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

Tuesday, August 16, 2016

Interpretation of remote sensing data

The basic principles of image interpretation are:
  1. Location
  2. Size
  3. Shape
  4. Shadow
  5. Tone and Colour
  6. Texture
  7. Pattern
  8. Height and Depth
  9. Situation and Association
Location refers to the geographic location and is an important tool that helps to identify the type of vegetation. This is because any type of vegetation is specific in its requirement of soil, climate and other factors that are typical to a certain location.

Size of objects on images is important with reference to the image scale. Length, width and perimeter are commonly measured. Measuring the size of an unknown object helps the interpreter to rule out possible alternatives. For example, the dimensions of standard objects are known and this makes it possible to determine the size of an unknown object by comparison.

Shape refers to the general form, configuration or outline of individual objects. In case of stereoscopic images, the objects height also defines the shape.

Shadows may either aid or hinder in interpretation. Extended shadows can make it difficult to understand other objects that can be identified easily.  A shadow cast by an object may be a key to the identity of another object. It is always recommended that the photos are oriented so that the shadow falls towards the interpreter otherwise a pseudoscopic illusion is produced leading to low points appearing high and vice versa.

Tone and Colour of all matter refers to different proportions of energy reflected in the blue, green, red and infra-red portions of the electromagnetic spectrum. This can be used as a spectral signature to identify the type of matter. Different shades of a colour are called as tone. The darker an object appears, the less light it reflects. Colour imagery is preferred as humans can detect thousands of colours. Colour help in the process of photo interpretation.

Texture is the frequency of tonal change on an image. It determines the overall smoothness or coarseness of image features. It is defined as the characteristic placement and arrangement of repetitions of tone or colour in an image. As the scale of an image is reduced, the texture of any given object or area becomes progressively finer and ultimately disappears. An interpreter can distinguish between features of similar reflectances based on their textural differences. For example: the contrasting textures of two tree species.

Pattern refers to the spatial arrangement of objects. Objects may be arranged systematically or randomly. A few other patterns are: Circular, Linear, Oval, Rectangular and Curvilinear to name a few. The repetition of a few general forms is characteristic of natural and constructed objects thus forming a pattern that helps the image interpreter in recognizing objects. For example: the spatial arrangement of trees in an orchard versus the random distribution of trees in a forest.

Height and depth is also known as elevation and baythymetry. It is one of the most important important diagnostic element of image interpretation. Any object that rises above local landscape will show some radial relief. This casts a shadow that provides information regarding its height.

Situation and Association Situation refers to the manner in which the objects in the image are organized and situated with respect to each other. Association refers to the fact of finding a particular activity in an image. Location, Situation and Association are normally interrelated to each other in an image. As an example, consider a commercial complex. It has several large buildings, huge parking areas and is usually located near a major road.

Remote sensing data products

The main products of remote sensing satellites are:

  • Sea Surface Temperature (SST), Ocean colour, Ocean winds and Sea surface height measured by satellite sensors.
  • Satellite derived chlorophyll concentration and ocean currents
  • Satellite remote sensing products can be used to generate potential habitat maps of aquatic life

The products of remote sensing data are extensively used in disaster management mainly in the following disasters:

  1. Extreme weather
  2. Floods
  3. Coastal hazards / Tsunamis
  4. Volcanoes
  5. Earthquakes
  6. Landslides
  7. Droughts
  8. Dust storms and
  9. Wild fires
Remote sensing data products have demonstrated their usefulness in combating or long term management of the following problems:
  1. Climate change
  2. Pollution monitoring
  3. Plant health
  4. Land usage
  5. Population density
  6. Deforestation and
  7. Desertification
Remote sensing capabilities can be used to provide situational awareness for a wide area in a very short time frame. The data products of remote sensing are:

For extreme weather:

  • Atmospheric temperature and water vapour profiles are used as input by forecasters
  • Sea surface winds, cloud cover, rainfall and cloud profiles are used as inputs to models
  • Remote sensing imagery is used for tracking storms and damage

For floods:

  • SAR generated DEMs are used to indicate risk areas
  • Weather forecasts can be made to warn the public
  • Satellite imagery is used to assess impact and track recovery
  • Remote sensing data can be used to predict risk due to areal precipitation
Drought:
  • Remote sensing products such as sea surface temperature and height are used to forecast e Nino
  • Snow cover, surface temperature and rain measurements are used to forecast available water
  • Soil moisture, rainfall and vegetation health are used to observe onset and progress of droughts
Pollution:
  • SAR imagery makes it possible to detect and track oil spills in the ocean
  • Atmospheric pollutants can be detected using Infra-Red (IR) radiation
  • Ocean colour is used to detect red tides