Monday, December 4, 2017

An overview of GPS

Global Positioning System (GPS)

  • The official name for GPS is NAVigation Satellite Timing And Ranging Global Positioning System or NAVSTAR GPS
  • It was developed in 1973 to overcome the limitations of previous navigation Systems. It was created by the United States department of Defence (USDOD) and was originally run with 24 satellites. It became fully operational in 1995. Brad Parkinson, Roger L. Easton and Ivan A Getting invented it. It is maintained by the United States Government and is freely accessible by anyone with a GPS receiver.
  • It consists of more than 30 satellites in medium Earth Orbit (2000 - 35000 km). Two dozen satellites working in harmony are known as a satellite constellation. It is basically used for 


  1. -Navigation
  2. -Map making and
  3. -Surveying


  • It consists of the following three segments:


  1. -Space segment
  2. -Control segment
  3. -User segment

Space segment consists of:

  1. -GPS satellites that fly in circular orbits at an altitude of 20,200 km and with a period of 12 hours
  2. -They are powered by solar cells
  3. -The satellites continuously orient themselves to point their solar panels toward the sun and their antenna toward the Earth
  4. -The orbital planes of the satellites are centered toward the Earth
  5. -Orbits are designed so that, atleast, six satellites are always within line of sight from any location on the plane.

Control segment consists of three units:

  1. -Master Control System
  2. -Moitoring stations and
  3. -Ground Antennas


  • -The master control station is located in Falcon Air Base in colorado springs
  • -It is responsible for overall management of the remote monitoring and transmission sites
  • -Here, a check-up is performed twice a day by each of the six stations as the satellites complete their journey around the Earth.
  • -It can reposition satellites to maintain optimal GPS constellation

Monitor stations:

  • -Check the exact altitude, position, speed and overall health of orbiting saellites.
  • -The control segment ensures that the GPS satellite orbits and clocks remain within acceptable limits
  • -A station can monitor upto 11 satellites at a time
  • -This "check-up" is performed twice a day, by each station

Monitor stations are located at:

  1. -Falco air base in colorado
  2. -Cape canaveral
  3. -Florida
  4. -Hawaii
  5. -Ascension island in Atlantic ocean
  6. -Diego Garcia Atoll in the Indian Ocean and
  7. -Kwajalein Island in the south Pacific ocean

Ground antennas:

  1. -Ground antennas are used to monitor and track the satellites from horizon to horizon 
  2. -They also transmit correct information to individual satellites
  3. -They also communicate with GPS satellites for command and control

User segment consists of the GPS receiverwhich in-turn consists of:

  1. -An antenna tuned to the frequencies transmitted by the satellite
  2. -Receiver processors and
  3. -A crystal oscillator as a highly stable clock


  • -The GPS receiver may also include a display for showing location and speed information to the user
  • -A receiver is often described by the number of channels signigying the number of satellites that it can monitor simultaneously
  • -Receivers usually have anythin between twelve to twenty channels

WORKING PRINCIPLE

  1. -GPS works on the principle of determination of any location if its distance from any two already known locations is available
  2. -GPS satellites orbit the Earth at an altitude of 11000 miles.
  3. -The orbits and locations of satellites are known in advance
  4. -GPS receivers store the orbit information or all satellites in an ALMANAC which is a file containing positional information for ALL the GPS satellites
  5. -A GPS receiver can tell its position by using its position data and comparing it with three or more GPS satellites
  6. -The distance of each satellite is measured by the time taken by the radio signal to travel from the satellite to the receiver
  7. -All electromagnetic radiation (Ex: Radio waves) travel at the speed of light
  8. -The distance from satellite to receiver is computed using the satndard formula and the receiver's position is determined using trilateration.

-The position calculated by a GPS receiver depends on three accurate measurements:

  1. -Current time
  2. -Position of the satellite and
  3. -Time delay for signal

Worst-case accuracy of a GPS signal is 7.8m at 95% confidence level

  • Sources of GPS signal errors are due to:


  1. -Satellite clock
  2. -Receiver clock
  3. -GPS jamming
  4. -Atmospheric errors
  5. -Multi-path error
  6. Accuracy can be improved using
  7. -Precision monitoring and
  8. -Augmentation
  9. Limitations


  • -GPS can provide worldwide, 3 dimensional positions, 24 hours a day in any kind of weather. However, ther must be a clear line-of-sight between the GPS antenna and four or more satellites
  • -The above condition may be a major problem in urban areas
  • -GPS signal may bounce-off nearby objects causing a problem called "multi-path interference"


APPLICATIONS

  1. -SURVEYING:
  2. -TELEMATICS:
  3. -Vehicle tracking
  4. -Military applications
  5. --GPS integrated into fighters, tanks, helicopters, ships, submarines, tanks, jeeps and soldier's equipment
  6. --Target tracking
  7. --Search and rescue operations


An overview of the concepts involved in spatial sciences

An overview of concepts in GIS and Remote Sensing
GIS -
GIS is an integrated collection of computer software and data used to view and manage information about geographic places, analyse spatial relationships and model spatial processes

Model-
A model is a simplification to describe, predict or analyse reality. It is usually done to answer a question or solve a problem.
Issues with models are that they represent a real world problem with several assumptions and simplifications involving compromise, sub-division,  reclassification, generalization and imposition of temporal limits resulting in applying subjective constraints.

GIS was developed in 1963 by Dr. Roger Tomlison who is regarded as the "Father of GIS".


  • Location data is information that describes the location and properties (attributes) of features. It may be stored as raster or vector data.
  • A map project references data files but does not contain them. Ex: ArcMap, QGIS
  • Map document has an extension .mxd in ArcMap
  • Vector data model defines objects with definite boundaries. Vector geometries are represented using (x,y,z) coordinate pairs: Point, (poly)line, Polygon
  • Vector data uses geographic coordinates and attribute information to locate and determine features.
  • Attribute types are: Nominal, Ordinal, Interval & Ratio.


  1. Nominal : Refers to QUALITY of a feature, NOT QUANTITY
  2. Ordinal : Refers to rank
  3. Interval: Refers to quality measurement that is linear (Ex: Temperature)
  4. Ratio : Quantity measurement that is linear, but has a fixed zero point.

Raster data model: It is a mixture of cells (PIXEL-Picture Element) organized into rows and colmns where each cell contains a value representing information. Size increases exponentially with increasing cell size.
Uses of raster:

  1. Base map - As a background for vector layers
  2. Surface map - Representing changing data in a landscape
  3. Thematic map - Grouping values into classes or categories

Methods to capture data: 

  1. Primary (mesuring data directly using instruments like GPS or techniques like surveying and remote sensing)
  2. Secondary (Digitizing maps from physical maps, photographs, using photogrammetry)


  • Remote sensing refers to images recorded from sensors without direct contact like UAVs (Unmanned Aerial Vehicles), Planes, Satellites. It is useful and economical for large areas.
  • Photogrammetry is the technology used to make measurements in the real world using photographs

The following are used to consider data quality assessment

  1. -Resolution
  2. -Scale
  3. -Age
  4. -Author
  5. -Source
  6. -Position & attribute
  7. -Accuracy
  8. -Completeness
  9. -Metadata


  • WFS- Web map service: It is a web mapping data format that represents map images (*.png, *.gif, *.jpg). It shares data online. It is the open geospatial consortium standard protocol for requesting georeferenced map images from a spatial database.
  • ESRI - Environmental Systems Research Institute is a private organization that created the popular GIS software ArcGIS for desktop and online
  • ArcMap - It is a software application created by ESRI to display, explore and edit GIS datasets
  • ArcCatalog- It is a software application created by ESRI to organise and manage geographic information for ArcGIS for desktop.
  • Vector file formats are those that can be stored as geodatabase classes in geodatabase and shapefile, CAD; shp, shx, dbf, prj, xml.
  • File geodatabase- Geodatabase is a collection of geographic datasets that is easily managed and scalable depending on the intended use. - IT IS EASIER TO STORE. It is a collection of files in a folder that can store, query and manage spatial and non-spatial data. It is composed of:


  1. -Feature classes
  2. -Feature dataset
  3. -Raster dataset
  4. -Non-spatial tables and
  5. -Toolboxes

Geodatabase is the native data model for ESRI software. It has the ability to handle different data models and datatypes all within one file folder.

  • Feature class - It is found in geodatabase files and is a collection of vectors with set attributes, but can also refer to annotations, multipoints or multipatches.

Raster file formats- They are saved in geodatabase as mosaic model, and tiff, jpeg, GeoTIFF, jpeg2000, DEM formats. The format determines:

  1. -How colours are handled and
  2. -How geographic data is stored.

Metadata - Metadata refers to data about data. It provides additional information about a feature and its attribute. For example:

  1. - Item description
  2. -Who created the data
  3. -Usage constraints


  • Geographic Coordinate system or GCS is a three dimensional surface used to determine locations on the Earth. A point can be referenced by longitude and latitude values measured from Earth's center to a point on the surface.
  • Spehroid is also known as an ellipsoid. It is a three dimensinal shape created from a two dimensional ellipse. It is a model of the Earth.
  • NORTH AMERICAN SPHHEROID = NAD83 - is the North American Datum of 1983
  • It is recommended to use a common datum in a dataset and convert the datum as required.
  • (x,y) coordinates are used to measure distance North or South of the equator and East or West of the prime meridian 

MAP PROJECTIONS:

  • -Conformal projection category preserves local angles. Ex: Mercator projection
  • -A PROJECTION CANNOT BE BOTH EQUAL AREA AND CONFORMAL
  • -Equal area projection category preserves area
  • -Equidistant projection category preserves scale in agiven direction
  • -Compromise projection category involves moderate distortion of SHAPE, AREA. DISTANCE, DIRECTION & SCALE.

MAP PROJECTION CLASS:

  • -CONIC
  • -CYLINDRICAL
  • -PLANAR

MAP PROJECTION CASE is a form of intersection that can be:

  • -TANGENT or
  • -SECANT

MAP PROJECTION is a mathematically described technique of representing the Earth's surface on a flat map. Projection can be described by:

  • -Class
  • -Projection case
  • -Aspect and
  • -Category

A UTM projection is composed of

  • -60 zones that are divided by North or South
  • -Each zone is a secant cylindrical mercator projection
  • -Standard lines are approximately 180 km to eah side of the centrall meridian

GCS is used to: 

  • -store data in a central database where users can project them as needed.
  • -make a quick map
  • -when distortion of shape, area and distance are irrelevant
  • -when spatial queies based on location and distance will not be performed.

Map projections are used to preserve a property
Ex: -Distance queries
  • -To measure areas
  • -GIS analysis
  • -Editing GIS features
  • -Correct visualisation
Transormation function can update the display without changing the dataset resulting in inaccurate measurement and data calculation

  • ArcScene and ArcGlobe  are 3D visualization applications
  • Georeferencing is a method of assigning real world spatial coordinates. It integrates new data into a GIS or assigns control points to reconizable features. It is used only for raster and CAD.
  • Measuring dimensions in GIS refers to quantifying characteristics of a feature by length, perimeter and area.
  • Measuring distance involves capturing the distance between two or more spatial entities (point, line, polygon)
  • Distance relationships can be drawn in a straight line on a map in the form of euclidean or great circle with consideration of time, perception and barrier distance.
  • Measuring density involves consideration of feature distribution in a landscape.
  • Standarzitaion refers to attribute data. It should be divided by a dimension of the spatial entity it relates to.
  • Summary statistics are statistical measurements of attribute data. Ex; Mean, Median, Variane and count
  • 3D measurements refer to measurement of spatial data that uses (x,y,z) coordinates including surface area and volume.
  • Surface area refers to 3D measurement that measures along slopes and it is always bigger than 2D surface area.
  • Categorical measurement refers to comparing categories of different factors using a common criteria. Ex: Suitability analysis, Weighted site selection. Metaphorically speaking, it allows analysts to compare "apples to oranges"

Following are the issues with data quality:

  1. -Accuracy
  2. -Source &
  3. -Metadata


SQL - Structured Query Language is a set of operators strung together to form a request. It is based on the input layer and a query is defined that searches for and selects records that satisfy the query.
Comparison operators used are:

  1. -Equal to (=)
  2. -Greater than (>)
  3. -Less than (<)
  4. -Greater than or equal to (>=)
  5. -Less than or equal to (<=)
  6. -Not equal to (<>)

Logical operators used are:

  1. -AND
  2. -OR
  3. -NOT
  4. -XOR

Wildcard search symbols

  1. -LIKE
  2. -'-'
  3. -'%'
  4. Null values

The operators are used to identify NULL values are:

  1. -IS
  2. -IS NOT

Spatial selection

  • -Accessing spatial data to select records that meet a set of spatial criteria Ex: Test relationship of different datasets


  1. -Intersection
  2. -Adjacency
  3. -Containment
  4. -Distance
  5. Joining data


  • -Combining data from multiple input tables into a single output table using a common key in the table
  • Types of relationships:


  1. ONE-TO-MANY
  2. MANY-TO-ONE


  • Attribute join involves appending the fields of one table to those of another through a field common to both tables
  • Benefit of attribute join is that all the data does not have to be stored in one table. NON-SPATIAL DATA can be mappable

SPATIAL JOIN

  • This operation joins the attributes of two layers based on the location of the features in the layers. This is possible ONLY if BOTH the layers have the SAME COORDINATE SYSTEM

The purpose of spatial join is:

  1. -To find the nearest feature
  2. -Contents of a polygon &
  3. -Use as a measurement tool


  • -THIS IS DONE USING SPATIAL JOIN TOOL (in the OVERLAY toolkit) or Join data by location

Web map Vs Digital map

  1. -Cheaper and less time-intensive to produce
  2. -Wide audience (accessible by anyone with internet access)
  3. -Easier to update
  4. -Interactive
  5. -Can be used to link to related information

Vector classification

  1. -Feature level classification to explore and display existing trends in data
  2. Thematic classification
  3. -It conveys information about a single topic or theme

Chloropleth
-It is a thematic map in which vector areas are distinctly colored or shaded to represent classed values of a particular phenomenon.

Classification techniques:

  1. -Equal interval, Defined interval
  2. -Quantile
  3. -Natural breaks (in ArcMap)
  4. -Petty breaks (in QGIS)
  5. -Standard deviation (How much a feature's attribute value varies from the mean)
  6. -Subjective (Manual)
  7. -Unclassified (Unique values)

Geographical standardization refers to standardization across different areas where the absolute data is divided by a dimension of the spatial entity. (For example: Using density instead of population)
Raster classification involves:

  1. -Reclassifying cells to genaralize existing trends
  2. -Creating themes in raster models. Ex: Chloropleth

Classification problems:

  1. -Confirmation bias
  2. -Ecological fallacy
  3. -Modifiable Areal Unit Problem (MAUP)

Components of geodatabase

  1. -VECTOR -Feature classes
  2. -RASTER -Raster datasets (A gridded spatial data model)
  3. -NON-SPATIAL -Tables made-up of rows and columns

Feature subtype: Geodatabase behaviour that represents a subset of features as a method to categorise data with same characteristics

TOPOLOGY

  • -Topology is defined as a set of geographic relationships of one or more feature classes with common geometries in a geodatabase.
  • -Topology describes how features are spatially related
  • -Shared features between feature classes can be managed using topology, nodes, edges and faces. Their relationship to one another and their features can be effectively discovered and assembled.
  • -Topology provides a mechanism to perform integrity checks on associated data thereby validating and maintaining better feature representations.

For example:

  1. -Navigating along features
  2. -Finding adjacent features

Features share geometry in a topology in the following ways:

  1. -Adjacent
  2. -Polygon topology
  3. Edge node topology

Geoprocessing is a GIS operation used to manipulate GIS data and derive new information
Useful geoprocessing tools are:

  1. -Clip 
  2. -Merge 
  3. -Append
  4. -Dissolve
  5. -Buffer

Buffers may be:

  1. - Fixed
  2. -Concentric or
  3. -Data derived

Geodesic buffer is an alternateive for large scale buffers

Types of Overlay analysis:

  1. -intersect
  2. -union
  3. -difference

The following are the problems with physical overlays:

  1. -Poor precision
  2. -Time consuming
  3. -Manual rescaling
  4. -Hard to make changes to analysis
  5. Plenty of error propogation

Problems with overlays:

  1. -Error propogation
  2. -Computationally intensive
  3. -Sliver creation
  4. -Scale

A Model builder consists of:

  1. -Tools
  2. -Input variables and
  3. -Connectors


  • Accuracy is defined as the closeness to true or known value
  • Precision is defined as closeness of two or more values to  each other

REMOTE SENSING
Types of sensors:

  1. -ACTIVE Ex: LiDAR, RaDAR
  2. -PASSIVE Ex: Visible panchromatic, Visible multispectral, InfraRed, Thermal


  • Reflectance is defined as the radiation that is given off by objects. Different classes of features reflect a different band of radiation atdifferent rates.
  • Image band is also known as raster band that is represented by a single matrix of cell values. It can also be a raster with multiple bands stored as a Digital Number(DN).

Landsat8 spectral bands:
-11 bands in total. Each band is good in reading specific features.

  1. -BAND-1 useful for mapping coastal & aerosol studies
  2. -BLUE Bathymetric mapping to distinguish soil from vegetation
  3. -GREEN Emphasizes peak vegetation to assess plant vigour
  4. -RED Discrimitaes vegetatin slopes
  5. -NIR Emphasizes biomass content and shorelines
  6. -SWIR-1 Emphasizes moisture content of soil and vegetation; penetrates thin clouds
  7. -SWIR-2 Improved SWIR-1
  8. -BAND-8 Panchromatic - 15 m resolution, sharper image definition
  9. -BAND-9 Improved detection of cirrus cloud contamination
  10. -BAND-10 TIRS-1 100m resolution, thermal imaging and estimated soil moisture
  11. -BAND-11 TIRS-2 Improved TIRS-1

TIRS - Thermal Infra Red Sensor
NVDI - Normalized Difference Varience Index

  • It uses NIR and Red bands ratio to read vegetative density more clearly (NIR-Red)/(NIR+Red)
  • Aerial photography - Data is colleted (multi-spectral, elevation) by a plane flying over the study area.


  1. - The study area is usually small
  2. - It has high resolution and is expensive

Satellite imagery

  1. - Data (multispectral, panchromatic, elevation) is collected by a satellite in orbit with 0.5 to 1 km resolution 
  2. Unmanned Aerial Vehicle (UAV) - Data (photographic, LiDAR, InfraRed, Thermal) is collected by unmanned aircraft controlled fro ground for a small area


  • Spatial resolution - Raster resolution that covers areas in pixel. Smaaller areas give higher resolution which implies largwer file and more expensive data.
  • Spectral resolution - It is the ability of a sensor to define fine wavelength ranges to separate them
  • Radiometric resolution - In terms of raster resolution, it is the ability of an imaging system to discriminate slight differences in energy using reflectance values
  • Temporal resolution - It is defined as the frequency with which a sensor can collect imagery of the same area (revisit period)
  • Surface analysis - This involves capturing and analysing the physical structure of the Earth in 3D. In raster, it forms DEM.

Examples of surface analysis are:

  1. -Surface interpretation
  2. -Hydrological analysis
  3. -Statisticl analysis
  4. -Image classification

Suitability analysis-Raster layers can be combined (overlay analysis) to model suitable area.
The steps involved in Suitability analysis are:

  1. -Using an established criteria
  2. -Reclassification into common values
  3. -Assigning weights to criteria
  4. -Overlay and
  5. -Evaluation

Components of raster resolution:

  1. -Spatial resolution
  2. -Spectral resolution
  3. -Radiometric resolution
  4. -Temporal resolution

History of GIS
- I generation (1993-99)
  1. - Zoom-in, Zoom-out
  2. - Not continuous surface
  3. - Layers cannot be toggled

- II generation (1999-2004)
  1. - GIS vendors developed server based softwares
  2. - Users could publish interactive maps on the web using GIS sofwares with interactivity and performance
  3. - In 1996 mapquest launched web service, users got directions but it was slow to load

- III generation (2005-present)
  1. - In 2005 google maps developed tiles
  2. - Tiles load faster than one big map. Maps are prepared at multiple scales. More data presented at each scale and web map loads only tiles needed by user.

Friday, October 20, 2017

Question Bank

QUESTION BANK
Q1. What is a map? Explain the various components of a map?
Q2. How are different geographic features represented on a map? Explain scale related generalization?
Q3. What is a map projection? Explain the three basic families of map projections with the help of a diagram?
Q4. List the salient features of UTM projection system
Q5. Briefly describe the features preserved in different types of projections and mention which area and country is best represented by it.
Q6. What are the various coordinate systems currently used to locate objects geographically
Q7. What is meant by map transformation and list the various map transformations being used
Q8. What is map analysis
Q9. Give a brief account of the historical development of GIS
Q10. List the standard GIS packages used along with the specific areas in which they are used
Q11. Briefly describe the use of GIS in (i) soil & water resources
(ii) agriculture
(iii) land use planning and
(iv) geology
Q12. How can GIS be used to make decisions under uncertainty
Q13. Describe with examples the various data types used in GIS
Q14. What is meant by data compression? Explain its necessity and describe the various types along with a list the compression algorithms currently in use.
Q15. What is a data structure in GIS?
Q16. Describe the various data formats used in GIS?
Q17. Write briefly about cartographic database
Q18. Describe digital elevation data and its use in GIS
Q19. Explain the object structural model in GIS
Q20. How is existing digital data incorporated into GIS?
Q21. Describe the process of manual digitization using a digitizing tablet
Q22. Describe the different type of scanners used to scan maps
Q23. Differentiate between vector data analysis and raster data analysis
Q24. What is SQL and how is it used to retrieve data from a database
Q25. Explain record overlay
Q26. What is a Digital Elevation Model (DEM) and how is it used in GIS?
Q27. Explain why is modeling done in GIS rather than an experimental study?
Q28. Explain cost and path analysis using GIS and analyse the advantages
Q29. Describe knowledge based systems
Q30. How is data organized for analysis in GIS
Q31. Describe the classification of various GIS models
Q32. How is analysis function used in GIS
Q33. Discuss the maintenance and analysis of non-spatial attribute data in GIS
Q34. Describe the various editing and query functions used in GIS
Q35. Explain the difference between conflation and edge matching
Q36. Describe spatial data transformation
Q37. Explain edge matching and editing of spatial data
Q38. Differentiate between spatial and non-spatial data with appropriate examples
Q39. Describe the various data formats used in GIS
Q40. What is a data structure and how is it implemented in a GIS
Q41. How is data entered in a GIS using a keyboard
Q42. Describe the process of manual digitizing using a digitizing board
Q43. Describe briefly the various types of scanners used to scan maps
Q44. What is the necessity of data compression in GIS and explain the commonly used compression algorithms
Q45. What is the role of remotely sensed data in GIS
Q46. What are the sources of existing digital data
Q47 What is a cartographic database and explain its role in GIS
Q50. What is Digital Elevation Data and illustrate its use in GIS with the help of an example
Q51. Define spatial analysis
Q52. Explain briefly about SQL
Q53. What is record overlay.  Explain with the help of an example
Q54. Compare and contrast vector data analysis and raster data analysis
Q55. Define modelling and what is the role played by GIS in this context
Q56. What is a Digital Elevation Model and illustrate its use in GIS
Q57. Explain cost and path analysis in GIS with the help of an example
Q58. Explain knowledge based systems in the context of GIS
Q59. Explain the classification of GIS
Q60. How is data organized for analysis using GIS
Q61. Explain the various analysis functions in GIS
Q62. Explain briefly: (i) Transformation
(ii) Conflation
(iii) Edge matching and
(iv) Editing operations in GIS
Q64. Describe the maintenance and analysis of non-spatial attribute data in GIS
Q65. Briefly describe the editing and query functions in GIS
Q66. Describe the various overlay and neighborhood operations used in GIS
Q67. What are connectivity functions and what are their applications in GIS
Q68. Describe cartographic modeling and illustrate its application in GIS with the help of an example
Q69. Describe the various types of output from GIS software
Q70. Define error and describe the various types of errors in GIS along with recommendations to overcome these errors
Q71. What are the various methods of sampling in GIS
Q72. What are the various components of data quality in GIS
Q73. Describe the characteristics of electromagnetic radiation with the help of a neat sketch
Q74. Explain the interaction of electromagnetic radiation with Earth’s surface with the help of a sketch
Q75. What are the various types of sensors used in remote sensing
Q76. List the various series of satellites used in Indian Remote Sensing program along with their important characteristics
Q77. Briefly describe the data products of remote sensing and methods of interpretation of this data
Q78. Explain how GIS software can be used for (i) Watershed modeling
(ii) Environmental modeling and

(iii) Visibility analysis

Wednesday, September 13, 2017

Using GIS for making decisions under uncertainty.

Using GIS for making decisions under uncertainty

Uncertainty can be defined as a state of being in doubt. GIS is primarily a decision support software that helps policy makers to take decisions regarding development and management with respect to geographical space. When there are several factors influencing the occurrence of an event, uncertainty arises regarding the outcome. Such situations fall under the ambit of "multi-criteria decision making".

Multi Criteria Analysis is a decision making is a tool developed for complex problems. It is used in a situation where multiple criteria are involved and confusion can arise when a logical, well-structured decision making process is not followed. In such cases, it becomes difficult to reach a consensus in a multi-disciplinary team. In such cases, each team makes a distinct identifiable contribution to arrive at a joint conclusion.

The theoretical basis of MCA
The various MCA methods in use are:
  • Ranking
  • Rating and 
  • Pairwise comparison in the AHP (Analytic Heirarchy Process)
GIS can help in multi-criteria analysis (MCA) as an application for setting priorities. This complexity is addressed effectively by GIS as it can handle:
  • Complex problems that require multi-disciplinary teams
  • A diversity of stake holders
  • Local, regional, continental and global scales
  • Multiple parameters of assessment
  • Uncertain and incomplete information
The Analytic Hierarchy Process (AHP) involves:
  • Scoring methodology
  • Categorizing empirical data and qualitative information
AHP helps organize the decision analysis in different levels. GIS tools and maps are utilized for making the decision. The various steps involved in GIS aided AHP are:
  • Defining goals, setting priorities, criteria and indicators
  • Prioritizing from indicators to criteria
  • Hierarchization from criteria to goals and priorities
Quantitative prioritization is done using weighted spatial overlay analysis. This is done by:
  • Collection of relevant spatial information data to use as indicators
  • Categorizing indicators and attaching weights to them
  • Overlaying weighted indicators to visualize each critera
Hierarchization from criteria to goals and priorities is done by prioritizing criteria by region. This is done by prioritizing regional criteria by:
  • Assigning weighted values to qualitative criteria by experts
  • Calculation of coefficients for each criteria weighted by region
The AHP method permits a structured discussion of complex problems by breaking them into different levels of importance. AHP methodology can be used in combination with GIS tools to help decision makers to analyse extensive information in maps to help in decision making. AHP helps to set priorities of options of different measurement parameters.

Thus GIS is an extremely useful tool for making decisions under uncertainty.

Wednesday, August 31, 2016

Software Scenario Functions: Watershed modelling

Watershed is a concept in hydrology that refers to the topographical boundary dividing two adjacent catchment basins. A watershed is an area of land that catches rain and snow and drains or seeps into a marsh, stream, river, lake or groundwater. Homes, farms, cottages, forests, small towns, big cities and more can make up watersheds. They come in all shapes and sizes and can vary from millions of acres, to a few acres that drain into a pond.

Modelling is the process of representing a real world object or phenomenon as a set of mathematical equations.

Watershed models study natural processes of flow of chemicals and microorganisms while determining the impact of human activities on these processes. Watershed modelling is an important tool to focus efforts to solve watershed based water resource, environmental, social and economic problems.

A watershed model can be used for:

  • Water resources planning, development, design, operation and management
  • Flooding
  • Droughts
  • Upland erosion
  • Stream bank erosion
  • Coastal erosion
  • Sedimentation
  • Non point source pollution
  • Water pollution from industrial, domestic and agricultural sources
  • Migration of microbes
  • Deterioration of lakes
  • Desertification and degradation of land
  • Irrigation of agricultural lands
  • Conjunctive use of surface and groundwater, etc

Watershed models are classified into

  • Black Box models that mathematically describe the relation between variables. 
           Ex: Unit hydrograph approach, ANN, Rational formula etc.
  • Lumped models that lie between the Black Box models and Distributed models. 
           Ex: Stanford watershed model, etc
  • Distributed models that are based on complex physical theory on the solution of real governing equation.
           Ex: St. Venant equations for watershed modelling, etc
GIS plays an important role in watershed modeling.
The areas in which GIS is applied in watershed modeling are:

  • Hydrologic assessment
  • Model setup
  • Parameter determination and
  • Modeling

Hydrologic assessment involves using GIS for the analysis of various hydrologic factors for the purpose of risk assessment or susceptibility to pollution, flood, drought, erosion, etc.

Model setup involves defining topography, boundaries and drainage networks of a watershed so as to form the basic framework for applying both lumped and distributed watershed models. DEM is the main data structure used for this work.
In the context of hydrologic assessment and model setup, GIS provides several valuable tools for data creation and management, automated feature extraction and watershed delineation.

Data creation is done by collecting elevations using GPS or digital contour maps to generate new DEMs where no data exists for the aera of interest. Sometimes, contour data on paper-based maps can be converted to digital format using GIS digitizing tools.

Automated feature extraction is performed by various GIS software packages that offer automated routines for delineating watershed boundaries and draining divides. GIS software can also be used for extracting surface drainage channel networks and generating other hydrography data from DEMs.   Ex: WMS and Archydro.

The application of watershed models with GIS requires data from a variety of sources in different formats into a common coordinate space for efficient processing or display. Most GIS software provides tools that assist transforming datasets into a common coordinate space.

An important aspect of modeling watershed processes is to determine parameter inputs. The Watershed Modeling System (WMS) is capable of processing both vector and raster data for land use, soil type, rainfall zone and flow path networks to develop important modeling parameters. 

Friday, August 19, 2016

Software Scenario Functions: Environmental modelling

A model is an abstraction of reality. This helps by representing complex reality in the simplest way. A change in any parameter of the model can be used to visualise the impacts on the entire model. This is the purpose of modeling. The best model is always that which achieves the greatest match between model outputs and real-world observations. Modeling is a powerful tool to understanding observations and can be used to develop and test theories. Moreover, it is faster to get a result by modeling than to actually spend time, energy and resources on the field. Environmental modeling is a powerful tool to understand the interactions between the environment, ecosystems and populations of animals. This is essential for monitoring and management of sustainable means of human dependency on environmental systems.

Environmental models integrate both time and space to understand the nature and functioning of the ecosystem under study. Environment models are multi-component in nature requiring the understanding of interactions between the biotic and abiotic systems. The complexity increases with the increasing number of components and an understanding of these systems requires breaking them into manageable components, combining them and explicitly describing the interactions occurring.

Environmental models cannot be built in the laboratory to adequately represent them. Environmental problems are multivariate, non-linear and complex. Modeling provides an integrated framework in which the individual disciplines can work on different aspects of the research problem and provide a module for integrating within the modelling framework.

GIS and environmental modeling have been used for decision making, planning and environmental management. This combination has been used along with environmental models for applications like:

  • monitoring of deforestation
  • agro-ecological zonation
  • ozone layer depletion
  • flood early warning systems
  • climate and weather prediction
  • ocean monitoring and mapping
  • soil mapping
  • wetland degradation
  • natural disaster & hazard assessment and mapping
  • land cover for input to global climate models

GIS models may be varied in space, in time or in state variables. GIS and remote sensing provide tools to extrapolate models in space as well as upscale models to smaller scales.

A few examples of environmental models used in GIS are listed below with brief descriptions:


  1. RUSLE - The Revised Universal Soil Loss Equation was successfully used with GIS. The process uses raster processing capabilities of the Map Analysis and Processing System (MAPS) to overlay data themes containing spatially distributed values for different RUSLE factors. This technique produces a map of relative levels of soil erosion potential caused due to rainfall, soil type, terrain, vegetation and erosion control practice. The terrain factor from DEM helps calculate soil loss potential for large areas.Thus the RUSLE and GIS interface can be used for soil degradation studies over a large scale.
  2. BIOCLIM - The BIOCLIM system determines the distribution of plants and animals based on climatic surfaces. Bioclimatic variables are used in species distribution modeling and related ecological modeling techniques. Worldclim is a set of global climate layers (gridded climate data) with a spatial resolution of about 1 km2. This data can be used for mapping and spatial modeling. GIS can be used in conjunction with BIOCLIM to make grid maps of distribution of biological diversity or it can also be used to find areas that have high, low or complementary levels of diversity. GIS can also be used to map and query climate data. BIOCLIM and GIS can also be used to predict species distribution. 
  3. CART -The Classification And Regression Tree (CART) model is a binary partioning methods yielding a class of models called tree -based models. The method is applied to several environemtal and ecological studies due to its capability of handling both continuous and discrete variables, its ability to model interactions among predictors and its hierarchical structure. When used in combination with GIS, the CART model output was converted into suitability maps that show the abrupt transitions between areas of high and low suitability.
  4. Monte Carlo simulation - The Monte Carlo method involves generation of random number of parameters to explore the behaviour of a complex process. The numbers are generated using a probability distribution function that describes the occurrence probability of an event. The power of this method lies in the number of simulated samples. The Monte Carlo simulation provides an answer to what may happen and the probability associated with each scenario. The Monte Carlo simulation technique is widely used in spatial analysis. It finds applications in spatial data disaggregation and statistical testing.

Wednesday, August 17, 2016

Characteristics of Indian Remote Sensing series of satellites

List of Indian Earth Observation Satellites:

  1. 1C
  2. P3
  3. 1D
  4. P4
  5. OceanSat-1
  6. TES
  7. P6 ResourceSat-1
  8. P5 CartoSat-1
  9. 2A CartoSat-2
  10. P7 OceanSat-2
  11. RISAT-1
  12. ResourceSat-2
  13. Megha-Tropiques
  14. RISAT-2
  15. ResourceSat-3
  16. HyperSpectral Image
  17. OceanSat-3
Current IRS missions:
ResourceSat-1
CartoSat-1
CartoSat-2

ResourceSat-1 (IRS-P6)
The main features of this satellite are listed below:
It has a circular polar Sun synchronous orbit
Its orbit height is 821 km at an inclination of 98.76
Its orbital period is 101.35 minutes and it performs 14 orbits per day
Its repetivity (LISS-3) is 24 days and revisit (AWiFS) is 5 days
Its 3-axis body is stabilized using reaction wheels, magnetic torquers and hydrazine thrusters
It is powered by a solar array generating 1250 W (at End of Life) using two 24 Ah Ni-Cd batteries
Its mission life is 5 to 7 years
The IRS-P6 has better radiometric resolution, red instead of pan-chromatic band and only one CCD array leading to better internal geometry
It is suitable for mapping and mobile cell phone planning
The LISS-IV camera can be operated in either monochromatic or multi-spectral mode

CartoSat-1
The main features of this satellite are listed below:
It has a circular polar Sun synchronous orbit
Its orbit height is 618 km at an orbit inclination of 98.87
Its orbit period is 97 minutes and it performs 15 orbits per day
Its 3-axis body is stabilized using reaction wheels, magnetic torquers and hydrazine thrusters
It is powered by a 5 sq. km solar array generating 1100 W (at EOL) using 24 Ah Ni-Cd batteries
Its mission life is 5 to 7 years
CartoSat has two panchromatic cameras for in-flight stereo viewing and this stereo data is provided to ground stations in real time
Its revisit capability is 5 days
Its swath is 27.5 km
It is capable of providing DEMs of approximately 4m elevation

CartoSat-2
The main features of this satellite are listed below:
Its orbit height is 630 km at an inclination of 97.91
Its orbit period is 97.4 minutes and it completes 14 orbits per day
Its revisit is 4 days and repetivity is 310 days
Its 3-axis body is stabilized using reaction wheels, magnetic torquers and hydrazine thrusters
It is powered by two 18Ah Ni-Cd batteries that generate 900 W using solar power
Its operational life is 5 years.
Its resolution is 0.81m and swath is about 9.6 km

Future IRS missions are:
ResourceSat-2 that is identical to ResourceSat-1 with a few sensor enhancements
ResourceSat-3 having increased resolution and more spectral bands along with addition of new sensors with 25 km swath
ResourceSat-4 adds new sensors with 12.5 km swath based on 500m optics
CartoSat series of satellites with increased resolution and more spectral bands
RISAT is the first Indian Remote Sensing Synthetic Aperture  Radar (IRS SAR) with:
            - C-band SAR
            - 10 km swath in spot mode and 240 km swath in scan mode
            -1 m to 50 m resolution
            -Single/Dual polarization