Journal of Research in Ecology
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ORIGINAL RESEARCH
Journal of Research in Ecology
Modeling the spatiotemporal pattern of farmland change in rural regions of Ahvaz County by remote sensing and landscape metrics Authors:
ABSTRACT: Land-use and land cover change (LUCC) has engrossed much attention due to its effects on global and regional environmental change. The spatial pattern of LUCC can reflect underlying human activities, involving urbanization processes and policies for social and economic development at local to region scales. Landscape pattern metrics are important for evaluating ecological processes and effects of LUCC. This study quantitatively analyzed spatiotemporal changes in land use and landscape pattern in a Ahvaz region of southwest Iran by comparing classified satellite images from 1986, 1998, and 2014, using a GIS, remote sensing, and landscape pattern metrics. The results showed an increase in farm lands during 1986–2014. Over the study period, 308% of newly-expanded farm lands were from bare lands and range lands. This process has brought about noticeable land use changes and farming Institution: growth at an unprecedented scale and rate, and consequently given rise to 1.Associate professor, substantial impacts on the landscape pattern. The results also disclosed that bare University of Tehran. lands and range lands were the major resources that were converted for farming development. Establishment of sugarcane agro-industry companies and policies of 2.Professor, University of Tehran. decision makers will increase positive significant impacts on agro ecosystem and 3.PhD Student, University of environment. To sum up, Trend analysis of landscape pattern metrics demonstrates integration of the farming landscape, with landscape pattern structure becoming Tehran. more homogeneous over the last three decades in the Ahvaz County. Hassanali Faraji Sabokbar1 Seyed Hassan Motiee Langrood2 and Hossein Nasiri3
Keywords: Land use Changes, Landscape Pattern, Decision Tree, Spatial Metrics, Rural Areas of Ahvaz county.
Corresponding author: Hossein Nasiri
Email ID:
Article Citation: Hassanali Faraji Sabokbar, Seyed Hassan Motiee Langroodi and Hossein Nasiri Modeling the spatiotemporal pattern of farmland change in rural regions of Ahvaz County by remote sensing and landscape metrics Journal of Research in Ecology (2016) 4(1): 083-093 Dates: Received: 05 May 2016
Web Address: http://ecologyresearch.info/ documents/EC0080.pdf
Journal of Research in Ecology An International Scientific Research Journal
Accepted: 31 May 2016
Published: 11 July 2016
This article is governed by the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0), which gives permission for unrestricted use, non-commercial, distribution and reproduction in all medium, provided the original work is properly cited.
083-093 | JRE | 2016 | Vol 4 | No 1
www.ecologyresearch.info
Faraji Sabokbar et al., 2016 change, therefore, has attracted increasing attention to
INTRODUCTION Land Use and Land Cover Change (LUCC)
remote sensing data for understanding spatiotemporal
significantly contributes to biodiversity loss, invasive
changes, and managing and restoring, the rural-urban
species spread, changes in biogeochemical cycles, and
ecosystems.
the loss of ecosystem services (Radeloff et al., 2012).
Furthermore, spatial resolution of remote sensing
Planning for a sustainable future of Land Use and Land
data and, image processing methods are also very crucial
Cover Change (LUCC) is a complex process (Irwin and
for the technical limitation of LUCC detection.
Geoghegan, 2001; Lambin and Geist, 2006) and
Developing an improved comprehending on how to
modeling these systems are challenging.
match applications and change detection techniques are
Rapid urban development, especially in the
still faced to several challenges. In spite of numerous
developing world, will continue to be one of the crucial
criteria affecting the choice of appropriate change
issues of global change in the 21st century (Sui and
detection
Zeng, 2001). This has resulted in an unprecedented scale
Component Analysis (PCA) and post classification are
and rate of urban expansion in Iran over the last three
most
decades. However, rapid urban growth and exploitation
demonstrate better performance compared with other
of natural resources have led to important impacts on
technics (Collins et al., 1996); Yuan and Elvidge, 1998;
ecosystem structure, function and dynamics which have
Lu et al., 2004; Jensen 2005).
methods,
commonly
image applied
differencing, methods
and
Principal usually
made urban and rural as fragile regions. This is
Much less known than remote sensing, spatial
particularly the issue in the economic-developed regions
metrics can be a useful tool for objectively quantifying
where dramatic rural-urban expansion and LUCC have
the structure and pattern of environment change from
induced serious environmental issues threatening rural-
thematic maps (Herold et al., 2005). Spatial metrics are
urban sustainable development (Yeh and Li 1999; Weng,
applied in landscape ecology, known as landscape
2001; Ji et al., 2001; Li and Yeh, 2004; Chen et al.,
metrics. Changes of landscape pattern can be detected
2005; Xiao et al., 2006; Liu et al., 2007). Accurate and
and delineated by the landscape metrics, which quantify
timely information on the status and trends of ecosystem
and classify complex landscape into identifiable patterns
Figure 1. Location of Ahvaz County at Khuzestan province, Iran 084
Journal of Research in Ecology (2016) 4(1): 083-093
Faraji Sabokbar et al., 2016 Table 1. Characteristics of remote sensing and ancillary data in this study Sensor type
Acquisition date
TM TM TM Dem
08/05/1986 09/05/1998 05/05/2014 -
WRS Path 165 165 165 -
WRS Row
Radiometric Resolution 1,2,3,4,5,7 1,2,3,4,5,7 1,2,3,4,5,7 1
38 38 38 -
Spatial Resolution 30m 30m 30m 10m
and disclose some ecosystem characteristics that are not
point of view and making preliminarily exploration of
directly observable (Antrop and Van Eetvelde, 2000;
the influence of urban development on these changes.
Turner et al., 2001; Wseng, 2007). In last decades, there
The overall objective of this study is to improve the
has been an increasing interest in using spatial metrics
understanding of the effects of landscape patterns and
method in rural and urban studies because of their use in
provide the basic information for distinguishing the
bringing out the spatial component in urban-rural
ecological impacts and proper decision-making towards
structure and in the dynamics of change and growth
environment sustainable development.
process (Deng et al. 2009). Furthermore, spatial metrics have the potential for more detailed analyzes of the
MATERIALS AND METHODS
spatiotemporal patterns of LUCC, and the assessment of
Study Area
environmental process. So, the integrated application of
Ahvaz is the capital of Khuzestan province, on
remote sensing and spatial metrics provide more spatially
Iran’s south west and is located between 31°24′ and 31°
consistent and detailed information about environmental
38′ latitude and 50°58′ and 51°11′ longitude (Figure 1).
structure and change.
The average level elevation of all residential areas is
Since
Iran
faced
land
reforms,
urban
located at the range of about 18 meters. This area is
development and a war in 1960-1988, tremendous land-
located in the west of the central Zagros Mountain,
use change has occurred in many rural regions of Iran
which is a region of complex geography located at the
such as the Khuzestan Province. As a case study in
intersection of large-scale atmospheric circulations and
Ahvaz City, one of the most rapidly developing cities in
major weather fronts effect on this area is from
Iran, this paper investigates the spatiotemporal variations
Mediterranean Sea. Therefore less rainfall occurs in this
and evolution of land use and landscape pattern in
region; consequently such a situation also occurs in the
response to the rapid urbanization and environmental
Ahvaz
process by combining remote sensing technics and
precipitation is about 213.4 mm, of which 90% occurs
spatial metrics. Bearing in mind the importance of
between December and April. The annual average
accurate and consistent land use change information over
maximum and minimum temperatures in this district are
a long-term, and finer resolution of remote sensing data
30° and 21°, respectively. Also, January is the coldest
in concert with the urban properties, this study proposes
month and July is the hottest month in this region. The
a feasible and cost-effective land use change detection
agricultural wells are the most important water resources
method by adopting a time series analysis of TM
in this plain. In recent years, due to the rapid
imagery spanning the last 29 years (1986–2014).
development of agricultural lands and long-term drought
Specifically, this study focuses on analysis of the
in these areas, the groundwater storage volume has
spatiotemporal dynamics of LUCC, a comprehensive
decreased sharply. Consequently, about half of the wells
investigation of landscape pattern from a comprehensive
were dried and others have low water in this plain.
Journal of Research in Ecology (2016) 4(1): 083-093
County.
However,
the
annual
average
085
Faraji Sabokbar et al., 2016 geometric
correction,
atmospheric
effects
were
normalized by dark object subtraction (DOS). Studies have shown that these corrections are useful in the classification and monitoring of LUCC (Liu et al., 2008; Olthof et al., 2005; Rรถder et al. 2005; Song et al., 2001). In this paper, all image processing was implemented using ENVI 4.5 software. The information from satellite images and groundwater resource data are shown in Table 1. Figure 2. General decision tree hierarchy (Richards and Richards, 1999).
Land use change detection method In the last decades, a variety of methods for land use/cover classification has been investigated including
Remote sensing data selection Landsat Thematic Mapper (TM) satellite images
maximum
likelihood,
minimum
distance,
and
(Path/Row 165 and 38) and ancillary data were used for
mahalanobis distance. The classifiers mentioned above
analysis.
have all been single stage in that only one decision is
The TM images of the study area were taken in
made about a pixel, as a result of which, it is labeled as
May of the years 1986, 1998, and 2014. Preprocessing of
belonging to one of the available classes or is left
the satellite images prior to image classification and
unclassified. Multi-stage classification techniques are
change detection process is necessary and commonly
also possible in which a series of decisions is taken in
comprises a series of sequential operations, containing
order to determine the correct label for a pixel. The more
atmospheric correction or normalization, geometric
common multi-stage classifiers are called decision trees
correction, and image enhancement (Coppin and Bauer
(DT).
1996). Geometric registration of these data sets was
They comprise of a number of connected
performed by image-to-image registration tie down
classifiers (or decision nodes) none of which is expected
through a first-order polynomial fit. The dates of imagery
to carry out the complete segmentation of the image data
were geo-referenced to a universal transverse Mercator
set. Instead, each component classifier only performs
(UTM) projection with an RMSE of 0.5 pixels. After
part of the task (Richards and Richards, 1999) (Figure 2).
Metrics Mean Shape Index (MSI) Number of Patch (NP) Mean Patch Size (MPS) Patch Size Coefficient of Variance (PSCOV) Area Weighted Mean Patch Fractal Dimension (AWMFD)
Mean Patch Edge (MPE) 086
Table 2. Spatial metrics selected in this study Description Measures the ratio between the perimeter of a patch and the perimeter of the simplest patch in the same area Total number of patches in the landscape The area occupied by a particular patch type divided by the number of patches of that type Coefficient of variation of patches Area weighted mean patch fractal dimension is the same as mean patch fractal dimension with the addition of individual patch area weighting applied to each patch. Because larger patches tend to be more complex than smaller patches, this has the effect of determining patch complexity independent of its size. The unit of measure is the same as mean patch fractal dimension. Average amount of edge per patch. Example: Mean Patch Edge Conifer
Unit metres/ hectare none square meter percent none
metres/patch
Journal of Research in Ecology (2016) 4(1): 083-093
Faraji Sabokbar et al., 2016
Figure 3. Classified TM images of Ahvaz County in three years 1986, 1998, and 2014 and Changes of each land use; Range land, Farm land, Water bodies, Built up land, and bare land in three periods: 1986–1998, 1998– 2014, and c 1986–2014. Perhaps the simplest type is the binary tree in which each
and configuration. Landscape composition refers to
component classifier, or node, is expected to perform a
features associated with the presence and amount of each
segmentation of the data into only one of the two
patch type within the landscape, but without being
possible classes, or groups of classes. In this study, the
spatially explicit. Landscape configuration refers to the
attributes of the samples fell into two categories: A)
physical distribution or spatial character of patches
remote sensing data, including the digital numbers of
within the landscape. There are various indexes for
each TM bands (scaled reflectance values), and
describing landscape patterns quantitatively (Mc Garigal
Normalized Difference Vegetation Index (NDVI) values
et al., 2002).
and B) GIS data, including Digital Elevation Model
In this paper, Mean Shape Index (MSI), Patch
(DEM) and slope map.
Size Coefficient of Variance (PSCOV), Area Weighted
Landscape pattern indexes
Mean Patch Fractal Dimension (AWMFD), Number of
Landscape pattern indexes are the quantitative
Patch (NP), Mean Patch Size (MPS), and mean patch
descriptions of landscape patterns. Landscape patterns
edge (MPE) at class level were chosen to be integrated
are recognized by spatial connection among component
into the LUC model. Calculations of spatial metrics were
parts, and can be described by both their composition
performed
Journal of Research in Ecology (2016) 4(1): 083-093
using
the
public
domain
software 087
Faraji Sabokbar et al., 2016 Table 3. Changes of each land use; range lands, farm lands, built up lands and bare lands during 1986-1998. Land use/Cover Classes
1986 Class Class Area (Km) Area (%)
Range lands Farm lands Water bodies Built up lands Bare lands Total
115.28 271.95 208 28.96 7588.32 8228.33
1998 Class Area Class Area (Km) (%)
1.4 3.31 2.53 0.35 92.39 100
68.69 561.52 106.57 41.81 7433.9 8228.33
0.837 6.48 1.3 0.4 90.6 100
Changes From 1986 to 1998 Area Area Changes Changes (%) (Km) -46.58 -40.41 289.98 106.48 -101.35 -48.76 3.63 44.36 -145.58 -2.03 -
FRAGSTATS version 4.2. However, FRAGSTATS
at a time in comparison with the other time are shown in
provides a large number of spatial metrics, a specific
Figure 3
subset of them was specifically selected for this study which is described in Table 2.
Figure 3. Classified TM images of Ahvaz City in three years 1986, 1998, and 2014 and Changes of each land use; Range land, Farm land, Water bodies, Built up
Results and Discussion
land, and bare land in three periods: 1986–1998, 1998–
Spatiotemporal land use change
2014, and c 1986–2014.
The TM images was classified using decision
According to Figure 3 and tables 3, 4 and 5, 40%
tree algorithm for the five land use; farm lands, range
of range lands in 1986 compared with 1998 is converted
lands, built up lands, water bodies, and bare lands that
to other land uses (farm land, built up land, and bare
are showed in Fig 3 in three years 1986, 1998, and 2014.
land) and about 89% and 93% are decreased in over
The ‘Kappa’ coefficient of classification results for 2014
period of 1998–2014 and 1986–2014 respectively; hence,
image is about 80% and with overall accuracy 88%.
the decreasing of range land leads to increased farm land.
According to Fig 3, with a quick look at the three images
Furthermore, land use changes are quite different in farm
presented, an increase in farm land (bright green) area
land than range land in over period of 1986-1998, 1998-
observed between 1986 and 1998. Generally, from 1986
2014, and 1986-2014. Temporal variation in farm lands
to 2014 the agricultural landscape trend towards the
show a severe increasing in over period of 29 years
integration. Bare lands were the largest area among of
(about 308%). This can be due to an increase in
land use types in the mentioned period. Also, decreasing
sugarcane agro-industry companies in surrounding of
area of range lands is evident between 1986, 1998 and
Ahvaz County.
2014. Spatiotemporal changes for each type of land use
However, most of the net changes in land uses
Table 4. Changes of each land use; range lands, farm lands, built up lands and bare lands during 1998-2014. Land use/ Cover Classes Range lands Farm lands Water bodies Built up lands Bare lands Total 088
1998 Class Area (Km) 68.69 561.52 106.57 41.81 7433.9 8228.33
Class Area (%) 0.837 6.48 1.3 0.4 90.6 100
2014 Class Area (Km) 7.5 1113.97 138.32 76.72 6892.86 8228.33
Class Area (%) 0.091 13.52 1.68 0.81 83.89 100
Changes From 1998 to 2014 Area Area Changes (Km) Changes (%) -61.4 550.19 -31.47 33.93 -554.04 -
-89.11 97.93 29.58 83.15 -7.46 -
Journal of Research in Ecology (2016) 4(1): 083-093
Faraji Sabokbar et al., 2016 Table 3. Changes of each land use; range lands, farm lands, built up lands and bare lands during 1986-2014. Land use/Cover Classes Range lands Farm lands Water bodies Built up lands Bare lands Total
1986 Class ArClass ea (Km) Area (%) 115.28 1.4 271.95 3.31 208 2.53 28.96 0.35 7588.32 92.39 8228.33 100
2014 Class Ar- Class Area ea (Km) (%) 7.5 0.091 1113.97 13.52 138.32 1.68 76.72 0.81 6892.86 83.89 8228.33 100
Changes From 1986 to 2014 Area Area Changes (Km) Changes (%) -107.8 -93.51 839.49 308.69 -69.9 -33.6 37.5 164.4 -699.04 -9. 34 -
are occurred in built up land. In over periods of 1986-
changes in farm lands of case study (Figure 4).
1998, 1998-2014, and 1986-2014, amount of positive
According to fig 4, the Number of Patch (NP) and Mean
changes for built up land has been calculated about
Patch Size (MPS) both increase monotonically. Metrics
44.36%, 83.15%, and 164.4% respectively. In fact, the
of NP, MPS and MPE at its lowest value in the 1986,
increasing of urban development, village to town
indicating a fragmented farming landscape that is
conversion, developing of industries leads to increase
composed of many small patches (Figure 4). For the
built up lands. Also, bare lands and water bodies have
1998, and 2014, NP, MPS and MPE increase gradually
swinging and negative changes for the entire periods
with distance from the 1986, indicating integration in
study
land parcels.
(1986-1998,
1998-2014,
and
1986-2014).
Therefore, results of application of remote sensing reveal
MSI increases first and then decreases due to
that this plain faced with severe variation in LUCC.
integration of farming parcels. Temporally, MSI value in
Hence,
change
the 1998 and 2014 has decreased, indicating that small
trajectories provides a critical component for land use,
agricultural parcels have been transformed into large
conservation and restoration planning in this region.
patches of developed lands. In contrast, Fractal
Farming landscape pattern analysis
dimension describes the complexity and fragmentation of
the
successful
identification
of
The results from landscape metric analysis along
a patch by a perimeter area proportion. Low values are
the Ahvaz city are shown in Figure 2. The diagrams
acquired when a patch condensed to a rectangular from
show the changes of landscape pattern both spatially
small perimeter areas comparatively. For more complex
along the Ahvaz city and temporally through three
and fragmented patches, the perimeter increases, yielding
decades. On each of the diagram, the horizontal axis
a higher fractal dimension. PSCOV and AWMPFD have
represents years and the vertical axis represents the
experienced swinging condition in the 1986, 1998, and
metric value being plotted. A major theme behind the
2014. These metrics decreases first, afterward increases
following interpretations is how the changes of landscape
due to drought condition and then decreases due to
pattern are related to the process of socioeconomically
combination of farming patches.
and urbanization.
The increasing growth of farm lands is evident in
In total, six commonly used landscape metrics
all of the output diagrams (Figure 4). Spatially, PSCOV
are examined systematically. The mean patch size,
and AWMPFD are positively related to the establishment
number of patches, mean shape index, mean patch edge,
of sugarcane agro-industry companies while PSCOV and
patch size coefficient of variance, and area weighted
AWMPFD are negatively related to the precipitation
mean patch fractal dimension all show a remarkably
changes. Hence, a farmed landscape is characterized by a
Journal of Research in Ecology (2016) 4(1): 083-093
089
Faraji Sabokbar et al., 2016
Figure 4. Changes of class-level MPS, NP, MSI, AWMPFD, MPE, and PSCOV along the Ahvaz County at three time points in farm lands large patch size. Temporally, the most significant
built up lands and subsequently integrated into farm
changes of landscape pattern in the past three decades
lands.
occur at the center of plain, where landscape pattern shifts from characteristics that represent bare landscapes
CONCLUSION
to those represent farming landscapes. Based on these
The interactions between human and physical
findings, it can be said that the results are consistent with
parameters created land use/ cover change patterns.
research findings such as Karami and Feghhi (2012),
Literature reviews of key factors of land use/ cover
Mirzaii et al (2014) and Talebi Amiri (2009). They
change assisted to analyze which theory is suitable in a
believe that forest lands changes to grassland and
specific region and increased the development of
agricultural
changes
theoretical knowledge. In this article an integrated
happened due to eliminating forest patches inside the
method is displayed to analyze the pattern of LUCC that
landscape
frequently.
These
allows a wide range of factors, from different disciplines, 090
Journal of Research in Ecology (2016) 4(1): 083-093
Faraji Sabokbar et al., 2016 to contribute to the explanation of land-use change.
Coppin Pol R and Marvin E Bauer. (1996). "Digital
Hence, this paper applies an integrated method of
change detection in forest ecosystems with remote
DT-landscape metrics for satellite images classification
sensing imagery." Remote sensing reviews, 13(3-4):207-
and land use changes monitoring. The landscape metrics
234.
is used in order to quantitating of farming changes. From the results of research, regardless of the changes occurred during three periods (1986-1998, 1998-2014 and 1986-2014) can be found that the land use changes has increasingly occurred into farm lands class. Compared to former classes, farm lands have changed a
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Herold Martin, Helen Couclelis and Keith CClarke.
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