Download STUDENT COURSEWORK SAMPLE Module P38333

STUDENT COURSEWORK SAMPLE
Module P38333 - GIS & Environmental Modelling
In this module, students learn how to perform a range of spatial analysis and
modelling methods that are commonly used in Environmental Assessment &
Management practice.
This coursework sample provides an extract from the student 'GIS Workbook'
produced by Jackie Jobes, submitted as part of the module assessment.
Question 3: Using GPS data, this question uses Kernel Density Estimation to model
the 'home-range' used by an individual fox.
Question 4: The first part of this question shows the results of a noise assessment,
using GIS spatial queries to define buildings in the study area that experience 'Major',
'Moderate' or 'Minor' noise disturbance from road traffic. The second part of the
question shows the results of a site search or "sieve mapping" exercise to identify
possible locations for a development proposal that meet predefined spatial criteria.
Question 6: Using air pollution dispersion modelling combined with spatial
interpolation of baseline data, this question tackles a 'critical loads assessment' for
nitrogen deposition at sensitive ecological sites (known as Sites of Special Scientific
Interest SSSIs).
Question 8: Using a digital terrain model constructed for the study area, this question
explores the use of viewshed analysis to determine the visual impact of a wind farm
proposal.
Question 9: Using the advanced ModelBuilder tool in ArcGIS, here the student has
developed a spatial model to target possible habitat suitable for the stone curlew, a
"Species of European Conservation Concern"
Workbook 2
J.Jobes - 14012119
P38333 GIS & Environmental Modelling
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Question 3: Density Surface Analysis
(OPTION 2 Kernel density analysis to model the home range of a fox using GPS collar data)
The purpose of the Kernel Density Surface (KDS) analysis was to attempt to construct a surface that
accurately reflects the likelihood of each cell being within the fox home and core range assuming a
cone-like distribution. The highest values are at the peak of the cone, which are the darker coloured
cells. Multiple cones add together to create the final KDS, giving a smoothing effect. These dark areas
therefore have a higher likelihood of being part of the fox home/core range.
By reducing the neighbourhood size, it decreases the search radius distance which is why in the 25m
map output is dotted more than blended. By thinking logically about a fox range, it is felt that the larger
neighbourhood size of 250m represents the physical roaming ability of a fox, rather than a fragmented
range which does not characterise how the fox may travel around a site range.
When considering the Kernel Density Surface (KDS) in comparison to the Minimum Covex Polygon (MCP)
method map output, the MCP is a crude interpretation. The MPC highlights the entire territory of the
area the foxes seem the live and travel. This is a very simple way of constructing boundaries of a home
range using a convex polygon. When looking at it against the KDS method, it is evident that the MCP has
overestimated the size of the home range and does not allow for range density to be assessed like KDS.
KDS offers a very clear output of ‘Home Range’ and ‘Core Range’. This can be very easily interpreted and
then actioned upon with confidence and therefore the recommended approach for modelling the
distribution of a fox.
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Question 4: Vector Overlay Analysis
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As shown in the “Alternative Development Locations” map (left) the current
development sites do not conform to the sieve criteria or guiding principles
that Dorset County Council (DCC) wish to be used within the planning process,
sitting outside of the suitable areas highlighted by the “Seive_Output”.
Corresponding with the criteria listed below in Table 2, the northern most
proposed site infringes criteria C and D, F, G and H. The south-eastern site
infringes the criteria B, G and H. The south-western site infringes the criteria
B and F. As the south-western site poses the least infringements, the criteria
that would need to be relaxed for the proposed site to gain approval would
be the proximity to woodland and to the railway station. All areas covered by
the red “Sieve_Output” meet the full criteria requested and therefore
locations within these sites would be recommended as alternatives for DCC,
in particular those to the north of Dorchester and those to the west.
Table 2. The series of guiding principles or criteria requested to be used as part of the plan
making proces of the new waste treatment sites.
A
B
C
D
E
F
G
H
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A site SHOULD be within 750m of a road
A site SHOULD be within 4000m of a railway station
A site should NOT be within 250m of a watercourse
A site should NOT be sited within the floodrisk zone
A site should NOT be within 1200m of Maiden Castle
A site should NOT be come within 50m of an area of woodland
A site should NOT be within 1500m of a school
A site should NOT be within 100m of an existing building
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Question 6: Air Pollution Impact Assessment
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Question 6: Air Pollution Impact Assessment
When evaluating the results gathered in Table 3 above, it is evident that the Predicted Environmental
Concentration (PEC) of Nitrogen deposition across all SSSI sites is likely to be very minimal and not
infringe on the critical loads outlined for each habitat. Data generation was also computed on a “worstcase” scenario, taking the upper NOx levels from the cartographic outputs if an SSSI bordered multiple
predicted NOx concentrations.
Due to a variety of different habitat types being considered within one SSSI the absolute maximum
critical load Nutrient Nitrogen and absolute lower load Nutrient Nitrogen was calculated using the
habitat type, as determined by APIS (2014). This is a highly crude way of measuring the NOx levels and
therefore is a significant flaw in the model’s capabilities which needs to be taken into consideration. For
instance, Acid grassland has the ability to cope with between 10 – 15 kg N/ha/yr whereas Neutral
grassland can cope with 20-30 kg N/ha/yr. This makes the prediction modelling very difficult to measure
when they both exist on the same site.
According to NADP (n.d.), a critical load is technically defined as “the quantitative estimate of an
exposure to one or more pollutants below which significant harmful effects on specified sensitive
elements of the environment are not expected to occur according to present knowledge.” As a result, it
is expected that with the data output above, these pollutants are unlikely to cause ecological changes
such as leaf discolouration, direct damage to mosses, liverworts and lichens, or changes in species
composition.
Limitations come with the assumptions made by the Gaussian dispersion model, which follows the
‘normal’ distribution of a plume. The assumptions include a constant rate of pollutants, a constant wind
speed, longitudinal, lateral and vertical movements as well as emission strength, height, concentration
and plume rise. These need to be taken into consideration when evaluating the likely effects.
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47%
PEC Nitrogen
Deposition (NO2)
7
Existing Nitrogen
Deposition (NO2)
PEC as a % of the
critical level for
NOx
23%
PC Nitrogen
Deposition (NO2)
(kg N/ha/yr)
NOx Background
Level (ug/m3)
30
Upper Critical
Load (kg N/ha/yr)
the PC as a % of
the critical level
for NOx
7
Main Habitat
Lower Critical
Load (kg N/ha/yr)
NOx Critical Level
(ug/m3)
SSSI
NOx “Process
Contribution” (PC)
(ug/m3)
Table 3. NOx Air Quality Compliance Risks at each SSSI and site specific nutrient nitrogen (NO2) critical loads for each SSSI.
10
30
1.47
1.47
2.94
Acid Grassland
Valley of Stones
Calcareous Grassland
Neutral Grassland
White Horse Hill
1.9
30
6%
7
30%
Calcareous Grassland
15
25
0.399
1.47
1.869
Black Hill Down
1.9
30
6%
7
30%
Calcareous Grassland
15
25
0.399
1.47
1.869
Blackdown (Hardy
Monument)
8.7
30
29%
7
52%
Dwarf Shrub Heath
10
20
1.827
1.47
3.297
7
30
23%
7
47%
Calcareous Grassland
15
25
1.47
1.47
2.94
5.3
30
18%
8.7
47%
Earth Heritage
No data
No data
1.113
1.827
2.94
10
30
1.113
2.562
3.675
No data
No data
1.47
1.47
2.94
15
30
1.113
1.47
2.583
10
25
0.399
1.47
1.869
15
30
1.47
1.47
2.94
10
25
1.47
1.47
2.94
10
25
1.827
1.47
3.297
Pitcombe Down
Upwey Quarries and
Bincombe Down
Fen, Marsh and Swamp
River Frome
5.3
30
18%
12.2
58%
Broad-leaved, Mixed and Yew Woodland
Dwarf Shrub Heath
Corton Cutting
Langford Meadow
7
30
23%
7
47%
Earth Heritage
5.3
30
18%
7
41%
Rich Fen, marsh and swamp
Acid Grassland
Giant Hill
1.9
30
6%
7
30%
Broad-leaved, Mixed and Yew Woodland
Calcareous Grassland
Neutral Grassland
Hog Cliff
7
30
23%
7
47%
Broad-leaved, Mixed and Yew Woodland
Calcareous Grassland
Court Farm, Sydling
7
30
23%
7
47%
Acid Grassland
Calcareous Grassland
Acid Grassland
Sydling Valley Downs
8.7
30
29%
7
52%
Broad-leaved, Mixed and Yew Woodland
Calcareous Grassland
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Question 8: Viewshed Analysis & Modelling
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Question 8: Viewshed Analysis & Modelling
The viewshed analysis interprets which locations are visible from the proposed windfarm, similar to a
searchlight from a single location. Terrain elevation has given a pseudo-3D effect where the height
creates ‘shade’. When introducing the implications of screening vegetation and buildings the visibility of
the turbines is greatly reduced when comparing it with simply topographic considerations as seen
Degree of Turbine Visibility.
The process does not account for shadows or visibility conditions, working only on maximum visibility.
The output also assumes that the terrain model is of high detail, so the quality of the dataset needs to
be taken into consideration when interpreting. Greater accuracy has been calculated in the final map
output, Visual Impact Index with Screening, which combines the outputs of the ‘Visual Impact Index’
map with the ‘Screening Vegetation and Buildings’ map to account for both the distance decay effect
and screening.
It would also be extremely valuable to utilise the Ordnance Survey resource for building height once
there is full UK coverage (OS, 2014) rather than taking an average of building height as done in this
scenario.
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Question 9: Targeting Stone Curlew Habitat
The map output has been generated with a number of considerations for Stone
Curlew preference as outlined in Table 4. The potential habitat indicates arable land
that is larger than 2ha, less than a 15° slope, within 1km of short, semi-natural
grassland as feeding ground. The habitat is also at least 1km away from a main road
due to their sensitivity to disturbance. See Figure 2 for the model canvas.
Limitations came with the inclusion of soil consideration, with a preference for free
draining rendzinas, as well as including a range of at least 30ha. In addition to their
disturbance sensitivity to main roads, minor roads could also be considered along
with the finer detail of public pathways where dog walkers may frequent. These
additions would further develop the model and enhance the output.
Table 4. Stone Curlew habitat criteria according to Thompson et al (2004).
Breeding Habitat
Arable Land
Semi-natural feeding
areas
Slope
Soils
Major Road Proximity
Minimum Size
Details
Suitable breeding habitat greater than 2ha
Devoid of tall/dense vegetation, short grassland within 1km of
breeding habitat. Home range 30ha feeding ground.
Less than 15°
Predominantly free draining rendzinas in arable situations
Greater than 1km from a major road or motorway
Two adjacent 1ha arable parcels optimal for viable breeding site
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Question 9: Targeting Stone Curlew Habitat
Figure 2. Stone Curlew Habitat Model Outline
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References
APIS (2014) Site Relevant Critical Loads and Source Attribution. Air Pollution Information System.
Available at: http://www.apis.ac.uk/srcl (accessed 01/05/2015).
Google Maps (2015) Google Maps: Grey’s Wood and Yellowham Wood, Dorchester. Google Maps.
Available at: https://www.google.co.uk/maps/@50.7409689,-2.3876368,1723m/data=!3m1!1e3
(accessed 11/03/15).
Heywood I, Cornelius S and Carver S (2011) An introduction to geographical information systems (4th
edition). Harlow, England: Prentice Hall.
NADP (n.d.) Critical Loads. National Atmospheric Deposition Program. Available at:
http://nadp.sws.uiuc.edu/lib/brochures/criticalloads.pdf (accessed 05/05/2015).
Natural England (n.d.) Publications and Maps: Ancient Woodland Inventory (Provisional) for England. GIS
Digital Boundary Datasets. Available at:
http://www.gis.naturalengland.org.uk/pubs/gis/tech_aw.htm (accessed 11/03/15).
OS (2014) New building height data released. Ordnance Survey. Available at:
http://www.ordnancesurvey.co.uk/blog/2014/03/new-building-height-data-released/ (accessed
05/05/2015).
Thompson S, Hazel A, Bailey N, Bayliss J, Lee JT (2004) Identifying potential breeding sites for the stone
curlew (Burhinus oedicnemus) in the UK. Journal for Nature Conservation 12: 229-235.
Tomlinson RF (2011) Thinking About GIS: Geographic Information System Planning for Managers (4th
edition). Redlands, California: Esri Press.
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