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Spatial and Temporal Landuse Change and Urban Sprawl of Ranchi, India

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Introduction:

Urban growing or urban conurbation is a spacial moral force phenomenon that indicates population growing, economic enlargement, big graduated table migration, metropolis importance etc. Urban conurbation has become a singular feature of urban development worldwide in the last few decennaries [ MGA1 ] . In developing states like India conurbation is taking its toll on the natural resources at an dismaying gait ( Kumar et al. 2007 ) . Urbanization is considered as the most influential drivers of land usage and land screen alteration associated with growing of population, economic system and mass migration.

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The integrating of remote detection ( RS ) and Geographical Information System ( GIS ) technique in urban surveies has become an effectual tool for observing urban growing and for future urban be aftering utilizing current and historical informations of the landuse. Remote detection is a cost effectual, technologically sound and an progressively used technique for the analysis of urban growing ( Sudhira et al. 2004, Yang and Liu 2005, Haack and Rafter 2006 ) . For about three decennaries, extended research attempts have been made for urban alteration sensing utilizing remotely sensed images ( Toll 1985, Royer et Al.

1988, Singh 1989, Lo and Shipman 1990, Gomarasca et Al. 1993, Green et Al. 1994, Yeh and Li 2001, Yang and Lo 2003, Haack and Rafter 2006 ) .

This survey focusses on Shannon’s Entropy based urban sprawl appraisal in Ranchi metropolis and its periphery countries. Entropy is originally a physical construct of statistical natural philosophies and thermodynamics. C. E. Shannon ( 1948 ) introduced the construct of information into information theory and named the productive theory “Information Entropy” in 1948. Information information can be used to reflect the grade of upset of a system and to quantitatively judge the evolutionary way of the system. A immense sum of literature is available on Shannon’s information based appraisal of landscape kineticss sing figure of spots as the landscape variable but in this survey we have used figure of spots every bit good as country of the spots to measure the Shannon’s information. Question is what is the significance of utilizing both for this appraisal? Then it is really of import to understand that figure of spots of any landuse category and country of those spots in the same category can non be in same ratio so the information calculated in both ways besides will non be same. Number of spots in a landuse category infact shows how a big spot has been converted to many little spots of different or same landuse category while Area of spots shows the sum of landscape kineticss among the landuse classes. So in this survey we used no. of spots to measure the Fragmentation form and country of spots has been used to gauge the sum of urban conurbation in Ranchi and its periphery countries. [ MGA2 ]

Sudhira et Al. ( 2003 ) focused on the analysis of urban growing form in the signifier of either radial or additive conurbation along the Bangalore-Mysore main road over a period of 1972–1998. They used classii¬?ed remote feeling image for 1998 and Survey of India topographical maps for the twelvemonth 1972. Then the Shannon’s information was used to find the conurbation in different waies.

Sudhira et Al ( 2004 ) calculated Shannon’s information and the landscape prosodies ( patchiness, map denseness ) in order to quantify the urban signifiers such as built-up in footings of spacial phenomenon for understanding the built-up kineticss sing built-up country for each small town as an single zone. The consequences show that the distribution of built-up in the part in 1972 was more spread than in 1999.

Kumari et Al ( 2006 ) demonstrated a simple attack to quantify the heterogeneousness of the flora of a landscape retrieved by orbiter images utilizing Entire Information Content ( Shannon Entropy ) in a flora index, NDVI. The survey is based on information theory, where Shannon’s information can be used to mensurate the grade of spacial concentration and scattering exhibited by any geo-graphical variable ( Theil, 1967 ; Thomas, 1981 ) .

Ramchandra et Al ( 2014 ) used Shannon’s information to understand way wise growing of urban country in a specific zone and whether the growing is compact or divergent every bit good. Writers found an increasing tendency for NE and SW waies and urban spots are acquiring fragmented in clip.

Aduah et Al ( 2013 ) applied Shannon’s information to analyze the urban conurbation in a coastal metropolis of Ghana. The land screen maps of the two day of the months ( 1988 and 2008 ) were reclassified into built-up and non-built-up. Shannon information was determined for each zone every bit good as the full survey country. The Shannon information for the survey country 1988 and 2008 indicated that sprawling in the survey country has non increased well between the two day of the months despite a monolithic ( more than 263 % ) addition in built-up countries.

In a similar type of work Kumar et Al ( 2014 ) used Shannon’s information in greater Hyderabad part to cognize the impacts of urbanisation in the rural urban periphery by spliting the survey country in East. West, North, South and Central zones. Writers used NDVI to minimise the seasonal fluctuation and atmospheric effects in the landuse categorization.

Jat M.K. et Al ( 2008 ) studied urban growing of Ajmer metropolis ( India ) in the last 29 old ages at mid-scale degree ( 5–25 m ) . Distant detection and GIS have been used to pull out the information related to urban growing, imperviable country and its spacial and temporal fluctuation. Statistical categorization attacks have been used to deduce the land usage information from orbiter images of eight old ages ( 1977–2005 ) . The Shannon’s information and landscape prosodies ( patchiness and map denseness ) are computed in order to quantify the urban signifier ( imperviable country ) in footings of spacial phenomena. Further, multivariate statistical techniques have been used to set up the relationship between the urban growing and its causative factors.

Sun H. et Al ( 2007 ) in their survey to mensurate the urban conurbation calculated Shannon’s information of 6 old ages taking 22 co-centric buffer zones around metropolis Centre for builtup and non-builtup category maintaining the thought in head that imperviable surfaces and concrete is a straightforward step of urban development ( Barnes et al. 2001 ) . In order to do the measuring of the 6 old ages of conurbation comparable, comparative information alternatively of absolute information was utilized because the scope of comparative

information is ever between 0 and 1.

Bhatta B. ( 2009 ) carried out a survey to place and analyze the urban growing form of Kolkata, India utilizing Shannons’s information and applied several statistical methods to acknowledge and analyze this form, which shows increasing dispersed development of the metropolis with the worsening population growing rate. Apart from the derived consequences, writer besides showed the potency of distant detection and GIS techniques for the analysis of urban growing form.

The current survey implements landuse categorization of urban and peri-urban parts to mensurate the landuse kineticss and urbanization form of metropolis of Ranchi, Jharkhand, India. Shannon’s information ( an urban conurbation index ) based on landuse categories at different activity locations all around the survey country of two different old ages ( 2000 and 2013 ) has been used to mensurate the urban conurbation. In this survey we calculated Shannon’s information with two methods a ) based on no. of spots in each category and ( B ) based on country covered by each category. Against the traditional method of making buffer zones from a individual point widening to full survey country we identified several locations which are more active or urbanised with regard to other countries such as major chowks naming them activity points or activity locations. We created 3 homocentric buffers of 1 Km each around every activity point. For both the old ages, for each buffer and for each location no. of spots in each category and country of each category has been calculated and Shannon’s information was calculated in both ways. As it is impossible that every category will be present in each buffer to normalise the value of entropy comparative Shannon’s information was besides calculated.

Study Area:

The survey country demarcated for the current work consist of 2 kilometres extension of Ranchi Ring Road. Ranchi is the capital territory of Jharkhand province of India. Ranchi is non a planned metropolis and since it has become the capital after the formation of new province of Jharkhand in twelvemonth 2000 metropolis has witnessed immense urban development and industrialisation that excessively in a haphazard mode in the metropolis and its periphery countries. It seems the metropolis will be extremely packed with concrete constructions in following few old ages doing major hazard to its environment and ecology. Keeping the hereafter of this metropolis in head appraisal of landscape kineticss and urban ecology has become really important at this minute to salvage its natural beauty and comforting clime.

Figure

Aims:

Aim of this survey is to qualify the spacial and temporal landuse alteration bespeaking the urban conurbation of Ranchi and its periphery countries on the footing of comparative Shannon’s information evaluated at the major activity Centres within the survey country utilizing two different attacks ( Patch based and country based ) over a span of 13 old ages. Along with appraisal of urban conurbation in the survey country, designation of most and least sensitive zones is besides a major purpose in this survey. Comparison of landuse kineticss based on Shannon’s information in different buffer zones along the activity centres has been used to place the most and least sensitive locations for urban conurbation between the old ages 2000 and 2013.

Methodology

Landuse/Landcover ( LULC ) Categorization:

Satellite images of the survey country were downloaded for the old ages 2000 and 2013 and LULC for both the old ages has been digitized with different landuse categories viz. Builtup, Builtup-vegetation, Crop land, Dense forest, Open forest, Plantation, Shrubs, Wasteland and Waterbody. Landsat 7 ETM+ image for the twelvemonth 2000 and Landsat 8 OLI_TIRS image for the twelvemonth 2013 were used in ArcGIS© for landuse/landcover categorization.

Figure

Designation of Activity Centers in the survey country

To analyze the landscape kineticss in Ranchi metropolis and its milieus 22 activity centres were identified in such a manner that these points cover the full survey country. These activity centres are really more developed and more populated than its environing countries like concern centres, route crossings ( chowks ) etc. The selected activity centres in the survey country are viz. Birsa Chowk, Booty More, Durga Soren Chowk, Firayalal Chowk, HEC Coloney, Hinoo Chowk, Kanke Block thru Tagore Hill, Kanke Chowk, Kanke Road Chowk, Kanta Toli Chowk, Karma, Kathitar, Lalgutua, Lalpur Chowk, Namkom, Pandra Krishi Bazar, Rampur, Sethio, Sidraul, Tati ( Khelgaon Road ) , Tatisilwai and Tipudana

Making Buffer zones around each activity centre ( 1km, 2Km and 3Km )

After placing the major activity locations all around the survey country 3 buffer zones of 1 kilometers, 2 kilometer and 3 kilometers have been created.

Figure

Calculation of Shannon’s Entropy for each buffer

Shannon’s information was calculated to analyse the type and way of landuse kineticss in the survey country concentrating on the activity locations. Shannon’s information is given by equation

WherePhosphorusIis the proportion of theIThursdaycategory andNis the entire figure of categories. Entropy value ranges from 0 tolog N. Here entropy closer to zero indicates compact distribution and closer tolog Nindicates dispersed distribution.

Shannon’s information was calculated for each buffer i.e. for 0-1Km buffer, 1-2km buffer and 2-3 kilometer buffer with two different attacks based on figure of spots and country of spots of each landuse category in each buffer.

  1. Spot based
  2. Areas based

Calculation of Relative Shannon’s Entropy

Since it is extremely possible that all the landuse categories will non be present in each buffer, hence to normalise the absolute Shannon’s information comparative Shannon’s information construct has been used to do it more precise by integrating existent figure of landuse categories in each buffer. This construct was given by Theil H. ( 1972 ) to give a broader reading to Shannon’s information as a step of divideness and spacial scattering. This construct indicates the proportion of the maximal possible scattering in which a variable is spread between classs or spacial zones ( it will be 1 if the variable is equally dispersed among all zones, and approaches 0 if the variable is concentrated in a little figure of zones ) . It is expressed by equation

where N is the figure of landuse categories in the peculiar buffer

Comparisons

Each year’s comparative Shannon’s information values were compared to happen the difference in each year’s information in both spot based method and country based method. Entropy differences were besides compared in each buffer for both the methods and for both the old ages. And at last a spot based vs country based comparing was besides observed to happen out whether these two attacks show some relationship or non.

Consequences and Discussion

Spot based Entropy

Comparing the spot based informations for different buffer zones around all activity locations it is found that in the twelvemonth 2000 in 1Km buffer zone minimal information is found at Lalpur chowk and upper limit at Pandra krisi bazaar where as in twelvemonth 2013 it minimum was at Kanke chowk and upper limit is at Kanke block route via Tagore hill. Similarly for 2km and 3 kilometers buffer zones minimal and maximal informations are shown in tabular array ( )

Table

Year

2000

2013

Buffer zone

1km

2km

3km

1km

2km

3km

Min Entropy

Lalpur Chowk

Firayalal Chowk

Booty More

Kanke Chowk

Karma

Tipudana

Max Entropy

Pandra Krishi Bazar

Rampur

Birsa Chowk

Kanke Block via Tagore hiill

Sethio

Tatisilwai

Area based Entropy

Comparing the country based informations for different buffer zones around all activity locations it is found that in the twelvemonth 2000 in 1Km buffer zone minimal information is found at Kanke route chowk and upper limit at Hinoo chowk where as in twelvemonth 2013 it was minimum at Kanke route chowk and upper limit is at Kanke block via Tagore hill route. Similarly for 2km and 3 kilometers buffer zones minimal and maximal informations are shown in tabular array ( )

Table ( )

Year

2000

2013

Buffer zone

1km

2km

3km

1km

2km

3km

Min Entropy

Kanke Road Chowk

Firayalal Chowk

Lalgutua

Kanke Road Chowk

Firayalal Chowk

Lalgutua

Max Entropy

Hinoo Chowk

Rampur

Durga Soren Chowk

Kanke Block via Tagore Hill

Sethio

Hinoo Chowk

Spot based information V Area based information

While analysing the consequences a spot based information V country based information comparing was besides performed find out the type of atomization form and urban conurbation in each buffer. Here it is of import to cognize that spot based information will give the sum of atomization while country based information will give the sum of conurbation. Table ( ) shows minimal and upper limit of spot V country information in each buffer.

Table ( )

Year

Buffer Zone

Minute

Soap

2000

1km

Spot

Lalpur Chowk

Pandra Krishi Bazar

Area

Kanke Road Chowk

Hinoo Chowk

2km

Spot

Firayalal Chowk

Rampur

Area

Firayalal Chowk

Rampur

3km

Spot

Booty More

Birsa Chowk

Area

Lalgutua

Durga Soren Chowk

2013

1km

Spot

Booty More

Kanke Block via Tagore Hill

Area

Kanke route Chowk

Kanke Block via Tagore Hill

2km

Spot

Karma

Sethio

Area

Firayalal Chowk

Sethio

3km

Spot

Tipudana

Tatisilwai

Area

Lalgutua

Hinoo Chowk

Plot of Patch based information V Area based information in each buffer did non demo any tendency line ( Fig ) . It indicates that there is no important relationship between them and both of the information approaches represent different significance. Plot of Patch based informations ( twelvemonth 2000 vs twelvemonth 2013 ) and secret plan of Area based informations ( twelvemonth 2000 vs twelvemonth 2013 ) besides did non demo any correlativity between them ( Fig ) . It indicates that fluctuation of information in these old ages do non demo any recognizable form and can be concluded that the conurbation form is random between 2000 and 2013.

Figure

Temporal analysis of Entropy

Temporal analysis of informations show that a noticeable sum of landuse alteration and frangmention has occurred in between twelvemonth 2000 and 2013. In 1km buffer zone a immense difference can be seen in spot based information and country based information of both old ages while in 3 kilometers buffer zone a minimum alteration form is shown in the graph. But in 2 kilometers buffer zone a important fluctuation is seeable in country based entropy while a negligible alteration is noticed in spot based information. An overall comparing of informations show that at Booty More, Kanke route chowk, Karma, Firayalal chowk, Tipudana and Lalgutua has minimal urban conurbation and atomization while at Kanke Block via Tagore hill route, Sethio, Tatisilwai and Hinoo chowk has maximum conurbations and landuse atomization.

Figure

Decision

On analysing the consequences of Shannon’s information in the survey country we are able to reason about the atomization form and sum of landuse kineticss in the survey country. A temporal comparing of spot based information and country based information in different buffer zones show that in in 1 kilometers buffer zone a important sum of landuse country has changed and atomization is besides maximal in this part. In 2 kilometers and 3 Km buffer zones severally a moderate and least atomization and landuse country alteration is observed for both years.. An overall comparing of informations show that at Booty More, Kanke route chowk, Karma, Firayalal chowk, Tipudana and Lalgutua has minimal urban conurbation and atomization while at Kanke Block via Tagore hill route, Sethio, Tatisilwai and Hinoo chowk has maximum conurbations and landuse atomization.

Mentions

B. Bhatta ( 2009 ) Analysis of urban growing form utilizing distant detection and GIS: a instance survey of Kolkata, India, International Journal of Remote Sensing, 30:18, 4733-4746, DOI: 10.1080/ 0143116080265196

Barnes et Al. ( 2001 )

Haack, B.N. and Rafter, A. ( 2006 ) . Urban growing analysis and mold in the Kathmandu Valley, Nepal. Habitat International, 30 ( 4 ) , pp. 1056-1065

Kumar, J.A.V. , Pathan, S.K. and Bhanderi, R.J. ( 2007 ) Spatio-temporal analysis for monitorin urban growth—a instance survey of Indore metropolis. Journal of Indian Society of Remote Sensing, 35, pp. 11–20.

Kumari J, Govind A and Govind A ( 2006 ) . Entropy Change as Influenced by Anthropogenic Impact on a Boreal Land Cover – A Case Study. Journal of Environmental Informatics 7 ( 2 ) : 75-83.

Theil H. ( 1967 ) . Economicss and Information Theory, North-Holland, Amsterdam.

Thomas R.W. ( 1981 ) . Information Statistics in Geography, WH Hutchins & A ; Sons, Norwich.

Shannon C.E. ( 1948 ) A Mathematical Theory of Communication. The Bell System Technical Journal, Vol. 27, pp. 379–423, 623–656,

Sudhira HS, Ramachandra TV, Karthik RS, Jagadish KS ( 2003 ) . Urban Growth Analysis Using Spatial and Temporal Data, J.Indian Soc. Remote Sens. , 31 ( 4 ) : 29-39.

Sudhira H.S. , Ramachandra T.V. and K.S. Jagadish ( 2004 ) . Urban conurbation: prosodies, kineticss and patterning utilizing GIS. International Journal of Applied Earth Observation and Geoinformation 5:29–39

Jat M.K. et Al ( 2008 ) Modelling of urban growing utilizing spacial analysis techniques: a instance survey of

Ajmer metropolis ( India ) . International Journal of Remote Feeling

Sun H. et Al ( 2007 )

Ramchandra T.V. , Aithal B.H. ( 2013 ) . Urbanization and Sprawl in the Tier II City: Prosodies, Dynamicss and Modeling Using Spatio-Temporal Data. International Journal of Remote Sensing Applications, 3 ( 2 ) :65-74

Yang and Liu 2005

Toll 1985

Royer et Al. 1988

Singh 1989

Lo and Shipman 1990

Gomarasca et Al. 1993

Green et Al. 1994

Yeh and Li 2001

Yang and Lo 2003

Cite this Spatial and Temporal Landuse Change and Urban Sprawl of Ranchi, India

Spatial and Temporal Landuse Change and Urban Sprawl of Ranchi, India. (2017, Jul 07). Retrieved from https://graduateway.com/spatial-and-temporal-landuse-change-and-urban-sprawl-of-ranchi-india/

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