Identifying Trends in Land Transformation in Jodhpur City...

S. L. Borana et al., International Journal of Research in Engineering, IT and Social Sciences, ISSN 2250-0588,

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This work is licensed under a Creative Commons Attribution 4.0 International License

Identifying Trends in Land Transformation

in Jodhpur City Using Remote Sensing and

GIS Technology

S. L. Borana1 and S. K. Yadav

2

1,2(RSG, DL, Jodhpur-342011, Rajasthan, India)

Abstract: This paper shows the trends in land use land cover (LU-LC) changes in the context of transformation

of Jodhpur city using Remote sensing (RS) and Geographical Information system(GIS) Technology. Five LU-LC classes were identified as built-up area, other area, vegetation, mining area and water bodies. Landsat TM and

ETM+ data of 1990 and 2010 were used for supervised classification to generate land use maps with overall

Kappa (κ) statistics are 0.7941, 0.8054 and 0.8398, respectively, for the classification of 1990, 2000 and 2010

images. A land use change detection analysis was carry out to determine the extent and rate of land use change

and transformation over 20 years period. Transition of land use, area calculation, reclassification and the

overlay methods are used in the study. Results showed significant change in other area (scrub and crop land)

and an overall increase in built-up area.

Keywords: LU-LC, Land Transformation, Landsat, GIS Technology.

I. INTRODUCTION The aim of this study was to analyze land use change using Landsat data of 1990 and 2010 in order to

observed changes in the land use. The study evaluates the land use transformation during the 20 year period.

Identified the major land use land cover transitions and trends, comparison and surface trend analysis technique

were used to characterize land changes between 1990 and 2010. The IDRISI Andes software provides an

efficient tool for rapid assessment of LU-LC change and their implications based on cross-tabulation principles.

The Land Change Modeler (LCM) for used evaluation of gains and losses in land use classes, land cover

persistence, and specific transitions between selected categories. Using the classified land-use maps from 1990

and 2010, this method was applied to identify spatial trends of land use changes and persistence.

Study Area

Jodhpur city is located at a latitude of 26º 18' North and longitude of 73º 1' East and is located in the

middle of the Thar Desert tract of western Rajasthan (Fig.1). Its general topography is characterized by the hills

located in the North and North-west. Jodhpur city is located at an average altitude of 241 m above Mean Sea

Level at railway station with fort and old city being much higher at 367.83 m and between 277.21m to 245.50 m respectively. The city has a natural drainage slope from North-North East to South-South East towards Jojari

River. The economy of Jodhpur thrives on industrial goods and cultural heritage.

Figure1: Study Area map





II. DATA AND METHODOLOGY The data used for this study were LANDSAT images (1990, 2000 and 2010) on scale 1:50,000 with

Survey of India (SoI) maps (Table-1). The images were geo-referenced (UTM WGS 84 projection) in ERDAS-

9.2 software. Satellite imagery were load into different bands to generate a false color composite (FCC), true

color composite (TCC) and the study was extracted by subset of the image. Land use classes were digitized in

GIS domain using ArcGIS 9.3 software. For classification five land use classes were identified and demarcated.

The change analysis, land use persistence, transformation spatial trend and pattern of built-up expansion were

calculated using IDRISI software. The detailed methodology adopted for this work is given in Fig-2.

Table1: Information of utilized Satellites Imagery

Year Date Acquired Satellite Sensor Spatial resolution

1990 Oct - 1990 LANDSAT 5 TM 30m

2000 Oct - 2000 LANDSAT 7 ETM 30m

2010 Oct - 2010 LANDSAT 7 ETM 30m

(Source: US Geological Survey).

Figure2: Methodology Flow Chart

III. RESULT AND DISCUSSION Change detection is the process of identifying differences in the state of an object or phenomenon by

observing it at different times (Singh, 1989). The land use change can be resulted by many factors a proper

analyze is needed in order investigate these factors. Transitions from one category to other ones can be observed

by change detection method which can help to understand the interaction between the categories. Area

calculation, reclassification and the overlay methods are used for this research. Reclassification is simply

classifying the one image according to new categories. Overlay simply means combining information from

different layers. By using these methods amount of gain and losses, net change and contributor to change from

each category can be calculated (Johnson, 2009).

LU-LC Change Detection Analysis: Land cover maps are generated for three temporal periods using

the approaches adopted in the methodology (Fig.3). Area estimates and change statistics are then computed. The

major land cover classes identified in the study periods covered includes five LULC classes, namely – built-up





area, other area, vegetation, mining area and water bodies. For each individual class area and change statistics

for the three periods are summarized in Table-2.

Table2: LULC Change Statistics for the Decade (1990 to 2010)

The data clearly shows that other area of land cover type is decreasing year by year and shows negative

change because of increasing built-up area among all the land cover types identified and in the three periods. It

is also found that there is approximately 4 per cent increase of vegetation area, nearly 0.18 per cent decrease of

water bodies since last 20 years. In case of built-up area there is about more than 15 per cent increase in area,

i.e. from 1990 to 2010, due to increasing human population & new settlements. There is a 21.64 per cent

decrease of other area. There is 2.34 per cent increase of mining area since last 20 years of the total area due to

availability of huge building stone with thousands of Mines/ Quarry.

Figure3: Land Use/ Land Cover Maps of three Temporal Periods

Accuracy Assessment Analysis: For the comparison of the classified images of years 1990 to 2010,

raster analysis was carried out to finding the variation, the gain and loss of the land use images. The indices used

for the evaluation were overall accuracy, overall Kappa (κ) as well as producer's and user's accuracy for individual land classes. The producer's and User's accuracy for individual land classes are given in Table-3. The

results show that the achieved overall classification accuracies are 84.05 per cent, 85.71per cent and 88.70per

Land Use/

Land Cover

Year 1990 Year 2000 Year 2010 2010-1990

(%) Extent

Rate of

Change Area

(km2)

%

Area

Area

(km2)

%

Area

Area

(km2)

%

Area

Built up Area 24.97 4.20 68.98 11.61 114.42 19.26 15.06 +1

Other Area 496.01 83.50 422.04 71.05 367.50 61.86 -21.64 -1

Vegetation 62.24 10.47 79.31 13.35 86.36 14.54 4.07 +1

Mining Area 10.33 1.74 22.38 3.76 24.25 4.08 2.34 +1

Water bodies 0.45 0.07 1.29 0.22 1.47 0.25 0.18 0

Total Area 594 100 594 100 594 100 -- --


Impact Factor: 6.452, Volume 07 Issue 11, November 2017, 5-52



cent and overall Kappa (κ) statistics are 0.7941, 0.8054 and 0.8398, respectively, for the classification of 1990,

2000 and 2010 images (Table-4).

Table3: Producer's and User's Accuracy for individual LULC Classes

Class Name 1990 (%) 2000 (%) 2010 (%)

PA UA PA UA PA UA

Built-up Area 87.88 93.55 89.74 94.59 93.75 95.74

Other Area 62.79 96.43 73.21 97.62 75.93 97.62

vegetation 92.31 70.59 92.68 77.55 91.43 71.11

Mining Area 92.86 90.70 91.18 81.58 96.88 91.18

Water Body 100.0 60.00 100.0 55.56 100.0 88.89

P= Producer's, U=User's, A= Accuracy

Table4: Overall Accuracy and Kappa (κ) Statistics

1990 2000 2010

Overall classification accuracy (%) 84.05 85.71 88.70

Overall Kappa (κ) statistics 0.7941 0.8054 0.8398

Gains and Losses by Category: Figure 4 demonstrates the gain and losses in LU-LC that occured in the

study area from 1990 to 2000, 2000 to 2010 and 1990 to 2010. The right side bars indicate the gain of each

class, while the left side define the loss of each class. The built-up area has increased over the years while there

is slight loss in this category. It means some parts of the previously existed built-up areas have converted to

some other land cover classes, while vast new area has transformed into built-up area from other classes. Gains

in built-up area are evident in all three combinations.

.

Figure4: Gains and Losses of Land Covers by Category (Unit: % of Area).





Contributors to Net Change Experienced by Built-up Area: Figure 5 illustrates which land cover type is

contributing more to net change in built-up area. It is found that other area is contributing most converting

towards built-up area followed by vegetation.

Figure5: Contributors to Net Change by Built-up Area (Unit: % of Area)

Transition to Built-up Area

Fig.6. Shows which areas from other land cover types have been converted to built-up areas. Overall it

can be concluded that built-up area is increasing and the contributions are mainly from other area (scrub and

crop land).

Figure6: Transition of Other Land Cover Types into Built-up Area (1990-2010)





Gains and Losses in Land Cover Types: The south eastern part of Jodhpur city has remained the same in case

of built-up area. While the north-east and south-west parts have converted to built-up areas. The northern part of

Jodhpur city has gained water body followed by a decrease in the southeast and south-west parts. No particular

pattern on gains or losses is found for vegetation. The changes are evident in eastern and western parts in cases of low land. Other area has decreased markedly and the losses are clear in north-western and mid parts (Fig.7).

Figure7: Gains and Losses in Land Cover Types (2000-10).

Spatial Trend of Change

It is possible to see the pattern of urbanization in the study area from the transition map. Moreover,

there is a mapping tool developed for Spatial Trend of Change. The analyst computes trend of the urban growth within the study area with Spatial Trend of Change since the main concern of the study is urban area. Three

polynomial orders have been tested in order to see the sprawling trend of the study area with the same tool. It

has revealed similar result that the built up is expanding to the West direction (Fig.8).





Figure8: Spatial Trend of Change of the Built-up (1990-2010)

CONCLUSION The study demonstrated that remote sensing and GIS techniques is useful in classify LU-LC maps of

the Jodhpur city from 1990 and 2010. Under Land cover classes significant losses were find out in land use

category of other area and gains in built-up areas. The built-up area is increased by 15 per cent i.e. from 1990 to

2010, due to increasing human population. The spatial trends of land use change were observed, has revealed

that the built up is expanding to the West direction of the Jodhpur city. Also concerning the transition from the

all category to the builtup category, the major trend was also observed in the west of the study area. Concerning

newly built-up areas, the major conversion to the class of developed was identified from the class of other area.

Acknowledgment

The authors are thankful to the Director DL, Jodhpur and Head, Department of Mining Engineering, Jai Narain Vyas University, Jodhpur for help and encouragement during the study.

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