Visual Impact analysis · visual impact. Doing such an analysis in an objective way can get complicated because factors like the distance to the building or weather conditions need

VISUAL IMPACT ANALYSIS Salzburg Airport Tower

Schendl Gabriel ID: 1120878

1

Abstract

This paper investigates the visual impact of the new and old airport tower of Salzburg. In the

introduction, the study object and the study area is presented. Further, some general information

of visual impact is provided and other studies of this effect are shortly introduced. In the

methodological part, it is explained why modelling visual impact is important and the three

applied methods in the analysis are discussed. In the results, the outcomes of the analysis are

visualized. A description and interpretation of these results is done in the discussion part of this

paper. In the conclusion, the most important findings of this study are summarized and finally,

an outlook is provided.

2

Contents Abstract ...................................................................................................................................... 1

List of tables ............................................................................................................................... 3

List of figures ............................................................................................................................. 3

List of equations ......................................................................................................................... 3

Introduction ................................................................................................................................ 4

Methods ...................................................................................................................................... 5

a) Modelling Visual Impact ................................................................................................. 5

b) Viewshed ......................................................................................................................... 6

c) Distance Decay Function / Fuzzy Viewshed ................................................................... 7

d) Overlay of viewsheds with landscape types and population ......................................... 10

Results ...................................................................................................................................... 11

a) Fuzzy Viewsheds ........................................................................................................... 11

b) Overlay of viewsheds with landscape types .................................................................. 12

c) Overlay of viewsheds with population data .................................................................. 13

Discussion ................................................................................................................................ 17

Conclusion ................................................................................................................................ 18

References ................................................................................................................................ 19

Data Sources in the analysis ..................................................................................................... 21

Attachment ............................................................................................................................... 22

3

List of tables Table 1: Variables in Equation 1 ................................................................................................ 9

Table 2: Zones for overlay of viewsheds ................................................................................. 12

Table 3: Overview Affected Population ................................................................................... 16

Table 4: Result of overlay viewsheds and land use raster for the first zone ............................ 22

Table 5: Results of overlay viewsheds and land use raster for the second zone ...................... 24

Table 6: Results of overlay viewsheds and land use raster for the third zone ......................... 26

Table 7: Results of overlay viewsheds and land use raster for the fourth zone ....................... 28

Table 8: Results of overlay viewsheds and land use raster for the fifth zone .......................... 30

List of figures Figure 1: Example for a binary viewshed .................................................................................. 7

Figure 2: Example for a distance buffer layer ............................................................................ 8

Figure 3: Fuzzy Viewshed of the old tower based on a DEM ................................................. 11

Figure 4: Fuzzy Viewshed of the new tower based on a DEM ................................................ 11

Figure 5: Affected Population Zone 1 ...................................................................................... 13




Figure 9: Affected Population for all zones ............................................................................. 15

List of equations Equation 1: Distance Decay Function ........................................................................................ 9

4

Introduction

The goal of this paper is to model the visual impact for the old and the new airport tower of

Salzburg and to find out which of the two towers has a higher visual impact.

In the year 2014, a new tower for the airport of Salzburg was put into operation. This new

building has a height of 53 meters (ORF 2014) and replaced the old tower which had been in

use from 1969 to 2014. It was necessary to construct a new tower because the old was not able

anymore to fulfil the current state of technology. The old tower had a height of 27 meters. After

the new tower was put into operation, the predecessor was not used anymore and parts of it

were dismantled in the meantime (ORF 2015). The study area is the surroundings of the city of

Salzburg within a distance of about 17 kilometers to the airport tower in the state of Salzburg.

Parts of the state of Bavaria which would be in this distance threshold are not considered in the

analysis.

The creation of new buildings leads to changes in viewing conditions of landscapes. This

resulting effect “is identified as a visual impact (Garnero and Fabrizio 2015)“. The investigation

of this effect of view obstructions is difficult. If a high building is created and if a building can

be perceived easily in the surrounding area, the visual impact of such a construction should be

analyzed. Besides it is necessary to find out “from where this building can be seen from

(Garnero and Fabrizio 2015)”. Geographic information systems can be used for the analysis of

visual impact. Doing such an analysis in an objective way can get complicated because factors

like the distance to the building or weather conditions need to be considered (Möller 2006).

This paper is not the first study of visual impact. Möller (2006) analyzed the visual impact of

wind power turbines in Denmark with a geographic overlay of viewsheds with population data.

Bishop and Miller (2007) did a visual assessment of wind turbines which were built along the

north coast of Wales. Loots, Nackaerts et al. (1999) calculated fuzzy viewsheds for a former

city defence system of the ancient Greeks at Sagalassos in Turkey. Rasova (2014) also created

fuzzy viewsheds of prehistoric monuments in the west part of Slovakia. Garnero and Fabrizio

(2015) applied a method to calculate visibility maps of a planned skyscraper in the city of Turin.

5

Methods

a) Modelling Visual Impact

If a new building is created, the viewing conditions of a landscape can change. This effect is

called visual impact. Analyzing this effect on the skyline can be difficult in rural and as well in

urban areas. In the second case, topographic aspects, the heights of buildings and atmospheric

conditions need to be considered in the analysis. Changes to urban landscapes which can be

investigated “are the variation of the skyline of the city and the visibility of the building from

visual corridors of the main streets (Garnero and Fabrizio 2015)”. If buildings can be perceived

easily from the surroundings, it is necessary to model the visual impact of a new building.

Another important aspect is to “understand where this building can be seen from and how much

of it can be seen (Garnero and Fabrizio 2015)“. This can be done with generating visibility

maps of high buildings. This simple method leads to accurate results (Garnero and Fabrizio

2015).

If such a new building is created, affected people complain about the visual impact of it. The

visual impact can be modelled with Geographic Information Systems. Visual impact analysis

is possible with using digital elevation models and population data with a high resolution. Land

use aspects can also be considered. The visibility of buildings can be calculated with viewsheds

with using a certain radius (Möller 2006).

Investigating “visual impact in an objective way is difficult (Möller 2006)”. A building which

can be perceived “from a location does not itself comprise an adverse impact because visibility

is not the same as an actually sensed visual-impact (Möller 2006)“. Influencing factors can be

“the distance to the building, the size, paint colour, structure weather conditions, how often,

how long, and where people are faced with (Möller 2006)”. Due to these factors, visual impact

analysis gets complicated. One approach “to quantify visual exposure is the geographic overlay

of viewsheds with landscapes types or population (Möller 2006)“.

6

b) Viewshed

The term viewshed was introduced in 1967 “as an analogy to the watershed (Nutsford, Reitsma

et al. 2015)“. One year later, the first computer algorithm was programmed for calculating

visibility (Nutsford, Reitsma et al. 2015).

The term viewshed analysis is defined as “a GIS function that identifies all the terrain surfaces

that are visible from a pre-defined observation point (Lee and Stucky 1998)“. The intervisibility

of a terrain surface is regarded as the base for this analysis. In GIS programs, digital elevation

models (DEMs) are used for representing terrain surfaces (Lee and Stucky 1998).

Viewshed analysis in GIS programs offers “a full 360 degree horizontal view orientation (Yang,

Putra et al. 2007)”. This analysis belongs to the 2.5 D concepts (Yang, Putra et al. 2007).

In most cases, viewsheds have an irregular and fragmented form. They usually consist of

discrete patches and not of some continuous polygon areas (Llobera 2003). Viewshed analysis

leads to a binary result: an object can be visible or not visible. The result is “a continuous spatial

representation of visibility that indicates whether or not a viewpoint on a surface can be seen

from a particular observer cell (Möller 2006)“.

In ArcGIS from ESRI (Environmental Systems Research Institute), viewsheds can be

calculated with Spatial Analyst and 3D Analyst tools. The used algorithm “determines the

visibility for each grid-cell centre by comparing the vertical angle to the centre of the cell with

the vertical angle to the local horizon (Möller 2006)“. The output can be seen as “a Boolean

variable that identifies whether each cell is visible (value 1) or not (value 0) from a certain

viewpoint (Garnero and Fabrizio 2015)“. As output, a new raster layer is created. Important

parameters can be the height of the pre-defined observation point or the radius of the viewshed

(Fisher 1993).

Figure 1 on page 7 shows the binary viewshed for the old airport tower based on a DSM with a

radius of 10 kilometers.

7

Figure 1: Example for a binary viewshed

c) Distance Decay Function / Fuzzy Viewshed

The viewshed described before cannot be regarded as a good tool for measuring visibility from

a human perspective. It is necessary to consider the distance between the pre-defined

observation point and the observer. The closer a visible object to an observer, the higher the

visual impact. The higher the distance between the object and the observer, the lower the visual

impact. Many factors like relative size of objects or object-background clarity influence this

process. They all “are a function of the distance between the perceived object and the observer

(Nutsford, Reitsma et al. 2015)”.

Due to these reasons, the viewshed analysis was technically improved. Distance decay functions

and fuzzy viewshed were introduced “in order to simulate the loss of visual resolution with

distance and to move from deterministic to probabilistic viewshed models (van Leusen

2002)(Chapter 6, page 13)“.

The fuzzy viewshed can be used “to more accurately model the real world view afforded by

various environmental conditions within a GIS (Beaulieu 2007)“. This is done with integrating

“a distance decay function into the standard binary viewshed (Beaulieu 2007)”. In contrast to

8

the binary viewshed where cell are identified as visible or not visible, “each cell within a fuzzy

viewshed is assigned a value from 0 to 1 representing the degree of visibility of the cell from

the viewpoint (Beaulieu 2007)“. A value near to 1 means that this cell has a high visibility, a

value near to 0 stands for a low visibility. The three main parts in creating fuzzy viewsheds are

(Beaulieu 2007):

a binary viewshed

a distance buffer and

a distance decay function.

The binary viewshed can be created in ArcGIS with using Spatial Analyst tools. As input data,

a digital elevation model and a pre-defined observer point are needed. The result contains cells

which are classified as visible or not visible from the observer point (Beaulieu 2007).

For creating a fuzzy viewshed, a layer with distance information is needed. This distance layer

is also created in ArcGIS with the Spatial Analyst function Euclidean distance. The output is

“a raster layer in which the value of each cell equals the distance from that cell to the viewpoint

(Beaulieu 2007)“.

Figure 2: Example for a distance buffer layer

9

The distance decay function can be regarded as the most important part in creating fuzzy

viewsheds. It is used “to model the drop in visual clarity that occurs with increasing distance

from the viewpoint (Beaulieu 2007)“. This function is based on the following equation (Fisher

1994):

Equation 1: Distance Decay Function

𝜇 (𝑥𝑖𝑗) = {11

(1+(𝑑𝑣𝑝→𝑖𝑗− 𝑏1

𝑏2)

2

)

for dvp→ij ≤ b1 and for dvp→ij > b1

The following table provides an overview of the variables used in Equation 1 (Beaulieu 2007):

Table 1: Variables in Equation 1

Variable Description

xij fuzzy membership at cell at row I, column j

dvp→ij distance from viewpoint to row i, column j

b1 maximum distance from viewpoint of clear visibility

b2 distance from viewpoint at which visibility drops to 50%

The next step is to complete the equation with choosing the distance variables for the decay

function. Possible values are 1 km for b1 and 2 km for b2. Then, a distance decay buffer has to

be calculated. This “is a raster layer created by applying the distance decay function to the

distance buffer layer (Beaulieu 2007)“. For this task, the raster calculator function of ArcGIS

can be used.

The calculation of a fuzzy viewshed is completed with combining “the layer representing the

distance decay function, and the layer classifying all cells as either not visible or possible visible

from the viewpoint based on the topography of the region (Beaulieu 2007)“. The distance decay

buffer layer has to be multiplied with the layer of the binary viewshed. Like in the step before,

the raster calculator function of ArcGIS is applied. The output layer contains cells represented

by values from 0 to 1. A value close to 1 stand for a high visibility, values close to 0 for a low

visibility (Beaulieu 2007).

10

The distance decay function can be adopted to consider factors which have an impact on the

visibility. Such factors can be “environmental factors affecting conditions between the observer

and the object under view, the physical properties of the object being viewed and constraints

imposed on visibility by the observer (Beaulieu 2007)”. Environmental factors with an impact

on the visibility can be “the presence or absence of atmospheric particulates and varying levels

and directions of illumination (Beaulieu 2007)“. Such atmospheric particulates with a negative

impact on visibility can be smoke, dust, fog, rain, sandstorms or swarms of insects. As physical

properties of the object, the size, the color, its reflectivity or the texture can be mentioned. The

amount and direction has an impact on the visibility. The more light available, the better the

viewing conditions. The direction of illumination needs to be considered because “the degree

of visibility is greater when looking away from the light source than when looking toward it

(Beaulieu 2007)“ like it is the case with sunrises or in sunsets. “Constraints imposed by the

observer on visibility refer to such things as the visual acuity of the observer and the observer’s

culturally influenced concepts of landscape and cognition (Beaulieu 2007)“. Here,

psychological and physiological restraints can be distinguished. Physiological restraints “limit

the visual acuity of the observer, while psychological restraints are cognitive limitations

imposed by the observers background or culture (Beaulieu 2007)“. Visual acuity investigates

the ability of the observer to see specific objects (Beaulieu 2007). Such adaptions were not done

in this analysis.

d) Overlay of viewsheds with landscape types and population

Visual impact can also be modelled with “geographic overlay of viewsheds with landscape

types or population, summarizing land use or population count for cumulative numbers of

visible objects (Möller 2006)“. This is a method for comparison of the visual impact and not

for modelling the total exposure. A zonal summary is carried out. The output of this method are

tables with statistical data. In ArcGIS, a landscape model is created. Input data are digital

elevation models and land cover data. The resulting landscape model can be used for

investigation of the visual impact. This impact of existing object is calculated for observer

locations based on certain topography. For overlaying population data with the viewsheds in

ArcGIS, zonal statistics functions are used. In the result, the affected population and the land-

use will be summarized as values in tables in the zonal statistics (Möller 2006).

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Results

a) Fuzzy Viewsheds Figure 1 shows the fuzzy viewsheds for the old airport tower based on a DEM.

Figure 3: Fuzzy Viewshed of the old tower based on a DEM

Figure 2 shows the fuzzy viewshed for the new airport tower based on a DEM.

Figure 4: Fuzzy Viewshed of the new tower based on a DEM

12

These two fuzzy viewsheds were both calculated with a toolbox which was downloaded from

the ArcGIS resources. It was not necessary to do all the described steps from before. Running

this model with a DSM as input data was not possible because it delivered results which are

presumably wrong. After the description of the toolbox, a Digital Elevation Model was

necessary as input data. The results were then classified after the following degrees of visibility:

0 – 0,94, 0,94 – 0,96, 0,96 – 0,98, 0,98 – 0,99, 0,99 – 0,995 and 0,995 – 1. For the fuzzy

viewshed of the new tower, only the highest degrees of visibility are visualized. For receiving

the lower degrees, it would have been necessary to set a higher radius of the fuzzy viewshed.

b) Overlay of viewsheds with landscape types

Based on the resulting zones after the degree of visibility of the fuzzy viewshed for the old

airport tower (Figure 3), binary viewsheds based on a DEM and a DSM were created for both

towers. Table 2 gives an overview of these visibility zones:

Table 2: Zones for overlay of viewsheds

Zone number Values in meters

1 0 – 5581

2 5581 – 7525

3 7525 – 10377

4 10377 – 12782

5 12782 – 17471

These viewsheds were then overlaid with a land use vector dataset. For the overlay, this vector

dataset was converted into a raster dataset with a spatial resolution of 10 meters using the tool

“Feature to Raster” in ArcGIS Pro. For the overlay of the viewsheds with the land use raster,

the Spatial Analyst tool “Zonal Statistics as Table” was applied. The resulting table were

converted into Excel files using the tool “Table to Excel”.

The resulting tables of this analysis can be found in the attachment. A value of 10 means that

10 cells are affected; each cell has a size of 100 m².

13

c) Overlay of viewsheds with population data

The binary viewsheds which were used before are now overlaid with a population density raster

layer in ArcGIS with the tool “Zonal Statistics as Table”. The resulting tables were converted

into Excel files to calculate the affected population.

Figure 3 shows the results for the first zone. The zones from the overlay of the viewsheds with

the land use raster are used again here except the fifth zone. An overlay of the population density

raster or the land use raster with fuzzy viewsheds including a distance decay function was not

applied because in this case the affected population respectively the number of affected cells

would have been multiplied with their degree of visibility. As results, values with decimals

would have been calculated instead of values without decimals.

Figure 5: Affected Population Zone 1

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Old Tower DEM New Tower DEM Old Tower DSM New Tower DSM

Affected Population

14

Figure 4 shows the results for the second zone.


Figure 5 shows the results for the third zone.


0

2000

4000

6000

8000

10000

12000

14000

16000

18000


Affected Population

0

500

1000

1500

2000

2500

3000

3500

4000


Affected Population

15

Figure 6 shows the results for the fourth zone.


Figure 6 shows the overall results from this analysis.

Figure 9: Affected Population for all zones

0

100

200

300

400

500

600


Affected Population

0

20000

40000

60000

80000

100000

120000


Affected Population

16

Table 3 gives an overview over the affected population. In the fifth zone, no people are affected

by the airport towers.

Table 3: Overview Affected Population

Viewshed Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Overall

Old Tower

DEM

61334 11173 2492 497 0 75496

New Tower

DEM

83757 15301 3357 551 0 102966

Old Tower

DSM

13110 1969 925 239 0 16243

New Tower

DSM

20005 3206 1561 267 0 25039

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Discussion

The results from calculating fuzzy viewsheds for both towers show that the new tower has a

higher visual impact because the red and the yellow zones extend over a higher distance

compared to the old airport tower.

The results from the geographic overlay of the viewsheds with the land use types show that

usually more raster cells are affected by the new airport tower. Only in a few cases, the old

airport tower had a higher visual impact. This could be caused by the fact that due to different

positions of the airport towers, the calculated viewsheds do not use exactly the same area. Some

land use types e.g. the category airport are not affected in all five zones. The results also show

that less cells are affected by the airport towers if the viewsheds are based on a digital surface

model. In case of a digital elevation model, more cells are affected.

The results from the geographic overlay of the viewsheds with the population density raser

show a similar pattern. More people are affected by the new airport tower. A large part of the

population lives in the first zone within a distance less than six kilometers to the airport towers.

The results also show that more people are affected by the airport towers if the viewsheds are

based on a digital elevation model. In case of a digital surface model, less people are affected.

In the fifth zone, nobody is affected by the airport towers. A systematic error which can occur

in the overlay of viewsheds based on the DSM with the population density raster is the fact that

in the case of buildings with several floors people who live in a lower floor have a higher

visibility compared to people living in a higher one. Another aspect is the fact that the results

from the overlay of viewsheds based on the DEM show that the number of affected people is

presumably too high. Due to the urban study area, these results should be treated with care. The

results from the overlay of viewsheds based on the DSM seem to be more reliable.

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Conclusion

The goal of this paper was to do a visual impact analysis for the old and the new airport tower

in Salzburg and to compare the visual impact of these two buildings.

The results from the three applied methods - calculation of fuzzy viewsheds, geographic overlay

of viewsheds with land use types and geographic overlay of viewsheds with population data -

show that the new airport tower has a higher visual impact than the old tower. This is

presumably caused by the greater height of the new airport tower.

A calculation of the fuzzy viewsheds for both airport tower based on a digital surface model is

missing in this analysis. Because the downloaded model did not work with a DSM as input data,

the calculation of the fuzzy viewsheds could be done manually after the described steps in the

methodological part of this paper. Also adaptions to the distance decay function were not

applied in the analysis e.g. in the case of environmental factors. Another further step would be

the investigation of the affected area in the state of Bavaria. Also a validation of the results from

the analysis is not possible.

Another problem is that uncertainty is given in viewsheds. Small changes of the viewing

location can lead to different viewing conditions. Further, every location is marked with a

specific probability of being visible or not; this probability can vary from a small to a large

likelihood. Personal experience leads to the fact “that the viewshed is not really a Boolean

phenomenon” (Fisher 1992). Also errors in digital elevation models like the root-mean-square

error (RMSE) are possible (Fisher 1992). For a higher accuracy of the viewsheds, it would have

been necessary to use a DEM or DSM with a better spatial resolution.

19

References

Beaulieu, T. (2007) Can you see that? Fuzzy viewsheds and realistic models of landscape

visibility.

http://www.georeference.org/forum/e31362F39303930322F46757A7A792076696577736865

647320616E64207265616C6973746963206D6F64656C73206F66206C616E6473636170652

07669736962696C6974792E706466/Fuzzy%20viewsheds%20and%20realistic%20models%

20of%20landscape%20visibility.pdf (accessed on March 29, 2016).

Bishop, I. D. and D. R. Miller (2007). "Visual assessment of off-shore wind turbines: The

influence of distance, contrast, movements, and social variables." Renewable Energy 32: 814 -

831.

Fisher, P. F. (1992). "First Experiments in Viewshed Uncertainty: Simulating Fuzzy

Viewsheds." Photogrammetric Engineering & Remote Sensing 58.3: 345 - 352.

Fisher, P. F. (1993). "Algorithm and implementation uncertainty in viewshed analysis."

International Journal of Geographical Information Science 7:4: 331 - 347.

Fisher, P. F. (1994). Probable and fuzzy models of the viewshed operation. Innovations in GIS:

selected papers from the First National Conference on GIS Research UK. M. F. Worboys.

London, UK, Taylor and Francis: 161 - 175.

Garnero, G. and E. Fabrizio (2015). "Visibility analysis in urban spaces: a raster-based approach

and case studies." Environment and Planning B: Planning and Design 42: 688 - 707.

Lee, J. and D. Stucky (1998). "On applying viewshed analysis for determining least-cost paths

on Digital Elevation Models." International Journal of Geographical Information Science 12:8:

891 - 905.

http://www.georeference.org/forum/e31362F39303930322F46757A7A792076696577736865647320616E64207265616C6973746963206D6F64656C73206F66206C616E647363617065207669736962696C6974792E706466/Fuzzy%20viewsheds%20and%20realistic%20models%20of%20landscape%20visibility.pdf




20

Llobera, M. (2003). "Extending GIS-based visual analysis: the concept of visualscapes."

International Journal of Geographical Information Science 17:1: 25 - 48.

Loots, L., et al. (1999). Fuzzy Viewshed Analysis of the Hellenistic City Defence System at

Sagalassos, Turkey. Archaeology in the Age of the Internet. CAA97. Computer Applications

and Quantitative Methods in Archaeology. Proceedings of the 25th Anniversary Conference.

University of Birmingham, Archaeopress, Oxford.: 82-81 - 82-89.

Möller, B. (2006). "Changing wind-power landscapes: regional assessment of visual impact on

land use and population in Northern Jutland, Denmark." Applied Energy 83: 477 - 494.

Nutsford, D., et al. (2015). "Personalising the viewshed: Visibility analysis from the human

perspective " Applied Geography 62: 1 - 7.

ORF (2014) Airport: Neuer Tower get in Betrieb. http://salzburg.orf.at/news/stories/2630894/

(accessed on March 2, 2016).

ORF (2015) Alter Flughafen-Tower wird zum Teil abgetragen.

http://salzburg.orf.at/news/stories/2733643/ (accessed on March 2, 2016).

Rasova, A. (2014). Fuzzy Viewshed, probable viewshed, and their use in the analysis of

prehistoric monuments placement in Western Slovakia. . Connecting a Digital Europe through

Location and Place. Proceedings of the AGILE'2014 International Conference on Geographic

Information Science. S. Huerta, Granell. Castellon.

van Leusen, M. (2002). Pattern to process: methodological investigations into the formation

and interpretation of spatial patterns in archaeological landscapes, Rijksuniversiteit Groningen.

Doctoraat: 356.

Yang, P. P.-J., et al. (2007). "Viewsphere: a GIS-based 3D visibility analysis for urban design

evaluation." Environment and Planning B: Planning and Design 34: 971 - 992.

http://salzburg.orf.at/news/stories/2630894/

http://salzburg.orf.at/news/stories/2733643/

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Data Sources in the analysis

The following datasets were used in the analysis:

Digital Elevation Model and Digital Surface Model of the state of Salzburg with a

spatial resolution of 10 meters (downloaded on March 15, 2016 from

https://www.salzburg.gv.at/themen/bauen-wohnen/raumplanung/sagis/download)

Data about land use of the districts Salzburg and Salzburg – Land (downloaded on April

26, 2016 from http://www.eea.europa.eu/data-and-maps/data/urban-atlas)

Population density raster of the city of Salzburg and its surroundings (ZGIS SBG

Population Point), accessed in ArcGIS via “Add Data From ArcGIS Online

Model to calculate fuzzy viewsheds (downloaded on March 26, 2016 from

http://www.arcgis.com/home/item.html?id=5e9cb4fd73fe4288a4cf534cc5a119aa)

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Attachment

Table 4 shows the results of the overlay of the binary viewsheds with the land use raster for the

first zone. As already mentioned in the results, a value of 5 means that five cells are affected,

each of them has a size of 100 m². Not all land use types can be found in all of the five following

tables.

Table 4: Result of overlay viewsheds and land use raster for the first zone

Land use

category

Affected cells

VS old tower

DEM

Affected cells

VS new tower

DEM

Affected cells

VS old tower

DSM

Affected cells

VS new tower

DSM

Continuous

Urban Fabric

(S.L. > 80%)

3361 4740 739 1551

Discontinuous

Medium

Density Urban

Fabric (S.L.:

30% - 50%)

30351 42078 4821 12628

Discontinuous

Low Density

Urban Fabric

(S.L.: 10% -

30%)

4563 6201 897 2190

Sports and

leisure facilities

6538 10459 783 2251

Discontinuous

Dense Urban

Fabric (S.L.:

50% - 80%)

37828 49273 5676 14784

Isolated

Structures

1544 1916 250 681

Agricultural +

Semi-natural

126131 170814 14976 71732

23

areas +

Wetlands

Discontinuous

Very Low

Density Urban

Fabric (S.L. <

10%)

240 269 37 50

Industrial,

commercial,

public, military

and private

units

42504 54375 5548 16282

Green urban

areas

13283 17422 4579 6686

Land without

current use

2443 3533 33 377

Other roads

and associated

land

15766 20598 1350 4281

Fast transit

roads and

associated land

5678 7192 363 2924

Railways and

associated land

4049 4919 53 320

Airports 18946 19240 14773 18107

Mineral

extraction and

dump sites

37 68 5 46

Construction

sites

1951 2349 225 708

Forests 53573 66727 16505 32430

Water bodies 1228 2821 28 274

24


second zone.

Table 5: Results of overlay viewsheds and land use raster for the second zone

Land use

category

Affected cells

old tower DEM

Affected cells

new tower

DEM

Affected cells

old tower DSM

Affected cells

new tower

DSM

Continuous

Urban Fabric

(S.L. > 80%)

272 343 4 38

Discontinuous

Medium

Density Urban

Fabric (S.L.:

30% - 50%)

10801 13829 1674 3962

Discontinuous

Low Density

Urban Fabric

(S.L.: 10% -

30%)

4519 5377 1603 2367

Sports and

leisure facilities

1156 1707 169 349

Discontinuous

Dense Urban

Fabric (S.L.:

50% - 80%)

7531 10257 806 2592

Isolated

Structures

1763 1835 530 707

Agricultural +

Semi-natural

areas +

Wetlands

52566 66059 14976 23969

Discontinuous

Very Low

107 139 27 53

25

Density Urban

Fabric (S.L. <

10%)

Industrial,

commercial,

public, military

and private

units

7778 11150 1315 3314

Green urban

areas

2342 2713 852 1378

Land without

current use

911 1290 61 109

Other roads

and associated

land

5140 6396 606 1213

Fast transit

roads and

associated land

1530 1765 729 909

Railways and

associated land

2031 2333 27 96

Mineral

extraction and

dump sites

520 506 295 293

Construction

sites

367 662 4 31

Forests 96467 101894 63039 67190

Water bodies 175 235 12 60

26


third zone.

Table 6: Results of overlay viewsheds and land use raster for the third zone

Land use

category

Affected cells

old tower DEM

Affected cells

new tower

DEM

Affected cells

old tower DSM

Affected cells

new tower

DSM

Discontinuous

Medium

Density Urban

Fabric (S.L.:

30% - 50%)

5319 6529 1500 2445

Discontinuous

Low Density

Urban Fabric

(S.L.: 10% -

30%)

3907 4579 1566 2205

Sports and

leisure facilities

172 523 8 61

Discontinuous

Dense Urban

Fabric (S.L.:

50% - 80%)

597 895 182 428

Isolated

Structures

3031 3405 1284 1530

Agricultural +

Semi-natural

areas +

Wetlands

84941 96448 47278 54726

Discontinuous

Very Low

Density Urban

Fabric (S.L. <

10%)

144 161 75 92

27

Industrial,

commercial,

public, military

and private

units

3226 4567 710 1844

Green urban

areas

17 73 28 169

Land without

current use

421 451 70 132

Other roads

and associated

land

3766 4470 1083 1421

Fast transit

roads and

associated land

674 771 0 99

Railways and

associated land

400 523 15 15

Mineral

extraction and

dump sites

868 972 272 370

Construction

sites

505 945 37 106

Forests 131988 141256 92113 98543


28


fourth zone.

Table 7: Results of overlay viewsheds and land use raster for the fourth zone

Land use

category

Affected cells

old tower DEM

Affected cells

new tower

DEM

Affected cells

old tower DSM

Affected cells

new tower

DSM

Continuous

Urban Fabric

(S.L. > 80%)

3 3 0 4

Discontinuous

Medium

Density Urban

Fabric (S.L.:

30% - 50%)

1906 2971 440 1037

Discontinuous

Low Density

Urban Fabric

(S.L.: 10% -

30%)

1567 1909 519 717

Sports and

leisure facilities

0 106 3 7

Discontinuous

Dense Urban

Fabric (S.L.:

50% - 80%)

304 641 48 199

Isolated

Structures

784 1073 382 491

Agricultural +

Semi-natural

areas +

Wetlands

16157 23051 5306 7294

Discontinuous

Very Low

43 51 9 11

29

Density Urban

Fabric (S.L. <

10%)

Industrial,

commercial,

public, military

and private

units

638 1009 229 444

Green urban

areas

156 198 75 99

Land without

current use

179 237 15 47

Other roads

and associated

land

1205 1683 405 546

Fast transit

roads and

associated land

280 322 162 197

Railways and

associated land

111 113 0 1

Mineral

extraction and

dump sites

311 289 167 171

Construction

sites

9 48 0 1

Forests 49055 53096 36874 39907


30


fifth zone.

Table 8: Results of overlay viewsheds and land use raster for the fifth zone

Land use

category

Affected cells

old tower DEM

Affected cells

new tower

DEM

Affected cells

old tower DSM

Affected cells

new tower

DSM

Isolated

Structures

5 3 3 3

Agricultural +

Semi-natural

areas +

Wetlands

770 2067 1302 1417

Other roads

and associated

land

26 24 0 5

Forests 2334 5228 2873 3807

Documents

Visual Impact analysis · visual impact. Doing such an analysis in an objective way can get complicated because factors like the distance to the building or weather conditions need