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

Friday, August 12, 2016

CHARACTERISTICS OF SENSORS

CHARACTERISTICS OF SENSORS

The characteristics of sensors are:
  • Spatial resolution
  • Spectral resolution
  • Radiometric resolution &
  • Temporal resolution
Spatial resolution Spatial resolution describes how much detail in a photographic image is visible to the human eye. The ability to distinguish between small details is one way to describe spatial resolution. Spatial resolution of images obtained from satellites sensor systems is usually expressed in m. 

Spectral resolution EMR patterns are recorded by sensors with separated spectral bands. Spectral reflectance curves or spectral signatures of different types of ground targets provide a knowledge base for extracting information. Spectral responses from ground targets are recorded in separate spectral bands by sensors. Spectral resolution refers to the number of bands and the width of each band. 

Radiometric resolution Radiometric resolution refers to the 'colour depth'. Higher radiometric resolution implies higher sensitivity of the sensor to detect minute changes in electromagnetic energy (sensitivity of sensor to detect differences in reflected or emitted energy). 

Temporal resolution Temporal resolution refers to the time (day or season) of image acquisition. A temporal resolution is helpful in evaluating the change, impacts and severity of the damage, if any. An important aspect in this regard is the revisit period.