remote sensing
Article
Time Series Analysis of Land Cover Change in Dry Mountains:
Insights from the Tajik Pamirs
Kim André Vanselow 1, * , Harald Zandler 2,3 and Cyrus Samimi 2,3
1
2
3
*
Citation: Vanselow, K.A.; Zandler,
H.; Samimi, C. Time Series Analysis
of Land Cover Change in Dry
Institute of Geography, University of Erlangen-Nuremberg, 91056 Erlangen, Germany
Department of Geography, University of Bayreuth, 95440 Bayreuth, Germany;
harald.zandler@uni-bayreuth.de (H.Z.); cyrus.samimi@uni-bayreuth.de (C.S.)
Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth,
95440 Bayreuth, Germany
Correspondence: kim.vanselow@fau.de
Abstract: Greening and browning trends in vegetation have been observed in many regions of the
world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends
of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding
of these changes and thus improve remote sensing studies in dry mountainous areas. The study
area is characterized by a complex set of attributes, making it a prime example for this purpose. We
used generalized additive mixed models for the trend estimation of a 32-year Landsat time series
(1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental
and socio-demographic data. With this approach, we were able to cope with the typical challenges
that occur in the remote sensing analysis of dry and mountainous areas, including background
noise and irregular data. We found that greening and browning trends coexist and that they vary
according to the land cover class, topography, and geographical distribution. Greening was detected
predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change.
At other sites, greening corresponds well with increasing temperature. Browning was frequently
linked to disastrous events, which are promoted by increasing temperatures.
Mountains: Insights from the Tajik
Pamirs. Remote Sens. 2021, 13, 3951.
Keywords: greening; browning; Central Asia; global change; vegetation dynamics
https://doi.org/10.3390/rs13193951
Academic Editors: Brigitte Leblon
and Jeff Harris
Received: 3 August 2021
Accepted: 28 September 2021
Published: 2 October 2021
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Attribution (CC BY) license (https://
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4.0/).
1. Introduction
Knowledge of land cover and vegetation dynamics is crucial for comprehending
changes in ecosystem structure, functions, and services and for revealing the underlying
drivers of change [1,2]. Evidence on a global scale shows that there has been a trend of
increasing green vegetation cover in recent decades in many regions of the world [3,4],
commonly referred to as greening [4]. Certainly, one prominent example discussed in the
literature is the greening of the Sahel (see, e.g., [5–8]). However, the majority of studies
on greening focus on high northern latitudes, particularly the boreal [9–13] and the arctic
tundra zones [13–21]. To a lesser extent, other regions across the planet have also attracted
attention, such as the mid-latitudes [22]; the subtropics [23]; the tropics [24]; arid and
semi-arid areas of the southern hemisphere [25]; and mountains—more specifically, the
Tibetan Plateau [26–28].
Globally, anthropogenic factors, such as fertilization and irrigation, are the drivers with
the highest impact on greening and by far outweigh the effect of climate change [4,13,29].
Yet, on a regional level, there are strong differences. Drivers of greening caused by
climate change play a major role in the high latitudes, in high mountains, and in drylands [4,11,25–27,30–33]. A prolonged growing season caused by increased temperatures is
responsible for most greening in the boreal zone and the arctic tundra [10,13,15–20,29,34],
whereas increased precipitation is the major driver for greening in arid and semi-arid
areas [6,11].
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https://www.mdpi.com/journal/remotesensing
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As opposed to greening, the term browning describes decreasing vegetation cover and
is usually associated with the degradation of vegetation. On a global scale, however, the
processes of vegetation browning are masked by those of greening [1,11,35]. While negative
trends of vegetation cover have been mainly attributed to climatic effects [1,36], there is also
evidence to suggest land-use impacts that cause browning, such as urbanization [31,37,38],
overgrazing [39], deforestation, and land conversion [8].
With the exception of the Tibetan plateau, dry mountains have rarely been the subject
of studies that focus on greening and browning trends, and research from regions with steep
and complex terrain is particularly scarce. However, since dry mountains provide vital
ecosystem services for the local population and adjacent lowlands, studies of this kind have
a high significance, among other things, because they are fundamental to understanding the
relationship between changes in land cover and ecosystem services. In total, mountainous
drylands represent more than one-third of all mountains and hold more than one-quarter of
the world’s biodiversity hotspots. Despite their relevance, scientific knowledge about these
ecosystems is still scarce. Nevertheless, it is evident that dry mountain areas are subject to
tremendous changes [40]. High mountain ecosystems are considered to be among those
most affected by global warming, as they tend to warm more rapidly than the average
rate [41–45] and suffer from extensive land-use changes [46,47].
To contribute to research on these poorly studied regions, we focus on greening and
browning trends in the Western Pamirs, a dry mountain area in Central Asia characterized
by high biodiversity [48,49]. There is a strong indication that the land cover and vegetation
in the Pamirs are changing, with negative impacts on ecosystem properties and their
associated functions and services [42,48–51]. About two-thirds of the population’s income
is generated in agriculture and animal husbandry [52]. Therefore, the availability and
preservation of arable land and pastures are of major importance.
Satellite remote sensing is a common and efficient tool used for the assessment of
vegetation and land cover change, especially for large and remote areas that are difficult
to access [53–56]. However, the estimation of reliable trends of land cover change in a
dry mountain ecosystem over a long period, based on remote sensing analysis, poses
several challenges:
First, the matter of scale is of critical importance: a common problem in remote sensing
studies is the discrepancy between different types of scale, in particular between the operational scale—i.e., the scale of action at which a certain land surface process occurs—and
the observational scale—i.e., the resolution of the data source used to analyze the processes [57,58]. In heterogeneous landscapes with small-scale environmental processes, both
the spatial and temporal resolution needs to be high enough to provide knowledge on
the operational scale [57–59]. In addition, a time series of satellite imagery that is long
enough to capture long-term changes are needed [60,61]. Unfortunately, most sensors that
provide sufficiently long time series are of too coarse a spatial resolution (e.g., Advanced
Very High Resolution Radiometer with a 1.1 km resolution at the nadir) to allow for the
adequate analysis of the land cover of any given mountainous terrain with a heterogeneous
topography [59]. The time series of high spatial resolution satellites, such as Sentinel-2
or Rapid Eye, are still short and unable to cover long-term trends and processes [62,63].
Therefore, in this study, we used Landsat data, as Landsat provides multispectral data of
sufficiently high spatial and temporal resolution and a comparatively long time series for
the study area—i.e., since 1988.
Second, available satellite imagery is often characterized by an irregular temporal
resolution. Landsat time series have a temporal resolution of 16 days in principal, but often
values are missing due to clouds, cloud shadows, and the technical failure of the Scan Line
Corrector (SLC) in Landsat 7 (see [64]), reducing the temporal resolution and causing the
irregular distribution of the data in time. Indeed, this is an issue for many standard time
series techniques requiring regular time series, such as autoregressive integrated moving
average (ARIMA) models. An appropriate methodological approach that can deal with
this issue is needed [65].
Remote Sens. 2021, 13, 3951
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Third, as the data are distributed irregularly in time, the applied approach should also
be able to deal with autocorrelation in the data [65,66].
Fourth, the remote sensing signal needs to be properly processed to reach sufficient
quality compared to background noise. The most prominent vegetation index used for
remote sensing-based change detection, including greening and browning trends, is the normalized difference vegetation index (NDVI) [11,21,67–70]. However, in vegetation-scarce
regions, such as drylands and mountains, the NDVI is distorted by a strong soil signal and
hence does not show enough sensitivity with regard to change [71–77]. Therefore, in this
study, we set up a time series of the modified soil adjusted vegetation index (MSAVI) [71].
This spectral vegetation index was tailor-made in order to cope with soil noise, and it has
proven its usefulness and importance in previous studies on drylands [78–82].
Fifth, the Landsat program consists of several generations of sensors, and their characteristics differ from one another. This complicates the comparison of the values derived
from the different sensors. The images used in this study originate from three different
sensors (Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI). To make use of all available
images and establish a single time series from those images, a suitable method is needed
that can take these differences into account [83].
Sixth, land cover time series often behave in a non-linear manner. Therefore, the
method of choice should be able to account for such non-linearity in the data [84,85].
To accommodate all these challenges, we decided to use generalized additive mixed
models (GAMMs, [86]) for our analyses of the time series in this study. GAMMs have been
applied successfully in the past for analyses of time series in ecological studies [87–91]
but have rarely been used in the context of the time series analysis of satellite images.
Lee et al. [83] used GAMMs in a post-classification analysis. However, to the best of our
knowledge, our study is the very first to use GAMMs directly for the analysis of raster time
series of vegetation and land cover change.
We aim to provide a comprehensive analysis of greening and browning trends in
the Western Rushan Range, Pamir Mountains, Tajikistan, and to contribute to a deeper
understanding of land cover changes, their distribution, and their potential causes and
impacts in dry mountains. Thereby, we present and discuss a novel approach that can
cope with the above-mentioned challenges and limitations of remote sensing studies in
dry and mountainous areas (Figure 1). We chose this region because it is characterized
by a complex set of attributes and drivers of change, including a semi-arid climate under
climate change, a steep mountainous terrain fostering gravitational mass movements, and
a radical change of the socio-economic system with impacts on land use, making it a prime
example to analyze the environmental change in a dry mountain system.
We investigate whether we are able to detect and quantify land cover changes in
terms of greening and browning with remote sensing imagery, and if so, whether these
changes possibly stand in relation to the development of temperature, precipitation, or
population or livestock numbers. Therefore, we utilize GAMMs for an elaborate multidata analysis combining a long-term time series of satellite images, historic vegetation
maps, ground truth records, and topographic, climate and socio-demographic data. This
approach intends to unveil the amount of change, its spatial patterns, and the driving
mechanisms of land cover trends. We hypothesize that (1) our approach is able to provide
detailed insight into the land cover change of the heterogeneous study area, including
small-scale processes; (2) both significant greening and browning trends exist in the study
area; (3) the characteristics of the detected trends differ according to the vegetation and land
cover class, as well as topographical and geographical distribution; (4) significant greening
trends correspond to increasing temperatures and increasing precipitation; (5) significant
browning trends correspond to increasing population and increasing livestock numbers.
Thereby, the presented study provides a novel example for a time series analysis of irregular
data in a dry and heterogeneous environment.
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Figure 1. General workflow of the study.
2. Materials and Methods
2.1. Study Area
The study area covers 1280 km2 of the western part of the Rushan Range in the ‐
Western Pamirs of Tajikistan (Figure 2). Administratively, it belongs to the Rushan and
Shugnan districts of the Gorno-Badakhshan
Autonomous Oblast (i.e., Province; GBAO).
‐
It is delimited by the river Gunt in the south, the river Panj in the west, the river Bartang
in the north, and the 72◦ E meridian in the east. The highest peaks reach more than
5000 m a.s.l., while the minimum elevation is found in the northwestern corner at 1980 m
a.s.l. The harsh climate is characterized by severe, long winters with minima below −35 ◦ C,
−
followed by short summers. The annual mean temperatures range between −7.1 ◦ C on
−
the Fedchenko Glacier and 8.7 ◦ C at the meteorological station in the region’s center in
Khorog [51,92–94]. Breckle [94] reports an annual precipitation in the Western Pamirs of
between 90 and 400 mm, with a distinct winter maximum. This region is also part of the
upper watershed of the Amu-Darya, rendering it crucial for the water supply for millions
‐
of people living downstream [95]. The Western Pamirs are considered to be a biodiversity
hotspot [48,49,51,96,97], and large parts of it are (though insufficiently) protected by the
Tajik National Park [98–100]. Around 2000 vascular plant species, including 160 endemics
and a high number of threatened animal species, such as the snow leopard (Panthera uncia)
and the Marco Polo sheep (Ovis ammon polii), are native to this area [49,101]. Furthermore,
numerous wild progenitors of cultured fruit trees are found in the region, constituting a
valuable genetic resource [50,93,96]. The Western Pamirs are also the habitat of rare juniper
forest patches and Tugai forests (azonal forests in river valleys), which play important roles
in the water regulation of rivers. Moreover, a large number of endemic dwarf shrub species
dominate the upper vegetation belts. Further information on the vegetation in the study
area is provided in Section 2.2.5.
Remote Sens. 2021, 13, 3951
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Figure 2. Overview map (source: Google Earth, SRTM [102]).
2.2. Data
We collected Landsat images of the study area and built a raster time series of the modified soil adjusted vegetation index (MSAVI; 2.2.1). Additionally, we gathered time series of
temperature and precipitation (2.2.2), population (2.2.3), and livestock data (2.2.4). Further‐
more, we used the vegetation and land cover data of a historic Soviet vegetation map and
‐
ground-truthing records from fieldwork (2.2.5), as well as topographic variables derived
from the shuttle radar topography mission (SRTM) digital elevation model (DEM; 2.2.6).
2.2.1. Landsat Data
ρ
ρ
ρ
ρ
We used the Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) on demand interface (https://espa.cr.usgs.gov/, accessed on
‐
ρ
ρ
6 July 2020) provided by the United States Geological Survey (USGS) to obtain pre-processed
satellite scenes and derived the MSAVI [71] from the Level-2 Surface Reflectance [103] of
the sensors Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI with a spatial resolution of
30 m. The calculation formula of the MSAVI is as follows:
‐
q
2ρ N IR + 1 − (2ρ N IR + 1)2 − 8(ρ N IR − ρred )
MSAVI =
(1)
2
where ρ N IR is the surface reflectance of the near-infrared band and ρred is the surface
reflectance of the red band.
Furthermore, a cloud and cloud shadow mask was calculated according to the CFMask
algorithm [104–106]. Altogether, we obtained 1267 scenes of the study area between 1988
and 2020, which we combined in a 32-year raster image time series.
2.2.2. Climate Data
We used the 2-m air temperature and precipitation data from three different data
sources. Daily climate data were obtained from the meteorological station in Khorog,
located in the Panj valley at 2084 m a.s.l. The data were downloaded from the Climate
Data Online (CDO) archive of the National Centers for Environmental Information [107].
Furthermore, we utilized reanalysis data from the European Centre for Medium-Range
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Weather Forecasts (ECMWF) Reanalysis v5-Land (ERA5-Land) dataset with a spatial
resolution of 0.1◦ [108], and the Modern-Era Retrospective analysis for Research and
Applications version 2 (MERRA-2) dataset with a spatial resolution of 0.5◦ × 0.625◦ [109],
as these products proved to be the most suitable for vegetation analysis in mountain regions
with limited data availability [110].
2.2.3. Population Data
Since the demographic development of a given region is a critical factor for both its
use of resources and its potential to originate labor force for cultivation, we included the
time series of population numbers in GBAO in our analysis. We combined two sources
published by the State Agency for Statistics under the President of the Republic of Tajikistan
(TajStat, [111,112]) in a 28-year time series (1990–2018) for the entire GBAO and in a 25-year
time series (1990–2015) for the individual districts.
2.2.4. Livestock Data
For the discussion of grazing impact on the development of land cover and vegetation,
we used numbers of large livestock (i.e., mainly cattle) collected by TajStat [113–115], which
differentiate between the entire GBAO and each district. A consistency check of the data
revealed that the time series could not be completely combined, as the numbers in the
oldest dataset (1981–1996) are three times lower than the numbers in the two other datasets
(1992–2011). Therefore, we decided to aggregate only the latest two of the datasets and
analyze the resulting two time series separately from each other.
2.2.5. Vegetation and Land Cover Data
To evaluate the results of the MSAVI trend analyses, we used a historical vegetation
map produced by the Soviet botanist Okmir Agakhanjanz in 1958–1961 (see [97] for details),
which distinguishes between 12 different vegetation and land cover classes in the study
area (Table 1). Additionally, to check the information from the maps and the results of
the trend analyses for plausibility, we recorded 76 ground-truth field plots all over the
study area.
2.2.6. Topographic Data
We evaluated relationships between the trends and topography using six topographic
variables derived from the SRTM DEM with a resolution of 1 arc-second (30 m) [102].
Elevation was directly extracted from the DEM and used as the basis to calculate the slope,
aspect, ruggedness, and distance to the valley bottom using the function terrain of the
R package raster [116]. The aspect was transformed into north and east exposedness by
calculating the cosine and sine of aspect, respectively [117].
2.3. Time Series Analysis Using Generalized Additive Mixed Models
Generalized additive mixed models (GAMMs) [86,118] were applied as an innovative
approach that has rarely been used in remote sensing studies, using the function ‘gamm’
of the R package ‘mgcv’ [119]. Depending on the type of time series used, our response
variables were MSAVI, temperature, precipitation, population, and livestock numbers,
respectively. Their expected values were estimated by the explanatory variable ‘year’. We
additionally included the explanatory variables ‘day of the year’ for the MSAVI time series
and the variable ‘month’ for the temperature and precipitation time series. In doing so,
we were able to separate the seasonal from the inter-annual component and hence could
uncover gradual as well as abrupt land cover changes. In contrast to seasonal changes,
which are mostly dependent on annual temperature and rainfall interrelations altering
plant phenology, gradual changes can be caused by inter-annual climate variability or land
degradation and land management. Abrupt changes are usually caused by disturbances
and extreme events.
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Table 1. Land cover classes of the study area (ordered from largest to smallest extent).
Land Cover Class
Extent
(km2 )
Extent
(%)
Elevation
(m a.s.l.)
Description
Barren land
964.0
75.3
Up to 5500
Rocks, screes, moraines; sparse vegetation cover.
Nival areas
113.5
8.9
>4600
Glaciers and firn fields without higher plants.
Cushion plant
vegetation
42.4
3.3
3000–4000
Dominant genus: Acantholimon (Prickly thrift).
Mountain deserts
36.3
2.8
2500–3400
Two main types: Teresken (Krascheninnikovia ceratoides)
deserts and Wormwood (Artemisia spp.) deserts.
Cryophytic and
subnival vegetation
33.7
2.6
4000–5000
Low-growing open aggregations composed of several herb
species (e.g., Oxytropis, Potentilla, Draba, Ranunculus).
Mountain steppes
26.6
2.1
3200–4400
Three main types: grass steppes (Poa, Stipa, Festuca),
herbaceous steppes (e.g., Cousinia, Ziziphora), Wormwood
steppes (dominated by Artemisia lehmanniana).
Cultivated land
25.3
2.0
Up to 3400
Agricultural land, clover meadows, gardens, settlements.
Juniper vegetation
13.3
1.0
2900–3800
Juniperus polycarpos var. seravschanica, associated with
umbellifer species (e.g., Prangos pabularia, Ferula jaeschkeana).
Mountain Tugai
13.3
1.0
Up to 3700
Alluvial scrubs and forests. Various woody species can
dominate—e.g., Salix, Betula, Populus, Hippophaë.
Floodplain
meadows
8.1
<0.1
Up to 4000
(sometimes 4800)
Limited to riparian habitats under the influence of
groundwater or melting snow. Several associations
dominated by Carex and Kobresia.
Mountain meadows
2.5
<0.1
2600–3400
Dominated by the genus Polygonum.
Rosaceae scrub
0.7
<0.1
2500–2900
Dominated by Rosa and Cotoneaster.
Due to the different periods and spatial scales at which the different response variables
operate, the trend comparisons had to be limited to qualitative discussions.
GAMMs are extensions of generalized additive models (GAMs; [120]) and, as such,
are able to estimate non-linear trends. They are a preferred method for ecological studies
since they allow for complex shapes of response and are well-suited to cope with irregular
spacing in a time series. Similar to other regression models, in a GAMM, a univariate
response variable is related to one or multiple explanatory variables. As in generalized
linear models (GLMs; [121]), the mean of the response variable in GAMMs is related to
the explanatory variables by a link function. However, as opposed to parametric models,
GAMMs make use of smoothing functions of the explanatory variables. The resulting
smoothing curves can take on many forms and allow for non-linear relationships between
the response and explanatory variables [66]. The general equation of a GAM is:
g(µ) = ∑f(X),
(2)
where g is a specified link function, (µ) is the expected mean response, and ∑f(X) is the
smooth functions of the covariates (X). In GAMMs, a random factor is added to this general
GAM equation. For a detailed description, readers are referred to Wood [86].
In this study, we used a Gaussian distribution and two types of smoothers—i.e., (1) thin
plate regression splines for the development of the data values over the years and (2) cyclic
cubic splines to account for the seasonality in the MSAVI, temperature, and precipitation
data. The value of the smoothness parameter had to balance between a model that was too
simple to sufficiently represent the data and an exceedingly complex model that overfitted
the data. With this in mind, the degree of smoothness was optimized by treating it as a
random effect using the maximum likelihood method [65,86,122]. As the values in our
time series are mostly irregularly distributed in time, we introduced a continuous-time
first-order autoregressive process (CAR(1)) to account for autocorrelation in the data that
Remote Sens. 2021, 13, 3951
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may bias the trend results [65,118]. To compensate for differences caused by the sensors,
which may otherwise distort the trend results, we used ‘sensor type’ (a factor variable with
the three levels Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI) as a random effect
(random intercept and random slope) variable in the GAMM models for the analyses of
the MSAVI time series [86].
We applied the GAMM function to each time series, and this approach allowed us to
address the time, amount, and direction of change within the trend component. For the
MSAVI time series, we present significance and amount of change as a map (Figure 3). In
doing so, the detected changes were localized on the vegetation map and subsequently
further evaluated. In particular, for the 1200-pixel time series, we conducted further
statistical analyses to obtain deeper insights into the development of the land cover. These
1200 comprise 100 time series for each of the 12 land cover types. Seventy-six of these
locations were visited in the field, giving us a better understanding of the on-site situation.
The remaining 1124 locations were stratified randomly and selected using the function
‘stratified’ of the R package ‘fifer’ [123].
Figure 3. Classification map of the trend type overlaid by land cover classes according to Agakhanjanz [76].
‐
Furthermore, we identified points of significant change in the time series. In contrast
to linear models, where each point in the time series always has the same slope of the
trend, in non-linear models, such as GAMMs, each time point may have a different slope.
To solve this difficulty, we estimated the first derivative of the fitted splines by calculating‐
finite differences for numerous time points in the time series [65]. Then, we evaluated if
the first derivatives at these time points comply with the null hypothesis of no change.
This was achieved by calculating a 95% confidence interval for the derivative estimates.
Points in the time series where zero is outside the confidence interval of the first derivative‐
indicate times of significant change. For a detailed description of this method, readers are
referred to Simpson [65].
3. Results
3.1. Vegetation Greening and Browning Trends as Indicated by MSAVI Time Series
‐
In this study, we refer to a significant positive (i.e., increasing) trend of MSAVI values
over the study period (1988–2020, p < 0.05) as greening, whereas a significant negative
(i.e., decreasing) trend (1988–2020, p < 0.05) is called browning. In total, we analyzed more‐
‐
‐
‐
‐
Remote Sens. 2021, 13, 3951
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than 1.4 million pixel time series of an area of 1280.2 km2 . For 16.5% of these time series,
there were too few observations to perform statistical analyses. In the remaining time series,
we detected greening for 385.9 km2 (36.1%). In contrast, only 17.2 km2 (1.6%) showed
browning. The majority of the time series (62.3%, 666.5 km2 ) did not exhibit a significant
trend (p > 0.05) of MSAVI increase or decrease (Figure 4).
Figure 4. Proportion of trend type in the entire study area and the individual land cover classes. ALL:
entire study area; BAR: barren land; CPV: cushion plant vegetation; MDS: mountain deserts; CSV:
cryophytic and subnival vegetation; MST: mountain steppes; CUL: cultivated land; JUN: juniper
‐
vegetation; MTU: mountain Tugai; FPM: floodplain meadows; MMD: mountain meadows; ROS:
Rosaceae scrub.
A visual evaluation of the trends suggests that the time series with no significant
changes are distributed all over the study area and, in particular, on slopes, ridges, and
high plateaus. Greening could be detected predominantly in the western part of the
‐ study
area—i.e., in Panj and its adjacent valleys, along the valleys of Bartang in the north, its
tributary valleys Geisev in the north-central, and Ravmed in the northeast—and in Gunt
Valley in the south of the study area. Time series with significant browning are partly
clustered in valley bottoms, with the biggest area located in Red Valley in the northwest
of the study area. The parts of the study area with too few observations for meaningful
statistical analyses predominantly belong to the nival areas with a permanent snow or ice
cover across the entire year (Figure 3).
This visual evaluation was supported by a quantitative analysis, which revealed that
trend types of the land cover classes, to some extent, differ fundamentally from the situation
in the entire study area and from each other (Figure 4). In four classes (mountain Tugai,
juniper vegetation, mountain meadows, and cultivated land), the number of time series
with significant greening exceeds the number of time series with no trend. The share of
time series with significant browning is particularly high in mountain Tugai and cultivated
land. Time series with no significant trend dominate, especially in mountain steppes
‐
and cryophytic and subnival vegetation. Nival areas also show a large proportion of
browning and no significant change. However, the very low number of valid observations
in these time series suggests spurious trends. Therefore, we decided‐ to refrain from
further interpretation.
−
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In the next step, we deepened the analysis based on the characteristics of 1100 test time
series, with 100 from each land cover class (except nival areas). First, we examined whether
the single years showed significant greening, significant browning, or no significant change.
We ascertained that 55% have no significant change in each of the 32 analyzed years. In
36.7%, MSAVI increased significantly in all years—i.e., we detected a continuous linear
trend over the entire study period. A continuous browning trend was revealed for less than
1% of the time series. Non-linear shapes—i.e., a mix of greening, browning and insignificant
change over the years—were found for 7.7% of the time series. However, the individual
land cover classes partly exhibit considerable differences from this result (Table 2). Nonlinear time series occur predominantly in mountain Tugai (19%) and cultivated land (16%).
Another remarkable land cover class is the mountain steppes. They have the highest
proportion of no change in all the years (76%), followed by cryophytic and subnival
vegetation (63%). Constant greening over the entire study period was especially detected
in juniper vegetation (46%) and Rosaceae scrubs (46%), followed by barren land (43%).
Table 2. Trend type according to land cover class, based on 1100 test plots (100 per land cover class).
Land Cover Class
Browning
(Linear)
Greening
(Linear)
No Trend
Non-Linear
Shape
Entire study area
Barren land
Cushion plant vegetation
Mountain deserts
Cryophytic and subnival vegetation
Mountain steppes
Cultivated land
Juniper vegetation
Mountain Tugai
Floodplain meadows
Mountain meadows
Rosaceae scrubs
6 (0.6%)
0
0
1
0
1
1
0
2
1
0
0
404 (36.7%)
43
35
40
33
18
40
46
31
32
40
46
605 (55.0%)
51
59
57
63
76
43
43
48
58
53
54
85 (7.7%)
6
6
2
4
5
16
11
19
9
7
0
Then, we compared the amount of change—i.e., the difference in MSAVI of the
component smooth functions for the variable ‘year’ between 1988 and 2020 and between
the different types of times series based on their median values: for all non-NA time
series, the median is 0.01, for time series with a significant greening trend it is 0.02, and
for time series with a significant browning trend it is −0.03 (Table 3). To put these rather
small values into perspective, we need to consider that the median MSAVI calculated over
all 1100 test plots for the whole time series is only 0.06. The comparison of the absolute
values did not unveil patterns of change between the land cover classes. In such cases, a
common approach is to compare the classes in terms of some measure of location [124,125].
We calculated the quartiles that are standard statistical location parameters to compare the
differences between land cover in the four different categories in terms of the amount of
change, as described below.
First, we calculated the quartiles of all change values in the test plots with a significant
positive change (n = 486). We call values above the 75% quantile a ‘very strong change’,
values above the median but smaller than the 75% quantile ‘strong change’, values above
the 25% quantile to the median ‘intermediate change’, and values below the 25% quantile
‘weak change’. Then, we compared the land cover classes based on their share of change
values in the four categories (Figure 5). In particular, cultivated land has many plots with
a very strong positive change, followed by mountain deserts. Mountain meadows is the
land cover class with the highest number of plots with strong positive change, followed
by floodplain meadows. The category intermediate change is led by juniper vegetation,
followed by Rosaceae scrubs. Plots with weak positive change are predominantly found in
juniper vegetation, followed by mountain Tugai and barren land.
‐
‐
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Table 3. Median of change according to land cover class and trend direction.
Land Cover Class
All Time Series
Greening
Entire study area
Mountain Tugai
Rosaceae scrub
Juniper vegetation
Mountain deserts
Cushion plant vegetation
Mountain steppes
Mountain meadows
Floodplain meadows
Cryophytic and subnival vegetation
Barren land
Cultivated land
0.01
0.01
0.01
0.01
0.02
0.02
0.01
0.02
0.01
0.01
0.01
0.03
0.02
0.02
0.02
0.02
0.03
0.03
0.02
0.03
0.02
0.02
0.02
0.04
Browning −
−0.03
−0.02
−0.02
−0.01
−0.04
−0.02
−0.03
−0.03
−0.02
−0.03
−0.03
−0.04
−
−
−
−
−
−
−
−
−
−
−
Figure 5. Strength of significant positive change for each land cover class. BAR: barren land; CPV:
cushion plant vegetation; MDS: mountain deserts; CSV: cryophytic and subnival vegetation; MST:
mountain steppes; CUL: cultivated land; JUN: juniper vegetation; MTU: mountain Tugai; FPM:
floodplain meadows; MMD: mountain meadows; ROS: Rosaceae scrubs.
Analogous to the plots with a significant positive change, we calculated the quartiles
of the modulus of the change values in the test plots with a significant negative change
(n = 26). Again, we compared the land cover classes based on their share of change values
in the four categories (Figure 6). There are no plots with a significant negative change
among Rosaceae scrubs, cushion plant vegetation, and cryophytic and subnival vegetation.
The highest number of negative changes was found in mountain Tugai, though most plots
fall into the category of weak negative change. Very strong and strong negative changes
predominantly occur in mountain meadows, followed by cultivated land.
Remote Sens. 2021, 13, 3951
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‐
Figure 6. Strength of significant negative change in each land cover class. BAR: barren land; CPV:
cushion plant vegetation; MDS: mountain deserts; CSV: cryophytic and subnival vegetation; MST:
mountain steppes; CUL: cultivated land; JUN: juniper vegetation; MTU: mountain Tugai; FPM:
floodplain meadows; MMD: mountain meadows; ROS: Rosaceae scrubs.
With regard to topography, we found significant differences (p < 0.05, Mann–Whitney
U-test) between the trend types for all analyzed variables (Table 4). Regarding elevation,
there ‐is a significant difference between all three trend types, with the highest values
discovered for time series with a significant browning trend, followed by time series with
no significant trend and time series with a significant greening trend. The slope values also
differ significantly between the three trend types, with the steepest slope for time series
with significant greening, followed by time series with no significant trend and time series
with significant browning. Furthermore, the time series of the three trend types differ in
terms of their north exposedness. The highest values could be detected for time series with
a significant browning trend, followed by time series with a significant greening trend and
time series with no significant trend. Moreover, the time series of the three trend types
show a significantly different east exposedness. The highest values appear in time series
with no significant trend, followed by time series with a significant browning trend and
time series with a significant greening trend. Considering the ruggedness of the terrain,
we also found significant differences between the time series of the three trend types, with
the highest values for greening time series, followed by no trend time series and browning
time series. Greening time series are the trend type closest to valley bottoms, followed by
browning time series and no trend time series.
Finally, with regard to the geographic position as indicated by Universal Transverse
Mercator (UTM) easting and northing, we also detected significant differences between
all three trend types. For UTM easting, the highest values occur in browning time series,
followed by no trend and greening time series. Regarding UTM northing, the values of
greening time series are significantly higher than the values for no trend and browning
time series, neither of which differ from each other.
‐
‐
‐
‐
‐
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Table 4. Median of topography variables in relation to trend type. Different shades of grey indicate a
significant difference between the values of classes based on Mann–Whitney U-Test.
Land Cover Class
Elevation
Slope
North exposedness
East exposedness
Terrain ruggedness
Distance to valley bottom
UTM easting
UTM northing
Entire Study Area
3909
32.3
0.04
−0.17
47.88
137.79
745,009
4,190,588
No Trend
4013
32.0
−0.12
0.17
47.23
154.3
746,222
4,190,339
Greening
3507
33.7
0.07
−0.64
50.92
126.44
740,691
4,192,771
Browning
4307
28.1
0.39
−0.1
40.99
131.1
747,237
4,189,769
3.2. Trends of Temperature and Precipitation
The visual inspection of the ERA5 and MERRA annual mean temperature values
shows increasing trends for both time series (Figure 7 left). This observation is supported
by the results of the GAMMs, where the component smooth functions of the variable ‘year’
revealed a significant (p < 0.01) increasing trend from 1988 to 2020 for all three analyzed
temperature time series (CDO Khorog, ERA5, MERRA). The CDO time series, however,
has many missing values; therefore, its result should be interpreted with caution, and
meaningful annual means could not be calculated and plotted. The biggest temperature
increase of 1.45 ◦ C over the study period was indicated by the MERRA time series, followed
by the ERA5 time series with 0.95 ◦ C and the CDO time series with 0.8 ◦ C.
Figure 7. Annual mean temperature time series (left) and annual mean precipitation time series (right) of ERA5 and MERRA from 1988 to 2020 (* indicates significant trends, p < 0.01; data
sources: [108,109]).
In contrast, we could not find consistent trends in the precipitation data (Figure 7
right). The results of the GAMMs for the ERA5 and MERRA time series also support the
assumption of no significant change over the study period (p > 0.05). Only the GAMM for
the CDO precipitation data of the meteorological station in Khorog exhibits a significant
increase in precipitation after 2007. However, this result is influenced by many missing
values before this date.
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3.3. Trends of Population Development
The population in the study area and its surroundings increased during the study
period (Figure 8). This is visible in the time series for the entire GBAO from 1990 to 2018,
including the city of Khorog, and for the rural population (2000–2018). The numbers steeply
increased until 2001, then slightly decreased from 2003 to 2007, and finally further increased
after 2008. The smooth functions in the GAMMs confirm a significant (p < 0.01) positive
trend. The population numbers (1990–2015) in the districts of Rushan, where the major part
of the study area is located, and Shugnan/Roshtqala, the district of the southern part of the
study area, show a positive trend as well. Rushan’s population significantly increased until
2001, then remained stable, and lastly slightly increased after 2006. In Shugnan/Roshtqala,
the numbers increased strongly until 2000, then remained stable on a high level, declined
in 2010, and again increased in recent years.
Figure 8. Population time series: the entire province of GBAO (total and rural population, left) and
the districts of Shugnan/Roshtqala and Rushan (right; * indicates significant trends, p < 0.01; data
sources: [111,112]).
3.4. Trends of Large Livestock Numbers
The numbers of large livestock in the study period first sharply decreased, at least
until 1996, but exhibited a strong increasing development after 2000 (Figure 9). The time
series for the entire GBAO from 1981 to 1996 shows stable numbers of around 42,000 large
livestock heads until the end of the 1980s, followed by a significant (p < 0.01) decrease
until 1996 to 19,440 animals. The GBAO time series from 1992 to 2011 exhibits a significant
(p < 0.01), near linearly increasing trend from 2000 to 2011. While the numbers stay below
80,000 in the 1990s, 105,011 animals were counted in 2011. The decline in the livestock
numbers at the beginning of the 1990s was also detected in the time series of the districts of
Shugnan/Roshtqala and Rushan, where the numbers decreased from 11,814 to 3136 and
from 4113 to 1610, respectively. The increase from 6904 to 8677 animals in Rushan after
2000 appears rather flat in the visualization but is nevertheless significant (p < 0.01). For
Shugan/Roshtqala, we detected a significant steep increase from 2000 to 2003, followed by
a further moderate increase.
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Figure 9. Time series of livestock numbers: the entire province of GBAO (left) and the districts of Shugnan/Roshtqala and Rushan (right; * indicates significant trends, p < 0.01; data
sources: [113–115]).
4. Discussion
To our knowledge, this is the first study that successfully uses GAMMs to analyze
vegetation and land cover change with a raster time series in complex dry mountain terrain.
By combining this approach with a relatively long Landsat time series and a particularly
suitable vegetation index, we addressed several common challenges of applied remote
sensing studies in dryland mountain regions, such as irregular data, high background
influence, and non-linearity.
4.1. Detection of Greening and Browning Trends
The analysis of the 32-year pixel-based MSAVI time series revealed significant land
cover changes in about 40% of the study area from 1988 to 2020. Concerning the direction
of change, the area exhibiting a greening trend by far exceeds the area with a browning
trend. This finding coincides well with observations on a global scale, where trends of
increasing green vegetation cover were detected in many regions of the world in recent
decades [3,4]. About 60% of the analyzed area showed no significant land cover change.
4.2. Distinction of Greening and Browning Trends
We detected greening in all land cover classes, though the largest proportions were
found in areas at or near the valley bottom. The amount of change was highest in cultivated
areas. This finding is not in line with our expectation that valley bottoms are characterized
mainly by browning due to overuse [49,51,59,63]. The highly elevated land cover classes
show a smaller percentage of greening; however, due to their large extent, the absolute
areas are still extensive. Cryophytic and subnival vegetation have a high proportion of
time series without significant change, which shows the stability of this high-elevation
land cover class. This is also an unexpected finding, as it contradicts the results of other
studies that expect greening, particularly in the highest altitudes [43,44,126].
Browning could be detected for less extensive areas, though it also occurred in all land
cover classes and predominantly in vegetation at or near the valley bottom. These time
series mostly show a non-linear development, indicating abrupt changes. A cluster of such
non-linear negative development was found in Red valley in the northwest of the study
area, which resulted from a debris flow in 2011 (Figure 10a).
Remote Sens. 2021, 13, 3951
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(a)
(b)
(c)
‐
Figure 10. Illustrative examples of partial effect plots for three selected ground-truthing
sites. The
plots show the component effect of the variable ‘year’ on the MSAVI value. The scale of the y-axis‐ was
shifted by the value of the intercept so that the plot represents the prediction of the output, assuming
the other variables included in the model are at their average value. The number in the parenthesis
‐
of the y-axis label gives the estimated degrees of freedom of the smooth: (a) a debris flow in 2011
destroyed the mountain Tugai in Red valley; (b) a mountain steppe site that is today dominated
by prickly Cousinia plants shows no significant change in MSAVI but rather in species composition;
‐
(c) example site from a barren land area without any vegetation that exhibits an implausible steep
increase in MSAVI.
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For the variable slope, browning sites have the lowest median, which can be explained
by the highest impact of disastrous events in the relatively weakly inclined valley bottoms,
whereas greening was detected in both steep and flat areas. The same explanations hold
true for north-exposedness, for which the median in sites with browning is the highest.
Again, this is because a relatively large cluster of browning was found in a north-exposed
valley. However, we assume that north-exposedness is not a general explanation for
browning trends, as similar events also occurred in south-exposed valleys outside the
study area—e.g., around the village of Barsem [94,127].
4.3. Relationship of Greening and Browning Trends to Temperature and Precipitation
For mountains, in particular, rising temperatures and a prolonged growing season
were discussed as major drivers for increasing vegetation cover and species richness in
alpine areas. As the temperature is one of the major limits on plant growth, warming
reduces this constraint [43,44,128]. For the study area, we found significantly rising temperatures. This result is in line with the findings of Haag et al. [129,130]; the review of
Robinson [59]; δ18O data from ice cores [131]; and the Intergovernmental Panel on Climate
Change (IPCC) report, which expects an increase of 3.7 ◦ C in the mean annual temperature
(temperature change for 2080–2099 in comparison to 1980–1999) in the region [132]. We conceive these results as a strong indication that warming also plays a role in the greening of
the study area. In most of the pixel time series that exhibit a greening trend, the increase
in MSAVI is linear. This finding corresponds well to the nearly continuous temperature
increase and corroborates the role of rising temperatures as a driver of greening. In arid
areas, increasing precipitation is a frequent driver of greening [6,11]. However, we did not
find significant trends of change in precipitation for the study area. This result is backed by
the findings reviewed by Robinson [59], whereas Deji et al. [131] found a wetting trend in
δ18O ice core data, and Haag et al. [129] detected increased summer precipitation.
In the highly elevated land cover classes, the increasing temperature that causes permafrost thawing is likely to contribute to the observed greening, particularly in moraine
material that is classified as barren land, as well as in cryophytic and subnival vegetation. Peng et al. [133] investigated permafrost–vegetation relationships in the northern
hemisphere and were able to show that the increased number of thawing days prolongs
the growing season and increases the active layer thickness, which facilitates vegetation
growth in permafrost regions. In the Pamirs, this is an important point, as 84.1% of the
GBAO was identified as a potential permafrost area. Projections based on a scenario of a
temperature increase of 4 ◦ C until the end of the century expect a disappearance of 40% of
the recent permafrost areas in the GBAO [134].
Furthermore, thawing permafrost decreases slope stability and hence promotes gravitational mass movements. Together with increased run-off induced by intensified melting of snow and glaciers [135–139], this may create catastrophic mass flows that devastate the valley bottoms and thus lead to an abrupt browning signal in remote sensing
data [127,134,140]. Indeed such events of gravitational mass movement regularly occur
in high mountains [127,140]. However, the prospective consistent temperature increase
within the next few decades [132] will accelerate such events. Additionally, heavy summer
rains in the study area in recent years resulting from disturbances triggered by the Indian
Summer Monsoon or originating from extratropical cyclones [139,141–143] exacerbate
these processes. In some cases, the enhanced melting can lead to the development of glacial
lakes and subsequently to glacial lake outburst floods (GLOFs). Such an event occurred in
Shakhdara valley, ca. 50 km south of the study area, where a GLOF destroyed the village
of Dasht in 2002, killing dozens of people and devastating vegetation [127,134].
4.4. Relationship of Greening and Browning Trends to Population and Livestock Numbers
The strong greening trend of the land cover classes at and near the valley bottom
can be explained by the increased cultivation efforts of a rising population size together
with improved land management practices linked to privatization since the breakdown
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of the Soviet Union [59]. Another argument in favor of this explanation is that most
of the greening areas are located in regions where the highest population numbers are
reported. Furthermore, the restoration and extension of irrigation systems are likely to
play important roles. After a major extension of these systems during the Soviet period,
their maintenance ceased with independence. However, many of these systems have been
reestablished—e.g., as community-based irrigation governance systems (see, e.g., [144]).
Similarly, projects of joint forestry management have helped to restore the Tugai scrubs
and forests [145].
In mountainous drylands, floods and debris flows are often disastrous, as arable and
irrigation lands, as well as associated villages, are predominantly situated on narrow river
terraces and alluvial plains in valley bottoms [144]. In this area, the Tugai scrubs and
forests play the dominant role in river discharge regulation and embankment stability, as
well as in the protection of soils and infrastructure. Directly after the Soviet breakdown,
these forests strongly degraded because of the energy crisis that forced the local population
to use the Tugai for fuelwood and timber, leading to the increased vulnerability of the
valley vegetation, arable land, and local communities [144–148]. In some areas, the state of
these forests has already improved, but there is still an increased vulnerability to natural
disasters in many areas due to poor forest conditions [149].
Another factor considered to be an important driver in browning is grazing impact.
Our results show that the livestock numbers in the study area declined after the Soviet
breakdown but strongly increased in the last two decades. This finding coincides with
the results of Robinson [59]. Furthermore, current trends towards a reduction in livestock
mobility and the abandonment of remote pastures are causing overgrazing in easily accessible areas and, in particular, around villages [59,62,150]. The increasing livestock numbers
are primarily driven by the increasing population that we found for GBAO since 1990.
In particular, the outbreak of the Tajik civil war in 1992 is seen as an important booster
initiating the rising numbers after the Soviet breakdown [63]. A higher population also
increases the need for other natural resources, such as fuelwood and timber. In the study
area, the Tugai forest is the most important provider for these ecosystem goods, which
explains the browning trend seen in some parts of this land cover class.
Indeed, we detected browning in land cover classes extensively used for grazing
livestock—e.g., in meadows—though the extent is low. Mountain steppes, another heavily
grazed land cover class, exhibit the largest proportion of pixel time series without significant
change (for an illustration, see Figure 10b).
According to our observations, grazing caused no changes in vegetation quantity here
but rather in vegetation quality. An explanation can be found in the long grazing history
of the study area and the change to vegetation dominated by prickly shrubs and herbs or
plant species that contain alkaloids [96,151]. Our field data support these findings. For
example, we found many unpalatable species, such as Senecio spp., and we frequently
detected a strong dominance of prickly herbs—in particular, of the genus Cousinia—in
areas where historical vegetation studies reported a much lower quantity of this plant
type [97]. We conclude that the change in the mountain steppes is not visible in the remote
sensing data but is evident on species and trait levels.
The inspection of 76 ground-truthing sites in the field and comparison with the historic
vegetation map support the plausibility of the findings derived from the remote sensing
analyses in most cases. However, the results for scree sites with no vegetation should be
interpreted with caution, as we detected unreliable steep positive trends in some rare cases
(see the example given in Figure 10c).
5. Conclusions
In this paper, we presented an innovative and promising approach for analyzing land
cover trends in dry mountain systems by combining long-term Landsat image time series
with environmental and socio-demographic data using GAMMs. Thereby, we were able
to tackle several challenges of remote sensing studies in mountainous drylands, such as
Remote Sens. 2021, 13, 3951
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irregular data, high background influence, non-linearity, and sensor issues. We found
that greening and browning trends coexist in the study area, with greening being predominant. The characteristics of the detected trends vary according to the vegetation and
land cover classes involved, as well as their topographical and geographical distribution.
With the applied approach, we were able to detect remarkable changes at a small spatial
scale—e.g., in narrow valley bottoms that remained undetected in previous long-term
remote sensing studies. These areas are predominantly used for agriculture and forestry,
which suggests direct anthropogenic drivers of change here. We also discovered a high
number of pixels with a greening trend that corresponds well with increasing temperature.
Trends of increasing precipitation, which would support greening, were not observed.
Detected browning was mainly attributed to disastrous events that were most probably
enhanced by increasing temperatures. Contrary to our expectations, browning trends
did not correspond well with an increasing population and increasing livestock numbers.
Grazing increased, but associated changes were not detectable with our approach. This
can be explained by our field observations, which showed that the respective effects are
most active on the species and trait level. Different approaches and future studies are
required in order to enhance our understanding of the processes involved at this resolution.
Nevertheless, this study presents trends of land cover change in the Pamir mountains
with a novel methodology that could serve as a basis for further research on vegetation
dynamics for management purposes—e.g., for sustainable pasture and forest management.
Thereby, the approach shows high potential in mountainous and dry areas globally.
Author Contributions: K.A.V. designed the study, conducted the analysis, and wrote the manuscript;
H.Z. and C.S. revised the manuscript; K.A.V., H.Z. and C.S. discussed the results and reviewed and
edited the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Belmont Forum, the German Research Foundation
(grant numbers SA 775/12-1 and VA 749/4-1), and the Schmauser Foundation.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: The authors are grateful for the logistic support of this study by Saidmir
Shomansurov (†), who sadly passed away in May 2020. We dedicate this study to his memory.
Martin Häusser, Malte Manne, Matthias von Stackelberg, and Jakob Wernicke deserve thanks for
supporting the data acquisition in the field. We also thank Andrei Dörre and Tobias Kraudzun for the
provision and translation of population and livestock statistics. Furthermore, we would like to thank
Ruth Tattershall, Christopher Dacosta, and Mattias Roth very much for language editing. Finally, we
thank three anonymous reviewers for their time and effort in reviewing the manuscript.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
References
1.
2.
3.
4.
5.
Pan, N.; Feng, X.; Fu, B.; Wang, S.; Ji, F.; Pan, S. Increasing Global Vegetation Browning Hidden in Overall Vegetation Greening:
Insights from Time-Varying Trends. Remote Sens. Environ. 2018, 214, 59–72. [CrossRef]
Nagendra, H.; Reyers, B.; Lavorel, S. Impacts of Land Change on Biodiversity: Making the Link to Ecosystem Services. Curr.
Opin. Environ. Sustain. 2013, 5, 503–508. [CrossRef]
Zeng, Z.; Peng, L.; Piao, S. Response of Terrestrial Evapotranspiration to Earth’s Greening. Curr. Opin. Environ. Sustain. 2018, 33,
9–25. [CrossRef]
Zhu, Z.; Piao, S.; Myneni, R.B.; Huang, M.; Zeng, Z.; Canadell, J.G.; Ciais, P.; Sitch, S.; Friedlingstein, P.; Arneth, A.; et al. Greening
of the Earth and Its Drivers. Nat. Clim. Chang. 2016, 6, 791–795. [CrossRef]
Brandt, M.; Mbow, C.; Diouf, A.A.; Verger, A.; Samimi, C.; Fensholt, R. Ground- and Satellite-Based Evidence of the Biophysical
Mechanisms behind the Greening Sahel. Glob. Chang. Biol. 2015, 21, 1610–1620. [CrossRef] [PubMed]
Remote Sens. 2021, 13, 3951
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
20 of 25
Fensholt, R.; Rasmussen, K.; Nielsen, T.T.; Mbow, C. Evaluation of Earth Observation Based Long Term Vegetation
Trends—Intercomparing NDVI Time Series Trend Analysis Consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT
VGT Data. Remote Sens. Environ. 2009, 113, 1886–1898. [CrossRef]
Trichon, V.; Hiernaux, P.; Walcker, R.; Mougin, E. The Persistent Decline of Patterned Woody Vegetation: The Tiger Bush in the
Context of the Regional Sahel Greening Trend. Glob. Chang. Biol. 2018, 24, 2633–2648. [CrossRef] [PubMed]
Mueller, T.; Dressler, G.; Tucker, C.J.; Pinzon, J.E.; Leimgruber, P.; Dubayah, R.O.; Hurtt, G.C.; Böhning-Gaese, K.; Fagan, W.F.
Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity. Remote Sens. 2014, 6, 5717–5731.
[CrossRef]
Forzieri, G.; Alkama, R.; Miralles, D.G.; Cescatti, A. Response to Comment on “Satellites Reveal Contrasting Responses of
Regional Climate to the Widespread Greening of Earth”. Science 2018, 360, 6394. [CrossRef] [PubMed]
Lucht, W.; Prentice, I.C.; Myneni, R.B.; Sitch, S.; Friedlingstein, P.; Cramer, W.; Bousquet, P.; Buermann, W.; Smith, B. Climatic
Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect. Science 2002, 296, 1687–1689. [CrossRef]
Liu, Y.; Li, Y.; Li, S.; Motesharrei, S. Spatial and Temporal Patterns of Global NDVI Trends: Correlations with Climate and Human
Factors. Remote Sens. 2015, 7, 13233–13250. [CrossRef]
Brehaut, L.; Danby, R.K. Inconsistent Relationships between Annual Tree Ring-Widths and Satellite-Measured NDVI in a
Mountainous Subarctic Environment. Ecol. Indic. 2018, 91, 698–711. [CrossRef]
Jeong, J.-H.; Kug, J.-S.; Kim, B.-M.; Min, S.-K.; Linderholm, H.W.; Ho, C.-H.; Rayner, D.; Chen, D.; Jun, S.-Y. Greening in the
Circumpolar High-Latitude May Amplify Warming in the Growing Season. Clim. Dyn. 2012, 38, 1421–1431. [CrossRef]
Cho, M.H.; Yang, A.R.; Baek, E.H.; Kang, S.M.; Jeong, S.J.; Kim, J.Y.; Kim, B.M. Vegetation-Cloud Feedbacks to Future Vegetation
Changes in the Arctic Regions. Clim. Dyn. 2018, 50, 3745–3755. [CrossRef]
Silapaswan, C.S.; Verbyla, D.L.; McGuire, A.D. Land Cover Change on the Seward Peninsula: The Use of Remote Sensing to
Evaluate the Potential Influences of Climate Warming on Historical Vegetation Dynamics. Can. J. Remote Sens. 2001, 27, 542–554.
[CrossRef]
Goetz, S.J.; Bunn, A.G.; Fiske, G.J.; Houghton, R.A. Satellite-Observed Photosynthetic Trends across Boreal North America
Associated with Climate and Fire Disturbance. Proc. Natl. Acad. Sci. USA 2005, 102, 13521–13525. [CrossRef]
Sturm, M.; Racine, C.; Tape, K. Increasing Shrub Abundance in the Arctic. Nature 2001, 411, 546–547. [CrossRef]
Sturm, M.; McFadden, J.P.; Liston, G.E.; Chapin, F.S., III; Racine, C.H.; Holmgren, J. Snow-Shrub Interactions in Arctic Tundra:
A Hypothesis with Climatic Implications. J. Clim. 2001, 14, 336–344. [CrossRef]
Bhatt, U.S.; Walker, D.A.; Raynolds, M.K.; Comiso, J.C.; Epstein, H.E.; Jia, G.; Gens, R.; Pinzon, J.E.; Tucker, C.J.; Tweedie, C.E.; et al.
Circumpolar Arctic Tundra Vegetation Change Is Linked to Sea Ice Decline. Earth Interact. 2010, 14, 1–20. [CrossRef]
Bunn, A.G.; Goetz, S.J.; Kimball, J.S.; Zhang, K. Northern High-Latitude Ecosystems Respond to Climate Change. Eos. Trans. Am.
Geophys. Union 2007, 88, 333–335. [CrossRef]
De Jong, R.; de Bruin, S.; de Wit, A.; Schaepman, M.E.; Dent, D.L. Analysis of Monotonic Greening and Browning Trends from
Global NDVI Time-Series. Remote Sens. Environ. 2011, 115, 692–702. [CrossRef]
Swann, A.L.S.; Fung, I.Y.; Chiang, J.C.H. Mid-Latitude Afforestation Shifts General Circulation and Tropical Precipitation. Proc.
Natl. Acad. Sci. USA 2012, 109, 712–716. [CrossRef] [PubMed]
Jin, Q.; Wang, C. The Greening of Northwest Indian Subcontinent and Reduction of Dust Abundance Resulting from Indian
Summer Monsoon Revival. Sci. Rep. 2018, 8, 4573. [CrossRef] [PubMed]
Haro-Carrión, X.; Waylen, P.; Southworth, J. Spatiotemporal Changes in Vegetation Greenness across Continental Ecuador:
A Pacific-Andean-Amazonian Gradient, 1982–2010. J. Land Use Sci. 2021, 16, 18–33. [CrossRef]
Kawabata, A.; Ichii, K.; Yamaguchi, Y. Global Monitoring of Interannual Changes in Vegetation Activities Using NDVI and Its
Relationships to Temperature and Precipitation. Int. J. Remote Sens. 2001, 22, 1377–1382. [CrossRef]
Chen, S.; Cui, X.; Liang, T. Response of Alpine Grassland Vegetation Phenology to Snow Accumulation and Melt in Namco Basin.
ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2018, 42, 185–192. [CrossRef]
Shen, M.; Piao, S.; Jeong, S.-J.; Zhou, L.; Zeng, Z.; Ciais, P.; Chen, D.; Huang, M.; Jin, C.-S.; Li, L.Z.X.; et al. Evaporative Cooling
over the Tibetan Plateau Induced by Vegetation Growth. Proc. Natl. Acad. Sci. USA 2015, 112, 9299–9304. [CrossRef]
Zhang, W.; Zhou, T.; Zhang, L. Wetting and Greening Tibetan Plateau in Early Summer in Recent Decades. J. Geophys. Res. Atmos.
2017, 122, 5808–5822. [CrossRef]
Jeong, S.-J.; Ho, C.-H.; Park, T.-W.; Kim, J.; Levis, S. Impact of Vegetation Feedback on the Temperature and Its Diurnal Range
over the Northern Hemisphere during Summer in a 2 × CO2 Climate. Clim. Dyn. 2011, 37, 821–833. [CrossRef]
Bogaert, J.; Zhou, L.; Tucker, C.J.; Myneni, R.B.; Ceulemans, R. Evidence for a Persistent and Extensive Greening Trend in Eurasia
Inferred from Satellite Vegetation Index Data. J. Geophys. Res. Atmos. 2002, 107, ACL 4-1. [CrossRef]
Fan, X.; Liu, Y.; Tao, J.; Wang, Y.; Zhou, H. MODIS Detection of Vegetation Changes and Investigation of Causal Factors in Poyang
Lake Basin, China for 2001–2015. Ecol. Indic. 2018, 91, 511–522. [CrossRef]
Keeling, C.D.; Chin, J.F.S.; Whorf, T.P. Increased Activity of Northern Vegetation Inferred from Atmospheric CO2 Measurements.
Nature 1996, 382, 146–149. [CrossRef]
Tucker, C.J.; Slayback, D.A.; Pinzon, J.E.; Los, S.O.; Myneni, R.B.; Taylor, M.G. Higher Northern Latitude Normalized Difference
Vegetation Index and Growing Season Trends from 1982 to 1999. Int. J. Biometeorol. 2001, 45, 184–190. [CrossRef]
Remote Sens. 2021, 13, 3951
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
21 of 25
Hinzman, L.D.; Bettez, N.D.; Bolton, W.R.; Chapin, F.S.; Dyurgerov, M.B.; Fastie, C.L.; Griffith, B.; Hollister, R.D.; Hope, A.;
Huntington, H.P.; et al. Evidence and Implications of Recent Climate Change in Northern Alaska and Other Arctic Regions. Clim.
Chang. 2005, 72, 251–298. [CrossRef]
Metternicht, G.; Zinck, J.A.; Blanco, P.D.; del Valle, H.F. Remote Sensing of Land Degradation: Experiences from Latin America
and the Caribbean. J. Environ. Qual. 2010, 39, 42–61. [CrossRef] [PubMed]
Zhou, L.; Tucker, C.J.; Kaufmann, R.K.; Slayback, D.; Shabanov, N.V.; Myneni, R.B. Variations in Northern Vegetation Activity
Inferred from Satellite Data of Vegetation Index during 1981 to 1999. J. Geophys. Res. Atmos. 2001, 106, 20069–20083. [CrossRef]
Piao, S.; Fang, J.; Zhou, L.; Zhu, B.; Tan, K.; Tao, S. Changes in Vegetation Net Primary Productivity from 1982 to 1999 in China.
Glob. Biogeochem. Cycles 2005, 19. [CrossRef]
Lü, Y.; Zhang, L.; Feng, X.; Zeng, Y.; Fu, B.; Yao, X.; Li, J.; Wu, B. Recent Ecological Transitions in China: Greening, Browning, and
Influential Factors. Sci. Rep. 2015, 5, 8732. [CrossRef] [PubMed]
Eddy, I.M.S.; Gergel, S.E.; Coops, N.C.; Henebry, G.M.; Levine, J.; Zerriffi, H.; Shibkov, E. Integrating Remote Sensing and Local
Ecological Knowledge to Monitor Rangeland Dynamics. Ecol. Indic. 2017, 82, 106–116. [CrossRef]
FAO; Mountain Partnership Secretariat; UNCCD; SDC; CDE. Highlands and Drylands-Mountains, a Source of Resilience in Arid
Regions; FAO, UNCCD, Mountain Partnership, Swiss Agency for Development and Cooperation, and CDE, with the support of
an international group of experts: Rome, Italy, 2011; p. 115.
Grabherr, G.; Gottfried, M.; Pauli, H. Climate Effects on Mountain Plants. Nature 1994, 369, 448. [CrossRef] [PubMed]
Salick, J.; Fang, Z.; Byg, A. Eastern Himalayan Alpine Plant Ecology, Tibetan Ethnobotany, and Climate Change. Glob. Environ.
Chang. 2009, 19, 147–155. [CrossRef]
Frei, E.; Bodin, J.; Walther, G.-R. Plant Species’ Range Shifts in Mountainous Areas—All Uphill from Here? Bot. Helv. 2010, 120,
117–128. [CrossRef]
Walther, G.-R.; Beißner, S.; Burga, C.A. Trends in the Upward Shift of Alpine Plants. J. Veg. Sci. 2005, 16, 541–548. [CrossRef]
Theurillat, J.-P.; Guisan, A. Potential Impact of Climate Change on Vegetation in the European Alps: A Review. Clim. Chang. 2001,
50, 77–109. [CrossRef]
Spehn, E.M.; Liberman, M.; Korner, C. Land Use Change and Mountain Biodiversity; CRC Press: New York, NY, USA, 2006;
ISBN 978-1-4200-0287-4.
Tasser, E.; Leitinger, G.; Tappeiner, U. Climate Change versus Land-Use Change—What Affects the Mountain Landscapes More?
Land Use Policy 2017, 60, 60–72. [CrossRef]
Kassam, K.-A. Viewing Change Through the Prism of Indigenous Human Ecology: Findings from the Afghan and Tajik Pamirs.
Hum. Ecol. 2009, 37, 677. [CrossRef]
Beg, G. Cross-border cooperation for biodiversity conservation and sustainable development: Case studies on Karakoram,
Hindukush and Pamir. In Experiences with and Prospects for Regional Exchange and Cooperation in Mountain Areas; Kreutzmann, H.,
Beg, G., Richter, J., Eds.; InWEnt-Internationale Weiterbildung und Entwicklung GmbH Press: Bonn, Germany, 2009; pp. 184–211.
Giuliani, A.; van Oudenhoven, F.; Mubalieva, S. Agricultural Biodiversity in the Tajik Pamirs. Mt. Res. Dev. 2011, 31, 16–26.
[CrossRef]
Akhmadov, K.M.; Breckle, S.-W.; Breckle, U. Effects of grazing on biodiversity, productivity, and soil erosion of alpine pastures in Tajik
Mountains. In Land Use Change and Mountain Biodiversity; CRC Press: New York, NY, USA, 2006; pp. 239–248. ISBN 978-0-8493-3523-5.
Mountain Societies Development Support Programme. In 2003 Baseline Survey of Gorno-Badakhshan Autonomous Oblast, Tajikistan;
MSDSP: Khorog, Tajikistan, 2004.
Wulder, M.A.; Hall, R.J.; Coops, N.C.; Franklin, S.E. High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization.
BioScience 2004, 54, 511–521. [CrossRef]
Kumari, N.; Saco, P.M.; Rodriguez, J.F.; Johnstone, S.A.; Srivastava, A.; Chun, K.P.; Yetemen, O. The Grass Is Not Always Greener
on the Other Side: Seasonal Reversal of Vegetation Greenness in Aspect-Driven Semiarid Ecosystems. Geophys. Res. Lett. 2020, 47,
e2020GL088918. [CrossRef]
Guo, B.; Yang, F.; Fan, Y.; Han, B.; Chen, S.; Yang, W. Dynamic Monitoring of Soil Salinization in Yellow River Delta Utilizing
MSAVI–SI Feature Space Models with Landsat Images. Environ. Earth Sci. 2019, 78, 308. [CrossRef]
Weiss, D.J.; Walsh, S.J. Remote Sensing of Mountain Environments. Geogr. Compass 2009, 3, 1–21. [CrossRef]
Wu, H.; Li, Z.-L. Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling. Sensors 2009, 9, 1768–1793.
[CrossRef]
Aplin, P. On Scales and Dynamics in Observing the Environment. Int. J. Remote Sens. 2006, 27, 2123–2140. [CrossRef]
Robinson, S. Land Degradation in Central Asia: Evidence, Perception and Policy. In The End of Desertification? Disputing Environmental Change in the Drylands; Behnke, R., Mortimore, M., Eds.; Springer Earth System Sciences; Springer: Berlin/Heidelberg,
Germany, 2016; pp. 451–490. ISBN 978-3-642-16014-1.
Forkel, M.; Carvalhais, N.; Verbesselt, J.; Mahecha, M.D.; Neigh, C.S.R.; Reichstein, M. Trend Change Detection in NDVI Time
Series: Effects of Inter-Annual Variability and Methodology. Remote Sens. 2013, 5, 2113–2144. [CrossRef]
de Beurs, K.M.; Henebry, G.M. A Statistical Framework for the Analysis of Long Image Time Series. Int. J. Remote Sens. 2005, 26,
1551–1573. [CrossRef]
Vanselow, K.A.; Kraudzun, T.; Samimi, C. Grazing Practices and Pasture Tenure in the Eastern Pamirs. Mt. Res. Dev. 2012, 32,
324–336. [CrossRef]
Remote Sens. 2021, 13, 3951
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
22 of 25
Breu, T.; Hurni, H. The Tajik Pamirs: Challenges of Sustainable Development in an Isolated Mountain Region; Centre for Development
and Environment (CDE): Berne, Switzerland, 2003.
U.S. Geological Survey. Landsat 7 Data Users Handbook; Earth Resources Observation and Science (EROS) Center: Sioux Falls, SC,
USA, 2018.
Simpson, G.L. Modelling Palaeoecological Time Series Using Generalised Additive Models. Front. Ecol. Evol. 2018, 6, 149.
[CrossRef]
Zuur, A.; Ieno, E.N.; Smith, G.M. Analyzing Ecological Data; Statistics for Biology and Health; Springer: New York, NY, USA, 2007;
ISBN 978-0-387-45967-7.
Huang, S.; Tang, L.; Hupy, J.P.; Wang, Y.; Shao, G. A Commentary Review on the Use of Normalized Difference Vegetation Index
(NDVI) in the Era of Popular Remote Sensing. J. For. Res. 2021, 32, 1–6. [CrossRef]
Liu, F.; Liu, H.; Xu, C.; Zhu, X.; He, W.; Qi, Y. Remotely Sensed Birch Forest Resilience against Climate Change in the Northern
China Forest-Steppe Ecotone. Ecol. Indic. 2021, 125, 107526. [CrossRef]
Alcaraz-Segura, D.; Cabello, J.; Paruelo, J. Baseline Characterization of Major Iberian Vegetation Types Based on the NDVI
Dynamics. Plant Ecol. 2009, 202, 13–29. [CrossRef]
Bai, Z.G.; Dent, D.L.; Olsson, L.; Schaepman, M.E. Proxy Global Assessment of Land Degradation. Soil Use Manag. 2008, 24,
223–234. [CrossRef]
Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A Modified Soil Adjusted Vegetation Index. Remote Sens. Environ.
1994, 48, 119–126. [CrossRef]
Khare, S.; Latifi, H.; Rossi, S. A 15-Year Spatio-Temporal Analysis of Plant β-Diversity Using Landsat Time Series Derived Rao’s
Q Index. Ecol. Indic. 2021, 121, 107105. [CrossRef]
Vanselow, K.A.; Zandler, H.; Samimi, C. Methods of Assessing Vegetation Dynamics and Pasture Potentials in Arid Mountain
Regions. In Exploring and Optimizing Agricultural Landscapes; Mueller, L., Sychev, V.G., Dronin, N.M., Eulenstein, F., Eds.; Innovations
in Landscape Research; Springer International Publishing: Cham, Switzerland, 2021; pp. 373–382. ISBN 978-3-030-67448-9.
Vanselow, K.A.; Samimi, C. Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote
Sensing and Random Forests. Remote Sens. 2014, 6, 6709–6726. [CrossRef]
Wu, Z.; Lei, S.; Bian, Z.; Huang, J.; Zhang, Y. Study of the Desertification Index Based on the Albedo-MSAVI Feature Space for
Semi-Arid Steppe Region. Environ. Earth Sci. 2019, 78, 232. [CrossRef]
Zandler, H.; Brenning, A.; Samimi, C. Quantifying Dwarf Shrub Biomass in an Arid Environment: Comparing Empirical Methods
in a High Dimensional Setting. Remote Sens. Environ. 2015, 158, 140–155. [CrossRef]
Zandler, H.; Brenning, A.; Samimi, C. Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid
Environment: Advantages and Limitations. Remote Sens. 2015, 7, 4565–4580. [CrossRef]
Xue, J.; Su, B. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. J. Sens. 2017,
2017, e1353691. [CrossRef]
Ge, L.; Juanle, W.; Yanjie, W.; Haishuo, W. Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015
via Remote Sensing. J. Resour. Ecol. 2019, 10, 676–684. [CrossRef]
Ding, Y.; Zhang, H.; Zhao, K.; Zheng, X. Investigating the Accuracy of Vegetation Index-Based Models for Estimating the
Fractional Vegetation Cover and the Effects of Varying Soil Backgrounds Using in Situ Measurements and the PROSAIL Model.
Int. J. Remote Sens. 2017, 38, 4206–4223. [CrossRef]
Mahmoud, A.M.A.; Hasmadi, I.M.; Alias, M.S.; Azani, A.M. Rangeland Degradation Assessment in the South Slope of the
Al-Jabal Al-Akhdar, Northeast Libya Using Remote Sensing Technology. J. Rangel. Sci. 2016, 6, 73–81.
Schmidt, H.; Karnieli, A. Sensitivity of Vegetation Indices to Substrate Brightness in Hyper-Arid Environment: The Makhtesh
Ramon Crater (Israel) Case Study. Int. J. Remote Sens. 2001, 22, 3503–3520. [CrossRef]
Lee, C.K.F.; Nicholson, E.; Duncan, C.; Murray, N.J. Estimating Changes and Trends in Ecosystem Extent with Dense Time-Series
Satellite Remote Sensing. Conserv. Biol. 2021, 35, 325–335. [CrossRef] [PubMed]
Ivits, E.; Cherlet, M.; Sommer, S.; Mehl, W. Addressing the Complexity in Non-Linear Evolution of Vegetation Phenological
Change with Time-Series of Remote Sensing Images. Ecol. Indic. 2013, 26, 49–60. [CrossRef]
Petit, C.C.; Lambin, E.F. Long-Term Land-Cover Changes in the Belgian Ardennes (1775–1929): Model-Based Reconstruction vs.
Historical Maps. Glob. Chang. Biol. 2002, 8, 616–630. [CrossRef]
Wood, S.N. Generalized Additive Models: An Introduction with R, 2nd ed.; Chapman and Hall/CRC: Boca Raton, FL, USA, 2017.
Goodbody, T.R.H.; Coops, N.C.; Hermosilla, T.; Tompalski, P.; Pelletier, G. Vegetation Phenology Driving Error Variation in
Digital Aerial Photogrammetrically Derived Terrain Models. Remote Sens. 2018, 10, 1554. [CrossRef]
Sudo, M.; Sato, Y.; Yorozuya, H. Time-Course in Attractiveness of Pheromone Lure on the Smaller Tea Tortrix Moth: A Generalized
Additive Mixed Model Approach. Ecol. Res. 2021, 36, 603–616. [CrossRef]
Gardiner, T.; Didham, R.K. Glowing, Glowing, Gone? Monitoring Long-Term Trends in Glow-Worm Numbers in South-East
England. Insect Conserv. Divers. 2020, 13, 162–174. [CrossRef]
Stelzer, R.S.; Parr, T.B.; Coulibaly, M. A Ten Year Record of Nitrate Retention and Solute Trends in a Wisconsin Sand Plains Stream:
Temporal Variation at Multiple Scales. Biogeochemistry 2020, 147, 125–147. [CrossRef]
Wang, H.; Hu, X.; Sterba-Boatwright, B. A New Statistical Approach for Interpreting Oceanic FCO2 Data. Mar. Chem. 2016, 183,
41–49. [CrossRef]
Remote Sens. 2021, 13, 3951
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
23 of 25
Miehe, G.; Winiger, M.; Böhner, J.; Yili, Z. The Climatic Diagram Map of High Asia: Purpose and Concepts (Klimadiagramm-Karte
von Hochasien. Konzept Und Anwendung). Erdkunde 2001, 55, 94–97. [CrossRef]
Stone, M.; Fuerle, R.D. On the Steppes of Central Asia; Spooner Press: New York, NY, USA, 1992; ISBN 978-0-9635918-0-7.
Breckle, S.-W. Ökologie der Erde Band 3-Spezielle Ökologie der Gemäßigten und Arktischen Zonen Euro-Nordasiens: Zonobiom VI-IX;
Schweizerbart: Stuttgart, Germany, 2021; ISBN 978-3-510-65422-2.
Unger-Shayesteh, K.; Vorogushyn, S.; Farinotti, D.; Gafurov, A.; Duethmann, D.; Mandychev, A.; Merz, B. What Do We Know
about Past Changes in the Water Cycle of Central Asian Headwaters? A Review. Glob. Planet. Chang. 2013, 110, 4–25. [CrossRef]
Wucherer, W.; Breckle, S.-W. Vegetation of the Pamir (Tajikistan): Land use and desertification problems. In Land Use Change and
Mountain Biodiversity; CRC Press: New York, NY, USA, 2006; ISBN 978-0-8493-3523-5.
Vanselow, K.A.; Samimi, C.; Breckle, S.-W. Preserving a Comprehensive Vegetation Knowledge Base–An Evaluation of Four
Historical Soviet Vegetation Maps of the Western Pamirs (Tajikistan). PLoS ONE 2016, 11, e0148930. [CrossRef]
Cunha, S.F. Perestroika to Parkland: The Evolution of Land Protection in the Pamir Mountains of Tajikistan. Ann. Am. Assoc.
Geogr. 2017, 107, 465–479. [CrossRef]
Haslinger, A.; Breu, T.; Hurni, H.; Maselli, D. Opportunities and Risks in Reconciling Conservation and Development in a
Post-Soviet Setting: The Example of the Tajik National Park. Int. J. Biodivers. Sci. Manag. 2007, 3, 157–169. [CrossRef]
Bragina, T.; Nowak, A.; Vanselow, K.A.; Wagner, V. Grasslands of Kazakhstan and Middle Asia: The Ecology, Conservation
and Use of a Vast and Globally Important Area. In Grasslands of the World Diversity, Management and Conservation; Squires, V.R.,
Dengler, J., Hua, L., Feng, H., Eds.; CRC Press: Boca Raton, FL, USA, 2018; pp. 139–167.
Agakhanjanz, O.E.; Breckle, S.P. Gebirge der Erde; Burga, C.A., Klötzli, F., Grabherr, G., Eds.; Ulmer: Stuttgart, Germany, 2004;
pp. 151–157.
Earth Resources Observation And Science (EROS) Center Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global 2017.
[CrossRef]
Masek, J.G.; Vermote, E.F.; Saleous, N.; Wolfe, R.; Hall, F.G.; Huemmrich, K.F.; Gao, F.; Kutler, J.; Lim, T.K. LEDAPS Landsat
Calibration, Reflectance, Atmospheric Correction Preprocessing Code; ORNL DAAC: Oak Ridge, TN, USA, 2012. [CrossRef]
Foga, S.; Scaramuzza, P.L.; Guo, S.; Zhu, Z.; Dilley, R.D.; Beckmann, T.; Schmidt, G.L.; Dwyer, J.L.; Joseph Hughes, M.; Laue, B.
Cloud Detection Algorithm Comparison and Validation for Operational Landsat Data Products. Remote Sens. Environ. 2017, 194,
379–390. [CrossRef]
Zhu, Z.; Woodcock, C.E. Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery. Remote Sens. Environ. 2012, 118,
83–94. [CrossRef]
Zhu, Z.; Woodcock, C.E.; Holden, C.; Yang, Z. Generating Synthetic Landsat Images Based on All Available Landsat Data:
Predicting Landsat Surface Reflectance at Any given Time. Remote Sens. Environ. 2015, 162, 67–83. [CrossRef]
National Centers for Environmental Information Climate Data Online. Available online: https://www.ncdc.noaa.gov/cdo-web/
(accessed on 13 January 2021).
Copernicus Climate Change Service C3S ERA5-Land Reanalysis. Available online: https://cds.climate.copernicus.eu (accessed
on 25 May 2020).
Huffman, G.J.; Stocker, E.F.; Bolvin, D.T.; Nelkin, E.J.; Jackson, T. GES DISC Dataset: GPM IMERG Final Precipitation L3 1 Month
0.1 Degree x 0.1 Degree V06 (GPM_3IMERGM 06). Available online: https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06
(accessed on 25 May 2020).
Zandler, H.; Senftl, T.; Vanselow, K.A. Reanalysis Datasets Outperform Other Gridded Climate Products in Vegetation Change
Analysis in Peripheral Conservation Areas of Central Asia. Sci. Rep. 2020, 10, 22446. [CrossRef] [PubMed]
State Agency for Statistics under the President of the Republic of Tajikistan. Collection of Statistics of Socio-Economic Developments of
the Gorno-Badakhshan Autonomous Oblast on the Occasion of the 22nd Anniversary of the Independence of the Republic of Tajikistan; State
Agency for Statistics under the President of the Republic of Tajikistan: Khorog, Tajikistan, 2015.
State Agency for Statistics under the President of the Republic of Tajikistan. Gorno-Badakhshan Autonomous Oblast. 25th Anniversary
of the Independence of the Republic of Tajikistan. Collection of Statistics; State Agency for Statistics under the President of the Republic
of Tajikistan: Khorog, Tajikistan, 2017.
State Agency for Statistics under the President of the Republic of Tajikistan (TajStat). Collection of the Economic and Social
Development of the Gorno-Badakhshan Autonomous Oblast on the Occasion of the 21st Anniversary of the Independence of the Republic of
Tajikistan; State Agency for Statistics under the President of the Republic of Tajikistan: Khorog, Tajikistan, 2014.
Qonunov, Y. Recent Changes in Pastoral Systems. Case Study on Tajikistan. In Pastoralism and Rangeland Management in Mountain
Areas in the Context of Climate and Global Change. Regional Workshop in Khorog and Kashgar; Kreutzmann, H., Abdulalishoev, K.,
Zhaohui, L., Richter, J., Eds.; Deutsche Gesellschaft für Internationale Zusammenarbeit, Bundesministerium für Wirtschaftliche
Zusammenarbeit und Entwicklung: Bonn, Germnay, 2010; pp. 82–101.
State Agency for Statistics under the President of the Republic of Tajikistan (TajStat). Copies of the Original Hand Written Tables of
the Statistical Commitee in Khorog; State Agency for Statistics under the President of the Republic of Tajikistan (TajStat): Khorog,
Tajikistan, 2014.
Hijmans, R.J. raster: Geographic Data Analysis and Modeling. R Package Version 3.1-5. Available online: https://CRAN.Rproject.org/package=raster (accessed on 13 January 2021).
Remote Sens. 2021, 13, 3951
24 of 25
117. Leyer, I.; Wesche, K. Multivariate Statistik in der Ökologie: Eine Einführung; Springer-Lehrbuch; Springer: Berlin/Heidelberg,
Germany, 2007; ISBN 978-3-540-37705-4.
118. Linear Mixed-Effects Models: Basic Concepts and Examples. In Mixed-Effects Models in S and S-PLUS; Pinheiro, J.C.;
Bates, D.M. (Eds.) Statistics and Computing; Springer: New York, NY, USA, 2000; pp. 3–56. ISBN 978-0-387-22747-4.
119. Wood, S.N. mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation. R Package Version 1.8-37.
Available online: https://CRAN.R-project.org/package=mgcv (accessed on 13 January 2021).
120. Hastie, T.J.; Tibshirani, R.J. Generalized Additive Models; CRC Press: Boca Raton, FL, USA, 1990; ISBN 978-0-412-34390-2.
121. McCullagh, P.; Nelder, J.A. Generalized Linear Models; Chapman and Hall/CRC Press: Boca Raton, FL, USA, 1989.
122. Wood, S.N. Thin Plate Regression Splines. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 2003, 65, 95–114. [CrossRef]
123. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020.
124. Wilcox, R.R. Comparing Two Independent Groups Via Multiple Quantiles. J. R. Stat. Soc. Ser. D (Stat.) 1995, 44, 91–99. [CrossRef]
125. Hedderich, J.; Sachs, L. Angewandte Statistik: Methodensammlung Mit R, 15th ed.; Springer: Berlin/Heidelberg, Germany, 2016;
ISBN 978-3-662-45691-0.
126. Lamsal, P.; Kumar, L.; Shabani, F.; Atreya, K. The Greening of the Himalayas and Tibetan Plateau under Climate Change. Glob.
Planet. Chang. 2017, 159, 77–92. [CrossRef]
127. Breckle, S.-W.; Mergili, M.B. Seeausbrüche und Muren im Pamir. In Warnsignal Klima: Hochgebirge im Wandel; Lozan, J.L., Breckle,
S.-W., Grassl, H., Eds.; GEO Magazin-Hamburg Press: Hamburg, Germany, 2020; pp. 23–27.
128. Steinbauer, M.J.; Grytnes, J.-A.; Jurasinski, G.; Kulonen, A.; Lenoir, J.; Pauli, H.; Rixen, C.; Winkler, M.; Bardy-Durchhalter, M.;
Barni, E.; et al. Accelerated Increase in Plant Species Richness on Mountain Summits Is Linked to Warming. Nature 2018, 556,
231–234. [CrossRef] [PubMed]
129. Haag, I.; Jones, P.D.; Samimi, C. Central Asia’s Changing Climate: How Temperature and Precipitation Have Changed across
Time, Space, and Altitude. Climate 2019, 7, 123. [CrossRef]
130. Haag, I.; Kassam, K.-A.; Senftl, T.; Zandler, H.; Samimi, C. Measurements Meet Human Observations: Integrating Distinctive
Ways of Knowing in the Pamir Mountains of Tajikistan to Assess Local Climate Change. Clim. Chang. 2021, 165, 1–22. [CrossRef]
131. Deji; Yao, T.; Yang, X.; Xu, B.; Zhao, H.; Li, J.; Li, Z.; Wu, G.; Yao, P.; You, C.; et al. Warming and Wetting Climate during
Last Century Revealed by an Ice Core in Northwest Tibetan Plateau. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2017, 487, 270–277.
[CrossRef]
132. IPCC. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change; Core Writing Team, Pachauri, R.K., Reisinger, A., Eds.; IPCC: Geneva, Switzerland, 2007.
133. Peng, X.; Zhang, T.; Frauenfeld, O.W.; Wang, S.; Qiao, L.; Du, R.; Mu, C. Northern Hemisphere Greening in Association With
Warming Permafrost. J. Geophys. Res. Biogeosciences 2020, 125, e2019JG005086. [CrossRef]
134. Mergili, M.; Müllebner, B.; Kopf, C.; Schneider, J.F. Changes in the glacial and periglacial environment of the European Alps and
the Central Asian mountains and their socio-economic implications: A comparison. In Proceedings of the Innsbruck Conference,
November 21–23, 2011; Borsdorf, A., Stötter, J., Veulliet, E., Eds.; IGF-Forschungsberichte; Verlag der Österreichischen Akademie
der Wissenschaften: Wien, Austria, 2011.
135. Lambrecht, A.; Mayer, C.; Wendt, A.; Floricioiu, D.; Völksen, C. Elevation Change of Fedchenko Glacier, Pamir Mountains, from
GNSS Field Measurements and TanDEM-X Elevation Models, with a Focus on the Upper Glacier. J. Glaciol. 2018, 64, 637–648.
[CrossRef]
136. Armstrong, R.L. The Glaciers of the Hindu Kush-Himalayan Region: A Summary of the Science Regarding Glacier Melt/Retreat in the
Himalayan, Hindu Kush, Karakoram, Pamir, and Tien Shan Mountain Ranges|HimalDoc; International Centre for Integrated Mountain
Development (ICIMOD): Kathmandu, Nepal, 2010.
137. Bolch, T.; Kulkarni, A.; Kääb, A.; Huggel, C.; Paul, F.; Cogley, J.G.; Frey, H.; Kargel, J.S.; Fujita, K.; Scheel, M.; et al. The State and
Fate of Himalayan Glaciers. Science 2012, 336, 310–314. [CrossRef]
138. Narama, C. Glacier Variations in Central Asia during the 20th Century. J. Geogr. 2002, 111, 486–497. [CrossRef]
139. Khromova, T.E.; Osipova, G.B.; Tsvetkov, D.G.; Dyurgerov, M.B.; Barry, R.G. Changes in Glacier Extent in the Eastern Pamir,
Central Asia, Determined from Historical Data and ASTER Imagery. Remote Sens. Environ. 2006, 102, 24–32. [CrossRef]
140. GAPHAZ Assessment of Glacier and Permafrost Hazards in Mountain Regions—Technical Guidance Document; Standing Group on
Glacier and Permafrost Hazards in Mountains (GAPHAZ) of the International Association of Cryospheric Sciences (IACS):
Zürich, Switherland; The International Permafrost Association (IPA): Lima, Peru, 2017.
141. Aizen, V.B.; Mayewski, P.A.; Aizen, E.M.; Joswiak, D.R.; Surazakov, A.B.; Kaspari, S.; Grigholm, B.; Krachler, M.; Handley, M.;
Finaev, A. Stable-Isotope and Trace Element Time Series from Fedchenko Glacier (Pamirs) Snow/Firn Cores. J. Glaciol. 2009, 55,
275–291. [CrossRef]
142. Oberhänsli, H.; Novotná, K.; Píšková, A.; Chabrillat, S.; Nourgaliev, D.K.; Kurbaniyazov, A.K.; Matys Grygar, T. Variability in
Precipitation, Temperature and River Runoff in W Central Asia during the Past ~2000 yrs. Glob. Planet. Chang. 2011, 76, 95–104.
[CrossRef]
143. Schiemann, R.; Glazirina, M.G.; Schär, C. On the Relationship between the Indian Summer Monsoon and River Flow in the Aral
Sea Basin. Geophys. Res. Lett. 2007, 34. [CrossRef]
144. Dörre, A.; Goibnazarov, C. Small-Scale Irrigation Self-Governance in a Mountain Region of Tajikistan. MRED 2018, 38, 104–113.
[CrossRef]
Remote Sens. 2021, 13, 3951
25 of 25
145. Haider, L.J.; Neusel, B.; Peterson, G.D.; Schlüter, M. Past Management Affects Success of Current Joint Forestry Management
Institutions in Tajikistan. Environ. Dev. Sustain. 2019, 21, 2183–2224. [CrossRef] [PubMed]
146. Mislimshoeva, B.; Samimi, C.; Kirchhoff, J.-F.; Koellner, T. Analysis of Costs and People’s Willingness to Enroll in Forest
Rehabilitation in Gorno Badakhshan, Tajikistan. For. Policy Econ. 2013, 37, 75–83. [CrossRef]
147. Herbers, H. Transformation in the Tajik Pamirs: Gornyi-Badakhshan-an Example of Successful Restructuring? Cent. Asian Surv.
2001, 20, 367–381. [CrossRef]
148. Xenarios, S.; Gafurov, A.; Schmidt-Vogt, D.; Sehring, J.; Manandhar, S.; Hergarten, C.; Shigaeva, J.; Foggin, M. Climate Change
and Adaptation of Mountain Societies in Central Asia: Uncertainties, Knowledge Gaps, and Data Constraints. Reg. Env. Chang.
2019, 19, 1339–1352. [CrossRef]
149. Boonstra, W.J.; Björkvik, E.; Haider, L.J.; Masterson, V. Human Responses to Social-Ecological Traps. Sustain. Sci. 2016, 11,
877–889. [CrossRef] [PubMed]
150. Vanselow, K.A.; Kraudzun, T.; Samimi, C. Land Stewardship in Practice: An Example from the Eastern Pamirs of Tajikistan.
In Rangeland Stewardship in Central Asia: Balancing Improved Livelihoods, Biodiversity Conservation and Land Protection; Squires, V., Ed.;
Springer: Dordrecht, The Netherlands, 2012; pp. 71–90. ISBN 978-94-007-5367-9.
151. Tayjanov, K.; Mamadalieva, N.Z.; Wink, M. Diversity of the Mountain Flora of Central Asia with Emphasis on Alkaloid-Producing
Plants. Diversity 2017, 9, 11. [CrossRef]