This document discusses different types of models used for modeling spatial processes in GIS for decision support. It describes natural and scale analogue models which use real-world events or objects as analogues. Conceptual models represent processes visually using diagrams. Mathematical models include deterministic, stochastic, and optimization models. Deterministic models show direct relationships while stochastic models use probabilities. Optimization models maximize or minimize outputs. The document argues that combining different modeling techniques in GIS allows for complex spatial process modeling to support decisions.
The concept of GIS was first introduced in the early 1960s, and it was subsequently researched and developed as a new discipline. The GIS history views Roger Tomlinson as a pioneer of the concept, where the first iteration was designed to store, collate, and analyze data about land usage in Canada.
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
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Role of electromagnetic Radiation in Remote Sensing
It should be clear by now that the electromagnetic waves are originator and
carrier of information in Earth observation. The information content of the products delivered by a given type of sensor is essentially related to the parameters, mainly frequency (or wavelength) and polarization, characterizing the observing system, including the geometry at which data are acquired. Therefore, the specifications of an EO system, which include the type of sensor, the band of operation, the observation angle, etc.
The concept of GIS was first introduced in the early 1960s, and it was subsequently researched and developed as a new discipline. The GIS history views Roger Tomlinson as a pioneer of the concept, where the first iteration was designed to store, collate, and analyze data about land usage in Canada.
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Role of electromagnetic Radiation in Remote SensingNzar Braim
Role of electromagnetic Radiation in Remote Sensing
It should be clear by now that the electromagnetic waves are originator and
carrier of information in Earth observation. The information content of the products delivered by a given type of sensor is essentially related to the parameters, mainly frequency (or wavelength) and polarization, characterizing the observing system, including the geometry at which data are acquired. Therefore, the specifications of an EO system, which include the type of sensor, the band of operation, the observation angle, etc.
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Explanation of very simple methods for atmospheric corrections and an example adapted from a paper of the Dept. of Thermodynamics, University of Valencia, Spain.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
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Discussion led by John Spencer and Mark Janko. This webinar shared new techniques in geospatial analysis and how they have the potential to transform data-informed decision making.
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Principal components analysis is a orthogonal transformational technique (preserving the symmetry between vectors and angles) to reveal new set of data arguably better from the original data set and better capture the essential information as well. It happens often that some variables are highly correlated with a lot of duplication. Instead of discarding the redundant data, principal components analysis condenses the info. in inter-correlated variables into a few variables, called principal components.
The main idea of Principal Component Analysis (PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Explanation of very simple methods for atmospheric corrections and an example adapted from a paper of the Dept. of Thermodynamics, University of Valencia, Spain.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
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Application of basic remote sensing in Geology. This presentation tries to discriminate the lithology in the Landsat-7 scene located Karachi West. Although other enhanced methodology available to discriminate the rock types, here just a band ratios and simple band combination used for lithology identification.
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Models of spatial process by sushant
1. GIS-MODELLING FOR DECISION SUPPORT:
MODELS OF SPATIAL PROCESSES:
NATURAL & SCALE ANALOGUE MODELS
CONCEPTUAL MODELS
MATHEMATICAL MODEL
Represented By
Sushant Sawant
Mtech I Geoinformatics
2. Introduction
• Models of spatial form are represented and analyzed using GIS. These
models can be used in many ways in data analysis operations;
however , they tell us nothing about the process responsible for
creating or changing spatial form.
• E.g. Population change, climate change, soil erosion. Etc.
3. Models of spatial process
• A process model simulates “real – world processes”.
• There are “2 reasons” for constructing such a model.
•
1. From a “pragmatic point of view” decision- need to be made and
actions taken about spatial phenomena. Model help this process.
• 2. From a “philosophical point of view” a process model may be the
only way of evaluating our understanding of the complex behavior of
spatial systems.( Buck et.al., 1995).
4. Why a classification of process models is a
good first step?
• There are many different approaches to process modelling
• To decide which is appropriate in a particular situation
• An understanding of the range of models available, their
strengths and weakness
6. Priori models
A “priori models” are used to model processes for which a body of
theory has yet to be established. In these situations the models is
used to help in the search for theory.
E.g. Scientist involved in research to establish whether – global warming
is taking place, would use a priori models, as the phenomenon of
“global warming” is still under investigation.
7. Posteriori Models
• “Posteriori Models” on the other hand, are designed to explore an
established theory. These models are usually constructed when
attempting to apply theory to new areas.
•
An a posteriori model might be developed to help predict
avalanches in a mountain area where a new ski piste has been
proposed.
E.g. Avalanche formation theory is responsible, well established, and
several models already exist which could be applied to explore the
problem.
8. • Beyond the a priori / a posteriori division, developing a further
classification of process models becomes quite complex.
•
However, 2 useful classifications ( Hardisty et al., 1993; Steyaert,
1993) have been integrated here to provide a starting point for
examining the different types of models.
• The classification includes:
1. natural and scale analogue models.
2. conceptual models.
3. mathematical models.
9. NATURAL & SCALE ANALOGUE MODELS
• Natural analogue models: uses actual “events or real-world”
objects as a basis for model construction (Hardisty et al., 1993).
• These events or objects occur either in “different places or at
different times”.
• E.g. a natural analogue model to predict area the formation of
avalanches in the previously unstudied area of a new ski piste might
be constructed by observing how avalanches form in an area of
similar character.
• The impact that avalanches would have on the proposed ski piste
could also be examined by looking at experiences of ski piste
construction in other areas.
10. SCALE ANALOGUE MODELS
•
There are also scale analogue models (Steyaert,1993) such as
topographic maps & aerial photographs, which are scaled down and
generalized replicas of reality.
•
These are exactly the sort of analogue models that GIS might use to
model the analogue prediction problem.
11. CONCEPTUAL MODELS
•
Conceptual process models are usually expressed in verbal or
graphical form, and attempt to describe in words or pictures
qualitative & quantitative interactions between real-world features.
•
The most common conceptual model is a systems diagram, which
uses symbols to describe the main components & linkages of the
model.
13. MATHEMATICAL MODELS
• Mathematical process models use a range of
techniques including:
1. deterministic
2. stochastic &
3. optimization models.
14. Deterministic
• There is only one possible answer for a given set of inputs. For example, a
deterministic avalanche prediction model might show a linear relationship
between slope angle & size of avalanche.
• E.g. The steeper the slope, the smaller the avalanche which results, since
snow build-up on the slope will be less.
• Such models work well for clearly defined, structured problems in which a
limited number of variables interact to cause a predictable outcome.
• However few simple linear relationships exist in geographical phenomena.
• In most situations there is a degree of randomness, or uncertainty, associated
with the outcome.
• E.g. this is true in the avalanche example.
15. Stochastic model
• Where there is uncertainty about the nature of the process involved, a
mathematical model known as a stochastic model is needed.
• Stochastic models recognize that there could be a range of possible
outcomes for a given set of inputs, and express the likelihood of each one
happening as a probability.
•
We know that slope angle and size of avalanche are related but that the
problem is much more complex than suggested by our deterministic models.
• However, in reality other variables will be involved, for example direction
of slope, exposure to wind, changes in temperature and underlying
topography.
•
• The predicted size of an avalanche is based on the probability of a number of
16. Optimization model
• These models are constructed to maximize or minimize some
aspect of the models output.
•
To help identify the area of minimum avalanche risk at a
given time..
17. Process Modelling & GIS
•
•
•
•
•
•
In GIS all the approaches – natural and scale analogue, conceptual &
mathematical modeling – are used to model spatial processes.
They may be used in isolation, substituted for each other in an interactive
development process or combined in an larger, more complex model.
The given case study shows how different modeling techniques can be used
together to build up complex models of spatial processes.
Unfortunately, proprietary GIS software provides few process models as part
of the standard set of functions.
Thus, generic models, which could be made available in GIS, would be far
too inflexible for widespread use.
In addition, many of the analytical functions provided by other modelling
software, provide an environment for constructing application-specific
models.
18. References
•
Heywood, I., Comelius, S., and Carver, S., (1988). An Introduction to
Geographical Information Systems, Addison Wiley Longmont, New York.
•
Burrough, P. A., and McDonnell, R., (2000). Principles of Geographical
Information Systems, Oxford University Press, London.
•
Research paper on Decision Support Systems by Marek J. Druzdzel and
Roger R. Flynn, Decision Systems Laboratory, School of Information
Sciences and Intelligent Systems Program, University of Pittsburgh,
Pittsburgh, PA 15260