Modeling the spatiotemporal pattern of farmland change in rural regions of Ahvaz County by remote se

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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

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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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