Department of Physical Geography
and Quaternary Geology
Phytomass and Soil Organic
Carbon Inventories Related to
Land Cover Classication and
Periglacial Features at Ari-Mas
and Logata, Taimyr Peninsula
Justine Ramage
Master’s thesis
Physical Geography and Quaternary Geology, 60 HECs
NKA 60
2012
Preface
This Master’s thesis is Justine Ramage’s degree project in Physical Geography and
Quaternary Geology, at the Department of Physical Geography and Quaternary Geology,
Stockholm University. The Master’s thesis comprises 60 HECs (two terms of full-time
studies).
Supervisors have been Gustaf Hugelius and Peter Kuhry at the Department of Physical
Geography and Quaternary Geology, Stockholm University. Examiner has been Peter
Jansson, at the Department of Physical Geography and Quaternary Geology, Stockholm
University.
The author is responsible for the contents of this thesis.
Stockholm, 13 September 2012
Lars-Ove Westerberg
Director of studies
Master Thesis
Phytomass and Soil Organic Carbon
Inventories Related to Land Cover
Classification and Periglacial Features at
Ari-Mas and Logata, Taimyr Peninsula
Justine Ramage
Dept. of Physical Geography and Quaternary Geology
Stockholm University
Master degree in Glaciology and Polar Environments, 2012
Supervised by Gustaf Hugelius and Peter Kuhry
A BSTRACT
The predicted increase in atmospheric temperatures is expected to affect the turnover
of soil organic carbon in permafrost soils through modifications of the soil thermal
regime. However, the tundra biome is formed of a mosaic of diverse landscape types
with differing patterns of soil organic carbon storage and partitioning. Among these,
differences in e.g. vegetation diversity and soil movements due to freeze-thaw processes are of main importance for assessing potential C remobilization under a changing climate. In this study, we described the storage of soil organic carbon (SOC)
and the aboveground phytomass carbon in relation to geomorphology and periglacial
features for two areas on Taymir Peninsula (Arctic Russia). An average of 29.5 kg
C m−2 , calculated by upscaling with a land cover classification, is stored in the upper soil meter at these two study sites. The mean C phytomass storage amounts to
ca.0.406 Kg C m−2 , or only 1.38% of the total SOC storage. The topography, at
different scales, plays an important role in the carbon partitioning. High amounts
of soil organic carbon are found in highland areas and within the patterned ground
features found in peatlands. The highest amounts of aboveground phytomass carbon
are found in deciduous shrubs and moss layers. The large variability in carbon distribution within land cover types among the sites reveals the challenge of upscaling the
carbon storage values over the Arctic and thus highlight the necessity to carry out
detailed field inventories in this region.
ii
ACKNOWLEDGEMENTS
Everyone interested in physical geography has been sitting for years in different classrooms that have a common characteristic: a planisphere. Everyone interested in polar
environments has nodded ones head quite often. So did I, and among those colourful
places one has always been more attractive – Siberia. I could not imagine that I would
discover a pixel of this huge unicoloured area for a master thesis. I could not imagine
that I will discover it by walking some kilometers in the tundra for harvesting plants.
That is the reason why I am grateful to Gustaf Hugelius and Peter Kuhry, my
supervisors, who gave me the chance to fulfil this amazing project. Thank you Juri
also for your help and your support in the field and later with your advice, good luck
with your PhD.
I am also thankful to the Cryocarb project that provided us with the funds and to
the whole team and especially to Kolia who taught me the Arctic species and shared
so much knowledge in the field.
And of course my family and friends, from Sweden to France through Germany,
Japan and Korea who have been supporting me, every day. And a special thanks to
Nico for having the patience and the energy to help me; and to Elissa for reviewing
it and sharing the coffees during the long study days in the computer rooms!
Acknowledging is an interesting part of the work, where I realize that the time
passed really fast and that so many people have been involved directly or indirectly
in this project.
iii
CryoCARB team at Ari-Mas, Taimyr Peninsula, Siberia, August 2011.
iv
N OMENCLATURE
BD
Bulk Density
LOI
Loss on Ignition
NDVI Normalized Difference Vegetation Index
NEP
Net Primary Production
N
Nitrogen
NPP
Net Primary Production
PCA
Principal Component Analysis
Pg C
Petagram Carbon
P
Phosphorus
Rh
Heterotrophic respiration
ROI
Region of Interest
SOC
Soil Organic Carbon
SOM
Soil Organic Matter
TOC
Total Organic Carbon
v
C ONTENTS
Chapter 1 – Introduction
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Chapter 2 – Background
2.1 Phytogeographic Zones and Floristic Provinces in the Arctic . . . .
2.1.1 Taimyr Peninsula . . . . . . . . . . . . . . . . . . . . . . . .
2.2 The Carbon Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Plant Productivity . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Plant Diversity in the Arctic . . . . . . . . . . . . . . . . .
2.2.3 Net Ecosystem Production and Soil Organic Carbon Stocks
2.3 Periglacial Geomorphology . . . . . . . . . . . . . . . . . . . . . . .
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Chapter 3 – Study area
Chapter 4 – Methods
4.1 Sampling Methods . . . . . . .
4.2 Carbon Analyses . . . . . . . .
4.3 Statistical Methods . . . . . . .
4.4 Multivariate Statistics Analyses
4.5 Remote Sensing Methods . . .
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Chapter 5 – Results
5.1 Soil Organic Carbon Distribution . . . . . . . . . . . . . . . . . . . . .
5.1.1 SOC Partitioning Related to Environmental Variables . . . . .
5.2 Vegetation Structure and Aboveground Phytomass Carbon . . . . . .
5.2.1 Aboveground Phytomass C Storage Related to Environmental
Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Carbon Storages Among Periglacial Features . . . . . . . . . . . . . .
5.3.1 SOC Distribution Among Periglacial Features . . . . . . . . . .
5.3.2 Vegetation Distribution Among Periglacial Features . . . . . .
5.4 Total Carbon Storage Using Land Cover Classification and Upscaling
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Chapter 6 – Discussion
6.1 The Effects of Landscape Topography on C Storages . . . .
6.1.1 High Amounts of SOC in Upland Areas . . . . . . .
6.1.2 Periglacial Patterned Ground Features . . . . . . . .
6.2 Predicted Evolutions in Plant Functional Type Abundances
6.3 Limits of Upscaling Methods . . . . . . . . . . . . . . . . .
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Chapter 7 – Conclusion
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52
C HAPTER 1
Introduction
Large amounts of carbon are stored within continuous permafrost terrains in Siberia.
Recent estimates place the total amounts of soil organic carbon (SOC) at 1672 Pg for
the northern permafrost region, this represents almost 50% of the estimated global
soil organic carbon pool [Tarnocai et al., 2009]. Permafrost, comprised of a deep layer
and an upper layer, covers 22% of the surface area in the Arctic [Zhang et al., 2008].
The deep layers remain frozen for at least two years, whereas the upper layer, defined
as the active layer thaws temporarily during the warm seasons. The water content in
permafrost soils is very variable but often high and mainly appears as ice. Thus permafrost degradation due to warmer temperature leads to soil subsidence. The Arctic
has undergone changes due to rapid warming in temperatures, which are considered
as a common climate change indicator as it integrates shifts in energy budget and
atmospheric circulation [McBean, 2005]. Warming occurs with high annual variability and the trend since 1980 shows an increase of about 1‰ per decade in winter
and spring and smaller in autumn. Temperatures are expected to increase by 7° to
8‰ by the end of the 21st century [McBean, 2005]. Environmental impacts on the
arctic ecosystem are large and show profound regional differences due to the complex
relationships between environmental elements [Serreze et al., 2000].
Of main focus in this thesis is to estimate the carbon storage distribution in permafrost terrain in Siberian tundra, as well as the consequences of warmer temperatures that have enhanced an increase in permafrost thaw due to changes in soilatmosphere properties, on the landscape. Changes in soil temperatures affect the
microbial decomposition of the soil organic matter whose consequences on the carbon
cycle are considered as one of the strongest potential feedback from soil terrestrial
ecosystem to the atmosphere [Schuur et al., 2008].
Thawing permafrost enhances changes in the landscape with increasing thermokarst
formations and the development of periglacial features that leads to the redistribution of horizontal carbon stocks [Schuur et al., 2008]. Moreover, few studies exist on
the spatial distribution and the amounts of carbon stored in the northern permafrost
regions [Tarnocai et al., 2009]. Recent studies show that 20% to 60% of the pool of
frozen organic carbon is in peatland soils of which 1% to 20% is intermixed with mineral soils [Smith et al., 2004]. Large amounts of carbon are found in deeper horizons
due to frost processes even though most of the carbon is stored in the active layer
1
2
since plant photosynthesis is responsible for carbon formation [Zimov et al., 2006].
Two carbon pools are analyzed and presented in this thesis; the storages of soil
organic carbon and the aboveground phytomass carbon distributions among plant
species. Vegetation plays an important role for the carbon storages since it uptake
carbon and insulate soils. In turn, modifications in soil and atmospheric properties
strongly impact plant physiology and the photosynthesis activity responsible for plant
development.
To understand the complex processes in permafrost soils, interdisciplinary approaches are gathered to provide with different scale information.
At a micro scale, a soil science approach is used in an attempt to understand
the microbiologic processes in permafrost soils and the frost processes responsible
for vertical carbon remobilization. Of main interest is the role of cryoturbation.
Cryoturbation is a process of soil movement that refers to the mixing of organic
horizons and mineral horizons showing vertical and horizontal sorting due to frost
action [Bockheim and Tarnocai, 1998].
Botanic studies deliver the knowledge needed on plant physiologies, abundances and
photosynthesis activity. Vegetation, through photosynthesis activity and its thermal
effects on heat and water fluxes, impacts significantly the carbon distribution and
ground processes [Walker et al., 2008].
At a small scale, geography and ecology field studies emphasize the processes responsible for carbon distribution and landscape interactions at different scales and
therefore give a better approach in understanding carbon storage partitioning and
distribution among different land cover types. Therefore, different scale perspectives
are involved in this thesis.
Field inventories are conducted over landscape areas and provide information on
the processes and the interactions between the environmental elements responsible
for carbon cycle. However, it is of main importance to be able to have an overview of
the changes affecting the whole ecosystem at small scales. Thus, remote sensing is an
important tool to estimate the shifts occurring in the tundra ecosystems in response
to climate change.
The intention of this thesis is to describe the soil organic carbon storage and the
phytomass abundances, using field inventory and remote sensing methods in two sites
located in the typical Siberian tundra. Different methods are adopted: statistics with
linear regressions, t-tests and principal components are used to highlight the relations
between environmental variables; remote sensing methods as region of interest analysis
and maximum likelihood classification allow to upscale the land cover classes for the
all landscape. The analysis aims at understanding the patterns of carbon distribution
in relation to the surrounding ecosystem and to discuss the potential impacts of
thawing permafrost on soil organic carbon remobilization. The thesis highlights the
tremendous variability of carbon distribution at different scales, thus emphasizing the
importance of multi-scale approach when analyzing landscape dynamics.
C HAPTER 2
Background
2.1
Phytogeographic Zones and Floristic Provinces
in the Arctic
A bioclimatic zone represents a climatic area characterized by specific vegetation
types in different habitats [Elvebakk, 1999]. This thesis uses the definition of tundra
by Yurtsev [1994], in which the tundra biome is defined as the phytogeographic region north of the Arctic treeline. Differences in bioclimatic zones are a consequence
of the past environment since the northern regions have been influenced by different climates over long periods, that have impacted the floristic migration [Elvebakk,
1999]. Different definitions are found in the literature to characterize the bioclimatic subzones in the tundra and a synthesis has been made by Elvebakk [1999] in
a attempt to contribute to the Circumpolar Arctic Vegetation Map (CAVM) project
[Walker, 2000, Walker et al., 2005]. Hence, the terminology used here refers to the
phythogeographic classification used by Walker et al. [2005], based on the classification made by Elvebakk [1999] and modified to fit the objectives of the CAVM project.
The Arctic is divided into five subzones, based on summer temperatures and vegetation (north-south transition in the stature of woody plants). The subzones can be
sorted into two groups defined by Walker [2000]; the arctic and the hypoarctic zones
that are again separated in four subzones, based on differences in vegetation found
on plakor habitats (Fig 2.1). The term plakor defines a zonal, flat and mesic habitat
[Matveyeva, 1994], characteristic of different regions.
ˆ The arctic group characterizes the high Arctic vegetation, found in the coldest
part of the Arctic. Bare ground surfaces are large and mosses and lichens are
mostly spread among the vegetated patches. The proportion of high vascular
plant coverage is less than 5% and they occur on mesic sites while woody plants
are absent [Walker, 2000]. The arctic group is subdivided into three subzones
that are defined as subzones A, B and C in the CAVM [Walker et al., 2005].
– Subzone A (or high Arctic tundra subzone) occupies 4.6% of the Arctic
landscape [Walker, 2000] and is characterized by a discontinuous plant
cover, with low-statute vegetation, mainly composed of mosses and lichens
3
2.1. Phytogeographic Zones and Floristic Provinces in the Arctic
4
Figure 2.1: Bioclimatic zones in the Arctic. Five zones are defined, based on summer
temperatures and vegetation (for a specific description of the zones, see chapter 2.1). From
Walker [2005].
on mineral soils [Walker et al., 2005].
– Subzone B (or Arctic tundra southern subzone of the Arctic group) occupies 35% of the tundra zone [Walker, 2000] and is characterized by shrub
vegetation with sedges and cotton grass vegetation on plakors.
– Subzone C is foremost characterized by a circumpolar occurrence of the
dwarf shrub Cassiope tetragona. This subzone can be considered as a
transition zone between the high and the low Arctic [Elvebakk, 1999].
ˆ The hypoarctic group refers to the vegetation in the subarctic regions (low Arctic), characterized by a dense vegetation cover. It corresponds to the subzones
D and E in the CAVM [Walker et al., 2005]. The boundary between Arctic
and hypoarctic vegetation relies on soil properties differences since in the high
Arctic, mineral soils are dominant while in the hypoarctic subzones, peaty soils
and moist tundra are prevalent [Aleksandrova, 1960]. The vegetation cover is
denser and the vascular plants occupy 50 to 80% of the area.
– The northern hypoarctic tundra (subzone D) is an ecotone toward the
Arctic tundra. Hypoarctic communities are interrupted by patches of bare
ground created by frost erosion [Walker, 2000].
– In the southern hypoarctic tundra (subzone E), the vegetation is dominated by shrubs and the boreal species occur on zonal sites. Some specific
subzones in the southern typical tundra, can be defined according to the geographical conditions ([Walker, 2000]). For example, in the oceanic regions
2.1. Phytogeographic Zones and Floristic Provinces in the Arctic
5
of hypoarctic subzones, the vegetation is replaced by heath tundra with
meadows and shrublands. The stlanik subzone, consisting of semi-prostate
shrubs, is specific to the tundra and north taiga of the northernmost parts
of Siberia [Razzhivin, 1994].
Tundra subzones have been subdivided into floristic regions (fig. 2.2), following
a west-east variation in vegetation [Yurtsev, 1994]. The variations in floristic composition at a large scale depends mainly on substrate characteristics, topography,
continental or oceanic influences [Walker, 2000]. Across the circum-Arctic, Yurtsev
[1994] distinguishes 6 provinces and 22 subprovinces, based on differences between
species in specific sectors.
Figure 2.2: Map presenting the floristic provinces in the Arctic. I] East Siberian: A]
Taimyr; B] Anabar-Olenek ; C] Yana-Kolyma II] Chukotka: A] Continental Chukotka ; B]
Beringian Chukotka ; C] South Chukotka ; D] Wrangel Island III] Alaskan: A] Beringian
Alaska ; B] Northern Alaska IV] Canada-Greenland: A] Central Canadian ; B] West Hudsonian ; C] West Greenland ; D] East Greenland ; E] Ellesmere-North Greenland V] BaffinLabrador VI] European-West Siberian: A] Kanin-Pechora ; B] Ural-Novaya Zemlya ; D]
Yamal-Gydan ; E] Svalbard Modified from Yurtsev [1994].
The subzones defined in the circumpolar Arctic vegetation map associate dominant
plant communities to the floristic provinces ([Walker et al., 2005]). Five physiognomic
categories, divided in 15 vegetation units defined by their dominant plant functional
types, are distinguished: barrens; graminoid-dominated tundras; prostrate-shrubdominated tundras; erect-shrub-dominated tundras; and wetlands. The dominant
2.1. Phytogeographic Zones and Floristic Provinces in the Arctic
6
plant functional type characteristics for each vegetation units are described. The
most spread vegetation unit over the different vegetation type of the Arctic region is
the unit composed of moist erect-dwarf shrub tundras, where erect dwarf shrubs are
mostly less than 40 cm tall with a continuous plant cover (80-100%) on zonal sites,
to sparse cover (5-50%) on dry ridges [Walker et al., 2005].
2.1.1
Taimyr Peninsula
Taimyr Peninsula is part of the East-siberian province ([Yurtsev, 1994]) and has the
particularity to be one of the only places in the Arctic in which all the tundra subzones
defined in the CAVM [Walker, 2000, Walker et al., 2005] are represented (Fig. 2.3).
Figure 2.3: Map of the Taimyr Peninsula showing the different zones and subzones. I.
Forest tundra; II. Southern tundra; III. Typical tundra; IV. Arctic tundra; V. Polar desert,
from [Matveyeva, 1994] .
A small area of taiga is spread out in the south of Taimyr [Shchelkunova, 2011].
The eastern part of the peninsula is characterized by larger abundances in high Arctic,
Arctic and alpine-Arctic species while in the west part, the oceanic influence is more
pronounced and southern species can grow [Yurtsev, 1994]. The high variability
between the tundra zones from north to south leads to a large variation in vegetation
structure [Matveyeva, 1994]. A west-east differentiation is observed where different
floristic provinces are distinguished between the western Taimyr (eastern part of the
Gydan-Yennissey province) and the center and eastern Taimyr (Pyasino-Khatang
province) [Shchelkunova, 2011]. Matveyeva [1994] shows that five major habitats are
2.2. The Carbon Cycle
7
found in Taimyr: plakors, snow beds, wetlands, south-facing slopes and fell-fields.
At a larger scale, the diversity in plant communities is also due to the complicated
geological structure of the peninsula. A large diversity of plains is found in the area
(low coastal marine plains, foothill plains covered by marine deposits, lacustrinealluvial plains, and glacial plains) from which results a diversity in soil structures
[Moskalenko, 2011].
2.2
The Carbon Cycle
Tundra biomes contain 1672 Pg C, this represents approximately 50% of the global
belowground organic carbon pool [Tarnocai et al., 2009]. Soil organic carbon (SOC)
storage is controlled by the balance of carbon inputs from plant production and outputs through decomposition [Jobbagy and Jackson, 2000]. In order to discuss the
carbon storage in terrestrial ecosystems, it is important to gain an understanding of
the properties of carbon uptake in vegetation and the outputs released through the
process of decomposition. Once this link between plant primary productivity and
carbon uptake through the impacts of environmental factors is understood, a better
understanding can be reached regarding the affect of climate change on carbon storage
systems.
2.2.1
Plant Productivity
Plants uptake carbon through photosynthesis. Thus phytomass is considered as a
carbon sink. Observations using normalized difference vegetation index (NDVI) show
an increase in the phytomass of circumpolar Arctic tundra of 19.8% (0.40 Pg C) over
the last 30 years [Epstein et al., 2012]. The following section develops the processes
invocate in the carbon production by phytomass.
Phytomass may be defined as the total amount of biomass in one area, that includes
the photosynthesizing part of the vegetation (leaves) and the stems [Laidler and Treitz,
2003]. Plant primary productivity is the increase in biomass, the dry content of an
organism that corresponds to the carbon gains resulting in the CO2 uptake from the
atmosphere [Hopkins, 1999]. Plant productivity is determined by the balance between
the carbon uptakes by photosynthesis (net primary production, NPP) and the carbon
losses by respiration (Rh) [Hopkins, 1999].
Photosynthesis is the process which allows the conversion of carbon dioxide in
organic compounds of a plant though light energy absorption by the leaves. It has
a prominent position in regulating biomass production [Hopkins, 1999]. Light is
essential in this process since it maintains the surface temperature and it allows
carbon fixation. Fixation is the process by which the carbon dioxide is incorporated
into an existing organic carbon sugar molecule [Klein and Klein, 1988]. The light
energy is absorbed by the plant through the leaves by a pigment, the chlorophyll.
This energy is absorbed by entering the leaves through the stomata (pores in the
leaves) and is diffused through the plant, thus providing an efficient mechanism for
CO2 absorption. CO2 and water are absorbed in order to be converted into sugars in a
carbon fixation process [Hopkins, 1999]. Respiration is the principal counterbalance
of photosynthesis. Through this process, the accumulated carbon is consumed in
order to obtain the energy required to increase and maintain the biomass [Hopkins,
1999].
2.2. The Carbon Cycle
8
Carbon assimilation, measured by the primary productivity, contributes to the development of plant biomass [Hopkins, 1999]. It can be measured by Growth Primary
Production (GPP) or by Net Primary Production (NPP). The GPP corresponds to
the total carbon assimilation through photosynthesis while the NPP measures the
net increase in carbon by correcting the GPP with the losses in carbon during the
respiration process [Hopkins, 1999].
Understanding how plant productivity is impacted by different environmental factors is complex since the components responsible for the photosynthesis activity respond with different rates and trigger numerous feedbacks [Roy et al., 2011]. In the
Arctic, the primary productivity shows a tremendous variability among vegetation
types and follows a general trend toward a decrease with increasing latitude [Roy
et al., 2011]. The growing season is short [Wilson, 1966] and affected by low mean air
temperature [Billings, 1974].
Air temperature is the main factor controlling the plant productivity in the Arctic
[Billings and Mooney, 1968] (Fig 2.4). In turn, temperatures are closely related to the
energy regime which is characterized by continuous daylight for a part of the summer
and continuous darkness in mid-winter [Roy et al., 2011], Thus, seasonality is a major
factor in the control of plant productivity since light is the major component of the
photosynthesis process [Grogan and Chapin, 1999]. Together with alpine species,
arctic plants are the only ones adapted to grow at low temperatures [Billings and
Mooney, 1968].
Figure 2.4: Effects of temperature on net primary production (NEP) . Temperature affects
directly the NEP heterotrophic respiration (Rh) and on the net primary production (NPP).
Temperature affects indirectly NPP and Rh through its impacts on species compositions,
nutrient mineralization, moisture, litter quantity and quality and soil organic matter (SOM)
quality. From Shaver [2000].
2.2. The Carbon Cycle
9
As in many other ecosystems, indirect effects of temperature changes on carbon
balances are likely to be more important than the direct effects themselves [Shaver
et al., 2000]. Warming-driven changes affect plant productivity through modification
in moisture, nitrogen availability, or species composition [Shaver et al., 2000]. Soil
characteristics are an important environmental factor in the control of Arctic plants
production. Plant growth on permafrost soils is controlled by the active layer depth
[Bliss, 1971]. Permafrost restricts the biological activity as it affects the root growing
systems and the deep soil drainage, which in turn affect the oxygen availability [Roy
et al., 2011]. Nutrients are essential for plant growth and their limitation restricts the
capacity of Arctic plant primary productivity [Roy et al., 2011]. Mineral nutrients
such as nitrogen (N) and phosphorus (P) are the major nutrients responsible for plant
biomass production in dwarf-shrub dominated Arctic ecosystems [Jonasson et al.,
1999]. Warmer temperatures lead to an increase in N and P uptake by the plants,
thus stimulate the plant primary productivity that is in turn responsible for a larger
carbon sink potential [Jonasson et al., 1999].
2.2.2
Plant Diversity in the Arctic
The primary productivity is affected by the mosaic pattern of species composition
due to their differences in carbon and nutrient use [Roy et al., 2011].
Different factors are involved for justifying the use of a classification into plant
functional types (PFT) . The different species are classified depending on their physiological characteristics and their adaptive strategies (relative growth rate, size, and
capacity to persist after disturbance) [Chapin et al., 1996]. A simplified classification
based on PFT distribution and development reactions to ecosystems processes was
made by Chapin et al. [1996]. In the tundra biome, vascular plants differ from the
non-vascular plants in their capacity to access the ground water and to develop under different conditions. Within vascular plants, the main difference between woody
plants and herbaceous is that woody plants have the capacity to expand their canopy
higher. Deciduous woody plants can be distinguished from evergreens in their shorter
season of photosynthetic activity, they need more resources and they produce a higher
quality of leaf litter.
Although tough climatic conditions are strongly controlling vegetation growth in
the Arctic, there is a large diversity between species’ communities (Fig. 2.5) that
occurs over short distances [Billings, 1974]. Local variations in topography, soil characteristics, climate and wind exposition impact the development of species in the
tundra [Roy et al., 2011]. It is challenging to describe a typical arctic vegetation form
except in the fact that it is mostly characterized by low stature without trees [Shaver
and Chapin, 1991].
2.2. The Carbon Cycle
10
Figure 2.5: Classification of the plant functional types for the arctic ecosystem. The species
are classified according to their physiology. From Walker [2000].
The low vegetation is widely composed of short-stemmed perennial herbaceous
plants (angiosperms), mosses, lichens and prostrate shrubs [Billings and Mooney,
1968]. Tundra plants have a distinctive characteristic determined by their rapid
growth within few weeks following the melting of the snow cover in spring [Billings
and Mooney, 1968]. Within the bioclimatic zone D, a simplified topographic gradient
(Fig. 2.6) has been defined [Elvebakk, 1999].
Among arctic plant communities, mosses and lichens are the only plant functional
types occurring in all subzones of tundra since they can tolerate a wide range of
temperatures [Turetsky, 2003]. They represent up to 50% of the total aboveground
biomass in some wet sites [Shaver and Chapin, 1991] and are important in carbon
and water exchanges with the soils. Their physiology differs from the vascular plants
thus they impact differently the surrounding ecosystem function by stabilizing soils
and producing organic matter [Turetsky, 2003]. They act as an insulating layer to
the soil and as a selective layer for nutrient leaching and water absorption [Hicklenton
and Oechel, 1976].
2.2.3
Net Ecosystem Production and Soil Organic Carbon Stocks
The net ecosystem production (NEP), defined as the carbon balance of an ecosystem
over a period, shows little relationship with the plant primary productivity in the
Arctic [Roy et al., 2011]. The net ecosystem production is particularly related to the
soil moisture regime [Roy et al., 2011] which is crucial in influencing carbon fluxes
through the impacts of soil characteristics on organic matter decomposition [Grogan
and Chapin, 1999]. Therefore, the NEP is closely related to the process of decomposition.
2.2. The Carbon Cycle
Prostrate dwarf-shrubs
(Dryas spp.)
11
Dominance of
dwarf-shrubs other than
(Betula nana s.,
Empetrum vaccinicum,
Salix)
Open Ridge
Vegetation
Discontinuous
Vegetation
Thermophilous herbs,
(e.g. Sibaldia,
Potentilla Crantzii)
Zonal
Vegetation
Snowbed
Vegetation
Mires of pleurocarpous
mosses in admixture
with Sphagnum spp.
Carex dominance
Thick
Peat
Continuous Vegetation
Figure 2.6: Simplified topographic vegetation gradient of the arctic zone D. Shrubs are
mostly vegetating the hill and slopes while peat surfaces are vegetated with graminoids and
mosses and lichens. From Elvebakk [1999].
Large amounts of carbon are stored within permafrost soils as soil organic matter
(SOM). A specificity of polar ecosystems is the predominance of dead SOM that store
high concentration in mineral nutrient due to the low rates in litter decomposition
[Zamolodchikov and Fedorov-Davydov, 2004]. Soil organic matter stocks result from
past accumulations under periods of warmer climate that have been later preserved
by cold and wet soils that inhibit the process of decomposition [McGuire et al., 2009].
Decomposition is an ecological process that recycles dead tissues from primary production into nutrients in the soil. Plant detritus (litter and dead roots) is the main
supply for SOM that form the inputs of the stock of organic matter while the outputs are the flux of carbon dioxide and methane from the soil surface released during
the process of decomposition [Davidson and Janssens, 2006]. The active layer depth,
depending on environmental factors such as topographic characteristics, substrate,
and vegetation are an important control in the decomposition rates [Kimble, 2004].
Organic matter decomposition is also slowed by soil movement disturbances defined
as cryoturbation since it leads to soil remobilization.
Considering the Arctic terrestrial ecosystem, the NEP presents a high variability.
It has been calculated that the organic matter budgets range from 0 to 30 g m−2 yr−1
over the whole tundra biome [Oechel and W.D., 1992]. Moreover, high decomposition
rates occurred in Taimyr Peninsula due to favorable environmental peculiarities of
the peninsula [Zamolodchikov and Fedorov-Davydov, 2004]. Therefore, soil organic
carbon stocks occur with a tremendous variability at the northern permafrost regional
scale. Soils are the largest pool of terrestrial organic carbon [Tarnocai et al., 2009].
New estimates of SOC show that a total of 495.8 Pg C for the upper first meter and
1024 Pg C for the upper three meter are stored in the northern permafrost regions
[Tarnocai et al., 2009]. Thus, SOC distribution in the Arctic presents some variability
in relation to environmental characteristics such as the soil temperatures and the
2.3. Periglacial Geomorphology
12
nature of the substrate. Large carbon pools were found in peatlands [Hugelius et al.,
2009], mainly in the permafrost. Another study shows that in the North American
Arctic Regions, the highest amounts of carbon are found in lowlands and in hilly
uplands [Ping et al., 2008].
2.3
Periglacial Geomorphology
In periglacial areas, the surface is often characterized by the presence of ground material arranged with symmetrical and geometric shapes, that are defined as patterned
ground. Periglacial landforms can be subdivided in two groups, slope landforms and
patterned-ground features. Specific landforms are associated with slopes whose process is driven by solifluction whereas on flat terrains patterned ground features result
in a differential frost heave.
The size, form, and age of active periglacial features vary in response to such factors as air and soil winter temperature, soil composition, ice content, ground drainage,
vegetation and snow cover. The dominant process responsible for the formation of
periglacial features within the bioclimatic subzone D is the differential frost heaving that leads to sorting and cryoturbation [Walker et al., 2005]. Vegetation abundance accelerates the development of periglacial features and affects their morphology
through their contribution to soil surface insulation [Walker et al., 2008]. Plant communities are more abundant within the stable areas between periglacial features. The
thickness of the moss layer and the abundance of phytomass are the factors affecting
at most the thermal soil regime of periglacial features in summer whereas in winter
the thickness of the snow cover is a more important factor controlling periglacial feature formation [Walker et al., 2011]. Mass movement is also one important factor
in the formation of periglacial features in sloped terrains [Walker et al., 2011]. Five
different types of periglacial features that represent the main features found in the
Taimyr Peninsula are listed.
ˆ Solifluction is a mass movement that occurs on slopes. It is induced by the
thawing of the active layer that leads to plasticity in soils due to water saturation. Gravitational forces lead the soil to flow down slope sliding on the
underlying permafrost table. Of major influence for the movement of solifluction is vegetation through its impact on stabilizing the slopes and maintaining
the soil structure [White, 2011].
ˆ Cryoturbation refers to the mixing of the organic and mineral horizons due to a
mass movement. It results in a vertical and horizontal sorting due to frost action
[Bockheim and Tarnocai, 1998]. Cryoturbation has a broad definition, one of
those defines irregular horizons resulting from soil mass movement or involutions
that originate from soil material displacement, due to cryostatic pressure and
gravity [Bockheim and Tarnocai, 1998]. It is a complex process and different
models have been presented to explain it. First, the cryostatic model assumes
that a pressure on the unfrozen material is created due the opposite movement of
two freezing fronts; downward from the surface and upward from the permafrost
table. In the convective cell equilibrium model, a heave subsidence process from
the surface leads the material to move downward and outward [Bockheim and
Tarnocai, 1998]. Cryoturbation is favored by imperfect drainage conditions and
often involves patterned-ground formation [Washburn, 1980]. Several studies
2.3. Periglacial Geomorphology
13
show that important amounts of carbon are stored in cryoturbated pockets
found in permafrost areas [Tarnocai and Smith, 1992, Bockheim, 2007, Hugelius
et al., 2009, Hugelius et al., 2010]. Thus cryoturbation is considered as a key
factor to carbon storage, through its role of burying organic pockets into deeper
horizons [Bockheim and Tarnocai, 1998].
ˆ Important to the Arctic is the formation of thermokarst lakes. It results from the
thaw of permafrost, accompanied by a ground subsidence that is filled by water
due to a change in the surface drainage. Thermokarst development is stronger
in areas of high ground ice content, especially where ice-wedge polygons are
well developed [Washburn, 1980]. Thermokarst terrains are strongly interacting
with local hydrology since subsidence impacts the surrounding drainage systems
[Schuur et al., 2008].
ˆ Polygons are large-scale polygonal forms of patterned-ground that illustrate
the marginal collapse structure of an ice-wedge due to repeating frost action
[Black, 1976]. They are characteristic of continuous permafrost terrains. Two
types of polygons have been identified in the sampling areas; high-centered and
low-centered polygons. The difference between these two types is that in the
low-centered polygons, the active ice-wedges are covered by rims of peat so the
margins of the polygon are higher than the surface of the center, while for the
high-centered polygon it is only the ice-wedge margins that collapse so a crack is
formed between the center of the polygon and the rims [TAGA Glossary, 2008].
ˆ Palsa-bogs complexes are defined as peat-covered mounds with perennially frozen
cores rising above the surface of peat bogs [TAGA Glossary, 2008]. They are
mainly composed of organic material and can have a frozen mineral core. The
formation of a palsa is dependent on the snow cover during winter and vegetation during the warmer seasons [Zuidhoff and Kolstrup, 2005]. Palsas develop
due to frost penetration associated with ice lens formation. During the initial
stage, supposed to happen in winter, it is shown that the absence of vegetation
might influence the deflation of snow, which leads to increased frost penetration
into the mound that grows. Palsas become higher until the biomass increases
and insulates the core that starts degrading [Zuidhoff and Kolstrup, 2005].
ˆ Non sorted-circles have a circular mesh and are not bordered by stones [TAGA Glossary, 2008]. Earth hummocks are among the most common periglacial feature
distributed within the permafrost terrains [Washburn, 1980]. Earth hummocks
are dome-shaped features characterized by raised-centers and cracks between
them. Raised-centers are mainly vegetated by sedges and grasses while the
depressions are covered by mosses.
C HAPTER 3
Study area
The field work was conducted in August 2011 in the Taimyr Peninsula, Siberia.
This area has been chosen according to the CryoCARB project objectives, which
focus on estimating organic carbon for cryoturbated soils and over different sites
in the tundra region of Eurasia Greenland and Svalbard [Cryocarb project website http://www.univie.ac.at/cryocarb/the-cryocarb-project/]. The Taimyr
Peninsula comprises a total area of about 400,000 km2 [Schmidt, 1999] and it is delimited by two rivers in the west and east (respectively the Yenisei and the Khatanga
River) and stretches from the Putorana Plateau in the south, up to the Arctic Ocean.
The peninsula is composed of three geomorphological units: a central mountain
range separates a wide coastal plain in the north from a large lowland plain in the
south [Schmidt, 1999]. The peninsula is affected by a cold dry-continental climate
[Matveyeva, 1994].
The two sampling sites (Fig. 3.1) proposed for Taimyr Peninsula are situated in
a large plain which is part of the North Siberian Lowland [Schmidt, 1999]. The
sites cover the typical tundra subzone D [Walker et al., 2005] and are located within
the continuous permafrost zone. In a study with plant inventories, climatological
characteristics of the area are of major importance for determining the dominant plant
functional types. The growing season is delimited by the frost period and in Taimyr
it lasts for about 2.5 months on average. However, there is a high variability between
the south of the peninsula and the north due to differences in temperature regimes.
In Khatanga, the closest meteorological station, the mean air annual temperature is
-13.5‰ and the mean temperature in July is 13‰ [Schmidt, 1999]. Typical tundra
sites in the peninsula are affected by an annual range of precipitation of 300-350 mm
and one third falls as rain in July and August [Schmidt, 1999].
14
15
Figure 3.1: Localization of the two study sites in Siberia, Taimyr Peninsula. A] Ari-Mas
(N 72.448871°, E 101.632461°) B] Logata (N 73.420524°, E 098.451784°).
The southern site, Ari-Mas (N 72.448871°, E 101.632461°, Fig. 3.1), is located on
the southern bank of the Novaya River and presents an interest since it is the northernmost isolated forest island in the world (72°30’), separated by 200 kilometers of
tundra from the closest forest. The study area is situated above the treeline and located in a zone defined in the Circumpolar Arctic vegetation map as moist erect-dwarf
shrub tundra [Walker et al., 2005]. In this defined zone, the erect-shrub dominated
tundra covers 16% of the subzone D, which comprises 26% of the total Arctic zone.
Following a typical vegetation profile, the plant cover was described across the river
16
valley of the Novaya River [Lashchinskiy, 2011]. On the terraces, grasslands dominated by Deschampsia sukatschewii and Stellaria crassifolia, grow on sandy soils. In
the north, small areas with willow community dominated by Tanacetum bipinnatum
and Salix lanata extend in the floodplains and the landscape is composed of peatlands
and thermokarst lakes. The southern side of the river is elevated and the transition
toward uplands is sharply marked by small cliffs. Above the cliffs, larch woodlands are
widespread among the tundra and are composed of Larix (Larix gmelinii, Petasites
frigidus, Carex vaginata, Dactylina arctica, Alopecurus alpinus, Bistorta elliptica,
Achoriphragma nudicaule,Flavocetraria nivalis, Luzula confuse). The vegetation in
the upland areas is dominated by a typical zonal tundra with dominant moss-cover
vegetation. Shrubby-moss tundra is composed of Vaccinium uliginosum, Eriophorum
vaginatum, Tomentypnum nitens and Aulacomnium palustre. Fens (Carex aquatilis,
Comarum palustre, Pedicularis sudetica, Hierochloe pauciflora, Caltha palustris, Eriophorum shceuchzeri and Dupontia fisheri ) and palsa-bog complex (Polytrichum strictum, Rubus chamaemorus) are prevalent on this side of the river (Fig. 3.2).
Figure 3.2: Vertical vegetation profile for Ari-Mas [Lashchinskiy, 2011].
The northern site, Logata (N 73.420524°, E 098.451784°, Fig. 3.1), is located close
to the Malaya Logata River. The vegetation over the transects consists of graminoids,
shrubs, forbs, mosses and lichens. Six vegetation syntaxa were defined (Fig.3.3).
Peatlands with arctophila community and grasses are widespread on the floodplains
along the Logata River. Sedge fens are also found within the floodplains and vegetated
with Carex aquatilis, Comarum palustre, Carex chordorrhiza, Hierochloe pauciflora
and Caltha palustris. Wet-moss tundra vegetation dominates in upland areas while the
slopes, affected by solifluxion, are covered with dryas tundra. In the depressions, the
fens are vegetated with eriophorum communities (Eriophorum shceuchzeri, Dupontia
fisheri). Palsa-bog complex are found also in the floodplains and are vegetated with
Polytrichum strictum and Rubus chamaemorus.
17
Figure 3.3: Vertical vegetation profile for Logata [Lashchinskiy, 2011].
Several evidence for active patterned ground features was found in the at Ari-Mas
and Logata. These periglacial features are described in the following figure (Fig.
3.4). Massive ice-wedges are found along the river and result of long-term variations
in the soil thermal regime. They form when seasonal cracks appear in winter and
cause soil contraction. When the temperature is above 0‰ , the ice starts to melt
and can refreeze in winter. At Logata, the ice wedges presented on the pictures are
eroded cliffs. Polygons are found at both sites on floodplains. They are formed by
similar thermal processes than the ice-wedges. As a result, large scale polygonal
features, outlined by shallow trenches, underlain by ice wedges are found between
thermokast lakes. Several palsas in peat plateau develop. They are formed when the
frost penetrate deeply into a frozen peat bog due to thin snow layer in winter. When
the core grows it exposes the top of the mount to more wind and less snow cover in
winter thus increases the hight of the palsa, until it degrades when the peat layer is
too thin to insulate the ice core. In the upland areas and on the slopes, dense patches
of earth hummocks are found. Hummocks result from the downward movement of
soil in depressions and the upward movement of soil in mounds due to freeze-thaw
process. The hollows are moist and covered with mosses while the mounts are often
drier.
18
Figure 3.4: Schema of the different types of patterned-ground in the study sites and their
genesis. Pictures from Gustaf Hugelius and Justine Ramage, 2011.
C HAPTER 4
Methods
Figure 4.1: Sketch summarizing the different methods used.
19
4.1. Sampling Methods
4.1
20
Sampling Methods
After an initial survey of the site, different transects were defined. The transect
locations were chosen to represent the main vegetation types and landscape geomorphology. The sites included different ecosystems located in upland and lowland areas,
with patterned ground landforms. A total of 8 transects of 400 to 1000 m length were
described. The sampling plots were chosen according to a defined sampling scheme
(Fig. 4.2). Sample plots were selected along each transect line at 100 m intervals
in which soil properties and vegetation descriptions were identified distinctly. Fixed
area circular vegetation descriptions with 10 m diameter were established at all sample
plots and a community using a relevé sample method was characterized.
Figure 4.2: Sampling scheme used during the field work at both sites, Ari-Mas and Logata.
Soil pits were dug in the active layer and the soils were described and sampled for
each horizon until the permafrost layer was reached. The depth of the frost table was
recorded at all sites. Soil samples in the active layer were collected every 10 cm using
fixed volume corers inserted horizontally into the wall of dug soil pits. Three replicates
of the topmost organic layer were collected in order to have a better understanding of
the soil variability. In the permafrost, soil samples were collected in 5 to 10 cm depth
increments, using a steel pipe which was hammered into the ground up to 100 cm in
depth. In fen peatlands, sediments were collected using a Russian Peat corer.
At Ari-Mas, 35 soil pits within 4 transects and 2 river bank exposures were sampled
and analyzed. These transects cover different landscapes on both sides of the Novaya
River and run perpendicular to the river. On the southern side of the river, the
transects are located in upland areas above the former river terraces. One of the
transect (AMT-2) covers a part of the larch woodlands. The floodplains are located
on the northern side of the river. The predominant mineral soil material is sandy-silt;
4.2. Carbon Analyses
21
during the sampling period, the mean active layer thickness was 76.6 cm. The pits
were dug to an average depth of 96 cm. For the 13 pits where it was not possible
to reach a depth of 100 cm, so the results were extrapolated in order to work with
homogeneous data.
At Logata, a total of 30 profiles along 4 transects and 3 profiles in different sections
of a polygon were sampled. One transect was located in the upland areas, the other
transects were spread out on the floodplains. The mineral soils are mainly composed
of silt and silty-sand and the active layer during the period of sampling was on average
50.8 cm deep (± 10.2 cm). The pits were dug up to an average depth of 82 cm and
the results were extrapolated to 100 cm.
The vegetation was estimated and classified using 1 x 1 m sample grid vegetation
sets 1 meter in front of the soil pits along the transect. The sample grid was used to
determine the cover of high vascular plants and of mosses and lichens. The term cover
refers to the percentage of ground surface covered by the different plant species. Two
replicate grids situated 3 meters to either side of the main point, perpendicular to the
direction of the transects were systematically described but not sampled (Fig. 4.2).
The descriptions focused on measuring plant functional type coverage and the level of
moisture. The level of moisture was estimated subjectively using a moisture gradient
from 1 to 4 (where 1 corresponds to dry-mesic areas; 4 correspond to water-logged
areas).
Topographic characteristics were taken into account since topography has a significant role in explaining vegetation abundances [Walker, 2000]. Differences in topographic conditions might indicate differences in substrate, in wind conditions, soil
drainage and other environmental characteristics [Walker, 2000]. The degree of the
slope and the aspect were systematically roughly estimated in the field. For a better
statistical analysis the sites have been classified into two groups regarding the degree
of their inclination ( sites gently inclined [<1°]; sites on slopes [up to 10°]).
The living aboveground plant biomass was sorted by plant functional types (FTP).
Individual species were grouped the into five major growth forms (graminoids, deciduous shrubs, evergreen shrubs, other vascular plant species (forbs), bryophytes and
lichens). The aboveground biomass was sampled using 25 x 25 cm quadrats for the
vascular plants and using 10 x 10 cm quadrats for the mosses and lichens. The litters
included all dead organic matter in contact with the soil and were collected in 25 x 25
cm quadrats. For the graminoids, we considered all live leaves as the current year’s
leaves even though it is known that some graminoid leaves remain alive through parts
of two growing seasons [Fetcher and Shaver, 1983, Shaver and Chapin, 1986]. The
quadrats were set in the corners of the sample grids, aboveground vascular plant phytomass was clipped at the top of the moss layer, and all moss biomass was harvested.
In the water-logged sites, the vegetation was sampled above the water table. A total
of 48 plots have been sampled for the vegetation along five transects and two more
plots were sampled at a palsa site in Ari-Mas. Vegetation samples remained in open
plastic bags and then in paper for the transportation to the laboratory.
4.2
Carbon Analyses
Soil and vegetation samples were sent to Novosibirsk, Siberia for analyses. Subsamples of soils were taken and oven-dried at 70‰ for 24 hours. For estimating the
4.3. Statistical Methods
22
SOC of each sample, the wet and dry weights, the bulk density and loss on ignition
(LOI) have been measured. The bulk density (BD = dry weight volume) is the dry
mass of soil per unit volume expressed as g/cm−3 . For mineral soils it is between 1
and 2 g cm−3 and it increases with compaction and depth. The loss on ignition (LOI)
gives an estimate of the organic matter in a soil by estimating the percentage of dry
mass of a soil. The first step consists in burning all the organic matter in an oven at
550‰ for 12 to 24 hours [Heiri et al., 2001].
LOI
550◦
= [(dry weight 105◦ − dry weight 550◦ )/dry weight 105◦ ) ∗ 100
where LOI 550◦ is the LOI at 550◦ (as a percentage), dry weight 550◦ is the dry
weight of the sample after combustion of organic matter at 550◦ and dry weight 105◦
is again the initial dry weight of the sample before the organic carbon combustion
[Heiri et al., 2001].
The soil organic carbon content is estimated from LOI, using a third order polynomial regression model based on individual soil samples in a study of a lowland tundra
site in the Usa Basin, European Russian Arctic [Kuhry et al., 2002]. The equation is
given by:
%C = (−0.00005x3 ) + (0.0059x2 ) + (0.362x)], with x the LOI
The soil organic carbon content was calculated for the 0-30 cm and 0-100 cm using
the formula:
SOC = (C ∗ BD ∗ depth) ∗ 10 , expressed as (kg C m−2 )
where C is the organic carbon (% weight), BD is the bulk density (g cm−3 ), depth
(cm) is the soil layer thickness.
The vegetation, classified into plant functional type was dried and weighted for
each plot. The weight signifies the total amount of aboveground phytomass. By
convention, the carbon content of phytomass is almost always found to be between
45 and 50% [Schlesinger, 1991]. Carbon content was calculated using the formula:
aboveground phytomass C (g C m−2 ) = 0.5 ∗ Phytomass
Total organic carbon (TOC) stored in each ecosystem was calculated as the sum
of soil organic carbon (SOC) including organic carbon of mineral soil horizons and
carbon of the organic horizons and carbon in plants of the vegetation community.
4.3
Statistical Methods
For phytomass data, the amounts of carbon per plots were homogenized for 1 x 1 m.
Some data were missing for a few plant species samples. To achieve more accuracy in
the data collected, extreme values that might reflect a mistake in the sampling or in
the laboratory analysis performed, were deleted using a maximum normed residual
test (Grubbs’ test) in order to detect outliers [Grubbs, 1969]. The test is based on the
difference of the mean of the sample (x̄) and the most extreme data (x) considering
the standard deviation (σ), using the formula:
g =
(x̄ − x)
σ
4.4. Multivariate Statistics Analyses
23
To determine missing values or replace outliers data points for the vascular plant
samples, linear regression was used to estimate the amount of carbon at these points.
The distributional properties of the variables were summarized by using histograms
and were statistically tested (coefficients of determination, r2 ; t-tests). Coefficients
of determination were used to measure how well, the regression line approximates
the real data points between aboveground phytomass carbon and environmental variables. Student t-tests were mainly used to estimate the potential differences between
the mean values of SOC between patterned-ground classes, especially between lowcentered and high-centered polygons, and to define the statistical differences between
the types of soil moisture, using the software Past 2.5 [Hammer et al., 2001].
4.4
Multivariate Statistics Analyses
Multivariate direct gradient analysis techniques are used (software Canoco 4.5, [Lepš
and Šmilauer, 2003]) whereby soil characteristics are related directly to a set of environmental variables. Principal component analysis has been chosen as the most
appropriate model for analysis since we had many species (soil characteristics) and
several environmental variables assumed to act potentially as explanatory variables,
with an unconstrained ordination (the statistic gradient length for each environmental variables was inferior to 1, which means that there is little variation in the data
[Ter Braak and Prentice, 1988].
The analyses focus on two data sets; one relates the soils characteristics and the
SOC partitioning; the other focuses on aboveground carbon distribution.
The ordination diagrams presenting PCAs (see chapter 5) show the main patterns
of variation of the soil distributions related to environmental variables. The SOC
stored in the upper meter is set as a supplementary variable which does not affect the
ordination. The diamonds represent the land cover types as nominal environmental
variables.
All the sampling sites are classified according to the land cover type from the
surrounded environment. Two other environmental variables impact the percentage
of carbon stored in different soils; the degree of slope and the active layer depth. The
distances between the points representing the sampling sites explain the dissimilarity
between them measured by their euclidean distance, according to the environmental
characteristics. The large arrows are the main environmental variables controlling the
ordination and the species samples. The soil characteristics are presented with thin
arrows: proportions of carbon stored in the organic soils (in the active layer and in
the permafrost), the mineral soils (in the active layer and in the permafrost), and the
cryoturbated pockets (in the active layer and in the permafrost). The arrows point at
the direction of the expected increase of value. Their length gives the intensity of the
correlation between the environmental variables and the species. The degree of the
angle between the arrows represents the correlations between species and variables.
The diamond symbols are the nominal variables (dummy variables).
4.5
Remote Sensing Methods
Remote sensing techniques are largely used throughout this study, especially to identify the land cover types and periglacial landforms in the study areas.
Quickbird images with a resolution of 2.4 m were acquired in 2011 for both sites; on
4.5. Remote Sensing Methods
24
July 11th for Ari-Mas and on August 11th for Logata. During the field work, each plot
was geolocalized using a handheld GPS receiver with Standard Positioning Service
and a typical horizontal accuracy of 10 m. The waypoints were added to the satellite
images in order to identify the plots at a smaller scale, using ENVI 4.8. A first land
cover recognition was described in the field, thus each sample plot was recognized.
Using the spectral reflection of the defined sample plots (Fig. 4.3), we used the tool
regions of interest (ROIs) in ENVI 4.8 in order to associate the spectral signature to a
land cover class. A total of 79 ROIs for Ari-Mas and 36 ROIs for Logata were defined
that have been grouped in 11 land cover classes (see chapter 5). Spectral signatures
of ROIs are used in supervised classifications in order to estimate the unknown values
of an object based on its spectral similarity [Fuchs et al., 2009].
Spatial filtering methods using mean filter were used to improve the image’s readability and extract the information needed by replacing the central pixels value by
the mean of the area.
A supervised classification is an image processing tool that develops a statistical
characterization of the reflectance for each information class, using variance and covariance between the classes. More specifically, a maximum likelihood classification
was used, where the pixels are assigned to the class of highest probability in order
to identify and portray the image in terms of land cover types. Ground truth analysis, using the regions of interest, was then used to estimate the confusions between
the class matrices and a kappa coefficient provide the measure of agreement between
the different classes. The Kappa coefficient ranges from -1 to 1; 1 being a perfect
agreement between classes, 0 being an agreement equivalent to random chance and -1
being no agreement between classes. Further classification of the landscape was then
Figure 4.3: Sketch of the process of a land cover classification [Tangjairong, 1999].
implemented in Arc Map 10 (Esri, ArcGIS) in order to define manually the land cover
types that could not be defined by the maximum likelihood classification due to lack
of clarity in spectral reflections. Manual delineation of areas through visual image
analysis (aided by field data) was applied at Ari-Mas for the palsa-bog complex and
the woodlands classes. Other basic image processes and arrangement were made in
Arc Map 10. Upscaling the results of SOC was then determined by multiplying the
SOC of the specific land cover classes by the area covered by the satellite image.
Using the spectral data provided by the satellite images, it is possible to measure
4.5. Remote Sensing Methods
25
vegetation indices. The normalized difference vegetation index (NDVI) is among the
most common indices used to calculate the ratio between the difference and the sum
of near infrared reflectance and red reflectance.
NDVI =
(NIR – VIS)
(NIR + VIS)
NIR is the reflectance in the near-infrared channel and VIS is the visible reflectance
of the red channel. NDVI is extensively used when studying phytomass distribution
and evolution. Green plants absorb the red wavelength during the photosynthesis
process (light absorption) and they have a high reflectivity in the near-infrared wavelength. NDVI relate variations in plant composition and abundances. An example of
the use of vegetation index is illustrated by the study from Raynolds, et al. [2006]
who used NDVI data in order to analyze phytomass distribution in the Arctic. They
found that NDVI decreases from south to north, correlating with the vegetation units
and is dependent on the substrate, elevation and other geographical characteristics.
Different synergies were used in an attempt to gather enough information and give
a better accuracy to the results.
C HAPTER 5
Results
5.1
Soil Organic Carbon Distribution
The soil organic carbon content was calculated for the upper meter from soil pits,
collected during a field work conducted in August 2011.
At Ari-Mas, an average of 27.6 kg C m−2 is stored in the top 100 cm (Table 5.1).
About 42% of the SOC is accumulated in organic horizons while 58% is stored in
mineral horizons. On average, more SOC is stored in the active layer (15.9 kg C
m−2 ) than in the permafrost (11.7 kg C m−2 ).
However, in palsa-bog complex, the highest amounts of SOC are stored in the
permafrost. In these types of periglacial features, the organic horizons are thick, and
the frost table is high. The organic horizons in permafrost have high values of SOC
(79.3 ± 20.2 kg C m−2 ).
Evidence of cryoturbation was found in 25 out of the 35 profiles, mainly in fens
(19.2 kg C m−2 ), larch woodlands (11 kg C m−2 ) and deep in the polygons (15.8 kg
C m−2 , only in the permafrost). Most of those sites with cryoturbation are located
in wet and flat terrains in lowlands. At Ari-Mas, there is no significant correlation
between the thickness of the organic layer and the amount of SOC stored in soils (r2
= 0.1) and the aboveground phytomass C (r2 = 0.3) although a weak trend show an
increase of carbon storage where the organic layer is thicker.
26
5.1. Soil Organic Carbon Distribution
27
Table 5.1: Mean SOC storage at Ari-Mas.
0-30 cm
LCC
0-100 cm
Organic
Mineral
Cryoturbated
Active layer
Permafrost
(CT)
(AL)
(PF)
CT in AL
CT in PF
Sites
7.5 ± 4.0
23.5 ± 7.3
3.9 ± 0.7
19.6 ± 4.5
11.0 ± 2.3
16.2 ± 4.2
7.3 ± 3.2
5.2 ± 3.9
5.8 ± 1.3
4
a) Shrub Tundra
3.8 ± 0.2
11.2 ± 5.9
1.4
9.8 ± 3.9
5.2
11.2 ± 0.0
0.0
5.2
0.0
2
b) Grass Tundra
4.0
10.5 ± 1.6
1.3 ± 0.1
9.2 ± 1.3
4.9 ± 0.1
5.5 ± 0.5
5.0
0.6
4.2
2
0.1 ± 0.2
5.9 ± 0.8
0.0
5.9 ± 0.8
0.0
5.9 ± 0.8
0.0
0.0
0.0
2
Polygons
7.6 ± 2.9
27.4 ± 6.8
4.5 ± 3.3
22.8 ± 6.3
15.8 ± 3.3
7.0 ± 2.6
20.4 ± 6.3
0.0
15.8 ± 3.3
4
Wet zonal Tundra
11.8 ± 4.1
25.3 ± 10.2
5.0 ± 2.3
20.9 ± 3.9
9.6 ± 3.4
19.5 ± 3.4
5.8 ± 3.1
5.7 ± 3.1
3.9 ± 3.7
7
Well-drained Tundra
10.2 ± 5.6
21.3 ± 9.9
4.6 ± 2.3
16.7 ± 5.5
4.6 ± 4.3
17.7 ± 5.1
5.7 ± 3.8
3.7 ± 4.3
0.9 ± 0.1
7
Palsa-bog Complexes
26.1 ± 2.7
70.3 ± 20.2
70.3 ± 20.2
0.0
0.0
21.0 ± 10.4
49.3 ± 17.5
0.0
0.0
4
Fens
11.4 ± 6.4
33.8 ± 11
6.2 ± 3.9
27.6 ± 5.5
19.2 ± 5.8
24.9 ± 5.6
8.9 ± 3.8
14.6 ± 3.3
4.6 ± 5.6
3
10.9
27.6
11.6
16
8.1
15.9
11.7
4.1
4.1
35
Woodlands
c) Sand Bars
Mean for study area
At Logata, the amount of SOC accumulated in the top 100 cm is 31.3 ± 11.6 kg C
m−2 (Table 5.2), mostly stored in mineral horizons (83.2%). An average of 23.5 kg C
m−2 was found in the active layer which means that 75% of the total SOC is stored
in the first top 0.5 meter.
An important difference between the amounts of SOC buried in lowland ecosystems
(polygons and fens) and in upland areas is observed (Fig. 5.2). Upland areas are the
main carbon pool with 36.7 kg C m−2 stored on average in the typical zonal tundra
while 22.4 kg C m−2 is stored in the lowland areas.
Shrub tundra, either in lowland or upland areas, have highest amounts of SOC
(respectively 40 and 38.4 kg C m−2 ) in the mineral horizons.
Among the larger amounts of SOC stored in the upland areas, a significant part is
buried within cryoturbated pockets in zonal (wet and well-drained) and shrub tundra
(19.2 kg C m−2 on average). Cryoturbation occurs mostly in the active layer with
samples showing double amounts of SOC than in the permafrost layer (13.6 kg C
m−2 and 5.57 kg C m−2 respectively). No significant correlation is found between
the thickness of the organic layer and the SOC, neither with the aboveground C (r2
= 0.04 and 0.06, respectively).
5.1.1
SOC Partitioning Related to Environmental Variables
The first ordination diagram for Ari-Mas (Fig. 5.1) shows the relations between
landscape partitioning of SOC and environmental variables. The arrow of SOC storage
indicates that the proportion of SOC increases along the first principal component
axis as does the proportion of SOC stored in the organic horizons. High proportion of
SOC of the top 100 cm is related to the palsa-bog complex. On the contrary, the sites
located on slopes and the sites with a deep active layer that are found in woodlands,
wet and well-drained zonal tundra, in floodplains and in sand bars are not significant
for explaining the total SOC of the top 100 cm. The proportion of SOC within the
mineral horizons and the cryoturbated pockets in the permafrost are associated to
the polygons.
5.2. Vegetation Structure and Aboveground Phytomass Carbon
28
Table 5.2: Mean SOC storage at Logata.
0-30 cm
LCC
0-100 cm
Organic
Mineral
Cryoturbated
Active layer
Permafrost
(CT)
(AL)
(PF)
CT in AL
CT in PF
Sites
Uplands Wet moss tundra
17.2 ± 4.2
37.8 ± 5.4
4.9 ± 2.6
28.7 ± 7.4
19.3 ± 9.6
32.5 ± 7.2
5.3 ± 3.1
16.4 ± 9.4
2.9 ± 3.7
7
Uplands Dryas tundra
13.2 ± 6.4
35.6 ± 1.9
5.3 ± 2.6
30.3 ± 5.2
18.4 ± 3.7
27.4 ± 5.2
8.2 ± 4.9
13.6 ± 4.2
4.9 ± 3.7
3
Uplands Shrub tundra
14.9 ± 6.1
37.8 ± 6.7
4.3 ± 0.7
33.5 ± 4.1
17.9 ± 2.5
24.8 ± 4.9
13.1 ± 3.1
9.7 ± 3.2
8.2 ± 0.2
3
Uplands Eroded zonal tundra*
No Data
No Data
No Data
No Data
No Data
No Data
No Data
No Data
No Data
0
Fens sedges
5.9 ±4.3
19.5 ± 8.5
3.6 ± 1.4
15.9 ± 5.3
8.7 ± 6.1
11.0 ± 2.9
8.5 ± 6.8
3.8 ± 2.8
4.9
4
Fens eriophorums
No Data
No Data
No Data
No Data
No Data
No Data
No Data
No Data
No Data
0
Polygons
13.7 ± 8.0
22.4 ± 11.6
8.2 ± 6.6
14.2 ± 3.8
8.2 ± 4.4
16.7 ± 5.6
5.1 ± 3.3
5.1 ± 4.6
3.1
8
Rims
17.2 ± 6.4
26.9 ± 10.9
9.8 ± 6.5
17.1 ± 4.4
9.9 ± 5.1
20.8 ± 5.7
5.8 ± 3.9
6.8 ± 5.2
3.2 ± 4.8
5
Center
7.9 ± 6.4
14.8 ± 8.5
5.5 ± 6.0
9.4 ± 1.4
5.2 ± 1.7
10.8 ± 4.1
4.1 ± 2.2
2.3 ± 0.9
2.9 ± 1.9
3
12.5 ± 2.7
40.0 ± 8.0
2.6 1.3
35.2 ± 7.0
25.4 ± 6.9
27.0 ± 7.8
13.0 ± 5.3
15.6 ± 8.5
9.8 ± 3.5
5
4.0
10.5 ± 1.6
1.3 ± 0.1
9.2 ± 1.3
4.9 ± 0.1
5.5 ± 0.5
5.0
0.6
4.2
2**
0.1 ± 0.2
5.9 ± 0.8
0.0
5.9 ± 0.8
0.0
5.9 ± 0.8
0.0
0.0
0.0
2**
11.0
31.3
5.2
26.1
15.1
23.5
7.8
10.1
5.0
30
Floodplains
Shrubs
Floodplains
grasses**
Sand bars ***
Mean for the study area
* No sites have been sampled for the eroded zonal tundra in upland areas. They are
characterized by grasses and bare ground. ** The values are assumed to be equal to
those from Ari-Mas.
At Logata, the storage of SOC in the top 100 cm decreases along the first component
axe and is strongly correlated with the degree of slope and the active layer depth
(Fig. 5.2). The proportion of SOC within mineral horizons in the active layer is
associated with wet tundra sites, with high storage of SOC within the top 100 cm
and is negatively correlated to the polygons. Shrub tundra in floodplains stores more
SOC in the organic horizons within the active layer and in the mineral horizon in the
permafrost. Proportionally, more SOC in cryoturbated pockets (both in the active
layer and in the permafrost) is found in well-drained tundra in the upland areas while
fens in floodplains (sedges) have a high proportion of SOC in the cryoturbated pockets
within the active layer.
5.2
Vegetation Structure and Aboveground Phytomass
Carbon
Among the different plant functional types, it is clear that mosses and lichens store
the highest amounts of carbon (74% of the total aboveground phytomass C) (fig.
5.3) and cover an average of 84% of the ground surface. Approximately, 12% of the
carbon is stored by shrubs (8% by deciduous and 5% by evergreens) which cover 32%
of the sampling plot surface areas. Graminoid species store 9% of the aboveground
C while they cover 30% of the surfaces. Aboveground phytomass C variability is the
highest within mosses and lichens. However, those species were not sampled in the
plots where the water content was high.
5.2. Vegetation Structure and Aboveground Phytomass Carbon
29
Figure 5.1: Principal component (PCA) ordination diagram (1st and 2nd principal components) showing the landscape partitioning of SOC storage at Ari-Mas in relation to environmental variables. The total SOC storage is set as a supplementary variable and do not
affect the ordination. Thin arrows with grey font are the species (the percentage of SOC
stored in the different soil horizons and in the cryoturbated pockets). Blue arrows with red
heads show the environmental variables. Green diamond symbols represent the types of land
cover, as dummy variables.
5.2. Vegetation Structure and Aboveground Phytomass Carbon
30
Figure 5.2: Principal component (PCA) ordination diagram (1st and 2nd principal components) showing the landscape partitioning of SOC storage at Logata in relation to environmental variables. The total SOC storage is set as a supplementary variable and do not
affect the ordination. Thin arrows with grey font are the species (the percentage of SOC
stored in the different soil horizons and in the cryoturbated pockets). Blue arrows with read
heads show the environmental variables. Green diamond symbols represent the types of land
cover, as dummy variables.
Figure 5.3: Aboveground phytomass C stored within the different plant functional types (graminoids, deciduous shrubs, evergreen shrubs, mosses and
lichens, other species) and in the litter at Ari-Mas and Logata. The palsa site is marked for further interpretation (see chapter 6).
5.2. Vegetation Structure and Aboveground Phytomass Carbon
31
5.2. Vegetation Structure and Aboveground Phytomass Carbon
32
Table 5.3: NDVI values related to the land cover types at Ari-Mas and Logata
LCC Ari-Mas
Wet zonal tundra
Well-drained Zonal tundra
Fens
LCC Logata
Wet zonal tundra /(uplands)
Dry zonal tundra /(uplands)
Shrub tundra
Floodplains Shrub tundra
Floodplains Fens (sedges)
Floodplains Fens (eriophorum)
Polygonal bogs
NDVI values
0.422
0.466
0.502
NDVI values
0.471
0.481
0.547
0.536
0.530
No Data
0.475
No correlations have been found between the percentage of plant coverage and
the amount of carbon stored by the phytomass. The NDVI values were related to
the aboveground phytomass C for each land cover type (Table 5.3). No statistical
correlation is found between NDVI and the total amount of carbon stored in the
aboveground phytomass (r2 = 0.02 for Ari-Mas; r2 = 0.05 for Logata).
At Ari-Mas, phytomass was only sampled in one transect with 3 samples in welldrained tundra, 4 samples in wet tundra and 3 samples in fens. Along this transect
the average aboveground phytomass C is 291.2 g C m−2 with 41.5% (364.8 g C m−2 )
stored in well-drained tundra, 31.2% (274.3 g C m−2 ) in wet tundra and 24.7% (240.1
g C m−2 ) in fens. The aboveground C represents 2.1% of the total C of the ecosystem
(soils and phytomass).
At Logata, 38 sites were sampled along 6 transects. Most of the sites were located
in the lowlands areas and one transect was located in an upland area (LGT-2). The
average aboveground phytomass C for the sampled sites is 454.3 g C m−2 . The highest
aboveground phytomass C values are found in three different land types, 24.3% (537.3
g C m−2 ) is stored within the polygons, 23.2% (512.8 g C m−2 ) is stored within welldrained tundra and 22.7% (501.2 g C m−2 ) within shrub-tundra. The aboveground
phytomass C represents 1.4% of the total C of the ecosystem (soils and phytomass).
5.2.1
Aboveground Phytomass C Storage Related to Environmental Variables
The surface wetness of the sites was estimated from field observations, since soil
moisture exerts a strong influence on tundra ecosystems and communities development (Walker, 2000). Five different types of soil moisture were noticed in the field
that have been grouped into three types (Fig. 5.4) when using Student’s t-tests to
estimate the statistical differences between them. The three types of soil present a
statistical difference (Student’s t-test, p ≤ 0.05). There is a gradient of decreased
aboveground phytomass with increased site wetness. Mesic surfaces correspond with
a well-balanced supply of moisture. Two different types of mesic surfaces were visible
in the field, some wetter and some drier. They represent a total of 30% of the sampled
5.3. Carbon Storages Among Periglacial Features
33
plots. As expected, the amount of carbon in the aboveground phytomass decreases
while the soil moisture increases and the highest rates of carbon are stored within
mesic surfaces which present an optimum soil environment for plant productivity.
Figure 5.4: Aboveground C in the sites at Ari-Mas and Logata, classified by the moist
aspect of the soil surface (3: hydric soils; 2: wet-mesic soils; 1: dry-mesic soils).
Among the 36 plots located on a flat surface (slope <1°), the total aboveground
phytomass C is 424.9 g C m−2 on average whereas among the 12 plots situated on
a slope (up to 10°) 357 g C m−2 is stored by the phytomass. Most of the sites (35)
are located in lowland areas where an average of 470.8 g C m−2 is stored in the
aboveground phytomass. Less aboveground phytomass C (350 g C m−2 ) is stored in
the upland areas, including the sites on slopes (fig. 5.5).
Specific analyses for each plant functional type have been made in order to estimate
how the plants are distributed among the sites in relation with the different environmental variables. Graminoids are found mostly in sites with high moisture content,
on flat terrains (slope < 1°). They are more spread out on hummocky terrains and
on terrains not affected by active periglacial processes. Deciduous shrubs are mostly
spread among wet-mesic sites on hummocky terrains, located on slopes (> 5°) whereas
evergreen shrubs are also spread out on hummocky terrains, on gently sloped (1°<
slope< 5°) dry-mesic terrains. Mosses and lichens are spread among all types of terrains and decrease when the moisture content increases. Other species (forbs) are
found on slopes among non-sorted circles with dry-mesic moisture content.
5.3
5.3.1
Carbon Storages Among Periglacial Features
SOC Distribution Among Periglacial Features
Statistically, a significant difference (Student’s t-test, p ≤ 0.05 ) is found when comparing the mean SOC stored in each type of periglacial geomorphological features in
Logata with Ari-Mas. Thus the results are described separately (fig. 5.6).
At Ari-Mas, an average of 28.9 kg C m−2 is stored in periglacial geomorphological
features that represents 87.7% of the total SOC stored in the sampled pits. Large
amounts of SOC are accumulated in the palsas (76 kg C m−2 ± 20.3 kg C m−2 ) mainly
in the permafrost (70%). Within the polygons, more SOC is stored in the center of
5.3. Carbon Storages Among Periglacial Features
34
Figure 5.5: Aboveground phytomass C distribution sorted by plant functional types in
relation to the topography.
low-centered polygons (LCP, 33.4 kg C m−2 ) than in in the rims. The amount of
SOC stored in the rims is equivalent to the amount found for the non-sorted circles
(NSC, 16 kg C m−2 and 16.2 kg C m−2 respectively). Among the sites not affected
by periglacial features, the amounts of SOC are high (24.2 kg C m−2 ± 12 kg C
m−2 ). There is a heterogeneity in the SOC distribution within periglacial features at
Ari-Mas (Fig. 20).
Almost same amounts of SOC as at Ari-Mas are found in the periglacial geomorphological features at Logata (30.6 kg C m−2 ). However, when comparing the SOC
distribution within the same types of periglacial features at Ari-Mas and Logata, the
the amounts of SOC stored in periglacial geomorphological features are higher at AriMas (Fig. 5.6). The difference is due to the SOC stored in the palsas at ari-Mas that
makes the average higher. At Logata, the carbon is more equally distributed among
periglacial geomorphological features (the standard deviation is 3.8 kg C m−2 ). More
SOC accumulate in non-sorted circles, hummocks and low-centered polygons (34.5 kg
C m−2 , 34.2 kg C m−2 and 33.5 kg C m−2 respectively).
The ordination diagrams (Fig. 5.7, A and B) present the first and second principal
components of a PCA focused on the carbon distribution (A] SOC and B] aboveground
phytomass C) among different types of periglacial features and land cover types.
Diamond symbols represent the dummy variables.
At Ari-Mas, the highest amounts of SOC found in the top 100 cm is explained by
the high percentages of soc found in the organic horizons of the palsas, mostly in the
permafrost. Proportionally, hummocks and non-sorted circles do not store much of
the total SOC. They are found on slopes and have deeper active layers. Together with
the sampled sites not affected by periglacial features, they explain the percentages of
soc in cryoturbation pockets found and in the mineral horizons in the active layer.
The polygons (centers and rims) hold most of the SOC stored in cryoturbated pockets
5.3. Carbon Storages Among Periglacial Features
35
Figure 5.6: SOC partitioning (0-100 cm) between different active and non-active periglacial
features at the two different sampling sites, A] Ari-Mas and B] Logata.
and mineral horizons in the permafrost. However, low-centered polygons have more
influence than the rims and the high-centered polygons on the percentage of SOC
found in those horizons.
At Logata, the highest amounts of SOC are stored on the slopes where periglacial
features (non-sorted circles and hummocks) have a high percentage of coverage. Large
amounts of SOC stored in the organic horizons within the active layer are found on
the rims. Terrains not affected by active periglacial processes are characterized by
higher soil moisture regime. The soil organic carbon found in high-centered polygon
is stored in cryoturbated pockets found in the permafrost, however high centered
polygons store little SOC.
In general, there are no correlations between the SOC storage and the thickness of
the organic layer within the periglacial features. However, among the low-centered
polygons (n = 7) a positive correlation (r2 = 0.57) is found, the pits with high amounts
of SOC are assumed to be the ones with the thickest organic layers. Those sites have
also a high percentage coverage of plants (r2 = 0.38).
5.3.2
Vegetation Distribution Among Periglacial Features
A variability in the phytomass distribution on different periglacial geomorphological features can be noticed, both between the two sites and between each type of
periglacial features (Fig. 5.8).
At Ari-Mas, only one transect has been sampled for the phytomass estimation.
The palsa accumulates the most aboveground C (399.5 g C m−2 ). However a strong
difference appears between the sides of the palsa and the top. The top is barely
vegetated with grasses while the side (western side) is covered by dense and high
shrub communities with large patches of mosses in the depressions. The amount
of aboveground phytomass C on this side is 546 g C m−2 whereas 253.2 g C m−2 is
stored by the grasses on the top. Less carbon is stored by the aboveground phytomass
on non patterned-ground terrains whereas the amount of carbon in the aboveground
phytomass on non-sorted circles (boils) is 374.4 g C m−2 .
At Logata, there is more aboveground phytomass than at Ari-Mas. The maximum
amount of aboveground phytomass C is stored on the rims (626 g C m−2 ) which
are mainly covered with shrub-tundra vegetation. It should be noticed that for the
5.3. Carbon Storages Among Periglacial Features
36
Figure 5.7: Principal component (PCA) ordination diagrams presenting the soil characteristics (the percentage of SOC stored in the different soil horizons and in the cryoturbated
pockets) at Ari-Mas [A] and Logata [B] along the principal component axes. SOC storage
(0-100 cm) is presented as supplementary variable. The environmental variables influencing
the ordination are the ground characteristics (low-centered polygons (LCP), high-centered
polygons (HCP), rims, palsas, hummocks, non-sorted circles and grounds non-affected by
active periglacial features and the soil aspect (slope, moisture and active layer depth).
Figure 5.8: Amount of carbon in the aboveground vegetation for the different types of
ground in A] Ari-Mas and B] Logata.
aboveground phytomass analysis, no distinction is made between shrub tundra in
lowland or in upland areas since the Student’s t-test between the two data sets show
a high probability for a same mean in the two land cover types (t = -0.007, p =
0.99). Non-sorted circles are found in the upland area in typical zonal tundra and
store on average 473.4 g C m−2 . Again, there is a high variability in the aboveground
phytomass C stored on non patterned-ground terrains even though on average the
aboveground C is high, 437.6 g C m−2 ± 350 g C m−2 . Low phytomass is found on
low-centered polygons (LCP) due to high moisture content. In the center of LCP,
5.3. Carbon Storages Among Periglacial Features
37
the water table is high and few types of vegetation grow. Moreover, the vegetation
was sampled above the water table, therefore the aquatic mosses are not taken into
account in this study.
Within the non-sorted circles (n = 8), the aboveground phytomass C is significantly
correlated with the thickness of the organic layer (r2 = 0.78) and with the percentage
of plant cover (r2 = 0.42).
The principal component ordination diagrams (Fig. 5.9) present the environmental
variables explaining the aboveground phytomass C (including patterned-grounds and
land cover types).
Figure 5.9: Principal component (PCA) ordination diagram presenting the aboveground C
distribution within species (graminoids (G); deciduous shrubs (DS); evergreen shrubs (ES);
mosses and lichens (ML); other species (O); litter (L)) related to environmental variables
(slope, moisture, SOC (0-100 cm), percentage coverage of patterned-ground (%PG), type of
patterned-ground and land cover type) at Ari-Mas [A] and Logata [B] along the principal
component axes. Diamond symbols represent the nominal variables. The total aboveground
phytomass C [AbvC] is presented as supplementary variable.
5.4. Total Carbon Storage Using Land Cover Classification and
Upscaling Methods
38
At both sites (Ari-Mas and Logata), the total aboveground phytomass C is strongly
related to mosses and lichens and deciduous shrub phytomass. At Ari-Mas, the slope,
influenced by the palsa side, is the main environmental variable explaining the aboveground C of mosses and lichens and deciduous shrubs. The top of the palsa is vegetated with graminoids while non-sorted circles and well-drained tundra are mostly
covered by evergreen shrubs and other species. The mass of litter is closely related to
the evergreen shrub phytomass.
At Logata, the total aboveground C is positively correlated with the shrub tundra
in the floodplains and with the well-drained tundra while it is negatively correlated to
high-centered polygons. In the lowland areas, the aboveground C is correlated with
the moisture content. Moisture and fens in floodplains are also correlated. Non-sorted
circles are mostly found in wet tundra. Other environmental variables such as slope,
percentage coverage of the patterned-ground and SOC storage are not significant in
the ordination.
5.4
Total Carbon Storage Using Land Cover Classification and Upscaling Methods
Land cover classifications, using upscaling methods, were performed for both sites,
combining data from field inventory (soil and vegetation sampling) and satelite imagery.
The total area at Ari-Mas is 92.6 km2 (Fig. 5.10) and is classified into 11 different
land cover types (Table 5.4). Almost 6% of the surface is bare ground and 18% of the
area is covered by lakes or rivers. No SOC measurements have been made for those
surfaces.
Palsa bogs store the highest amounts of SOC (70.3 kg C m−2 ). However, they
represent only 4.5% of the total area, so their contribution in the SOC total storage
is lower (0.3 Tg C) than the carbon stocks in wet zonal tundra or polygonal-bogs
(0.48 and 0.47 Tg C, respectively). Within the polygonal-bogs, SOC is stored in the
centers within soil horizons under 30 cm depth. Proportionally, less SOC is stored
in the rims whose contribution is equivalent to well-drained tundra. In wet tundra,
(0.22 Tg C in the top 100 cm for the whole landscape) 48% of the SOC stored in the
top 100 cm is in the upper 30 cm. The surface covered by larch woodlands represents
approximately 9.5% of the total area and 0.21 Tg C are stored in the top 100 cm.
The amount of carbon stored by the phytomass (0.02 Tg C) represents 0.8% of the
total ecosystem C storage.
5.4. Total Carbon Storage Using Land Cover Classification and
Upscaling Methods
39
Figure 5.10: A] Quickbird satellite image of Ari-Mas. B] Land cover classification for
Ari-Mas, using ENVI 4.8 and ArcGis 10.
7.2
19.0
4.4
4.2
2.1
16.6
5.6
92.6
Center polygons
Wet zonal tundra
Well-drained zonal tundra
Palsa bog complexes
Fens
Water
Bare ground
Total area
10.1
Rims
1.4
Sand bars
Floodplains
17.3
9.3
Grasses
Floodplains
Polygons
4.0
Shrubs
Floodplains
7.8
100
6.1
27.63
No Data
No Data
33.8
2.3
17.9
70.3
21.3
25.3
33.4
4.5
4.7
20.5
21.3
27.4
18.7
10.9
5.9
10.5
10.0
1.5
11.2
23.5
(kg C m−2 )
Mean SOC 0-100 cm
4.3
9.5
(%)
(Km−2 )
8.8
Total area
Total area
Larch woodlands
LCC Ari-Mas
2.22
No Data
No Data
0.07
0.29
0.09
0.48
0.24
0.22
0.47
0.01
0.10
0.04
0.21
(Tg C m−2 )
Total SOC 0-100 cm
100
No Data
No Data
3.2
13.2
4.2
21.5
10.8
9.7
21.3
0.4
4.4
2.0
9.3
0-100 cm (%)
Contribution
10.86
No Data
No Data
11.4
26.1
10.2
11.8
9.7
5.5
7.6
0.1
4.0
3.8
7.5
(Tg C m−2 )
Mean SOC 0-30 cm
0.31
No Data
No Data
0.02
0.11
0.04
0.22
0.07
0.06
0.13
0.00
0.04
0.02
0.07
(Tg C m−2 )
Total SOC 0-30 cm
100
No Data
No Data
3.4
15.4
6.3
31.6
9.9
7.9
18.7
0.0
5.2
2.1
9.4
0-100 cm (%)
Contribution
2.91
No Data
No Data
0.48
0.35
0.73
0.55
No Data
No Data
No Data
No Data
No Data
No Data
No Data
(kg C m−2 )
Mean Abv C
0.0163
No Data
No Data
0.0091
0.0007
0.0064
0.0024
No Data
No Data
No Data
No Data
No Data
No Data
No Data
(Tg C m-2)
Total Abv C
0.73
No Data
No Data
0.13
0.00
0.07
0.01
No Data
No Data
No Data
No Data
No Data
No Data
No Data
in the total SOC (%)
Abv C
Table 5.4: Land cover classification and carbon (SOC and AbvC) estimations for the area of Ari-Mas, Taimyr Peninsula, Siberia.
35
0
0
3
4
7
7
2
2
4
2
2
2
4
Sites
5.4. Total Carbon Storage Using Land Cover Classification and
Upscaling Methods
40
5.4. Total Carbon Storage Using Land Cover Classification and
Upscaling Methods
41
The total area covered by the Quickbird image in Logata is 117.8 km2 (Fig. 5.11).
In total, 11 land cover types were classified (Table 5.5). The total SOC storage for
the all area is 2.73 Tg C. No data are provided for waters, eroded zonal tundra and
eriophorum fens which represent respectively 6.3%, 0.5% and 2.8% of the landscape.
In total, larger stocks of SOC are stored in shrub tundra, either in upland areas or
on floodplains. They cover approximately 30% of the total area and thus contribute
1.35 Tg C. A clear difference appears between upland and lowland areas. The upland
areas, especially the wet zonal tundra (0.95 Tg C), have more SOC accumulated in
the top 100 cm of the soils.
Proportionally, shrub tundra in upland areas accumulate more SOC but they represent 15.2% of the area which is less than the area covered by wet zonal tundra.
1.7% of the area is covered by well-drained zonal tundra, on which is stored 0.07 Tg
C.
There is a larger variability within the amount of carbon stored in the different
land cover types in lowland areas. Shrub tundra on floodplains have higher SOC
stocks, while polygonal-bogs, grass tundra and sand bars store less SOC. However,
the data for sand bars and grasses on floodplains are taken from the calculations made
at Ari-Mas, assuming that the SOC stored is equivalent.
In general, approximately 40% of the SOC stocks are accumulated in the top 30
cm. However, in polygonal-bogs 61% is found in this upper 30 cm, especially in the
center of the polygons (64%).
In total, 0.053 Tg C is accumulated by the phytomass that represents 2% of the
total ecosystem C storage. Shrub tundra store more carbon than other land cover
types.
5.4. Total Carbon Storage Using Land Cover Classification and
Upscaling Methods
42
Figure 5.11: A] Quickbird satellite image of Logata. B] Land cover classification for Logata,
using ENVI 4.8 and ArcGis 10.
11.5
Rims
7.4
117.8
Water
Total area
)
100
6.3
31.3
No Data
5.9
10.5
1.2
2.1
40.0
14.8
5.0
14.3
26.9
22.4
14.8
9.8
19.5
No Data
2.8
No Data
37.8
35.6
37.8
(kg C m
−2
)
Mean SOC 0-100 cm
4.8
0.5
15.2
1.7
21.4
(%)
Total area
2.7
No Data
0.01
0.02
0.67
0.17
0.16
0.39
No Data
0.11
No Data
0.68
0.07
0.95
(Tg C m
−2
)
Total SOC 0-100 cm
100
No Data
0.5
0.6
24.6
6.3
5.8
14.3
No Data
4.1
No Data
24.8
2.7
34.9
0-100 cm (%)
Contribution
11.0
No Data
0.1
4.0
3.8
7.9
17.2
13.7
No Data
5.9
No Data
14.4
13.2
17.2
(Tg C m
−2
)
Mean SOC 0-30 cm
1.4
No Data
0.00
0.01
0.21
0.09
0.10
0.24
No Data
0.03
No Data
0.27
0.03
0.43
(Tg C m
−2
)
Total SOC 0-30 cm
100
No Data
0.0
0.4
14.9
6.4
7.2
17.0
No Data
2.4
No Data
18.9
1.9
30.8
0-100 cm (%)
Contribution
0.45
No Data
No Data
No Data
0.60
0.29
0.66
0.44
No Data
0.39
No Data
0.59
0.51
0.27
(kg C m
−2
)
Mean Abv C
0.054
No Data
No Data
No Data
0.0100
0.0017
0.0076
0.0077
No Data
0.0022
No Data
0.0106
0.0011
0.0068
(Tg C m-2)
Total Abv C
1.5
No Data
No Data
No Data
1.5
1.9
2.5
2.0
No Data
2.0
No Data
1.6
1.4
0.7
in the total SOC (%)
Abv C
* No sites have been sampled for the eroded zonal tundra in upland areas. They are characterized by grasses and bare ground.
** The values are assumed to be equal to those from Ari-Mas.
2.4
Sand bars **
1.4
grasses**
17.4
Polygons
Floodplains
3.3
Fens eriophorums
5.9
5.7
Fens sedges
16.8
0.5
Eroded zonal tundra*
Shrubs
17.9
Uplands Shrub tundra
Floodplains
2.1
Uplands Dryas tundra
Center
25.2
(Km
−2
Total area
Uplands Wet moss tundra
LCC Logata
Table 5.5: Land cover classification and carbon (SOC and AbvC) estimations for the area of Logata, Taimyr Peninsula, Siberia.
30
2*
2*
5
3
5
8
0
4
0
3
3
7
Sites
5.4. Total Carbon Storage Using Land Cover Classification and
Upscaling Methods
43
C HAPTER 6
Discussion
About the same amounts of SOC are stored in the top meter of soils in the two study
areas located in bioclimatic subzone D; that represents an average of 29.5 kg C m−2
for 100 km2 . The main results show that largest SOC stocks are stored in mineral
horizons found in the active layer. However, when looking at the carbon partitioning
and the land cover, a tremendous variability across the tundra biome at a large scale
is easily perceptible. Moreover, complex interactions between environmental variables
impact the carbon distribution within soils and vegetation.
Different aspects will be discussed in the following section in order to explain the
potential remobilization of permafrost carbon following ongoing permafrost thaw.
First, the impacts of topography at different scales on carbon storage and its potential
remobilization are highlighted. Second, to settle the issue of potential changes in the
ecosystem energy balance, the impacts of periglacial feature on the carbon cycle are
explained. In each of the sections, the principal offset to soil organic carbon losses,
that is the potential increase in aboveground phytomass C, is discussed giving the
actual phytomass abundance and distribution. Finally, in order to properly discuss
the results, the limits of a land cover classification applied for the tundra is presented.
6.1
The Effects of Landscape Topography on C Storages
Geomorphology impacts the SOC distribution at both sites at diverse scales; at Logata
through the differences of carbon storage between upland and lowland areas; at AriMas through the effects of microtopography.
6.1.1
High Amounts of SOC in Upland Areas
Highest amounts of SOC are found in upland ecosystems and in shrub tundra, especially at Logata. A majority of studies present different patterns in carbon distribution over the tundra, where more carbon is stored in lowland ecosystems and
peatlands due to the lower rates of SOM decomposition on these soils [Sommerkorn,
2008, Schuur et al., 2008, Hugelius et al., 2009, Hugelius et al., 2010, Hugelius, ].
44
6.1. The Effects of Landscape Topography on C Storages
45
Higher stocks of SOC are expected to be stored in lowland ecosystems due to the
lack of oxygen availability in saturated soils, which reduces decomposition [Hobbie
et al., 2000]. However, in hilly uplands soils high carbon stores have been found for
the North American Arctic Region [Ping et al., 2008].
At a landscape scale, 62% of the total carbon stored in the upper meter accumulates
in upland ecosystems at Logata, that represents 39.1 ± 5.6 kg C m−2 , for a percentage
coverage of 38.3% of the total area (this includes wet, well-drained and shrub tundra).
Highest totals of carbon are found on flat uplifted areas vegetated by wet zonal
tundra. Both parent material and vegetation cover can be responsible for this specific pattern. The ice content is important on the sites and massive ice wedges are
found in the vicinity. A comparison over 48 sites in Alaska shows that soil organic
carbon stocks are dependent on landscape type and were higher in lowland and hilly
upland soils [Ping et al., 2008]. Upland woodlands and tundra classes in the studied
areas present similar average amounts of carbon on the order of 21 to 25 kg C m−2 .
Those results confirm the averages found for the Usa Basin, European Russian Arctic
[Kuhry et al., 2002].
The analyses of the SOC vertical distribution highlight the role of cryoturbation
in SOC storages. Important SOC stocks in the upland ecosystems at Logata can
be explained by the prominence of cryoturbation. An average of 21.4 kg C m−2
is in cryoturbated pockets within upland areas, mostly accumulated in the active
layer which stores 31 kg C m−2 . Cryoturbation affects the rates of decomposition,
by stabilizing the organic matter into permafrost soils. It is considered as the main
process in SOC movement from the surface to depth in permafrost affected soils [Ping
et al., 2008].
In this study, the four sites located on south facing slopes have been included in
the upland ecosystems measurements. While analyzing them separately, an average
of 43.6 kg C m−2 is found on the slopes. The majority is in the active layer (35 kg C
m−2 ). There is a difference between the SOC stored in down slope positions (49.18
kg C m−2 ) and the SOC stored on the upper slopes (38 kg C m−2 ). SOC stocks
increased from top to down slopes. Only four sites are taken into account in those
results but a similar pattern is also found in a study in arctic Alaska from [Michaelson
et al., 1996]. Cryoturbated pockets on slopes store an average of 28 kg C m−2 , of
which 80% is within the active layer. Cryoturbation is widely found on the slopes that
are also affected by mass movements. Gravity is an important factor in the process of
cryoturbation [Bockheim and Tarnocai, 1998]. Ice-rich permafrost slopes affected by
thawing processes involve cryoturbation through solifluction enhanced by permafrost
thawing (Fig 6.1). In this mass movement process, permafrost thawing leads to soil
saturation, therefore it mobilizes the active layers that slide slowly on the unthawed
permafrost table. Permafrost degradation enhances the process of soil movement on
the slopes by exposing the surface to solar radiation and aerobic conditions [Hobbie
et al., 2000]. The rate of permafrost degradation on slopes and ecological changes
depend on the ice content in the ground [Osterkamp et al., 2000]. Thermal erosion
has major impacts on the ecosystem SOC storage by remobilizing soil horizons and
degrading the vegetation cover. Consequently the role of cryoturbation in SOC remobilization for the upland ecosystems might be linked to the process of solifluction.
Different patterns in phytomass distribution are also related to the topography.
At Logata, the soil material in the area is unique as it is composed of fine glaciomarine silty-sands sediments. As a result, surfaces are colonized by specific vegetation.
6.1. The Effects of Landscape Topography on C Storages
46
Figure 6.1: Photograph of a retrogressive thaw slump on a slope at Logata (From Laschinskiy [2011])
Among the species found are Descurainia sophoides, Taraxacum sp. and Taraxacum
taimirense. More aboveground phytomass carbon was found in lowland areas (57%
of the total aboveground phytomass carbon) due to the importance in moss cover
in lowland areas (61% of the total aboveground moss carbon). In upland areas,
phytomass densities of evergreen shrubs, forbs and graminoids are related to better
drainage that creates better conditions for those plants to grow.
The highest amounts of phytomass are found in shrub tundra, with an average of
596 g C m−2 . At the same time, important stocks of SOC are found in shrub tundra
both in lowland and upland areas at Logata (on average 39 kg C m−2 ). Shrub tundra
are defined as the ecosystems where shrubs dominate but coexist with mosses, thus
the high level of aboveground phytomass C in shrub tundra can be explained by the
carbon accumulation capacity of both the shrubs and mosses.
In summary, large amounts of SOC found in upland areas are linked with soils
that are affected by important remobilization, due to a combination of solifluction
on slopes and cryoturbation occurring in upland surfaces and on slopes. On the
contrary, the aboveground phytomass C is higher in lowland ecosystems, especially in
mesic soils, due to a denser cover in mosses. However the land cover type has more
relevance when explaining aboveground phytomass C as it is directly related to the
plant species. The highest amounts are found in shrub tundra.
6.1. The Effects of Landscape Topography on C Storages
6.1.2
47
Periglacial Patterned Ground Features
Evidence for impacts of microtopography on SOC storage is emphasized at Ari-Mas.
For example active periglacial features are found both in plateaus (palsas, hummocks
and frost boils) and in floodplains (polygons) and illustrate differences in the SOC
distribution patterns. Furthermore, the principal component analysis revealed underlying relationships between patterned-ground terrains, measured phytomass and
environmental variables.
Frost boils at Ari-Mas store more SOC in the active layers, especially in cryoturbated pockets.
In palsa-bog complexes, the SOC is stored in the organic layers, mostly in the permafrost core. In total, palsa-bog complexes contribute for 13.2% to the total SOC
of the area (0.29 Tg C) while they represent only 4.5 % of the surface. The active
layer on the top of the palsas is on average 30 cm thick at Ari-Mas. However, the
thickness of the peat layer that insulates the frozen core in summer is cyclic and varies
with changes in climatic factors [Seppälä, 1982]. The vegetation cover also acts as
an insulating layer during summer and palsas act as positive factors for vegetation
development. The slopes are covered with shrubs and mosses. On the palsa studied
at Ari-Mas, twice as much phytomass is on the slopes as on the mount, which is covered with graminoids (Fig. 6.2). Those results are consistent with the high amounts
of carbon found in palsa and plateau-bog deposits in northeastern European Russia
[Hugelius et al., 2009].
Figure 6.2: A] Soil profile of a palsa mount at Ari-Mas. B] Vegetation cover on the side of
the palsa.
Polygons are widely spread on the floodplains at both sites. They cover 18.7% of
the area at Ari-Mas and 14.8% at Logata and contribute to 21.3% and 14.3% of the
total SOC stored in those two areas. Mineral horizons in the permafrost contribute
6.1. The Effects of Landscape Topography on C Storages
48
to a large part of the total SOC stored in the polygons. Cryoturbated pockets in
polygons accumulate 60% of the total SOC at Ari-Mas and 36.5% at Logata. While
looking closer at the different elements that compose the polygons, an important
difference between the amounts of SOC stored in the center of the polygons and
the rims is observed. At both sites, an average of 33.5 kg C m−2 is stored in the
low-centered polygons while 22.8 ± 5.3 kg C m−2 is accumulated in the rims. This
difference can be explained by the lower rates of decomposition in the center of the wet
and waterlogged low-centered polygons due to the anaerobic conditions that limit the
decomposition process, while aerobic conditions in the rims facilitate the SOC lability.
The ice content is higher in the rims since they are the polygon wedges, thus that is
another reason for lower amounts of SOC in the rims. More SOC in the rims remains
in cryoturbated pockets within the active layer.
The vegetation patterns differ between the rims and the centers. The rims are
covered with abundant wet shrub-tundra vegetation whereas in the polygons centers
are found graminoids and aquatic vegetation (Arctophila community). Thus there
is twice as much phytomass on the rims than in the centers due to better mesic
conditions that boost photosynthesis. Rims and high centered polygons have similar
amount of SOC.
Periglacial patterned ground features that store the highest amount of SOC (mainly
palsa-bogs) are found on terrains with thick peat layers. Similar results were found
for a northeastern European Russia lowland taiga-tundra transition site, where the
vulnerable permafrost carbon pool was found in palsa and peat plateau bog deposits
([Hugelius et al., 2009]). Moreover, the role of patterned ground in the redistribution
of SOC in those environments is highlighted in several studies [Raynolds et al., 2006,
Walker et al., 2008].
A part of the aboveground phytomass C is accumulated on the rims between the
polygons. They are associated with moist or dry vegetation, mainly composed of
graminoids and mosses. The same type of vegetation (moist or dry) is found on the
high centered polygons, the dry hummocks and the non-sorted circles that are accumulating the same amounts of aboveground phytomass C (on average 406 g C m−2 ).
Graminoids accumulate substantial amounts of carbon on raised patterned ground
features. A study in Prudhoe Bay, Alaska, highlighted the fact that patterned ground
creates a mosaic of vegetation that characterized favorable environmental conditions
for some type of plants. Moist or dry vegetation occurred in relatively elevated sites
located in ice-wedge polygons in association with polygon rims, peat hummocks, and
high polygon center [Walker, 2000].
At the scale of several meters, Arctic landscapes exhibit a large amount of heterogeneity that is caused by patterned-ground features, including palsa-bog complexes,
polygons, circles and hummocks. The soil organic carbon distribution differs between
periglacial features. Palsa-bog complexes accumulate large amounts of SOC in the
permafrost while frost-boils store less SOC and mostly in the active layer. Patterned
ground features impact the distribution and abundance of phytomass that, in turn
has a role in the patterned ground development by insulating the permafrost.
6.2. Predicted Evolutions in Plant Functional Type Abundances
6.2
49
Predicted Evolutions in Plant Functional Type Abundances
With changes in temperatures, phytomass abundances are expected to adapt. The
impacts in regard to aboveground phytomass C changes are uncertain over the different types of tundra. A shift in the species dominance in the tundra might lead to
changes in the whole ecosystem carbon balance due to an increase in the leaf density
that impact the light penetration under the canopy.
Air temperatures in the Arctic have increased approximately twice as much as the
global rates in response to rising atmospheric CO2 [McBean, 2005]. Over the past 40
years the temperatures in the Arctic have increased by 1.6‰ [McBean, 2005]. Arctic
ecosystems are sensitive to environmental disturbances that affect the soils and vegetation. Arctic vegetation is particularly sensitive to changes in air and soil temperatures
due to their impact on soil moisture regime and nutrient availability. In turn, phytomass has strong impacts on soil properties and on carbon fluxes. Significant and
rapid changes in tundra vegetation are predicted to occur under a moderate increase
in temperatures that leads to a positive response of plant productivity, mainly driven
by an increase in the abundance of shrubs [Walker, 2000, Walker et al., 2006, Forbes
et al., 2010, Hudson and Henry, 2009].
Increasing leaf densities, due to denser shrub cover, leads to an increase in the net
absorbed radiation rates and thus enhances the photosynthesis activity [Walker et al.,
2006]. Comparisons of normalized difference vegetation index (NDVI) between 1981
and 2000 across Canada show a direct response of tundra photosynthetic stimulates
to maximum summer temperatures [Bunn et al., 2005]. Evidence for close relationship between NDVI and aboveground vegetation phytomass have been highlighted for
several sites [Jia et al., 2006, Raynolds et al., 2006]. Summer warming has enhanced
deciduous shrub growth which in turn has been responsible for observed increases in
NDVI values that are correlated to shrub annual growth [Forbes et al., 2010]. Thus,
a change in the carbon distribution in the tundra is ongoing [Forbes et al., 2010]. A
transition toward more shrub-dominated tundra may impact the surface energy balance, the permafrost table and the carbon storage capacity [Walker et al., 2006, Forbes
et al., 2010, Blok et al., 2011].
Landscapes with high abundances in shrub communities might be significantly affected by a change in temperatures, which lead to differences in the moisture regime
and photosynthesis activity. The consequence of an expansion in shrub tundra is an
increase in carbon uptake in the ecosystem by the shrubs. Moreover, denser vegetation cover increases the soil insulation, thus protecting the permafrost from thawing
in summer. Thus, the effects on the rates of carbon released to the atmosphere in
shrub tundra are not clearly defined. Given that aboveground phytomass C in shrub
tundra represents a substantial fraction of all ecosystem carbon (the aboveground
phytomass C represents 1.5% of the total ecosystem carbon at Logata), increasing
shrub phytomass might offset the amount of SOC that is expected to be released due
to increases in the active layer depth.
However, each vegetation type responds differently to climate change and the adaptive capacity of arctic plants varies largely [Jia et al., 2006]. One consequence of a
change in temperatures is a decrease in species diversity due to the increase in temperature and soil moisture regime [Walker et al., 2006]. Given that shrub communities
are expected to increase, the canopy might be denser due to an increase in vascular
6.3. Limits of Upscaling Methods
50
plant [Douma et al., 2007]. Thus, the light that reaches the moss layer might decrease, thereby resulting in a diminution in moss primary productivity [Douma et al.,
2007]. Since mosses act as a powerful insulating layer for the soil, a diminution in
their coverage would allow the active layer to increase in summer, and would increase
the carbon remobilization. Among all plant functional types, mosses store the most
carbon (74.2%) for the studied sites. They are covering almost all surfaces and vary
in density and in species composition depending on the soil moisture regime and other
environmental characteristics. At Logata, moss phytomass is highest in shrub tundra
(45% of the total moss phytomass is in shrub tundra). Moreover, moss-dominated
ecosystems account for 35% of the world active carbon pool [McGuire et al., 1995],
thus a decrease in moss cover might have important impacts on SOC releases.
The consequences of a warming on plant species are different over the Arctic regions. A study conducted in the northwest Russian Arctic, show that due to warmer
temperature, the height and cover of deciduous shrubs (willows) and graminoids increased [Forbes et al., 2010]. In northern Sweden and Svalbard, a decrease in moss
contribution to photosynthetic activity of the ecosystem occurred when the high vascular plants cover increased due to a competition for the light between species [Douma
et al., 2007]. However, over 27 years in a site in Nunavut, Canada, phytomass changes
have led to increased moss and evergreen shrub abundances, while graminoid, deciduous shrub, forb, and lichen cover was not affected [Hudson and Henry, 2009].
Both deciduous shrubs and mosses are major contributors to the aboveground phytomass C and have an important role in controlling carbon exchange in the tundra.
The results of different studies suggest that a shift in plant cover dominance, enhanced by temperature warming effects, could have important effects on element
cycling through litter and soil processes. A shift in plant community distribution
may be the most influential factor altering carbon and nutrient cycling in tundra
ecosystems, [Grogan and Chapin, 1999] through specific differences in rates of litter
decomposition and nutrient release, with possible feedbacks to soil carbon storages
[Hobbie et al., 2000, Mack et al., 2004].
6.3
Limits of Upscaling Methods
Remote sensing data with high resolution images (2.4 m for Quickbird images) are
extremely valuable for understanding vegetation distribution at different scales and
are of main interest to support field inventory data. Combining field inventories with
remotely sensed data allows having a better understanding of scale dependent spatial
heterogeneity in the landscape [Wu, 2004].
However, error propagations are one important limit of the upscaling methods. The
approximations made at each spatial scale analysis aggregate, and thus increase the
level of uncertainty at each scale level. First, some misinterpretations of the landscape might have been made during field inventories. Other problems can appear
when processing samples in the laboratory, even though they are limited by mixing
each sample of soil and using subsamples for each analysis. Another loss in accuracy
results from linking field data to remote sensing data as this is made by using statistics that aggregate the information (soft classifications as the Maximum Likelihood
classification). A typical problem has been raised in the thesis while using the upscal-
6.3. Limits of Upscaling Methods
51
ing methods for Ari-Mas: the results may overestimate some of the classes because
the woodland class was misclassified with palsa-bogs and polygon classes due to the
difficulty of differentiating their spectral reflection due to the high accuracy provided
by the Quickbird images.
The Maximum Likelihood classification is the most common supervised classification method used in remote sensing and is statistically strong as it takes into account
the marginal distributions of the data sets and their internal correlations [Conese and
Maselli, 1992]. Data aggregation that is made when using a supervised classification
allows comparisons but leads to losses in information and creates a scale effect due
to a decrease in the variance of the dataset. It is known that there is a relationship
between the coefficients of correlation and the size of the spatial units of the data
(Gehkle and Biehl, 1934 in [Josselin et al., 2008]). Thus, it leads to a problem when
providing individual statistics for analysis at an aggregated level, known as the Modifiable Area Unit Problem [Josselin et al., 2008]. The use of high resolution images
is a step to decrease the problem of a supervised classification but it does not solve
the problem of mixed pixels, due to the occurrence of different objects among a same
pixel. Thus, estimating the results from different spatial dimension allows better
estimates. As well as taking into consideration larger datasets.
C HAPTER 7
Conclusion
The thesis aims at understanding the patterns of carbon distribution in relation to
the surrounding ecosystem using field inventories and remote sensing data. Two main
methods were used in order to achieve this goal; field inventories during the summer
of 2011 and remote sensing analysis using high resolution images (Quickbird).
The complex geomorphology and the diversity in ecosystems motivate to comprehend the changes in Arctic landscapes at different scales. At a regional scale, similar
amounts of carbon are found in the two sampling sites that are localized in northern
hypoarctic tundra; on average 29.5 kg C m−2 .
The study highlight the fact that the tundra is composed of a mosaic of diverse
ecosystems that impact the carbon distribution at a smaller scale.
The first difference in carbon storages is explained by the effect of topography;
more SOC is accumulated in upland ecosystems that in lowland ecosystems. In the
upland ecosystems at Logata, 39.1 kg C m−2 is stored in the top 100 cm on average,
which is 62% of the total SOC in the area. Those results are linked with the important amount of SOC found in cryoturbated pockets in upland ecosystems, which is
related to the process of solifluxion on the slopes. The aboveground phytomass C is
higher in lowland ecosystems (57% of the total aboveground phytomass C), due to
the importance in moss abundances. Most of the aboveground C is found in mosses
and lichens (74.2%) that are widely spread out over all land cover types.
Differences in carbon storage are found between different patterned ground types.
Highest amounts of SOC are found in palsa-bogs on peatlands with an average of 70.3
kg C m−2 . More SOC is stored in the center of the polygons (33.5 kg C m−2 ) than
n the rims (22.8 kg C m−2 ), probably due to differences in decomposition. Patterned
ground features are suitable for the development of a mosaic of vegetation patterns.
The highest amounts of aboveground phytomass C are found on the rims.
The results are important to comprehend the impact of ongoing climate change
in the Arctic on the carbon storage at a large scale. Field inventories, and remote
sensing analysis as complementary methods, provide accurate estimates for the total
carbon store over a surface area. However, the study emphasizes the importance of
taking into account a large number of soil profiles and relevés over different landscape
52
53
types to avoid large averages in carbon stores when upscaling. Therefore, multiscale
analysis is necessary in order to discern landscape heterogeneity.
Due to the high variability in the Arctic soil distribution and plant composition,
better estimates at the circumpolar scale can be made by using more data, collected
in different regions of the Arctic. Further research is ongoing in order to enlarge
the existing Northern Circumpolar Soil Carbon Database (NCSCD) that is currently
made up of 1647 pedons [Hugelius, in preparation].
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