lecture 1 geog2590 – gis for physical geography1 gis for physical geography geog2590 dr steve...

Lecture 1 GEOG2590 – GIS for Physical Geography

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GIS for Physical GIS for Physical GeographyGeographyGEOG2590GEOG2590Dr Steve Carver

School of Geography


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Introduction to moduleIntroduction 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 outlineModule outline1. Principles of GIS for Physical Geography

applications2. Working with environmental data3. Error and uncertainty4. Interpolating environmental datasets5. Grid-based modelling6. Terrain modelling 1: the basics7. Terrain modelling 2: applications8. Hydrological modelling9. Land suitability modelling10. Spatial decision support systems11. Reading week


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AimsAimsOn 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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ObjectivesObjectives

• 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 outcomesLearning 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.Lecture 1. Principles of GIS for Principles of GIS for physical geography physical geography

applicationsapplications

• Outline– what makes physical geography

applications of GIS different?– environmental science and

management– the role of GIS?


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What makes physcial What makes physcial geography applications geography applications

of GIS different?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 Spatio-temporal variationvariation

• Range of variability over a range of spatial and temporal scales– variation depends on the scale of

observatione.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 Spatio-temporal scales of operationof 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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ComplexityComplexity

• 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…Question…

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


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Sampling theorySampling 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 theorySampling theory

DEM Cell size 1

Cell size 2

Time

Rate

1 wavelength

amplitude


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Question…Question…

• How do we choose appropriate sampling frequencies?


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Advantages of GISAdvantages 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(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 analysisuncertainty


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GIS alone is not enoughGIS 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 systemsIntegrated 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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NELUPNELUP


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ConclusionsConclusions

• 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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PracticalPractical• 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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PracticalPractical• 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 outcomesLearning 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…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

lecture 1 geog2590 – gis for physical geography1 gis for physical geography geog2590 dr steve...

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