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\begin{document}

\title{Revealing past and future land-cover transitions from 1985 to 2036 in
the drylands of Central Iran}



\author[1]{Bibizahra Mazloum}%
\author[1]{Saied Pourmanafi}%
\author[1]{Alireza Soffianian}%
\author[2]{Abdolrassoul Mahini}%
\author[3]{Alexander Prishchepov}%
\affil[1]{Isfahan University of Technology}%
\affil[2]{Gorgan University of Agricultural Sciences and Natural Resources}%
\affil[3]{University of Copenhagen}%


\vspace{-1em}



  
  \date{March 17, 2025}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Land serves as a vital production resource, and therefore, land planning
plays an important role in sustainable land-use design. Increasing the
global population alters landscapes via land-use and land-cover change
across different landscapes, including the drylands. Iran includes large
areas of dryland, where the population increased by 60\% from 1985 to
2016. Further population increase in Iran would require more land
resources to be allocated for human needs. However, the pace and
patterns of these changes remain unclear. The aim of this study was to
map land-cover change from 1985 to 2016 and predict future land-cover
change in the Zayandehrood ecologic sub-basins of Central Iran. By using
multiseasonal Landsat imagery, nine thematic classes were mapped with a
random forest classifier for 1985, 1998, and 2016 with an overall
accuracy of 80\% for each period. Classification results revealed that
from 1985 to 2016 residential areas doubled and industrial areas
increased at the expense of rangelands. Our study also revealed cropland
expansion at the expense of rangelands, cropland abandonment and
contraction of croplands due to residential and industrial development.
Prediction of changes by 2036 with a multi-layer perceptron neural
network and Markov chain analysis revealed further expansion of
industries and residencies particularly nearby the protected areas such
as Ghamashlu Wildlife Refuge. Predicted contraction of some degraded
agricultural lands and concomitant agricultural expansion in the
agricultural frontier by 2036, underscore the importance of sustainable
land management in highly arid areas of Iran and improvement of the
strategies for the protection of rangelands.%
\end{abstract}%



\sloppy


\textbf{Revealing past and future land-cover transitions from 1985 to
2036 in the drylands of Central Iran}

Bibizahra Mazloum\textsuperscript{1}*, Saied
Pourmanafi\textsuperscript{1}, Alireza Soffianian\textsuperscript{1} and
Abdollrasoul Salman Mahini\textsuperscript{2}, Alexander V.
Prishchepov\textsuperscript{3}

\textsuperscript{1.} College of Natural Resources, Isfahan University of
Technology, Iran

\textsuperscript{2} Department of Environment, Gorgan University of
Agricultural Sciences \& Natural Resources, Iran

\textsuperscript{3} Department of Geosciences and Natural Resource
Management (IGN), University of Copenhagen, \selectlanguage{ngerman}Øster Voldgade 10, DK-1350
København K, Denmark

* Corresponding author: bibizahramazloum@gmail.com, phone: +4535331386

Abstract

Land serves as a vital production resource, and therefore, land planning
plays an important role in sustainable land-use design. Increasing the
global population alters landscapes via land-use and land-cover change
across different landscapes, including the drylands. Iran includes large
areas of dryland, where the population increased by 60\% from 1985 to
2016. Further population increase in Iran would require more land
resources to be allocated for human needs. However, the pace and
patterns of these changes remain unclear. The aim of this study was to
map land-cover change from 1985 to 2016 and predict future land-cover
change in the Zayandehrood ecologic sub-basins of Central Iran. By using
multiseasonal Landsat imagery, nine thematic classes were mapped with a
random forest classifier for 1985, 1998, and 2016 with an overall
accuracy of 80\% for each period. Classification results revealed that
from 1985 to 2016 residential areas doubled and industrial areas
increased at the expense of rangelands. Our study also revealed cropland
expansion at the expense of rangelands, cropland abandonment and
contraction of croplands due to residential and industrial development.
Prediction of changes by 2036 with a multi-layer perceptron neural
network and Markov chain analysis revealed further expansion of
industries and residencies particularly nearby the protected areas such
as Ghamashlu Wildlife Refuge. Predicted contraction of some degraded
agricultural lands and concomitant agricultural expansion in the
agricultural frontier by 2036, underscore the importance of sustainable
land management in highly arid areas of Iran and improvement of the
strategies for the protection of rangelands.

Introduction

Land serves as a vital production resource and therefore land planning
plays an important role in sustainable land use design (Foley \emph{et
al.} , 2011; Lambin \& Meyfroidt, 2011). Human-induced land-use and
land-cover change (LULCC) are the key proximate causes that shape
landscapes at different scales (Foley, 2005; Lambin \& Geist, 2006).
Evidence shows that LULCC increased over the last 100 years due to
population growth, and subsequent increasing land demand and changing
consumption patterns, among other factors (Gerbens-Leenes \& Nonhebel,
2005; Goldewijk, 2001). It is envisaged that the purpose of land
planning is to direct the agents that change land use (e.g., landowners,
farmers) to a point that results in the balance between meeting societal
needs and protection of the environment. Hence, the quantitative
assessment of the state of land cover and land use and revealing the
potential changes of land cover is crucial for better sustainable
land-use planning (Herrmann \& Osinski, 1999).

Earth observation, such as satellite remote sensing, is a prominent
approach to monitor land changes and produce land-cover maps (Cohen \&
Goward, 2004; Coppin \emph{et al.} , 2004; Hansen \& Loveland, 2012).
There has been substantive progress over the last three decades with the
aid of remote sensing to monitor how humans appropriate land; for
instance, the dynamic of residential and industrial areas (Reba \& Seto,
2020), agriculture and forestry activities (Eisavi \emph{et al.} , 2015;
Hansen \emph{et al.} , 2013; Rufin \emph{et al.} , 2019), and also the
intensity of appropriation of rangelands (Dara \emph{et al.} , 2020;
Jakimow \emph{et al.} , 2018), including drylands (Dubovyk, 2017). In
the past, scientists have relied on field surveys and interpretation of
aerial photographs to map LULCC, often over smaller areas
(Nebiker\emph{et al.} , 2014). As the size of the area of study becomes
bigger, field surveys and interpretation of aerial photos become very
costly and time-consuming. Therefore, satellite remote sensing is an
excellent source of information to study LULCC at local and regional
scales because satellite images, from on-board of Landsat satellites for
instance, can cover a large extent and have a higher temporal coverage
compared to field based observations and aerial surveys (Hansen \&
Loveland, 2012; Loveland \& Dwyer, 2012). Landsat images from the freely
accessible United States Geological Survey (U.S.G.S.) archive are used
widely to evaluate the quantitative parameters of land, especially land
use/cover change due to long historical repetitive coverage, the spatial
resolution of Landsat images that match LULCC phenomena and relevant
radiometric resolution suited to map vegetation change, including
drylands and rangelands (Eisavi \emph{et al.} , 2015; Ridwan \emph{et
al.} , 2018; Roy \emph{et al.} , 2014). Moreover, the change of paradigm
about free accessibility of Landsat records made this data relevant for
the scientific community to monitor LULCC (Wulder \emph{et al.} , 2012).

There has also been progress in the application of semi-automatic
classification methods to derive thematic classes from satellite imagery
and produce LULCC maps. Machine-learning algorithms gained popularity
recently to classify satellite imagery due to the ability to handle
non-linearity among spectrally complex classes, such as various urban
landscapes, agriculture and rangelands (Dara \emph{et al.} , 2020;
Khatami \emph{et al.} , 2016; Kraemer \emph{et al.} , 2015; Löw \emph{et
al.} , 2015). Random forest, which is probably the most popular
machine-learning classification method, is an ensemble classifier,
where, instead of one single tree (CART and C4.5), multiple trees from
training data are produced. Such an approach allows the model overfit to
be reduced (Belgiu \& Dr\selectlanguage{polish}ă\selectlanguage{english}gu\selectlanguage{polish}ţ, \selectlanguage{english}2016; Gislason \emph{et al.} , 2006). At
the same, compared to CART no pruning of produced trees is implemented
in a random forest, which allows increasing classification accuracy with
random forest (Belgiu \& Dr\selectlanguage{polish}ă\selectlanguage{english}gu\selectlanguage{polish}ţ, \selectlanguage{english}2016; Gislason \emph{et al.} , 2006).
High precision, ability to learn nonlinear relationships, ability to
determine variables, non-parametric nature, high computation speed, and
resistance to over-fitting are among the advantages of random forest
classifier (Belgiu \& Dr\selectlanguage{polish}ă\selectlanguage{english}gu\selectlanguage{polish}ţ, \selectlanguage{english}2016; Gislason \emph{et al.} , 2006;
Millard \& Richardson, 2015). It has been also noted, classification
error decreases by increasing the number of trees; both the number of
trees and the number of utilized variables for branches of each node can
be parameterized (Belgiu \& Dr\selectlanguage{polish}ă\selectlanguage{english}gu\selectlanguage{polish}ţ, \selectlanguage{english}2016; Gislason \emph{et al.} ,
2006). The random forest classifier has been widely used over the last
two decades for land-change mapping (Chen \emph{et al.} , 2019;
Jakimow\emph{et al.} , 2018; Millard \& Richardson, 2015). In sum, a
random forest classifier can be a promising classifier to map LULCC in
spectrally complex areas, such as the drylands of Iran (Eisavi \emph{et
al.} , 2015).

Various models have been used for investigating the dynamics of land use
and predicting the land-use changes in order to improve decision-making
(Castella \& Verburg, 2007; Verburg \emph{et al.} , 2006; Zhang \emph{et
al.} , 2015). Some models, especially dynamic models such as Markov,
dynamic systems, automatic cells, multi-agent models, and conversion of
land use and its effects at small regional extent (CLUE-S), can help the
decision-makers to evaluate potential land-cover change patterns under
the different scenarios (Mallampalli \emph{et al.} , 2016; Sun \emph{et
al.} , 2016; Verburg \emph{et al.} , 2002). Each of these models has
advantages and disadvantages for predicting land-cover change. For
instance, geometry modification technology (GEOMOD) can model only two
land-cover change classes (Pontius Jr \& Chen, 2006) and SLEUTH (slope,
land use, exclusion, urban extent, transportation and hillshade) is
primarily used for urban development and cannot capture the driving
forces behind urban growth. Additionally, SLEUTH has limitations on the
number of input variables and can be implemented only under the UNIX
system (Chaudhuri \& Clarke, 2013; Sekovski \emph{et al.} , 2015).
SLEUTH is deliberately focused more on form and dynamics, i.e.,
``where'' could the development take place, but not ``why'' (e.g.,
demography and socioeconomic processes) (Sekovski \emph{et al.} , 2015).

Land Change Modeler, available via Terrset software (former Idrisi
software, Clark Labs) (Eastman, 2015), can model multiple land-cover
change trajectories simultaneously in comparison to the above methods
and tools. One of its advantages is that it is available via Terrset and
can be run with Windows operating system, therefore it is more available
for a broader community. It has no limitation on the number of input
variables. It provides more capabilities for tuning than other methods,
in addition to its simplicity. Land Change Modeler has been used to
evaluate and model various land-cover changes in Iran (Ansari \& Golabi,
2019).

Unsustainable land-use activities may result in substantial negative
ecological consequences with cascade effects even when land-use change
occurs at the local level (Newbold \emph{et al.} , 2015; Yaghobi
\emph{et al.} , 2019). Reduction of the negative environmental impacts
of land-use change while maintaining economic viability and social
acceptability is a particular challenge for most developing countries in
Asia, including Iran (Zhao \emph{et al.} , 2006). For instance, in Iran,
the population increased by 60\% from 50 million to 80 million people
just within a 30-year course from 1986 to 2016 (Danaei \emph{et al.} ,
2019). Over the same course, the urban population increased and by 2016,
it comprised 70\% (Danaei \emph{et al.} , 2019). Most of the territory
of Iran can be considered dryland with vast rangelands and irrigated
croplands lying in semi-desert conditions (Mesgaran \emph{et al.} ,
2017; \emph{The Cambridge History of Iran} , 1968). There has been
documented evidence of environmental degradation caused by unsustainable
urban and agricultural expansion (Minaei \emph{et al.} , 2018; Yaghobi
\emph{et al.} , 2019; Zareie \emph{et al.} , 2016). This all increased
the concern of the scientific community on the negative impact on the
environment and the relevance of quantification and prediction of such
impacts.

The ultimate goal of this study was to assess land-cover change with the
aid of multiseasonal Landsat satellite imagery from 1985 to 2016 in
Zayandehrood ecologic sub-basins of Central Iran, an area of rapid
agricultural and urban/industrial development and to determine the
required land amount for various land uses in the future. Our specific
research questions were:

\begin{enumerate}
\tightlist
\item
  What are the major trajectories of land-cover change in Zayandehrood
  ecologic sub-basins of Central Iran from 1985 to 2016?
\item
  What will be the future land-cover/ use transitions in the study area
  by 2036?
\end{enumerate}

Methods

2.1 Study area

The study area includes the three sub-basins of Zayandehrood: Lanjanat,
Najafabad, and Mahyar Shomali, which are located at 50 57' to 51 51' of
eastern longitude and 31 45' to 32 50' of northern latitude with an area
of 527,148 hectares in the Isfahan province of Iran (figure 1).

The population in the study area increased from 321,000 in 1996 to 2.26
million people in 2016 (https://www.isfahan.irib.ir). The climate ranges
from arid and semi-arid (east and southeast) to cold semi-humid (west
and southwest) with an elevation range from 1,569 m (plains, hills) to
3,235 m (mountains). Rainfall increases with elevation, while
temperature decreases. The mean annual temperature is 13 \selectlanguage{ngerman}°C, and the
mean annual rainfall is 209 mm. The Zayandehrood River is the biggest
and most important river in the study site, which originates in the
Zagros Mountains (figure 1). This river provides water for farming,
residential areas and industries in the study site and finally goes to
the Gavkhoni wetland in the east of the watershed (Akbari \emph{et al.}
, 2015; Iranmehr \emph{et al.} , 2015). The Ghamashlu National Park in
the north and Kola Ghazi National Park in the Southeast host charismatic
herbivores, \emph{Capra aegagrus} and \emph{Gazella subgutturosa} .
Among protected native plants in the study area are \emph{Fritillaria
imperialis} and \emph{Pistacia atlantica} . Therefore, despite semi-arid
conditions, the study area has high biodiversity and importance for
environmental protection. Different land uses, such as residential
areas, agriculture, industries and mining, emphasize the importance of
obtaining accurate information about the state of land use and of
developing strategies for sustainable land use in the future.

\textless{}Figure 1\textgreater{}

2.2 Data

In this study, U.S.G.S. Landsat multiseasonal imagery for the years
circa 1985, 1998, and 2016 were used to match the development of
land-planning policy over the last decades in Iran. Landsat images are
well-suited to distinguish different land-cover types due to the
spectral and spatial resolution of the sensors (TM, ETM+, OLI) on board
Landsat satellites, and also due to the temporal revisits. To determine
different phonological stages of spring and summer crops, image-dates
for May and August were selected. Systematically corrected (topography
and atmosphere) level 2 Landsat imagery from TM and OLI sensors were
acquired from U.S.G.S. for Word Reference System -2 (WRS-2) path 37 and
38 and row 164. We then mosaicked satellite imagery from different
paths.

2.3 Classification with random forest classifier

Image classification is a method for extracting environmental
parameters, such as land cover and land use (LCLU) (Jensen, 2004). An
expert who conducts a supervised classification should have prior
knowledge about study area conditions and land uses. For this study,
training samples were determined by using available maps, aerial images,
field data and ancillary spatial information, such as soil types. We
used a random forest classifier implemented in the package ``random
Forest'' in statistical R software. Based on the literature and
preliminary tests, we used 1,000 trees to parameterize the random forest
classifier. We used the Blue, Green, Red, NIR, SWIR-1, and SWIR-2 bands
for Landsat 4, 5 TM, Landsat 7 ETM+ and Landsat 8 OLI to produce LCLU
change maps with a random forest classifier. The iterative
classification approach was used with an increase of training samples
selected for each thematic class until the classification accuracy for
individual thematic classes did not increase anymore.

We developed a classification catalog, which presented a mix of LCLU
classes (Table 1). Our classification catalog consisted of eight
thematic classes: `forest', `agriculture', `rangeland', `water',
`rocks', `industry', `residences', and `other'. The `forest' class
consisted of urban and industrial green space with trees and tree
plantations, `agriculture' consisted of tilled agricultural land used
for crops and clean or green fallow land as a part of crop rotation. The
`rangeland' class consisted of native natural plants with low (type I)
to medium (type II) density. `Water' represented water objects, such as
rivers, natural wetlands and man-made water ponds. The `rock `class
consisted of rocky outcrops and the mountains. `Industry' consisted of
industrial areas and mines. `residences' comprised urban and rural
settlements and main paved roads and the `other' class was bare soil,
industrial and mineral wastelands, and dirt roads.

\textless{}Table 1\textgreater{}

2.4 Field data collection for parameterization of the classifier and
accuracy assessment

Field data, which was later utilized for training and accuracy
assessment, was collected following several steps. Field campaigns were
conducted and information about land-cover types was recorded with a
non-differential GPS during May and August 2016. Notes were also taken
on land-cover and land-use types. For a better interpretation of the
results, photos were taken in the field as well. We also interviewed the
farmers about their activities to reconstruct land use by recalling
memories about land use, back to 1985. Field-collected data were then
used to parameterize and correct the random forest classifier, and
assess the classification accuracy of produced maps. We showed satellite
imagery available via Google Earth mapping services during the
interviews with farmers if we were not sure about the type of LCLU.

2.5 Post-classification change detection

The process of LCLU change determination is attributed to change
detection over time (Coppin \emph{et al.} , 2004; Lu \emph{et al.} ,
2004). A post-classification change detection approach was used to
assess land transitions from 1985 to 1998 and 2016. More specifically,
five trajectories were assessed in more detail: ``rangeland to
residences'', ``rangeland to forest'', ``agriculture to residences'',
``rangeland to agriculture'', and ``rangeland to industry''.

2.6 Accuracy assessment of classified land-cover/ use maps

The accuracy of produced maps was examined quantitatively with
validation data produced by visual expert interpretation of
multiseasonal Landsat imagery, high-resolution satellite imagery
available via Google Earth\textsuperscript{TM} mapping service and the
use of field-collected data. Accuracy assessment was performed for 1985,
1998 and 2016 maps. In total, 1,277 points were used to evaluate the
accuracy of the thematic maps, which were produced with a stratified
random sampling approach. Overall, producer's and user's accuracies were
calculated based on contingency tables between validation data and
classified maps.

2.7 Prediction of land cover/ use change

Land cover/ use change can be determined by biophysical, technological,
economic, social and political drivers but also by trigger events
(Bürgi\emph{et al.} , 2004; Geist \& Lambin, 2002; Hersperger \emph{et
al.} , 2010). Here we used biophysical site conditions to predict the
spatial characteristics of LCLU change from 1985 to 2016 with the
Terrset Land Change Modeler, where we developed several sub-models.
Based on potential transition scenarios, following ``business-as-usual''
namely, rangeland to other land uses and agriculture to residences,
time-dependent (dynamic) and time-independent (static) descriptive
variables were determined in order to assess the transition potentials
of pixels of one land use/cover to another (De Rosa \emph{et al.} ,
2016). Cramer coefficient, which ranges from ``0'' (no correlation) to
``1'' (strong correlation), was used to determine the correlation of
model variables (independent variables) with LCLU classes (dependent
variables). The prediction of LCLU transition was made separately for
five transition classes, namely ``rangeland to residences'', ``rangeland
to forest'', ``agriculture to residences'', ``rangeland to
agriculture'', and ``rangeland to industry''. LCLU change was predicted
for 2016 and 2036 with a multi-layer perceptron (MLP) neural network. To
determine the amount of change that will occur up to 2036, we used the
Markov chain prediction process.

Several spatial biophysical and socioeconomic variables, namely,
distance to water, residences, industries, agriculture and rangelands
(based on classification results for 1985 and 2016), slope, distance to
roads and flood potential, determined the likelihood of LCLU change
transitions (figure 6). The slope was calculated based on a 30-meter
shuttle radar topography mission (SRTM) digital elevation model (DEM)
(figure 6). The Euclidian distance to roads was calculated based on the
digital map of main roads. Similarly, the Euclidian distance to water
was calculated with the account of the Zayandehrood River and the main
water channels. We also assessed the susceptibility to floods (flood
potential), which was determined based on the calculation of the
distances to seasonal and permanent water flows based on hydrologic maps
for the study area. All produced maps were calculated with a 30-meter
pixel size to match the LULCC maps.

Results

3.1 Land use/cover classification and accuracy assessment

Our classified land-cover maps for 1985, 1998, and 2016 consisted of
`rocks', `rangeland type I', `rangeland type II', `water',
`agriculture', `residences', `forest', `industry' and `other' thematic
classes, and were produced with plausible classification accuracies
(figure 2, Table 2). To increase the accuracy for the `agriculture'
class, lands with clean and green fallow, tilled soil and lands under
crops were combined. To increase the accuracy of the `residences' class,
post-classification assessment was performed with a reverse comparison
of the residences' patterns; if they first appeared in 1985, they should
also appear in the 1998 and 2016 maps (the assumption being that there
was no reduction in residential area over time).

\textless{}Figure 2\textgreater{}

Table 2 shows the accuracy of maps and classes produced with a random
forest classifier. The `agriculture' thematic class had a high accuracy
in all years. The accuracies for the `rangeland type I', and `forest'
thematic classes were high as well. The user's and producer's accuracies
for the `residences' class varied from 0.67 to 0.80. The classes
`rocks', `other', and `rangeland type I' were the least accurate ones
due to low spectral separability among these classes.

\textless{}Table 2\textgreater{}

3.2 Land use/cover change detection

Results showed that the `water' and `rocks' thematic classes had a
negligible decrease during the studied period. The `water' thematic
class, which included rivers, water channels and big man-made pools,
might exhibit inter-annual change due to fluctuation of precipitation
levels for the three studied periods (figure 3). The presence of snow in
one of the images for 1998 could affect the extent of the `rangeland
type II' thematic class. `Industry', `forest', `residences' and
`agriculture' thematic classes increased from 1985 to 2016, while the
`rangeland type I' thematic class steadily decreased (figure 4). From
1985 to 2016, the `forest' thematic class increased ten times, the
`industry' thematic class three times, and the `residences' class two
times.

\textless{}Figure 3\textgreater{}

\textless{}Figure 4\textgreater{}

A detailed visual inspection of classified maps revealed that the
`agriculture' thematic class expanded mostly at the expense of the
`rangeland type I' thematic class, with 3.3\% growth in area from 1985
to 2016 (table 2, table 3). Visual qualitative inspection of land
use/cover change also revealed forests and urban greenery that had been
planted mainly around the industries and mines from 1985 to 2016 (figure
5). Spatial growth of residential areas from 1985 to 2016 primarily
occurred at the expense of agricultural lands, `rangeland type I', and
in close proximity to existing residential areas and roads (figure 5).

\textless{}Table 3\textgreater{}

\textless{}Figure 5\textgreater{}

Results also showed that the conversion of `agriculture' to `residences'
and `forest' was more pronounced from 1998 to 2016 than from 1985 to
1998, which was different for the transition from `rangeland type I' to
`industry' (which dominated from 1985 to 1998).

\emph{3.3 Land-cover/} \textbf{use \emph{change prediction}}

By using the VALIDATE method in the Terrset GIS software, agreement was
measured between predicted maps and the classified map for 2016. The
Standard Kappa Index, Quality Kappa Index (no location), and Location
Kappa Index for 2016 model validation were 0.91, 0.92, and 0.93,
respectively (table 4). The accuracy of the predicted map for 2016 was
88\%.

The predicted LULCC map from 2016 to 2036 revealed multiple trajectories
of land transformation, such as an increase of some classes often at the
expense of rangeland type I. For instance, `residences' should increase
to 9000 (ha) at the expense of class `agriculture' and `rangeland type
I' (figure 7, 8).

\textless{}Table 4\textgreater{}

\textless{}Figure 6\textgreater{}

\textless{}Figure 7\textgreater{}

\textless{}Figure 8\textgreater{}

Discussion

Land-cover/use change has various implications for the environment (De
Rosa \emph{et al.} , 2016). LULCC prediction models provide the
possibility to assess the change of land cover and land use and supply
important information for better land-use planning to managers and
stakeholders (Ansari \& Golabi, 2019; Herrmann \& Osinski, 1999). Often,
watersheds are considered as an important assessment unit for land
planning. Therefore, we determined our study area based on the ecologic
border of the Lenjan and Najafabad watersheds of the Zayandehrood River
to prepare a suitable estimation of the spatial transition potential of
the land. The study area is within the province administrative units and
the results of the study can be used for the development of land
management plans.

We reconstructed land cover/ use change with multiseasonal Landsat
imagery for 1985, 1998 and 2016 for the watershed surrounding the
Zayandehrood River, which is the main water resource of the study area
and our study revealed multiple trajectories of land change. For
instance, our study showed both agricultural expansion, but also
abandonment (an increase of fallow and completely abandoned areas from
1985 to 2016). Several studies suggested that human factors, such as
over-using underground water resources, and water transfer plans are the
main reasons for water decrease including the Zayandehrood River and,
consequently, resulting in abandonment of agriculture plots where
irrigation and production costs on degraded lands are not compensated by
the returns from farming (Azadi \emph{et al.} , 2016; Fazli, A \& Badi,
Sh, 2017; Minaei \emph{et al.} , 2018). Therefore, the current water
management plans are not sustainable as they result in exhausting water
resources and salinization of some plots as in other parts of Iran
(Iranmehr \emph{et al.} , 2015; Yaghobi \emph{et al.} , 2019;
Zeaiean\emph{et al.} , 2005).

Our classification results also revealed the conversion of agricultural
lands to residences and recreational and industrial areas in close
proximity to residential areas, in drought-prone areas, and with
deficient water resources. These findings were in line with earlier
studies in other parts of Iran, which also found a contraction of
agricultural lands in proximity to residential areas (Eisavi \emph{et
al.} , 2015; Mosammam \emph{et al.} , 2017; Soffianian \& Madanian,
2015). Such finds are worrisome because Iran also relies on food
imports. Any additional contractions of croplands in Iran may pose a
threat for food insecurity.

Our findings also showed that residential areas and even new industrial
towns such as Majlesi were built at the expense of the rangelands.
Contrary to the contraction of agricultural lands in the vicinity of
residential areas, we also observed an increase in agricultural land at
the expense of rangelands. Rangelands that were not converted to
croplands, were often left at the marginal areas. Thus, any new cropland
expansion at the expense of rangelands suggested cropland encroachments
in agro-environmental frontiers with unstable yields and in areas prone
to abandonment (Kraemer \emph{et al.} , 2015; Yaghobi \emph{et al.} ,
2019). Rangelands in many parts of the world, including Iran, have a
weak protective status. At the same time, rangelands host charismatic
umbrella species and support the livelihoods in rural areas, including
rangeland livestock herding (Behnke \emph{et al.} , 2016; Dara \emph{et
al.} , 2020; Kerven \emph{et al.} , 2006). Therefore, the permanent
conversion of rangelands to industrial areas should be controlled, and
zoning should be developed to avoid the unintended land conversions and
reduction of valuable ecosystem services rangelands provide
(Gudka\emph{et al.} , 2014; Kamp \emph{et al.} , 2015). With the current
land management policies and prediction of land-change we provide, the
protected areas, especially Ghamashlu Wildlife Refuge, in our study area
(figure 8) are at risk. For instance, our study revealed ongoing
residential expansion in proximity to the Wildlife Refuge.

The presence of mineral sources in our study area, particularly in the
mountains, resulted in the expansion of opened-cast mining, thus
creating conflicts with environmental preservation strategies. Our
land-change predictions and evaluation of existing plans about mining
suggest further continuation of the negative impact of mining and
supplementary road construction on rangelands and other drylands
(Madadi\emph{et al.} , 2014; Makki \emph{et al.} , 2012). Expansion of
big industrial towns and various mines over the last 30 years has also
intervened between natural resource management and exploitation of water
resources with other land uses such as farming (Soffianian \& Madanian,
2015). Given a shortage of water resources in the study region of Iran,
a vicious circle between the expansion of various land-use practices and
water resources may result in further contraction of valuable land
resources, and therefore, our results are extremely relevant for better
land-use planning in the drylands of Iran.

In this study, we evaluated land-cover changes starting from 1985 to
2016. Land-change transformations started much earlier than our first
year of observation (1985), for instance following the change of the
political and institutional course in Iran in 1978. However, we captured
an overall course of post-1978 land-cover change driven by the aim of
satisfying the needs of the growing population. Before 1985, Landsat
satellite imagery were scattered (Landsat 4 and 5 TM) or came with
coarser spatial and radiometric resolution (Landsat 1-3 MSS). Similarly,
we made projections about land-cover change by 2036 based on the overall
course of land transformation in the past, therefore reflecting the
''business-as-usual'' scenario. There might be other counterfactual
scenarios that may mediate the rates and patterns of land-cover change,
such as adjustment of existing land-use policies (Mallampalli \emph{et
al.} , 2016; Prishchepov \emph{et al.} , 2019), change of economic
conditions in Iran, etc. However, our projection can be used as a
starting point of evaluation of existing policies and additional
counterfactual scenarios can be built around.

Our study showed multiple pathways of land transformations in the
dryland of central Iran, for instance, when both cropland expansion and
abandonment concomitantly are taking place and go hand in hand with
urban sprawl at the expense of rangelands. Despite the extreme climatic
conditions, global drylands, which occupy almost half of the global
surface (Pr\selectlanguage{polish}ă\selectlanguage{english}v\selectlanguage{polish}ă\selectlanguage{english}lie, 2016), are experiencing rapid land transformation due
to the overall population increase. Moreover, a large portion of
drylands are in developing countries, such as Iran, where livelihoods
strongly depend on agriculture and environmental goods (Brandt \emph{et
al.} , 2017; Emg, 2011; Tong \emph{et al.} , 2017). Despite a growing
population across dryland, both cropland expansion and abandonment have
also been found in other parts of the drylands of Northern Eurasia, such
as Kazakhstan (Chen \emph{et al.} , 2013; Horion \emph{et al.} , 2016;
L\selectlanguage{ngerman}öw\emph{et al.} , 2015), Mongolia (Dong \emph{et al.} , 2011;
Jamsranjav\emph{et al.} , 2018; Sankey \emph{et al.} , 2018) and
Uzbekistan (Dubovyk\emph{et al.} , 2012). Considering the adverse
climatic projections that drylands of Eurasia will face during the
21\textsuperscript{st} century (Groisman \emph{et al.} , 2017; Groisman
\& Soja, 2009), better land-use policies should be developed toward
sustainable use of land and water resources to satisfy predicted
population growth.

Acknowledgements

Authors thank DFF-Danish ERC Support Program (grant number: 116491,
9127-00001B) and the University of Copenhagen for a six month research
stay at the University of Copenhagen, Department of Geosciences and
Natural Resource Management (IGN).

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\textbf{Table 1: Thematic land-cover and land-use classes.}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Class} & \textbf{Short description}\tabularnewline
\midrule
\endhead
Rocks & Rocky outcrops\tabularnewline
Rangeland type I & Rangelands with low-density vegetation\tabularnewline
Rangeland type II & Rangelands with moderate density
vegetation\tabularnewline
Water & Rivers, artificial ponds, wetlands and water
channels.\tabularnewline
Agriculture & Agricultural land used for crops, orchards\tabularnewline
Forest & Forest, urban and industrial green areas (e.g.,
parks)\tabularnewline
Residences & Urban and rural settlements and paved roads\tabularnewline
Industry & Industrial areas and mines\tabularnewline
Others & Industrial and mineral wastelands, dirt roads, bare
soil\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2: Accuracy and extent of land use/cover classes (in
hectares)}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllllll@{}}
\toprule
& 1985 & 1985 & 1985 & 1985 & 1985 & 1998 & 1998 & 1998 & 1998 & 1998 &
2016 & 2016 & 2016 & 2036\tabularnewline
\midrule
\endhead
Classes & User\textsuperscript{,}s & Producer\textsuperscript{,}s &
Producer\textsuperscript{,}s & Area & Area & User\textsuperscript{,}s &
Producer\textsuperscript{,}s & Producer\textsuperscript{,}s & Area &
Area & User\textsuperscript{,}s & Producer\textsuperscript{,}s & Area &
Area\tabularnewline
Rocks & 0.49 & 0.77 & 0.77 & 93404 & 93404 & 0.49 & 0.78 & 0.78 & 93253
& 93253 & 0.49 & 0.81 & 93136 & 93196\tabularnewline
Other & 0.41 & 0.87 & 0.87 & 6100 & 6100 & 0.57 & 0.67 & 0.67 & 6230 &
6230 & 0.65 & 0.68 & 6302 & 6233\tabularnewline
Rangeland type I & 0.89 & 0.83 & 0.83 & 314779 & 314779 & 0.88 & 0.81 &
0.81 & 285816 & 285816 & 0.90 & 0.75 & 257809 & 212172\tabularnewline
Rangeland type II & 0.79 & 0.51 & 0.51 & 7089 & 7089 & 0.72 & 0.42 &
0.42 & 3930 & 3930 & 0.83 & 0.54 & 4841 & 3930\tabularnewline
Water & 1 & 0.50 & 0.50 & 969 & 969 & 1 & 0.90 & 0.90 & 1088 & 1088 & 1
& 0.80 & 1001 & 1088\tabularnewline
Agriculture & 0.87 & 0.80 & 0.80 & 101595 & 101595 & 0.86 & 0.88 & 0.88
& 123124 & 123124 & 0.82 & 0.93 & 138604 & 169443\tabularnewline
Forest & 0.94 & 0.90 & 0.90 & 654 & 654 & 0.79 & 0.78 & 0.78 & 4138 &
4138 & 0.75 & 0.85 & 6716 & 10591\tabularnewline
Residences & 0.80 & 0.80 & 0.67 & 9498 & 9498 & 0.90 & 0.90 & 0.69 &
0.69 & 14105 & 0.88 & 0.80 & 20269 & 29197\tabularnewline
Industry & 0.76 & 0.76 & 0.81 & 3071 & 3071 & 0.90 & 0.90 & 0.75 & 0.75
& 5475 & 0.83 & 0.83 & 8480 & 11304\tabularnewline
Total accuracy & & & 0.80 & 0.80 & & & & 0.80 & 0.80 & & & 0.80 &
&\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3: Land cover/ use transition matrixes based on classified
maps 1985, 1998, and 2016}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Forest} & \textbf{Industry} & \textbf{Residences} & \textbf{1985
and 1998}\tabularnewline
\midrule
\endhead
232 & 168 & 2775 & Agriculture\tabularnewline
2760 & 2228 & 1739 & Rangeland type I\tabularnewline
\textbf{Forest} & \textbf{Industry} & \textbf{Residences} & \textbf{1998
and 2016}\tabularnewline
8233 & 518 & 4142 & Agriculture\tabularnewline
2568 & 1439 & 2102 & Rangeland type I\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
Rangeland to & Rangeland to & Rangeland to & Rangeland to & Rangeland to
& Rangeland to & & & Agriculture to & Agriculture to\tabularnewline
\midrule
\endhead
Residences & Forest & Industry & Industry & Agriculture & Agriculture &
Agriculture & & & Residences\tabularnewline
89\% & 87\% & 87\% & 80\% & 80\% & 81\% & 81\% & & 83\% &
83\%\tabularnewline
Standard Kappa Index Quality Kappa Index Location Kappa Index & Standard
Kappa Index Quality Kappa Index Location Kappa Index & Standard Kappa
Index Quality Kappa Index Location Kappa Index & Standard Kappa Index
Quality Kappa Index Location Kappa Index & Standard Kappa Index Quality
Kappa Index Location Kappa Index & & & & 0.91 0.92 0.93 & 0.91 0.92
0.93\tabularnewline
Accuracy of the predicted map 2016 & Accuracy of the predicted map 2016
& Accuracy of the predicted map 2016 & Accuracy of the predicted map
2016 & Accuracy of the predicted map 2016 & Accuracy of the predicted
map 2016 & Accuracy of the predicted map 2016 & & 0.88 &
0.88\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4: Accuracy of sub-models and prediction model}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1-1/Figure-1-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5-1/Figure-5-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-6/Figure-6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-7-1/Figure-7-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-8-1/Figure-8-1}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

