Regional dynamics in an industrial region of south Gujarat ... · Regional dynamics in an industrial region of south Gujarat, India -A geospatial perspective Bikramjit Barooah 1,

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 5, No 4, 2015

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4380

Submitted on January 2015 published on May 2015 592

Regional dynamics in an industrial region of south Gujarat, India

-A geospatial perspective Bikramjit Barooah1, Tathagata Ghosh2, Rolee Kanchan3

1- Research Student, Department of Geography, Faculty of Science, The Maharaja Sayajirao

University of Baroda, Vaodara-390002, Gujarat

2- Temporary Assistant Professor, Department of Geography, Faculty of Science, The

Maharaja Sayajirao University of Baroda, Vaodara-390002, Gujarat

3- Professor, Department of Geography, Faculty of Science,The Maharaja Sayajirao

University of Baroda, Vaodara-390002, Gujarat

[email protected]

ABSTRACT

The process of industrialization and urbanization puts its effect on its immediate surroundings

leading to transformation in Landuse and Landcover. In the present study, an attempt has

been made to analyze the Landuse and Landcover dynamics of south Gujarat in the last two

decades (1990 to 2011). Traditional supervised and unsupervised classification along with the

band rationing technique has been employed to understand the spatial pattern of different

Landuse and Landcover categories. Broad categories like built-up land, agricultural land,

vegetation, water body, fallow land and riverine sediments were extracted from the analysis.

The built-up land and agricultural land showed significant changes where, former was related

to positive change while the later detected negative changes. The band rationing techniques

assisted in determining the changes that has taken place in each of the category and

correlation among these categories showed important inter-relationship.

Keyword: Regional dynamics, land use and land cover, band rationing.

1. Introduction

Regional dynamics in response to the fast economic and social transformation has its impact

on the immediate surroundings. In the last few decades, the accelerated process of

industrialization and higher rate of population growth resulted into large scale impact on land

use and land cover over the earth surface. Urban centers are the major hubs of the population

concentration where economic and industrial activities are coagulated. These growth centers

and growth poles play key role in initiating the process of industrialization and urbanization.

The spread effect from the centers to the outer peripheral region gradually transforms the

scenario. This transformation has an influence on the existing vegetation cover, transport

system, settlements etc. This type of transformation generates employment, increase the

density, intensity and quality infrastructure and better livelihood. One of the major focus of

the Country after the Independence was to attain self sufficiency in the sector of agriculture

and accelerate the process of industrialization to attain balanced regional development which

brings changes in the agricultural policies and industrial settings. Hence, it is essential to

analyze the changing scenario of land use and land cover as they are direct representative of

the socio-economic transformation of the region. Li and Yeh (2003) analysed the land use

and land cover changes in the Pearl river delta of South China. The result illustrated that the

market economy has played an important role in reconstructing the land use pattern. To

depict the spatial pattern indicators like compactness index and entropy were used in the

Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective

Bikramjit Barooah et al.,

International Journal of Geomatics and Geosciences

Volume 5 Issue 4, 2015 593

study. Similar type of work has been done by Deng et al. (2009) in the Zeijhihan province of

China. In this study, other than using traditional remote sensing techniques, different matrixes

like Shannon’s diversity index, Mean Patch Size etc. were taken into consideration. The

results revealed the applicability of both the methods and also identified the landscape pattern

dynamics in different time periods. Besides using conventional land classification methods by

supervised and unsupervised classification, Porter-Bolland et al. (2007) adopted statistical

techniques like principal component analysis and image differentiations for landuse and

landconver pattern in the La-montana region. Prakash and Gupta (1998) analyzed three

methods viz. image differentiation, image rationing and NDVI and no significant variations

in results were observed among these methods. Indices like NDVI, SAVI, PCA and KT was

analyzed by Rogan and Yool (2001) in Pelonciollo mountains of Arizona and New Mexico. It

was found that KT provided the best result among all of the indexes. The combinations of

traditional unsupervised classification with GIS based spatial reclassification and post

classification provided better results in the study of Yang and Lo (2002) in the metropolitan

area of Atlanta, Georgia.

Visual image interpretation is one of the method that was used from 1970’s when the satellite

images were not available. Loveland et al. (2002) applied the visual interpretation technique

on fine scale MSS, TM and ETM+ to identify the land use and land cover change rate. Zhu

and Woodcock (2014) implemented Continuous Change Detection and Classification

(CCDC) technique in the parts of New England. In this technique, observed and predicted

images were taken into considerations using Landsat satellite images. As the pixel value

exceeds the threshold value a pixel was generated and the algorithm exhibited 90% accuracy.

For the identification of detection of land cover disturbances in the Lappi reindeer herding

district, Kivinen and Kumpula (2014) adopted traditional method of land use and land cover

using multiple sources like MSS, TM, aerial photographs. The analysis suggested that since

late 1960’s the area under study experienced significant disturbances which are about 27% of

the total area. Rawat et al. (2013) attempted land use and land cover change detection in the

parts of Uttarakhand state of India using geospatial technique. According to the study, built-

up land and agricultural area showed significant change particularly in the south of the

National Highway no. 121.

In the present study, an attempt has been made to incorporate different types of techniques to

observe the changes that have taken place in the last two decades (1990-2011).

2. Study area

The study area is located in the southern portion of the state of Gujarat. Geographically, the

region extends between 72o56’38” E to 73o5’22” E longitude and 21o39’25” N to 21o46’14”

N latitude covering an area of 192.33 km2 (Figure.1) (DCHB 2001). The west of the study

area is bounded by Gulf of Khambat. In the north and south, river Mahi and Narmada flows

in the east to west direction respectively. Physiographically, the entire region is considered as

flat terrain with an elevation ranging from m 17 to 24 m (SOI toposheet No. 46C/14 and

46G/2). The National Highway No. 08 traverses through the study area and is a part of

Golden Corridor. This is one of the major reasons behind the faster rate of industrialization in

and around the city of Bharuch. The major industries of this region are chemical,

petrochemical, pharmaceuticals, engineering and textile.





Figure 1: Study area

3. Methodology

Present study mainly depends upon Landsat satellite image of 1990 and 2011. Both the

images of 14th November (Row/Path 148/45) were downloaded from the USGS website

(http://glovis.usgs.gov/). First three visible bands (Band-1=Blue, Band-2=Green and Band-

3=Red) were stacked. From the stacked images, area of interest was subset (72o56’38” E to

73o5’22” E longitude and 21o39’25” N to 21o46’14” N latitude). Survey of India (SOI)

toposheet No 46C/14 and 46G/2 were used for image registration. Initially, unsupervised

classification was done on both the images with higher number of classes, later on the basis

of visual interpretation similar classes were merged with each other. After the completion of

the unsupervised classification, supervised classification was done. Finally, seven classes

were identified on both the images (vegetation cover, agricultural land, water body, fallow

land, reverie sediments and built up land) and their area in km2 was calculated. Band

rationing techniques like Normalized Difference Vegetation Index (NDVI), Normalized

Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI) and

Normalized Difference Bareness Index (NDBaI) were adopted. 1500 pixels were randomly

generated on each of the indices and its corresponding pixel values were extracted (Figure.2).

The extracted pixel values were used to map in GIS platform to depict the spatial distribution

of the indices using kriging interpolation method. Multiple correlation among the indices also

been done for both the images. ERDAS Imagine 9.1 and ArcGIS 10 was used for the image

processing and mapping respectively.

3.1 Band Rationing Indices

Band rationing is the method where a particular portion of the bandwidth is taken into

consideration. Every entity on the surface of the earth reflects the incoming solar radiation at





a particular wavelength. Thus, that particular wavelength will depict much better information

than the entire wavelength.

Figure 2: Workflow of the study

On the basis of this concept, number of band ratios have been evolved to depict the

corresponding entity of the earth surface. Some of the common band ratio indices are

Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index

(NDBI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Water

Index (NDWI). The calculation of the indexes are as follows

Li and Fox (2012)





Zhang et al. (2009)

Maki et al. (2004)

Chen et al. (2006)

Where –

NDVI= Normalized Difference Vegetation Index,

NDBI= Normalized Difference Built-up Index,

NDWI= Normalized Difference Water Index,

NDBaI= Normalized Difference Bareness Index,

NIR=Near Infrared, SIR=Shortwave Infrared.

4. Results and discussion

4.1 Spatial Pattern of land use and land cover

The spatial pattern of the land use and land cover showed significant variations during the

study period. A large patch of build up land was observed in the central portion of the study

area mostly confined in the northern bank of the river Narmada. In recent years, the patch

was sprawled from the central to the outward periphery in all the directions except in the

south. Some smaller patches can also be observed in the north central portion which had

grown as an outgrowth in the recent years. In the south eastern segment of the study area

some smaller patches of settlements have recently emerged. Conspicuous changes can be

observed in the agricultural land. In the former year (1990) the agricultural land was mostly

observed in the northern part of the river. In a span of two decades new settlements have

emerged and they occupied a significant portion resulting into a decline in the proportion of

agricultural land. On the other hand, in recent years the agricultural land was largely confined

to the south of river Narmada. Some small patches of agricultural land had also emerged on

the small deposition on bed of river Narmada located in the eastern part. The major segment

of vegetated area had shifted from the northern portion of the study area to the south of the

river in continuous patch. Similarly, the fallow land had dispersed and confined to the south

eastern part of the study area.

4.2 Land use / land cover and band rationing

In 1990, 15.62 km2 area was covered by built-up land which was about 8.12% to the total

area. In 2011, this area increased to 30.49 km2 (15.85%) (Figure.3). Thus, in the time span of

21 years an increase of 7.73% was observed where as a decline was observed in agricultural

land. In 1990, 79.44 km2 (41.30% of the total area) was under agricultural land while in 2011

the area declined to 72.39 km2 (37.64% of the total area) (Table.1). Thus, 3.66% decrease in

the agricultural land was evident in the last two decades (Figure.4).





Figure 3: Landuse and landcover map of Bharuch city and its surroundings (1990)

Figure 4: Land use and land cover map of Bharuch city and its surroundings (2011)

Table 1: Percentage wise Land use and land cover category (1990-2011)

Categories

Area in km2

(1990)

% to Total

Area

Area in km2

(2011)

% to Total

Area

% Change in

Two

Decades

Vegetation 58.72 30.53 54.89 28.54 -1.99

Agriculture 79.44 41.30 72.39 37.64 -3.66





Figure 5: Percentage change in land use and land cover categories

(Between 1990-2011).

During this period the percentage of land under vegetation cover (-1.04%), water bodies

(-1.15%) and coastal sediments (-1.04%) also declined. No significant change (0.05%) was

observed in the fallow land (Figure. 5). Normalized Difference Vegetation Index showed that

during 1990 the data ranged between +0.556 to -0.144 while in 2011 (Figure. 6a and 6b), it

varied between +0.049 to -0.067. Normalized Difference Built-up Index (NDBI) depicts the

changes that took place in-terms of buildup land consisting of Shortwave Infrared and

Near Infrared wavelength. In 1990, the NDBI ranged between +0.393 to -0.575 while in 2011

it varied between +0.513 to -0.538 (Figure. 6c and 6d).

b

Buildup Land 15.62 8.12 30.49 15.85 7.73

Fallow Land 11.31 5.88 11.40 5.93 0.05

Water Body 14.32 7.45 12.11 6.30 -1.15

Riverine

Sediments 12.92 6.72 10.93 5.68 -1.04

Total Area 192.33

192.33





a

c d

e f

g h

Figure 6: Band rationing indices (a and b-NDVI of 1990 and 2011, c and d-NDBI of 1990

and 2011, e and f- NDWI of 1990 and 2011, g and h-NDBaI of 1990 and 2011)

A significant change was noted on the positive side while in the lower side, value did not

showed any important variation. Reversely, NDWI depicted changes that took place in last 21

years considering NIR and SIR (Figure. 6e and 6f). The bareness index (NDBaI) was

computed by using SWIR and TIR band rationing. During 1990, the NDBaI ranged between

+0.039 to -0.900 while during 2011, the variation was between +0.216 to -0.878. Thus, many

fold increasing trend was noted at the higher end while at the lower end a small decrease was





observed (Figure. 6g and 6h). Spatial distribution of the NDVI illustrated that in the central

and western portion of the study area, a big patch of higher index was observed while in the

2011 this patch shrunk. The central patch moved further north and the patch in the western

part is almost absent in the recent years. The southern segment of river Narmada experienced

a significant decrease in the vegetation cover.

4.3 Correlation among the band rationing indices

Figure 7: Coefficient of correlation matrix of different indices.

Each of the band rationing indices depicted a particular type of physical entity in respect to

the particular wavelength. Relationship among each of the index can be useful to show the

relation over certain time span. Between NDWI and NDVI, a negative correlation was

observed for both the years. In 1990, the correlation was -0.228. Perfect negative correlation

was found between NDWI and NDBI for both years. The relationship between NDVI and

NDBI was positive for both the years but relationship weakened in 2011 (+0.228 in 1990 and

+0.028 in 2011) (Figure.7). A significant change was observed among the parameters of

NDWI and NDBaI. In 1990, the correlation between them was +0.778 while in 2011 it was

+0.793. On the other hand, the correlation between NDWI and NDBaI in 1990 was -0.778

while in 2011 it became -0.793. In case of NDVI and NDBaI it was -0.653 and -0.535 in

1990 and 2011 respectively.

5. Conclusion

From the present study the following inferences could be drawn-

1. The city of Bharuch has expanded in multi-direction with 7.73% change in the last

two decades which clearly indicated the rapid process of urbanization. The major hub

of growth was observed in the northern portion of the river Narmada with few smaller

settlements that has recently emerged due to the spread effect of the city centre.

2. Fast expansion of the city centre has put it effect on different parameters (like

vegetation cover, agricultural activities and water bodies) which are located in the

close association with each other’s.





3. Computer aided knowledge based map depicted the changes in spatial pattern of land

use and land cover while band rationing techniques (like NDVI, NDWI, NDBaI and

NDBI) successfully enumerated individual parameters (like vegetation, water body,

bareness and built-up area).

4. Coefficient of correlation matrix showed that all the indices are in close relationship

to each other (either positive or negative) and affect each other.

6. References

1. Chen, X.-L., Zhao, H.-M., Li, P.-X., & Yin, Z.-Y., (2006), Remote sensing image-

based analysis of the relationship between urban heat island and land use/cover

changes. Remote Sensing of Environment, 104, pp 133–146.

2. DCHB, District Census Hand Book, (2001), Bharuch District, Census of India.

3. Deng, J. S., Wang, K., Hong, Y., & Qi, J. G., (2009), Spatio-temporal dynamics and

evolution of land use change and landscape pattern in response to rapid urbanization.

Landscape and Urban Planning, 92(3), pp 187-198.

4. Kivinen, S., & Kumpula, T., (2014), Detecting land cover disturbances in the Lappi

reindeer herding district using multi-source remote sensing and GIS data.

International Journal of Applied Earth Observation and Geoinformation, 27, pp 13-19.

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a fast growing region using remote sensing and GIS. Landscape and urban planning,

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6. Li, Z., & Fox, J. M, (2012), Mapping rubber tree growth in mainland Southeast Asia

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D.E, (2002), A strategy for estimating the rates of recent United States land-cover

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Regional dynamics in an industrial region of south Gujarat ... · Regional dynamics in an industrial region of south Gujarat, India -A geospatial perspective Bikramjit Barooah 1,