GIS for Physical Geography
GEOG2590
Dr Steve Carver
School of Geography
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Introduction to module
• Module outline:
– Convenor: Dr Steve Carver
– 11 x 1 hour lectures
– 11 x 2 hour GIS practicals
• Assessment:
– 3 x 500 word equivalent practical assignments
(20% each) to be submitted in weeks 19, 22
and 24.
– 1 x 1 ¼ hour written examination (40%)
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Module outline
1.
Principles of GIS for Physical Geography applications
2.
Working with environmental data
3.
Error and uncertainty
4.
Interpolating environmental datasets
5. Grid-based modelling
6.
Terrain modelling 1: the basics
7.
Terrain modelling 2: applications
8.
Hydrological modelling
9. Land suitability modelling
10. Spatial decision support systems
11. Reading week
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Aims
On completion of this module 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
• 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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Objectives
• Identify principles and functional issues pertaining
to physical geography applications of GIS;
• Examine and review specific application areas
where GIS is a useful tool;
• Investigate techniques provided by GIS which have
particular relevance to physical geography
applications and problem solving; and
• Identify and address problem areas such as data
sources, modelling, error and uncertainty
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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 GIS to
problems in physical geography;
– show an appreciation of the space-time variability
within environmental data and what this means for GIS
applications in the field; and
– demonstrate a high level of skill in the application of
GIS software (principally ArcGIS) to the solving of
environmental problems.
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Lecture 1.
Principles of GIS for physical
geography applications
• Outline
– what makes physical geography applications of
GIS different?
– environmental science and management
– the role of GIS?
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What makes physcial
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



Lecture 1
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 (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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Next week…
• Working with 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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