Derivation of Urban Planning Indicators (UPIs) using Worldview-3

Imagery and GEOBIA Method for Split Settlement, Croatia

Rina Milošević

, Silvija Šiljeg

and Ivan Marić

University of Zadar, Department of Geography, Trg kneza Višeslava 9, 23 000 Zadar, Croatia

Keywords: Urban Planning Indicators (UPIs), Worldview-3 (WV3), GEOBIA, Split.

Abstract: In most urban environments, loss of natural vegetation, the reduction of open spaces, and the rapid invasive

transformation of the natural environment into impervious has happened. These changes can lead to a decline

in life quality and in an increase of various economic, social, ecological, and infrastructural problems and

risks. The complexity of the urban environment at various scales requires the application of high spatial and

temporal resolution data in the process of urban planning. In this paper, specific urban planning indicators

(UPIs), divided into two groups, have been derived for statistical circles (SC) of Split settlement in Croatia.

Vegetation indicators (TCR - tree cover ratio, LCR - lawn cover ratio, GCR - green cover ratio) and

indicators of urbanization (SCR - street cover ratio, BCR - building cover ratio, IMR - impervious surface

ratio) were derived from the derived land cover model. It was generated from WorldView-3 (WV3) imagery

with the GEOBIA method. A supervised machine learning technique support vector machine (SVM) was

used. A significant spatial variability between UPIs at SCs was observed. The UPIs values at the studied level

are the reflection of the historical spatial-functional development of the Split settlement. These type of UPIs

can be used at the neighborhood level of urban planning and analysis of different issues in an urban

environment.

1 INTRODUCTION

Urbanization is one of the most striking features of

our history, especially since the industrial era. It is

characterized by constant, rapid growth. Based on the

UN-projections by the year 2050, more than 80% of

the world population will live in urban areas. In

European countries, the level of urbanization is

around 74% (Duffin, 2019).

Urbanization has led to the replacement of natural

vegetation-dominated surfaces by various impervious

materials. This had a significant impact on the

environment. Some of the observed consequences are

reduction of the open spaces (Liu, 2009), increased risk

of pluvial floods (Du et al., 2015), endangerment of the

drinking water quality (Wang et al, 2020), the

appearance of the urban heat islands (UHI) (Petralli et

al., 2014), various environmental pollution problems

(Sleavin et al., 2000) and ultimately a decline in life

quality (Sinha, 2019). Therefore, efficient urban

planning has become a central tool of governance,

https://orcid.org/0000-0002-5473-2579

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https://orcid.org/0000-0002-9723-6778

through which these major issues of urban

development will have to be addressed (Watson, 2009,

pp.3). This challenge requires new analytic approaches

and new sources of data and information (Miller and

Small, 2003) in urban planning.

There are numerous definitions of urban planning

(Hall, 2002; Davidson 1996; Anguluri, and

Narayanan, 2017). It is regarded as a complex,

technical, and political process that includes land use

control, urban environment design, and

environmental protection. Its primary purpose is to

improve the decision-making process (Levy, 2016).

In the context of urban planning, there is a notion of

the urban environment (Sénécal, 2007, Blaschke et al.

2011) which is defined as a physical place that

includes different land use patterns, built

infrastructure, and transportation system (Brownson

et al., 2009; Gong et al., 2016). The increasing

availability of geospatial data in combination with

traditional data sources could facilitate the

development of new tools in understanding urban

Miloševi

c, R., Šiljeg, S. and Mari

c, I.

Derivation of Urban Planning Indicators (UPIs) using Worldview-3 Imagery and GEOBIA Method for Split Settlement, Croatia.

DOI: 10.5220/0010465102670273

In Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2021), pages 267-273

ISBN: 978-989-758-503-6

267

environment complexity (Blaschke et al. 2011).

Urban environment planning requires a

multidisciplinary approach and the application of

modern research methods (Abbate et al. 2003,

Blaschke et al., 2011, Tenedório et al., 2016) through

the application of various geospatial technologies

(GST) (Herold et al., 2003, Lo, 2007; Bodzin, 2009,

Blaschke et al. 2011). GST is defined as a set of

methods, techniques, and procedures used in

modeling of complex processes and features in

different levels of detail (LoD) depending on the

research purpose (Marić, et al., 2019). GST includes

GIS, elements of remote sensing (RS), a global

positioning system (GPS), and other related

geospatial technologies (Dibiase et al., 2006, LeGates

et al., 2009). The application of various GST enables

the derivation of various urban planning indicators

(UPIs) (Zhao et al., 2011, Rosales, 2011, Petralli et

al., 2014, Chrysoulakis et al., 2014, Chatzipoulka et

al., 2016) which serves decision-makers, with

measuring performance role, in the planning of urban

environment (Zhao et al., 2011). The UPIs are usually

determined at the very beginning of planning and

serve as a basis for the entire planning and process

design. UPIs are crucial in the monitoring of urban

morphology and urban development intensity. They

are derived for different purposes, among which

stands out research about urban thermal islands (Zhao

et al., 2011, Lin et al., 2017), the building of

sustainable cities, and sustainable urban development

(Rosales, 2011, Shen et al., 2011, La Rosa, 2014,

Chrysoulakis et al., 2014), and achieving sustainable

urban governance (Chrysoulakis et al., 2014). The

LoD and spatial resolution of data used to derived

specific UPIs depends on the level (eg. local,

neighborhoods, metropolitan, regions) (Bryant, 2006)

or scale (macro - micro) (Elshater, 2017) at which the

urban planning process is performed. In this research,

we use high-resolution WorldView-3 imagery to

derive specific UPIs for the city of Split, Croatia. The

research was performed within the INTERREG Italy-

Croatia PEPSEA (Protecting the Enclosed Parts of

the Sea in Adriatic from pollution) project. UPIs were

calculated from a land cover model which was

derived using geographic object-based image

analysis (GEOBIA) (Hay and Castilla, 2008).

2 STUDY AREA

Split is the administrative center of Split-Dalmatia

County. It is the largest city in the Dalmatia region

and the second-largest city in the Republic of Croatia

(HR) (Fig. 1B). At the latest census (2011), the total

population of Split was 178 102. Split is located on

the peninsula and surrounded by hills. Mosor hill is

located on the northeast side of the city (Fig. 1C).

Kozjak hill is located on the northwest side. Split is

surrounded by the islands of Brač, Hvar, Šolta, and

Čiovo (Fig. 1B). The city of Split consists of 92

statistical circles (SC) (Fig. 1C). A statistical circle is

one of the smallest statistical spatial units in the HR.

They were established in 1959 and revised in each

previous census. They represent a permanent network

of spatial units, covering the entire mainland of the

HR (Šiljeg et al., 2018).

Figure 1: A) Split settlement in the HR; B) location of Split

peninsula in Split-Dalmatia Country and B) statistical

circles (IDs of Split settlement.

ID (Figure 1) - Name of statistical circle

1-SK0049298, 2-SK0049301, 3-SK0049310, 4-SK0109819, 5-SK0109827, 6-

SK0109835, 7-SK0109843, 8-SK0109851, 9-SK0109860, 10-SK0109878, 11-

SK0109886, 12-SK0109894, 13-SK0109908, 14-SK0109916, 15-SK0109924,

16-SK0109932, 17-SK0109959, 18-SK0109967, 19-SK0109975, 20-SK0109983,

21-SK0109991, 22-SK0110019, 23-SK0110027, 24-SK0110035, 25-SK0110043,

26-SK0110051, 27-SK0110060, 28-SK0110078, 29-SK0110086, 30-SK0110094,

31-SK0110108, 32-SK0110116, 33-SK0110124, 34-SK0110132, 35-SK0110159,

36-SK0110167, 37-SK0110175, 38-SK0110183, 39-SK0110191, 40-SK0110205,

41-SK0110213, 42-SK0110221, 43-SK0110230, 44-SK0110248, 45-SK0110256,

46-SK0110264, 47-SK0110272, 48-SK0110299, 49-SK0110302, 50-SK0110329,

51-SK0110337, 52-SK0110345, 53-SK0110353, 54-SK0110361, 55-SK0110370,

56-SK0110388, 57-SK0110396, 58-SK0110400, 59-SK0110418, 60-SK0110426,

61-SK0110434, 62-SK0110442, 63-SK0110469, 64-SK0110477, 65-SK0110485,

66-SK0110493, 67-SK0110507, 68-SK0110515, 69-SK0110523, 70-SK0110531,

71-SK0110540, 72-SK0110558, 73-SK0110566, 74-SK0110574, 75-SK0110582,

76-SK0110604, 77-SK0110612, 78-SK0110639, 79-SK0110647, 80-SK0110655,

81-SK0110663, 82-SK0110671, 83-SK0110680, 84-SK0110698, 85-SK0110701,

86-SK0110710, 87-SK0113034, 88-SK0113069, 89-SK0113107, 90-SK0113115,

91-SK0113123, 92-SK0148652

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3 MATERIAL AND METHODS

3.1 GEOBIA Extraction of Land Use

Model using Worldview-3 (WV-3)

Imagery

The land cover model of the Split settlement was

derived from WorldView-3 (WV-3) satellite imagery.

WV-3 was launched on 13 August 2014 by Digital

Globe (Ye et al., 2017). WV-3 is one of the most

advanced commercial satellites. It provides one of the

highest spatial resolutions for multispectral data (0.31

m for panchromatic data and up to 1.24 m for

multispectral bands) (Maxar Technologies, 2019).

The derivation of land cover was done through

several steps (Fig. 2).

Figure 2: Scheme of WV-3 image processing using

GEOBIA method.

The first step involved creation of a multispectral

image (MS) using the Composite bands tool. Then

spatial resolution of the MS was enhanced using a

panchromatic image (Choi et al., 2019). This was

done in the Geomatica Banff 2018 Trial with

PANSHARP tool. The product of this process was

pansharpened MS.

Then segmentation of pansharpened image was

done. The Segment Mean Shift tool in ArcGIS

software was used. Quality of land cover is highly

determined by the selection of user-defined

parameters: Spectral Detail, Spatial Detail,

Min_Segment_Size, and Band Indexes.

The Spectral Detail sets the level of importance

given to the spectral differences of features in the

imagery (ESRI, 2020).

The Spatial Detail sets the level of importance

given to the proximity between features. In both cases,

values range from 1 to 20.

The Min_Segment_Size parameter identifies

blocks of pixels that are too small (in relation to

defined value) to be considered as a fragment (ESRI,

2020). All segments that are smaller than the

specified value will be merged with their best fitting

neighbor segment.

Band_Indexes parameters refer to the selection of

the bands used in multispectral image segmentation.

It is necessary to choose bands that offer the most

noticeable differences between features. However,

there is no clearly defined rule about the optimal

segmentation parameters values (Benarchid and

Raissouni, 2014).

To define the best combination for UPIs

extraction, we have tested different parameter values

using (Fig. 3) the visual interpretation method (trial-

and-error) (Benarchid and Raissouni, 2014).

Three segmented images were generated using

different parameter values (Fig. 3). The visual

interpretation showed that the third segmented model

gave the best result (Fig. 3). In it, the values of

spectral and spatial detail are high enough to separate

features of similar spectral characteristics and to

create not too spatialy smooth classes. On this model

training samples are taken for identification of the

land cover classes. About fifty training samples were

marked for each defined class (n=8).

In the next step, train Esri classifier definition

(.ecd) file using the Support Vector Machine (SVM)

was created. Some researches have shown that SVM

in urban environments (Kranjčić et al., 2019) is

achieving higher classification accuracy than

traditional methods (Chen et al., 2019). In the final

step, the land cover model for Split settlement was

generated (Fig. 2). In future research, the accuracy

Derivation of Urban Planning Indicators (UPIs) using Worldview-3 Imagery and GEOBIA Method for Split Settlement, Croatia

269

Figure 3: Tested segmentation parameter values.

assessment of land cover (overall accuracy and class

by class) will be performed using the very high-

resolution multispectral model generated with Mica

Sense RedEdge-MX mounted on Matrix 600 Pro.

3.2 Derivation of Urban Planning

Indicators (UPIs)

The UPIs for each statistical circle were derived from

the generated land cover. UPIs used in this study

were:

1. Lawn Cover Ratio (LCR): percentage of the

study area that is covered by low vegetation (%);

2. Tree Cover Ratio (TCR): percentage of the

study area that is covered by trees (%);

3. Green Cover Ratio (GCR): percentage of the

study area that is covered by any kind of vegetation

(%) (GCR = LCR + TCR) ;

4. Street Cover Ratio (SCR): percentage of the

study area that is covered by concrete surfaces (%);

Macadam is broken stone of even size used for surfacing roads.

5. Building Cover Ratio (BCR): percentage of the

study area that is covered by buildings (%)

6. Impervious Surfaces Ratio (ISR): percentage of

the study area that is covered by impervious surfaces

(buildings + concrete surfaces, houses) (%).

The first three UPIs are recognized as vegetation

indicators of urban planning. They are important

because the spatio-temporal distribution of vegetation

is regarded as a fundamental variable in some aspects

of urban planning. The use of such vegetation

indicators is a common approach in vegetation

monitoring. The other three UPIs (SCR, BCR, and

ISR) are indicators of urbanization and major

contributors to the environmental impact of

urbanization.

4 RESULTS AND DISCUSSION

4.1 Land Cover Model

Total eight land use classes have been identified and

extracted; tree cover, lawn cover, street cover,

buildings, houses, macadam

, shadows, and other

objects (Fig. 4). Shadows are observed as a deficiency

in this MS imagery. This has become especially

notable in the urban environment modeling where

they are potentially the main source of

misclassification (Zhan et al, 2005). This problem is

particularly pronounced when using advanced

sensors with very high resolution (Shahi et al., 2014).

Therefore, in this research shadows are detected and

classified as a separate category (Zhang et al., 2018).

Figure 4: Land cover model of Split settlement.

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In this case study, most of them are detected on the

northern side of the objects due to sun position during

the satellite recording and are caused by a pronounced

height of specific objects. The percentage of shadow

class in the total area of SK varies significantly The

highest percentages (around 15%) are found in

smaller statistical circles (SK0110485, SK0110361)

in which tall, residential buildings predominate.

4.2 Urban Planning Indicators (UPIs)

The main results of this study are derived UPIs (Fig.

5). Vegetation indicators have a higher percentage in

the outskirts of the city, with the exception of the

SK0109932 located in the western part of the city,

dominated by the Marjan Forest Park which has the

highest GCR among all circles (92.38%). Forest-Park

is one of eleven nature protection categories in

Croatia. Tree cover is making 85.72% of the

SK0109932. The high GCR is also noticed in adjacent

units south (SK0109959, SK0109908) of Marjan and

in the eastern part (SK0110701) where GCR mostly

consists of lawn cover (Fig. 5). This SK includes the

Mejaši, a relatively young neighborhood that was

merged with the city in the 2000s. In recent times new

residential buildings have been constructed in this

area. As expected, these statistical circles have the

lowest ratio of impervious surfaces.

The old city center along with the wider city

center area stands out as the most built-up part,. These

units (SK0109827, SK0109860, SK0109843,

SK0110205, SK0109894, etc.) are characterized by a

prevalence of impervious surfaces (67-82%) and the

lack of green areas. The most dominant type of

impervious surfaces in this area are streets and

buildings. These are older residential neighborhoods

(Klempić, 2004). The northern outskirts (SK

0110582, SK 0110655) of the city are also

characterized by a high presence of impervious

surfaces (Figure 5). However, this part of the city is

highly industrialized. Statistical circles characterized

with the highest values of SCR (SK 0110205, SK

0110582, SK 0110060, SK 0110574, SK 0110655,

SK 0110647) are located nearby industrial zone,

central bus station, and passenger port.

A large percentage of the IMP (buildings, roads,

houses) in statistical circles is not surprising given the

history of spatial-functional development of the Split

settlement. Namely, in the period from the Second

World War to the 1990s, housing construction in Split

was marked by socially-oriented collective

construction with the objective to build as many

residential buildings as possible on the smallest area

possible. After the intensified industrialization

Figure 5: Derived UPIs for Split settlement.

process on the outskirts of the city, due to cheaper

land, the construction of individual, mostly illegal

housing units is taking place, which in the 1990s

became the dominant form of housing construction

(Klempić, 2004).

Derivation of Urban Planning Indicators (UPIs) using Worldview-3 Imagery and GEOBIA Method for Split Settlement, Croatia

271

To our knowledge, there are not many papers in

which WV3 imagery was used to generate specific

UPIs. In this study, it was demonstrated that WV3

imagery provides a good background to generate

spatially detailed and up-to-date land cover data for a

large urban area. This data is invaluable input for

many activities within urban planning and

development.

5 CONCLUSION

In this paper, we have demonstrated the use of WV-3

imagery and the GEOBIA method in the derivation of

UPIs for the Split settlement. These types of UPIs can

be used at the neighborhood level of urban planning

and analysis of different issues in an urban

environment. UPIs provided in this study, can form a

basis for future planning and spatial organization of

Split settlement. Significant spatial variability

between UPIs is observed at the level of the statistical

circles. The UPIs values at the studied level are the

reflection of the historical spatial-functional

development of the Split settlement. Urban expansion

of the Split city is limited by its geographical location

and orographic features of the surrounding area.

Therefore, further development of the city is

envisaged within the existing boundaries. In that

context, the importance of knowing the exact, up-to-

date UPIs cannot be overemphasized.

In future research, we are planning to analyze the

UPIs within the specific SK using MS images of very-

high resolution (<5 cm). Such detailed data would

enable the accurate assessment of the UPIs generated

from WV3 imagery. Also, it could help in

establishing a system of indicators for urban

management and in providing the assistance in

monitoring of urban development at micro-level of

research.

ACKNOWLEDGEMENTS

This work has been supported by INTERREG Italy-

Croatia PEPSEA (Protecting the Enclosed Parts of the

Sea in Adriatic from pollution) and Croatian Science

Foundation under the project UIP-2017-05-2694.

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