Gis & Rs_Prof A Abhyankar PDF

RS and GIS By Dr. Abhijat Arun Abhyankar Associate Professor and Interim-Dean, School of Energy and Environment NICMAR University, Pune [email protected]  Outline of talk Part One Basics of Remote sensing a...

RS and GIS By Dr. Abhijat Arun Abhyankar Associate Professor and Interim-Dean, School of Energy and Environment NICMAR University, Pune [email protected]  Outline of talk Part One Basics of Remote sensing and GIS Part two Application of Remote sensing and GIS  Any guesses? Different types of Resolutions FCC NDVI Atmospheric window Radiometer Spectral Reflectance Curve CCD Sun synchronous satellite distance from Earth? Supervised vs. Unsupervised Classification  Software -RS and GIS ERDAS Imagine PCI Geomatica Idrisi for window GRASS ENVI ILWIS MapInfo Arc GIS QGIS Intergraph GRAM++  Remote sensing "Remote sensing is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and A: illuminating source recording reflected or B: radiation from the Atmosphere emitted energy and C: interaction with the target processing, analyzing, and D: recording of Energy by the Sensor E: transmission, reception, and applying that information". processing F: Interpretation and Analysis 1. Energy Source or Illumination (A) - the first requirement for remote sensing is to have an energy source which illuminates or provides electromagnetic energy to the target of interest. 2. Radiation and the Atmosphere (B) - as the energy travels from its source to the target, it will come in contact with and interact with the atmosphere it passes through. This interaction may take place a second time as the energy travels from the target to the sensor. 3. Interaction with the Target (C) - once the energy makes its way to the target through the atmosphere, it interacts with the target depending on the properties of both the target and the radiation. 4. Recording of Energy by the Sensor (D) - after the energy has been scattered by, or emitted from the target, we require a sensor (remote - not in contact with the target) to collect and record the electromagnetic radiation. 5. Transmission, Reception, and Processing (E) - the energy recorded by the sensor has to be transmitted, often in electronic form, to a receiving and processing station where the data are processed into an image (hardcopy and/or digital). 6. Interpretation and Analysis (F) – the processed image is interpreted, visually and/or digitally or electronically, to extract information about the target which was illuminated. 7. Application (G) - the final element of the remote sensing process is achieved when we apply the information we have been able to extract from the imagery about the target in order to better understand it, reveal new information, or assist in solving a particular problem.  A System View of Remote Sensing Remote Sensing Passive Active Reflected Thermal Passive Visible/IR Active Microwave Aerial Thermal Imaging Passive Altimetry, Laser photography microwave Scatterometry, profiling and Visible/Near radiometry Synthetic Aperture LASER IR imaging Radar  Stages in Remote Sensing 1. Electromagnetic energy reflected / emitted by earth surface features 2. Energy received by the remote sensors 3. Energy converted to electrical signal 4. Electrical signal converted to DIGITAL form 5. Digital signal transmitted to ground 6. Ground station organizes data on CDs/DVDs 7. Data distributed to users 8. Users analyze data and produce information products  Remote Sensing Process 1. Statement of Problem 2. Data acquisition In situ measurements (GPS, biomass, soil moisture, spectro-radiometer, etc.) Remote Sensing Data (passive and active remote sensing ) 3. Data analysis Visual interpretation Digital Image Processing Scientific Visualization 4. Information presentation  Basis of Remote Sensing Different terrain Objects /phenomena being of different type/different chemical composition/physical condition radiate (reflect/emit) electromagnetic energy differently at different wavelength Interaction with terrain features-multispectral responses at different wavelengths)  Sydney Harbor with Opera House 61 cm panchromatic Quickbird image. Digital Globe  CSRE 0.6m x 0.6m 13 6m x 6m 14 23m x 23m 15  Application of Remote Sensing Agriculture and crop monitoring Bathymetry Cartography Climatology Coastal erosion Disaster monitoring Forestry geology glaciology oceanography meteorology pollution monitoring, snow resources soil characterization urban mapping and Infrastructure/Construction Management water resources mapping and monitoring Advantages of Remote Sensing Data can be gathered from a large area (synoptic) of the Earth’s surface or atmosphere in short span of time. In-situ measurements are time consuming and costly. Remote Sensing can be cost effective. has wide range and multidisciplinary applications Limitations of Remote Sensing It is often oversold. The use of the data must offer some tangible advantages to justify the cost of acquiring and analyzing them. It provides some information about objects on the earth’s surface, but not all required in depth research. It provides only spatial, spectral and temporal information. There may be error in the geophysical parameters retrieved using Remotely Sensing data.  Solar Radiation Wavelength s Energy % (Sun) Spectral region Wavelength (um) Percent of Energy Gamma and X rays Less than 0.01 0.02 Far UV 0.01-0.2 Middle UV 0.2-0.3 1.95 Near UV 0.3-0.4 5.32 Visible 0.4-0.7 43.50 Near IR 0.7-1.5 36.80 Middle IR 1.5-5.6 12.00 Far IR 5.6-1000 0.41 Microwave and radio waves Greater than 1000 Total 100  Atmospheric window Blue zones (absorption bands) mark minimal passage of incoming and/or outgoing radiation, whereas, white areas (transmission peaks) denote atmospheric windows, in which the radiation doesn't interact much with air molecules and hence, isn't absorbed. Electromagnetic Spectrum Bands used in remote sensing Region Name/Bands Wavelength Comments Available for remote sensing the Visible 0.4 to 0.7 micrometers Earth. Available for remote sensing the Infrared 0.7 to 100 micrometers Earth. Available for remote sensing the Reflected Infrared 0.7 to 3.0 micrometers Earth. Near Infrared 0.7 to 0.9 micrometers. Available for remote sensing the Thermal Infrared 3.0 to 14 micrometers Earth. Longer wavelengths of this band can pass through clouds, fog, Microwave or Radar 0.1 to 100 centimeters and rain. Images using this band can be made with sensors that actively emit microwaves. Not normally used for remote Radio > 100 centimeters sensing the Earth.  Interactions with features and Surface ► All EM energy reaches earth's surface must be reflected, absorbed, or transmitted ► The proportion of each depends on: type of features, wavelength, angle of illumination EI (λ)= ER (λ) + EA (λ) + ET(λ) Reflection Absorption Transmission Typical spectral reflectance curve of water, vegetation and bare soil 22 Features Landsat1,2,3 Landsat 4,5 SPOT IRS-IA IRS-IC Nature Sun Sys Sun Sys Sun Sys Sun Sys Sun Sys Altitude (km) 919 705 832 904 817 Orbital 103.3 99 101 103.2 101.35 period (minutes) inclination 99 98.2 98.7 99 98.69 (degrees Temporal 18 16 26 22 24 resolution (days) Revolutions 251 233 369 307 341 Equatorial 09.30 09.30 10.30 10.00 10.30 crossing (AM) Sensors RBV,MSS MSS,TM HRV LISS-I,LISS-II LISS-III, PAN,WIFS 23 Basic Interactions between Electromagnetic Energy and the Earth’s Surface  Digital Image Processing Image rectification-geometric correction Image enhancement-Contrast manipulation Image classification-Supervised classification, NDVI (NIR-Red/NIR+Red), Unsupervised Classification Post classification smoothing-majority filter Accuracy assessment-Kappa coefficient 25 1.0 m Spectral bands VIS 0.55-0.75 m IR 10.5-12.5 m Resolution at sub-satellite point : VIS 2 km IR 8 km Data rate : 526.5 kbs  Resolutions Spatial Spectral Radiometric Temporal Resolution Spatial resolution (GRE) The measure of how closely lines can be resolved in an image is called spatial resolution Spectral resolution- width of the spectral band Radiometric resolution Radiometric resolution determines how finely a system can represent or distinguish differences of intensity, and is usually expressed as a number of levels or a number of bits Temporal resolution- revisit time  Points to remember As we go from visible to NIR the spectral resolution becomes coarser i.e. spectral bands become wider As we go from visible to Near IR and TIR the spatial resolution also become coarse Binary: 1 0 1 0 1 1 0 1 Decimel: 1×27 + 0×26 + 1×25 + 0×24 + 1×23 + 1×22 + 0×21 + 1×20 = ? 128+0+32+0+8+4+0+1=173 1 1 1 1 1 1 1 1 128+64+32+16+8+4+2+1=2 55  Display of Satellite Images To display a satellite image on our computer monitor, let us note that the monitor has – Red gun – Green gun – Blue gun – Data needs to be fed to the three guns to display the color picture Example: Red Display Example: Green Display Example: Blue Display Example: Color Display Flood Simulation Flood Stage “Sea Level” Flood Stage 1m Flood Stage 2m Flood Stage 3m Flood Stage 4m Flood Stage 5m Flood Stage 6m Flood Stage 8m Flood Stage 10 m Flood Stage 12 m Flood Stage 14 m Flood Stage 16 m Flood Stage “Sea Level” Flood Stage 1m Flood Stage 2m Flood Stage 3m Flood Stage 4m Flood Stage 5m Flood Stage 6m Flood Stage 8m Flood Stage 10 m Flood Stage 12 m Flood Stage 14 m Flood Stage 16 m Thermal Infrared Image  MAPPING AND CHANGE DETECTION OF MANGROVES AROUND MUMBAI USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (GIS) Landcover map of study area of year 2004 Landcover map of study area of year 2013 Change detection map of the study area (2004 to 2013)  Case study: Orissa Supercyclone Orissa Districts affected During Orissa super cyclone Multi-date georeferenced FCC dataset October 11, 1999 November 2, 1999 November 4, 1999 98 Landcover Map and area of each landcover in the Kendrapara district using IRS 1D LISS III data Area (thousand Landcover hectares) Water 21.286 Forest 9.877 Fallow land 49.776 Other vegetation 41.295 Rice 132.786 Area of Kendrapara in thousand hectares=255.02 Spatial distribution of water in October 11, 1999 imagery of Kendrapara district of Orissa at threshold of –14.0 dB Spatial distribution of water in November 2, 1999 imagery of Kendrapara district of Orissa at threshold of –14.0 dB Spatial distribution of water in November 4, 1999 imagery of Kendrapara district of Orissa at threshold of –14.0 dB  Spatial distribution of completely submerged rice in post event images of November 2, 1999 and November 4,1999 using Deterministic technique Spatial distribution of rice completely submerged in Kendrapara district of Orissa on November 2, 1999 at threshold of -14.0 dB Spatial distribution of rice completely submerged in Kendrapara district of Orissa on November 4, 1999 at threshold of -14.0 dB Advantages of Remote Sensing Data can be gathered from a large area of the Earth’s surface or atmosphere in short space of time. In situ measurements are time consuming and costly. Remote Sensing is considered as cost effective. It has many applications in wide number of areas. Limitations of Remote Sensing It is often oversold. The use of the data must offer some tangible advantages to justify the cost of acquiring and analysing them. It provides some information about objects on the earth’s surface, but not all required for research. It gives only spatial, spectral and temporal information. INTRODUCTION TO GIS  Overview and Definition of GIS It brings together the ideas developed in various fields such as Computer Science, Mathematics, Civil Engineering, Surveying, Economics, Agriculture and Geography to name a few. Focus of GIS activity centers around Hardware and software Information processing Applications  Selected Definitions of GIS DoE (1987) - A system for capturing, storing, checking, manipulating, analysing and displaying data which are spatially referenced to the Earth. Aronoff (1989) - Any manual or computer based set of procedures used to store and manipulate geographically referenced data Carter (1989) - An institutional entity, reflecting an organizational structure that integrates technology with a database, expertise and continuing financial support over time Parker (1988) - An information technology which stores, analyses, and displays both spatial and non-spatial data Dueker (1979) - A special case of information systems where the database consists of observations on spatially distributed features, activities, or events, which are definable in space as points, lines, or areas. A GIS manipulates data about these points, lines and areas to retrieve data for adhoc queries and analyses  Selected Definitions of GIS Smith et al. (1987) - A database system in which most of the data are spatially indexed, and upon which a set of procedures operated in order to answer queries about spatial entities in the database. Ozemoy, Smith and Sicherman (1981) - An automated set of functions that provides professional with advanced capabilities for the storage, retrieval, manipulation and display of geographically located data Burrough (1986) - A powerful set of tools collecting, storing, retrieving at will, transforming and displaying spatial data from the real world Cowen (1988) - A decision support system involving the integration of spatially referenced data in a problem-solving environment Koshkariov, Tikunov and Trofimov (1989) - A system with advanced geo-modeling capabilities  Advantages of GIS Reduction in data redundancy Data integration Maintaining data consistency Capability of data updating Capability of data storage and retrieval Capability of data processing and modeling Automated mapping  Where is a GIS from ? Geography Cartography CAD and computer graphics Surveying and photogrammetry Remote Sensing and space technology  Key components are… Hardware High end workstations to desktop systems Software Geo processing engine of GIS Major Functions – collect, store, manage, query, analyse and present GIS data base (spatial and related data) Live ware People responsible for designing, implementation and using GIS  Geographic Information System Organized collection of – Hardware Software – Software People – Network – Data Data – People Network – Procedures Procedures Hardware Example : Administrative boundaries - Spatial Census Data - Attribute data In GIS, spatial data is the key feature which differentiates it from other information systems Geographical data are expensive to collect, store and manipulate Large volumes of data are needed for a good study Data collection cost higher than cost of hardware and software National and Global digital databases are getting developed  LandCapability Soil Spatial Roads Data Layers VillagesBnd Location Landuse  What Does A GIS Do? GIS can answer the following questions: 1. Location - What is at a given location? 2. Condition - Where does it occur? 3. Routing - What is the best way? 4. Trend - What has changed? 5. Pattern - What is the pattern? 6. Modeling - What happens if ?  Reasons for Success of GIS Great proliferation of information about cultural and natural environment Remote Sensing satellites, market surveys, topographic surveys etc. produce large quantities of digital data. Many of these data have some type of explicit or implicit geographical reference This geographical reference has helped in linking data sets together and this principle is one of the reasons for the success of GIS Have great commercial applications They address significant global, national, local, social and scientific problems Rapid reduction in the cost of computer hardware and software  Development of GIS Applications In the initial phase, the main activity was assembling, organizing and understanding an inventory of features like forest resources maps, soil types, utility networks etc. In this phase, the systems were primarily used for data queries such as locations and condition questions The second phase got evolved for covering complex analytical operations They require data spread across several layers and use of statistical and spatial analytical techniques. Applications such as determining the suitability of land for locating a retail store or monitoring changes in a region The third and most developed phase is the evolution of GIS as a decision support system. Special emphasis is on spatial, analytical and modeling activities  APPLICATIONS ▪ Resources Management ▪ Transportations ▪ Landuse Planning Management ▪ Agriculture ▪ Telecommunication ▪ Forestry ▪ Mining ▪ Water Resources Management ▪ Government Agencies ▪ Rural/Urban Planning ▪ Defense ▪ Environmental Management ▪ Emergency Operations ▪ Risk Management ▪ Crime Management ▪ Business /Marketing ▪ Epidemiology ▪ Real Estate ▪ Archaeology ▪ Facility Mapping  Business of GIS GIS industry is worth over $12 billion – Software – Data – Services – Publishing – Education  Data Sources In GIS Analog Maps Topographic Maps Aerial Photographs Satellite Images Ground Surveys Ground Surveys With GPS Government of India – Primary Survey Depts. State Government – Primary Survey Depts. City, Town, and Village level maps and Records Reports and Publications  Map Map is a fundamental language of geography which gives the descriptive information about the world A map is a small scale conventional representation of the earth (or part) as seen from above A Cartographic representation without scale should not be called a map. It should be considered as a sketch or a diagram.  Essential Map Elements Title: Describes what a map Shows Legend: Defines the Symbols Scale: Shows the relationship of map distance to actual distance on the ground. Direction: Refers to the cardinal directions and is shown by an arrow Source: The institution or resource from which the information on the map was compiled. Date: Shows when the map was made and the date of information on the map Border : Defines the edges of the map and separates the map from the text Author : The Institution or the individual that created the map Ground Relationship: Ground and water features differentiated  Thematic Maps can also be Grouped into Two Qualitative Maps Quantitative Maps Qualitative Map shows the spatial distribution or location of a kind of normal data. For example, a map showing wheat fields in a map would be a qualitative map. It would not show how much wheat is produced from that field. Example: Soil Map, Landuse Map, Crop Distribution, Political, Physical Maps etc. Quantitative Maps displays the numerical aspect of spatial data. A map showing wheat production (Volume) in the wheat field would be quantitative map Other Examples: Maps showing quantitative distribution of any theme, It may be a Choropleth map (population, production, income etc), Isopleth (Contours), or a Graphical representation of volume, number, or quantity.  Map Scale Map scale is a ratio between the distance on the map to distance on the earth’s surface. Scales are shown in 3 ways on the maps ▪ RF Scale (Representative Fraction) Example : 1:50,000 ▪ Verbal Statement (Descriptive Scale) Example : 1 cm = 5 Km or 1 Inch = 1 Mile ▪ Bar Scale (Graphical Scale) Example :  Map Limitations A map is a representation of 3-dimesional curved Surface on a 2-dimensional flat surface. The correct representation is a globe not a map. A map is a summary of a selected facts about the reality. A very large scale ,map of your garden might be quite accurate even to the point of showing the location of different types of plants. A map of a larger area such as Tehsil or a District are more selective. It can attempt to show important features but no single map could show all types of features.  Functions Of Maps Navigation Visualization Measurements Storing Spatial Data  Advantages of maps Descriptive Good Planning Tool Solve Complex Problem Objective and Efficient  GIS DATA MODEL Data Model is a set of guide lines for the representation of the logical organization of the data in a data base consisting of logical units of data and the relationships between them Each data model fits to certain types of data and applications Two Major Choices of Data Models Raster and Vector  RASTER MODEL – Raster Model divides the entire study area into regular grid of cells in a specific sequence – It is space filling and every location in the study area corresponds to a cell in the raster model – The size of the cell defines the level of spatial detail – All variations within the cell is lost – One set of cells and associated values is a layer (soil, landuse, elevation etc.) – Raster model tells what occurs at each place in the area  CREATION OF RASTER DATA LAYER – To create a raster layer, lay a grid pattern over a map (like soil) and code each cell with a value that represents the soil type – Cell is called as raster or grid or pixel – These coded values are in ASCII and can be entered manually through keyboard. It will be time consuming and tedious – Currently scanners are used to create raster data layers – Remote Sensing directly gives the digital data raster model  RASTER REPRESENTATION Legend Cultivated Area Forest Pond Settlement Open Area Each color represents a different value of a nominal-scale field denoting land cover class.  Raster Data Layer Resolution ▪Resolution can be defined as the minimum linear dimension of the smallest unit of geographical space for which data are encoded ▪Higher resolution refers to raster with small cell dimensions. It gives more detail and the storage requirement increases Value ▪It is item of information stored in a layer for each pixel or cell ▪Cells in the same zone have the same value Location ▪Generally location is identified by an ordered pair of coordinates (row and column numbers) that identify the location of each unit of geographic space in the raster ▪Usually the true geographic location of one or more corners of the raster is also known Pixel Values ▪The type of values contained in pixels in raster depend upon the map being coded ▪Raster Data Values may be ▪0 - 255 (8 bit value) Remote Sensing Image ▪Integers ▪Real Values (DTM) ▪Integer values act as code numbers which point to names in an associated table or legend ▪One pixel or cell is assured to have only one value ▪The boundary of two classes may run across the middle of the pixel. In such cases, the pixel is given the value of the largest fraction of the cell.  RASTER OPERATIONS 7/4/2024  RASTER OPERATIONS A raster GIS must have capabilities for Input of data, various house keeping functions, operations on layers, output of data and display layers Basic Display Ø Simplest type of values to display are integers On a colour display , each integer value is assigned a unique colour (Fig.1) Ø If the values have a natural order, a sequence of colours is used (Fig.2) There must be a legend to explain the meaning of each colour. Other Types of Display Ø Display the data as a surface Ø Contours can be shown through the pixels along the lines of constant value (Fig.3) Ø The surface can be shown in oblique-perspective view (Fig.4)  Fig. 1 Fig. 2 Fig. 3 Fig. 4  LOCAL OPERATIONS produce a new layer from one or more input layers the value of each new pixel is defined by the values of the same pixel on the input layers(s) neighbouring or distant pixels have no effect Arithmetic operations make no sense unless the values have appropriate scales of measurement Regrouping Is carried out using only one input layer 1. assign a new value to each unique value on the input layer useful when the number of unique input values is small  LOCAL OPERATIONS 2. assign new values by assigning pixels to classes or ranges based on their old values useful when the old layer has different values in each cell, e.g., elevation or satellite images 3. sort the unique values found on the input layer and replace by the rank of the value e.g. 0, 1, 4, 6 on input layer become 1, 2, 3, 4 respectively applications : assigning ranks to computed scores of capability, suitability etc. some systems allow a full range of mathematical operations  Overlay Operations an overlay occurs when the output value depends on two or more input layers many systems restrict overlay to two input layers only (Fig. 5) Examples : 1. output value equals arithmetic average of input values 2. output value equals the greatest (or least) of the input values 3. layers can be combined using arithmetic operations 4. combination using logical conditions e.g. if y > 0, then z = 1, otherwise z = 0 Fig. 5  NEIGHBOURHOOD OPEATIONS The value of a pixel on the new layer is determined by the local neighbourhood of the pixel on the old layer Filtering A filter operates by moving a "window" across the entire raster e.g. many windows are 3x3 cells the new value for the cell at the middle of the window is a weighted average of the values in the window by changing the weights we can produce different effects:  NEIGHBORHOOD OPEATIONS Examples filters: 1..11.11.11.11.11.11.11.11.11 Replaces each value by the simple unweighted average of it and its eight neighbouring values smooths the spatial variation on the layer 2. 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 slightly smooths the layer 3. -.1 -.1 -.1 -.1 1.8 -.1 -.1 -.1 -.1 slightly enhances local details by giving neighbours negative weights  Slopes and aspects if the values in a layer are elevations, we can compute the steepness of slopes by looking at the difference between a pixel's value and those of its adjacent neighbours the direction of steepest slope, or the direction in which the surface is locally "facing", is called its "aspect“ (Fig. 6) slope and aspect are useful in analyzing vegetation patterns, computing energy balances and modeling erosion or runoff aspect determines a direction of runoff it can be used to sketch drainage paths for runoff DEM Slope Aspect Fig. 6  OPERATIONS ON EXTENDED NEIGHBOURHOODS Distance calculate the distance of each cell from a cell or the nearest of several cells each pixel's value in the new layer is its distance from the given cell(s) Buffer zones buffers around objects and features are very useful GIS capabilities e.g. build a buffer of 500 m wide around the road network buffer operations can be visualized as spreading the object spatially by a given distance (Fig. 7) Fig. 7  COMMANDS TO DESCRIBE CONTENTS OF LAYERS One layer generate statistics on a layer e.g. mean, median, most common value, other statistics More than one layer compare two maps statistically e.g. is pattern on one map related to pattern on the other? e.g. chi-square test, regression, analysis of variance Zones on one layer generate statistics for the zones on a layer e.g. largest, smallest, number mean area EXAMPLE ANALYSIS USING A RASTER GIS EXAMPLE ANALYSIS USING A RASTER GIS  Environmental Study for Barvi Reservoir, Maharashtra.  EXAMPLE ANALYSIS USING A RASTER GIS Objective Identify erosion prone areas : An area that satisfies the following criteria: has high intensity rainfall has with less soil depth has less vegetation has steeper slope Raster description : Resolution 100 m, area 0.5 km by 0.5 km Layer 1 : Slope Map 1 steeper slope 2 lower slope 1 1 1 2 2 1 1 1 2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 2 2  EXAMPLE ANALYSIS USING A RASTER GIS Layer 2 : Soil Depth Map 1 low 2 high 1 1 1 1 2 1 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 Layer 3 : Vegetation Map 1 less 2 more 1 1 1 2 2 1 1 1 2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 2 2 Layer 4 : Rain Fall Map 1 high 2 low 1 1 1 2 2 1 1 2 2 2 1 1 1 2 2 1 1 1 2 2 1 2 2 2 2  ANALYSIS STEPS Layer 1: Layer 2: Layer 3: Layer 4: Slope Soil Depth Vegetation Rain Fall Overlay Overlay Layer 5: Layer 6: Steeper Slope & Low Less Vegetation Soil Depth & High Rainfall Overlay Layer 7:Erosion Prone Areas  ANALYSIS STEPS Layer 5 : Steep Slope and low soil depth 1 1 1 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Layer 6 : Low Vegetation and High Rain Fall 1 1 1 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 Layer 7 : Erosion Prone Areas 1 1 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0  Vector Representation Used to represent points, lines, and areas All are represented using coordinates – One per point – Areas as polygons Straight lines between points, connecting back to the start Point locations recorded as coordinates – Lines as polylines Straight lines between points Point Line Polygon Layer Layer Layer Digitization Errors  Topology Science and mathematics of geometric relationships – Simple features + topological rules – Connectivity – Adjacency – Shared nodes / edges Topology uses – Data validation – Spatial analysis (e.g. network tracing, polygon adjacency) Polygon Topology Model  Raster vs Vector Volume of data – Raster becomes more voluminous as cell size decreases Source of data – Remote sensing, elevation data come in raster form – Vector favored for administrative data Applications – Some Applications better suited to raster model, some to vector model Triangular Irregular Network  Vector Analysis ▪ Identifies spatial relationship within a layer or between the spatial layers. ▪ Can be carried out using both spatial and attribute data. ▪ Vector analysis functions are limited compared to raster analysis functions. 169  Non spatial Query Use the attribute data base to select features that meet certain criteria. Select the villages in a Block that have at least one primary school and a bank. Nonspatial query runs on a single layer. 170 171 Operations Involving Two Layers Union Intersect Clip Update Erase 172 Union Operates on two layers. A new polygon layer is created by overlaying features from two input polygon layers. Union makes a spatial join. It is equivalent to ‘or’ Boolean operator. The output layer contains the contained polygons. attributes of both the layers. Area extent combines the area extents of both input layers 173 Union Illustration 1 1 2 1 2 U2 1 2 = 3 4 SID Concatenated UID 1 Soil 1 & Slope 1 SID UID SID UID 1 Soil1 1 Slope 1 2 Soil 1 & Slope 2 2 Soil2 2 Slope 2 3 Soil 2 & Slope 1 4 Soil 2 & Slope 2 174 Intersect It is an overlay operation created by overlaying 2 layers. A new output layer is created. Layer 1 can be point or line or polygon layer. Layer 2 is a polygon layer. Layer 1 Layer 2 Output Layer Point Polygon Point Line Polygon Line Polygon Polygon Polygon 175 Intersect Contd. 2 1 2B 1 2 B 1B 1A 2A + A = 4 3B 3A 4A 3 176 ▪It is equivalent to ‘and’ Boolean operator. ▪The output layer contains only those portions of features that are in the area occupied by both the input layers. 177  Overlay Operations And Topology ▪ Operations union and intersect create new layers and new topology gets built. ▪ Attribute tables are updated. The attribute table contains items from both the input layers. ▪ Therefore all items from the input layers’ attribute tables are retained except for the geometric measures (area and perimeter in the case of polygon layers). 178 Clip It operates on 2 layers input. Extracts a part of an input layer that intersects with the clip layer. The features of the input layer are retained in the output layer. 179 Clip Contd. ▪The attribute table of the output layer contains the same attributes as of the input layer. ▪Input layer can be point, line or polygon layer. ▪Clip layer must be a polygon layer. ▪The output layer is of the same feature type as the input layer. Input layer Clip layer Output layer 180 Erase It operates on 2 layers. Erase is similar to clip, except that the input layer features that overlap with erase layer polygons are erased in the output layer. Input layer can be point, line or polygon layer. Erase layer must be a polygon layer. The output layer is of the same feature type as the input layer. When input and erase layers are polygon layers, interchange of layers give different results. 181 Erase Illustration Input layer Erase layer Output layer 182 Update It operates on 2 polygon layers. The features of the input layer are updated with the features of update layer. When input and update layers are polygon layers, interchange of layers give different results. 183 Update Illustration 1 2 0 1 2 7 8 7 8 4 9 9 3 3 4 Input layer Update layer Output layer 184 Single Layer Operations Eliminate Dissolve Buffer 185 Eliminate It operates on a single layer. Merges selected polygons with neighboring polygons that have the largest shared border between them, or that have the largest area. Often used to remove sliver polygons created during an overlay operation of 2 layers. During overlay operation of 2 layers, the layers have a nearly perfect boundary match, but not exact match which creates thousands of thin sliver polygons. 186 Eliminate Illustration Before Elimination After Elimination ▪Eliminate command removes these very skinny polygons (Slivers). ▪The sliver is reassigned to the polygon with which it shares the longest boundary. 187 Dissolve Operation Output It operates on a single layer. Dissolve merges adjacent polygons or lines which have the same User ID. In polygon layer, it removes the segment between adjacent polygons containing same User IDs. 188 Buffer Buffer creates buffer polygons around specified features in a layer. Buffer creates a new polygon layer. Input layer can be point or segment or polygon layer. In a segment or polygon layer, one can create inside or outside or both side buffers. Using a single buffer distance, it creates buffer zones of the same width around the selected features. Incremental buffers are created for a set of distances around a selected feature. 189  Illustration Point Buffer Line Buffer Internal Buffer External Buffer 190 Spatial Modeling What is spatial modelling? It is the process of manipulating and analyzing spatial geographic data to generate useful information for solving complex problems. Why? It finds the relationship that exist among the spatial features. 191 How to do? ▪Identify the problem. ▪Breakdown (simplify) the problem. ▪Organize the data required to solve the problem. ▪Develop clear and logical flow chart using well defined operations. ▪Run the model and modify it if necessary. 192 THANK YOU