Forestry
An International Journal of Forest Research
Forestry 2013; 86, 429 – 439, doi:10.1093/forestry/cpt014
Advance Access publication 10 June 2013
Productivity and carbon sequestration of Populus euphratica
at the Amu River, Turkmenistan
Allan Buras1*, Niels Thevs1, Stefan Zerbe2 and Martin Wilmking1
1
Institute of Botany and Landscape Ecology, University of Greifswald, Grimmer Str. 88, D-17487 Greifswald, Germany
2
Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università 5, I-39100 Bolzano, Italy
*Corresponding author. Tel.: +493834864188; Fax: +493834864114; E-mail: [email protected]
Received 25 September 2012
Deserts belong to the least productive terrestrial ecosystems, but along rivers, they may exhibit a high productivity.
In central Asia, Populus euphratica Oliv. is the dominant tree species of the riparian ‘Tugai’ vegetation. In terms of
climate change mitigation, ‘Tugai’ restoration may be traded for on the international carbon market. However,
detailed knowledge of ‘Tugai’ productivity is lacking. Within this study, we modelled the productivity of P. euphratica
based on tree-ring data (r 2 ¼ 0.73, P ,, 0.001) and applied the derived model to estimate the stand productivity of
this species within the Kabakly nature reserve on the Amu River, Turkmenistan. Productivity estimates ranged from
0.3 to 3.0 t ha21 year21 and expressed the same magnitude as the data mentioned previously in the literature.
Forest productivity appeared to be negatively correlated with distance from the river, which was consistent with
Quickbird remote sensing data. Quickbird net differenced vegetation index (NDVI) was strongly correlated with
Landsat NDVI (r ¼ 0.91, P , 0.001), indicating the general potential to upscale net primary productivity estimates
for P. euphratica. Tree ring series expressed no synchrony, for which possible explanations are discussed. Under ideal
conditions P. euphratica sequesters CO2 and thus may be considered a suitable tree species for carbon trade
mechanisms and climate change mitigation.
Introduction
Among all terrestrial biomes, desert ecosystems are among the
least productive (e.g. Cao and Woodward, 1998; Townsend et al.,
2008). This comparably low productivity is mainly a consequence
of water scarcity (due to low precipitation) and high potential
evapo-transpiration (due to comparably high solar angles; e.g.
Shmida et al., 1986;Walter and Breckle, 1991). However, highly productive ecosystems, such as plantations close to oases or natural
vegetation along rivers, can exist, where groundwater is available
in sufficient quantities.
In central Asian desert ecosystems, such as the Karakum,
Kysylkum and Taklamakan deserts, the locally most productive
ecosystems are found along the rivers, in particular the Amu
River, the Syr
River and the Tarim River (e.g. Walter and Breckle, 1994; Thevs,
2005). Historically, these rivers were bordered by the so-called
Tugai vegetation, which mainly consists of Populus euphratica
Oliv., Phragmites australis Trin. ex Steud. and Tamarix L. spec., but
in the past century, large areas of Tugai vegetation have been converted into agricultural land mainly for cotton cultivation (Lavrenko, 1956; Zhao et al., 2001; Ogar, 2003; Thevs et al. 2007).
The agricultural practices (in particular intensive irrigation) in
these areas have lead to severe soil salinization, resulting in significant decreases of agricultural yields – a process that is likely to intensify in the next decades (Hoppe, 1992; Ibrakhimov et al., 2007,
Forkutsa et al., 2009). In terms of climate change mitigation and
combating desertification, it may become economically feasible
to restore moderately salinized abandoned fields with the
common species of Tugai vegetation which are naturally salttolerant. As they would sequester CO2 at intermediate time
scales, these restored areas could be traded for on the international carbon market, thus providing financial benefits for the local
land-owners whose land has become worthless for farming (e.g.
UNFCCC, 2002a, b).
To be traded on the carbon market, it is necessary to know the
potential CO2 sequestration, and hence the net primary productivity (NPP) of the reforested/replanted species. There have been few
investigations of Tugai forest NPP and these are either on a rather
local basis (Wang et al., 1996; Gries et al., 2003) or without detailed
information of the contributions of individual species (Jiang et al.,
1999). The NPP of P. australis has been investigated (Thevs et al.,
2007), but there is little knowledge available for P. euphratica –
the dominant tree species within Tugai forests (Wang et al., 1996).
Estimates on local carbon stocks and productivity give a first
insight into the feasibility of applying carbon trade mechanisms
to P. euphratica. However, to assess the total re-sequestration potential related to P. euphratica forests in central Asia, large-scale
investigations are needed. One way to estimate carbon stock and
productivity of plant species and ecosystems on a large scale is
remote sensing analyses. Local assessments are necessary to
obtain ground-truth data, which in turn enables modelling
carbon stocks and productivity using remote sensing information.
Application of the resulting models to a set of calibrated satellite
# Institute of Chartered Foresters, 2013. All rights reserved. For Permissions, please e-mail: [email protected].
429
Forestry
images covering the region of interest then allows large-scale estimates (Lillesand et al., 2004;Eisfelder et al., 2010,2012,Thevs et al.,
2012a).
The objectives of our study were to:
- understand the main drivers of total NPP (i.e. annual dry
biomass accumulation per hectare) in P. euphratica-dominated
forests;
- generate models to approximate total NPP of stands in the
Kabakly nature reserve on the Amu River, Turkmenistan;
- apply best-fit models to estimate the average NPP and thus
CO2-uptake of Tugai forests along the transition zone from the
Amu River to the Karakum Desert;
- assess the potential for upscaling the results by means of
remote sensing.
Figure 1 Schematic map of the study area showing the forest transition
from the border of the Amu Darya River (site A) towards the Karakum
Desert (site D). The Tugai forest was divided into four classes, based on
forest density and dominant vegetation resulting in sites A, B, C and D. A
darker shading of forest classes in the schematic map corresponds with a
higher forest density.
Material and methods
Study species and study site
Populus euphratica belongs to the family Salicaceae and differs from other
poplar species in its high temperature and salinity tolerance (Wang et al.,
1996). It is considered a phreatophyte, i.e. it is dependent on groundwater
resources to meet its water demand. Within the central Asian deserts – its
main distributional area – it is therefore restricted to the riparian ecosystems, where it is a keystone species in the so-called Tugai forest (e.g.
Thevs et al., 2012b). The limiting factors for the growth of P. euphratica
are groundwater shortage and relatively high soil salt concentrations
(.2%). When growing close to these limits, heart rot is common from the
age of 40 and upwards (Wang et al., 1996; Gries et al., 2003).
Field work was carried out within the Kabakly nature reserve on the Amu
River middle reaches, in Turkmenistan (Lat: 39.848N; Lon: 62.528E). With the
establishment of the nature reserve in 1982, foraging by domesticated
animals and firewood-logging – the main anthropogenic disturbances of
this site – were banned. Thus, the forests within the nature reserve can
be described as ongoing secondary succession. We chose this particular
site for our investigations, as it represents relatively undisturbed conditions,
compared with non-protected areas within Turkmenistan.
Close by the river, the forest is relatively dense (960 trees ha21, see
Thevs et al., 2012b) and mainly consists of P.euphratica with P. australis as
understorey. Towards the desert, forest density decreases (410 trees ha21,
Thevs et al., 2012b) with larger proportions of P. australis and the local presence of Tamarix (Figure 1).
The main water supply for the vegetation in Kabakly is the Amu River,
which has summer floods from May to August and a large intra- and interannual runoff variation in flow. Major precipitation-events occur at the
onset of the growing season (March/April) and are a less important plant
water source than river runoff (Figure 2).
General study design
Within the nature reserve, we investigated a roughly 1-km-long transect,
representing the forest transition zone from the river bank towards the
desert, during March 2010. The transition zone transect was separated
into four equidistant sites (300 m distance between the sites, respectively): site ‘A’ close by the Amu River, sites ‘B’ and ‘C’ in rather dense Tugai forest
with increasing forest structure heterogeneity (including reed beds) and site
‘D’ close by the Karakum desert, where the forest becomes patchy with
Tamarix shrubs as understory (Figure 1). At each site, three equidistant subsites (200 m distance between the sub-sites, parallel to the river) were
located. However, of the resulting twelve sub-sites, only nine were accessible for field sampling due to dense reed-beds, which hampered the access
430
Figure 2 Average monthly runoff of the Amu Darya at Dargan-Ata (100 km
downstream of the study site) based on runoff measurements between
1988 and 2007 (solid line, dashed lines indicate min and max values for
the time period) and average monthly precipitation (bars; period 1961–
1990) in Repetek (200 km southeast of the study site). Data provided by
the Ministry of Water Economy of Turkmenistan.
to three sub-sites among sites ‘A’ and ‘B’. Although site ‘A’ thus is represented by only one sub-site instead of three, forest structure heterogeneity
is rather low at site ‘A’.
At each of the accessible sub-sites, 20 P. euphratica individuals were
chosen to represent stand characteristics using the Point Centered
Quarter method (PCQ, Mueller-Dombois and Ellenberg, 1974; see also
Thevs et al., 2012b for further details on the sampling design). In addition,
tree-cores were extracted from the four trees at the central cross of each of
the nine sub-sites using an increment borer. Altogether, 170 P. euphratica
trees were measured (transect data) and tree cores were extracted from
36 trees (dendro data). From the dendro data, we derived NPP and age
models which were applied to the transect data to represent the variability
of forest NPP.
Transect data
For each investigated P. euphratica tree, individual tree height (H), diameter
at breast height (DBH) and canopy area (CAN) were recorded. The canopy
Productivity and carbon sequestration
area was derived from two perpendicular measurements of canopy diameter. Canopy diameters were determined from the ground using a straightedge that was held vertically to define the outer margin of the canopy in the
respective directions. In addition, the distance to the nearest neighbour was
measured. The average distance between individuals was used to estimate
the number of trees per hectare (N, Clark and Evans, 1954). Stand biomass
and productivity was estimated by multiplying N by the average single tree
biomass and NPP (e.g. Thevs et al., 2012b).
Total biomass of all investigated trees was calculated using allometric
formulas for P. euphratica (Chen and Li, 1984), which use H and DBH to
predict total tree biomass (B09, sum of oven-dry above- and belowground
biomass):
log B09 = log a + b × log DBH2 × H
(1)
B ¼ biomass, a and b vary depending on which partition of the biomass is
calculated (stem, trunk, leaves, root or total biomass, for further details
see Chen and Li, 1984; Thevs et al., 2012b).
GPS coordinates (UTM, WGS 84) were recorded for each individual tree to
calculate the river distance (RD) and analyse its potential explanatory
power.
Dendro data
Of the sampled 36 tree cores, three were excluded due to heart rot. Heart rot
made an age-determination impossible and likely also reflected the low vitality of these trees (Wang et al., 1996). Tree cores of the remaining 33 trees
were sanded until rings became visible. Tree ages were determined by
counting tree-rings. In four cases the pith was not hit by the corer, but
the number of missing rings was estimated from ring-curvature. For these
individuals, 2 years were added to tree age. Ring-widths (as a proxy of
annual tree growth – thus NPP) were measured using a LinTAB 5 measuring
stage (RINNTECH, Heidelberg, Germany) and the measurements were processed with TsapWin Professional (Version 0.59, RINNTECH, Heidelberg,
Germany).
To align the tree ring width series among sample trees, we attempted to
cross-date our measurements. Cross-dating analyses revealed that many
ring width series either had single missing or false rings, as indicated by
somewhat synchronous sequences followed by sequences with a temporal
lag of 1 year. However, the synchronous periods varied among trees, this
made it difficult to clearly identify which rings were false or missing. Due
to the low synchronicity of the ring width series, we were able neither to generate a master-chronology nor to carry out traditional response function
analyses (e.g. Fritts, 1977). To generate NPP models based on the dendro
data, we therefore decided to base our analyses on the ring widths of
2009, the final growth year before sampling. To validate this approach,
we further investigated NPP models based on average ring widths over
2005– 2009 (5 year average for each tree) as this averaging procedure
likely minimizes errors due to false or missing rings.
Average Gleichlaeufigkeit (glk) and mean sensitivity are standard dendrochronological parameters and were computed for all trees. glk is a
measure of the synchronicity among (at least) two time series regarding
their relative temporal changes (e.g. patterns of growth increments from
the tree cores). If the time series vary synchronously, glk will be close to 1,
if they vary anti-synchronously glk will be close to 0 and if they vary asynchronously, glk will be close to 0.5. Thus, glk can be regarded as a
measure whether time series (re-)act synchronously or not. In dendrochronology, high glk values (generally above 0.65 but dependent on the
tree species) justify the generation of so-called master chronologies, i.e.
averaging the time series with high glk to one time series. Mean sensitivity
is a measure of the inter-annual variability of tree growth. If the interannual tree growth varies widely, the tree is considered to be sensitive
and mean sensitivity values will be high (maximum value of 2). Trees with
more or less the same ring width over their lifespan are considered
complacent and express low mean sensitivity values (minimum value of
zero). Sensitive trees are considered to react more to environmental conditions (e.g. Fritts, 1977). Here, we used mean sensitivity to assess whether
the investigated trees showed comparable growth variability among sites.
A comparison of ring width sums with measured DBH of the trees
revealed that the cores were shrunk due to the drying process. Therefore,
a linear model was calculated (r 2 ¼ 0.96, P , 0.001) and applied to ring
width values in order to represent the true DBH increments of each year
(△ DBH, i.e. corrected ring width ×2). △ DBH of the outermost rings (△
DBH09) was used for the estimation of total tree NPP (i.e. annual above-and
belowground dry biomass accumulation per hectare). In addition, average
ring width was calculated for each tree over its whole lifespan (△ DBHav). As
height increments of the last year (△ H09) were unknown, average △ Hav was
calculated by dividing tree height by tree age. Then, △ Hav was multiplied
with the tree-specific ratio of △ DBH09/△ DBHav to estimate △ H09. Although
this procedure is a rather rough approximation of height increments, it is
legitimated by data from Wang et al. (1996) that showed a significant correlation (r ¼ 0.43, P ¼ 0.02) between DBH and height increments.
As mentioned above, we decided to analyse the average NPP from 2005
to 2009 and test whether it produced results comparable with the NPP estimates of 2009. For this, we computed average △ DBH for the past 5 years
(i.e. 2005 –2009 △ DBH05 – 09) and from this we derived △ H05 – 09 in the
same manner as for △ H09.
To calculate NPP of the cored trees, △ DBH09 and △ H09 were subtracted
from measured DBH and H to achieve DBH08 and H08. Then, biomass was
calculated for 2008 (B08) by applying DBH08 and H08 to formula one
(chapter 2.2.1). The difference between B09 and B08 was then defined as
biomass increment of 2009 (i.e. NPP09). In the same manner, we derived
NPP09_five from DBH05 – 09 and △ H05 – 09 to obtain an NPP estimate for
2009, but based on average DBH and H increments of the past 5 years.
Average tree C-contents were obtained from small wood samples that
were cut from the tree cores using a razor blade. From each core, heart wood
and sap wood samples were mixed to integrate carbon contents over wood
of differing age. Finally, the mixed wood-samples were analysed for
C-contents in a C-N-analyser (Vario el III, Elementar Analyser, Hanau,
Germany). To estimate annual carbon sequestration, average C-contents
of the tree cores were multiplied by the modelled NPP09.
Statistical analyses
The transect data were grouped according to sub-sites and then tested for
normal distribution using the Shapiro test. Due to the comparably lower
sample size, the dendro data were only pooled at the site scale. To determine significant differences among sub-sites and sites, Kruskal– Wallis multisite comparison (accounting for non-normally distributed data) was
carried out (pgirmess package, R foundation for Statistical Computing,
Vienna, Austria). △ DBH09, △ H09 and NPP09 were tested for significant differences compared with DBH05 – 09, △ H05-09 and NPP09_five using the Wilcoxon
test. Furthermore, relationships between all measured and calculated variables were investigated using Spearman’s rank correlation.
In order to predict age and NPP for transect trees, a suite of candidate
models was developed from the dendro data. However, linear models do
not represent the real nature of relations among NPP and plant stature
for intercept =0. Therefore, the intercept (which for all NPP models was
close to 0) was set to 0 if justified according to the 95% confidence intervals
of bootstrapping with 10,000 iterations. For the age-models however, the
omission of the intercept was not justified. Best-fit models were determined
by calculating Akaike’s Information Criterion (AIC; Akaike, 1978) and subsequently validated through random sampling and splitting of the data
(50/50) with a remodelling of the subsample data in 100 iterations. We
did so to support the reliability in the models, accounting for the comparably low representation of site A. Subsample models were applied to the
remaining samples and r2 was averaged among both. Average r2 and standard deviation of these 100 iterations were estimated to reflect the
431
Forestry
robustness of models. Finally, the validated best-fit models were applied to
the transect data in order to estimate the productivity and age structure of
P. euphratica within the study site. All statistical analyses were carried out
using ‘R’ (Version 2.12, R Foundation for Statistical Computing, Vienna,
Austria).
Remote sensing
In addition to the analyses mentioned above, we investigated remote
sensing data covering the study site. We did this for three different reasons:
(1) To verify the representativeness of the field data set regarding the stand
structure close by the Amu River by an independent approach (NPP representation through net differenced vegetation index (NDVI) instead
of ring widths);
(2) To test the relationship among RD and forest NPP found in the field data;
(3) To assess the feasibility of upscaling the results derived within this study
using remote sensing data, which would provide a basis for further,
larger-scaled investigations dealing with Tugai forests.
A Quickbird (QB) satellite image (recorded the 14 June 2009; resolution:
0.6×0.6 m) covering the area of the study site was analysed. NDVI was
derived from near-infrared (NIR) and red (RED) channels [NDVI ¼
(NIR2RED)/(NIR + RED)]. NDVI values (ranging from 21 to +1) were
added to 1 (achieving values between 0 and 2) and multiplied by 1024 to
achieve 11 bit compatible raster values (from 0 to 2048), which was a necessary procedure for the further GIS processing of the data.
NDVI is known to be a good proxy of vegetation biomass and NPP (Jiang
et al., 1999; Lillesand et al., 2004). As vigorous vegetation has a higher reflectance in the NIR compared with the RED spectrum, it generally has positive NDVI values (i.e. values above 1024 if transformed to 11 bit compatible
values). In contrast, non-vegetated terrestrial surfaces have an equal reflectance in the NIR and RED spectra, and therefore, NDVI values of such
surfaces will be close to 0 (1024 for 11 bit compatible files). Negative
values (below 1024 for 11 bit files) represent water surfaces, as water has
a higher reflectance in the RED compared with the NIR spectrum (Lillesand
et al., 2004).
To reflect the observed decrease in forest productivity towards the
desert and test the representativeness of site ‘A’, seven training areas
were analysed for NDVI (see also Figure 7, Results section). The training
areas were defined to span 1000 ×2000 pixels (i.e. an area covering
600×1200 m, northing and easting, respectively). One of these transect
areas corresponds with the same area of the investigated forest transect
(chapter 2.2.1). The other six NDVI transects (referred to as control transects) were analysed, to investigate the general variability of NPP gradients
towards the desert.
NDVI transects were generated by averaging NDVI-values over each
Pixel-column within each transect (Figure 3). NDVI values for the 1000
pixels with the same easting were averaged and this was done for all of
the 2000 columns along the East– West gradient. This generalization was
necessary, as single-row NDVI-transects had high variation and reflected
micro-scale forest heterogeneity. The NDVI results were tested for Spearman’s rank correlation with RD. Furthermore, a linear regression slope
was obtained for NDVI vs RD for each transect.
The slopes of the six NDVI control transects were compared with the
slope for the NDVI transect corresponding with the study site, using Wilcoxon test. In addition, the mean inter-series correlation coefficient was
calculated to compare NDVI gradients. To further assess the variability of
Tugai forest productivity close to the Amu River, we calculated mean
NDVI values for each transect within 300 m of the river bank. The averaged
values of the six NDVI control transects were compared with the respective
part of the field-measured transect using Wilcoxon test.
Finally, to assess the feasibility of upscaling based on LANDSAT TM
images (LS), QB NDVI was averaged for 50 ×50 pixels (30×30 m), i.e. the
area of one LS pixel. A variety of powers of NDVI(1-10) were tested for
432
Figure 3 Schematic example of NDVI transect generation only covering
19×14 pixels to simplify the explanation. The brightness of the pixels
corresponds with the respective NDVI values (the brighter the higher). For
each column (14 different NDVI values with the same Easting but 14
different Northing values) the average NDVI was computed. For further
explanations, see text.
correlation with stand biomass (derived from Thevs et al., 2012b) and
stand NPP estimates (derived within this study) of the corresponding subsites. The powers of NDVI were varied, as the scatterplot between NDVI
and NPP or biomass intended a slight curvature in the relationship. To
assess the representativeness of this approach, we further calculated
average QB NDVI-values of 800 areas that correspond with pixels from an
LS scene (Path: 158, Row: 32) taken on 1 July 2009. These average NDVI
values were tested for correlation with the NDVI values from the LS
scene. All remote sensing analysis was carried out using Quantum-GIS
(Version 1.6.0, Open Source Geospatial Foundation), GRASS-GIS (Version
6.4.0, Open Source Geospatial Foundation) and the rgdal package in ‘R’
(Version 2.12, R Foundation for Statistical Computing, Vienna, Austria).
Results
NPP models
Average glk among all trees was 0.52 with no significant differences among sites (i.e. all sites had an average glk of roughly
0.5). Eight per cent of glk-values were .0.7, but they appeared to
be randomly distributed among sites, and a visual comparison of
those series revealed that, despite the comparably high glk, trees
exhibited different absolute inter-annual variations. While tree
height significantly decreased from site A (close by the Amu
River) towards site D (close by the Karakum Desert), DBH and tree
biomass showed no clear trend. Tree ages, however, increased
towards site D but not significantly. Average ring-widths and
mean sensitivities decreased significantly from site A towards
site D (Figure 4).
Productivity and carbon sequestration
Figure 4 Stature-related tree features, mean ring-width and mean sensitivity of the ‘dendro dataset’ separated among sites. Different letters at the top of
each panel refer to statistically different groups according to Kruskal multi-site comparison (P , 0.05).
All growth-related variables (i.e. △ DBH09, △ DBH05 – 09, △ DBHav,
△ H09, △ H05 – 09, △ Hav, NPP09, NPP09_five and NPP09_av decreased
significantly from site A to site D (Figure 5). △ DBH09, △ H09 and
NPP09 did not differ significantly compared with △ DBH05 – 09,
△ H05 – 09 and NPP09_five, respectively (P . 0.05 for all comparisons).
The average carbon contents of the measured wood samples were
47.3% with a standard error of 0.2%.
DBH significantly increased with tree age, tree biomass, tree
height and both NPP estimates (Table 1). Tree height significantly
increased with tree age, tree biomass and both NPP estimates
(NPP09 and NPP09_five) and decreased with increasing RDs. Tree
age significantly increased with tree biomass and both NPP
estimates. Both NPP estimates significantly increased with tree
biomass, and decreased with RD. The two NPP variables were
highly correlated with each other.
NPP models (Table 2) either for 5-year averages (NPP09_five) or
2009 (NPP09) values had significant increases in explained variance
if RD was included into the models. Best-fit models (according to
AIC) had an explained variance of 0.70 and 0.73, respectively. For
NPP09 models, the impact of RD on NPP appeared to be lower
than for NPP09_five models. As for NPP models, the inclusion of RD
into age models increased explained variance. The L3- and A2models had the highest r 2 and lowest AIC (see Table 2) and were,
therefore, used to estimate NPP09, age structure and CO2-uptake.
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Forestry
Figure 5 Growth-related tree features of the ‘dendro dataset’ separated among sites. Different letters at the top of each panel refer to statistically different
groups according to Kruskal multi-site comparison (P , 0.05).
Table 1 Spearman’s rank correlation among selected variables from the
dendro data
H
age
B09
RD
NPP09_five
NPP09
DBH
H
Age
B09
0.64***
0.85***
0.96***
0.16
0.66***
0.64***
0.5**
0.8***
20.51**
0.81***
0.81***
0.84***
0.29
0.44**
0.46***
20.02
0.76***
0.75***
RD
NPP09_five
Remote sensing analyses
20.36*
20.44**
0.92***
DBH ¼ diameter at breast height, H ¼ tree height, B09 ¼ tree biomass in
2009, RD ¼ river distance, NPP09_five ¼ NPP in 2009 derived from average
DBH increments in the period 2005– 2009, NPP09 ¼ NPP in 2009 derived
from DBH increments in 2009.
*P , 0.05, **P , 0.01, ***P , 0.001.
Model application
Forest density decreased from site ‘A’ towards site ‘D’, while modelled tree ages increased (though not significantly) in the same direction. The modelled average tree NPP09 showed an overall
decrease from site A towards site D but with irregular variations
434
in between. Sites B and D expressed the lowest tree NPP09, while
site A showed the maximum absolute values. Both tree biomass
and tree NPP09 significantly increased with canopy area (r ¼ 0.48,
P , 0.001; r ¼ 0.28, P , 0.001, respectively). Stand NPP09 (combining forest density and average tree NPP09) decreased towards the
desert (Table 3 and Figure 6).
The averaged transect NDVI-values decreased significantly with
increasing RD (average correlation coefficient of all transects ¼
20.94+0.02, P ,, 0.001 in all cases; Figure 7). Regression
slopes (mean+SD: 0.201+0.03) of the six NDVI control transects
did not differ significantly from that representing the investigated
forest transect (0.198, P ¼ 0.54). The mean inter-series correlation
among the seven NDVI transects was 0.95+0.03. Finally, the NDVI
values within 300 m distance to the river did not differ significantly
among control transects (1477+19) and sampled transect (1478,
P ¼ 0.84). The sub-site-based NDVI values on the LANDSAT TM
scale showed a significant correlation (compared with other
powers of NDVI) with sub-site stand biomass and stand NPP. A
scatterplot of the variables intended a non-linear relationship.
Therefore, several powers of NDVI values were tested for correlation with stand biomass and stand NPP. Highest correlations
Productivity and carbon sequestration
Table 2 Model parameter and evaluation analyses of the top three models for NPP based on either 5-year averaged NPP (models F1– F3) or last year
NPP (models L1– L3), and for age models (models A1 and A2)
△
AIC
Model
Explanatory
Intercept
Slope
R2
F1
F2
F3
L1
L2
L3
B09
DBH*H
(DBH*H)/log(RD)3
B09
DBH*H
(DBH*H)/
log(RD)1.5
DBH
DBH*log(RD)
0a
0a
0a
0a
0a
0a
3.638e202
2.214e204
5.594e202
4.267e202
2.517e204
4.137e203
0.50+0.03
0.58+0.04
0.70+0.03
0.61+0.03
0.67+0.02
0.73+0.02
93.54
87.84
75.77
87.59
81.96
75.18
17.77
12.07
0
12.41
6.78
0
7.2
7.6
1.511
0.224
0.77+0.02
0.83+0.01
182.19
165.43
16.76
0
A1
A2
AIC
a
Intercept of zero justified according to bootstrapping with 100 iterations.
B09 ¼ tree biomass 2009, DBH ¼ diameter at breast height, H ¼ tree height, RD ¼ river distance.
Table 3 Stand density (N), age, average tree NPP, stand NPP and stand CO2 uptake for the transect sub-sites. Superscript letters indicate significant
differences (P , 0.05) according to Kruskal multi-site comparison
Sub-site
N [trees ha21]
Age [years] Mean+SE
NPP [kg a21] Mean+SE
Stand NPP [t ha21 a21]
Stand CO2 uptake [t ha21 a21]
AI
BII
BIII
CI
CII
CIII
DI
DII
DIII
718
571
860
511
511
511
217
437
352
25+4a
19+3ab
21+2a
24+2a
26+4a
29+1ac
33+13ac
23+3a
22+3a
4.2+0.5a
1.0+0.0b
1.2+0.0b
1.7+0.1ab
1.4+0.0ab
2.4+0.0a
2.2+0.3a
0.8+0.0b
0.8+0.0b
3.0
0.6
1.0
0.8
0.7
1.2
0.5
0.3
0.3
5.2
1.0
1.7
1.4
1.2
2.1
0.9
0.5
0.5
were found for the seventh power of NDVI values (r ¼ 0.95 and 0.98
respectively, P ≪ 0.001; Figure 6) and the respective scatterplot
revealed a linear relationship (not shown). Average NDVI values
calculated for 800 LS pixel equivalent areas were strongly correlated with the corresponding NDVI values of the LS scene (r ¼
0.91, P , 0.001, Figure 8). This high correlation is likely due to
almost identical spectral ranges of the respective channels
[QUICKBIRD: RED (630 –690 nm), NIR (760 –900 nm); LANDSAT:
RED (630– 690 nm), NIR (780–900 nm)].
Discussion
In this investigation, we developed models to derive estimates for
Tugai-forest NPP from tree and stand features determined in the
field. The field-derived estimates of NPP were contrasted with
NDVI obtained via remote sensing. We found that current tree
size and NPP were well related, which is likely explained by the increasing crown area with increasing tree size. This is supported by
Kozlowski et al. (1991), who report that growth of individual trees
is correlated with crown size. However, at some age, the effect of
tree-size on NPP will diminish. This is, because older trees show a
decreased ratio of photosynthetic to non-photosynthetic tissue
resulting in generally reduced growth rates (Kozlowski et al.,
1991). Furthermore, Wang et al. (1996) report a higher rate of
heart rot for P. euphratica trees above 40 that are growing under unfavourable conditions, this likely decreasing single tree NPP. In our
study, tree NPP significantly increased with tree age (as did
biomass) and we did not find indications of age trends in our ring
width curves. Therefore, we assume that the range of tree age
(14 –58 years) represented by our model data is marginally
affected by age-related growth decline for vital trees. Since trees
with heart rot (and thus a lower vitality) were not considered in
our model, for older trees and/or trees with a lower vitality the
model likely will overestimate NPP. In our study, the number of treecores with heart rot among the sampled individuals (3 out of 36, i.e.
8 per cent) was low. On the other hand, growth rates of very young
trees are likely underestimated by our model. The intercept in the
derived age models was between 7 and 8 years, indicating that
radial growth – and thus also likely shoot growth – is comparably
higher for very young trees. As is to be expected with empirical
models, our model is mainly applicable to vital trees within the
age range represented by our study.
435
Forestry
Figure 6 stand NPP, stand biomass and average LS NDVI values for the transect sub-sites. The correlation among the three plotted variables is clearly
visible.
Figure 7 Overview of the seven computed NDVI transects. Left: the background image shows the Quickbird NDVI raster image. Transect boundaries are
indicated by the black horizontal lines. The crosses mark the sub-site locations. Right: averaged NDVI values plotted against RD. The numbers indicate the
slope of a regression among NDVI and RD. Maximum values close by the river are in all transects around 1500, representing comparable productivities (see
text for test statistics). The increasing slopes towards the south likely reflect the increased drainage related to a channel which surpasses the study site
leading to the increasingly humped shape of the NDVI - RD curves in the same direction.
The effect of RD is very pronounced. Trees close to the river bank
had larger ring widths and were significantly taller even though
they were younger than trees closer to the desert. Gries et al.
(2003) have shown that shoot growth of P. euphratica is negatively
correlated with groundwater depth. In addition, Westermann et al.
(2008) found that radial growth of degraded P. euphratica adjacent
436
to the lower Tarim River significantly increased as soon as water
conditions became more favourable. Thevs et al., (2012b) have
reported decreasing groundwater levels towards the desert
within our study site . . . Another possible effect of RD could be
higher salt concentrations in the soil towards the desert, as these
soils – if at all – only rarely are flooded (and are thus rarely
Productivity and carbon sequestration
Figure 8 The scatterplot of LS and QUICKBIRD NDVI values of the
corresponding areas highlights the strong correlation between the two
variables (r ¼ 0.91, P , 0.001).
flushed) by freshwater throughout the summer floods. Neill (1993)
showed that regularly flooded salinized soils expressed significantly decreased soil ion concentrations and lead to increases in plant
productivity compared with non-flooded soils. To test whether this
applies to our study site, further analysis of soil ion concentrations
would be necessary. The 27% of unexplained variance in our
best-fit model (L3) could be explained by measurement errors,
competition for light, disease and/or insect herbivory, which we
did not investigate within our study. The incorporation of RD as
an explanatory variable limits the application of our models to
this particular site. At other locations, groundwater properties
may exhibit differing levels, gradients and qualities, e.g. through
different runoff characteristics of the river, higher substrate heterogeneity, water-ion load and/or geomorphological variations of the
river bed.
The trends in NDVI for the eight transects from the river bank to
the desert are consistent with the decrease of NPP with increasing
RD. These results demonstrate the representativeness of our
ground-truth data, despite the fact that we only were able to
sample one subsite close by the river bank. The NDVI transition gradients became steeper towards the south, which is likely caused by
a channel that crosses the study area from the southeast towards
the northwest (compare Figures 1 and 7). Along the channel, the
vegetation expresses a higher productivity, reflected in the locally
higher NDVI values. In contrast, west of the channel the forest
expresses a steep NDVI decrease as a result of decreased water
supply due to the northwestward drainage within the channel. As
a consequence, NDVI gradients express increasingly humped
curves towards the south (Figure 7). Nevertheless, the NDVI gradients of the comparable transects did not differ significantly from
the forest transect, and there was the high mean inter-series correlation among all transects.
The NPP models derived for P. euphratica were based upon ring
width increments which we obtained from sampled tree cores.
However, the measured ring-width curves expressed no glk ¼
0.52 and cross dating of the measured ring width series turned
out to be doubtful. Possible explanations for this discrepancy are
(1) unidentified rings due to the strong brightness of P. euphratica
sapwood, (2) false rings, i.e. intra-seasonal density fluctuations
due to harsh environmental conditions which were falsely interpreted as annual rings (e.g. Fritts, 1977) and (3) inter- and intraspecific competition for light and water resources resulting in differentiated radial growth of individual trees. For instance, young
trees within stands may allocate more resources into shoot
growth to overcome shading suppression. Furthermore, there
may be inter-specific competition for water-resources within
dense reed stands. Reeds are known to contribute to high soil
water consumption (evapo-transpiration of reed-beds can be
0.5 –6.5 mm/day; e.g. Zhou and Zhou, 2009).
Despite the observed discrepancies among ring width series, the
derived NPP models for 2009 and for the 2005– 2009 period (L and
F models, Table 2) showed consistent patterns and were strongly
correlated. Therefore, we assume that our estimates for NPP09
are representative of the average growth of P. euphratica. Wooddensitometric analyses may help to better detect false rings or
sapwood rings, which could allow for more successful cross
dating of ring width curves (e.g. Schweingruber, 2012). Further investigation into the ring series discrepancies should be carried
out to better understand the ecology of P. euphratica.
Based on our models, the dry-weight stand productivity of P.
euphratica within the study site ranged from 0.3 to
3.0 t ha21 a21, which means a net CO2 uptake of 0.5 –
5.2 t ha21 a21. This is comparable with the result of Wang et al.
(1996), who found that the dry-weight stand productivity of a P.
euphratica forest in Xinjiang, China, ranged from 1.8 to
3.5 t ha21 a21. Wang et al.’s higher minimum value of
1.8 t ha21 a21 likely reflects their minimum tree density of
800 sph compared with 217 sph in our study. Moreover, differing
groundwater conditions could explain the variations in productivity
ranges between these studies.
The estimated range of CO2 uptake reveals that – under favourable conditions, represented by site A in our study – P. euphratica is
capable of considerable CO2 uptake (5.2 t ha21 a21) from the atmosphere. Together with P. australis – which in Tugai forests commonly grows as understory and may sequester equal amounts of
CO2 (Thevs et al., 2007) – Tugai ecosystems thus may be considered a suitable and ecologically adapted biome for restoration
and carbon-trade in terms of climate change mitigation activities.
Within the study site, the density of P. australis ranges from scattered reed patches close to the desert to mono-specific reed-beds
within forest clearances. Therefore, to include the potentially large
contribution of reed beds to total ecosystem productivity, further
analyses of reed productivity, distribution and density are necessary. Additional ground truth data would also clarify how NPP is
spatially separated among P. euphratica and P. australis dominated
areas.
Based on our results, we suggest that large-scale Tugai forest
productivity studies should aim at the derivation of NDVI-based
models. On this scale, the inclusion of site-specific explanatory
variables (e.g. RD or soil salt concentrations) that need to be determined at each site can be avoided. Nevertheless, collection of
spatial data of forest productivity along transects in the field, as
we have done provides the basis for a calibration of NDVI-based
models. In this first assessment, we found a high correlation
between the NDVI of 800 LS pixels and averaged NDVI values
from QUICKBIRD pixels, showing the general feasibility of remote
sensing for large-scale estimates.
437
Forestry
Conclusions
-alkalisierungen im nördlichen Tarim-Becken (Xinjiang). Institut für
Asienkunde, Hamburg. In German.
Our results indicate that it is theoretically possible to model and
upscale Tugai forest NPP estimates. Further ground-based investigations focusing on P. euphratica and P. australis as the dominant
Tugai vegetation are necessary. Determination of Tugai forest distribution along the Amu River is mandatory as a reliable basis for
large-scale classifications and large-scale estimates of NPP and
carbon sequestration. The relationship between P. australis NPP
and satellite image NDVI needs to be more broadly investigated
and modelled. The output of such an analysis would allow for a
more accurate evaluation of Tugai forests as ecosystems to be
traded for on the international carbon market.
Ibrakhimov, M., KHamzina, A., Forkutsa, I., Paluasheva, G., Lamers, J.P.A. and
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Acknowledgements
We thank all staff-members from the Ernst-Moritz-Arndt-University,
Greifswald, who gave valuable comments and remarks on the data
evaluation as well as our cooperation partners from the ministry of Water
in Turkmenistan – in particular Dr. Kurban Ovezmuradov and Amangul
Ovezberdyeva. Furthermore, we thank Dr. Steve Mitchell and two
anonymous referees, who helped improving our manuscript.
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Funding
Schweingruber, F.H. 2012 Der Jahrring. Kessel Verlag, RemagenOberwinter, Germany, 234 pp.
The Bauer-Hollmann Stiftung and Rudolf und Helene Glaser Stiftung within
the Stifterverband für die Deutsche Wissenschaft funded our travel to Turkmenistan as well as the job position of the corresponding author Allan
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