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GEOG2750 Earth Observation & GIS of the Physical

Environment

20 Credit Level 2 Module

Louise Mackay & Steve Carver

Module Information

See alsohttp://www.geog.leeds.ac.uk/courses/level2/geog2750/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 AimsOn 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 applications15. Spatial & Temporal variability and environmental data16. Error & Uncertainty17. Interpolation of environmental data18. Principles of grid-based modelling19. Terrain modelling: the basics20. Reading week21. Terrain modelling: applications22. Hydrological modelling23. Environmental assessment24. 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

Time

Rate

1 wavelength

amplitude

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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 modellingdecision 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 datavolumetric analysisuncertainty

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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 (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