Revealing past and future land-cover transitions from 1985 to
2036 in the drylands of Central Iran
Bibizahra Mazloum1∗, Saied
Pourmanafi1, Alireza Soffianian1 and
Abdollrasoul Salman Mahini2, Alexander V.
Prishchepov3
1. College of Natural Resources, Isfahan University of
Technology, Iran
2 Department of Environment, Gorgan University of
Agricultural Sciences & Natural Resources, Iran
3 Department of Geosciences and Natural Resource
Management (IGN), University of Copenhagen, 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 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 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 et al. , 2015;
Hansen et al. , 2013; Rufin et al. , 2019), and also the
intensity of appropriation of rangelands (Dara et al. , 2020;
Jakimow 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 (Nebikeret 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 et al. , 2015; Ridwan et
al. , 2018; Roy 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 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 et al. , 2020;
Khatami et al. , 2016; Kraemer et al. , 2015; Löw 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 & Drăguţ, 2016; Gislason 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 & Drăguţ, 2016; Gislason 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 & Drăguţ, 2016; Gislason 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 & Drăguţ, 2016; Gislason et al. , 2006).
The random forest classifier has been widely used over the last two
decades for land-change mapping (Chen et al. , 2019; Jakimowet 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 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 et al. , 2006; Zhang 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 et al. , 2016; Sun et
al. , 2016; Verburg 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 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 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 et al. , 2015; Yaghobi 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 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 et al. , 2019). Over the
same course, the urban population increased and by 2016, it comprised
70% (Danaei 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 et al. , 2017; The
Cambridge History of Iran , 1968). There has been documented evidence of
environmental degradation caused by unsustainable urban and agricultural
expansion (Minaei et al. , 2018; Yaghobi et al. , 2019;
Zareie 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:
- What are the major trajectories of land-cover change in Zayandehrood
ecologic sub-basins of Central Iran from 1985 to 2016?
- What will be the future land-cover/ use transitions in the study area
by 2036?
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 °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 et al. ,
2015; Iranmehr et al. , 2015). The Ghamashlu National Park in the
north and Kola Ghazi National Park in the Southeast host charismatic
herbivores, Capra aegagrus and Gazella subgutturosa . Among
protected native plants in the study area are Fritillaria
imperialis and 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.
<Figure 1>
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.