Biogeosciences, 15, 1319–1333, 2018
https://doi.org/10.5194/bg-15-1319-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Climate effects on vegetation vitality at the treeline
of boreal forests of Mongolia
Michael Klinge1, Choimaa Dulamsuren2, Stefan Erasmi1, Dirk Nikolaus Karger3, and Markus Hauck2
1Institute of Geography, University of Goettingen, Goldschmidtstr. 5, 37077 Goettingen, Germany
2Albrecht-von-Haller Institute for Plant Sciences Plant Ecology and Ecosystems Research, University of Goettingen,
Untere Karspuele 2, 37073 Goettingen, Germany
3Swiss Federal Research Institute WSL, Zuericherstrasse 111, 8903 Birmensdorf, Switzerland
Correspondence: Michael Klinge (mklinge1@gwdg.de)
Received: 31 May 2017 – Discussion started: 10 July 2017
Revised: 24 January 2018 – Accepted: 3 February 2018 – Published: 5 March 2018
Abstract. In northern Mongolia, at the southern boundary of
the Siberian boreal forest belt, the distribution of steppe and
forest is generally linked to climate and topography, mak-
ing this region highly sensitive to climate change and human
impact. Detailed investigations on the limiting parameters of
forest and steppe in different biomes provide necessary in-
formation for paleoenvironmental reconstruction and prog-
nosis of potential landscape change. In this study, remote
sensing data and gridded climate data were analyzed in or-
der to identify main distribution patterns of forest and steppe
in Mongolia and to detect environmental factors driving for-
est development. Forest distribution and vegetation vitality
derived from the normalized differentiated vegetation index
(NDVI) were investigated for the three types of boreal for-
est present in Mongolia (taiga, subtaiga and forest–steppe),
which cover a total area of 73 818 km2. In addition to the
forest type areas, the analysis focused on subunits of forest
and nonforested areas at the upper and lower treeline, which
represent ecological borders between vegetation types. Cli-
mate and NDVI data were analyzed for a reference period of
15 years from 1999 to 2013.
The presented approach for treeline delineation by iden-
tifying representative sites mostly bridges local forest dis-
turbances like fire or tree cutting. Moreover, this procedure
provides a valuable tool to distinguish the potential forested
area. The upper treeline generally rises from 1800 m above
sea level (a.s.l.) in the northeast to 2700 m a.s.l. in the south.
The lower treeline locally emerges at 1000 m a.s.l. in the
northern taiga and rises southward to 2500 m a.s.l. The lat-
itudinal gradient of both treelines turns into a longitudinal
one on the eastern flank of mountain ranges due to higher
aridity caused by rain-shadow effects. Less productive trees
in terms of NDVI were identified at both the upper and lower
treeline in relation to the respective total boreal forest type
area. The mean growing season temperature (MGST) of 7.9–
8.9 ◦C and a minimum MGST of 6 ◦C are limiting parameters
at the upper treeline but are negligible for the lower treeline.
The minimum of the mean annual precipitation (MAP) of
230–290 mm yr−1 is a limiting parameter at the lower tree-
line but also at the upper treeline in the forest–steppe eco-
tone. In general, NDVI and MAP are lower in grassland, and
MGST is higher compared to the corresponding boreal for-
est. One exception occurs at the upper treeline of the subtaiga
and taiga, where the alpine vegetation consists of mountain
meadow mixed with shrubs. The relation between NDVI and
climate data corroborates that more precipitation and higher
temperatures generally lead to higher greenness in all eco-
logical subunits. MGST is positively correlated with MAP
of the total area of forest–steppe, but this correlation turns
negative in the taiga. The limiting factor in the forest–steppe
is the relative humidity and in the taiga it is the snow cover
distribution. The subtaiga represents an ecological transition
zone of approximately 300 mm yr−1 precipitation, which oc-
curs independently from the MGST.
Since the treelines are mainly determined by climatic pa-
rameters, the rapid climate change in inner Asia will lead
to a spatial relocation of tree communities, treelines and bo-
real forest types. However, a direct deduction of future tree
vitality, forest composition and biomass trends from the re-
cent relationships between NDVI and climate parameters is
Published by Copernicus Publications on behalf of the European Geosciences Union.
1320 M. Klinge et al.: Climate effects on vegetation vitality
challenging. Besides human impact, it must consider bio-
and geoecological issues like, for example, tree rejuvena-
tion, temporal lag of climate adaptation and disappearing
permafrost.
1 Introduction
Due to the highly continental environment in northern cen-
tral Asia, Mongolia is subjected to dry and winter-cold cli-
mate conditions. The landscape and vegetation development
is highly sensitive to changes in temperature and/or precip-
itation (Dulamsuren et al., 2010a; Gunin et al., 1999). The
intensity and impact of climate parameters on vegetation
strongly varies in space caused by different factors like to-
pography, latitude and air circulation. Corresponding to the
change in climatic conditions from cold semihumid in the
north to warm and arid in the south, a latitudinal zonation
of the vegetation occurs, which is modified by an altitudi-
nal zonation in the mountains (Hilbig, 1995). From north
to south, these vegetation zones include taiga, forest–steppe,
steppe and the Gobi desert. Taiga, subtaiga and fragmented
forests in the forest–steppe ecotone represent the southern
edge of the Eurosiberian boreal forest. The grassland belongs
to the region of the Mongolian–Chinese steppe. The distribu-
tion of the different vegetation zones, boreal forest types and
treelines is mainly controlled by air temperature, evapotran-
spiration and precipitation (Walter and Breckle, 1994). How-
ever, site-specific edaphic parameters, including soil tem-
perature, soil moisture and nutrient availability, also play a
role. Moisture conditions are a key limiting factor control-
ling the distribution of deserts and steppes as well as for
the lower boundary of mountain forests at the transition to
drylands. In contrast, thermal conditions control position of
the upper treeline and the alpine vegetation belt (Klinge et
al., 2003, 2015; Körner, 2012; Paulsen and Körner, 2014).
Both the upper and the lower treelines of Mongolia’s bo-
real forests represent an obvious visual boundary between
vegetation zones of highly different ecological requirements,
though their current state can be strongly influenced by hu-
man impact (Klinge et al., 2015).
The mean temperature of the growing season (MGST)
is more relevant for describing the thermal environment
at the upper forest line than mean annual air temperature
(MAAT), because winter temperatures are of minor signif-
icance for tree growth (Jobbágy and Jackson, 2000; Körner,
2012). To define temperature conditions at the upper tree-
line the warmest month isotherm of 10 ◦C is commonly used
(Walter and Breckle, 1994). For the northern Tian Shan,
Klinge et al. (2015) indicated a minimum monthly mean
temperature of 5 ◦C during the growing season. Paulsen and
Körner (2014) defined the minimum MGST as 5.5 to 7.5 ◦C
and the mean temperature as 6.4 ◦C during a period of daily
temperatures > 0.9 ◦C in a minimum growing season of 94
days for the upper treeline in a global context. A lower tree-
line occurs in the semiarid region of central Asia between
relatively humid mountain regions and arid basins. The for-
est distribution is generally limited by annual precipitation,
which has its minimum between 300 and 200 mm yr−1 (Du-
lamsuren et al., 2010a; Holdridge, 1947; Miehe et al., 2003;
Walter and Breckle, 1994).
In the forest–steppe, the spatial distribution of vegetation
is highly correlated with terrain parameters (Hais et al., 2016;
Klinge et al., 2015). Less solar radiation input causes lower
temperatures and reduces the evapotranspiration pressure on
north-facing slopes, leading to higher humidity, higher soil
moisture and more widespread permafrost. The higher water
availability supports tree growth (Dashtseren et al., 2014).
The dominant tree species in Mongolia’s boreal forests is
Siberian larch (Larix sibirica). On south-facing slopes higher
solar irradiation produces hydrological conditions which are
too dry for the establishment of forests and thus favor grass-
land (Bayartaa et al., 2007).
With respect to global climate change, the question of po-
tential shifts in growth conditions arises. Vegetation indices
like the most commonly applied NDVI (normalized differen-
tiated vegetation index), which are derived from multispec-
tral satellite images (Landsat, MODIS, SPOT VGT), provide
information about the “greenness” and vitality of the vege-
tation cover. The various investigations into recent trends in
climate and NDVI which exist for the region of Mongolia
state partially diverging results (Dashkhuu et al., 2015; Eck-
ert et al., 2015; Miao et al., 2015; Poulter et al., 2013; Van-
dandorj et al., 2015). Instrumental climate data from weather
stations in Mongolia are often discontinuous and time se-
ries of climate measurements are not available from moun-
tain areas since climate stations are located near settlements
in the basins. Thus, representative climate parameters must
be modeled by different regionalization processes (Böhner,
2006). Various gridded datasets of reanalyzed climate pa-
rameters with different spatial and temporal resolution exist,
which are mainly used for climate trend analysis; examples
include CRU-TS (Harris et al., 2014), ERA-interim (Dee et
al., 2011) and CHELSA (Karger et al., 2017) (Figs. S1 and
S2 in the Supplement). While the quality, origin and reso-
lution of climate records are potential sources of uncertainty,
the results and interpretations of the correlations between cli-
mate and NDVI trends occasionally suffer from disregarding
the specific bio-ecological restrictions of the different vege-
tation zones.
Batima et al. (2005) analyzed climate station data and ob-
served an increasing MAAT of 1.7 ◦C for Mongolia between
1940 and 2001. Eckert et al. (2015) stated that temperatures
have not varied much since the year 2000. Dulamsuren et
al. (2014) found a trend toward warmer temperature extremes
starting around 2000. Measurements of permafrost distribu-
tion and active layer development in Mongolia show a gen-
eral trend of permafrost degradation, which has been accel-
erating since the 1990s (Sharkhuu et al., 2007; Sharkhuu,
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M. Klinge et al.: Climate effects on vegetation vitality 1321
2003). This is due to climate warming but reinforced by a
loss of vegetation caused by livestock grazing in some steppe
areas and tree cutting in the forests. Permafrost degradation
is more intense in the Khuvsgul area than in the Khentei and
Khangai Mountains (Sharkhuu et al., 2007).
The trends in precipitation in Mongolia are not spatially
uniform and can strongly depend on the period of observa-
tion used for climate analysis (Erasmi et al., 2014; Giese et
al., 2007). Batima et al. (2005) found a negative trend of an-
nual precipitation in the period between 1970 and 2001. In
the driest regions of western and southern Mongolia, no spe-
cific trends occurred at all. Based on tree-ring data, Dulam-
suren et al. (2010b) documented increasing drought stress for
larch trees in the Khentei Mountains, which they attributed to
increasing aridity caused by rising summer temperatures and
decreasing summer precipitation during the last 50 years. Al-
though trees at the outer boundary of the forest stands might
be better adapted to drought stress, obvious margins of dead
trees surrounding the forest islands were recently found in
many places in the forest–steppe. For the period from 1980
until 2005, Bayartaa et al. (2007) reported a strong increase
in burnt forest area in Mongolia, starting in 1996, which was
caused by very dry winter and spring seasons but may also
be combined with weakened governmental management dur-
ing the period of political transition. A general tendency of
decreasing lake levels during recent decades in two great
lakes of interior drainage in the Gobi with an catchment
area south of the Khangai Mountains was observed by Szu-
min´ska (2016). This lake-level decline was associated with
trends in reduced precipitation and increased evapotranspira-
tion resulting from rising temperatures.
Eckert et al. (2015) analyzed the general trend for NDVI
in Mongolia during the period between 2001 and 2011 us-
ing the MODIS NDVI dataset and found mostly positive
trends in northern and eastern Mongolia, stable conditions
in southern Mongolia and large areas of negative trends in
the northern Mongolian Altai and in the east of the Khangai
Mountains. Based on the same dataset and a similar period
from 2000 to 2012, Vandandorj et al. (2015) analyzed the
seasonal variation of NDVI for individual vegetation zones.
High variations of NDVI occur particularly in the steppe re-
gions where the vitality and density of grassland is closely
related to the amount of annual precipitation due to low stom-
atal control of transpiration by the grassland vegetation. Low
variations in NDVI occur in forested regions, since trees exert
a much stricter stomatal control of transpiration than herbs
and grasses, and in the sparsely vegetated desert regions.
Poulter et al. (2013) investigated the influence of recent cli-
mate trends on the forests in inner Asia by the temporal dis-
tribution of a greening value using specific vegetation indices
from remote sensing data and environmental datasets. They
found a trend toward earlier greening induced by increas-
ing spring temperatures and earlier browning associated with
decreasing summer precipitation. Based on these relation-
ships they projected better future forest conditions for Mon-
golia until 2100. In opposition to these findings, Bayartaa et
al. (2007) reported that climate scenarios would indicate a
significant decrease in forest area and its total biomass for
Mongolia until the middle of the 21st Century, which is in
accordance with the recent trends from dendrochronologi-
cal data from Mongolia (Dulamsuren et al., 2010a, b, 2014;
Khansaritoreh et al., 2017). Lu et al. (2014) investigated the
applicability of different remote sensing-based biomass esti-
mation approaches. They found that the biomass estimation
method via NDVI was sufficient in low-density forests. Du-
lamsuren et al. (2016) showed the NDVI to be well suited to
estimating the tree biomass of Mongolian forests. The best
fit of linear regression was found between biomass and the
mean NDVI of April for the period 1999–2013. This shows
that in addition to the vegetation vitality the NDVI is a valu-
able indicator for tree biomass in open forest stands.
With regard to the diverse and in parts contradictory ob-
servations on climate and vegetation status, interdependen-
cies, and recent trends in Mongolia that are reported here,
this study investigates the present distribution of forest areas
and its relation to the actual climate and topography based
on high-resolution satellite and gridded climate data. In ad-
dition to existing studies, here, the specific impact of climate
parameters related to different boreal forest types and eco-
logical subunits is analyzed in order to delineate potential
turning points for environmental changes. The following hy-
potheses were tested:
– Every type of boreal forest is delimited by a spe-
cific climatic envelope. The statistical correlations be-
tween NDVI and climate parameters in different forest
types and at the corresponding treelines reflect climate-
ecological relationships and limitations.
– Different spatial gradients of climate-induced vitality
change exist for different types of boreal forest. This
applies in particular to the treelines as an indicator of
extreme ecological site conditions.
– Forest and grassland of the same zone of boreal forest
type show different spatial gradients and relations to cli-
mate.
2 The study area
Mongolia is situated in northern central Asia in the transition
zone between the Siberian taiga in the north and the Gobi
desert in the south (Fig. 1). Mongolia extends from 87◦45′
to 119◦56′ E and from 41◦34′ to 52◦09′ N and covers a total
area of 1 562 950 km2. Wide basins of interior drainage oc-
cur at elevations between 900 and 1500 m a.s.l. with the low-
est areas below 720 m a.s.l. There are five principal mountain
systems in Mongolia: the Mongolian Altai (MA) in the west
(highest peak is Tavan Bogd, 4374 m a.s.l.), the Gobi Altai in
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1322 M. Klinge et al.: Climate effects on vegetation vitality
Figure 1. The vegetation zones of Mongolia (modified from Gunin and Vostokova, 2005, and Landsat 8 supervised classification).
the south (Ikh Bogd, 3957 m a.s.l.), the Khangai Mountains
(KaM) in the center (Otgon Tenger, 3964 m a.s.l.), the Khen-
tei Mountains (KeM) in the northeast (Asralt Kharj khan,
2799 m a.s.l.), and the Khuvsgul region in the eastern Sayan
Mountains (Munkh Saridag, 3460 m a.s.l.). The mountain-
tops are shaped by pronounced flat surfaces at elevations be-
tween 2500 and 3500 m a.s.l. (Academy of Sciences of Mon-
golia and Academy of Sciences of USSR, 1990; Murzaev,
1954).
The climate of Mongolia is highly continental with semi-
humid, semiarid and arid conditions. In wintertime, the
Siberian high-pressure cell produces cold and dry weather
with little snowfall and mean temperatures between−15 and
−30 ◦C (Barthel, 1983; Klinge, 2001). The main rainfall oc-
curs from June to August during the short summer and is
induced by westerlies and cyclone precipitation, with the dry
season starting again in autumn. The mean summer tempera-
tures range between 10 and 27 ◦C. Mean annual precipitation
is lower than 50 mm in the interior basins, around 125 mm in
the southern desert and up to 350 mm in the northern steppes,
whereas it rises to more than 500 mm in the high mountains.
According to the climatic conditions, the vegetation zones
are arranged in characteristic sequences along latitudinal and
altitudinal gradients (Hilbig, 1995). Dark mountain taiga
with coniferous trees (Pinus sibirica, Picea obovata, Abies
sibirica, Larix sibirica) occurs as closed forests in northern
Mongolia and locally as mountain taiga in the upper KaM
in central Mongolia (Dulamsuren, 2004). The subtaiga for-
est type with needle and deciduous broadleaf forests (Larix
sibirica, Pinus sylvestris, Betula platyphylla) represents a
type of light taiga beneath and surrounding the mountain
taiga. In northern Mongolia, the forest often extends into
the valley bottoms and open grassland is restricted to in-
tramontane basins. The vegetation in central Mongolia con-
sists of steppe grassland in the basins and forest–steppe in
the mountains. In this forest boundary ecotone of semiarid
climate conditions, deciduous conifer forests consisting of
Larix sibirica are primarily limited to north-facing slopes
(Treter, 1996). In the high mountains, dense alpine meadow
vegetation occurs between forest–steppe and the periglacial
zone of frost debris. The main perennial rivers are accompa-
nied by floodplain meadows and alluvial forests of Populus
spp. and Ulmus pumila (Hilbig, 1995).
Missing forest management and extensive forest use by
tree cutting and wood pasture led to forest degradation and
local deforestation in many regions of Mongolia during re-
cent decades (Tsogtbaatar, 2004). In addition, hazardous for-
est fires destroyed large forest areas (Bayartaa et al., 2007;
Goldammer, 2002, 2007; Hansen et al., 2013). Although it
is supposed that most of the recent forest fires in Mongolia
were primarily set by humans, there is an additional ecolog-
ical exposure to fire susceptibility (Dorjsuren, 2009), which
is caused by climate warming, permafrost retreat and insect
calamities.
3 Methods
Figure 2 shows a scheme of the complete analysis process,
and the individual steps are described in detail below. Fig-
ure 3 provides visualizations of the different spatial resolu-
tion of the basic datasets used for the analysis. The forested
area of Mongolia and its surroundings was mapped from
50 Landsat 8 satellite images (spatial resolution 30 m). Im-
ages of the years 2013 and 2014 were used as a baseline,
and, in areas of low quality or high cloud coverage, were sup-
plemented by Landsat 5 images from 2009 to 2011 (spatial
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M. Klinge et al.: Climate effects on vegetation vitality 1323
Figure 2. Processing workflow for treeline delineation, NDVI and climate analysis.
resolution 30 m). The mapping process consists of two steps.
Initially a maximum likelihood supervised classification was
carried out and subsequently the resulting forest polygons
were visually proofed and manually corrected.
The elevation of the actual treeline was calculated from se-
lected points of a digital elevation model (DEM) taken from
SRTM data (spatial resolution 90 m; Fig. 3c). Points repre-
senting the treeline were established using a kernel model,
which evaluates, for every pixel covered by forest, whether
(1) it has a slope of more than 2◦, (2) there is any forested
area in the surroundings in a higher or lower position and
(3) there is any woodless area representing the existence of
the next vegetation zone beyond the potential forest bound-
ary, to exclude relief-related distribution limits. The specific
search parameters for the upper and lower treeline are given
in Fig. 2. Körner (2012) proposes a minimum vertical range
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1324 M. Klinge et al.: Climate effects on vegetation vitality
Figure 3. Examples for the spatial resolution of the different data: (a) mean growing season NDVI 1999–2013, (b) mean growing season
temperature 1999–2013, (c) upper and lower treeline boundary from Landsat and SRTM data.
of 100 m from the upper treeline (UT) to the summit to pre-
vent the summit effect on tree development and to receive a
true climatic treeline value. Due to extensive planation sur-
faces in the area of KaM, the widespread alpine belt occurs
with less than 100 m vertical distance between the upper tree-
line and the flat mountaintops. Thus, it was necessary to re-
duce the minimum distance for defining the summit effect
in the modeling to only 10 m to prevent UT values beyond
large alpine areas from being excluded. After visual proof
and deletion of strong outlying points, a final number of
7081 points for the UT and 5220 for the lower treeline (LT)
were used for the spatial interpolation of the treeline surfaces
applying the natural neighbor method (Watson, 1992). Sub-
sequently, the vertical distance of the treeline surfaces and
the area above and below the treeline were calculated. A
buffer of 1000 m around these areas was chosen to represent
the treeline boundary area. This distance meets the spatial
resolution of the SPOT VGT and climate data (Fig. 3b).
The distribution of the different zones of boreal forest type
was adapted from the Ecosystems Atlas of Mongolia (Gunin
and Vostokova, 2005). At places where the map does not
match the position of the landscape elements represented in
the remote sensing data, the spatial deviations were corrected
to the position of the satellite images. The different ecosys-
tem units were generalized to the main vegetation zones
(desert, desert steppe, steppe, forest–steppe, subtaiga, taiga,
alpine vegetation). Forests of floodplain areas, which are hy-
drologically favored by groundwater, were excluded from
this analysis. Where forest areas were found in steppe re-
gions, those parts were changed into forest–steppe. In the up-
per elevation belts where the strong disparity between north-
facing slopes with forest and south-facing slopes with steppe
dissipates, the areas with slopes covered by forests in ev-
ery direction were reclassified as mountain subtaiga. Sub-
sequently, the mapped forest areas were combined with the
vegetation zones to achieve a spatial differentiation between
forested area and open grassland within the total ecological
(TE) units of the forest–steppe, subtaiga and taiga. These
three types of boreal forest comprise the area under inves-
tigation in the present study.
Here, the statistical approach to use one mean value in a
period of 15 years (1999–2013) for every parameter was cho-
sen in order to eliminate annual changes and interannual vari-
ations, which derive from phenology and climate variability.
Thus, normalized variables representing the mean site con-
ditions were computed and spatially analyzed, although this
is a simplification since the plant species respond to interan-
nual variations and extreme values. NDVI, temperature and
solar radiation are integrated to the MGS (mean growing sea-
son). Precipitation during the winter season is retained in the
soil and additionally available during the MGS. The vegeta-
tion index from SPOT VGT satellite data was used for the
time span from 1 January 1999 to 31 December 2013, which
originally consists of SPOT-Vegetation 10-daily NDVI com-
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M. Klinge et al.: Climate effects on vegetation vitality 1325
posites (spatial resolution 1 km, Fig. 3a). These data were
aggregated to monthly values using the maximum value of
the three 10 day composites. Monthly NDVI data were fur-
ther aggregated to the mean of the growing season from May
to September (MGS-NDVI) for the period 1999 to 2013. We
used reanalyzed climate data from the CHELSA dataset with
30 arcsec resolution (approx. 1 km, Fig. 3b), because it incor-
porates terrain parameters and wind effect for better repre-
senting climate parameters in the relief (Karger et al., 2017)
(Figs. S1 and S2 in the Supplement). Monthly data from
1999 to 2013 were averaged to cover the same period as the
MGS-NDVI dataset. While MGSTs were calculated from the
monthly means from May to September, the mean annual
precipitation (MAP) represents the average of the total an-
nual sum of the period from 1999 to 2013. The sum of solar
radiation input (MGSR; Wh m−2) for the MGS (day 121–
273) was simply calculated with a GIS-tool based on STRM-
DEM data for 2007 and was assumed to be relatively constant
for the observation period 1999 to 2013.
Up to 3000 random points for both forest and grassland
area in the three types of boreal forest and at the upper and
lower forest boundary were chosen for statistical analysis
(Tables 1, 2). The total number of random points was reduced
for treeline subunits, which have only a small spatial distri-
bution to prevent a point density that is too large. Areas of
larger valleys where extensive forest occurs below the LT are
excluded from the treeline analysis.
For each of the three boreal forest types (forest–steppe,
subtaiga, taiga), first, the total area (total ecological unit, TE)
is considered, and then the TE is divided into forest (f) and
grassland (s) and further separated into the 1 km boundary
area of both treelines (LT, UT). This categorization leads
to 18 ecological subunits. Multiple comparisons between
means were calculated with Duncan’s multiple range test af-
ter testing for normal distribution using SAS 9.4 software
(SAS Institute Inc., Cary, North Carolina, USA). In addi-
tion to the mean values, the standard deviation specifies the
variation range of the climate parameters for every subunit.
Pearson and multiple correlation coefficients between NDVI,
MAP, MGST and MGSR were computed as a statistical base
for the interpretation of regression gradients. Due to the high
amount of random points, the performance of a t test was
opposed because the significance level (p value) is always
< 0.05. The correlations at the level of the TE are used to
analyze the controlling climatic conditions and the environ-
mental range with respect to the ecological distribution of the
entire type of boreal forest.
4 Results
4.1 Treeline distribution
The actual total area of Mongolian southern boreal forest was
estimated at 73 818 km2 (Dulamsuren et al., 2016). The pro-
Table 1. Arithmetic mean± standard deviation of different climate
parameters (MAP: mean annual precipitation, MGST: mean grow-
ing season temperature, MGS-NDVI: mean growing season normal-
ized differentiated vegetation index) and vegetation units (subunits
are TE: total ecological unit, LT: lower treeline, UT: upper treeline,
s: portion of grassland, f: portion of forest). Within one row, mean
values sharing a common uppercase letter, do not differ significantly
(p ≤ 0.05, Duncan’s multiple range test, dfmodel= 2). Within one
subunit (forest–steppe, subtaiga, taiga), mean values sharing a com-
mon lowercase letter do not differ significantly (p ≤ 0.05, Duncan’s
multiple range test, dfmodel = 5.13295).
Subunit forest–steppe Subtaiga Taiga
MAP (mm yr−1)
TEf 266± 62 Aa 339± 70 Ba 357± 69 Ca
TEs 256± 63Ab 309± 68Bbe 331± 73Cb
LTf 251± 60Ac 294± 60Bc 292± 56Bc
LTs 253± 62Abc 286± 57Bd 290± 53Bc
UTf 231± 52Ad 305± 72Be 333± 80Cbd
UTs 227± 54Ae 314± 73Bb 339± 80Cd
MGST (◦C)
TEf 11.0± 2.1Aa 11.7± 2.3Ba 11.1± 1.4Ca
TEs 11.6± 2.5Ab 11.7± 2.7Ba 11.1± 1.7Ca
LTf 11.5± 2.2Ab 12.1± 2.6Bb 11.5± 1.7Ab
LTs 12.1± 2.3Ac 12.8± 2.4Bc 11.7± 1.6Cc
UTf 8.4± 0.8Ad 7.9± 1.2Bd 8.9± 1.3Cd
UTs 8.4± 0.9Ad 7.5± 1.2Be 8.5± 1.3Ce
MGS-NDVI
TEf 0.51± 0.08Aa 0.60± 0.08Ba 0.63± 0.06Ca
TEs 0.47± 0.08Ab 0.55± 0.09Bb 0.55± 0.09Cb
LTf 0.46± 0.08Ab 0.54± 0.08Bc 0.58± 0.09Cb
LTs 0.44± 0.08Ac 0.51± 0.08Bd 0.55± 0.08Cc
UTf 0.44± 0.06Ac 0.47± 0.07Be 0.51± 0.09Cd
UTs 0.42± 0.07Ad 0.44± 0.08Bf 0.47± 0.09Ce
portion of forested areas related to the total areas of the eco-
logical units and subunits at the treelines are given in Ta-
ble 3. The approximate forest proportion for all three eco-
logical units is 40 % and the highest proportions occur in the
taiga and at all UTs. As expected for an ecotone, low forest
densities occur in the forest–steppe, but this is also true for
LTs of all forest types. Figure 4 shows the forest distribution,
the treelines, the vertical distance of the forest belt and the
area beyond the treelines in northern Mongolia. No treeline
continuance is indicated in the southern part of Mongolia due
to missing boreal forests in the desert. The treeline distribu-
tion in western Mongolia generally corresponds to the results
from Klinge et al. (2003), who investigated forest distribution
in the Altai Mountains based on topographic maps.
Large areas above the UT occur in the MA, in the south-
ern part of KaM and east of Lake Khuvsgul. In the KeM, ar-
eas above the treeline in > 2500 m a.s.l. are small. The UTs
show a general rise from 2200 m a.s.l. at the mountains in
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1326 M. Klinge et al.: Climate effects on vegetation vitality
Table 2. Correlation matrix showing Pearson and multiple correlation coefficients (r) between NDVI, climate, and terrain parameters for
different types of boreal forest and ecological subunits. (MAP: mean annual precipitation, MGST: mean growing season temperature, MGS-
NDVI: mean growing season normalized differentiated vegetation index, MGSR: mean growing season solar radiation input; subunits are
TE: total ecological unit, LT: lower treeline, UT: upper treeline, s: portion of grassland, f: portion of forest). Bold letters highlight strong
correlations between the different parameters.
Subunit Forest- Subtaiga Taiga Forest- Subtaiga Taiga Forest- Subtaiga Taiga Forest- Subtaiga Taiga
steppe steppe steppe steppe
MGS-NDVI/MAP MGS-NDVI/MGST MGS-NDVI/MGSR
TEf 0.58 0.44 0.22 0.49 0.62 0.55 −0.15 −0.24 −0.09
TE g 0.57 0.38 0.19 0.49 0.55 0.57 −0.26 −0.17 −0.18
LTf 0.53 0.33 0.51 0.56 0.52 0.60 −0.09 −0.20 −0.18
LTg 0.55 0.39 0.39 0.61 0.52 0.46 −0.29 −0.29 −0.30
UTf 0.34 0.11 0.34 0.31 0.59 0.71 0.22 0.19 0.08
UTg 0.42 0.10 0.33 0.25 0.55 0.66 0.15 0.17 0.08
MGS-NDVI/MAP; MGSR MGS-NDVI/MGST; MGSR MGS-NDVI/MAP; MGST MGS-NDVI/MAP; MGST; MGSR
TEf 0.58 0.47 0.24 0.51 0.62 0.56 0.62 0.71 0.64 0.63 0.72 0.65
TEg 0.58 0.41 0.26 0.50 0.56 0.58 0.62 0.67 0.68 0.63 0.67 0.68
LTf 0.53 0.36 0.52 0.59 0.52 0.60 0.60 0.56 0.72 0.63 0.57 0.72
LTg 0.56 0.43 0.42 0.61 0.52 0.50 0.64 0.58 0.57 0.65 0.58 0.58
UTf 0.36 0.24 0.35 0.37 0.60 0.71 0.43 0.62 0.74 0.45 0.64 0.75
UTg 0.42 0.21 0.34 0.30 0.56 0.66 0.47 0.58 0.69 0.47 0.59 0.69
MAP/MGSR MGST/MGSR MAP/MGST
TEf −0.23 −0.16 0.05 −0.54 −0.48 −0.29 0.50 0.16 −0.18
TEg −0.29 −0.05 0.01 −0.67 −0.42 −0.26 0.47 0.00 −0.28
LTf −0.22 −0.21 −0.16 −0.46 −0.47 −0.24 0.63 0.23 0.20
LTg −0.37 −0.29 −0.41 −0.58 −0.44 −0.23 0.65 0.24 0.12
UTf 0.25 −0.18 0.01 0.05 0.11 0.04 0.12 −0.11 0.18
UTg 0.20 −0.10 −0.05 0.00 0.12 0.16 0.11 −0.15 0.17
Table 3. Proportion of forest area (f) and total area of different boreal forest types and corresponding treelines. (TE: total ecological unit, LT:
lower treeline, UT: upper treeline).
Area (km2) TE TEf %f LT LTf %f UT UTf %f
Forest–steppe 62 678 17 983 28.7 17 275 3894 22.5 3525 1822 51.7
Subtaiga 87 648 38 747 44.2 7558 2135 28.2 3168 1341 42.3
Taiga 31 710 17 088 53.9 1234 401 32.5 949 495 52.2
Sum 182 036 73 818 40.6 26 067 6430 24.7 7642 3658 47.9
the north of Uvs Nur and from 1800 m a.s.l. south of Lake
Baikal to 2700 m a.s.l. in the southern parts of the MA and
the KaM (Fig. 4a). At the southwestern side of the MA the
UT rises steeply from 2100 to 2600 m a.s.l. in a northeastern
direction. In the large mountain systems of the MA and KaM
the UT stays in a relative constant altitude between 2400 and
2600 m a.s.l. Northeast of KaM, the UT has an explicit longi-
tudinal direction and a UT depression of up to 800 m occurs
in the basin of the Selenga River. It was verified using the for-
est cover change data of Hansen et al. (2013) that the extraor-
dinarily low UT at 1800 m a.s.l. is not related to burnt forest.
In large burnt areas, as they occur for example in the northern
KaM, it could be expected that the actual treeline is shifted
and may not represent the natural limit. However, small for-
est patches that remain vital represent the potential forested
area. Relic forest stands provide valuable treeline values in
the modeling process and help to identify areas of human or
natural forest disturbance (Klinge et al., 2015; Miehe et al.,
2003).
Large areas below the LT exist in the great basins and
along the main river valleys, but they are also present in the
intermontane basins (Fig. 4b). While the subtaiga is border-
ing the meadow–steppe, the lower treeline seldom occurs in
the taiga and forests extend continuously into the valley bot-
tom. Nevertheless, at smaller intermontane basins and val-
leys a lower forest boundary is still detectable in the Mon-
golian taiga. Concordant with the intensifying aridity, the LT
is generally rising southward from 1000 to 2500 m a.s.l. in
eastern Mongolia. The strong rise of the LT at the north- and
southwestern slopes of the Altai Mountains is due to the con-
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M. Klinge et al.: Climate effects on vegetation vitality 1327
Figure 4. Treeline distribution maps of Mongolia: (a) upper treeline, (b) lower treeline, (c) vertical distance between upper and lower
treeline (a.a.t.= area above the upper treeline, a.b.t.= area beneath the lower treeline, f.b.l.t.= forest below the lower treeline, a.s.l.= above
sea level).
vective rainfall in the western ranges and the eastward inten-
sification of aridity in the MA.
The forested area of central Mongolia, which remains be-
tween the large areas beyond the treelines, is small from the
top-down view. However, the spatial expansion of forests has
a particular vertical component (Fig. 4c). The maximum al-
titudinal expansion of the forest belt of up to 1000 m vertical
distance occurs in the northwestern subtaiga and taiga. In the
mountain forest–steppe of the central MA, the western KaM
and in the mountains at Lake Khuvsgul, the altitudinal extent
of forests reduces below 400 m. In the southeastern part of
the MA, the UT and LT converge, the forest belt disappears,
and the mountain steppe directly passes over into the alpine
belt. Due to the extraordinarily low UT, thin forest belts also
occur in the area northeast of the KaM and in the southwest-
ern part of KeM. This can be related to human impact by
forest clearing in a more populated region.
Most precipitation is combined with westerlies, which
produce humid condition at the western side of the Altai
Mountains. In the rain shadow at the eastern side in the cen-
tral MA and in the Valley of the Great Lakes dry condi-
tions occur. This causes an extraordinarily high LT and the
small vertical extent of the forest belt in this region (Klinge
et al., 2003). The southern side of the KaM is still arid, but
its northern part and particularly the KeM receive more pre-
cipitation coming from the northeast along the Selenga river
depression. The tree species composition of the different bo-
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1328 M. Klinge et al.: Climate effects on vegetation vitality
Figure 5. Mean annual precipitation (MAP) and mean air temperature during the growing season (MGST) related to the mean growing
season NDVI of random points of different ecosystem units (values averaged for the investigation period 1999–2013). The straight lines
represent the linear regressions between climate parameters and NDVI. The distribution curves represent the frequency of random points
(%). Dashed lines represent forest values (f), continuous lines represent grassland values (s), yellow colors represent lower treeline values
(LT), green color represents upper treeline values (UT) and black colors represent the total ecological unit values (TE). Vertical grey dashed
lines indicate the deduced minimum values for tree growth.
real forest types and subunits is given in Fig. S3 in the Sup-
plement.
4.2 Specific climate parameters of boreal forest types
The zonal statistics for the climate parameters and MGS-
NDVI in different boreal forest types are given in Table 1
and the correlation matrix among MGS-NDVI, MAP, MGST
and MGSR is presented in Table 2. Figure 5 illustrates the
frequency distributions and linear regressions between these
parameters. The average MAP of the TE forests generally
rises from 266 mm yr−1 in the forest–steppe to 339 mm yr−1
in the subtaiga and 357 mm yr−1 in the taiga (Table 1). Due
to the expected hydrological limitation, the MAP at the LT is
lower than the respective average of the TE. This is also true
for all forest subunits at the UTs, where the MAP is about
30 mm yr−1 lower than the mean of the TE forests. This phe-
nomenon is due to the lower temperatures in higher moun-
tains, which reduce the evapotranspiration pressure. More-
over, the average MAP at the UT of the forest–steppe is
even lower than at the LT. However, sites with extremely low
MAP, below 190 mm yr−1 (Fig. 5a), receive additional soil
water supply. The grassland has predominantly lower aver-
age values of MAP than the forest of the corresponding eco-
logical unit. This general relation inverts at the UTs of the
subtaiga and taiga, while there are nearly equal values at the
LTs of the forest–steppe and taiga.
The average MGST of any TEs are very similar between
11.0 and 11.7 ◦C. However, the maximum of 16 ◦C in the
taiga is lower than in the forest–steppe and subtaiga where it
is up to 18 ◦C (Fig. 5b). While all average values of MGST
at the LTs equate to the TE values, the UTs show frequency
maxima of the MGST between 7.5 and 8.9 ◦C (Table 1). With
the exception of the UT in the subtaiga and taiga, in all sub-
units, the grassland has similar or slightly higher tempera-
tures as the forest of the same unit. The phenomenon of an
inversion of the general relation at the UT of the subtaiga
and taiga, which occurs simultaneously to the MAP, is due to
a change in grassland vegetation. Alpine shrub and meadow
vegetation are supported by the cold and more humid climate
and replace the mountain meadow steppe. The MGST of all
TEs and LTs shows similar frequency distributions with wide
value ranges and slightly higher values at the LTs (Fig. 5b).
However, the narrow and uniform frequency distributions of
all UTs indicate that the MGST is the main controlling pa-
rameter for forest distribution at the UT with an absolute
minimum value of 6 ◦C. A considerable portion of MGSTs
at the UTs occur between 10 and 13 ◦C, which is marginal in
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M. Klinge et al.: Climate effects on vegetation vitality 1329
the forest–steppe and subtaiga but becomes more important
in the taiga.
4.3 Relationship between climate and NDVI in
different types of boreal forest
The average values of MGS-NDVI of Table 1 show only
small variation between the TE and the treeline subunits. The
values rise from forest–steppe to taiga and are higher in the
forested area compared to the grassland of the same subunit.
The inverse relation between forest and grassland of the same
subunit, which occur for MAP and MGST at the UT of sub-
taiga and taiga, does not exist for the NDVI. The frequency
distributions of MSG-NDVI for the subunits in the forest–
steppe are nearly similar but clearly separated in the other
types of boreal forest (Fig. 5c). The UTs have the lowest and
the TEs have the highest NDVI values, which is generally
due to less favorable ecological site conditions at the forest
boundaries. In Table 2 most of the TEs show good correla-
tions between NDVI and the climate parameters (r = 0.44–
0.71), with an obvious exception of the MAP and the taiga.
Linear regressions of the terrain parameter MGSR are omit-
ted in Fig. 5, because MGSR is only weakly correlated to the
NDVI in all subunits.
In accordance with the correlation coefficients given in Ta-
ble 2, the linear regressions between MGS-NDVI, MAP and
MGST (Fig. 5) illustrate the relationship between the envi-
ronmental conditions and the types of boreal forest and their
respective treelines. The regression trends indicate a poten-
tial susceptibility of the ecological unit to climate changes.
There are mostly low correlations between MGS-NDVI and
MAP at most subunits. The only exceptions are the TE and
the LT of the forest–steppe and particular the LT in the for-
est subunit of the taiga. However, the gradients of linear re-
gression indicate potential relations between NDVI and MAP
for all LTs and particularly for all subunits in the forest–
steppe (Fig. 5a). Both the correlation values and the linear
regressions between MGS-NDVI and MGST (Fig. 5b) in-
dicate strong dependencies for all subunits; the UT of the
forest–steppe is an exception from this rule, since only weak
correlation was found. However, the steep gradient of the lin-
ear regressions at all UTs accentuates the temperature as the
main limiting parameter with increasing influence towards
the taiga. Presupposing that at least precipitation, tempera-
ture and solar radiation input control the vitality of the veg-
etation and the treeline distribution, but with different in-
tensities for every subunit, the multi-regression correlations
between NDVI and MAP, MGST and MGSR are generally
higher. However, the combination of the two climate param-
eters MAP and MGST shows the best correlations with the
NDVI, while the combination of all three parameters only
leads to a marginal improvement (Table 2).
The high positive correlations between MAP and MGST
and the high negative correlation between MGST and MGSR
in the TE and at the LT of the forest–steppe indicate a specific
environmental interrelation and potential autocorrelation ef-
fects between these two climate parameters in the semiarid
climate zone. This is due to the fact that in the forest–steppe
the increasing atmospheric vapor pressure deficit, which re-
sults from higher temperatures, must be compensated for by
more precipitation, on the one hand, and by less solar ra-
diation input, on the other hand. However, the weak corre-
lation between MAP and MGST in all subunits of the sub-
taiga and taiga indicates a climate-independent factor. This
is notably attributable to permafrost distribution as a supple-
mental ecological parameter, which is not included in our re-
gression models but modifies the soil hydrological regime.
Regression gradients between MAP and MGST of the TEs
change from the strong positive gradient in the forest–steppe
into a less precipitation-dependent gradient in the subtaiga
and then into a negative gradient in the taiga (Fig. 5d). The
rising MAP produces a more humid climate in the taiga and
reduces the dependency of vegetation vitality in the TE on
precipitation limits. Low temperatures as a zonal climatic pa-
rameter become a dominating limit for tree development to-
wards higher latitudes. Concordant with the transformation
of ecological conditions, the physiological constitution of in-
dividual trees and the tree species composition change from
drought-adapted individuals to low-temperature-adapted but
more drought-sensitive individuals.
5 Discussion
Trees grow and exist for several decades or centuries and
establish an autochthonous microclimate below the canopy;
thus, forests represent mean climatic conditions of a longer
period. In contrast, the vitality of annual or perennial grasses
and herbs of the steppes and meadows respond to interan-
nual variation in climate conditions, and the vegetation den-
sity represents small-scale periods (Bat-Oyun et al., 2016).
The treelines represent boundaries of forest distribution at the
ecological limits and it is hypothesized that changes in cli-
mate or environmental conditions at these boundaries lead to
an alteration of the treelines. On the one hand, forest expan-
sion needs a longer period of favorable conditions for seed
formation, as well as seedling and sapling establishments.
The requirements can be different from those of mature trees.
On the other hand, declines in the forest area can be induced
by short hazardous events like drought, freeze, calamities or
fire. Human impact on the forest area since prehistoric times
is another important influence on the actual treeline (Klinge
et al., 2015; Miehe et al., 2003). The treeline might be shifted
as the result of climate changes with a certain time lag. Al-
though the treeline may not directly correspond to the current
climatic envelope for forest, it represents at least the mini-
mum potential forest area. In view of the ecological relations,
the spatial accuracy of the actual database, and the regional
scale of the investigation, it is reasonable to calculate average
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1330 M. Klinge et al.: Climate effects on vegetation vitality
values for a longer period to receive representative parame-
ters.
The lower boundaries of the distribution curves (Fig. 5a)
and the standard deviation of MAP (Table 1) indicate that
an approximate MAP of 190 mm yr−1 can be regarded as
the minimum amount of direct rainfall for tree development
in Mongolia. Dulamsuren et al. (2010a) reported an annual
precipitation between 230 and 400 mm for larch trees (Larix
sibirica) at the lower forest boundary in northern and central
Mongolia. For the northern Tian Shan, Klinge et al. (2015)
state a minimum MAP of 250 mm for the distribution limit
of spruce trees (Picea schrenkiana). Sites with lower MAP
values, occurring in parts of the forest–steppe, are favored by
additional soil water supply from upslope areas or melting
permafrost ice, which can support tree growth under these
dry conditions where rainfall is insufficient (Dulamsuren et
al., 2014). This explains why Dulamsuren et al. (2014) found
coniferous forests in regions with an annual precipitation of
around 120 mm in the MA. The annual amount of precip-
itation is highly varying in the steppes region and the per-
mafrost layer can bridge drought years by accumulating soil
water in the soil ice reservoir during moist years (Sugimoto et
al., 2002). The vegetation vitality as expressed by the NDVI
is generally lower in the forest–steppe than in the subtaiga
and the taiga. This fact reflects the extreme ecological limita-
tions of forests in the forest–steppe ecotone. Recently emerg-
ing margins of dead trees around the forest islands are ap-
parently induced by the trend in increasing temperature, in-
sufficient precipitation and missing soil water storage from
disappearing discontinuous permafrost.
The proportion of open grassland area to forest islands
in the southern forest–steppe changes towards northern lat-
itudes with the expansion of forest area. In the large val-
leys of the taiga and subtaiga in northern Mongolia, where
trees are apparently less limited by water shortage, a LT does
not exist. However, inside the dense woodland of the taiga,
the grassland occurs in intramontane basins (Dulamsuren et
al., 2005; Gunin et al., 1999; Hilbig, 1995). The rain shadow
of the surrounding mountains keeps precipitation extraordi-
narily low and thus a LT is present. The high correlation of
the detected LTs to MAP in the taiga suggests a primarily
drought-induced and not anthropogenic position of the LT.
This finding points to a high vulnerability of the trees at the
taiga’s LT to climate warming. This conclusion is supported
by ecophysiological, dendrochronological and palynological
studies from the LT of the mountain taiga of western Khentei
(Dulamsuren et al., 2009a, 2010b; Schlütz et al., 2008).
There is a close correlation between NDVI and MGST at
the UT in the taiga and the subtaiga (Table 2). At the UT of
the forest–steppe, precipitation is an additional limiting fac-
tor at higher elevations. While a MGST of 6 ◦C tends to be
the general minimum temperature for tree growth in the study
area, at some places at the UT of the subtaiga, trees occur at
MGST as low as 4 ◦C (Fig. 5b). Here, the low MGST is as-
sociated with high MAP of roughly 350 mm yr−1 (Fig. 5d).
In the low temperature range between 6 and 8 ◦C, the linear
regressions between MAP and MGST at the UT show that
different MAP conditions exist simultaneously for the dif-
ferent types of boreal forest (Fig. 5d). In the forest–steppe at
6 ◦C MGST, MAP is approximately 200 mm yr−1, whereas it
amounts to 320 mm yr−1 in the subtaiga and 400 mm yr−1 in
the taiga. This combination between both low precipitation
and temperature is extreme at the LT of the forest–steppe. In
the range of 6–8 ◦C MGST, MAP tends to be below the tree
growth minimum of 190 mm yr−1, which emphasizes again
the impact of permafrost, as the permafrost is also associated
with low temperatures.
Differing frequency distributions show that the NDVI at
the UT and LT is generally lower than in the TEs of the taiga
and the subtaiga, except for the forest–steppe (Fig. 5c). The
low NDVI values indicate low vegetation vitality. This sug-
gests that forests composing the treelines in the taiga and the
subtaiga and the complete forest–steppe ecotone are exposed
to physiological stress. Forests in the taiga receive generally
more precipitation and thus have developed higher stand den-
sities and are also home to more water-demanding dark taiga
tree species like Abies sibirica and Pinus sibirica (Dulam-
suren, 2004; Dulamsuren et al., 2010a). Reports of increased
drought stress, reduced stemwood formation, reduced for-
est regeneration and increased tree mortality, especially in
the Larix sibirica-dominated forest–steppe ecotones of inner
Asia, support this conclusion (Dulamsuren et al., 2010a, b,
2013; Liu et al., 2013).
Recent climate change scenarios predict a temperature in-
crease in Mongolia of up to 5 ◦C until the end of the cen-
tury (Ministry of the Environment Japan, 2015), while pro-
jections for precipitation trends show spatial differences be-
tween decreasing precipitation amounts in the north and an
increase in the southern parts (Sato et al., 2007). In general,
climate modeling also suggests a future increase of sum-
mer droughts and a decrease in soil moisture (Sato et al.,
2007). Higher temperatures yield higher evapotranspiration
and hence to less relative humidity, even if a slight increase of
precipitation simultaneously occurs. The consequences of in-
creasing aridity and an increasing atmospheric vapor deficit
are a reduction in tree vitality, which finally might lead to
widely increased tree mortality and forest area loss in the
forest–steppe, subtaiga and taiga. In addition, this trend could
promote of Pinus sylvestris in parts of the Larix sibirica-
dominated forest–steppe (Dulamsuren et al., 2009b).
6 Conclusion
Using high-resolution remote sensing and climatic data al-
lows the characterization of the climatic envelope of the three
types of boreal forest in Mongolia as well as identification
of hotspots of additional natural or anthropogenic factors. It
was shown that the NDVI distribution between forest and
grassland of the same ecological subunits differs, which is
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M. Klinge et al.: Climate effects on vegetation vitality 1331
mainly controlled by different photosynthetic activity, vege-
tation density and seasonal growth. However, with respect to
the small-scale variation of the vegetation and the resolution
of the NDVI data, a spatial overlap producing mixed data
values cannot be totally avoided. The ecological relationship
between climatic parameters and forest or treeline distribu-
tion was verified by the NDVI as an indicator for vegetation
vitality. It can be assumed that local site conditions like per-
mafrost distribution, soil parameters and hydrology may also
play an important role in vegetation vitality. The statistical
results on geoecological relations presented in this work are
suited to be used for modeling of potential past, current and
future forest areas.
The observed recent increase of forest greening indices
from remote sensing data and stemwood increment found
in several places by Poulter et al. (2013) is combined with
increasing summer temperature but also promoted by ad-
ditional soil water supply from melting permafrost. How-
ever, disappearing permafrost and increasing drought stress,
as projected by climate modeling, may cause dramatic loss
of forest cover in future. The widespread occurrence of dead
tree margins around forest islands shows that this trend is al-
ready ongoing as the result of climate warming. Trees suffer-
ing from drought stress are more vulnerable to insect calami-
ties. The impact of forest fires also increases under dryer con-
ditions. For all LTs and for the TE of the forest–steppe, in-
creasing temperatures are likely to result in increased tree
mortality, the reduction of forested area and shifting of the
LTs.
Research on NDVI trends and climate development in
Mongolia is often lacking detailed spatial separation of the
different ecological units. Every vegetation unit has its own
temporally defined ecological environment, which produces
different spatial and temporal gradients in remote sensing-
derived vegetation indices. Changes in climate conditions
will lead to more or less vitality in the limited physiologi-
cal range of the individual trees, which are adapted to recent
local climate and soil conditions. Forest dynamics and forest
development from the biological point of view mean change
in the vegetation structure and biodiversity, which cannot be
exclusively modeled by greening indices (Busing and Mailly,
2004; Miao et al., 2015; Poulter et al., 2013). It was shown
that the creation of detailed landscape stratification and of
small-scale ecological classifications could assist in incorpo-
rating spatial and temporal transitions of vegetation units in
environmental modeling.
Data availability. The GIS data for the upper and lower treeline as
well as the areas above and below the treeline are provided in the
Supplement of this article.
The Supplement related to this article is available online
at https://doi.org/10.5194/bg-15-1319-2018-supplement.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. The authors would like to thank the US Ge-
ological Survey and VITO, Belgium, for making the satellite data
freely available for scientific research. We acknowledge support by
the Open Access Publication Funds of Göttingen University. We
very specially thank Jan Degener for his scientific support in data
processing and intensive discussion. We also thank Udo Schickhoff
and an anonymous referee for the valuable comments which im-
proved the paper.
Funded by the Deutsche Forschungsgemeinschaft (DFG) –
Projektnummern FR 877/32 and DU 1145/4-1.
This open-access publication was funded
by the University of Göttingen.
Edited by: Jochen Schöngart
Reviewed by: Udo Schickhoff and one anonymous referee
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