Relationship between Landscape Pattern and Human Disturbance in

Serbia from 2000 to 2018

LUÍS QUINTA-NOVA1,2,a, JOSÉ MANUEL NARANJO GÓMEZ2,4,b, ANA VULEVIC5,c,

RUI ALEXANDRE CASTANHO2,6,d, LUÍS LOURES2,7,e

1Instituto Politécnico de Castelo Branco, Escola Superior Agrária,

PORTUGAL

2VALORIZA - Research Center for Endogenous Resource Valorization,

Instituto Politécnico de Portalegre (IPP),

PORTUGAL

4University of Extremadura,

SPAIN

5CIP, Belgrade,

SERBIA

6Advanced Research Centre, European University of Lefke, Lefke, Northern Cyprus, TR-10 Mersin,

TURKEY

7Research Centre for Tourism, Sustainability and Well-being (CinTurs),

University of Algarve, 8005-139 Faro,

PORTUGAL

aORCiD: https://orcid.org/0000-0002-8464-7527

bORCiD: https://orcid.org/ 0000-0001-7998-9154

cORCiD: https://orcid.org/0000-0001-5150-4477

dORCiD: https://orcid.org/0000-0003-1882-4801

eORCiD: https://orcid.org/0000-0002-6611-3417

Abstract: - This study intends to verify how the alteration of the landscape configuration, represented by

different metrics of configuration and diversity, is related to the intensity of human disturbance. The objectives

of the study are: (1) to quantify the change in land use/land cover (LULC) patterns and the degree of human

disturbance in Serbia between 2000 and 2018, and (2) to study the relationship between LULC configuration

and the impact resulting from human disturbance under different levels of intensity, to understand how

changing trends in landscape pattern can serve as indicators to estimate landscape changes resulting from

human actions. The Hemeroby Index (HI) was calculated to quantify the impacts on ecosystems resulting from

disturbance caused by human actions. Based on the analysis of the variation in the value corresponding to the

HI for the period between 2000 and 2018, the level of naturalness increased by only 5% of the territory of

Serbia, with this change being verified mainly in SE Serbia. The landscape pattern was quantified using a set of

LULC metrics. We used the Spearman method to identify the existing statistical correlations between the

geometric parameters of the landscape and the HIs values. At the landscape level, the Mean Shape Index, Edge

Density, Mean Patch Fractal Dimension, and Shannon Diversity Index show a strong negative correlation with

HI. This correlation suggests that landscapes with greater structural complexity are good indicators of low

levels of hemeroby. At the class level, Edge Density and Mean Patch Size correlate significantly with the HI for

artificial surfaces, agricultural areas, forests, and semi-natural areas.

Key-Words: - Hemeroby level, Land-cover changes, Land-use, LULC metrics, Serbia, Sustainable

development.

Received: June 15, 2023. Revised: March 6, 2024. Accepted: April 11, 2024. Published: May 16, 2024.

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DOI: 10.37394/232015.2024.20.17

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1 Introduction

Anthropogenic actions significantly impact the

landscape's structure and functionality, which is a

growing concern, and their monitoring has become

one of the critical points and essential areas of

research in landscape ecology, [1], [2]. Many

researchers have focused on the spatial variability of

disruption resulting from such actions and its

relationship with the evolution of landscape

patterns, [3], [4].

Contextually, environmental indices play a

substantial role in delivering information about the

environment's condition. For example, they assist in

decision-making processes or monitoring and

evaluating the efficiency of political and

administrative measures, [5]. Moreover, considering

the broad scope of landscape ecology, such indices

are developed to quantify landscapes' state and

changes.

The ecological pattern and process paradigm

indicates that a landscape's configuration influences

ecological processes, and landscape metrics provide

a suitable means of quantifying these patterns. Thus,

landscape metrics reflect the spatial configuration of

the landscape mosaic, [6], [7], [8].

Landscape metrics also present an exhaustive

view of landscape structure, measuring and

describing the landscape's configuration and

composition, [6]. Given the recent advances in

remote sensing and computation, analyzing large

datasets representing various aspects of landscape

patterns has become even more feasible.

In this regard, the Hemeroby Index (HI) is

essential for evaluating human interventions'

repercussions on ecosystems - once it has been

widely used in different studies, [3], [4], [9] to

quantify the intensity of anthropic changes in

landscape structure and function resulting from such

activities in the ecological environment, [10].

Therefore, we can say that the degree of

hemeroby, as indicated by the HI, serves as an

integrative measure of the impacts of human

activities on ecosystems, whether intended or not. It

measures an area's human impact level/naturalness,

indicating the deviation from potential natural

vegetation, [11], [12]. As hemeroby levels rise,

human influence becomes more detrimental,

resulting in increased landscape disturbance and

alteration, [3]. It can be classified into seven levels

concerning the degree of naturalness, [5].

Multiple authors underscore that examining

landscape patterns at the class level offers a more

effective method for estimating human disturbance

levels than landscape-level analyses based solely on

the total number of patches, [3]. Therefore, it is

essential to complement the study of the

relationships between the alteration of the landscape

mosaic and the degree of hemeroby at the landscape

level with the changes at the level of the LULC

classes to achieve a more detailed analysis of the

LULC tendencies and to select the more suitable

indicators. In this study, we used both approaches

(landscape and class level) to understand the

phenomena better.

In short, environmental indices are valuable

tools for understanding the impact of human

interventions on ecosystems and landscape patterns.

They aid decision-making, monitoring

environmental changes, and formulating effective

landscape management policies promoting

sustainable development.

Using land based on suitability involves using

tools to regulate the implementation of land use

planning measures in Serbia, [13]. The Law on soil

protection, spatial planning, and utilizing natural

resources and commodities in alignment with

spatial, urban, and other planning documents are

vital in preventing land degradation, [14].

Efforts were made twice in present-day Serbia

to adopt a land planning approach - during the post-

World War II socialist period in the former

Yugoslavia and the transition period to free-market

democracy after 2000, [15].

Decades of large-scale urbanization, combined

with ineffective and unsustainable attempts at

regional and urban planning, have led to ecosystem

degradation on a global scale. The rural

abandonment-urban concentration gap is anticipated

to further widen during the XXI century, [16]. Land

use is significant in addressing various sustainability

issues, including conserving biodiversity, mitigating

climate change, ensuring food security, alleviating

poverty, and promoting sustainable energy. These

issues are interconnected and require attention to

achieve long-term sustainability. Land systems

exhibit complex behaviors and often experience

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irreversible changes, making it crucial to adopt

sustainable land management practices, [17].

Brundtland presented sustainability as “(…)

development that meets the needs of the present

generation without compromising the ability of

future generations to meet their own needs”, [18].

To assess sustainability, suitable approaches

must be employed that consider its diverse

dimensions, including its environmental, economic,

and social aspects, at various spatial and temporal

scales, [19], [20], [21].

To achieve the desired sustainability,

understanding how societies use, manage, and

interact with land is crucial, [22], [23]. For this

reason, a combined and comprehensive approach to

land use implies many compromises between these

three pillars, and consequently, it is necessary for a

right European policy, [24], [25]. Thus, member

State approaches to spatial and land-use planning

are also vital factors in shaping the impact of EU

policies on land, [26] and they can be resolved by

employing integrated sectoral policies and targeted

policy instruments, [27].

Institutional arrangements are pivotal in

supervising the processes of information gathering,

monitoring, and evaluation of land use policies,

which are indispensable for attaining territorial

cohesion, [25], [26], [27], [28]. The adoption of

CORINE land cover classification (CLC) is

intended to facilitate the advancement of complex

spatial analyses covering a wide range of land-use

categories, [29]. The geodatabase is structured

hierarchically, with the first tier encompassing

primary land use types and land cover categories

such as artificial areas, agricultural areas, forest and

semi-natural areas, wetlands, and water bodies.

Subsequently, the second tier comprises 15

departments, and the third tier consists of 44

departments. Additionally, the CLC incorporates

registered data from different years, namely 1990,

2000, 2006, 2012, and 2018, [29], [30], [31].

The CLC records changes are beneficial for new

research at the regional level, which has conducted

spatial analysis studies based on Geographic

Information System (GIS) tools and CORINE data

methodological approaches to landscape mosaic

dynamics with different types of land cover

countries, regions, islands, or cities, [29], [32], [33],

[34], [35].

Since the CLC2000 project and databases were

developed in Serbia in 2005, a considerable interest

in using the data has been experienced in different

institutions. Back then, the production of the

CLC2000 database followed standard CORINE

procedure: computer-aided visual arrangement of

Landsat 7 satellite imagery sustained with ancillary

data produced under the CARDS Programme and

field checking. The methodology created a polygon-

based vector dataset that seamlessly integrates

various spatial features. This method's essential

mapping criteria consist of a map scale set at

1:100,000, a minimum mapping unit of 25 hectares,

and a minimum width specification for linear

elements (100 meters), [28], [29], [30].

The IMAGE2000 database was used as source

data, consisting of orthorectified Landsat 7 ETM+

images in national projection. The images are from

2000 with a tolerated deviation of +/- one year. The

CLC Changes database compares CLC2000 and the

satellite images from 2000 (IMAGE2000), [36].

Also, the ESPON Project SUPER, [37], has created

a database to perform analyses by merging data on

land use with possible drivers of land-use change.

Thus, all data were collected or converted to NUTS

3 (2016 limits) for the four dates of the CLC (2000,

2006, 2012, and 2018). The database is customized

to enable user-generated queries and is publicly

available.

As for former research in Serbia using land use

changes related to CLC, it is noticeable as several

works describe farmer lands being replaced by

urban areas, [38], [39], [40] and as the forest cover

in certain regions experiencing depopulation

increased, [41], [42], [43]. Nonetheless, the number

of studies related to hemeroby is still being

determined, [44]. Finally, most of these studies

establish that sustainable land use policies should be

defined at the national and regional level, [14], [15],

[38], [39], [40], [41], [45].

Contextually, the aim of this study is (1) to

quantify the change in land use/land cover patterns

and the degree of human disturbance in Serbia

between 2000 and 2018 and (2) to study the

relationship between landscape metrics and the

impact resulting from human disturbance under

different levels of intensity, to understand how

changing trends in landscape pattern can serve as

indicators to estimate landscape changes resulting

from human actions.

2 Methodology

Situated in Southeast Europe on the Balkan

Peninsula, Serbia is a continental country covering

88,361 km2. Serbia experiences a warm-humid

continental climate characterized by cold and

relatively dry winters and humid summers. The

northern region is predominantly flat, while the

central parts comprise highlands. Moving south, the

hills gradually transform into mountains, with the

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Dinaric Alps and the Carpathian Mountains

stretching to the south and southeast. The capital

city of Serbia is Belgrade, situated at the

confluence of the Sava and Danube rivers. In the

north, the Autonomous Province of Vojvodina

stands out for its highly developed agricultural

production. Areas with agricultural, forest, and

pasture activities are dominant. The ongoing

transition to a market-oriented economy has

increased demand for land use changes, particularly

for constructing industrial, infrastructure, and

recreational facilities.

The Corine Land Cover (CLC 2000; CLC

2006; CLC 2012 and CLC 2018) Land Use and

Cover (LULC) databases were used to calculate the

values of the different landscape indices. Class-

level and landscape-level metrics were calculated

for the 88 squares of 30 km2 each, corresponding to

a grid covering Serbia. The Patch Analyst

extension included in the Arc GIS 10.8 software

was used to calculate the landscape metrics.

The LULC classes were transformed into a

scale representing different levels of hemeroby,

ranging from ahemerobic (no anthropogenic

influence) to metahemerobic (destroyed

biocenosis). This seven-point scale enabled the

classification of LULC based on their

corresponding degrees of hemeroby, as indicated in

Table 1. Subsequently, an average value was

computed for each 10 km2 grid using the following

equation:

  

󰏇  



 (1)

 - Number of categories of hemeroby

(here:  = 6)

ƒ - Proportion of the area of the category 

h - Hemeroby-factor

 - HI

The landscape structure was quantified through

a set of landscape metrics shown in Table 2. Those

landscape metrics were selected based on some

criteria, namely: (1) these should represent and

define the dimensions of the characteristics of

spatial patterns; (2) these should be easily

calculated and not be redundant; and (3) they were

previously adopted in similar studies considered

relevant. In selecting the metrics to be used in the

study, the results of their application in various

research work with identical objectives to this study

were also considered, [3], [46], [47], [48].

Metrics that describe the patch area

distribution, such as the Mean Patch Size (MPS),

allow for characterizing the area distribution

between patches at the class or landscape level. The

Mean Shape Index (MSI) describes the patch

structure in the landscape as that of the average

patch characteristic and indicates the level of

landscape fragmentation. Edge Density (ED)

standardizes the length of edges on a per-unit area

basis. Mean Patch Fractal Dimension (MPFD)

describes landscape complexity, [49].

The correlation between the values of

landscape metrics and those associated with the HI

at both the landscape and class levels was

determined using IBM SPSS Statistics 22 software.

The Shapiro-Wilk test was initially applied to

assess the normal distribution of the variables.

However, most of the variables did not follow the

normal distribution. Hence, a non-parametric

Spearman's correlation coefficient was utilized,

[50]. Using LULC maps from 2000, 2006, and

2018, the study identified landscape metrics that

showed a statistically significant relationship with

the HI at a significance level of 0.01.

Table 1. Assignment of LULC types onto the Hemeroby scale, [14]

Degree of hemeroby

LULC types

Oligohemerobic

weak human impacts

Potential natural vegetation (PNV) forest. Natural habitats and other

seminatural areas, like dunes and inland marshes.

Mesohemerobic

moderate human impacts

Forest stands (not PNV). Scrub and/or herbaceous vegetation

associations. Sparsely vegetated areas.

β-euhemerobic

Moderate to strong human impacts

Pastures. Green urban areas. Inland waters. Heterogeneous agricultural

areas with natural vegetation.

α-euhemerobic

strong human impacts

Arable land and permanent crops. Artificial, non-agricultural

vegetated areas

Polyhemerobic

very strong human impacts

Discontinuous urban areas. Mine, dump, and construction sites.

Metahemerobic

Excessively strong human impacts.

Biocoenosis destroyed

Continuous urban areas. Industrial, commercial, and transport units.

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Table 2. Landscape metrics used in the study

3 Results

Figure 1 presents the LULC maps for the four

years: 2000, 2006, 2012, and 2018.

Agriculture in Serbia is primarily concentrated

in the northern region and near the major rivers. In

recent years, there has been a decline in agricultural

and pasture areas, impacting them the most. In

2018, agriculture was the dominant sector and

covered a significant portion of Serbia's territory

(Figures 2, Figure3 and Table 3).

From 2000 to 2018, the decrease in agricultural

areas was evident (3.53%), and forest area has been

increasing since 2000 with a variation of 1.34%,

representing about 29.82% of Serbia's territory in

2018. Also, the artificial areas increased in spatial

coverage from 3.29 % to 3.74 %.

(Corine Land Cover)

Structural

feature

Index

Name

Description

Edges

Edge Density

Calculating the edge length within a landscape

considers the distribution of patch types per square

km, including the landscape boundary and

background segments.

Area

MSI

Mean Shape Index

The measurement of average patch shape (complexity)

refers to quantifying the spatial structure of patterns,

typically land cover, within a specific class or for all

patches present in the landscape.

MPS

Mean Patch Size

The Mean Patch Size is calculated by dividing the

cumulative area occupied by patches within the

landscape (or a designated class) by the total

number of patches within that area.

Shape

complexity

Diversity

MPFD

MPAR

SDI

Mean Patch Fractal

Dimension

Mean Perimeter-Area

Ratio

Shannon Diversity Index

MPFD characterizes the complexity of a patch based on

its perimeter and area, describing the relationship

between the patch's size and shape.

Indicator of polygon shape complexity. Unlike other

shape parameters, the perimeter-area ratio is not

standardized to a simple Euclidean shape.

It reflects landscape heterogeneity and represents the

degree of landscape diversity.

(a)

(b)

(c)

(d)

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Fig. 2: Land use changes between 2000 and 2018 (km2).

Table 3. Distribution of land uses (CLC - level 1) in Serbia from 2000-2018

2000

2006

2012

2018

Artificial surfaces

3.29%

3.61%

3.65%

3.74%

Agricultural areas

54.97%

53.38%

53.33%

53.03%

Pastures

2.08%

2.15%

1.97%

Forest

29.43%

29.71%

29.68%

29.82%

Seminatural areas

8.54%

9.40%

9.45%

9.63%

Bareground

0.28%

0.27%

Wetlands

0.32%

0.35%

0.34%

0.40%

Water bodies

1.09%

1.14%

(a)

(b)

(c)

(d)

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During the study period, the average HI value for

Serbia decreased. In 2000, the index value was 3,847

± 0.747, which decreased to 3,834 ± 0.758 in 2006,

3,835 ± 0.759 in 2012, and further decreased to

3,829 ± 0.758 in 2018. These values correspond to a

β-euhemerobic level, indicating moderate to strong

human impacts.

Figure 4 presents the change in the average

values for the spatial metrics obtained at a landscape

level. MSI and MPFD values have decreased since

2006, which indicates a decrease in landscape

complexity. On the other hand, the SDI value has

steadily increased since 2000, corresponding to a

higher diversity of land uses. The MPS value has

decreased since 2006, corresponding to a reduction

in the average patch dimension.

The results of the analysis shown in Table 4

demonstrate that the HI has a significant negative

correlation (p < 0.01) with landscape pattern indexes,

specifically Mean Shape Index (MSI), Total Edge

(ED), Shannon Diversity Index (SDI), and Mean

Patch Fractal Dimension (MPFD) and, at a

Landscape Level during the study period from 2000

to 2018. This suggests that complex landscapes serve

as reliable indicators of low levels of hemeroby. The

change of the average values for the landscape

metrics obtained at a class level is presented in

Figure 5.

Fig. 4: Landscape pattern indexes and HI values change between 2000 and 2018 at a Landscape Scale: (a) Edge

Density (ED); (b) Mean Shape Index (MSI); (c) Mean Patch Size (MPS); (d) Mean Patch Fractal Dimension

(MPFD); (e) Mean Perimeter-Area Ratio (MPAR); (f) Shannon Diversity Index (SDI)

(a)

(b)

(c)

(d)

(e)

(f)

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Table 4. Spearman’s correlations between the HI and the landscape metrics - Landscape Level (2000 - 2018).

* Correlation is signiﬁcant at the 0.01 level.

Fig. 5: Landscape pattern indexes average values change between 2000 and 2018 at a Class Scale: (a) Edge

Density (ED); (b) Mean Shape Index (MSI); (c) Mean Patch Size (MPS); (d) Mean Patch Fractal Dimension

(MPFD); (e) Mean Perimeter-Area Ratio (MPAR)

2000

2006

2012

2018

Edge Density (ED)

-0.417*

-0.425*

-0.424*

-0.433*

Mean Shape Index (MSI)

-0.493*

-0.654*

-0.639*

-0.645*

Mean Patch Size (MPS)

-0.280*

-0.203

-0.194

-0.149

Mean Patch Fractal Dimension

(MPFD)

-0.438*

-0.586*

-0.589*

-0.616*

Mean Perimeter-Area Ratio (MPAR)

0.084

0.094

0.097

-0.037

Shannon Diversity Index (SDI)

-0.323*

-0.375*

-0.377*

-0.383*

(a)

(b)

(c)

(d)

(e)

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At a class level, we notice a gradual increase in

edge density, particularly in forest, agricultural, and

seminatural areas. Also, a slight increase in other

spatial metrics, like Mean Shape Index, Mean Patch

Size, and Mean Patch Fractal Dimension values,

were verified for the same land uses. Those results

indicate increased LULC fragmentation and

complexity for those land use categories.

The results presented in Table 5 demonstrate the

correlation between hemeroby and landscape pattern

indexes at a Landscape Level for various land types,

including artificial surfaces, agricultural areas,

forests, and seminatural areas, during the study

period from 2000 to 2018. The analysis reveals that

Edge Density (ED) and Mean Patch Size (MPS)

significantly correlate with the HI in these land

categories over the mentioned time frame.

Based on the analysis of the variation in HI value

verified between 2000 and 2018 (Figure 5), we

verified that the hemeroby degree decreased by 5%

for the 1 km2 quadrats and increased by 3.5% for the

quadrats. In the remaining territory, the hemeroby

level remained stable.

The increase in naturalness, mainly in the SE

part of Serbia, could be related to forestation.

Contextually, the rise in hemeroby level is consistent

with the urban growth rates seen in and around

settlements.

Table 5. Spearman’s correlations between the HI and the landscape metrics - Class Level (2000 - 2018)

*Correlation is signiﬁcant at the 0.01 level.

MSI

MPS

2000

2006

2012

2018

2000

2006

2012

2018

2000

2006

2012

2018

Artificial surfaces

0.763*

0.734*

0.547*

0.509*

0.648*

0.085

0.721*

0.706*

0.830*

0.777*

0.806*

0.799*

Agricultural areas

-0.421*

-0.365*

-0.346*

-0.331*

-0.792*

-0.122

-0.742*

-0.726*

0.673*

0.731*

0.889*

0.887*

Pastures

-0.116

-0.226

-0.225

-0.146

-0.192

-0.197

-0.280*

-0.217

-0.049

-0.232

-0.167

-0.093

Forest

-0.866*

-0.846*

-0.835*

-0.831*

-0.596*

-0.204

-0.500*

-0.520*

-0.935*

-0.904*

-0.923*

-0.924*

Seminatural areas

-0.693*

-0.726*

-0.718*

-0.737*

-0.694*

-0.045

-0.742*

-0.746*

-0.634*

-0.714*

-0.656*

-0.678*

Bare ground

-0.209

-0.223

-0.278

-0.207

-0.298

-0.307

-0.232

-0.117

-0.154

-0.085

-0.274

-0.197

Wetlands

0.230

0.381

0.377

0.420*

0.277

0.201

0.458*

0.471*

0.130

0.276

0.267

0.299

Water bodies

0.415*

0.386*

0.387*

0.371*

0.399*

0.280

0.389*

0.345*

0.379*

0.342*

0.365*

0.362*

MPFD

MPAR

2000

2006

2012

2018

2000

2006

2012

2018

Artificial surfaces

0.430*

-0.035

0.286*

0.158

-0.554*

-0.019

-0.585*

-0.587*

Agricultural areas

-0.894*

-0.189

-0.875*

-0.861*

-0.651*

-0.076

-0.933*

-0.934*

Pastures

-0.319*

0.000

-0.355*

-0.322*

-0.047

0.123

-0.039

-0.118

Forest

-0.285

-0.152

-0.212

-0.222

0.686*

0.209

0.651*

0.659*

Seminatural areas

-0.416*

0.077

-0.578*

-0.568*

0.181

0.006

0.299*

0.319*

Bare ground

-0.276

-0.341

-0.207

-0.104

0.057

-0.240

0.233

0.242

Wetlands

0.348*

0.074

0.506*

0.519*

0.538*

0.091

0.448*

0.356

Water bodies

0.325*

0.137

0.302*

0.290

-0.156

0.094

-0.141

-0.145

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Fig. 5: The estimated change in the HI values between 2000 and 2018

4 Discussion

Based on the results, the landscape pattern response

to human disturbance differs depending on the

scale. At a landscape level, the HI has a significant

negative correlation (p < 0.01) with landscape

pattern indexes that are good indicators of land-

scape complexity (ED, MSI, MPFD, SDI). This

suggests that decreased human disturbance would

lead to more complex landscapes. A tendency to

increase LULC fragmentation and complexity was

also verified at a class level, especially for forest,

agricultural, and seminatural areas. Other studies

showed the exact relation between hemeroby level

and landscape complexity, [9], [46].

By analyzing the correlation between

hemeroby and landscape metrics in different LULC

classes, it was also found that the effect of human

disturbance on landscape pattern was more intense

in low-level hemeroby areas (i.e., forest,

agricultural areas, semi-natural areas) but less

severe in artificial areas. Therefore, it is better to

focus on monitoring the change in the existing

agroforest ecosystems, as their ecological quality is

more sensitive to human disturbances.

The results obtained through applying both

qualitative and quantitative landscape pattern

indicators helped identify and interpret the main

trends of land transformation in Serbia. However,

some limitations still need to be further improved,

namely the reduction of the scale effect in the

interaction of multiple land-scape functions, which

may have affected the results to a certain extent.

Although agriculture is nominally one of the

most important land uses in Serbia, every year, a

significant proportion of the agricultural land in

Serbia changes to another use. In the fifties, Serbia

lost approximately 222,000 ha of agricultural land

irrecoverably to constructing industrial, mining,

energy, and traffic infrastructure, [50]. It should be

noted that in the period immediately before 2000,

there was a planned trend of reducing land use with

agriculture in the long term, which was

demonstrated by the quantitative analysis of

planning and management processes of the territory

at the local level, [51].

It is essential to understand the circumstances

under which land use transitions occur since

planning instruments give a territorial expression to

societies for sustainable development. Land use

changes may have negative feedback resulting from

the depletion of critical resources and/or a decline

in the supply of essential ecosystem goods and

services. Conversely, modifications in LULC can

be driven by socioeconomic transformations and

innovations that operate independently of the

ecological system, following their dynamics, [52].

In reality, land use change phenomena exhibit a

remarkable diversity of geographical and historical

contexts, presenting several aspects of high

complexity in ecological and social systems, [53].

Consequently, a more integrated, broad, and

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contemporary land use planning and management

policy is needed, [28].

Furthermore, data on land use in Serbia are

encountering multiple obstacles - i.e., political and

economic transition or the pursuit of sustainable

development of energy production itself. 2005-

2006, CORINE Land Cover (CLC) databases were

assembled for Serbia. Since then, they have

allowed us to acquire data on land cover for the

whole territory of the Republic of Serbia, [28].

The ubiquitous emphasis on an integrative

approach to using and preserving agricultural and

forest land as the most critical resource is

nevertheless in conflict with a practice that shows

the conversion of agricultural and forest land into

building land, the expansion of settlements, and

illegal construction at the cost of agricultural land,

which is subsequently "legalized", [54], [55].

Alongside the enactment of laws, regulations,

and strategies for safeguarding and conserving land

resources, it's apparent that organizations and

institutions in the Republic of Serbia face

challenges in effectively implementing these legal

measures [56]; consequently, it is expected that

systems of spatial planning and territorial

governance should intervene at different levels

from national to local. As a strategic document for

spatial planning, the new RS Spatial Plan 2021-

2035 [57] aims at more rational use of previously

occupied agricultural land, increasing the area of

Serbia’s territory under forest use to 41.4% by 2050

and restructuring other regions. There is about

923,000 ha of agricultural land in state ownership,

and the problems faced by the competent Ministry

of Agriculture are numerous: a threat to

agrobiodiversity, threat to land quality,

undeveloped land market, restitution process that is

still ongoing, are just a few examples.

The reduction of the area under agricultural

land is caused by the changes in the agricultural

sector since 2000, which continues even after the

restitution, and is also economically conditioned for

the conversion of land "(…) if we take into account

that in the Republic of Serbia in 2004, the value of

construction land was approx. 1,000 times higher

than the initial value of the original (agricultural or

forest) land that is converted into construction land

(World Bank document, 2004; Strategy for

Sustainable Urban Development of the RS until

2030, 2019)", [58]. Also, in demographically

depleted, peripheral, remote, and mountainous

areas, we have a case of converting agricultural

land into forest land due to the absence of the

possibility for someone to cultivate that land.

The primary shifts in purpose occurred during

the expansion of urban settlements. In the post-

socialist era, there is a need to harmonize the legal

framework of spatial and urban planning systems

and practices, [59], [60]. Due to significant changes

in the purpose of agricultural land for construction

and the needs of public purposes, two by the

concepts of neutral degradation and land safety, as

well as for residential construction, planning

solutions are submitted during the preparation of

planning and urban planning documents in Serbia

for the opinion of the competent Ministry for the

environmental protection.

Given what was mentioned in the previous

paragraphs, it is crucial to characterize the changes

in Serbia's territorial planning policy and the

implications for land occupation and its associated

impacts, emphasizing the transition to a free-market

democracy after 2000.

5 Conclusions

Based on the results obtained, we can conclude that

the landscape metrics work as good indicators of

the quality of the landscape mosaic, being

appropriate to describe its degree of hemeroby and

allowing us to anticipate possible changes in

naturalness/artificialization based on LULC at a

regional scale. This confirms the results of previous

studies developed in different countries.

No index is interpretable by itself, as there is a

wide range of elements of uncertainty regarding

ecological interpretation. Only an approach using

various spatial metrics and indicators, including the

HI, allows a comprehensive analysis of changing

use trends and consequences.

The results obtained through the application of

different indicators of the state of the environment

and LULC can be integrated into a nationwide

system for monitoring the implementation of

spatial planning measures and their impact on land

systems. Therefore, consistently computing the

mentioned metrics over time could significantly

enhance the qualitative and quantitative

understanding of Serbia's land use and land cover

(LULC) changes. This data would give decision-

makers and the public comprehensive insights into

territorial transformations across various levels.

Adapting planning models based on these

indicators and the relations between them can

effectively prevent and mitigate anthropic impacts

on the ecosystem.

In sum, and broadly, the theoretical explanation

of the relationship between landscape patterns and

human disturbance lies in landscape metrics,

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disturbance theory, and human-environment

interactions. Through these theoretical frameworks,

we can understand how human activities shape

landscape patterns and, in turn, how landscape

patterns influence the distribution and impacts of

human disturbance on ecological systems. This

knowledge is essential for guiding landscape

management, conservation efforts, and sustainable

development strategies that balance human needs

with environmental protection.

Future research should pay more attention to

other determining factors influencing changes in

use, namely population growth, economic

development, and technological progress. It is also

suggested that a territorial observatory be

implemented to enable the systematic collection of

data and, consequently, the monitoring and

subsequent analysis of Territorial Dynamics. This

observatory may have the configuration of a spatial

data infrastructure.

Acknowledgments:

The authors would like to acknowledge the

financial support of the National Funds provided by

FCT-Foundation for Science and Technology to

VALORIZA-Research Center for Endogenous

Resource Valorization (project UIDB/05064/2020).

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