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GIS and Remote Sensing Training at Pitney Bowes Software


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This presentation was made for internal training in Pitney Bowes. The content has many references across and has not been compiled. A simple ppt will help a lot.

This presentation was made for internal training in Pitney Bowes. The content has many references across and has not been compiled. A simple ppt will help a lot.

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  • 1. A journey to Geographical Information System and Remote Sensing Technology Dr. Nishant Sinha
  • 2. Journey Expectations ▪ GIS – Basics of GIS – Components of GIS – GIS Data Models (Raster andVector) – GIS DataTypes and Metadata – Various GIS Data formats and GIS Data Products – Process of GIS Data Generation/creation to Analysis – Data Conversions – WebGIS –WMS,WFS
  • 3. Journey Expectations ▪ Remote Sensing – Basics of Remote Sensing – Image Processing and Enhancements techniques (Image algebra, Band Rationing, NDVI) – Image classification (Supervised and Unsupervised) and other recent trends in classification – Image ClassificationAccuracyAssessment, Post processing, Rectification and finalization for preparing Map Layouts for printing ▪ Cartography – Scale and types of scales – Projections and coordinate systems – Map and Map elements, UnderstandingToposheets
  • 4. Journey Expectations ▪ 3D Data representation and visualization – Real world objects (buildings, towers, etc.) and rendering on Google Earth – Digital Elevation Model (DEM) – Walk through / fly through ▪ GPS ( (In a Nut Shell) – How it works and its functionalities – Capturing GPS Points and transferring to GIS environment – Few Applications
  • 5. Spatial is Special ▪ “Everything is related to everything else, but near things are more related than distant things” Tobler, W. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46, 234–40 ▪ Sometimes called the First Law of Geography (because it is generally true!).
  • 6. How do we describe geographical features? ▪ by recognizing two types of data: – Spatial data which describes location (where) – Attribute data which specifies characteristics at that location (what, how much, and when) How do we represent these digitally in a GIS? ▪ by grouping into layers based on similar characteristics (e.g hydrography, elevation, water lines, sewer lines, grocery sales) and using either: – vector data model – raster data model ▪ by selecting appropriate data properties for each layer with respect to: – projection, scale, accuracy, and resolution How do we incorporate into a computer application system? ▪ by using a relational Data Base Management System (RDBMS) Representing Geographic Features
  • 7. ▪ Continuous ▪ Elevation ▪ Rainfall ▪ Ocean salinity ▪ Discrete – Polygon areas: ▪ unbounded: landuse, market areas, soils, rock type ▪ bounded: city/county/state boundaries, ownership parcels, zoning – Line networks ▪ roads, transmission lines, streams – Points Location : ▪ fixed: wells, street lamps, addresses Spatial Data Types
  • 8. Categorical name – nominal ▪ no inherent ordering ▪ land use types, county names – ordinal ▪ inherent order ▪ road class; stream class ▪ often coded to numbers eg SSN but can’t do arithmetic Numerical Known difference between values – interval ▪ No natural zero ▪ can’t say ‘twice as much’ ▪ temperature (Celsius or Fahrenheit) – ratio ▪ natural zero ▪ ratios make sense (e.g. twice as much) ▪ income, age, rainfall ▪ may be expressed as integer [whole number] or floating point [decimal fraction] Attribute data tables can contain locational information, such as addresses or a list of X,Y coordinates. ArcView refers to these as event tables. However, these must be converted to true spatial data (shape file), for example by geocoding, before they can be displayed as a map. Attribute data types
  • 9. ContainTables or feature classes in which: – rows: entities, records, observations, features: ▪ ‘all’ information about one occurrence of a feature – columns: attributes, fields, data elements, variables, items (ArcInfo) ▪ one type of information for all features The key field is an attribute whose values uniquely identify each row Parcel Table Parcel # Address Block $ Value 8 501 N Hi 1 105,450 9 590 N Hi 2 89,780 36 1001 W. Main 4 101,500 75 1175 W. 1st 12 98,000 entity AttributeKey field Data Base Management Systems (DBMS)
  • 10. Geographic Information System A system that doesn't hold maps or pictures but holds a database
  • 11. GIS Defined ….. ▪ A computer-based system for the manipulation and analysis of geospatial information in which there is an automated link between a data object and their spatial location. (Free on-line textbook)
  • 12. Roger F. Tomlinson, (born 17 November 1933) is an English geographer and the primary originator of modern computerized geographic information systems (GIS), and has been acknowledged CM as the "father of GIS"
  • 13. What is GIS? One word at a time…
  • 14. G Information S ▪ Data is a fact or collection of facts ▪ Data that is processed, organized, structured or presented in a given context to make them useful, are called Information
  • 15. G Information System A set of components for: Storing Displaying Analyzing DATA Information System Data Storage Query Information One example of an Information System: Microsoft Access database
  • 16. What is the S in GIS? ▪ 1980s: Geographic Information Systems – technology for the acquisition and management of spatial information – software for professional users, e.g. cartographers – Example: MapInfo ▪ 1990s: Geographic Information Science – comprehending the underlying conceptual issues of representing data and processes in space-time – the science (or theory and concepts) behind the technology – Example: design spatial data types and operations for querying ▪ 1990s: Geographic Information Studies – understanding the social, legal and ethical issues associated with the application of GISy and GISc ▪ 2000s: Geographic Information Services – Web-sites and service centers for casual users, e.g. travelers – Service (e.g., GPS, mapquest) for route planning
  • 17. What is GIS?
  • 18. Geographic Information System A means of: Storing Mapping Analyzing Spatial Data Information System Geographic Position
  • 19. Geographic Information System Leaving us with a simple way to start learning about GIS: A tool for deriving information from any data with a geographic / spatial component
  • 20. What is GIS? Basics of Storing, Mapping, and Analyzing Spatial Data…
  • 21. What is GIS? and answers the following…
  • 22. Location - What is at………….? The first of these questions seeks to find out what exists at a particular location. A location can be described in many ways, using, for example place name, post code, or geographic reference such as longitude/latitude or x/y.
  • 23. Condition - Where is it………….? The second question is the converse of the first and requires spatial data to answer. Instead of identifying what exists at a given location, one may wish to find location(s) where certain conditions are satisfied (e.g., an unforested section of at-least 2000 square meters in size, within 100 meters of road, and with soils suitable for supporting buildings)
  • 24. Trends - What has changed since…………..? The third question might involve both the first two and seeks to find the differences (e.g. in land use or elevation) over time.
  • 25. Patterns - What spatial patterns exists….? This question is more sophisticated One might ask this question to determine whether landslides are mostly occurring near streams. It might be just as important to know how many anomalies there are that do not fit the pattern and where they are located.
  • 26. Modelling - What if………..? "What if…" questions are posed to determine what happens, if a new road is added to a network or if a toxic substance seeps into the local ground water supply. Answering this type of question requires both geographic and other information (as well as specific models). GIS permits spatial operation.
  • 27. Aspatial Questions "What's the average number of people working with GIS in each location?" is an aspatial question the answer to which does not require the stored value of latitude and longitude; nor does it describe where the places are in relation with each other.
  • 28. Spatial Questions " How many people work with GIS in the major centres of Delhi" OR "Which centres lie within 10 Kms. of each other? ", OR " What is the shortest route passing through all these centres". These are spatial questions that can only be answered using latitude and longitude data and other information such as the radius of earth. Geographic Information Systems can answer such questions.
  • 29. Storing Geographic Data One GIS data layer combines both Geographic Features and their Attributes Geographic Features indicate “where”
  • 30. Storing “Everyday” Geographical Objects ▪ Points ▪ The fundamental primitive is the point, a 0-dimensional (0-D) object that has a position in space but no length. – home, day-care, health clinics, schools, retail and tobacco outlets, crimes & graffiti, bus stops, neighborhood anchor institutions, community assets, resources and risks ▪ Lines ▪ A line is a 1-D geographic object having a length and is composed of two or more 0-D point objects. – roads, railway, pathways, walking or bus routes, rivers ▪ Areas (Polygons) ▪ A polygon is a geographic object bounded by at least three 1-D line objects or segments with the requirement that they must start and end at the same location (i.e., node) – census unit, ZIP code, school district, police precinct, health service areas, counties, states, provinces, watersheds
  • 31. Mapping Geographic Data – India States India Airports (point layer) India States (polygon layer)
  • 32. Analyzing Geographic Data • Query GIS data layers based on attributes or geography, or both  Which states’ population was more than 75 million in 2011?
  • 33. Analyzing Geographic Data • Query GIS data layers based on attributes or geography, or both  Which are the neighboring states of Madhya Pradesh
  • 34. What is GIS? In more details…
  • 35. Representing Spatial Elements
  • 36. Scale of GIS data Global to Local
  • 37. What is Scale? ▪ Ratio of distance on a map, to equivalent distance on the earth's surface. – Large scale: large detail, small area covered (1”=200’ or 1:2,400) – Small scale -->small detail, large area (1:250,000) – A given object (e.g. land parcel) appears larger on a large scale map – Scale can never be constant everywhere on a map because of map projection – Scale representation ▪ Verbal: (good for interpretation.) ▪ Representative fraction (RF) (good for measurement) (smaller fraction=smaller scale: 1:2,000,000 smaller than 1:2,000) ▪ Scale bar (good if enlarged/reduced) 0ne inch each equals one statute mile 1: 63,360 Miles 0 1 2
  • 38. Scale Examples Common Scales 1:200 (1”=16.8ft) 1:2,000 (1”=56 yards; 1cm=20m) 1:20,000 (5cm=1km) 1:24,000 (1”=2,000ft) 1:25,000 (1cm=.5km) 1:50,000 (2cm=1km) 1:62,500 (1.6cm=1km; 1”=.986mi) 1:63,360 (1”=1mile; 1cm=.634km) 1:100,000 (1”=1.58mi; 1cm=1km) 1:500,000 (1”=7.9mi; 1cm=5km) 1:1,000,000(1”=15.8mi; 1cm=10km) 1:7,500,000(1”=118mi); 1cm=750km) Large versus Small large: above 1:12,500 medium: 1:13,000 - 1:126,720 small: 1:130,000 - 1:1,000,000 very small: below 1:1,000,000 ( really, relative to what’s available for a given area; Maling 1989) Map sheet examples: 1:24,000: 7.5 minute USGS Quads (17 by 22 inches; 6 by 8 miles) 1:7,500,000 US wall map (26 by 16 inches) 1:20,000,000: US 8.5” X 11”
  • 39. Precision or Resolution - it’s not the same as scale or accuracy! Precision: the exactness of measurement or description ▪ the “size” of the “smallest” feature which can be displayed, recognized, or described ▪ Can apply to space, time (e.g. daily versus annual), or attribute (douglas fir v. conifer) ▪ For raster data, it is the size of the pixel (resolution) – e.g. for NTGISC digital orthos is 1.6ft (half meter) ▪ raster data can be resampled by combining adjacent cells; this decreases resolution but saves storage – eg 1.6 ft to 3.2 ft (1/4 storage); to 6.4 ft (1/16 storage) ▪ Resolution and scale – generally, increasing to larger scale allows features to be observed better and requires higher resolution – but, because of the human eye’s ability to recognize patterns, features in a lower resolution data set can sometimes be observed better by decreasing the scale (6.4 ft resolution shown at 1:400 rather than 1:200) ▪ Resolution and positional accuracy – you can see a feature (resolution), but it may not be in the right place (accuracy) – Higher accuracy generally costs much more to obtain than higher resolution – Accuracy cannot be greater (but may be much less) than resolution ▪ e.g. if pixel size is one meter, then best accuracy possible is one meter) 1.6ft 3.2ft 3.2ft
  • 40. Accuracy: Rests on at least four legs, not one! Positional Accuracy (sometimes called Quantitative accuracy) – Spatial ▪ horizontal accuracy: distance from true location ▪ vertical accuracy: difference from true height – Temporal ▪ Difference from actual time and/or date Attribute Accuracy or Consistency: the validity concept in experimental design/stat. inf. – a feature is what the GIS/map purports it to be – a railroad is a railroad, and not a road Completeness--the reliability concept from experimental design/stat. inf. – Are all instances of a feature the GIS/map claims to include, in fact, there? – Partially a function of the criteria for including features: when does a road become a track? – Simply put, how much data is missing? Logical Consistency: The presence of contradictory relationships in the database – Non-Spatial ▪ Data for one country is for 2000, for another its for 2001 ▪ Data uses different source or estimation technique for different years (again, lineage) – Spatial ▪ Overshoots and gaps in road networks or parcel polygons
  • 41. ▪ Consists of discrete coordinates to store the geographic position of – Points ▪ Points: People or Cities (center) – Lines ▪ Roads or Other Linkages – Polygons ▪ CensusTract ▪ Vector Data Model – Geographic features stored as X,Y coordinate pairs – Each vector layers has an attribute table – Each feature corresponds to a row in the table Data Types: Vector Data
  • 42. ▪ Raster data represents a continuous surface divided into a regular grid of cells ▪ Often used as background map layer ▪ Points: People or Cities (center) – Lines ▪ Roads or Other Linkages – Polygons ▪ CensusTract ▪ Raster Data Model – Stores images as rows and columns of numbers, forming a regular grid structure – Great for computational analysis or modeling – Bad for mapping precise locations Data Types: Raster Data Raster AttributeTables
  • 43. Vector vs Raster Vector • Low data volume • Faster display • Can also store attributes • Less pleasing to the eye • Does not dictate how features should look in the GIS Raster • High data volume • Slower display • Has no attribute information • More pleasing to the eye • Inherently stores how features should look in the GIS
  • 44. Coordinate Systems ▪ Describing the correct location and shape of features requires a framework for defining real-world locations ▪ A geographic coordinate system is used to assign geographic locations to objects. ▪ GIS data layers must have a coordinate system defined to integrate with other layers
  • 45. Map Projections Transforming 3-dimensional space (Earth) onto a 2-dimensional map (GIS) Mercator Azimuthal Equidistant Albers Equal Area Conic Lambert Conformal Conic Robinson
  • 46. Map Projection is important ▪ Small-scale (large area) maps – Interested in Comparing shapes, areas, distances, or directions of map features? – Measurement errors can be quite substantial: New York New York Los AngelesLos Angeles Projection: Mercator Distance: 3,124.67 miles Projection: Albers Equal Area Distance: 2,455.03 miles Actual distance: 2,451 miles
  • 47. Editing Errors in GIS
  • 48. Data collected may need to be reorganized and checked for errors, before being used for spatial analysis, or mapping project. Error detection and correction may include: - Compare data with input document - Check topology of spatial objects - Check attributes of spatial objects - Check for missing spatial objects Data Storage and Editing
  • 49. Three major types of error: (1) Entity error (positional error). Entity error can take three different forms: missing entities, incorrectly placed entities, and disordered entities. (2) Attribute error.Attribute error occurs in both vector and raster systems. (3) Entity-attribute agreement error (logical consistency). Of the three basic types of error found in GIS databases, the last two are the most difficult to find.
  • 50. Detecting and Editing Errors of Diff. Types ▪ Negative cases of the following statements will cause errors: 1. All entities that should have been entered are present. 2. No extra entities have been digitized. 3. The entities are in the right place and are of the correct shape and size. 4. All entities that are supposed to be connected to each other are connected . 5. All entities are within the outside boundary identified with registration marks.
  • 51. Spatial Errors ▪ Dangling node, can be defined as a single node connected to a single line entity. Dangling nodes are also called dangles. ▪ Dangles can result from three possible mistakes: (1)Failure to close a polygon (2)Failure to connect the node to the object it was supposed to be connected to (called an undershoot) (3)Going beyond the entity you were supposed to connect to (called an overshoot).
  • 52. Source of Errors ▪ Dangles can also be a result of incorrect placement of the digitizing puck, or improper fuzzy tolerance distance setting. Distance between left dangle and its above line segment is 0.25mm Fuzzy tolerance = 0.1mm, if you change it to o.3mm, dangle will disappear.
  • 53. Spatial Errors ▪ Sliver polygons ▪ This occurs when the software uses a vector model that treats each polygon as a separate entity. (or spatial object) ▪ Solution: Use a GIS that does not require digitizing the same line twice. ▪ Weird polygons ▪ Polygons with missing nodes. ▪ Missing Arcs/segments
  • 54. Labeling Errors
  • 55. Attribute Errors: Raster and Vector Missing attributes For raster: A. Missing row B. Incorrect or misplaced attributes ForVector Incorrect attribute values are very difficult to detect.
  • 56. Checklist to Avoid Errors As geospatial analyst, you should always approach a project with the obvious sources of error discussed firmly on you mind. Therefore, when given a task to perform, and the associated data, the following should act as a good checklist: – Is the data current? – Were the data mapped at the correct scale? Do they have the same accuracies? – What is the resolution of the data? Will it support the kinds of analysis we want to perform? – Do we have all the data for the project areas, or is there some data missing? – If we need other data sets, are they available, or will we have trouble getting them?
  • 57. Obvious Errors ▪ The statement “to err is human” is very applicable to creating spatial data. Humans make a lot of errors. Typing in the wrong value in a computer is a common mistake that humans make. However, there are other sources of obvious error besides human error: – Age: a map is a representation of real-world objects at a given point in time. The reliability of a dataset typically goes down as it gets older. This is especially true of data that would frequently change such as housing within a city. Many GIS projects take years to complete, and it is entirely possible that much of the data collected in the beginning of a project may be out of date by the end of the project. – Map Scale: In general, larger scale maps show more detail than smaller scale maps. Also, larger scale maps tend to have greater accuracy than smaller scale maps, especially maps within the “same family” such as the differences between 1:250,000, 1:100,000 and 1:24,000 GIS will process any of your data, whether the processing is appropriate or not. Therefore, you can combine data from different scales rather easily, however, doing so may not be a good idea due to the different accuracies of the products. – Data Format: The way we represent data also presents an obvious source of error. For example, a raster map of landuse represented by 10 meter grid cells will differ significantly from a raster map of landuse represented by 100 meter grid cells. The following is a grid of landuse values around Ithaca, NewYork. You can see the differences in representation between a map with 10 meter grid cells, 30 meter grid cells, and 100 meter grid cells.
  • 58. Problems with Age The following maps show the different land cover types between 1968 and 1995. You can see how the data has changed over 30 years, and why using older data might present a problem.
  • 59. Components of Data Quality ▪ Positional Accuracy ▪ Attribute Accuracy ▪ Resolution ▪ Completeness
  • 60. Spatial Accuracy ▪ positional accuracy relates to the coordinate values for the geographic objects. But, even positional accuracy is divided into two different categories: – Absolute accuracy: refers to the actual X,Y coordinates of a geographic object. If one knows the correct position of the geographic object, they can compare the differences with the position represented in the geographic database. Typically, absolute accuracy will measure the total different between an object, or the difference in the X coordinate and the difference in theY coordinate. – Relative accuracy: refers to the displacement of two or more points on a map (in both the distance and angle), compared to the displacement of those same points in the real world.
  • 61. Errors Associated with Spatial Analysis ▪ Errors in Digitizing a Map – Source errors ▪ Distortion ▪ Boundaries drawn on a map have a “thickness” – 1 mm line ▪ 1.25 m wide on 1:250 map ▪ 100m wide on 1:100000 ▪ Estimates show that 10% of a 1:24000 soil map may represent the boundary lines alone – Digital Representation ▪ Curves are approximated by many vertices ▪ Boundaries are not absolute, but should have a confidence interval
  • 62. Errors Resulting from Natural Variations from Original Measurements ▪ Measurement Error – Accuracy vs. Precision ▪ Accuracy: extent to which an estimated value approaches the true value ▪ Precision: measure of dispersion of observations about a mean
  • 63. Accuracy and Precision ▪ Accuracy is defined as displacement of a plotted point from its true position in relation to an established standard while Precision is the degree of perfection; or repeatability of a measurement. ▪ For mapping, accuracy is associated with position of an object to its true position. ▪ Precision is then the ability to repeat a measurement, or how likely you are to return to the same location time and time again. ▪ The figures to the right illustrate the differences between accuracy and precision. ▪ Therefore, if there are natural variations in either the instruments used for measurement, or the object you are measuring, the accuracy or precision may be effected.
  • 64. Digitizing errors from duplicate lines include slivers and missing labels for the sliver polygons. Slivers are exaggerated for the purpose of illustration. Digitizing errors
  • 65. Digitizing errors of overshoot (left) and undershoot (right) Digitizing Errors- Overshoot & Undershoots
  • 66. Digitizing errors of an unclosed polygon Digitizing errors-Unclosed Polygon
  • 67. Pseudo nodes, shown by the diamond symbol, are nodes that are not located at line intersections Digitizing errors- Pusedo Nodes
  • 68. The from-node and to-node of an arc determine the arc’s direction. Digitizing Arc
  • 69. Digitizing error of multiple labels due to unclosed polygons Digitizing Unclosed Polygon –Multi labels
  • 70. The dangle length specified by the CLEAN command can remove an overshoot if the overextension is smaller than the specified length. In this diagram, the overshoot a is removed and the overshoot b remains. Removing Dangles - Using Clean Command
  • 71. Typical Digitizing Situations this is ideal, but... overshoot, and what to do with it undershoot, and what to do
  • 72. Remote Sensing The science and art of acquiring of information about an object without being in physical contact with it
  • 73. Remote Sensing As you view the screen of your computer monitor, you are actively engaged in remote sensing. HOW ? THE ANSWER IS A physical quantity (light) emanates from the screen, which is a source of radiation.The radiated light passes over a distance, and thus is "remote" to some extent.
  • 74. Remote Sensing 1 2 3 5 6 1. energy source 2. atmospheric interaction 3. ground object 4. data recording / transmission 5. ground receiving station 6. data processing 7. expert interpretation / data users 4 7
  • 75. Balloon Remote Sensing, Paris, 1858
  • 76. Pigeon Remote Sensing
  • 77. Actual Pigeon Pictures
  • 78. Wilbur Wright and his first aerial photograph of France.
  • 79. Apollo Spacecraft Mission
  • 80. Satellite Remote Sensing
  • 81. LandSAT Satellite Ox-Bow of the Mississippi
  • 82. Indian Remote Sensing (IRS) Satellite Bihar State Map on AWiFS Data
  • 83. Remote Sensing Sensors
  • 84. True color film Infrared film
  • 85. Landsat Images
  • 86. Quickbird Image
  • 87. ▪ Area is covered by grid with (usually) equal-sized cells ▪ Cells often called pixels (picture elements); raster data often called image data ▪ Attributes are recorded by assigning each cell a single value based on the majority feature (attribute) in the cell, such as land use type. ▪ Easy to do overlays/analyses, just by ‘combining’ corresponding cell values: “yield= rainfall + fertilizer” (why raster is faster, at least for some things) ▪ Simple data structure: – directly store each layer as a single table (basically, each is analagous to a “spreadsheet”) – computer data base management system not required (although many raster GIS systems incorporate them) Representing Data using Raster Model corn wheat fruit clover fruit oats
  • 88. Raster Array Representations ▪ Raster data comprises rows and columns, by one or more characteristics or arrays – elevation, rainfall, & temperature; or multiple spectral channels (bands) for remote sensed data – how organise into a one dimensional data stream for computer storage & processing? ▪ Band Sequential (BSQ) – each characteristic in a separate file – elevation file, temperature file, etc. – good for compression – good if focus on one characteristic – bad if focus on one area ▪ Band Interleaved by Pixel (BIP) – all measurements for a pixel grouped together – good if focus on multiple characteristics of geographical area – bad if want to remove or add a layer ▪ Band Interleaved by Line (BIL) – rows follow each other for each characteristic File 1: Veg A,B,B,B File 2: Soil I,II,III,IV File 3: El. 120,140,150,160 A,I,120, B,II,140 B,III,150 B,IV,160 A,B,I,II,120,140 B,B,III,IV,150,160 Note that we start in lower left. Upper left is alternative. A B B B III IV I II 150 160 120 140 Elevation Soil Veg
  • 89. The generic raster data model is actually implemented in several different computer file formats: ▪ GRID is ESRI’s proprietary format for storing and processing raster data ▪ Standard industry formats for image data such as JPEG, TIFF and MrSid formats can be used to display raster data, but not for analysis (must convert to GRID) ▪ Georeferencing information required to display images with mapped vector data – Requires an accompanying “world” file which provides locational information File Formats for Raster Spatial Data Image Image File World File TIFF image.tif image.tfw Bitmap image.bmp image.bpw BIL image.bil image.blw JPEG image.jpg image.jpw
  • 90. Remote Sensing: Imagery Types TES 1 m Quickbird 61cm IRS 1D 23.5 m IRS 1D 5.8 m High Resolution Imagery Low Resolution Imagery Panchromatic Imagery Multi-spectral Imagery
  • 91. Resolution ▪ The Ability to Discriminate ▪ Types of Resolution – Spatial: Discrimination by Distance – Spectral: Discrimination by Wave length – Radiometric: Discrimination by energy levels – Temporal: Discrimination byTime 5.8m 5.8m Radiometric Resolution 8-bit (0-255) Spectral Resolution 0.4-0.7 μm Day 1 Day 48 Day 96
  • 92. What is an image? ▪ Data that are organized in a grid of columns and rows ▪ Usually represents a geographical area X-axis
  • 93. An image refers to any pictorial representation, regardless of what wavelengths or remote sensing device has been used to detect and record the electromagnetic energy. A photograph refers specifically to images that have been detected as well as recorded on photographic film. Based on these definitions, we can say that all photographs are images, but not all images are photographs. Difference between Image and Photographs
  • 94. Pixels ▪ Resulting images are made of a grid of pixels • Each pixel stores a digital number (DN) measured by the sensor • Represents individual areas scanned by the sensor • The smaller the pixel, the easier it is to see detail
  • 95. Continuous data Two types: • Panchromatic ( 1 Band/layer) • Multispectral ( 2 or more Bands)
  • 96. Viewing continuous images ▪ Each band or layer is viewable as a separate image Thematic Mapper Band 1 Band 4 Band 5
  • 97. Blue Green Red NIR SWIR Part of spectrum Monitor color guns Viewing images ▪ Three bands are viewable simultaneously Band 4 Band 3 Band 2 Band 4 Band 5 Band 3 Band 1 Band 2 Band 3
  • 98. Geomteric Corrections ▪ All remote sensing imagery inherently subject to geometric distortions caused by various factors ▪ Geometric corrections intended to compensate for these distortions ▪ Required so that geometric representation of the imagery is as close as possible to the real world ▪ Geometric registration of the imagery to a known ground coordinate system must be performed
  • 99. ▪ Radiometric Corrections – changing the image data BVs to correct for errors or distortions ▪ atmospheric effects (scattering and absorption) ▪ sensor errors ▪ Geometric Corrections – changing the geometric/spatial properties of the image data – Also called image rectification or rubber sheeting Image Preprocessing
  • 100. Geometric Registration ▪ Image-to-map registration – Involves identifying the image coordinates (i.e. row, column) of several clearly discernible points, called ground control points (or GCPs), in the distorted image (A - A1 to A4), and matching them to their true positions in ground coordinates (e.g. latitude, longitude). – True ground coordinates are measured from a map (B - B1 to B4), either in paper or digital format ▪ Image-to-image registration – Performed by registering one (or more) images to another image, instead of geographic coordinates ▪ Several types of transformations applied on image co- ordinates to transform into real world coordinates: – Plane transformations - keep lines straight, being on the first order – Curvilinear (polynomial) - higher order transformations that do not necessarily keep lines straight and parallel – Triangulation. – Piecewise transformations - Break the map into regions, apply different transformations in each region
  • 101. Geometric Corrections ▪ All remote sensing imagery inherently subject to geometric distortions caused by various factors ▪ Geometric corrections intended to compensate for these distortions ▪ Required so that geometric representation of the imagery is as close as possible to the real world ▪ Geometric registration of the imagery to a known ground coordinate system must be performed
  • 102. ▪ Distortions factors – the perspective of the sensor optics – the motion of the scanning system – the motion of the platform – the platform altitude – attitude, and velocity – the terrain relief and – the curvature and rotation of the Earth ▪ Distortions type – Systematic (predictable in nature) ▪ Accounted through accurate modeling of sensor and platform motion and ▪ Geometric relationship of the platform with the Earth – Unsystematic (random) errors cannot be modeled and corrected Earth Rotation AltitudeVariation PitchVariation SpacecraftVelocity RollVariation YawVariation Non Systematic Distortions Systematic Distortions Image distortions Scanner distortions Actual Velocity Nominal Velocity Mirror Angle time Mirror velocity variations Scan Skew
  • 103. Geometric Correction ▪ Four Basic Steps of Rectification 1. Collect ground control points (GCPs) 2. “Tie” points on the image to GCPs. 3. Transform all image pixel coordinates using mathematical functions that allow “tied” points to stay correctly mapped to GCPs. 4. Resample the pixel values (BVs) from the input image to put values in the newly georeferenced image
  • 104. Geometric Correction ▪ ThreeTypes of Resampling – Nearest Neighbor - assign the new BV from the closest input pixel.This method does not change any values – Bilinear Interpolation - distance- weighted average of the BVs from the 4 closest input pixels – Cubic Convolution - fits a polynomial equation to interpolate a “surface” based on the nearest 16 input pixels; new BV taken from surface 1 2 3 4 1 2 3 4
  • 105. Image Enhancements ▪ Procedures of making a raw image more interpretable for a particular application ▪ Improve the visual impact of the raw remotely sensed data on the human eye ▪ Classification – Contrast (global) enhancement: Transforms raw data using statistics computed over whole data set ▪ Examples - Linear contrast, histogram equalized and piece-wise contrast stretch – Spatial (local) enhancement - Local conditions considered only that vary over image ▪ Examples - Image smoothing and sharpening
  • 106. Image Enhancements ▪ Procedures of making a raw image more interpretable for a particular application ▪ Improve the visual impact of the raw remotely sensed data on the human eye ▪ Contrast (global) enhancement: Transforms raw data using statistics computed over whole data set (Examples - Linear contrast, histogram equalized and piece-wise contrast stretch) ▪ Spatial (local) enhancement - Local conditions considered only that vary over image (Examples - Image smoothing and sharpening)
  • 107. Image Enhancement: Example ▪ Contrast Enhancement - “stretching” all or part of input BVs from the image data to the full 0-255 screen output range
  • 108. Image Fusion LISS III PAN Brovey Multiplicative PCA Wavelet
  • 109. Image Classification ▪ To label the pixels in the image with meaningful information of the real world. ▪ Classification of complex structures from high resolution imagery causes obstacles due to their spectral and spatial heterogeneity ▪ Two types – Unsupervised classification – Supervised classification 117
  • 110. Supervised vs. Unsupervised Approaches – Unsupervised: statistical "clustering" algorithms used to select spectral classes inherent to the data, more computer-automated Posterior Decision – Supervised: image analyst "supervises" the selection of spectral classes that represent patterns or land cover features that the analyst can recognize Prior Decision
  • 111. Edit/evaluat e signatures SelectTraining fields Classify image Evaluate classification Identify classes Run clustering algorithm Evaluate classification Edit/evaluate signatures Supervised Unsupervisedvs
  • 112. Image Enhancements ▪ Procedures of making a raw image more interpretable for a particular application ▪ Improve the visual impact of the raw remotely sensed data on the human eye ▪ Classification – Contrast (global) enhancement: Transforms raw data using statistics computed over whole data set ▪ Examples - Linear contrast, histogram equalized and piece-wise contrast stretch – Spatial (local) enhancement - Local conditions considered only that vary over image ▪ Examples - Image smoothing and sharpening
  • 113. Band Combinations ▪ Features can become more obvious Vegetation Urban 2,3,1 (RGB) 4,3,2 (RGB)4,5,3 (RGB)
  • 114. Keys to Image Interpretation ▪ Shape ▪ Size ▪ Shadows ▪ Tone ▪ Color ▪ Texture ▪ Pattern
  • 115. Interpretation Principles • Shape • Size • Shadow • Tone/Color • Texture • Pattern • Relationship to Surrounding Objects
  • 116. ▪ The photo on the right is a black and white photo of the City of Ithaca and the Cornell University campus taken in 1991. More specifically, it was taken on April 4, 1991 (look in the upper left hand corner). ▪ So lets take a quick tour of the photograph Image Interpretation
  • 117. ▪ Size: the size of an object is one of the most useful clues to its identity. Also, understanding the size of one object may help us understand the sizes of other objects. ▪ For example, most of us have a feeling for the size of a baseball field, and football field. When we observe these objects on a photograph, it will help us to understand the sizes of other objects on the photograph. ▪ For example, on another part of the photograph we have a trailer park. This could easily be confused with a parking lot, but when we understand the size of the objects we will realize that the objects in the trailer park are much too large to be cars. Image Interpretation
  • 118. ▪ Shape: Shapes can often give away an object’s identity. For example, a cloverleaf is a very distinctive feature of a highway, while a stream’s meandering gives away its identity. ▪ And again, the baseball diamond we just looked at also has a distinctive shape. Image Interpretation
  • 119. ▪ Shadow: shadows often give us an indication of the size and shape of an object. When we look at aerial photographs we often see a vantage point we are not used to: an overhead view. ▪ Shadows can let us “cheat” alittle to see the side of an object. The photos on the right show the CornellTheory Center, which casts a rather large shadow, indicating the building size, and a water tower on one of the farms on campus. If you look closely, you can see the “legs” of the watertower. Image Interpretation
  • 120. ▪ Shadow: while shadows are helpful, they can also be a hindrance. As we try to look down into the gorge on the Cornell campus, we can see very little due to the shadows cast. Image Interpretation
  • 121. ▪ Tone:You can see the tonal contrast between Cayuga Lake and the land area. Also, there is good tone representation for wet or dry soils. Image Interpretation
  • 122. ▪ Texture: In this photo we see the Cornell Plantations and Botanical Garden, as well as the experimental agricultural plots. Especially in the Plantations, you will see the different textural characteristics between the mowed lawns and the grassy areas. Notice too, the small pond in the Plantations (an example of tone) ▪ Additionally, around another natural area on campus you can see the textural difference of trees vs. more of a grassland area. ▪ And again, as you look at the agricultural plots you will notice a different texture from the forested areas. ▪ Finally, in the golf course shown below there are obvious patterns between managed lawns vs. the unmanaged lawns, in addition to the tonal differences between the lawns and sand traps. Image Interpretation
  • 123. ▪ Pattern:There are so many examples related to pattern. These would include the rectilinear pattern of the older, urban neighborhoods in Ithaca, the straight lines of trees in an orchard, the rectilinear shape of the experimental agricultural plots, and the configuration of a parking lot. ▪ Also, the pattern of the golf course with greens, tees, traps, and fairways is very easy to spot. Image Interpretation
  • 124. ▪ Pattern: the drainage pattern for a particular property on this photo is easy to see. Also, because the drainage is relatively straight, we can assume that a moderate to steep slope exists, as water did not have much opportunity to meander. Image Interpretation
  • 125. ▪ Relationship: observing relationships on photographs is one of the most fun observations. For example, a school and a plaza are interpreted differently due to relationships: – While both have many large structures on them, schools typically have playing fields – Also, plazas usually have larger parking areas ▪ Here we see the East Hill Shopping Plaza (no athletic fields, but a campus of buildings), and the Ithaca High School campus (with athletic fields) Image Interpretation
  • 126. ▪ Relationship: here is another example of relationship that shows a middle school and an elementary school. Notice that it have buildings like the high school, and a parking lot, but no real athletic fields to speak of. What it does have, however, is what appears to be a playground, and is surrounded by a residential community. ▪ The structures on the top are an apartment complex. They could be tractor trailers, but “size” gives them away.They are too large to be tractor trailers when you consider the size of the schools below. ▪ Notice that just north of the apartment complex is a large pool. How do we know it’s a pool, well, the tone gives us a clue… Image Interpretation Apartments School School
  • 127. ▪ interpretation is putting together our observations bit by bit to form a coherent understanding of the image. For instance, identifying the water treatment plant forces us to use shape, pattern, tone, and relationship to make the connection: – We see the water holding areas in black (tone) – We see the large tanks (shape) – And when you’ve seen one treatment plant, you’ve seen them all (pattern)!! ▪ Notice that across the water is a park. Why do we know it’s a park? Well, again, we see multiple ball fields, not enough buildings to be a school, and a very large pool. Image Interpretation
  • 128. Cartography The foremost cartographers of the land have prepared this for you; it’s a map of the area that you’ll be traversing
  • 129. Spatial is Special “Everything is related to everything else, but near things are more related than distant things” Tobler, W. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46, 234–40. Sometimes called the First Law of Geography (because it is generally true!).
  • 130. Cartography – amalgam of art, science and technology ▪ The art, science and technology of making maps ▪ Help to produce and analyze maps to enable communication of ideas ▪ Basic principles of map making – Has a clear motive or goal – Is directed toward an audience – Uses appropriate design elements to clearly convey its message
  • 131. Cartography dynamics – An example ▪ Phone call Cartography
  • 132. Cartography – a communication bridge The geographic environment Compile Recognize Select Classify Simplify Symbolize Read Analyze Interpret Imagine Map user MapMap CartographerReality Reality?
  • 133. Cartographic elements ▪ Maps show how different features are related ▪ All maps, however, share the same common elements – Title – Legend – Scale – Directional Indicator – Inset Maps – Projection System Beauty is NOT the MAIN objective
  • 134. The Elements of a Map: Title ▪ Most important element of the map for acquiring information efficiently is the title ▪ Identifies the map area and the type of map ▪ Cartographers may list the title simply or artistically ▪ Typically appear at the top of the map, but not always Map of Indiana Showing Its History, Points of Interest,1967, GRMC, Ball State University Libraries
  • 135. The Elements of a Map: Legend ▪ Another important feature on a map is the legend or map key ▪ Contains information needed to read a map ▪ Most maps use symbols or colors to represent different geographic features, the meaning of which is determined by legend This legend simply identifies the roads ofTexas, but the cartographer chose to be creative in the design of the legend to add character to the map. (Texas Guide Map, 194-, GRMC, Ball State University Libraries).
  • 136. The Elements of a Map: Scale ▪ Almost all maps have scales ▪ Scales compare a distance measured on the map to the actual distance on the surface of the earth. ▪ Scales appear on maps in several forms, but most cartographers draw a line scale as a point of reference This scale from a historical map of Kentucky features an image of GeorgeWashington surveying the land. (Kentucky:The Dark and Bloody Ground, An Historical and Geographical Map of the State of Kentucky, 1933, GRMC, Ball State University Libraries).
  • 137. The Elements of a Map: Directional Indicator ▪ A directional indicator on a map helps determine the orientation of the map. ▪ Some cartographers place an arrow that points to the North Pole on the map.This is a “north arrow.” ▪ Other maps indicate direction by using a “compass rose,” with arrows pointing to all four cardinal directions Ancient cartographers drew elaborate, artistic directional indicators—most commonly a compass rose. (Floridae Americae Provinciae: Recens & Exactissima Descriptio, 1564, engraved reproduction, GRMC, Ball State University Libraries).
  • 138. The Elements of a Map: Inset Map ▪ Some maps feature inset maps - smaller maps on the same sheet of paper. ▪ Provide additional information not shown on the larger map ▪ Drawn at a larger, more readable scale ▪ Usually feature areas of interest related to the larger map The larger map above is a modern map of the NileValley.The inset map shows the ancient Egyptian Empire in the same area.(National GeographicThe NileValley: Land of the Pharaohs, 1963, GRMC, Ball State University Libraries).
  • 139. ▪ Earth is round and maps are flat, getting information from a curved surface to a flat one involves a mathematical formula called a map projection , or simply a projection ▪ This process of flattening the earth will cause distortions in one or more of the following spatial properties and No projection can preserve all these properties: – Distance – Area – Shape – Direction ▪ Type of projections based on different types of distortion – EQUIVALENCY Correct representation of area – CONFORMALITY Correct representation of shapes – EQUIDISTANCY Correct representation of distance – AZIMUTHALCY Correct representation of direction ▪ Type of projections according to the projection surface – CONIC – CYLINDRICAL – PLANAR In simple terms where the ‘paper touches the Earth’ there are no distortions. But the further the ‘paper’ is away from the surface of the Earth the greater distortions. The mathematics in different projections attempt to overcome this problem – but none remove all distortions. The Elements of a Map: Map Projections
  • 140. Earth Models and Datums Geoid Ellipsoid Sphere Sea Level Height Figure 2.4 Elevations defined with reference to a sphere, ellipsoid, geoid, or local sea level will all be different. Even location as latitude and longitude will vary somewhat. When linking field data such as GPS with a GIS, the user must know what base to use. Terrain
  • 141. The Datum ▪ An ellipsoid gives the base elevation for mapping, called a datum. ▪ Examples are NAD27 and NAD83 Everest ▪ The geoid is a figure that adjusts the best ellipsoid and the variation of gravity locally. ▪ It is the most accurate, and is used more in geodesy than GIS and cartography.
  • 142. Geographic Coordinates Parallels Equator PrimeMeridian PrimeMeridian Meridians
  • 143. Geographic Coordinates as Data
  • 144. Standard parallels
  • 145. Equivalency ▪ Also known as Equal area Projection, that preserves the area of displayed features ▪ Shape, distance and angles are distorted ▪ The meridians and parallels may not intersect at right angles but they are marked in such a way that the area represented in each quadrangle is same with the adjacent one ▪ Shapes of the features are generally distorted for a larger area ▪ in smaller areas it is difficult to visualize unless it is measured Example: Albers Equal-Area Conic projection
  • 146. Conformality ▪ Also known as Conformal Projection, which preserves the local shape ▪ To preserve the individual angles describing the spatial relationship this projection must show the perpendicular graticules intersecting at right angles ▪ Issue - Shapes of the larger areas cannot be preserved ▪ Used for smaller regions i.e. for large- scale maps ▪ Useful for navigational charts and weather maps Example: Mercator projections
  • 147. Equidistancy ▪ Also known as Equidistant Projection, which preserves the distance between two points ▪ Scale is not maintained correctly throughout an entire map ▪ There are one or two lines on a map along which the scale is maintained correctly irrespective of the fact that whether they are great or small circles and straight or curved Example:
  • 148. Azimuthalcy ▪ Also known as True direction Projection ▪ Preserves or maintains some of the great circle arcs, giving the directions or azimuths of all points on the map correctly with respect to the center Example: Azimuthal Equidistant projections
  • 149. Conic Projections ▪ Based on the concept of the ‘piece of paper’ being rolled into a cone shape and touching the Earth on a circular line – Tip of the cone is positioned over a Pole – Line of latitude where the cone touches the Earth is called a Standard Parallel ▪ Used for regional/national maps of mid- latitude areas – such as Australia and the United States of America. ▪ Characteristics – Fan shaped when used to map large areas – Have distortions increasing away from the central circular line (the ’touch point of the paper‘) – Have very small distortions along the central circular line (the ’touch point of the paper‘) – Shapes are shown correctly, but size is distorted – Usually have lines of longitude fanning out from each other and have lines of latitude as equally spaced open concentric circlesThis is a typical example of a world map based on the Conic Projection technique. This map is centred on central Australia and the Standard Parallel is 25° South. Note how the shapes of land masses near the Standard Parallel are fairly close to the true shape when viewed from space – see the images at the beginning of this section. This includes Australia, South America and the ’tip‘ of Africa. Also note how land masses furthest away from the Standard Parallel are very distorted when compared to the views from space. Particularly note how massively large northern Canada and the Arctic icecaps look.
  • 150. Cylindrical Projections ▪ Based on the concept of the ‘piece of paper’ being rolled into a cylinder and touching the Earth on a circular line – Cylinder is usually positioned over the Equator, but this is not essential ▪ Usually used for world maps or regional/national maps of Equatorial areas – such as Papua New Guinea ▪ Characteristics – Rectangular or oval shaped – but this projection technique is very variable in its shape – Have lines of longitude and latitude at right-angles to each other – Have distortions increasing away from the central circular line (the ‘touch point of the paper’) – Have very small distortions along the central circular line (the ‘touch point of the paper’) – Show shapes correctly, but size is distorted.. his is an example of a cylindrical map projection and it is one of the most famous projections ever developed. It was created by a Flemish cartographer and geographer – Geradus Mercator in 1569. It is famous because it was used for centuries for marine navigation. The sole reason for this is that any line drawn on the map was a true direction. However, shapes and distances were distorted. Notice the huge distortions in the Arctic and Antarctic regions, but the reasonable representation of landmasses out to about 50° north and south.
  • 151. Planar Projections ▪ Based on the ‘flat piece of paper’ touching the Earth at a point. The point is usually a Pole, but this is not essential – Cylinder is usually positioned over the Equator, but this is not essential ▪ Also known as Azimuthal or Zenithal Projection ▪ Characteristics – Have distortions increasing away from the central point – Have very small distortions near the centre point (the ’touch point of the paper‘) – Compass direction is only correct from the centre point to another feature – not between other features – Not usually used near the Equator, because other projections better represent the features in this area When the centre of the map is the North or South Pole maps produced using Azimuthal Projections techniques have lines of longitude fanning out from the centre and lines of latitude as concentric circles. These projections are often called polar projections.
  • 152. What are my cartographic objectives? Why?Map objectives Convey information Illustrate analysis results Highlight spatial relationships Easier comprehension of complex events How? Design objectives Fulfill map objectives Assign meaningful symbology Ensure truthful depiction of reality Fulfill communication objectives
  • 153. What are my communication objectives? PopulationSoils Focused information Importance can vary Symbols can dominate Variety of information Equal importance Subtle symbology General map Thematic map Qualitative Quantitative
  • 154. ▪ More than 3000 ▪ No perfect projection ▪ All have distortions Cylindrical Conical Azimuthal Direction Distance Shape Area What projection should I chose?
  • 155. Displace? Aggregate?Omit? Simplify? Collapse? Typify? Exaggerate?Classify?Refine? C E B C2 C3 E1 B1 E5C5 B4 How much detail should I include?
  • 156. Color Shape Texture/Pattern Gray tone value Size Qualitative Quantitative What symbols should I use?
  • 157. What colors should I use? ▪ Connotations ▪ Conventions ▪ Preferences ▪ For screen or paper R G B Y 100 70 40 10% 20 30 50 85% Hue Value Saturation Dimensions
  • 158. 12 colors Maximum 7- 8 shades What are the eye limitations?
  • 159. What about the color impaired? ▪ Avoid pure green / red ▪ Vary shapes, textures ▪ Use brightness contrast (not more colors) Normal eye Red defective Green defective Blue defective
  • 160. How do I represent names on my map? ▪ Legibility issues – Text color vs background color – Uppercase vs lowercase – No fancy fonts River RIVER River Form Tigris Orlando Color Baghdad Basra Style Qualitative San Diego Redlands Size Redlands San Diego Value Tigris Orlando Color River RIVER River Form Quantitative
  • 161. • Readability issues Makramville Jackville1 2 How do I label names? How do I place names at POINTS? • Ambiguity issues How do I place names at LINES? • Faster reading issues How do I label contours?
  • 162. 0 3 ft Are these dog houses? Must be visually easy to read Correct? Easy to use? ? What about my scale?
  • 163. Objective Map form Quantity of information Easy / Complex Quality Authenticity Symbol size, color Reality Audience Conditions of use Technical limits Scale Generalization What factors control my design?
  • 164. ALWAYS THINK of the? ? ? USER
  • 165. Global Positioning System “Geographers never get lost.They just do accidental field work.”
  • 166. What is Global Positioning System ? 24+ satellites revolving around earth provide 24-hour, real time, all- weather, global coverage Satellites are equipped with atomic clocks Precise time signals are broadcast on L-band radio frequencies Four satellite signals enable receivers to triangulate position
  • 167. Satellites broadcast •Precise time •Orbit data •Satellite health Receiver measures time delay from satellites, and by triangulation calculates • Location • Elevation •Velocity How GPS System Works?
  • 168. GPS applications Car Navigation ▪ On-board navigation ▪ Fleet management ▪ Roadside assistance ▪ Stolen vehicle recovery Consumer/Recreational ▪ Portable GPS receivers for fishermen, hunters, hikers, cyclists, etc. ▪ Recreational facilities -- golf courses, ski resorts ▪ Integration of GPS into cellular phones ▪ E-911 requirement
  • 169. GPS applications Surveying/Mapping/GIS ▪ Rural electrification ▪ Telecom tower placement ▪ Pipelines ▪ Oil, gas, and mineral exploration ▪ Flood Plain Mapping ▪ Sub-centimeter accuracy ▪ 100%-300% savings in time, cost, & labor Tracking/Machine Control ▪ Package/cargo delivery ▪ Fleet and asset management ▪ Theft recovery ▪ Public safety and services ▪ Farming, mining, and construction equipment ▪ DGPS required for many applications
  • 170. 3-D GIS The application of GIS is limited only by the imagination of those who use it
  • 171. Journey Expectations • 3D Data representation and visualization – Real world objects (buildings, towers, etc.) and rendering on Google Earth – Digital Elevation Model (DEM) – Walk through / fly through
  • 172. 3D GIS ▪ The earth is not flat. In the real world, surfaces with vertical dimensions exist • True 3D – Store data in structures that actually reference locations in 3D space (x,y,z) – Here z is not an attribute but an element of the location of the point – If z is missing, object does not exist! ▪ 3D enable interactive perspective viewing and navigation, including pan, zoom, rotate, tilt, fly-through simulations, and export utilities for display on the Web ▪ 3D GIS assist – Construction of surface models such asTINs and raster from any data – Extrude buildings and vector features from a surface – Aerial photography can be draped onto a 3D model to project more realistic look – Supply analytical functions to calculate slope, aspect and hill shading The Eagle map of the United States engraved for rudiments of National Knowledge. 1833.
  • 173. 3D Prevue: “Coastal Terrain Model” … a surface that integrates topography and bathymetry + = Integrated Topo-Bathy Model BathymetryTopography
  • 174. Why do we need 3D GIS? ▪ Simulation of complex systems provide understanding on how the system operates different perspectives aided by high quality visualization and interaction ▪ Observation of system features that would be too small or too large to be seen on a normal scale system ▪ Access to situations that would otherwise be dangerous or too remote or inaccessible ▪ Enable high degree of interaction ▪ Provide a sense of immersion of the environment – Where the user can appreciate the scale of change and visualise the impact of building design on the external environment and the inhabitants
  • 175. Why do we need 3D GIS? ▪ To offer consensual reading, with which people will have an immediate perception of relief, orientation, slope and visibility ▪ Allow to export to popular multimedia formats such as video (.avi or .mpeg) or VRML (.vrl or .vrml) that provide the following benefits – No prior knowledge of 3D GIS required – Only require intuitive and easy to use interface to operate the 3D model – Inherent flexibility/adaptability – these multimedia are 3D cross-platform display and non-browser specific which enable expensive data to be used more widely – Fast and slow time simulation – Ability to control timescale by incorporating a sequence of captured events into the key frames of the motion videos
  • 176. Surface defined... ▪ Surfaces involve a third 'z' dimension (height/elevation/magnitude, quantity) in addition to x,y planimetric location ▪ Any type of continuous data can be represented as a surface, whether it be ground elevation, barometric pressure, rainfall, crop yield, noise levels, population density, sales intensity, land value, income, crime rates, etc. ▪ 3 basic methods for representing a surface – DEM (digital elevation model) – TIN (Triangulated Irregular Network) – Contour lines
  • 177. Digital Elevation Model (DEM) ▪ Set of regularly spaced sampled ground points in the x and y dimensions (although spacing not necessarily the same in each) accompanied by an elevation measure (z dimension) ▪ Terminology introduced by USGS ▪ General term digital terrain model (DTM) may be used to refer to any of the above surface representations when in digital form ▪ Two concepts used for determining elevation at points within the grid cells: – Lattice: each mesh point represents a value on the surface only at the center of the grid cell.The z-value is approximated by interpolation between adjacent sample points; it does not imply an area of constant value – Surface grid considers each sample as a square/rectangular cell with a constant surface value
  • 178. Triangulated Irregualr Network (TIN) ▪ A set of adjacent, non-overlapping triangles computed from irregularly spaced points, with x, y horizontal coordinates and z vertical elevations ▪ Terminology introduced by USGS ▪ General term digital terrain model (DTM) may be used to refer to any of the above surface representations when in digital form ▪ Two concepts used for determining elevation at points within the grid cells: – Lattice: each mesh point represents a value on the surface only at the center of the grid cell.The z-value is approximated by interpolation between adjacent sample points; it does not imply an area of constant value – Surface grid considers each sample as a square/rectangular cell with a constant surface value
  • 179. Triangulated Irregular Network (TIN) Irregular set of points Irregular set of points connected through triangulation Triangulation to create surface • TIN – A set of adjacent, non-overlapping triangles computed from irregularly spaced points, with x, y horizontal coordinates and z vertical elevations. • Why triangulation? – Can capture significant slope features (ridges, etc) – Efficient since require few triangles in flat areas – Easy for certain analyses: slope, aspect, volume – Avoids “weak” geometry and “thin” triangulation – Creates a unique network (barring a minor exception)
  • 180. Contour (isolines) Lines ▪ Contour lines, or isolines, of constant elevation at a specified interval, ▪ Advantages – Familiar to many people – Easy to obtain mental picture of surface • Close lines = steep slope • UphillV = stream • DownhillV or bulge = ridge • Circle = hill top or basin ▪ Disadvantages – Poor for computer representation: no formal digital model – Must convert to raster orTIN for analysis – Contour generation from point data requires sophisticated interpolation routines, often with specialized software such as Surfer from Golden Software, Inc., or ArcView Spatial Analyst extension
  • 181. Spatial Interpolation ▪ Critical component for raster surface creation ▪ Used to create surface which contain equally spaced cells from irregularly spaced point data ▪ Five basic methods of interpolation – Inverse Distance Weighting – Spline – Trend – Natural Neighbor – Kriging
  • 182. Spatial Interpolation ▪ Critical component for raster surface creation ▪ Used to create surface which contain equally spaced cells from irregularly spaced point data ▪ Five basic methods of interpolation – Inverse Distance Weighting – Spline – Trend – Natural Neighbor – Kriging
  • 183. Inverse Distance Weighting ▪ Inverse DistanceWeighting (IDW): assumes that each input point has a local influence that diminishes with distance, which is achieved by weighting the points closer to the processing cell greater than those farther away. ▪ Output values for each cell may be based on – Nearest neighbor option which weights a specified number of points (the “k" nearest neighbors). If k=n then all points are included. – or Radius option which uses all points within a specified radius. ▪ A Power Parameter controls the significance of the surrounding points upon the interpolated value: the higher the value the less influence from distant points. – In essence, this is the rate of attenuation with distance; a higher value means a steeper slope for attenuation ▪ A barrier input line theme can be specified to represent a cliff, ridge, or some other interruption in a landscape; points beyond the barrier are excluded from the weighting. A choice of No Barriers will use all points specified in the No. of Neighbors or within the identified radius. ▪ Use IDW, for example, to interpolate a surface of consumer purchasing. More distant locations have less influence, because people are more likely to shop closer to home; a barrier could be railroad lines assuming nobody "crosses the tracks" to shop. ▪ An IDW surface passes through the measured points (referred to as an “exact interpolation”), and interpolated surface is never above or below the highest/lowest measured points
  • 184. Spline ▪ Spline is a general purpose interpolation method that fits a minimum-curvature surface exactly through the input points. ▪ Conceptually, it is like bending a sheet of rubber to pass through the points, while minimizing the total curvature of the surface. ▪ The Regularized option yields a smoother surface ▪ the weight parameter defines the “weight of the third derivatives of the surface in the curvature minimization expression.”The higher its value the smoother the surface -The Tension option tunes the stiffness of the surface according to the character of the modeled phenomenon. ▪ the weight parameter defines the “weight of the tension”.The higher its value the coarser the surface ▪ The number of points parameter identifies the number of points per region used for local approximation. Higher values produce smoother surfaces. ▪ The spline method is best for gently varying surfaces such as elevation, water table heights, or pollution concentrations. It is not appropriate if there are large changes in the surface within a short horizontal distance, because it can overshoot estimated values. ▪ Like IDW it is an “exact interpolation” in that the surface passes through the measured points, but unlike IDW the surface can extend above or below measured points
  • 185. Interpolation with cubic "natural" splines between three points Note how smooth the curves of the terrain are; this is because Spline is fitting a simply polynomial equation through the points
  • 186. Trend ▪ Trend: Uses a polynomial regression to fit a least-squares surface to the input points.The surface is chosen so that the sum of the squared differences (vertical distances on the z axis) between the original points and their estimate on the surface is minimized for the entire surface. ▪ The resulting surface seldom passes through the original points since it performs a best fit for the entire surface, thus it is an “inexact interpolation.” The lower the RMS error, the more closely the interpolated surface represents the input points. ▪ Order of the polynomial used to fit the surface is specified by user.The most common order of polynomials is 1 through 3. – A first-order linear trend surface interpolation simply performs a least-squares fit of a plane to the set of input points. – When an order higher than 1 is used, the interpolator may generate a Grid whose minimum and maximum might exceed the minimum and maximum of the input points. – As the order of the polynomial is increased, the surface being fitted becomes progressively more complex. A higher order polynomial will not always generate the most accurate surface, it is dependent upon the data. ▪ A logistic option is available for generating a trend surface for prediction of presence/absence of certain phenomena (in the form of probability) for a given set of locations (x,y) in space.The z value is a categorized random variable with only two possible outcomes, for instance, the existence of an endangered species or the lack of existence of that species. These two values of z can be coded as 1 and 0, respectively. It creates a continuous probability Grid with cell values between 1 and 0. A maximum likelihood estimation is used to calculate the logistic probability surface model. Trend surface interpolation creates smooth surfaces. (see Interp MakeTrend Discussion in AV Help)
  • 187. Natural Neighbor ▪ Natural neighbor: similar to IDW in that it applies weights to a set of neighbor points, so it is a local rather than global interpolation – However, point selection is based on delauney triangles, and weighting is based on area of thiessen polygons ▪ “robust” and “conservative, artifice free” – interpolated heights will be within range of sample heights (none above or below) and it will not produce peaks, ridges, etc. that are not in the sample data ▪ Exact in that surface passes through all input points ▪ Requires no user input such as search radius, sample count, etc.. ▪ Can handle large numbers of points that may crash other methods ▪ Works with regularly and irregularly distributed points ▪ Also known as Sibson interpolation
  • 188. Natural neighbor interpolation.The colored circles. which represent the interpolating weights, wi, are generated using the ratio of the shaded area to that of the cell area of the surrounding points.The shaded area is due to the insertion of the point to be interpolated into the Voronoi tessellation
  • 189. Kriging ▪ Kriging is an advanced procedure that assumes the distance or direction between sample points shows spatial correlation that describes the surface. ▪ Kriging assumes that the same pattern of variation can be observed at all locations on the surface. ▪ This pattern is estimated by constructing Variograms based on sets of n points or all points within a specified radius. ▪ The best estimation method for generating the output surface is then based on an interactive examination of theVariogram. ▪ This approach is most useful when you already know about spatially correlated distance or direction bias within the data. It is most commonly used in geology and soil science.
  • 190. Example of one-dimensional data interpolation by kriging, with confidence intervals. Squares indicate the location of the data.The kriging interpolation is in red.The confidence intervals are in green.
  • 191. Data sampling ▪ Method of sampling is critical for subsequent interpolation... Regular Random Transect Stratified random Cluster Contour
  • 192. Systematic sampling pattern Easy Samples spaced uniformly at fixed X,Y intervals Parallel lines Advantages Easy to understand Disadvantages All receive same attention Difficult to stay on lines May be biases Systematic sampling
  • 193. Random Sampling Select point based on random number process Plot on map Visit sample Advantages Less biased (unlikely to match pattern in landscape) Disadvantages Does nothing to distribute samples in areas of high Difficult to explain, location of points may be a problem Random Sampling
  • 194. Cluster Sampling Cluster centers are established (random or systematic) Samples arranged around each center Plot on map Visit sample (e.g. US Forest Service, Forest Inventory Analysis (FIA) Clusters located at random then systematic pattern of samples at that location) Advantages Reduced travel time Cluster Sampling
  • 195. Adaptive sampling Higher density sampling where feature of interest is more variable. Requires some method of estimating feature variation Often repeat visits (e.g. two stage sampling) Advantages Efficient in large homogeneous areas with higher spatial variation. Disadvantages If no method of identifying where features are most variable then several you need to make several sampling visits. Adaptive Sampling
  • 196. 3D GIS in action: DMRC GPS Control: 5 cm accuracy Digital Elevation Model: 50 cm accuracy triangulation
  • 197. 3D GIS in action: DMRC 3D Topology Texturing 3D Visualization 3D Rendering