GEOG2750
Earth Observation & GIS of the Physical
Environment
20 Credit Level 2 Module
Louise Mackay & Steve Carver
Module Information
See also
http://www.geog.leeds.ac.uk/courses/level2/geog2750/index.html
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GEOG2750 – Earth Observation & GIS of the Physical Environment
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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:
1. Knowledge of the use of GIS across a range of
applications in physical geography including
terrain analysis, hydrology, landscape evaluation
and environmental assessment;
2. Familiarity with the use and application of the
ArcGIS package; and
3. 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 11
Introduction 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



–
–
now - sec - min - day - year - century - etc.
e.g. Climatology





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Micro : runoff plots, infiltrometer, hillslope
Meso: sub-catchment, headwaters, reach
Macro: whole catchment, region, watershed
Seconds:
Minutes:
Day:
Year:
Millennium:
Wind speeds
Incoming solar radiation
Anabatic/katabatic winds
Annual temperature variation
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



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specialist programming languages (e.g. AML and Avenue)
universal programming languages (e.g. Java and Visual Basic)
access to source code (e.g. GRASS)
GEOG2750 – Earth Observation & GIS of the Physical Environment
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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 (1:10,000 OS Profile data)
– 50m resolution DEM (1:50,000 OS Panorama data)
– 10m interval contour data (1:10,000 OS Profile data)
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Practical
•
Steps:
1. 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.
2. 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/
nelup.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
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