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AI8702 REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEM Unit 4 Data Input and Analysis
GIS Data Two kinds of data are usually associated with geographic features: Spatial and Non- spatial data. Spatial data refers to the shape, size and location of the feature. Non- spatial data refers to other attributes associated with the feature such as name, length, area, volume, population, soil type, etc ..
Spatial vs. Non-spatial data
Spatial Data Spatial data is the physical representation of earth features. It represents the location, size, and shape of the object in the earth i.e., building, ponds, mountains, administration, boundaries, etc. Spatial Data is available in two primary formats 1. Vector 2. Raster
Raster Data A raster data is a representation of images in a matrix of cells/ pixels into rows and columns. The raster data set and data values are stored in rows and columns. To have high accuracy data, GIS professionals use high-resolution raster datasets. As it comes with the own challenges and difficulties to manage, Map info advancement introduces to a specially designed data format, multi- Resolution Raster (MRR). There are different raster types, Image, Image Palette, Classified and Continuous, or discrete. These types are stored as two significant formats, single color data, and composite color data.
Raster Data
Raster File Formats  Portable Network Graphics (PNG)  Joint Photographic Experts Group (JPEG2000)  JPEG File Interchange Format (JFIF)  Multi-resolution Seamless Image Database (MrSID)  Network Common Data Form (netCDF)  Digital raster graphic(DRG)  ARC Digitized Raster Graphic (ADRG)  Enhanced Compressed ARC Raster Graphics (ECRG)  Compressed ARC Digitized Raster Graphics (CADRG)
Raster File Formats  Raster Product Format (RPF)  Binary file – Band Interleaved by Pixel (BIP), Band Interleaved by Line (BIL), Band Sequential (BSQ)  Enhanced Compressed Wavelet (ECW)  Extensible N-Dimensional Data Format (NDF)  GDAL Virtual Format (VRT)  Tagged Image File Formats (TIFF)  Geo Tagged Image File Formats (GeoTIFF)  Graphic Interchange Format (GIF)  Digital Elevation Model (DEM)
Raster File Formats  RS Landsat  ArcInfo Grid  Airborne Synthetic Aperture Radar (AIRSAR) Polarimetric  Bitmap (BMP), device-independent bitmap (DIB) format, or Microsoft Windows bitmap  BSB  Controlled Image Base (CIB)  Digital Geographic Information Exchange Standard (DIGEST)  File geodatabase  ENVI Header
Raster File Formats  Golden Software Grid (.grd)  GRIB  Hierarchical Data Format (HDF) 4  HGT  High-Resolution Elevation (HRE)  Integrated Software for Imagers and Spectrometers (ISIS)  Shuttle Radar Topography Mission (SRTM)  Terragen terrain
Vector Data Vector data are represented in points, lines and polygons. Polygon data are used to describe areas such as the boundary of a city (on a large scale map), forest, and lakes. Polygon features are two dimensional. It can be used to measure the area and perimeter of a geographic feature.
Vector Data Line data represents the linear features. Some Common examples for the representation of line features are rivers, roads, etc. The line is a one-dimensional representation. It gives only the length of the element.
Vector Data Point data is used to represent non-adjacent features and to represent discrete data points. Points have zero dimensions, and it gives latitude & longitude of the respective location. The point feature will not provide the length and area of the features. Examples would be schools, points of interest such as hospitals, schools, colleges, worship centers, and more other locations.
Vector File Formats  Shapefiles  ArcInfo Coverage  E00 ArcInfo Interchange  Spatial Database engine (ArcSDE)  Digital Line Graph (DLG)  GeoJSON  AutoCAD DXF  Keyhole Markup Language (KML)  TIGER
Vector File Formats  Vector Product Format (VPF)  Esri TIN  Geography Markup Language (GML)  SpatiaLite  OSM (OpenStreetMap)  Scalable Vector Graphics  National Transfer Format (NTF)  SOSI  MapInfo TAB format
Vector File Formats  GPS exchange Format (GPX)  IDRISI Vector  Geographic Base File-Dual Independent Mask Encoding (GBF- DIME)  Delimited Text Files
Spatial vs. Non-spatial data
Non-Spatial Data  Non-spatial data are represented in table formats. For example, the administrative boundary table has population information, district name, provinces, sex ratio, etc.
Data Models in GIS
Data Models in GIS
Data Models in GIS
Raster Data Model
Raster Data Model
Raster Data Model
Raster Data Model
Raster Data Model
Vector Data Model
Vector Data Model
Vector Data Model
Vector Data Model
Vector Data Model
Vector Data Model
Vector Data Model
Vector Data Model
Vector Data Model
Vector Data Model
Triangulated Irregular Network (TIN)
Triangulated Irregular Network (TIN)
Triangulated Irregular Network (TIN)
Digital Elevation Model (DEM)
Digital Elevation Model (DEM)
Digital Elevation Model (DEM)
Functions of GIS  Data Entry  Storing of Data  Data Analysis
Functions of GIS 1. Data Entry  Both spatial and attribute data are entered into computer system by different input devices like scanner, digitizer, keyboard, mouse, etc.  Scanner, digitizer, mouse are used for entering spatial data.  The attribute data available as reports, tables, etc. are entered through keyboard.  As the data is drawn from different sources, they have different scales, projections, referencing system, etc. Therefore, there is need to standardize the database to common standard.  GIS software enables this operation by ‘georeferencing’ method.
Functions of GIS 2. Storing of data  The different spatial entities which represent different features of real world can be stored in two different formats in the computer – Raster format & Vector format  The knowledge of these formats in which spatial data are stored, is required for decision makers as it affects the accuracy of the data, their analysis, storing capacity of computer, etc.
Functions of GIS 3. Data Analysis (Map Analysis)  Different types of spatial data analysis can be performed by GIS, performing queries, network analysis, overlay analysis, model building, etc.  Since GIS stores both spatial and non-spatial data and links them together, it can perform different types of queries.  For example, by joining the spatial data and its attributes and then by performing queries, one can see on map, the water of which tube wells having chlorine content more than 200 mg/liter.  Similarly, one can see on map, the roads constructed before 1980 which needs to be repaired.  In the same way, which area of a given forest having more than 60% tree density on Map.
Functions of GIS 3. Data Analysis (Map Analysis) Proximity Analysis can be done through buffering i.e., identifying a zone of interest around a point, line or polygon. For Ex, 10m around on tube well can be marked for planting flower plants, (or) 50m along National Highways (both sides) can be buffered for planting trees. A specified distance around the forest can be buffered as no habitation zone.
Functions of GIS Proximity Analysis
Functions of GIS 3. Data Analysis (Map Analysis) Network Analysis is another important analysis done through GIS. For example, optimum bus routing can be determined by examining all the field or attribute data linked to road map/ spatial data.
Functions of GIS Network Analysis
Functions of GIS 3. Data Analysis (Map Analysis) Overlay Analysis can be done through GIS by overlaying/ integrating a number of thematic maps. Overlay operation allows creation of a new layer of spatial data by integrating the data from different layers. For example, a particular land use class having saline soils, slope less than 20%, drainage density less than 10m per sq.km can be created from four different thematic maps, through land use map, soil map, topographic map and water resource map.
Functions of GIS 3. Data Analysis (Map Analysis) Model building capability of GIS is very helpful for decision makers. It is usually referred to as ‘What if’ analysis. For example, if a certain amount of water is released from a dam, how much area would be inundated? GIS has the capabilities of analysing a large amount of data within no time.
Data Models A data model is a description or view of the real world. Data modeling is a process that formalizes the description or view at different levels of data abstraction. Since, the real world is made up of complex spatial objects and phenomena, it is practically impossible for a single data model to represent everything that is present. This means that different users may have different data models when they attempt to collect data in the same location.
Data Models 1. Conceptual models The different views of the same urban area obtained by the engineer, the developer and the geographer are called conceptual models. It represents the user’s perception of the real world. Here, data abstraction is strictly limited to the description of the information contents of the user’s view of the real world, without any concern for computer implementation.
Data Models 2. Logical data models It represents an implementation – oriented view of the database. It represents the real world by means of diagrams, lists and tables designed to reflect the recording of data in terms of some formal language. It is software dependent. There are three classic logical data models.  The relational data model  The network data model  The hierarchical data model
Data Models 3. Physical data models It represents the hardware implementation – oriented view of the database. It is the 3rd level of data abstraction. It describes the physical storage (or file format) of the data in the computer by record format, record ordering and access paths. It is hardware dependent. It is intended for system programmer and database administrator, and not for general end users.
Data Models 4. Spatial data models The term spatial data model (geographic model) is used to describe, how geographical data are organized within a GIS in order to represent real world phenomena. GIS uses one of the two spatial data models (sometimes both).  Raster data models  Vector data models
Data Models Raster data models  Raster models divide the study area into cells, usually rectangular grid cells.  It is location based because emphasis is placed upon the location of each cell relative to other cells.  It is frequently used to model field data.  They correspond to regularly spaced points on a continuous surface.
Data Models Vector data models  Vector models are used to represent discrete phenomena, represented by geometric primitives (points, lines & polygons).  It is object-based.  Field based conceptualizations tends to favour a raster model.  Object based conceptualizations tends to favour a vector model.  3D surfaces can be represented by isolines (Ex: Contour lines) or Triangulated irregular network (TIN)  Isolines are familiar in cartography, but TINs are much more efficient in GIS modelling.
Database Models A separate data model is used to store and maintain attribute data for GIS software. These data models may exist internally within the GIS software, or may be reflected in external commercial Database Management Software (DBMS). A variety of different data models exist for the storage and management of attribute data. The most common are:  Tabular  Hierarchical  Network  Relational  Object Oriented The tabular model is the manner in which most early GIS software packages stored their attribute data. The next three models are those most commonly implemented in database management systems (DBMS). The object oriented is newer but rapidly gaining in popularity for some applications.
Database Models – Tabular Models  The simple tabular model stores attribute data as sequential data files with fixed formats (or comma delimited for ASCII data), for the location of attribute values in a predefined record structure.  This type of data model is outdated in the GIS arena. It lacks any method of checking data integrity, as well as being inefficient with respect to data storage, e.g. limited indexing capability for attributes or records, etc.
Database Models – Tabular Models
Database Models – Hierarchical Network  A hierarchal database management system is a system in which the data elements have a one to many relationship (1: N). This DBMS organize data in a tree-like structure, similar to a folder structure in your computer system.  The hierarchy starts from the root node, connecting the child node to the parent node. This DBMS is good for storing the data about the items describing its features, attributes, and so on.
Database Models – Hierarchical Network
Database Models – Hierarchical Network  The hierarchical database organizes data in a tree structure.  Data is structured downward in a hierarchy of tables.  Any level in the hierarchy can have unlimited children, but any child can have only one parent.  Hierarchical DBMS have not gained any noticeable acceptance for use within GIS.  They are oriented for data sets that are very stable, where primary relationships among the data change infrequently or never at all.  Also, the limitation on the number of parents that an element may have is not always conducive to actual geographic phenomenon.
Database Models – Hierarchical Network
Database Models – Network Model  A Network database management system is a system in which the data elements have a one to one relationship (1: 1) or many to many relationship (N: N). This DBMS also has a hierarchical structure, but it organizes data in a graph-like structure, and is allowed to have more than one parent for one single record.  For example, a teacher in a college teaches in two departments. Note: This DBMS is the most widely used database system before the introduction of the relational database management system.
Database Models – Network Model
Database Models – Network Model  The network database organizes data in a network or plex structure. Any column in a plex structure can be linked to any other. Like a tree structure, a plex structure can be described in terms of parents and children. This model allows for children to have more than one parent.  Network DBMS have not found much more acceptance in GIS than the hierarchical DBMS. They have the same flexibility limitations as hierarchical databases; however, the more powerful structure for representing data relationships allows a more realistic modelling of geographic phenomenon. However, network databases tend to become overly complex too easily. In this regard it is easy to lose control and understanding of the relationships between elements.
Database Models – Network Model
Database Models – Relational Model  A relational database management system (RDBMS) is a system in which the data is organized in the two-dimensional tables using rows and columns. This database management system was introduced by E.F Codd in 1970.  It is called a ‘relational’ database because data within each table is related to each other. Also, tables may be related to other tables in the database by using certain concepts of keys. Each table in a database has a key field that uniquely identifies each record. This system is the most widely used DBMS. Relational database management system software is available for large mainframe systems as well as workstations and personal computers.  For example, Oracle Database, MySQL, Microsoft SQL Server, and IBM DB2.
Database Models – Relational Model Emp_id Emp_name Emp_salary Emp_address 101 Arun 42,000 Delhi 102 Aman 40,000 Moradabad 103 Rakesh 43,000 Meerut 104 Shivam 44,000 Noida 105 Tarun 42,000 Gurgaon 106 Yash 40,000 Delhi In the above table employee, Emp_id, Emp_name, Emp_salary, and Emp_address are the attributes containing their values. Here, Emp_id is a primary key attribute which is uniquely identifying each record in the Employee table.
Database Models – Relational Model  The relational database organizes data in tables. Each table, is identified by a unique table name, and is organized by rows and columns.  Each column within a table also has a unique name. Columns store the values for a specific attribute, e.g. cover group, tree height.  Rows represent one record in the table. In a GIS each row is usually linked to a separate spatial feature, e.g. a forestry stand.  Accordingly, each row would be comprised of several columns, each column containing a specific value for that geographic feature.  The following figure presents a sample table for forest inventory features. This table has 4 rows and 5 columns.  The forest stand number would be the label for the spatial feature as well as the primary key for the database table. This serves as the linkage between the spatial definition of the feature and the attribute data for the feature.
Database Models – Relational Model UNIQUE STAND NUMBER DOMINANT COVER GROUP AVG. TREE HEIGHT STAND SITE INDEX STAND AGE 001 DEC 3 G 100 002 DEC-CON 4 M 80 003 DEC-CON 4 M 60 004 CON 4 G 120
Database Models – Relational Model  Data is often stored in several tables. Tables can be joined or referenced to each other by common columns (relational fields).  Usually the common column is an identification number for a selected geographic feature, e.g. a forestry stand polygon number. This identification number acts as the primary key for the table.  The ability to join tables through use of a common column is the essence of the relational model.  Such relational joins are usually ad hoc in nature and form the basis of for querying in a relational GIS product.  Unlike the other previously discussed database types, relationships are implicit in the character of the data as opposed to explicit characteristics of the database set up.
Database Models – Relational Model  The relational database model is the most widely accepted for managing the attributes of geographic data.  There are many different designs of DBMSs, but in GIS the relational design has been the most useful. In the relational design, data are stored conceptually as a collection of tables. Common fields in different tables are used to link them together. This surprisingly simple design has been so widely used primarily because of its flexibility and very wide deployment in applications both within and without GIS.
Database Models – Relational Model
Database Models – Relational Model  In the relational design, data are stored conceptually as a collection of tables. Common fields in different tables are used to link them together.  In fact, most GIS software provides an internal relational data model, as well as support for commercial off-the-shelf (COTS) relational DBMS'. COTS DBMS' are referred to as external DBMS'. This approach supports both users with small data sets, where an internal data model is sufficient, and customers with larger data sets who utilize a DBMS for other corporate data storage requirements. With an external DBMS the GIS software can simply connect to the database, and the user can make use of the inherent capabilities of the DBMS. External DBMS' tend to have much more extensive querying and data integrity capabilities than the GIS' internal relational model. The emergence and use of the external DBMS is a trend that has resulted in the proliferation of GIS technology into more traditional data processing environments.
Database Models – Relational Model The relational DBMS is attractive because of its:  simplicity in organization and data modelling.  flexibility - data can be manipulated in an ad hoc manner by joining tables.  efficiency of storage - by the proper design of data tables redundant data can be minimized; and  the non-procedural nature - queries on a relational database do not need to take into account the internal organization of the data. The relational DBMS has emerged as the dominant commercial data management tool in GIS implementation and application.
Database Models – Relational Model The following diagram illustrates the basic linkage between a vector spatial data (topologic model) and attributes maintained in a relational database file. Basic linkages between a vector spatial data (topologic model) and attributes maintained in a relational database file
Database Models – Relational Model
Database Models – Object Oriented Model  An object-oriented database management system is a system in which information or data is represented in the form of objects, as used in the object-oriented programming. It is a combination of relational database concepts such as concurrency control, transactions, etc. and OOPs principles, such as data encapsulation, inheritance, and polymorphism.  This database system permits data, information, software components, computing environments, and products to be shared easily.  Object-Oriented Programming + Relational Database Features = Object-Oriented Database management system
Database Models – Object Oriented Model
Database Models – Object Oriented Model The object-oriented database model manages data through objects. An object is a collection of data elements and operations that together are considered a single entity. The object-oriented database is a relatively new model. This approach has the attraction that querying is very natural, as features can be bundled together with attributes at the database administrator's discretion. To date, only a few GIS packages are promoting the use of this attribute data model. However, initial impressions indicate that this approach may hold many operational benefits with respect to geographic data processing. Fulfilment of this promise with a commercial GIS product remains to be seen.
Database Models – Object Oriented Model
Data Input and GIS Data input is the procedure of encoding data into a computer- readable form and writing the data to the GIS data base. There are two types of data to be entered in a GIS - spatial (geographic location of features) and non-spatial (descriptive or numeric information about features). There are three types of data entry: •Manual (via typing on keyboard or importing text files); •Digitizing; •Scanning;
Data Input and GIS – Manual Data Entry  Manual data entry can bring into GIS either collected or measured data.  These data exist as simple text files or binary files.  Text files should have at least two columns with X and Y coordinates.  These columns allow georeferencing of the file i.e. association of it with specific geographic coordinate system.  Binary files are usually a product of the software package associated with measuring device (for example files from Global Positioning System data collection).  They also have X and Y data, associated with description of the collected features, but in encoded format that could be read by special software.
Data Input and GIS – Digitization & Scanning  Digitizing is a process of entering digital codes of analyzed data into computer.  Digitizing can be manual (using digitizing tablet) or automatic (using scanner).  The difference between two methods is that digitizing tablet allows to do georeferencing during the digitizing process, while scanning require georeferencing later, after digital file (usually TIFF, GIF or JPEG image) has been created.  Another difference between methods is speed and accuracy of the data processing.  Apparent slowness of the work on digitizing tablet compensates often for the amount of editing after scanning process.
Data Input and GIS
Data Input and GIS
Data Input and GIS
Data Input and GIS
Data Input and GIS  At the same time good scanning allows automatic layer separation (for example, separation of red-colored roads from brown-colored contour lines), while digitizing of the map on a tablet requires manual creation of separate themes.  In this case the condition of the original hardcopy is very important.  Since human operator can use more cognitive tools and knowledge than the software support for scanning device, digitizer can handle better the hardcopy in a poor condition .  Special kind of scanned data is remote sensing image, taken either by satellite camera, digital camera or video camera.
Data Input and GIS - Digitization  Digitizing in GIS is the process of converting geographic data either from a hardcopy or a scanned image into vector data by tracing the features. During the digitizing process, features from the traced map or image are captured as coordinates in either point, line, or polygon format.
Data Input and GIS - Digitization  There are several types of digitizing methods. Manual digitizing involves tracing geographic features from an external digitizing tablet using a puck (a type of mouse specialized for tracing and capturing geographic features from the tablet). Heads up digitizing (also referred to as on-screen digitizing) is the method of tracing geographic features from another dataset (usually an aerial, satellite image, or scanned image of a map) directly on the computer screen. Automated digitizing involves using image processing software that contains pattern recognition technology to generated vectors.
Data Input and GIS – Digitization Errors  Since most common methods of digitizing involve the interpretation of geographic features via the human hand, there are several types of errors that can occur during the course of capturing the data. The type of error that occurs when the feature is not captured properly is called a positional error, as opposed to attribute errors where information about the feature capture is inaccurate or false.
Data Input and GIS – Digitization Errors An open polygon caused by the endpoints not snapping together. Dangles or Dangling Nodes Dangles or dangling nodes are lines that are not connected but should be. With dangling nodes, gaps occur in the linework where the two lines should be connected. Dangling nodes also occur when a digitized polygon doesn’t connect back to itself, leaving a gap where the two end nodes should have connected, creating what is called an open polygon.
Data Input and GIS – Digitization Errors Example of a weird polygon where the line folds back on itself. Switchbacks, Knots, and Loops These types of errors are introduced when the digitizer has an unsteady hand and moves the cursor or puck in such a way that the line being digitized ends up with extra vertices and/or nodes. In the case of switchbacks, extra vertices are introduced and the line ends up with a bend in it. With knots and loops, the line folds back onto itself, creating small polygon like geometry known as weird polygons.
Data Input and GIS – Digitization Errors The circle represents the area of the snap tolerance. The line being digitized will automatically snap to the nearest nodes within the snap tolerance area. Overshoots and Undershoots Similar to dangles, overshoots and undershoots happen when the line digitized doesn’t connect properly with the neighboring line it should intersect with. During digitization a snap tolerance is set by the digitizer. The snap tolerance or snap distance is the measurement of the diameter extending from the point of the cursor. Any nodes of neighboring lines that fall within the circle of the snap tolerance will result in the end points of the line being digitized automatically snapping to the nearest node. Undershoots and overshoots
Data Input and GIS – Digitization Errors Slivers Slivers are gaps in a digitized polygon layer where the adjoining polygons have gaps between them. Again, setting the proper parameters for snap tolerance is critical for ensuring that the edges of adjoining polygons snap together to eliminate those gaps. Where the two adjacent polygons overlap in error, the area where the two polygons overlap is called a sliver. Gap and Sliver Errors in Digitized Polygons
Data Input and GIS – Digitization Errors
Data Input and GIS – Scanners  Scanning coverts paper maps into digital format by capturing features as individual cells, or pixels, producing an automated image.  Maps are generally considered the backbone of any GIS activity.  But many a time paper maps are not easily available in a form that can be readily used by the computers.  Most of the paper maps had been prepared on the basis of old conventional surveys.  New maps can be produced using improved technologies but this requires time as it increases the volume of work. Thus, we have to resort to the available maps.
Data Input and GIS – Scanners  These paper maps have to be first converted into a digital format usable by the computer.  This is a critical step as the quality of the analog document must be preserved in the transition to the computer domain.  The technology used for this kind of conversions is known as scanning and the instrument used for this kind of operation is known as a scanner.  A scanner can be thought of as an electronic input device that converts analog information of a document like a map, photograph or an overlay into a digital format that can be used by the computer. Scanning automatically captures map features, text, and symbols as individual cells, or pixels, and produces an automated image.
Data Input and GIS – Working of a Scanner  The most important component inside a scanner is the scanner head which can move along the length of the scanner.  The scanner head contains either a charged-couple device (CCD) sensor or a contact image (CIS) sensor.  A CCD consists of a number of photosensitive cells or pixels packed together on a chip.  The most advanced large format scanners use CCD’s with 8000 pixels per chip for providing a very good image quality.
Data Input and GIS – Working of a Scanner  While scanning a bright white light from the scanner strikes the image to be scanned and is reflected onto the photosensitive surface of the sensor placed on the scanner head.  Each pixel transfers a gray tone value (values given to the different shades of black in the image ranging from 0 (black) – 255 (white) i.e. 256 values to the scan board (software).  The software interprets the value in terms of 0 (Black) or 1 (white), thereby, forming a monochrome image of the scanned portion.  As the head moves ahead, it scans the image in tiny strips and the sensor continues to store the information in a sequential fashion. The software running the scanner pierces together the information from the sensor into a
Data Input and GIS – Working of a Scanner  Scanning a colour image is slightly different in which the scanner head has to scan the same image for three different colours i.e. red, green, blue.  In older colour scanners, this was accomplished by scanning the same area three times over for the three different colours. This type of scanner is known as three-pass scanner.  However, most of the colour scanners now scan in one pass scanning all the three colours in one go by using colour filters.
Data Input and GIS – Working of a Scanner  In principle, a colour CCD works in the same way as a monochrome CCD. But in this each colour is constructed by mixing red, green and blue. Thus, a 24-bit RGB CCD presents each pixel by 24 bits of information. Usually, a scanner using these three colours (in full 24 RGB mode) can create up to 16.8 million colours.  Nowadays a new technology: full width, single-line contact sensor array scanning has emerged in which the document to be scanned passes under a line of LED’s which capture the image. This new technology enables the scanner to operate at previously unattainable speeds.
Data Input and GIS – Types of Scanner There are several different types of scanners performing the same job but handling the job differently using different technologies and producing results depending on their varying capabilities. Hand-held scanners although portable, can only scan images up to about four inches wide. They require a very steady hand for moving the scan head over the document. They are useful for scanning small logos or signatures and are virtually of no use for scanning maps and photographs.
Data Input and GIS – Types of Scanner Flatbed scanners The most commonly used scanner is a flatbed scanner also known as desktop scanner. It has a glass plate on which the picture or the document is placed. The scanner head placed beneath the glass plate moves across the picture and the result is a good quality scanned image. For scanning large maps or toposheets wide format flatbed scanners can be used.
Data Input and GIS – Types of Scanner Drum scanners Then there are the drum scanners which are mostly used by the printing professionals. In this type of scanner, the image or the document is placed on a glass cylinder that rotates at very high speeds around a centrally located sensor containing photo-multiplier tube instead of a CCD to scan. Prior to the advances in the field of sheet fed scanners, the drum scanners were extensively used for scanning maps and other documents.
Data Input and GIS – Types of Scanner Sheet fed scanners Finally, there are the Sheet fed scanners which work on a principle similar to that of a fax machine. In this, the document to be scanned is moved past the scanning head and the digital form of the image is obtained. The disadvantage of this type of scanner is that it can only scan loose sheets and the scanned image can easily become distorted if the document is not handled properly while scanning. However, the new generation of the wide format sheet fed scanners has overcome this problem and have become indispensable for scanning maps, imageries and other large sized documents.

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Data Input and Analysis.pptx engineering

  • 1.
    AI8702 REMOTE SENSING ANDGEOGRAPHICAL INFORMATION SYSTEM Unit 4 Data Input and Analysis
  • 2.
    GIS Data Two kindsof data are usually associated with geographic features: Spatial and Non- spatial data. Spatial data refers to the shape, size and location of the feature. Non- spatial data refers to other attributes associated with the feature such as name, length, area, volume, population, soil type, etc ..
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    Spatial Data Spatial datais the physical representation of earth features. It represents the location, size, and shape of the object in the earth i.e., building, ponds, mountains, administration, boundaries, etc. Spatial Data is available in two primary formats 1. Vector 2. Raster
  • 5.
    Raster Data A rasterdata is a representation of images in a matrix of cells/ pixels into rows and columns. The raster data set and data values are stored in rows and columns. To have high accuracy data, GIS professionals use high-resolution raster datasets. As it comes with the own challenges and difficulties to manage, Map info advancement introduces to a specially designed data format, multi- Resolution Raster (MRR). There are different raster types, Image, Image Palette, Classified and Continuous, or discrete. These types are stored as two significant formats, single color data, and composite color data.
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    Raster File Formats Portable Network Graphics (PNG)  Joint Photographic Experts Group (JPEG2000)  JPEG File Interchange Format (JFIF)  Multi-resolution Seamless Image Database (MrSID)  Network Common Data Form (netCDF)  Digital raster graphic(DRG)  ARC Digitized Raster Graphic (ADRG)  Enhanced Compressed ARC Raster Graphics (ECRG)  Compressed ARC Digitized Raster Graphics (CADRG)
  • 8.
    Raster File Formats Raster Product Format (RPF)  Binary file – Band Interleaved by Pixel (BIP), Band Interleaved by Line (BIL), Band Sequential (BSQ)  Enhanced Compressed Wavelet (ECW)  Extensible N-Dimensional Data Format (NDF)  GDAL Virtual Format (VRT)  Tagged Image File Formats (TIFF)  Geo Tagged Image File Formats (GeoTIFF)  Graphic Interchange Format (GIF)  Digital Elevation Model (DEM)
  • 9.
    Raster File Formats RS Landsat  ArcInfo Grid  Airborne Synthetic Aperture Radar (AIRSAR) Polarimetric  Bitmap (BMP), device-independent bitmap (DIB) format, or Microsoft Windows bitmap  BSB  Controlled Image Base (CIB)  Digital Geographic Information Exchange Standard (DIGEST)  File geodatabase  ENVI Header
  • 10.
    Raster File Formats Golden Software Grid (.grd)  GRIB  Hierarchical Data Format (HDF) 4  HGT  High-Resolution Elevation (HRE)  Integrated Software for Imagers and Spectrometers (ISIS)  Shuttle Radar Topography Mission (SRTM)  Terragen terrain
  • 11.
    Vector Data Vector dataare represented in points, lines and polygons. Polygon data are used to describe areas such as the boundary of a city (on a large scale map), forest, and lakes. Polygon features are two dimensional. It can be used to measure the area and perimeter of a geographic feature.
  • 12.
    Vector Data Line datarepresents the linear features. Some Common examples for the representation of line features are rivers, roads, etc. The line is a one-dimensional representation. It gives only the length of the element.
  • 13.
    Vector Data Point datais used to represent non-adjacent features and to represent discrete data points. Points have zero dimensions, and it gives latitude & longitude of the respective location. The point feature will not provide the length and area of the features. Examples would be schools, points of interest such as hospitals, schools, colleges, worship centers, and more other locations.
  • 14.
    Vector File Formats Shapefiles  ArcInfo Coverage  E00 ArcInfo Interchange  Spatial Database engine (ArcSDE)  Digital Line Graph (DLG)  GeoJSON  AutoCAD DXF  Keyhole Markup Language (KML)  TIGER
  • 15.
    Vector File Formats Vector Product Format (VPF)  Esri TIN  Geography Markup Language (GML)  SpatiaLite  OSM (OpenStreetMap)  Scalable Vector Graphics  National Transfer Format (NTF)  SOSI  MapInfo TAB format
  • 16.
    Vector File Formats GPS exchange Format (GPX)  IDRISI Vector  Geographic Base File-Dual Independent Mask Encoding (GBF- DIME)  Delimited Text Files
  • 17.
  • 18.
    Non-Spatial Data  Non-spatialdata are represented in table formats. For example, the administrative boundary table has population information, district name, provinces, sex ratio, etc.
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  • 43.
    Functions of GIS Data Entry  Storing of Data  Data Analysis
  • 44.
    Functions of GIS 1.Data Entry  Both spatial and attribute data are entered into computer system by different input devices like scanner, digitizer, keyboard, mouse, etc.  Scanner, digitizer, mouse are used for entering spatial data.  The attribute data available as reports, tables, etc. are entered through keyboard.  As the data is drawn from different sources, they have different scales, projections, referencing system, etc. Therefore, there is need to standardize the database to common standard.  GIS software enables this operation by ‘georeferencing’ method.
  • 45.
    Functions of GIS 2.Storing of data  The different spatial entities which represent different features of real world can be stored in two different formats in the computer – Raster format & Vector format  The knowledge of these formats in which spatial data are stored, is required for decision makers as it affects the accuracy of the data, their analysis, storing capacity of computer, etc.
  • 46.
    Functions of GIS 3.Data Analysis (Map Analysis)  Different types of spatial data analysis can be performed by GIS, performing queries, network analysis, overlay analysis, model building, etc.  Since GIS stores both spatial and non-spatial data and links them together, it can perform different types of queries.  For example, by joining the spatial data and its attributes and then by performing queries, one can see on map, the water of which tube wells having chlorine content more than 200 mg/liter.  Similarly, one can see on map, the roads constructed before 1980 which needs to be repaired.  In the same way, which area of a given forest having more than 60% tree density on Map.
  • 47.
    Functions of GIS 3.Data Analysis (Map Analysis) Proximity Analysis can be done through buffering i.e., identifying a zone of interest around a point, line or polygon. For Ex, 10m around on tube well can be marked for planting flower plants, (or) 50m along National Highways (both sides) can be buffered for planting trees. A specified distance around the forest can be buffered as no habitation zone.
  • 48.
  • 49.
    Functions of GIS 3.Data Analysis (Map Analysis) Network Analysis is another important analysis done through GIS. For example, optimum bus routing can be determined by examining all the field or attribute data linked to road map/ spatial data.
  • 50.
  • 51.
    Functions of GIS 3.Data Analysis (Map Analysis) Overlay Analysis can be done through GIS by overlaying/ integrating a number of thematic maps. Overlay operation allows creation of a new layer of spatial data by integrating the data from different layers. For example, a particular land use class having saline soils, slope less than 20%, drainage density less than 10m per sq.km can be created from four different thematic maps, through land use map, soil map, topographic map and water resource map.
  • 52.
    Functions of GIS 3.Data Analysis (Map Analysis) Model building capability of GIS is very helpful for decision makers. It is usually referred to as ‘What if’ analysis. For example, if a certain amount of water is released from a dam, how much area would be inundated? GIS has the capabilities of analysing a large amount of data within no time.
  • 53.
    Data Models A datamodel is a description or view of the real world. Data modeling is a process that formalizes the description or view at different levels of data abstraction. Since, the real world is made up of complex spatial objects and phenomena, it is practically impossible for a single data model to represent everything that is present. This means that different users may have different data models when they attempt to collect data in the same location.
  • 54.
    Data Models 1. Conceptualmodels The different views of the same urban area obtained by the engineer, the developer and the geographer are called conceptual models. It represents the user’s perception of the real world. Here, data abstraction is strictly limited to the description of the information contents of the user’s view of the real world, without any concern for computer implementation.
  • 55.
    Data Models 2. Logicaldata models It represents an implementation – oriented view of the database. It represents the real world by means of diagrams, lists and tables designed to reflect the recording of data in terms of some formal language. It is software dependent. There are three classic logical data models.  The relational data model  The network data model  The hierarchical data model
  • 56.
    Data Models 3. Physicaldata models It represents the hardware implementation – oriented view of the database. It is the 3rd level of data abstraction. It describes the physical storage (or file format) of the data in the computer by record format, record ordering and access paths. It is hardware dependent. It is intended for system programmer and database administrator, and not for general end users.
  • 57.
    Data Models 4. Spatialdata models The term spatial data model (geographic model) is used to describe, how geographical data are organized within a GIS in order to represent real world phenomena. GIS uses one of the two spatial data models (sometimes both).  Raster data models  Vector data models
  • 58.
    Data Models Raster datamodels  Raster models divide the study area into cells, usually rectangular grid cells.  It is location based because emphasis is placed upon the location of each cell relative to other cells.  It is frequently used to model field data.  They correspond to regularly spaced points on a continuous surface.
  • 59.
    Data Models Vector datamodels  Vector models are used to represent discrete phenomena, represented by geometric primitives (points, lines & polygons).  It is object-based.  Field based conceptualizations tends to favour a raster model.  Object based conceptualizations tends to favour a vector model.  3D surfaces can be represented by isolines (Ex: Contour lines) or Triangulated irregular network (TIN)  Isolines are familiar in cartography, but TINs are much more efficient in GIS modelling.
  • 60.
    Database Models A separatedata model is used to store and maintain attribute data for GIS software. These data models may exist internally within the GIS software, or may be reflected in external commercial Database Management Software (DBMS). A variety of different data models exist for the storage and management of attribute data. The most common are:  Tabular  Hierarchical  Network  Relational  Object Oriented The tabular model is the manner in which most early GIS software packages stored their attribute data. The next three models are those most commonly implemented in database management systems (DBMS). The object oriented is newer but rapidly gaining in popularity for some applications.
  • 61.
    Database Models –Tabular Models  The simple tabular model stores attribute data as sequential data files with fixed formats (or comma delimited for ASCII data), for the location of attribute values in a predefined record structure.  This type of data model is outdated in the GIS arena. It lacks any method of checking data integrity, as well as being inefficient with respect to data storage, e.g. limited indexing capability for attributes or records, etc.
  • 62.
    Database Models –Tabular Models
  • 63.
    Database Models –Hierarchical Network  A hierarchal database management system is a system in which the data elements have a one to many relationship (1: N). This DBMS organize data in a tree-like structure, similar to a folder structure in your computer system.  The hierarchy starts from the root node, connecting the child node to the parent node. This DBMS is good for storing the data about the items describing its features, attributes, and so on.
  • 64.
    Database Models –Hierarchical Network
  • 65.
    Database Models –Hierarchical Network  The hierarchical database organizes data in a tree structure.  Data is structured downward in a hierarchy of tables.  Any level in the hierarchy can have unlimited children, but any child can have only one parent.  Hierarchical DBMS have not gained any noticeable acceptance for use within GIS.  They are oriented for data sets that are very stable, where primary relationships among the data change infrequently or never at all.  Also, the limitation on the number of parents that an element may have is not always conducive to actual geographic phenomenon.
  • 66.
    Database Models –Hierarchical Network
  • 67.
    Database Models –Network Model  A Network database management system is a system in which the data elements have a one to one relationship (1: 1) or many to many relationship (N: N). This DBMS also has a hierarchical structure, but it organizes data in a graph-like structure, and is allowed to have more than one parent for one single record.  For example, a teacher in a college teaches in two departments. Note: This DBMS is the most widely used database system before the introduction of the relational database management system.
  • 68.
    Database Models –Network Model
  • 69.
    Database Models –Network Model  The network database organizes data in a network or plex structure. Any column in a plex structure can be linked to any other. Like a tree structure, a plex structure can be described in terms of parents and children. This model allows for children to have more than one parent.  Network DBMS have not found much more acceptance in GIS than the hierarchical DBMS. They have the same flexibility limitations as hierarchical databases; however, the more powerful structure for representing data relationships allows a more realistic modelling of geographic phenomenon. However, network databases tend to become overly complex too easily. In this regard it is easy to lose control and understanding of the relationships between elements.
  • 70.
    Database Models –Network Model
  • 71.
    Database Models –Relational Model  A relational database management system (RDBMS) is a system in which the data is organized in the two-dimensional tables using rows and columns. This database management system was introduced by E.F Codd in 1970.  It is called a ‘relational’ database because data within each table is related to each other. Also, tables may be related to other tables in the database by using certain concepts of keys. Each table in a database has a key field that uniquely identifies each record. This system is the most widely used DBMS. Relational database management system software is available for large mainframe systems as well as workstations and personal computers.  For example, Oracle Database, MySQL, Microsoft SQL Server, and IBM DB2.
  • 72.
    Database Models –Relational Model Emp_id Emp_name Emp_salary Emp_address 101 Arun 42,000 Delhi 102 Aman 40,000 Moradabad 103 Rakesh 43,000 Meerut 104 Shivam 44,000 Noida 105 Tarun 42,000 Gurgaon 106 Yash 40,000 Delhi In the above table employee, Emp_id, Emp_name, Emp_salary, and Emp_address are the attributes containing their values. Here, Emp_id is a primary key attribute which is uniquely identifying each record in the Employee table.
  • 73.
    Database Models –Relational Model  The relational database organizes data in tables. Each table, is identified by a unique table name, and is organized by rows and columns.  Each column within a table also has a unique name. Columns store the values for a specific attribute, e.g. cover group, tree height.  Rows represent one record in the table. In a GIS each row is usually linked to a separate spatial feature, e.g. a forestry stand.  Accordingly, each row would be comprised of several columns, each column containing a specific value for that geographic feature.  The following figure presents a sample table for forest inventory features. This table has 4 rows and 5 columns.  The forest stand number would be the label for the spatial feature as well as the primary key for the database table. This serves as the linkage between the spatial definition of the feature and the attribute data for the feature.
  • 74.
    Database Models –Relational Model UNIQUE STAND NUMBER DOMINANT COVER GROUP AVG. TREE HEIGHT STAND SITE INDEX STAND AGE 001 DEC 3 G 100 002 DEC-CON 4 M 80 003 DEC-CON 4 M 60 004 CON 4 G 120
  • 75.
    Database Models –Relational Model  Data is often stored in several tables. Tables can be joined or referenced to each other by common columns (relational fields).  Usually the common column is an identification number for a selected geographic feature, e.g. a forestry stand polygon number. This identification number acts as the primary key for the table.  The ability to join tables through use of a common column is the essence of the relational model.  Such relational joins are usually ad hoc in nature and form the basis of for querying in a relational GIS product.  Unlike the other previously discussed database types, relationships are implicit in the character of the data as opposed to explicit characteristics of the database set up.
  • 76.
    Database Models –Relational Model  The relational database model is the most widely accepted for managing the attributes of geographic data.  There are many different designs of DBMSs, but in GIS the relational design has been the most useful. In the relational design, data are stored conceptually as a collection of tables. Common fields in different tables are used to link them together. This surprisingly simple design has been so widely used primarily because of its flexibility and very wide deployment in applications both within and without GIS.
  • 77.
    Database Models –Relational Model
  • 78.
    Database Models –Relational Model  In the relational design, data are stored conceptually as a collection of tables. Common fields in different tables are used to link them together.  In fact, most GIS software provides an internal relational data model, as well as support for commercial off-the-shelf (COTS) relational DBMS'. COTS DBMS' are referred to as external DBMS'. This approach supports both users with small data sets, where an internal data model is sufficient, and customers with larger data sets who utilize a DBMS for other corporate data storage requirements. With an external DBMS the GIS software can simply connect to the database, and the user can make use of the inherent capabilities of the DBMS. External DBMS' tend to have much more extensive querying and data integrity capabilities than the GIS' internal relational model. The emergence and use of the external DBMS is a trend that has resulted in the proliferation of GIS technology into more traditional data processing environments.
  • 79.
    Database Models –Relational Model The relational DBMS is attractive because of its:  simplicity in organization and data modelling.  flexibility - data can be manipulated in an ad hoc manner by joining tables.  efficiency of storage - by the proper design of data tables redundant data can be minimized; and  the non-procedural nature - queries on a relational database do not need to take into account the internal organization of the data. The relational DBMS has emerged as the dominant commercial data management tool in GIS implementation and application.
  • 80.
    Database Models –Relational Model The following diagram illustrates the basic linkage between a vector spatial data (topologic model) and attributes maintained in a relational database file. Basic linkages between a vector spatial data (topologic model) and attributes maintained in a relational database file
  • 81.
    Database Models –Relational Model
  • 82.
    Database Models –Object Oriented Model  An object-oriented database management system is a system in which information or data is represented in the form of objects, as used in the object-oriented programming. It is a combination of relational database concepts such as concurrency control, transactions, etc. and OOPs principles, such as data encapsulation, inheritance, and polymorphism.  This database system permits data, information, software components, computing environments, and products to be shared easily.  Object-Oriented Programming + Relational Database Features = Object-Oriented Database management system
  • 83.
    Database Models –Object Oriented Model
  • 84.
    Database Models –Object Oriented Model The object-oriented database model manages data through objects. An object is a collection of data elements and operations that together are considered a single entity. The object-oriented database is a relatively new model. This approach has the attraction that querying is very natural, as features can be bundled together with attributes at the database administrator's discretion. To date, only a few GIS packages are promoting the use of this attribute data model. However, initial impressions indicate that this approach may hold many operational benefits with respect to geographic data processing. Fulfilment of this promise with a commercial GIS product remains to be seen.
  • 85.
    Database Models –Object Oriented Model
  • 86.
    Data Input andGIS Data input is the procedure of encoding data into a computer- readable form and writing the data to the GIS data base. There are two types of data to be entered in a GIS - spatial (geographic location of features) and non-spatial (descriptive or numeric information about features). There are three types of data entry: •Manual (via typing on keyboard or importing text files); •Digitizing; •Scanning;
  • 87.
    Data Input andGIS – Manual Data Entry  Manual data entry can bring into GIS either collected or measured data.  These data exist as simple text files or binary files.  Text files should have at least two columns with X and Y coordinates.  These columns allow georeferencing of the file i.e. association of it with specific geographic coordinate system.  Binary files are usually a product of the software package associated with measuring device (for example files from Global Positioning System data collection).  They also have X and Y data, associated with description of the collected features, but in encoded format that could be read by special software.
  • 88.
    Data Input andGIS – Digitization & Scanning  Digitizing is a process of entering digital codes of analyzed data into computer.  Digitizing can be manual (using digitizing tablet) or automatic (using scanner).  The difference between two methods is that digitizing tablet allows to do georeferencing during the digitizing process, while scanning require georeferencing later, after digital file (usually TIFF, GIF or JPEG image) has been created.  Another difference between methods is speed and accuracy of the data processing.  Apparent slowness of the work on digitizing tablet compensates often for the amount of editing after scanning process.
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  • 92.
  • 93.
    Data Input andGIS  At the same time good scanning allows automatic layer separation (for example, separation of red-colored roads from brown-colored contour lines), while digitizing of the map on a tablet requires manual creation of separate themes.  In this case the condition of the original hardcopy is very important.  Since human operator can use more cognitive tools and knowledge than the software support for scanning device, digitizer can handle better the hardcopy in a poor condition .  Special kind of scanned data is remote sensing image, taken either by satellite camera, digital camera or video camera.
  • 94.
    Data Input andGIS - Digitization  Digitizing in GIS is the process of converting geographic data either from a hardcopy or a scanned image into vector data by tracing the features. During the digitizing process, features from the traced map or image are captured as coordinates in either point, line, or polygon format.
  • 95.
    Data Input andGIS - Digitization  There are several types of digitizing methods. Manual digitizing involves tracing geographic features from an external digitizing tablet using a puck (a type of mouse specialized for tracing and capturing geographic features from the tablet). Heads up digitizing (also referred to as on-screen digitizing) is the method of tracing geographic features from another dataset (usually an aerial, satellite image, or scanned image of a map) directly on the computer screen. Automated digitizing involves using image processing software that contains pattern recognition technology to generated vectors.
  • 96.
    Data Input andGIS – Digitization Errors  Since most common methods of digitizing involve the interpretation of geographic features via the human hand, there are several types of errors that can occur during the course of capturing the data. The type of error that occurs when the feature is not captured properly is called a positional error, as opposed to attribute errors where information about the feature capture is inaccurate or false.
  • 97.
    Data Input andGIS – Digitization Errors An open polygon caused by the endpoints not snapping together. Dangles or Dangling Nodes Dangles or dangling nodes are lines that are not connected but should be. With dangling nodes, gaps occur in the linework where the two lines should be connected. Dangling nodes also occur when a digitized polygon doesn’t connect back to itself, leaving a gap where the two end nodes should have connected, creating what is called an open polygon.
  • 98.
    Data Input andGIS – Digitization Errors Example of a weird polygon where the line folds back on itself. Switchbacks, Knots, and Loops These types of errors are introduced when the digitizer has an unsteady hand and moves the cursor or puck in such a way that the line being digitized ends up with extra vertices and/or nodes. In the case of switchbacks, extra vertices are introduced and the line ends up with a bend in it. With knots and loops, the line folds back onto itself, creating small polygon like geometry known as weird polygons.
  • 99.
    Data Input andGIS – Digitization Errors The circle represents the area of the snap tolerance. The line being digitized will automatically snap to the nearest nodes within the snap tolerance area. Overshoots and Undershoots Similar to dangles, overshoots and undershoots happen when the line digitized doesn’t connect properly with the neighboring line it should intersect with. During digitization a snap tolerance is set by the digitizer. The snap tolerance or snap distance is the measurement of the diameter extending from the point of the cursor. Any nodes of neighboring lines that fall within the circle of the snap tolerance will result in the end points of the line being digitized automatically snapping to the nearest node. Undershoots and overshoots
  • 100.
    Data Input andGIS – Digitization Errors Slivers Slivers are gaps in a digitized polygon layer where the adjoining polygons have gaps between them. Again, setting the proper parameters for snap tolerance is critical for ensuring that the edges of adjoining polygons snap together to eliminate those gaps. Where the two adjacent polygons overlap in error, the area where the two polygons overlap is called a sliver. Gap and Sliver Errors in Digitized Polygons
  • 101.
    Data Input andGIS – Digitization Errors
  • 102.
    Data Input andGIS – Scanners  Scanning coverts paper maps into digital format by capturing features as individual cells, or pixels, producing an automated image.  Maps are generally considered the backbone of any GIS activity.  But many a time paper maps are not easily available in a form that can be readily used by the computers.  Most of the paper maps had been prepared on the basis of old conventional surveys.  New maps can be produced using improved technologies but this requires time as it increases the volume of work. Thus, we have to resort to the available maps.
  • 103.
    Data Input andGIS – Scanners  These paper maps have to be first converted into a digital format usable by the computer.  This is a critical step as the quality of the analog document must be preserved in the transition to the computer domain.  The technology used for this kind of conversions is known as scanning and the instrument used for this kind of operation is known as a scanner.  A scanner can be thought of as an electronic input device that converts analog information of a document like a map, photograph or an overlay into a digital format that can be used by the computer. Scanning automatically captures map features, text, and symbols as individual cells, or pixels, and produces an automated image.
  • 104.
    Data Input andGIS – Working of a Scanner  The most important component inside a scanner is the scanner head which can move along the length of the scanner.  The scanner head contains either a charged-couple device (CCD) sensor or a contact image (CIS) sensor.  A CCD consists of a number of photosensitive cells or pixels packed together on a chip.  The most advanced large format scanners use CCD’s with 8000 pixels per chip for providing a very good image quality.
  • 105.
    Data Input andGIS – Working of a Scanner  While scanning a bright white light from the scanner strikes the image to be scanned and is reflected onto the photosensitive surface of the sensor placed on the scanner head.  Each pixel transfers a gray tone value (values given to the different shades of black in the image ranging from 0 (black) – 255 (white) i.e. 256 values to the scan board (software).  The software interprets the value in terms of 0 (Black) or 1 (white), thereby, forming a monochrome image of the scanned portion.  As the head moves ahead, it scans the image in tiny strips and the sensor continues to store the information in a sequential fashion. The software running the scanner pierces together the information from the sensor into a
  • 106.
    Data Input andGIS – Working of a Scanner  Scanning a colour image is slightly different in which the scanner head has to scan the same image for three different colours i.e. red, green, blue.  In older colour scanners, this was accomplished by scanning the same area three times over for the three different colours. This type of scanner is known as three-pass scanner.  However, most of the colour scanners now scan in one pass scanning all the three colours in one go by using colour filters.
  • 107.
    Data Input andGIS – Working of a Scanner  In principle, a colour CCD works in the same way as a monochrome CCD. But in this each colour is constructed by mixing red, green and blue. Thus, a 24-bit RGB CCD presents each pixel by 24 bits of information. Usually, a scanner using these three colours (in full 24 RGB mode) can create up to 16.8 million colours.  Nowadays a new technology: full width, single-line contact sensor array scanning has emerged in which the document to be scanned passes under a line of LED’s which capture the image. This new technology enables the scanner to operate at previously unattainable speeds.
  • 108.
    Data Input andGIS – Types of Scanner There are several different types of scanners performing the same job but handling the job differently using different technologies and producing results depending on their varying capabilities. Hand-held scanners although portable, can only scan images up to about four inches wide. They require a very steady hand for moving the scan head over the document. They are useful for scanning small logos or signatures and are virtually of no use for scanning maps and photographs.
  • 109.
    Data Input andGIS – Types of Scanner Flatbed scanners The most commonly used scanner is a flatbed scanner also known as desktop scanner. It has a glass plate on which the picture or the document is placed. The scanner head placed beneath the glass plate moves across the picture and the result is a good quality scanned image. For scanning large maps or toposheets wide format flatbed scanners can be used.
  • 110.
    Data Input andGIS – Types of Scanner Drum scanners Then there are the drum scanners which are mostly used by the printing professionals. In this type of scanner, the image or the document is placed on a glass cylinder that rotates at very high speeds around a centrally located sensor containing photo-multiplier tube instead of a CCD to scan. Prior to the advances in the field of sheet fed scanners, the drum scanners were extensively used for scanning maps and other documents.
  • 111.
    Data Input andGIS – Types of Scanner Sheet fed scanners Finally, there are the Sheet fed scanners which work on a principle similar to that of a fax machine. In this, the document to be scanned is moved past the scanning head and the digital form of the image is obtained. The disadvantage of this type of scanner is that it can only scan loose sheets and the scanned image can easily become distorted if the document is not handled properly while scanning. However, the new generation of the wide format sheet fed scanners has overcome this problem and have become indispensable for scanning maps, imageries and other large sized documents.