Application Of Remote Sensing And GIS Techniques In Urban .
International Journal of Scientific & Engineering Research Volume 10, Issue 6, June-2019ISSN 2229-55181127Application of Remote sensing and GISTechniques in Urban Planning,Development and Management.(A case study of Allahabad District, India)Kamaluddeen A. Baba, Deepak Lal, Abdulkadir BelloAbstract— Management and planning of urban space requires spatially accurate and timely information on land use andchanging pattern. Evaluation provides the planners and decision-makers with required information about the current state ofdevelopment and the nature of changes that have occurred. Remote sensing and Geographical Information system (GIS) providesvital tools which can be applied in the analysis at the district and as well, as the city level. This study evaluates the proposed futureland use plan (2021) in context to the existing land use (2015) in Allahabad district. Land use /land cover map was prepared usingsupervised classification of landsat 8 (OLI) and LISS IV satellite data The results obtained from classified image were comparedwith the land use information obtained from National Remote Sensing Centre (NRSC, Govt. of India) for the Allahabad district.Though the land use information obtained from NRSC is for the period 2011-12, however in the absence of a more recent data, theone available from NRSC was used for assessing/ verifying the overall pattern and distribution of the land use. Evaluation of theproposed plan (2021) was done by assessing the various types of land use and their respective areas existing under each proposedland use class. It was found that agriculture will be the most affected land use followed by bare land accounting 97.4 km2 and 32km2 respectively. The least affected class is the water encompasses approximately 2.5 km2.IJSERKeywords: Remote sensing, GIS, Urban Planning, Proposed land use plan, Landsat 8 (OLI),1.0 IntroductionWhile the change in climate is a hot topic thesedays, the rapid transformation of a world’s cities isequally dramatic. As the population explodes andglobal resources wane, a new set of urban obstaclesdemands visionary thinking from architects,planners and policy makers. There is an unequalurban growth, which is taking place all over theworld, but the rate of urbanization is very fast inthe developing countries, especially in Asia (Fazal,S. 2013). In 1800 A.D., only 3% of the world’spopulation lived in urban centre, but this figurereached to 14% in 1900 and in 2000, about 47% (2.8billion) people were living in urban areas (Demers,M. N. 2014).To support this increase inpopulation and physical growth rate observed,constant monitoring of the urban growth isrequired to be sustained by anthropogenicactivities. (Ade, M.A. and Afolabi Y.D. 2013). Kamaluddeen A. Baba is currently a Lecturer at Lecture III atDepartment of Social Science, Geography Unit, Kano statecollege of education and preliminary studies, Nigeria, PH2347031020607. E-mail:Kamaluddeen kamaluddeenababa@gmail.com Deepak lal is currently a senior lecturer and head of GIS UnitShuats University India Abdulkadir Bello is currently Lecture II at Department of SocialScience, Geography Unit, Kano state college of education andpreliminary studies, Kano, Nigeria. Email:belloabdulqadir@gmail.comIn India for example, people no longer lives invillages. A figure shows that 79 million people livein urban areas as of 1961, but it went up to 285million in 2001(Census of India 2001). In India andChina alone, there are more than170 urban areaswith populations of over 750,000 inhabitants(United Nations Population Division, 2001).Statistics show that India’s urban population is thesecond largest in the world after China, and ishigher than the total urban population of allcountries put together barring China, USA andRussia. In 1991, there were 23 metropolitan cities inIndia, which increased to 35 in 2001 (Census ofIndia, 1991 and 2001).Urban planning (urban, merged urban regions,regional, city, and town planning) is a technicaland political process concerned with the use ofland and design of the urban environment,IJSER 2019http://www.ijser.org
International Journal of Scientific & Engineering Research Volume 10, Issue 6, June-2019ISSN 2229-5518including air and water and infrastructure passinginto and out of urban areas such as transportationand distribution network (Wikipedia 2014). Urbanplanning guides and ensures orderly developmentof settlements and satellite communities whichcommute into and out of urban areas or shareresources with it. It concerns itself with researchand analysis, strategic thinking, architecture, ns,implementationandmanagement (Omojola, M. F. 2014). It is possible tocreate a cadastral layer or parcel boundary layerbecause of very high spatial resolution data (M.Raghunath,2006). A plan can take a variety offorms including strategic plans, comprehensiveplans, neighborhood plans, regulatory andincentive strategies, or historic preservation plans.Planners are often also responsible for enforcingthe chosen policies. The modern origins of urbanplanning lie in the movement for urban reform thatarose as a reaction against the disorder of theindustrial city in the mid-19th century. Urbanplanning can include urban renewal, by adaptingurban planning methods to existing cities sufferingfrom decline. Alternatively, it can concern themassive challenges associated with urban growth.Therefore, this paper evaluates the proposedfuture plan (2021) in context to the existing landuse (2015) in Allahabad district.region was known in antiquity as the Vats country.To its south and southeast is the Bagelkhandregion, to its east is middle Ganges valley of NorthIndia, or Purvanchal, to its southwest is theBundelkhand region, to its north and northeast isthe Awadh region and to its west along withKaushambi it forms the part of Doab i.e. the LowerDoab region. In the North district Pratapgarh, inthe south Rewa (M.P.), in the east Sant Ravi DasNagar and in the west kaushambhi districts arelocated.Ithasanaverageareaof5482km2.IJSERThe specific objectives are:1.2.3.To prepare a land use/land cover map ofthe study area using supervisedclassificationTo calculate the area covered by theindividual land use in the proposed plan(2021)Examine the level at which the currentdevelopment (2015) comply with theproposed future plan (2021) in the studyareaMaterial and MethodStudy Area:Allahabad is located at 25.45 N 81.84 E in thesouthern part of the Uttar Pradesh at an elevationof 98 meters (322 ft.) and stands at the confluenceof two, the Ganges and Yamuna (Figure 3.1). The1128Figure 1: Study AreaIJSER 2019http://www.ijser.org
International Journal of Scientific & Engineering Research Volume 10, Issue 6, June-2019ISSN 2229-55181129Data usedSatellite data: Two satellite data are used inconducting the analysis in this study; the landsat 8(OLI) and LISS IV data:S/NSatelliteSensorDateSpatial ResolutionBandPath/Row1Landsat 8OLIMay-15309143/42 & 143/432IRS-P6L4fxDec-135.83101/54Table 1: Satellite Data Used in the StudyThe Landsat 8 OLI/TIRS Pre-WRS-2 data setcontains nearly 10,000 scenes acquired by theOperational Land Imager (OLI) and/or ThermalInfrared Sensor (TIRS) sensors after launch(February 11, 2013) through April 10, 2013, whenthe satellite achieved operational orbit (WRS-2).The sensor provides improved signal-to-noise(SNR) radiometric performance quantized over a12-bit dynamic range. (This translates into 4096potential grey levels in an image compared withonly 256 grey levels in previous 8-bit instruments.)Improved signal to noise performance enablebetter characterization of land cover state andcondition. Products are delivered as 16-bit images(scaled to 55,000 grey levels) and have a large filesize, at approximately 1 GB compressed. The datais used in this study for classification and obtainingthe pixels count of land uses as proposed in thefuture plan (2021) of the Allahabad district. Thetable below provides the spectral band, spatialresolution and wavelength of the data.Another satellite data used in the present analysisis LISS-IV data which is the advanced highresolution camera operating in three spectralbands, in the visible 0.52-0.59 microns (Green Band2) and 0.62-0.68 microns (Red Band 3); and in theNIR: 0.76-0.86 microns (Band 4) with spatialresolution of 5.8 m and a swath of 70 km. It is usedin this study to identify land use /land cover in thestudy areaIJSERTable 3: Spatial resolution and wavelength ofLISS IV DataTable 2: Spatial resolution and wavelength oflandsat 8 (OLI)Landsat 8 (OLI) BandsBand 1 - Coastal aerosolBand 2 - BlueBand 3 - GreenBand 4 - RedBand 5 - Near Infrared(NIR)Band 6 – SWIR 1Band 7 - SWIR 2Band8PanchromaticBand 9 - 4-0.67Spatialresolution(m)303030300.85 - 0.881.57 - 1.652.11 - 2.293030300.50 - 0.681.36 - 1.381530-IRS P-6 LISS IVBandsWavelength(um)Spatial(m)Band 2-Green0.52-0.595.8Band 3-Red0.62-0.685.8Band 4-NIR0.77-0.865.8resolutionOther data usedOther data used includes topographic map ofAllahabad district (2014), proposed future land useplan for Allahabad (2021) and Global positioningsystem (GPS) data.Software usedThe software used in carrying out the analysisincludes the following:ArcGIS software (ArcGIS 10.2)ArcGIS is vector- based software that is used ininputting, storing, manipulating and analyzing thespatial data. It is designed by (ESRI)IJSER 2019http://www.ijser.org
International Journal of Scientific & Engineering Research Volume 10, Issue 6, June-2019ISSN 2229-5518Erdas software (Erdas imagery 9.2 & Erdasimagine 2014)ERDAS Imagine is a raster-based software packagedesigned by leica Geosystems specifically toextract information from imagery. It provides avariety of tools such as image orthorectification,mosaicking,reprojection, classification andinterpretation.1130LISS IV 2013 ImageLandsat 8 (OLI) 2015 ImageGround truth DataLayer stackingImage MosaickingBhuvan land use map 2011-2012Supervised ClassificationMicrosoft officeMicrosoft office is an office suite of applications,servers and services. It was first announced by BillGates of Microsoft on 1 August 1988 at COMDEXin Las Vegas. It contained Microsoft Word,Microsoft Excel and Microsoft PowerPointClassified imageAccuracy AssessmentMethodologyFinal MapThe methodology applied to find solutions for theresearch problems and objectives is given below:IJSERPreparation of Existing Land Cover/LandUse Map using supervised ClassificationSupervised Classification is the type ofclassification in which the image analystsupervises the pixel categorization process byspecifying, to the computer algorithm, numericaldescriptors of the various land cover types presentin a scene (Nnam, V.C. 2013). Representativesample sites of known cover type, called trainingareas, are used to compile a numericalinterpretation key that describes the spectralattributes for each feature type of interest. Eachpixel in the dataset is then compared numericallyto each category in the interpretation key andlabeled with the name of the category it looks mostlike (Addink, E.A, et’ al 2010). Land cover/land usemap is prepared using image classification oflandsat 8. Layer stacking of the image is performedbefore mosaicking of the two scenes covering thestudy area. Seventy-five sample site points werecollected representing the study area (Allahabad(District) using Global positioning systemFigure 2: Flow chart of the classificationprocedureThe colonies selected for purpose of obtaining theground control points includes Civil lines, Nayakatra, Lukarganj, Malviya nagar, George Town,Allahpur, Katra, Police lines, Tagore nagar,Mumford ganj, Gongotri nagar, Bahrana,Rambagh, Dariyabad, Daraganj, Arail, Mahewa,Central jail, Chak nounian, Jhusi, High court line,Ashok nagar, Mutti ganj, Dandi, and classificationwas carried out in which five classes wereclassified (Built up area, Agriculture, Forest, Waterbody and Barrel land. Maximum likelihoodalgorithm is used because it has an advantage overthe other types. Maximum likelihood classificationmethod applies the probability theory to theclassification task. From the training set classes, themethod determines the class centers and thevariability in raster values in each input band foreach class (Lillesand, T.M, 2001). This informationallows the process to determine the probabilitythat a given cell in the in the input raster setbelongs to a particular training set class. Theprobability depends upon the distance from thecell to the class centre, and the size and shape ofthe class in spectral space. The maximumlikelihood method computes all of the classprobabilities for each raster cell and assigns the cellto the class with the highest probability valueIJSER 2019http://www.ijser.org
International Journal of Scientific & Engineering Research Volume 10, Issue 6, June-2019ISSN 2229-5518Comparing the Future Land Use Plan (2021)with the Classified Images (2015)The proposed future plan of the study area wascompared with the classified images in order toexamined the level at which the plan comply withthe current development in the study area. Thehard copy of the plan design of the study area(Allahabad district) was obtained from themunicipal corporation of Allahabad district; it wasscanned into a picture format (tiff.), andgeoreferenced to the same coordinate system(UTM Zone 44 N) as the satellite imagery using theobserved coordinates of some ground controlpoints. After georeferencing, shape files werecreated for different land uses and the design wasvectorised. After the vectorisation of the design,the proposed land use was evaluated withreference to 2015 classified image. The imagesegmentation was carried out on the image usinguser defined constraints which controls thesegmentation of different image objects intoindependent objects. Segmentation is the divisionof an image into spatially continuous, disjoint andhomogeneous regions, i.e. the objects (Jensen, J.R.,2014). After the segmentation the image objects aregiven meaning or identification by carrying outtraining which is based on information obtainedduring field survey pertaining to the Land use.During the process of survey, ground coordinatesof image points of the different themes (classes)were recorded as well as the different land uses,this information enabled us to check the accuracyof the image classification process that was carriedout. The shape file of the individual land use of theproposed plan was overlaid on the classified imageof the study area in order to assess the level ofdevelopment. Finally, Area of the individualclasses was obtained by multiplying the number ofcount with the resolution.Land use Plan (2021)1131Landsat 8(OLI) 2015Topo Sheet 2014GeoreferencingGround Coordinates DataVectorisationBhuvan Land use 2011-2012ClassificationIJSEROverlayPresentation of the current developmentFigure 3: General evaluation flow chartResult and DiscussionFuture Development Plan, Allahabad District,2021The 2021 plan map for Allahabad district wasobtained as a hard copy map which wasgeoreferenced with the topo sheet of Allahabaddistrict and imported into a GIS environment. Eachland use from the map was digitized andconverted into vector format. The various land useclasses present in the map are provided in table 4.1and the map is presented in figure 4.1Table 4: Various Land use under the proposedplanS.NO123456IJSER 2019http://www.ijser.orgLand roposed roadArea(km2)1235179371% Areacovered53274160
International Journal of Scientific & Engineering Research Volume 10, Issue 6, June-2019ISSN 2229-551878TotalPasture anddairy farmParks15242311132711100IJSERFigure 5: Distributions of land use/land cover inthe study areaNRSC Land use (2011-12), Allahabad DistrictFigure 4: Land use /land cover, AllahabadDistrict, 2015In the absence of most recent Land use /land coverinformation of the study area, a land use map wasprepared for the study area using Landsat 8 imageacquired on May 2015. The entire study area wasclassified into five classes that included Built uparea, Agriculture, Forest, Water, and Barren land(Table &Figure 4.2).Table 5: Various land use obtained fromclassified landsat 8 imageS.No.Land useArea(km2)% AreaCovered1Bare Land575112Water15633Agriculture4069784Built up25155Forest16335214100TotalThe results obtained from classified image werecompared with the land use information obtainedfrom National Remote Sensing Centre (NRSC,Govt. of India) for the Allahabad district. Thoughthe land use information obtained from NRSC isfor the period 2011-12, however in the absence of amore recent data, the one available from NRSCwas used for assessing/ verifying the overallpattern and distribution of the land use. The landuse information and its comparison to the land useobtained from the classified Landsat 8 image issummarized in table 4.3Table 6: Comparison between NRSCclassification and 2015 classified imageClassifiedImageArea(km2)%DifferenceS.No.Land useNRSCArea(km2)1Bare uilt up14625172IJSER 2019http://www.ijser.org
International Journal of Scientific & Engineering Research Volume 10, Issue 6, June-2019ISSN 2229-55185ForestTotal11331541636proposed land use class. Thesummarized in the figure below511152142Summary and ConclusionConsidering the above table, five types of land usewas compared (Bare land, water, Agriculture, builtup and forest) The total area of NRSC andclassified image are 5111km2 and 5214km2respectively, whereas the percentage difference is10% for bare land, -51% for water, 2% foragriculture, 6% for forest and 72% for built up area.Table 7: Existing Land Use AreasresultsareThe paper is aimed to evaluate the proposed futureland use plan (2021) designed by Allahabaddevelopment Authority/Municipal corporationAllahabad, with reference to existing land usewhich was obtained by classifying a Landsat 8satellite image acquired on May 2015. The resultsof the classified image were compared with theland use information available from Bhuvan (20112012). Conclusively, eight proposed land useclasses were evaluated which includes residential,commercial, industrial, offices, colleges, proposedroad, pasture and dairy farm and parks. The totalarea of the proposed land use classes under theexisting 2015 classified image is 231 km2.Agriculture class will be the most affected classIJSERExisting land use Area (km2) 2015Proposed Land Use Classes(2021)S.No.TotalBare landWaterAgricultureBuilt 0.054496Proposed road0.10.0050.40.417Pasture and Dairy ation of Proposed Land Use Plan (2021)with Current Land Use (2015), AllahabadDistrictThe future land use plan (2021) for Allahabaddistrict proposed by the Allahabad DevelopmentAuthority was evaluated with the existing scenarioof land use in the district obtained from theclassified 2015 Landsat 8 image. The evaluationwas done by assessing the various types of landuse and their respective areas existing under eachfollowed by bare land accounting 97.4 km2 and 32km2 respectively. The least affected class is thewater encompasses approximately 2.5 km2.REFERENCESA, A. M., & Afolabi, Y. D. (2013). Monitoring Urban Sprawlin the federal capital territory of Nigeria using remotesensing and GIS techniques. Ethiopian Journal ofEnvironmental Studies and Management Vol. 6 No.1, 82-95.IJSER 2019http://www.ijser.org
International Journal of Scientific & Engineering Research Volume 10, Issue 6, June-2019ISSN 2229-5518Addink, E.A., Van Coillie, F.M.B., De Jong, S.M., 2012.Introduction to the GEOBIA 2010 special issue: from pixelsto geographic objects in remote sensing image analysis.International Journal of Applied Earth Observation and Geoinformation 15, 1–6.Demers, M. N. (2014). Fundamentals of Geographic InformationSyste
Remote sensing and Geographical Information system (GIS) provides vital tools which can be applied in the analysis at the district and as well, as the city level. This study evaluates the proposed future land use plan (2021) in context to the existing land use (2015) in Allahabad district. Land use /land cover map was prepared using supervised classification of landsat 8 (OLI) and LISS IV .
PRINCIPLES OF REMOTE SENSING Shefali Aggarwal Photogrammetry and Remote Sensing Division Indian Institute of Remote Sensing, Dehra Dun Abstract : Remote sensing is a technique to observe the earth surface or the atmosphere from out of space using satellites (space borne) or from the air using aircrafts (airborne). Remote sensing uses a part or several parts of the electromagnetic spectrum. It .
Scope of remote sensing Remote sensing: science or art? The remote sensing process Applications of remote sensing Information flow in remote sensing The EMRreflected, emitted, or back-scattered from an object or geographic area is used as a surrogatefor the actual property under investigation.
Proximity Sensor Sensing object Reset distance Sensing distance Hysteresis OFF ON Output Proximity Sensor Sensing object Within range Outside of range ON t 1 t 2 OFF Proximity Sensor Sensing object Sensing area Output (Sensing distance) Standard sensing object 1 2 f 1 Non-metal M M 2M t 1 t 2 t 3 Proximity Sensor Output t 1 t 2 Sensing .
Jul 28, 2014 · imagery analysis are forms of remote sensing. Remote sensing, a term which refers to the remote viewing of the surrounding world, including all forms of photography, video and other forms of visualization (Parcak 2012) can be used to view live societies. Satellite remote sensing allows
4 Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 2021 Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 2021 5 Supply side and economic factors driving adoption Remote sensing, which includes both space and earth observation (EO), is the
Chapter 3 Introduction to Remote Sensing and Image Processing 17 Introduction to Remote Sensing and Image Processing Of all the various data sources used in GIS, one of the most important is undoubtedly that provided by remote sensing. Through the use of satellites, we now have a continuing program of data acquisition for the entire world with time frames ranging from a couple of weeks to a .
Oregon, USA. In: Greer, Jerry Dean, ed. Natural resource management using remote sensing and GIS: Proceedings of the Seventh Forest Service Remote Sensing Applications Conference; 1998 April 6-10; Nassau Bay, TX. Bethesda, MD: American Photogrammetry and Remote Sensing Society: 79-91 Nassau Bay, Texas April 6-10, 1998 Sponsored by:
One of the fundamental remote sensing tasks is scene classification. Cheng et al. [3] defined scene classification as the categorization of remote‐sensing images into a discrete set of meaningful land‐cover and land‐use classes. Scene classification is a fundamental remote‐sensing task and
