Textbook of Remote sensing and geographical information systems (Third Edition): Part 2

8 Spatial Data Modelling 8.1 Introduction Burrough (1986) observed that the human eye is highly efficient at recognising shapes and forms but the computer needs to be instructed exactly how spatial patterns should be handled and displayed Computers require precise and clear instructions on how to turn data about spatial entities into graphical representations The process is the second stage in designing and implementing a data model At present there are two main approaches in which computers can handle and display spatial entities They are the raster and vector approaches The data structures that have little to with the graphic representation of cartographic objects are simple lists, ordered sequential files and indexed file systems These three systems are discussed in the next chapter under attribute database management The human mind is capable of producing a graphic abstraction of space and objects This representation is actually quite sophisticated if we use computers to handle graphic devices A map appears as a graphic device which contains an implied set of relationships about the spatial elements, such as, monuments, roads/rivers, and parks Lines are connected to other lines and together are linked to create areas Spatial Data Modelling or polygons The lines are related to one another in space through angles and distances Some are connected , but others are not Some polygons have neighbours, but others are isolated The list of possible relationships that can be contained on a graphic diagram is virtually endless From this endless relationships among objects, there should be a way to find and represent each object and relationships by means of a set of rules These rules then assist the computer to recognise all the points, associated lines, and areas to represent something on the earth The representation may be with respect to explicit locations related to other objects within space, absolute and/or relative location , proximity of each object and many other relationships In order to extract all such information, we need to create a language, known as language of spatial relationships through spatial modelling Spatial modelling is very much useful in understanding the geographical problems In general , spatial modelling in GIS can be split into two parts: a model of spatial form and a model of spatial processes The model of spatial form represents the structure and distribution of features in geographical space, while the interaction between these features are considered in spatial data processing models 8.2 Stages of GIS Data Modelling The construction of models of spatial form can be taken as a series of stages of data abstraction By applying this abstraction process the GIS designer moves from the position of observing the geographical complexities of the real world to one of simulating them in the computer This process involves, (i) Identifying the spatial features from the real world that are of interest in the context of an application (ii) Representing the conceptual model by an appropriate spatial data model This involves choosing between one of the two approaches: raster or vector (iii) Selecting an appropriate spatial data structure to store the model within the computer The spatial data structure is the physical way in which entities are coded for the purpose of storage and manipulation Fig 8.1 provides an overview of the stages involved in creating a GIS data model At each stage in the model-building process, we move further away from the physical representation of a feature in reality and closer to its abstract representation in the computer In this chapter, the definition of entities and graphical representation of the surface features in the computers are considered along with the different spatial data models and structures available The modelling of more complex features and the difficulties of including the third and fourth dimensions in a GIS model are also presented 241 Remote Sensing and GIS Real world Spatial data model Raster 000 001 1 1 1 100 000 1 1 1 1 1 000 10100000011 1111110000 1 1 1 000 1 111 111 100 Vector arcs 1 Spatial data model Computer Fig 8.1 Stages in creating a GIS model 242 124569 124584 125484 124598 125999 124569 234568 234578 234789 234756 234768 234980 Spatial Data Modelling 8.3 Graphic Representation of Spatial Data An entity is the element in reality It is a phenomenon of interest in reality that is not further subdivided into phenomena of the same kind For example, a city can be considered an entity A similar phenomena stored in a database are identified as entity types All geographi~al phenomena can be represented in two dimensions by three main entity types: points, lines, and areas Fig 8.2 shows how a spatial data model could be constructed using points, lines, and areas Fig 8.2 also introduces two additional spatial entities: networks and surfaces These are an extension of the area and line concepts Areas entity Parks and forest Indira park in Hyderabad Line entity Point entity + Spatial model of Hussain sagar lake and its environs Hotels and prominent places : Viceroy Hotel, NTR Ghat and others Tankbund, boundary of the lake Nework Surface R Road Networks (Necklece road) Elevation (DTM) Fig 8.2 Spatial entity Data model 243 Remote Sensing and GIS A surface entity is used to represent continuous features or phenomena For these features there is a measurement or value at every location , as in the case of elevation, temperature and population density This makes representation by a surface entity appropriately The continuous nature of surface entities distinguishes them from other entity types (points, lines, areas, and networks) which are discrete, that is, either present or absent at a particular location A network is a series of interconnecting lines along which there is a flow of data, objects or materials, for example, the road network, along which there is a flow of traffic to and from the areas Another example is that of a river, along which there is a flow of water Others not visible on the land surfaces, include the sewerage and telephone systems considered network type of entities The dynamic nature of the world poses two problems for the entity-definition phase of a GJS project The first is how to select the entity type that provides the most appropriate representation for the features being modelled Is it best to represent a forest as a collection of pOints (representing the location of individual trees), or as an area (the boundary of which defines the territory covered by the forest)? The second problem is how to represent changes over time A forest, originally represented as an area, may decline until it is only a dispersed group of trees that are better represented by USing points The definition of entity types for real-world features is also hampered by the fact that many real-world features simply not fit into the categories of entities available An area of natural woodland does not have a clear boundary as there is normally a transition where trees are interspersed with vegetation from a neighbouring habitat type In this case, if we wish to represent the woodland by an area entity, where we place the boundary? The question is avoided if the data are captured from a paper map where a boundary is clearly marked, as if someone has already made a decision about the location of the woodland boundary But is this the true boundary? Vegetation to an ecologist may be a continuous feature (which could be represented by a surface), whereas vegetation to a forest is better represented as series of discrete area entities Features with 'fuzzy' boundaries, such as the woodland, can create problems for the GIS deSigner and the definition of entities, and may have an impact on later analysis Deciding which entity type should be used to model a real-world feature is not always straightforward The way in which individuals represent a spatial feature in two dimensions will have a lot to with how they conceptualise the feature In turn this will be related to their own experience and how they wish to use the entity they produRe An appreciation of this issue is central to the design and development of all GIS applications There are two fundamental methods of representing geographical entities They are (i) Raster method , and (ii) Vector method 244 Spatial Data Modelling 8.3.1 Raster Data Representation In raster representation , the terrain is divided into a number of parcels or quantised the space into units A parcel or a unit is called a grid cell Although a wide variety of raste~ shapes like triangles or hexagons are possible, it is generally simpler to use a series of rectangles, or more often squares, called grid cells Grid cells or other raster forms generally are uniform in size, but this is not absolutely necessary For the sake of simplicity, we will assume that all grid cells are of the same size and that, therefore, each occupies the same amount of geographic space as any other Raster data structures not provide precise locational information because geographic space is now divided into discrete grids, as much as we divide a checkerboard into uniform squares Instead of representing points with their absolute locations, they are represented as a single grid cell (Fig 8.3) This stepped appearance is also obvious when we represent areas with grid cells All points inside the area that is bounded by a close set of lines must occur within one of the grid cells to be represented as part of the same area The more irregular the area , the more stepped the appearance In grid-based or raster GIS, there are two general ways of including attribute data for each entity The simplest is to assign a single number representing an attribute like a class of land cover, for each grid cell location By positioning these numbers, we, ultimately, are allowing the position of the attribute value to act as the default location for the entity For example, if we assign a code number of 10 to represent water, then list this as the first number in the X or column direction, and the first in the Y or row direction, by default the upper left grid cell is the location of a portion of the earth representing water The larger the grid cell , the more land area is contained within it a concept called resolution The coarser the resolution of the grid, the less we know about the absolute position of points, lines, and areas represented by this structure Raster structures, especially square grid cells, are pieced together to represent an entire area Raster data structure may seem to be rather undesirable because of the lack of absolute locational information Raster data structures have numerous advantages over other structures Notably, they are relatively easy to conceptualise as a method of representing space Remotely sensed data acquired by a sensor is one of the well known example of raster data representation In fact, the relationship between the pixel used in remote sensing and the grid cell used in GIS allows data from satellites to be readily incorporated into raster-based GIS without any changes A characteristic feature of grid-based systems is that many functions, especially those involving the analysis and modelling of surfaces and overlay operations, are simple to perform with this type of data structure The major disadvantages of the raster data structure are a reduced spatial accuracy, decrease of the reliability of area and distance measures, and the need for large storage capacity associated with having to record every grid cell as a numerical value 245 Remote Sensing and GIS sional) Ground surface i:IiJ Buildings rarest cover boundary - Fig 8.3 Raster Graphic Data Representations 246 Spatial Data Modelling The raster view of the world Happy V~lIey spatial entities The vector view of the world filii • • • • y • X X • Points: hotels •• X Y Lines : Tankbund X Areas : parks y Network : roads ••••••••••• ••• ••• • • •.ii •• •• •• ••••• •••••••••••• Surface : elevation Fig 8.4 Raster and vector spatial data models 247 X Remote Sensing and GIS The raster spatial data model is one of a family of spatial data models described as tessellations (Demers, 1999) In the raster world individual cells are used as the building blocks for creating images of point, line, area, network, and surfaces: Fig 8.4 shows how a range of different features represented by the five different entity types can be modelled using the raster approach Hotels are modelled by single and discrete cells, the tankbund is modelled by linking cells into lines, the forest by grouping cells into blocks, and the road network by linking cells into networks The relief of the area has been modelled by giving every cell in the raster image an altitude value In Fig 8.4 the altitude values have been grouped and shaded to give the appearance of a contour map 8.3.2 Vector Data Representation The second method of representing geographic space, called vector, allows us to give specific spatial locations explicitly In this method it is assumed that geographic space is continuous , rather than being quantised as small discrete grids This perspective is acquired by associating points as a single set of coordinates (X and Y) in coordinate system ,lines as connected sequences of coordinate pairs of pOints, and areas as sequences of interconnected lines whose first and last coordinate points are the same (Fig 8.5) Anything that has a single (X, Y) coordinate pair not physically connected to any other coordinate pair is a point (zero-dimensional) entity Point • (x, y) Line (~ , Y2) (x4' yJ (Xl ' Yl) (XS' Y~ (lS, Y.) (~, Y2) (lS· Y.) (X4' yJ (Xs· y~ Polygon (Xl' Yl) Fig 8.5 Vector graphic data representation 248 Spatial Data Modelling A vector spatial data model uses two-dimensional Cartesian (x, y) coordinate system to store the shape of a spatial entity In the vector world the point is the basic building block from which all spatial entities are constructed The simplest spatial entity, the point, is represented by a single (x, y) coordinate pair Line and area entities are constructed by connecting a series of points into chains and polygons Fig 8.4 shows how the vector model has been used to represent various features The more complex the shape of a line or area feature, the greater the number of points required to represent it Selecting the appropriate number of points to construct an entity is one of the major problems in vector based GIS data representation In the vector data model, the representation of networks and surfaces is very complex and closely linked to the way the data are structured for computer encoding The representation of the vector data is much more representative and generally, we combine the entity data with associated attribute data kept in a separate file through a database management system, and then link them together It means that the entity data and corresponding attribute data in the form of tables can be stored and linked through a software linkage In vector data structures, a line consists of two or more coordinate pairs, again storing the attributes for that line in a separate file This is explained in the next section under vector models For straight lines, two coordinate pairs are enough to show location and orientation in space More complex lines will require a number of line segments, each beginning and ending with a coordinate pair For complex lines, the number of line segments must be increased to accommodate the many changes in angles The shorter the line segments, the more exactly will they represent the complex line Thus we see that although vector data structures are more representative of the locations of objects in space, they are not exact but are still an abstraction of geographic space 8.3.3 Spatial Data Models Spatial data structures provide the information that the computer requires to reconstruct the spatial data model in digital form Although some lines act alone and contain specific attribute information that describes their character, other more complex collections of lines called networks add a dimension of attribute characters Thus not only does a road network contain information about the type of road or similar variables, but it will also indicate, that travel is possible only in a particular direction This information must be extended to each connecting line segment to advise the user that movement can continue along each segment until the attributes change-perhaps 249 Tai lieu Luan van Luan an Do an References Turner., A K (ed.) 1992 Three-Dimensional Modelling with Geoscientific Information Systems, Dordrecht : Kluwer Academic Publishers, 443 pp Vincent, RK., 1973, A Thermal Infrared Ratio Imaging Method for Mapping Compositional Variations Unpublished Ph D Thesis, Univ of Mich., Ann Arbor, Mich., 102 p Vincent., R K., Rowan, L C Gillespie, R E., and Knapp, C., 1975, Thermal Infrared Spectra and Chemical Analysis of Twenty-Six Igneous Rock Samples Remote Sensing of Environment, 4.4, pp 199-210 Walsh., S J., 1987, Comparison of NOAA AVHRR Data to Meteorologic Drought Indices, Photogrammetric Engineering and Remote Sensing, 53, pp1069-74 Weibel., R, and Heller., M., 1991, Digital Terrain Modelling: In Maguire D J., Goodchild., M F., Rhind D W., (eds), Geographical Information Systems Principles and Applications, Long man, London, Vol 1, pp 269-97 Witthuhn., B.D., D P Brandt, and G J Demo, 1974, Discovery in Geography, Dubuque, IA, Kendall/Hunt Publishing Company Wolfe., W L, andZissis, G J., 1989, The Infrared Handbook The Infrared Information Analysis Center, Environmental Research Institute of Michigan, USA Wolf, P R, 1983, Elements of Photogrammetry, 2nd ed McGraw Hill Book Co New York o 447 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an SUBJECT INDEX B A band interleaved by line 325 absolute location 230 band interleaved by pixel 325 active microwave remote sensing 55,57 band width 84 binary encoding 367 active remote sensing 26 additive noise 164 black body 30, 37, 38, 41, 45 adjacency 213 block coding 236, 245 aerial photographs 123,124,127,130,131, 134,140,141,210 c aeronautical charts cadastral maps 16, 19 airborne 78, 87 cartography 2,316 amendment vector 281 CCD 87,92 analogue data 290 cell grid modelling 215 ARC/INFO 242 chain coding 236, 244 area entities 229,235,247 choropleth maps 6,316 ascending node 77 classification stage 184, 192 atmospheric windows 75 Cluster-busting capabilities 368 attribute accuracy 312 COGO 369 attribute completeness 315, 316 computer assisted design 211 attribute data 286, 288, 291, 298, 300 computer coding scale 146 attribute database management 225, 250 attributes conceptual accuracy 313 conicalorthomorphic 158, 159, 160 automated digitisation 324 conical projection 13, 16 autor.;tatic seed 368 connectivity automation 205 containment 213 azimuthal projections 13 continuous 3,4,5,21 448 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an Subject Index contrast enhancement 28 emissivity 75, 78 convolution windows 168 entropy 26 cosmetic operations 154 envionmentalstudies 77 cultural maps envionmental quality cylinders 12 equalised histogram 165 cylindrical projection 12, 13 equatorial crossing time 116 o 418 ERS 91,105,109 ERTS 91 dangling node 300 euclidean distance 214 data accuracy 309 F data stream 287 DBMS 259, 260, 284 DEM 343,345,348 FCC 119,120,122 digital cartography 205, 208, 209, 222 flood monitoring 102, 109 digital number 145,146,151,162,163,165, 168, 170, 175, 176, 179 fiux 28,29,37,41,42 digital surveying 362 fractional scale digital terrain modelling 343, 344 frequency 146,165,168,172 digitisation 290, 292, 294, 296, 298, 300 fuzzy 229 digitiser 291 fuzzy classification 368 discrete 3, 4, fuzzy edge 126 discrete edge 126 format conversion 218,324 G discriminant function 184, 186, 187 diSjoint entities 214 gause elimination method 160 doppler shift 68 GBF/DIME model 251 doughnut buffer 335 geographic coordinate system 9,212 dropout line 154 geographical entities 212 drought indicator 178 geographical information systems 11 DTM generation 343, 344, 347 geoid 12 dual independent map encoding 206 geomatics 2, 367 dynamic urban land use 382 geometric correction 148, 149, 157, 160 geometric distortion 148, 149, 150 E geomorphic landforms 125 edge enhancement 28 georeferencing system 237 electromagnetic spectrum 26,29,30,33,34, 39, 42, 46, 55 geospatial data 308,309,314,315 geostationary 76, 79, 102 ellipsoid 12,13,14,18 449 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an Subject Index global network 105 global positioning system 296, 362 GOES 102, 105 graphic devices 225 graphical representation 7, 19 graphical user interface 367,371 GRASS 209,215 GRID Model 239,241 grid origin 16 ground track 25 ground truth 312,322 H K karst topography 129, 130 kettles 130 keycoding 291 L land evaluation 119 land use 135,141,419,425 landsat 77, 78, 81, 82, 87, 91, 92 laplacian edge 173, 176 laser altimetry 345 layover 71, 73 HCMM satellite 102 HRV sensor 92 hybrid data model 275 hybrid projection 13 Hydrogeomorphology 22 least common geographical units 265 line entities 235, 256 lineament analysis 176 LlSS III data 82 lithology 176 low pass filters 168 IFOV 79,80,81,82,83,84,88 IGES 369 IKONOS 82,116 image enhancement 148,161,168 image interpretation 119, 122123,125,127, 129,130,135,136,141 image registration 148,157 IMGRID 238,239,241 incident angle 59, 68, 70, 71, 72 incident electromagnetic energy 26 inclination 76,77,109 indexed file systems 225 indiscrete 175 instrumental parameter 75, 79 integrated terrain units 265 integrity 259, 260, 275, 281 INTERGRAPH 277 IRS 77,78,82,84,91,92,95,109 LUNAR 238 LUT 148,167 M management system 68 MAGI 238,241 MAP model 238, 242 map overlay analysis 324 map projections 2,10,11,13,14, '16 mapping quantitative point 212 maripeda mandai 418 mean filter 168, 170, 172 mechanical scanner 150 metric aspects 213 microwave band 55 microwave region 26,35 minimum mapping unit 126 450 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an Subject Index mirrorsweep 150,151 panorama 151 mission life time 76 modules 208,216,217 passive land use 382, 383 MSMR 109 MSS 78, 82, 87 passive microwave remote senSing 55 passive sensor 26 multiband 86, 87, 88 photogrammetry multispectral sensor 324 photographic 119,120,121,122,123,135,140 pictorial data product 119, 123 passive microwave radiometry 76 N pictorial form 143 nadir 24, 25, 46, 56, 59 NASA 91,102,105 pixel 82,143,144,145,146,148,152,154, 155,158,189,202 natural resources management 1,21 planar projections 13 navigational database planck 30, 37 neighbourhood 168,170 neighbourhood function 334 network analysis 324,352 planes 12 point spread function 81, 82 precision 310,313,314,316 network maps principal component analysis 161, 177, 182 network systems 268,272,270 NIMBUS Satellite 105 prism maps Projections 2, 10, 11, 12, 13, 14, 15, 18 NOAA 78,102 proximity 5,226 noise suppression 155 push broom scanner 84 non-coordinate systems north hemisphere 18 Q o quadtree data structure 236, 238 quantum theory 31, 33 object oriented 265 R observation angle 75, 78 ocean primary 109 radar 76, 80, 86 OCEANSAT 109 ordered sequential files 225 orthophotoscopes 363 radar module 367 overshoots 293, 294, 360 radarsat 82, 91, 105, 109 radar shadow 71, 73 radiance 25, 28, 29, 40, 42, 43 p radiation 75 PAMAP data structure 370 radiant energy 28,33,37 PAN 95,116 radiant flux density 28, 29 panchromatic 95,116 radiometers 56 451 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an Subject Index radiometric 146, 148, 151, 154, 161 selective key 124 radiometric calibration 90, 91 sensor parameter 79 radiometric correction 151 sensor platform 84 raster data 295, 302 signal conversion 144 raster data structures 230 signal peaks 83 raster GIS 331,332,333,334,342 site sensitivity index 432 raster Overlay 341 slope foreshortening 71,72 RDBMS 268, 273, 284, 285 small scale maps 5, reclassification 218,219 softcopy photogrammetry 363 red square 239 solar irradiance 26, 38, 41 reflectance 28,29,43,45,46,47,48,49,50, 51,52,151,154,161,178, space craft 149,150 registration 294 spaghetti model 247,249,250,256 relational database model 268,274 SPANS 245 relative location 226 spatial accuracy 309,310,312,313 remote sensing 2, 10,21,22 spatial data 1, 2, 10, 21, 22 repeat cycle 109 spatial dimension resampling 302 spatial distribution resolution 308,313,314 spatial elements 225 return beam vidicon 87,91 spatial entities revisit internal 75 spatial information systems 2,21 root mean square error 322 spatial modelling 226 run length 236 spatial objects spaceborne 87 spatial referencing spatio-temporal data 277, 279 satellite 119 speckle noise 70 satellite image 123,127,136,141 spectral exitance 36,37,38 satellite motion 148 spectral patterns 143, 147 satellite platforms 25 satellite track 25 spectral reflectance curve 46,47,48,48,54 spikes 300 scale 2,5,6,7,8, 10, 14, 1516, 18,20 spor scale factor 11 standard query language 273, 338 scanning 290, 295, 296, 300, 304 stereoplotters 363 scattering 34, 39, 40, 42 sun-synchronous seasat 82,91,105,109 surface entity 229 security 259, 260, 275, 285 surveying 77,91,92 452 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn 77 Tai lieu Luan van Luan an Do an Subject Index u swath width 78, 80 SWIR 95 symbol libraries undershoots 293,294, 300 synchronisation 260 unsupervised classification 363,367 synthetic aperture radar 26, 80, 91, 105, 109 urban plan maps utility maps T v temporal accuracy 309,312 temporal resolution 78 value completeness 316 tessellations 233 vector data 298, 300, 302 thermal radiation 76 89 vector data structures 234,247 thinning 296, 302 vector GIS 331,332,338 TIGER model 251 tiling 302 vector overlay 339, 341 topographical maps 1, 5, 15, 19 topological model 247,250,251 vegetation dynamics 102,115 verbal scale topological overlay 339 vignetting 54 topology 5, 247, 249, 254 visual interpretation 436 vector tessellation 371 training data 26, 28 w training dataset 184 training sample 183,186,189,191 wasteland 119 transformation 176, 177, 180 waveband 29,35 triangular tessellations 212 triangulated irregular network 345 TVcamera 87 wavefront angle 73 WiFS 95 o 453 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an .+ 1/ • LEGEND Pediplain with moderate weathering Source: Pediplain with moderate weathering covered with alkaline soils SOl Toposheets of 1:25,000 56K/16/SE, 56/K/16/SW, 56U13/NE IRS 1C PAN LlSS III Pediplain with shallow weathering Inselberg Residual hill Water body ~ Village [2SZJ:Lineament Hydrogeomorphological Map of Shivannagudem Watershed , Andhra Pradesh , India Plate No.1 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an Sally Farms as seen by LlSS-I, LlSS-1i and LlSS-1i1 sensors Plate No.2 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an Band Band Band IRS- 10 LlSS-11i FCC image (bands 2, 3, 4) and corresponding Black and White Images of band 2, band and band data of path 108, row 56 , showing Calcutta and Surrounding Areas Plate No.3 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an 22' 27' 32' 37' 78°42'E 17°29' N 24' 19' 14' 17°9' E 22' 27' 32' 37' Pseudocolour Image showing NDVI of Hyderabad City used for Environmental Planning under Clean and Green Program Plate No.4 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn 78°42' E Tai lieu Luan van Luan an Do an 78°16'E 21 ' 26' 31' 36 ' 78°41E 17' 33'N 17' 33 ' N 28' 28' 23' 23 ' 18' 18' 13' 13' 17"8'N 17"8'N 78°16'E -• • 21' 26' 31' 36' 78°41E LEGEND BUILT-UP (D) BUILT-UP (M) BUILT-UP (S) KHARlF RABI RESERVED FOREST BARREN SCRUB (D) SCRUB (S) GRASS LAND WITHOUT SCRUB POLLUTED WATER CLEAR WATER STONE QUARRY Land Use / Land Cover of Hyderabad : an output of Maximum likelyhood Classifie r, Plate No.5 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an Digital Terrain Model (DTM) Plate No Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an PAN + LISS III Image of IRS-l D of Hyderabad with its Muncipal Corporation Boundaries Plate No.7 Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn Tai lieu Luan van Luan an Do an Stt.010.Mssv.BKD002ac.email.ninhddtt@edu.gmail.com.vn