GEOG2750%20Earth%20Observation%20

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GEOG2750 Earth Observation GIS of the Physical
Environment20 Credit Level 2 Module

• Louise Mackay Steve Carver
• Module Information
• See also
• http//www.geog.leeds.ac.uk/courses/level2/geog275
  0/index.html

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Module Outline

• Runs Semester 1 2.
• Semester 1 Earth Observation of the Physical
  Environment Louise Mackay
• Semester 2 GIS of the Physical Environment
  Steve Carver
• Two complimentary technologies for monitoring
  understanding the Earths physical environment

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GIS Aims

• On completion of semester 2 students should have
  
• Knowledge of the use of GIS across a range of
  applications in physical geography including
  terrain analysis, hydrology, landscape evaluation
  and environmental assessment
• Familiarity with the use and application of the
  ArcGIS package and
• Knowledge of environmental data sources, skills
  in the interpretation of spatial environmental
  data and an awareness of specific problems and
  issues relating to data quality, spatial data
  models and methods of interpolation.

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GIS Objectives

1. Identify principles and functional issues
   pertaining to physical geography applications of
   GIS
2. Examine and review specific application areas
   where GIS is a useful tool
3. Investigate techniques provided by GIS which have
   particular relevance to physical geography
   applications and problem solving and
4. Identify and address problem areas such as data
   sources, modelling, error and uncertainty.

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Overall Learning Outcomes

• On completion of this module students should be
  able to
• Demonstrate a clear knowledge and understanding
  of the key concepts concerning the application of
  Earth observation and GIS to problems in physical
  geography
• Critique and evaluate the applicability of Earth
  observation and GIS in relation to physical
  geography applications and
• Demonstrate a high level of skill in the
  application of Earth observation GIS software
  to the solving of environmental problems.

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Dates Times

• GIS Semester 2
• 10 x 1hr lectures, Monday 10-11am, Geography
  Lecture Theatre
• 10 x 2hr practicals, Tuesday 3-5pm or Friday
  9am-1pm, Textiles G34 Computer Lab

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Module Assessment

• Semester 2 - GIS
• 5 practical worksheets contributing 5 each to
  the final module mark
• 1 x 1hr exam (short answer) at the end of the
  semester (2 questions from 5) contributing 25 of
  module mark
• Overall assessment based on
• 10 Practicals 50 of final module mark (5 x
  Earth Observation 25 done already last
  semester)
• 2 exams 50 of final module mark (Earth
  Observation 25 done already last semester)

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GIS Syllabus Semester 2 (Weeks)

• 14. Introduction to GIS for environmental
  applications
• 15. Spatial Temporal variability and
  environmental data
• 16. Error Uncertainty
• 17. Interpolation of environmental data
• 18. Principles of grid-based modelling
• 19. Terrain modelling the basics
• 20. Reading week
• 21. Terrain modelling applications
• 22. Hydrological modelling
• 23. Environmental assessment
• 24. Making Decisions

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Lecture 11Introduction to GIS for
environmental applications

• Outline
• what makes physical geography applications of GIS
  different?
• environmental science and management
• the role of GIS?

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What makes physical geography applications of GIS
different?

• The natural environment is
• extremely complex
• highly variable (space and time)
• complicated further by human action
• Understanding of natural systems
• very basic
• multiple approaches to natural science

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From this
to this
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Spatio-temporal variation

• Range of variability over a range of spatial and
  temporal scales
• variation depends on the scale of observation
• e.g. vegetation (species, community, ecosystem)
• sliding scale to represent both spatial and
  temporal variability
• i.e. space from infinitesimal (zero) to infinite
• i.e. time from the instantaneous to for ever

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Spatio-temporal scales of operation

• Variety of spatial and temporal scales
• micro scale - meso scale - macro scale
• e.g. Hydrology
• Micro runoff plots, infiltrometer, hillslope
• Meso sub-catchment, headwaters, reach
• Macro whole catchment, region, watershed
• now - sec - min - day - year - century - etc.
• e.g. Climatology
• Seconds Wind speeds
• Minutes Incoming solar radiation
• Day Anabatic/katabatic winds
• Year Annual temperature variation
• Millennium Glacial/interglacial periodicity

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Complexity

• Complex nature of environmental systems makes
  possibility of realistic modelling seem remote
• Frustrated by lack of understanding
• e.g. influence of human activity
• Variations in complexity
• most GIS applications model only 1 or 2 processes
  with assumptions/simplification

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Question

• How can sampling strategies be matched to
  spatio-temporal scales?

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Sampling theory

• Sampling spatial processes
• the sampling frequency needs to be small enough
  to record local variations without undue
  generalisation of spatial pattern but coarse
  enough so as to avoid data redundancy
• Sampling temporal processes
• in order to record variations in temporal
  processes sampling frequency needs to be about
  half the wavelength of the process to avoid
  measurement bias and too much detail
• Sampling dependent on process(es) operating

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Sampling theory
DEM
Cell size 1
Cell size 2
1 wavelength
Rate
amplitude
Time
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Question

• How do we choose appropriate sampling frequencies?

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Advantages of GIS

• GIS is good at
• handling spatial data
• visualisation of spatial data
• integrating spatial data
• framework for
• analysis and modelling
• decision support

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(dis)Advantages of GIS

• GIS is not so good at
• handling temporal data
• visualisation of temporal data
• integrating spatial and temporal data
• framework for
• analysis and modelling of time dependent data
• volumetric analysis
• uncertainty

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GIS alone is not enough

• Integrated systems
• limited off-the-shelf spatial analysis and
  modelling
• framework for developing better integrated
  systems
• GIS - image processing systems
• GIS - modelling systems
• GIS - statistical software
• facilitated through
• specialist programming languages (e.g. AML and
  Avenue)
• universal programming languages (e.g. Java and
  Visual Basic)
• access to source code (e.g. GRASS)

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Integrated systems

• Combined (symbiotic) systems
• Example
• NERC/ESRC Land Use Programme (NELUP) decision
  support for land use change in UK
• GRASS GIS
• models hydrological (SHE), agricultural
  economics and ecological
• Graphic User Interface (GUI)
• Spatial Decision Support System (SDSS)

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NELUP
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Conclusions

• The physical world is complex and our
  understanding simple
• environmental data is highly variable
• implications for GIS applications
• GIS has important role to play in environmental
  science and management
• handling and analysing spatial data
• problems with temporal data

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Practical

• Spatial variability in environmental data
• Task Investigate the spatial variability in
  terrain datasets and determine the effects of a)
  sampling strategy, and b) resolution on the data.
•  Data The following datasets are provided for
  the Leeds area
• 10m resolution DEM (110,000 OS Profile data)
• 50m resolution DEM (150,000 OS Panorama data)
• 10m interval contour data (110,000 OS Profile
  data)

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Practical

• Steps
• Display both elevation datasets in ArcMap and
  look for visible differences - do these result
  from differences in sampling strategy or
  resolution or both? Use the IDENTIFY tool to
  interrogate the images.
• Calculate the slope (gradient) from both the 10m
  and 50m data is there any striping in the
  slope data and what might this be due to? (use
  the slope tool in ArcMap or ArcGRID to calculate
  slope)

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Learning outcomes

• Familiarity with scale issues especially
  resolution and sampling in relation to spatial
  variation in environmental data
• Experience/practice in use of analysis and
  display functions in ArcMap
• Familiarity with OS terrain model products

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Useful web links

• NELUP web site
• http//www.ncl.ac.uk/wrgi/wrsrl/projects/nelup/nel
  up.html

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Next week

• Spatial and temporal variability and
  environmental data
• general characteristics of environmental data
• environmental data sources
• toward integrated databases
• Practical Using Digimap to access OS data
  products