assumed and C pools for the reference year were calculated based on historical forest age,
disturbance, climate change, nitrogen and CO2 dynamics. Aside from the NPP and LAI inputs
which were produced during this study all other InTEC inputs were derived from existing
Canada wide data sets used during previous studies conducted by Ju et al. (2006) and Ju and
Chen (2008). Spatial subsets of the Canada wide inputs were created to encompass only the
88
modelling domain of this study. The resulting C pools produced by InTEC were then used as
inputs to BEPS to calculate NEP for the reference year and all subsequent years of the study.
7.1.2.4 Soil specific inputs
Soil specific information was derived from Agriculture and Agri-food Canada Soil
Landscapes of Canada Survey (SLC). Soil texture data were adapted from Canada-wide data
sets used in previous BEPS modelling initiatives done by Liu et al. (2002) and Liu et al. (2003).
Soil water or available water holding capacity (AWC) – the portion of water in the soil
between field capacity and wilting point that can be readily extracted by plants was adapted from
the previously cited sources on the basis of the SLC database. AWC data are input into the model
as a single value per pixel for the first hour on the first day of the simulation. These values were
then used by the model to initialize the soil water calculations which were done on an hourly
time step.
Soil temperature was calculated in a similar way by BEPS (starting point value used to
initialize hourly calculations) and was assigned an initial value equal to the air temperature for
the first day of simulations in each year. BEPS calculated soil temperature at the top and bottom
of 5 soil layers based on the difference between soil and air temperature scaled by thermal
diffusivity which increases with soil depth.
7.1.2.5 Land cover
The land cover input file was created based on aerial overview survey data of MPB
disturbance produced by the BC Ministry of Forests and Range (BCMoFR) (Westfall and Ebata,
2008). The data were acquired in ARC/VIEW polygon shape file format. The polygons were
classified by severity as follows: trace (<1%), light (1-10%), moderate (11-30%), severe (31-
50%) and very severe (>50%) (Table 1). The percentages reflected the amount of each polygon
89
Figure 19. Mountain pine beetle (MPB) cumulative affected areas used to create land cover (all conifer) input mask for Boreal Ecosystem Productivity Simulator (BEPS) modelling. Red boxes delineate the 6 sub-regions used for net ecosystem productivity (NEP) calculations in order to allow for multiple processor use for a single year.
showing red attack signs. The shape-files were then converted to raster format. The ‘trace’
classification was not utilized since it captured only several trees and the resulting polygons were
on the order of ten square meters which could not be represented by the 1 km2 resolution utilized
in this study. The resulting raster pixels often cover a larger area than the polygons from which
they were derived. This presents a problem of the modelling domain containing a significant
percentage of unaffected pixels in any given year. The solution was to model each year for the
cumulative extent of MPB outbreak. Due to the significant overlap of intra-year affected areas,
particularly during the latter stages of the outbreak, the influence of non-affected areas was
eliminated or at the very least equalized between years. The cumulative affected areas were
assigned a conifer designation and used as the land cover input in BEPS. The cumulative
affected area contained 245,424 pixels corresponding to square kilometres. The model was run
for this same area every year.
90
The land cover file was used as a mask to assign the pixels for which NEP was
calculated. Furthermore, the modelling region was partitioned into six sub-regions (Figure 19) in
order to allow for simultaneous processing of a single year using multiple computers. Each of the
six subsets required seven consecutive days of simulation on one standard PC resulting in a 45
day modelling period per year. Thirty PC’s were assembled resulting in a 14 day continuous
modelling time line for the ten year period. Hourly outputs were summed to daily and yearly
NEP totals. The NEP sub-region maps were then mosaiced back to a single image for analysis
and mapping.
7.1.2.6 Other input
The snow depth input was a single per pixel value for the initial snow condition. The
initial snow depth file for every simulation year was produced as the sum of precipitation from
the last seven days of precipitation data in the previous year. Snow depth in the first year (1999)
was computed as the sum of precipitation during the first seven days of that year. This value was
then used by BEPS to initialize snow depth calculations which were based on air temperature,
radiation and the accumulation of new snow. This is an important input in that it dictates the
availability of soil water to plants. When the snow is present on the ground soil water within the
model is unavailable to plants (accounts for frozen ground conditions) thus limiting
photosynthesis in winter months (Liu et al., 2002). Such an assumption neglects the insulating
capacity of snow and may induce error in C calculations particularly during spring and fall when
temperature limitations on photosynthesis are reduced but snow cover is still present.
A single clumping index file was used for the duration of the study and the average
measured lodgepole pine value (0.55) was used for each pixel in the study area.
91
Two files, the first containing latitude and the second containing longitude information
for the centre of each pixel were created and used as inputs in BEPS. These files are necessary
for the correct calculation of radiation dynamics which rely on the solar zenith angle which is
dependent on geographic location at every time step during the simulations.
7.1.3 Biological and ecosystem specific input parameters
Photosynthesis, stomatal conductance and respiration are based on biophysically derived
relationships. The mathematical equations that quantify these relationships are driven by cover-
type-specific parameters. Some examples include maximum carboxylation rate (maximum
amount of photosynthesis) (Vmax) required for photosynthesis calculation, minimum stomatal
conductance (gsmin), response of respiration to temperature (Q10) (Table 6). The proper choice of
such parameters is critical in accurately quantifying the naturally occurring processes of the
ecosystem within the modelling framework. The origins of the values used in this study are
primarily based on previous research which through various measurement methods quantified
them (Kimball et al., 1997; Wang and Leuning, 1998; Chen et al., 1999b; Arain et al., 2002). It
must be noted that the parameterization of BEPS in such a way may yield significant error. The
variability of the parameters may be significant between different stand age characteristics and
tree species. Furthermore environmental conditions such as light and nutrient availability may
alter the specific values. Seasonal variation may also affect the specific values. Ideally, spatially
distributed inputs of critical parameters particularly Vmax are needed. At the very least a sunlit
shaded division of the parameters may be included in BEPS to alleviate some error associated
with large ecosystem variability.
92
Table 6. Critical parameters for net ecosystem production (NEP) calculations in the Boreal Ecosystem Productivity Simulator (BEPS) model, particularly for photosynthesis and stomatal conductance.
Symbo Unit Description Value Reference Ω - Clumping index 0.55 This study gsmin mm/s Minimum Stomatal Conductance 0.001 Chen et al. (1999) Vmax μmol/m2 Maximum carboxylation rate
050 Arain et al. (2002)
Jmax μmol/m2 Maximum electron transport rate 0
100 Arain et al. (2002) Topt oC Optimal Temperature (250C) 25 Kimball et al. (1997) k - Ball-Berry slope 6 Wang and Leuning b’ mm/s Residual stomatal conductance of 0.017 Wang and Leuning b’ 1.6 mm/s Residual stomatal conductance of 0.011 Wang and Leuning Q10 - Temperature sensitivity factor 2.3 Kimball et al. (1997)
93
CHAPTER 8.0: SITE LEVEL NEP VALIDATION USING FLUX TOWER MEASUREMENTS
Annual NEP maps produced by the methodology outlined in the previous chapter require
comparison to field derived measurements of NEP for the purpose of validation. Modelled NEP
results must be compared with flux measurements in order to draw conclusions on the accuracy
of the modelling efforts. Two measurement sites located within the northern portion of MPB
affected areas – Crooked River (MPB-03) and Kennedy Siding (MPB-06) operated by Andrew
Black at the University of British Columbia (UBC), Biometeorology & Soil Physics Group
(Biomet), were used for NEP validation.
Measurement activities at these sites were carried out using flux towers which were fitted
with various gas analysing instruments measuring the exchange of C and water between the
biosphere and atmosphere. The instruments (combination of Campbell Scientific 3 dimensional
ultrasonic anemometer – measures wind vectors and air temperature and Li-COR Inc open path
infrared gas analyser – turbulent fluxes of CO2 and water vapour) function on the principle of
eddy correlation (EC). The EC technique is a measure of the covariance between vertical wind
velocity and the concentration of gases within it associated with turbulent motion. This is based
on the principle that within the turbulent boundary layer directly above a vegetation canopy
horizontal air flow includes a turbulent component made up of vortices or eddies in the vertical
direction. At the point of measurement on the tower downward and upward air movement is
measured along with gas concentration, temperature and humidity of each air parcel. With this
information known gas flux is determined as the covariance between wind speed deviations from
zero and deviations of gas concentration from the mean value (Brown et al., 2010; Baldocchi,
2003).
94
The EC technique is not without its flaws. Gap filling techniques which correct
anomalous data and interpolate missing data can impart substantial errors in the final flux
measurements. Such methods are based on simple empirical relationships and linear regressions
along moving windows and may not represent reality in a sufficiently robust way (Barr et al.,
2004). Furthermore, during low wind conditions flux measurements may become inaccurate due
to insufficient wind velocity for the sensor to register. In other words the signal to noise ratio
becomes very low. Wind profiles alter the foot print area of measured fluxes making it difficult
to extrapolate measured fluxes to the surrounding landscape. Finally, wintertime fluxes may be
exaggerated due to the effect of the sensor heating the surrounding air and artificially creating
turbulent systems which register as net flux uptake and result in overestimation of fluxes. In
other words, the measured ecosystem source in the winter time is not as large as in reality
(Baldocchi, 2003; Burba et al., 2008; Brown et al., 2010).
8.1 Site descriptions
Both sites are located in BC’s Northern Interior region with MPB-06 at Kennedy siding
170 km north of Prince George and MPB-03 adjacent to Crooked River Provincial Park 70 km
north of Prince George (Figure 5). Both sites were dominated by lodgepole pine overstory
between 85 to 110 years old with moss and lichen cover on the ground. MPB-03 had a
significant secondary overstory component consisting of subalpine fir and spruce trees with
deciduous willows and birch trees. The soils at both sites were coarse grained sandy loams with
significant gravel components and in general well drained. The first signs of MPB attack at
MPB-06 occurred in the summer of 2006 with approximately 5% visible red attack. In May of
2007 nearly 80% of the lodgepole pine overstory was experiencing red attack. Measurements at
this site began on 18 July 2006. MPB-03 was first attacked in 2003 and by 2007 nearly 95% of
95
the lodgepole pine overstory had been killed. Measurements at this site began on 20 March 2007.
Both sites used identical measurement instruments and techniques as described in the previous
section (Brown et al., 2010).
8.2 Site specific BEPS model inputs
A version of BEPS (point version) modified to simulate hourly NEP for a single 1 km2
pixel was used for years 2007 and 2008 at MPB-03 and MPB-06. This version was used rather
than the regional version since all inputs are in text format and thus easily created and modified
unlike the binary inputs used in regional BEPS. All biological parameters used for
photosynthesis calculations were identical to those for regional modelling (Table 6).
VEGETATION LAI produced by the process outlined in Chapter 3 and soil C pools discussed in
the previous chapter were the same as those used in regional modelling. The site specific inputs
were meteorology and soil characteristics and the methods for their production are described
below.
Half-hourly measurements of air temperature, downward shortwave radiation, relative
humidity, precipitation and wind speed were provided by UBC Biomet Group and were scaled to
hourly values. Measurements at MPB-03 began in March of 2007 resulting in lack of values for
the first 3 months of the year. Furthermore gaps in measurements due to instrument malfunction
and insufficient wind conditions were fairly prevalent in the data sets. The scaled NCEP
interpolated meteorology data described in the previous chapter were used to fill these gaps.
Furthermore, all wintertime (November to March) precipitation measurements were replaced
with scaled NCEP data due to the inaccuracy of tipping bucket gauges in snow precipitation
measurement.
96
Soil specific characteristics required by BEPS as inputs were provided by UBC Biomet
Group along with the meteorological data. The soil texture at both sites was reported as coarse
textured gravel soil of glacio-fluvial origins dominated by sand particles. The closest matching
soil texture based on percentages of sand silt and clay recognized by BEPS was the sandy loam
classification. Measured soil moisture and temperature on the first simulation day (Jan 1) for
both sites was used in BEPS simulations.
8.3 NEP validation results
The NEP data received from UBC Biomet was in the form of total monthly values
normalized by area (gC/m2). Wintertime fluxes (November to March) were provided as one
cumulative value. The measured versus modeled comparison was made under the assumption
that EC measurements at the tower sites represented a 1km2 footprint area (equal to the
resolution of the BEPS input data). The comparisons were evaluated based on statistical
measures of RMSE, SEE, R2 and percent over or under estimation. The results suggested that
decreases in NEP due to MPB attack were strongly mitigated by the increased production of
secondary overstory, remaining healthy lodgepole pine trees and understory growth.
8.3.1 Measured versus modelled NEP
Figure 20 displays time series data comparing measured versus modelled estimates of
NEP for both MPB-03 and MPB-06 in 2007 and 2008, while Table 7 shows the total and
average fluxes of the above mentioned sites and years. The total annual flux per unit area was
overestimated by BEPS at MPB-06 2007, MPB-03 2007, 2008 by 4, 23 and 22 percent
respectively and underestimated at MPB-06 2008 by 32% yielding an average deviation between
measured and modelled results of 20%. A monthly average value for the cumulative five month
measured wintertime fluxes was used in the statistical analysis in order to maintain data
97
Figure 20. Measured versus modelled annual net ecosystem production (NEP) time series comparisons for MPB-03 and MPB-06 validation areas. Total monthly measured NEP in gC/m2 derived form eddy covariance (EC) flux tower data is used for the comparison.
consistency. The correlation coefficients (R2) between measured and modelled NEP at MPB-06
and MPB-03 were 0.70 and 0.86, respectively (Table 8). Such values reflect a relatively good
ability of BEPS to capture site level ecosystem dynamics and are consistent with past study as
discussed previously in Chapter 7. Scatter plots of measured versus modelled NEP are shown
in Figure 21.
-30-20-10
01020304050
NE
P (g
C/m
2 )
2007 Month 2008
MPB-06 (2007,2008)
MeasuredModelled
-20
-10
0
10
20
30
40
NE
P (g
C/m
2 )
2007 Month 2008
MPB-03 (2007, 2008)
MeasuredModelled
98
Figure 21. Scatter plots of measured versus modelled net ecosystem production (NEP) from 2007 and 2008 for flux tower validation sites. Cumulative measured wintertime fluxes were plotted as the five month (November to March) average.
Table 7. Annual total and average carbon (C) flux (net ecosystem production) comparison between measured and modelled site level validation procedures.
Site Year Average Sum
(gC/m2)
MPB-06 2007 Measured -10.25 -82 Modelled -10.70 -85.62
MPB-06 2008 Measured -4.03 -32.3 Modelled -6.00 -48.01
MPB-03 2007 Measured -6.94 -55.5 Modelled -5.33 -42.65
MPB-03 2008 Measured 0.74 5.9 Modelled 0.67 7.64
Table 8. Results of statistical analysis for measured versus modelled site level validation of net ecosystem production (NEP).
Site Year Average Sum R2 RMSE SEE
(gC/m2)
MPB-06 2007,2008 Measured -7.14 -114.3
0.70 10.37 1.38 Modelled -8.54 -136.64
MPB-03 2007,2008 Measured -3.1 -49.6
0.86 5.92 1.23 Modelled -2.19 -35.01
R² = 0.7003-30
-20
-10
0
10
20
30
40
50
-20 0 20 40
Mod
elle
d N
EP
(gC
/m2 m
onth
)
Measured NEP (gC/m2 month)
MPB-06 (2007,2008)
R² = 0.8552-20
-10
0
10
20
30
40
-20 0 20 40
Mod
elle
d N
EP
(gC
/m2
mon
th)
Measured NEP (gC/m2 month)
MPB-03 (2007, 2008)
99
The total two year measured CO2 flux at MPB-06 was -144.3 gC/m2 as compared to the
modelled result of -136.63 gC/m2 with a RMSE of 10.4 gC/m2. The RMSE was found to be 10%
of the total measured flux indicating that modelled results were 90% accurate in quantifying the
total two year flux. Measured CO2 flux at MPB-03 totalled -49.6 gC/m2 as compared to the
modelled result of -35 gC/m2 with RMSE of 5.9 gC/m2. The RMSE at this site was 12% of total
measurements indicating an 88% accuracy of modelled annual total results. Furthermore, the
SEE was calculated for both sites. MPB-06 and MPB-03 exhibited values of 1.38 and 1.23
respectively. This indicated that modelled data showed relatively little deviation from the
expected relationship with MPB-03 producing a relatively less scattered relationship than that of
MPB-06 (Table 8).
8.3.2 Secondary structure and healthy tree growth response to MPB attack
In 2007 both sites were C sources to the atmosphere as shown by both the measurements
and modelled results. In 2008 with increasing MPB activity the production at both increased with
source to sink conversion at MPB-03 as reflected by both measurements and modelling results
(Table 7). MPB-06 exhibited an increase in NEP by 60% in 2008 with only 21% healthy trees.
The secondary overstory effect and understory vegetation growth at this site were negligible. The
increased production in 2008 was attributed to increased vitality of the remaining healthy trees as
a result of increased nitrogen, soil water and light availability (Brown et al., 2010).
Dendrochronological analysis performed by Berg et al., (2006) following insect
disturbance events in Alaska and the Yukon found an elevated growth response of surviving
trees following insect disturbance up to magnitudes of 80% stand mortality. On average over a
five year time period surviving trees grew at a rate two times faster than during non-attack
conditions. This growth release was attributed to a stand thinning effect which increased resource
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availability for the surviving trees. MPB-03 transitioned from a source to a sink of C by
exhibiting a tenfold increase in NEP in 2008 with nearly 95% mortality of the primary lodgepole
pine overstory. Brown et al. (2010) speculated that the difference in site response between MPB-
06 and MPB-03 was primarily a function of species composition. MPB-06 had a significant
secondary overstory component comprised of shade acclimated sub-canopy conifers (subalpine
fir, spruce) and deciduous trees (willow, birch). The secondary structure component responded
very positively to increased light and nutrient availability following removal of the dominant
lodgepole pine trees.
Several studies have investigated the effect of increased tree growth response and
secondary structure response to insect disturbance or stand thinning activities (Waring and
Pitman, 1985; Heath and Alfaro, 1990; Veblen et al., 1991; Yang, 1998; Alfaro and Campbell,
2004; Berg et al., 2006; Smirnova et al., 2008; Brown et al., 2010; DeRose and Long, 2010).
These studies showed a fairly consistent increased growth response with a 2 – 6 year lag period
following disturbance. Waring and Pitman (1985) conducted an experimental study of growth
response to MPB attack in northern Oregon. They concluded that non-attacked host trees
exhibited an increase in growth efficiency defined as the addition of stem biomass per square
meter of foliage 2 – 3 years post attack. The growth efficiency was found to be 30% greater than
pre-attack conditions during the three year period. Waring and Pitman (1985) attributed this to
decreases in leaf area of attacked trees allowing for increased light availability for the remaining
non-attacked trees and resulting in higher levels of photosynthesis. Heath and Alfaro (1990)
concluded that the above discussed growth release began between 2 to 6 years following
relatively severe MPB outbreaks during the mid 1980s in the central interior of BC. Relatively
recent work by Alfaro and Campbell (2004) utilized dendrochronology to investigate three major
101
historical MPB outbreaks in the Chilcotin Plateau of central BC. The average period of increased
growth following MPB attack was found to be 14 years for the surviving trees. Veblen et al.
(1991) used similar techniques as Alfaro and Campbell (2004) to investigate a spruce beetle
outbreak in Colorado in 1940. It was found that tree species not affected by the beetle and
relatively shade tolerant such as fir and spruce showed accelerated growth post beetle attack. The
resultant increase in growth rates was in the order of a 2 to 5 fold increase in mean tree ring
diameters. Furthermore, this effect lasted more than 40 years particularly in fir species which
became dominant in severely affected stands. Both Veblen et al. (1991) and DeRose and Long
(2010) found that regeneration of trees from the seed bank was not a dominant form of stand
regeneration. In other words, saplings of lodgepole pine trees growing from seeds on the forest
floor could not become established as the older spruce and fir species out-competed them for
resources. Yang (1998) conducted research on the effects of thinning (similar to patchy
distribution of MPB associated mortality) in mid-age lodgepole pine stands near Hinton Alberta.
It was observed that needle length, tree height and basal area increased significantly 5 to 10 years
post thinning resulting in a foliar increase of 29%.
8.3.3 Assessment of BEPS performance
In general BEPS performed well against measurements but at both sites during both years
monthly NEP values were overestimated by the model. That is to say, that both the wintertime C
source and the summertime C sink were too large. This may have been a function of scaling
errors. The EC measurement footprint is highly variable based on wind patterns and may not
have represented a 1 km2 footprint as was assumed during the model validation. Furthermore,
wintertime fluxes measured at the sites may have been underestimated due to problems with EC
instruments artificially heating their surroundings resulting in the inaccurate measurement of C
102
sequestration during wintertime. With respect to physical principles the wintertime
overestimation may be attributed to errors in the representation of vegetation production.
VEGETATION LAI values decrease in the winter time rather significantly in some areas. The
model may be missing actual wintertime C sequestration as a result. Summertime overestimation
may be attributed to an oversimplification of Rh processes. Due to the lack of exchange between
C pools represented in BEPS fast C decomposition may be underrepresented causing
overestimation of NEP. To some extent this effect may have been compensated by the reduction
in fine root accumulation in MPB affected trees. Fine roots are accumulated annually and
decompose rapidly. Due to widespread mortality fine root decomposition is limited to the initial
year of attack.
BEPS was able to capture the effects of secondary structure growth and to some extent
the increased production of healthy trees and the response of understory growth to MPB
outbreak. VEGETATION LAI captured the secondary structure as a component of overstory
LAI reducing the effect of lodgepole pine mortality on NEP. The growth response of healthy
trees was partially captured by their contribution to total overstory LAI. This effect was most
pronounced at MPB-06 which lacked a secondary overstory component but retained 20% healthy
lodgepole pine. The relatively low growing season correlation coefficient at MPB-06 (0.67) in
comparison to MPB-03 (0.83) whose recovery was dominated by secondary structure growth
suggests the inability of BEPS to fully capture the effect of primary overstory growth response to
MPB. Such a comparison also underlined the success of BEPS at capturing secondary overstory
growth. Understory vegetation as outlined in previous sections was computed in the model as a
simple decreasing exponential relationship with increasing overstory LAI. This may not have
been the most accurate formulation in that sites such as MPB-06 which exhibited significant
103
drops in overstory LAI with increasing MPB attack did not posses significant amounts of
understory vegetation. As such the understory component of these sites may have been
overestimated in the BEPS. The reverse is also true in that sites with significant understory may
have been underrepresented in the model. Furthermore, the modelling scheme considered
understory as being the same cover type as overstory resulting in underestimation of production
due to deciduous understory growth. The response of accelerated understory growth may
function on a time lag post-disturbance. The characterization of understory response in the
modeling scheme does not capture this dynamic as the understory vegetation increases instantly
with decreases in overstory LAI in the model.
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CHAPTER 9.0: CARBON VARIATION AS A FUNCTION OF MPB ATTACK
Annual maps of NEP were produced and investigated in order to quantify the distribution
of C sources and sinks for MPB-affected areas in BC from 1999 to 2008. In addition,
relationships between C cycle components and their respective controlling factors were
examined. These included the dependence of NPP on LAI, the temperature and precipitation
controls on Rh and the subsequent consequence to NEP. It was speculated that ecosystem
resilience acted as a mitigating factor against the impacts of MPB attack on C cycling.
Comparisons between the results presented by this study and past studies with respect to
ecosystem C dynamics and their response to MPB outbreak were examined.
9.1 Annual NEP mapping and NPP and Rh results
The results of NPP, Rh and NEP simulations are outlined in Table 9 and displayed
graphically in Figure 22. NPP over the ten year period was characterized by a significant
decrease from 1999 to 2001 followed by stabilization and slight recovery until 2005 followed by
a rise towards pre-outbreak levels from 2006 to 2008. The NPP range was 451 to 488 gC/m2, the
average was 463 gC/m2 with a standard deviation of 10.85 gC/m2. NPP decreases were primarily
a function of reduced foliage cover as quantified by LAI for MPB affected trees. The average
decrease in NPP from pre-outbreak condition (1999) was 5.6% with a range between 3.5 and
7.3%. The relatively small reductions in NPP as a function of MPB outbreak were potentially a
function of reductions in autotrophic respiration compensating for reduced photosynthesis due to
foliage loss. Furthermore, NPP may have been propped up by the accelerated production of
unaffected trees and the rapid growth of secondary structure as quantified by LAI.
105
Table 9. Annual values of modelled net ecosystem production (NEP), net primary production (NPP) and heterotrophic respiration (Rh) for the cumulative mountain pine beetle affected area in British Columbia.
Year AVG NEP NEP AVG NPP NPP AVG Rh Rh
gC/m2yr MtC/yr gC/m2yr MtC/yr gC/m2yr MtC/yr 1999 9.89 2.43 488.46 119.88 478.57 117.45 2000 -13.42 -3.29 467.68 114.78 481.10 118.07 2001 -32.71 -8.03 451.45 110.80 484.15 118.82 2002 -3.79 -0.93 452.79 111.13 456.58 112.06 2003 6.97 1.71 461.10 113.17 454.13 111.45 2004 -18.95 -4.65 459.39 112.75 478.35 117.40 2005 -31.75 -7.79 455.13 111.70 486.88 119.49 2006 -14.08 -3.46 471.11 115.62 485.19 119.08 2007 -12.71 -3.12 463.69 113.80 476.40 116.92 2008 -7.51 -1.84 466.59 114.51 474.10 116.36
SUM -118.05 -28.97 4637.39 1138.13 4755.44 1167.10 AVG -11.81 -2.90 463.74 113.81 475.54 116.71
Figure 22. Net ecosystem production (NEP), net primary production (NPP) and heterotrophic respiration (Rh) for mountain pine beetle (MPB) affected areas from 1999 to 2008.
‐35.00
‐30.00
‐25.00
‐20.00
‐15.00
‐10.00
‐5.00
0.00
5.00
10.00
15.00
420.00
430.00
440.00
450.00
460.00
470.00
480.00
490.00
500.00
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
NEP
(gC/m
2 yr)
NPP
, Rh (gC/m
2 yr)
Year
NPP
Rh
NEP
106
Rh remained fairly steady throughout the study duration with a range of 454 to 486
gC/m2, an average value of 475 gC/m2 and standard deviation of 11.4 gC/m2. From 1999 to 2001
Rh showed a steadily increasing trend with a large decline in 2002 and 2003 in response to
drought conditions followed by a return to pre-drought levels from 2004 to 2006 and a
decreasing trend in 2007 and 2008. The Rh peak occurred in 2005, exhibiting a 2% increase from
pre-outbreak conditions. A major increase in Rh due to the addition of large amounts of dead
wood biomass and foliage as a result of MPB induced tree mortality was evident. This may be
due to the fact that dead trees remain standing for several years after mortality and the majority
of detritus was in the form of coarse woody material which typically decays over long periods of
time. Furthermore, the large additions of foliage elements which typically decay at a fast rate
may have been offset by the decreased production of fine root elements which also exhibit rapid
turnover. Initial increases in fine root decay were not accounted for in this study and may have
caused underestimation of Rh. The enhanced production of secondary structure and surviving
healthy trees may compensate for a future release of C as a function of decaying coarse wood
elements. Also due to the slow turnover of woody detritus a sudden C release in the form of Rh
would not be expected rather a more gradual increasing trend as evident from 1999 to 2001 and
2004 to 2005 would be more realistic.
NEP was highly variable through the duration of the study exhibiting a range in values of
-31.75 to 9.89 gC/m2 with an average annual value of -11.81 gC/m2 and standard deviation of
14.14 gC/m2. NEP dropped in all years as compared with 1999 with a maximum decrease of 4.3
times in 2001 and minimum decrease of 29% in 2003. The average NEP change as compared
with pre-outbreak conditions was a decrease of 90% annually. The MPB affected areas
transitioned from a sink of 9.89 gC/m2 in 1999 to a source of -32.71 gC/m2 in 2001, followed by
107
a recovery to sink conditions of 6.97 gC/m2 in 2003. From 2004 to 2005 NEP decreased again to
a source of -31.75 gC/m2, followed by a steady recovery trend from 2006 to 2008, reaching an
NEP of -7.51 in 2008. The initial decline in NEP was attributed to the rapid expansion of the
MPB outbreak and the resulting ecosystem shock. The return to sink status by 2003 during a
relatively severe outbreak year can only be attributed to rapid vegetation regrowth, secondary
structure development and increased vigour of surviving trees as seen in the LAI trends. The
second major collapse in NEP in 2005 was a function of extremely rapid MPB expansion both in
range and severity of attack during that time period. The recovery trend beginning in 2006 was
again a function of new opportunities with respect to light and nutrient availability made
available for surviving and secondary vegetation.
The spatial and temporal source and sink distribution was altered due to MPB attack as
compared with pre-outbreak conditions in 1999 (Figure 23). In 1999 a fairly uniform east to
west sink to source pattern was evident. Small hotspots of large scale sources (-400 to -600
gC/m2) were evident along a central transect of the cumulative MPB affected area. A fairly weak
but spatially consistent source (0 to -200 gC/m2) area was found in along the western edge and
central region of the attack. The remaining areas were a sink of C ranging between 200 and 600
gC/m2. From 2000 to 2004 the strength and area of the central severe sinks grew consistently
ranging from -400 to -800 gC/m2. The areas in the central interior which were a fairly small but
consistent source grow in size but not in overall source strength. By 2002, these areas began to
show signs of recovery with patchy appearance of sink areas. From 2005 to 2006, severe source
108
Figure 23. Annual net ecosystem production (NEP) for the cumulative mountain pine beetle (MPB) affected area from 1999 to 2008.
109
Table 10. Annual carbon (C) source and sink distribution for mountain pine beetle affected areas displayed as total area (km2) and percentage of total area.
areas reverted back to a scattered, isolated distribution pattern with individual clusters localized
near the central and south eastern outbreak regions. The majority of the devastated central
interior region became a weak source (0 and -100 gC/m2) with scattered and sink areas
throughout. The concentration of sink areas was largest along the fringes of major outbreak
centres. The source and sink distribution with respect to the percentage of total area remained
fairly equal. The percentage of sinks from 1999 to 2001 rose from 53 to 57% indicating the
strength of source areas increased due to the drop in NEP associated with this period.
Stabilization of sink areas at about 55% through 2005 was evident while a steady decrease to
42% occurred through 2007 with a similar distribution in 2008 (Table 10). The period between
2006 and 2008 was interesting in that the percentage of source areas was increasing and
occupied a total area larger than the sinks but the NEP was steadily on the rise. This indicated an
increase in the strength of existing sink areas potentially induced by increased growth and vigour
of surviving trees.
The total NEP over the ten year period was -28.05 ± 3.4 MtC with an average annual loss
of -2.9 MtC (Figure 24). Such values suggest that the impact of MPB outbreak on C dynamics is
Year C sink C source C sink C source
Km2 % area 1999 130755 114669 53 47 2000 133320 112104 54 46 2001 140985 104439 57 43 2002 134890 110534 55 45 2003 136799 108625 56 44 2004 132475 112949 54 46 2005 121655 123769 50 50 2006 118404 127020 48 52 2007 103457 141967 42 58 2008 121844 123580 50 50
110
Figure 24. Cumulative net ecosystem production (NEP) for mountain pine beetle (MPB) affected areas in British Columbia’s Interior pine forests from 1999 to 2008.
severe yet not as significant as past predictions, namely those of Kurz et al. (2008). Forests have
adapted to function and even thrive during times of severe disturbance. Forests have the ability to
change their vegetation composition in response to disturbance by mechanisms which encourage
new tree development namely reduced competition for resources. Furthermore, forests in general
exhibit the characteristic to strengthen their resilience to disturbance as a function of the
disturbance itself. This concept can provide insight into the simulated NEP trend and is described
below.
111
Past investigation by Waring and Pitman (1985) who studied the effects of MPB on
lodgepole pine stands in west-central Oregon yielded interesting insight into the increase in
surviving tree vigour as a function of the insect disturbance itself. It was observed that within a
three year period stands which had undergone MPB attack with an average reduction of live
canopy of 16% with cumulative losses of 26% exhibited an increase in surviving tree vigour
exceeding 100 g of new wood production per square meter. Pre-outbreak conditions averaged
tree vigour of 70 g wood production per square meter. Through tree comparisons it was found
that trees exhibiting high growth efficiency (tree vigour) required larger numbers of beetles to
induce mortality. Trees with growth efficiency of 100 g/m2 were rarely attacked by beetles and
those which were, showed less than 5% mortality. It was predicted that within three years the
trees with increased growth efficiency would have been highly resistant to MPB attack. Such a
finding underlined the historical observations of outbreak subsidence between 3-5 years after
initial outbreak and the fact that beetle outbreaks rarely last longer than nine years (Cole and
Amman, 1980). The effect of increased growth efficiency provided a suitable explanation for the
patterns simulated by this study particularly in the similar durations of NEP decline and recovery
and the initiation of final recovery due to the decline in MPB outbreak size approximately eight
years after the initial outbreak.
9.2 Controlling factors of NPP and Rh
Overstory LAI was found to be a significant control on NPP. On average a 20% decrease
in LAI resulted in 6% decrease in NPP. This comparison was made utilizing the total ecosystem
NPP (both understory and overstory) and the satellite-derived overstory LAI. The reason for the
non-linearity of the LAI to NPP relationship was a function of the understory LAI. Overstory
LAI decreases were compensated by understory LAI increases by an exponential decreasing
112
Figure 25. Annual net primary production (NPP) response to changes in leaf area index (LAI) as a result of mountain pine beetle (MPB) outbreak. NPP is plotted on the primary axis, while LAI is on the secondary axis.
relationship. In other words as overstory LAI decreased understory LAI increased exponentially.
The 14% difference between LAI and NPP declines was found to be the result of increased
understory production contributing to total NPP. Nevertheless, LAI had a dominant influence on
NPP when compared with other environmental factors such as radiation, water availability and
temperature. This is evident in the intra-annual NPP and LAI trends which were nearly identical
in the trajectory of increases and declines (Figure 25). Furthermore, the inter-annual variability
of NPP was found to average between 120 and 830 gC/m2yr. The range of these values indicated
the importance of biological controls on photosynthesis, namely stomatal conductance (gs) and
foliage clumping (Ω). High gs values and low foliage clumping (conifer forests) allowed for
greater CO2 uptake and increased sunlit leaf area respectively, thus photosynthesis and in turn
NPP increased.
Rh was found to be controlled by precipitation (total in the growing season) which was
used as a proxy indicator for soil moisture (Figure 26). The magnitude of the Rh response to
precipitation was governed by air temperature (average in the growing season) which was used
0
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1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
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Year
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LAI
113
as a proxy for soil temperature (Figure 27). Temperature over the ten year period was found to
be steadily increasing with a range of 6.92 to 9.84 oC. Precipitation during the time period was
variable following a pattern of several wet years followed by dry years with a range of 296 to
454 mm. The years 1999, 2002, 2003, 2006 were dry while 2001, 2004, 2005, 2007 and 2008
were wet. Rh was found to increase with increasing precipitation and temperature. The nature of
the relationship between the three variables was most evident from 2006 to 2008 during which
precipitation increased but Rh decreased slightly due to a significant drop in temperature by
1.15oC from 2006 to 2008. The strength of the Rh to precipitation was stronger than the
temperature to Rh relationship with R2 values of 0.45 and 0.05 respectively. The low R2 values
are not surprising since Rh is not only a function of temperature and moisture but also soil texture
and lignin content of litter pools. Furthermore, Rh is calculated as the sum of C released from all
C pools both detritus and soil and the response of these pools is stratified by soil layers which
exhibited different moisture and temperature conditions. Therefore the Rh to temperature and
moisture relationships are not as strong as would be predicted by simpler empirical models.
9.3 Site level LAI and NEP recovery
In order to investigate the response of LAI and NEP to MPB attack and the ecosystem
resilience concept in further detail with consideration of their spatial distributions, localized site-
level analysis was undertaken. Three sites were chosen each at different latitudes to capture the
spatial variability in the NEP and LAI response. Site A was the MPB-03 flux tower site located
in the northern range of MPB attack, site B was a mid range site in the affected area and site C
was from the southern extent of the outbreak. Site A was a mature pine site with a relatively
114
Figure 26. The response of heterotrophic respiration (Rh) to average growing season air temperature.
Figure 27. The response of heterotrophic respiration (Rh) to average total growing season precipitation.
430
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1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
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Rh
115
Figure 28. Net ecosystem production (NEP) and leaf area index (LAI) change as a function of mountain pine beetle (MPB) increasing severity for flux measurement site MPB-03 with late attack history. Value in brackets under the x axis denote the percentage of trees killed that year.
Figure 29. Net ecosystem production (NEP) and leaf area index (LAI) change as a function of mountain pine beetle (MPB) increasing severity at a mature pine site with early attack history. Value in brackets under the x axis denote the percentage of trees killed that year.
Figure 30. Net ecosystem production (NEP) and leaf area index (LAI) change as a function of mountain pine beetle (MPB) increasing severity at a mid age pine site with late attack history. Value in brackets under the x axis denote the percentage of trees killed that year.
0
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50100150200
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Year (%attack)NEP (A)LAI (A)
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late attack history, Site B was also mature but with an early attack history and site C was a mid
age site with a late attack history. Site A exhibited ten year average LAI of 2.38 with cumulative
NEP of 307 gC/m2 (Figure 28). The first major attack occurred in 2003, and nearly 90% of
overstory mortality occurred by 2006. LAI was on the rise between 2000 and 2003 followed by a
70% in 2005 and a steady recovery until 2008. NEP followed a similar trend with source to sink
conversion in 2005 but recovery of the sink in 2008. This underlines the effect of secondary
overstory and increased healthy tree vigour in the relatively short recovery period (three years) of
NEP. Site B was characterized by an average LAI of 2.42 and cumulative NEP of -672 gC/m2
(Figure 29). Initial attack occurred in 2000 with the most severe period from 2004 to 2006
(>50% mortality). Nevertheless, during this period both LAI and NEP showed a steady recovery
following large drops in 2002 underlining the increased tree growth efficiency of the remaining
healthy trees. Site C was characterized by an average LAI of 3.1 and cumulative NEP of 1242
gC/m2 (Figure 30). The first attack at this site occurred in 2004 with severity reaching 31-50%
tree mortality by 2007. During the severe period, LAI decreased by 40% and NEP was converted
from a sink to a source in 2005. The area returned to a sink with steadily rising trajectory only
two years following the attack. This relatively southern site characterized by mid age trees had a
much greater capacity to sequester C and showed much higher resistance to MPB outbreak albeit
not as severe as those in more northern regions.
In general, recovery trends moved from north to south, and were more rapid in the south.
All sites exhibited recovery during the most severe outbreak years which may have been a
function of the expansion of secondary structure and increased resistance of surviving trees as a
result of reduced nutrient limitation. Furthermore, the rapid ascension of NEP post attack may
117
have been attributed to an inhibited strength of MPB outbreak as a result of increased tree growth
efficiency.
9.4 Comparison with previous results
Kurz et al. (2008) conducted a modelling study on the effect of disturbance (MPB,
harvest and fire) for MPB affected areas in BC from 2000 to 2006 and projected the future
impact of the insect disturbance until 2020. The study utilized the Carbon Budget Model of the
Canadian Forest Sector (CBM-CFS3), an empirically driven, accounting based stand and
landscape level model which simulated forest C dynamics. The model is spatially distributed in
that its basic modelling unit is a forest stand. The CBM-CFS3 is driven by inventory based forest
data which are used to determine 21 C pools within the ecosystem. The changes in the C pools
are simulated at an annual time step and reflect forest growth, litter fall, turnover, decomposition,
natural disturbance and forest management. NPP is simulated as a function of a tree growth
module and no estimates of GPP or Rh are made. Net biome production (NBP) which is NEP
reduced by direct forest fire emissions and harvesting activities is calculated based on a rather
advanced mechanism for C transfer between pools. The transfers are dictated by empirical
transfer coefficients and temperature-dependant, pool-specific constraints are utilized. C transfer
as a result of fire, harvest and insects is partitioned into various pools according to empirically
derived proportions, and the direct emission related to these disturbances is accounted for (Kurz
and Apps, 1999; Kurz et al., 2008). The CBM-CFS3 model does not consider non-disturbance
factors such as climate, nitrogen deposition, CO2 fertilization or the process-based relationships
which drive ecosystem dynamics such as photosynthesis quantification.
In order to compare between the results of this study and those of Kurz et al. (2008) an
adjustment for C removed from the ecosystem due to harvesting was necessary. Annual harvest
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data were received from the BCMoFR for the managed forest area represented in the modelling
domain of Kurz et al. (2008). Since the total area modeled by Kurz et al. (2008) was 35% larger
than the total area modelled by this study annual harvest figures were reduced by a factor of
0.65. Furthermore, it was assumed that 85% of the harvested C was transferred to the forest
products sector and the remaining 15% remained in the environment as coarse detritus on the
forest floor, foliage detritus and root detritus. Thus the harvest values were further reduced by a
factor of 0.85.
Both sets of results show similar cumulative NBP results but the details and trajectory of
the results differ considerably. Kurz et al. (2008) reported a cumulative C loss to the atmosphere
as a result of MPB and harvest of -106.82 MtC (-11.86 annual average) while this study showed
a C loss of -93.73 MtC (-9.37 annual average). This study simulated 12% less cumulative C loss
than did Kurz et al. (2008) (Table 11). The ten year trajectory comparison between these two
studies yielded rather different results (Figure 31). From 2000 until 2001 this study reported a
significantly larger C source than did Kurz et al. (2008). But the trajectory of the curve had
changed to reflect increasing NBP in 2002 and 2003 in this study, while Kurz et al. (2008) saw a
large drop in NBP from 2002 to 2003 followed by a recovery in 2004 and then a period of
gradually decreasing values until 2008, with the expectation of recovery beginning in 2010 and
the return to pre-outbreak levels by at least 2040. This study followed Kurz et al. (2008) in 2004
and 2005 with declines in NBP but deviated from the similarity beginning in 2005 with a steady
increasing trend to 2008. The main difference in the two sets of results was the fact that Kurz et
al. (2008) showed steady decline in NBP after 2005 while this study showed a steady increase in
NBP from 2005.
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The reasons for the deviation between the two studies are twofold. The first is due to the
lack of the positive effects associated with non-disturbance factors, particularly CO2 fertilization,
nitrogen deposition and climate warming. The second and most relevant in the context of this
study is the positive effect of insect disturbance on secondary structure growth and increased
growth response in healthy trees. A principle conclusion of this study is that forest recovery post
disturbance is more rapid than expected and as has been shown by Kurz et al. (2008) potentially
due to the impact of ecosystem resilience as a mitigating factor to disturbance. Furthermore, the
effectiveness of the disturbance to provide an opportunity in the form of alleviating nutrient and
light limitations may have substantially increased the strength of ecosystem resilience forcing
rapid growth and increased production at a higher level than during pre-disturbance times.
Table 11. Comparison between annual values of net biome production (NBP) from Kurz et al. (2008) and this study and the annual amounts of harvested carbon (C).
YEAR Kurz et al. (2008)
This Study (harvest) Harvested C
MtC/yr 1999 - -3.10 5.52 2000 0.59 -9.09 5.80 2001 0.59 -13.55 5.52 2002 -2.5 -7.01 6.07 2003 -17.5 -4.64 6.35 2004 -10.5 -10.78 6.13 2005 -15 -14.64 6.85 2006 -19 -11.08 7.62 2007 -21 -10.74 7.62 2008 -22.5 -9.08 7.23
SUM -106.82 -93.72 64.75 AVG -11.87 -9.37 6.475
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Figure 31. Carbon (C) balance of British Columbia’s pine forests undergoing mountain pine beetle (MPB) outbreak over the last decade from 2 studies: (1) Carbon Budget Model of the Canadian Forest Sector (Kurz et al., 2008) based on forest inventory data and changes in age structure due to disturbance. Results from 2006 and 2007 are projections based on probable MPB spread patterns. (2) Spatially explicit, process-based modelling accounting for changes in meteorology, forest health – leaf area index, and soil moisture conditions (This study). (3) The results of this study with the annual harvested lumber subtracted from net ecosystem production (NEP).
-25.00
-20.00
-15.00
-10.00
-5.00
0.00
5.00
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Eco
syst
em C
arbo
n (M
tC/y
r)
Year
This StudyCBM-CFS3 (Kurz et al 2008)This study (harvest)
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CHAPTER 10.0: CONCLUSIONS AND SUMMARY
10.1 Summary
The spatial and temporal patterns of LAI and NEP were investigated in this research. The
major objectives of the research were: (1) to produce satellite based LAI maps for MPB affected
areas in BC from 1999 to 2008 and validate these maps through field measurements of LAI using
optical instruments, (2) to use the LAI maps as major inputs in spatially distributed, process-
based C cycle modelling of C dynamics for MPB affected areas in BC from 1999 to 2008 and
validate the results with measured C flux data, and (3) to investigate the potential of ecosystem
resilience as a mitigating force combating the presumed traumatic negative effect of MPB on C
cycling.
LAI images were produced from SPOT VEGETATION reflectance data at a ten day time
step from 1999 to 2008 for the cumulative extent of MPB affected areas in BC. The maps were
produced using the RSR vegetation index which limited the background effects on the retrieval
of LAI by applying a MIR scalar sensitive to liquid water contained in background vegetation.
Empirically derived RSR to LAI relationships developed for conifer forests were then applied to
convert the RSR images into LAI. Extensive field validation was carried out by measuring LAI
with LAI-2000 and TRAC spectral instruments. The resulting comparisons between measured
and satellite-based LAI retrievals revealed strong correlation between the RSR index and LAI
(R2 = .68), while other indices namely SR (R2 = 0.31) and NDVI (R2 = 0.42) were less effective.
Fine resolution LAI images covering the measurement areas were scaled to fit over the coarse
resolution VEGETATION products producing fairly good agreement between the different
spatial scales (R2 = 0.65). The resulting LAI maps along with meteorological, soil characteristic,
land cover and C pool (produced by the InTEC model) data were used as inputs to the spatially
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distributed, process-based C cycle model BEPS. C cycle modelling was carried out for the
cumulative extent of the MPB outbreak on hourly time steps at 1 km resolution. Daily and
annual total NEP images were created and examined for spatial and temporal relationships
between various C cycle components including NEP, NPP and Rh. The model outputs were
validated against tower flux measurements at two sites in the Northern Interior region of BC.
BEPS was able to capture the monthly variability in measured NEP at both sites during 2007 and
2008. The relationship between measured and modelled results was characterized by R2 values
averaging 0.78 but some overestimation was evident both in the size of the winter time C source
and growing season C sink. The temporal dynamics of LAI and NEP were investigated in order
to find potential mechanisms underlining the hypothesized existence of an ecosystem resilience
function mitigating the full effects of MPB outbreak.
Both LAI and NEP were characterized by negative responses to MPB attack. Increased
tree mortality was reflected in decreases in LAI and NEP from pre-outbreak conditions.
Regardless of the large scale and severity of the MPB attack, both LAI and NEP showed
dramatic signs of recovery during very severe years and during the last three years of the study
where a steadily increasing trend was established for both variables. It was speculated that this
apparent resistance to the affects of MPB was a manifestation of ecosystem resilience. The first
major contribution to ecosystem resilience may have been the response of secondary overstory
trees as evident in annual increases in LAI. Increased opportunity as a function of increased light
and nutrient availability due to overstory mortality may have allowed secondary trees, not
affected by beetles to exhibit a rapid and sudden increase in production. Furthermore, as evident
in site level measurement studies the reduction of overstory allowed for an increase in understory
vegetation. The second major contribution of ecosystem may have been the increased growth
123
efficiency of surviving lodgepole pine trees. Past studies have shown that trees not affected by
the MPB at the beginning of the attack developed resistance to the beetle by increasing their
vigour or growth efficiency. These trees began to accumulate more wood per square meter than
during pre-outbreak conditions and subsequently became more resilient to MPB attack. This may
have been a fundamental reason for the 8 year duration of the outbreak which began to show
signs of decline in 2008.
10.2 Research limitations
Several research limitations should be noted for both the LAI mapping and NEP
modelling components of this study. The procedure for producing spatially distributed LAI maps
relied on the ability of the RSR vegetation index to remove the understory contribution to total
LAI. Although the RSR index is more capable than the SR and NDVI indices in removing the
effect of the background some residual effects may remain. A possible solution can be found in
the use of multiple angle remote sensing which can detect the overstory and understory
reflectance separately (Pisek et al., 2010).
A further limitation is the quantification of understory LAI. The relationship used to
calculate understory LAI in BEPS is based on a comparison between measured overstory and
understory LAI in healthy forest stands. The response of understory vegetation in MPB affected
areas may have been governed by factors other than the amount of overstory LAI. Furthermore,
the calculation of understory LAI may have overestimated the rate of natural vegetation growth.
Understory vegetation responded instantly to decreases in overstory LAI in the modeling
scheme, when in reality the response may have exhibited a significant time lag. This effect may
have been somewhat neutralized due to the fact that understory LAI would be overestimated in
some areas and underestimated in others. MPB affected forest stands with no developed
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understory would have been unrealistically represented as having an understory component
based on their overstory LAI. On the other hand, stands with a significant understory component
may have been underrepresented in modelling scheme.
A significant limitation of this research was the lack of consideration of increased foliage
inputs into the C pools used for Rh computation as a function of MPB induced tree mortality.
Due to the assumption of constant C pool magnitude for the duration of the study Rh may have
been underestimated in turn overestimating NEP. The sudden mortality of trees due to the MPB
attack may have contributed significantly to Rh through the decomposition of needles on the
forest floor and fine root components in the soil. Although this contribution may have been
somewhat offset by the reduction in fine root production and subsequent annual decomposition,
it potentially contributed significant error to the Rh quantification. Coarse woody detritus such as
tree stems and branches decompose at a rate slower than that of foliage and fine root components
potentially limiting C transfer in these pools at the decadal scale of this study.
Further limitations include the single value parameterization of critical biophysical inputs
to BEPS, of which most significant is Vmax or the maximum capacity of plants to
photosynthesize. Values such as this can show significant variability between ecosystems, forest
age, and species composition. In addition, the large scale tree mortality associated with the MPB
outbreak, and the subsequent changes in vegetation composition may have induced significant
variability in the single value parameters used in BEPS.
10.3 Future work
This study ended at a critical point in the recovery of BC’s forests post-MPB disturbance.
The C cycling dynamics in the next ten years will provide the true test of ecosystem resilience
functioning as a mitigating factor to MPB disturbance. Will the steady increases in NEP shown
125
by this study during the 2006 to 2008 period continue and eventually return to pre-outbreak
levels? When will the ecosystem return to a sink of C? The answers to these questions are critical
in order to fully understand the C consequence in BC’s forest as a function of the MPB outbreak.
Process-based modelling investigations need to be carried out for the next several years in order
to answer such critical questions.
A natural progression of this research is to develop models which simulate future insect
spread patterns. Such models can be coupled to existing C process models in order to estimate
the potential effects of insect disturbance on currently unaffected ecosystems. The projected
increased prevalence of insect disturbance as a function of climate change further adds to the
necessity of such research.
As a result of the large scale MPB outbreak examined in this study changes to ecosystem
species composition may occur. Forests once dominated by lodgepole pine stands may transition
to fir and spruce ecosystems. In the long term these areas may even transition to grass land
ecosystems greatly limiting the future C uptake capacity. An investigation on the forest
succession post-disturbance is necessary to establish the long term health of the ecosystem.
The results of this research may be used to initiate further study on potential forest
management practices post-MPB disturbance. According to the Mountain Pine Beetle Action
Plan produced by the BC government a major management practice for mitigating the effects of
MPB is the intensive replanting of lodgepole pine trees in affected areas. This practice is based
on the assumption that severely affected areas dominated by a single species (lodgepole pine)
lack significant diversity to recover without the aid of reforestation efforts. Furthermore,
recovery in these areas is expected to function over time periods in excess of 20 years. The
results of this study and those of site level EC measurements suggest that recovery may be rapid
126
due to the increased production of unaffected species. As such, intensive replanting initiatives
may not be necessary in order to achieve levels of ecosystem health comparable to pre-
disturbance conditions.
10.4 Conclusion
In conclusion the research outlined in the preceding chapters underlines the importance of
a process-based approach driven by remote sensing inputs for the study of C cycle dynamics in
order to increase the understanding of critical climate change feedbacks that dictate both the
magnitude and sign of the C source and sink distribution. This approach utilized inputs (LAI)
that characterize the ecosystem condition thus more accurately represented the complex
changing reality as a result of disturbance. The process-based model provided a set of rules
which governed individual ecosystem functions but allowed those functions to change based on
environmental circumstances which enabled such an approach to at least somewhat capture the
effect of ecosystem resilience.
The response of ecosystems to climate change and the feedback mechanisms that either
accelerate or mitigate climate change are not well understood. The potential of forests to increase
C uptake with increased temperatures and offset the growing CO2 concentrations in the
atmosphere will depend on the response of ecosystems to increasing disturbance. It is therefore
imperative to gain a fundamental understanding into the nature of the ecosystem response by
conducting research such as that presented here. Future large scale disturbances such as the BC
MPB outbreak will undoubtedly reduce the capacity of ecosystems to sequester C but the
magnitude of that capacity will depend on factors associated with ecosystem resilience and
governed by complex inter relations within affected ecosystems.
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REFERENCES
Alfaro, R., and R. Campbell. (2004). Dendrochrological reconstruction of mountain pine beetle outbreaks in the Chilcotin plateau of British Columbia. In Mountain pine beetle symposium: Challenges and solutions., eds. T. L. Shore, J. E. Brooks and J. E. Stone, 258-266. Victoria, BC: Canadian Forest Service.
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