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Environmental Processes

, Volume 5, Supplement 1, pp 213–237 | Cite as

Land Accounts in the River Basin Districts of Greece

  • Georgios BariamisEmail author
  • Georgios Paschos
  • Evangelos Baltas
Original Article
  • 39 Downloads

Abstract

Land is a highly valuable natural resource, where all human activities take place by exploiting natural goods for socio-economic development. The competition for land resources is increasing with pristine soils of unoccupied land being very precious for agricultural use (food production) and infrastructure development. International efforts have been made in assessing and quantifying the natural capital reserves and ecosystem services through lately led conceptual approaches such as the land and ecosystems extend accounting. This research conducted at the national level of Greece and in the 14 river basin districts (RBDs), revealed the main drivers of change in land cover stocks, as well as future trends in a non-spatially explicit context. In Greece, the analysis for the last 23 years has shown that the land resources are intensively exploited with significant piece of land being transformed into urban fabric and other man-transformed surfaces supporting a more urbanized manner of socio-economic development. The expansion of artificial surfaces is a phenomenon met in all RBDs of the country. The significant reduction of forests increases the flood risks, especially in peri-urban areas, while the reduction of forests in low-lying lands used for intense agriculture reduces aquifer recharge and impairs water quality due to the use of fertilizers and pesticides. An attempt has been made to estimate the main trends with a 50-year projection horizon; the current trends are anticipated to continue (+17% expansion of urban fabric) with significant land surface being converted from natural to other uses.

Keywords

Land accounts Urbanization Agricultural development Forest loss Natural capital 

1 Introduction

Last century’s development has not taken into account the limits of the environmental capital for the provision of goods and has led to an unsustainable global development pattern. Extensive urban expansion, exploitation of mineral resources for energy production and increased water demand for the agricultural and industrial sectors are some of the pressures exerted on the natural environment (Millennium Ecosystem Assessment 2005).

Following the global trends of urbanization, it is projected that the population living in urban areas will comprise 66% of the global population by 2050 (United Nations 2014). The current projections are that the world population will continue to move towards urban areas at an increasing rate (Seto et al. 2012), resulting in a rising need for more natural lands to be “consumed” by the urban sprawl, reducing this way the ecosystem productivity through loss of habitat, biomass, and carbon storage. The urbanization of lands has also measurable impact on the quantity (Kaplan et al. 2014; Chen et al. 2015; Miguel Ayala et al. 2016; Gyamfi et al. 2016) and quality (Wilson 2015; McGrane 2016) of water resources.

Deterioration of natural lands and ecosystems remains a major environmental concern at a global scale. Although future scenarios include projections of deforestation and natural habitat loss, especially in tropical ecosystems (Blasco et al. 2015), the general expectations differ in Europe, where the amount of forests and shrub lands is increasing according to recent studies (FAO 2011). This recovery of natural vegetation appears as a consequence of reduction in land use intensity or even land abandonment at local regions that were initially affiliated with the agricultural sector (Bariamis et al. 2015; Karp et al. 2015). Although there is a variety of reasons for this, such as depopulation of rural areas, mechanization of farming operations, and the European Union agricultural policy (Corbelle-Rico et al. 2015), the extent of these changes and the impact they will have on currently existing land systems are likely to lead to a change in overall assessment approaches as the valuation of ecosystem services progresses at global scale (Costanza et al. 2014).

In the pursuit of sustainable development, reduction of emissions from deforestation, forest degradation, and enhancement of forest carbon stocks (REDD+) is a global strategy to mitigate climate change (Atmadja and Sills, 2016). Mediterranean ecosystems are a particular type of dryland which accounts for less than 5% of the Earth’s surface but host about 20% of the world’s plant species (Cowling et al. 1996). Land degradation, a primary concern in such ecosystems, describes the substantial decrease in the biological productivity of a land system as a result of human activities (Geri et al. 2010). Loss of natural vegetation cover is often a precedent to soil erosion and deterioration of the water storage capacity (Schulz et al. 2010). The physical changes to forest cover have also various implications including impacts to surface albedo, evapotranspiration rates and surface runoff (Drummond and Loveland, 2010). The general conclusion is that forest removal or harvesting increases streamflow, whereas reforestation reduces aquifer water recharge (Zhou et al. 2010). Therefore, investigating the dependencies and coupling mechanisms among soil, water and vegetation interactions can help to understand the land surface development processes and biogeochemical balances in dry lands (Wang et al. 2012).

The challenges that are emerging from this shift, have transformed the global economy and require a holistic approach in assessing economic growth, environmental protection and future social welfare. The United Nations initiated the UN-Water, an inter-agency mechanism, to coordinate the implementation of the agenda defined by the Millennium Declaration (United Nations 2000) and the World Summit on Sustainable Development (United Nations 2002). Following up to more detailed implementation framework steps, the 2030 Sustainable Development Agenda (United Nations 2015) recognized a set of 17 Sustainable Development Goals (SDGs), which include safe access and sustainable use of water resources, food security and sustainable agriculture, actions to combat climate change, sustainable management of forests, combat against desertification, and halting and reversal of land degradation. Additional international initiatives include the monitoring of the global natural capital as part of the WAVES partnership (World Bank 2010), and the evaluation of the ecosystem services (Maes et al. 2013).

The increasing competition for the finite and scarce natural resources, which are either depleting or have been systematically driven to quality degradation, stimulated the development of the environmental accounting theories and models. More specifically, the natural resources are considered as natural capital inputs and are measured as environmental assets which are the main material inputs and sources of the economy. All the environmental processes had been standardized in a comprehensible way under the System of Environmental and Economic Accounting (SEEA) – Central Framework (United Nations 2014). The SEEA provides a set of useful tools and has already been implemented throughout the world in the water (Bariamis et al. 2016; Dutta et al. 2017; Pedro-Monzonís et al. 2016; Paschos et al. 2017; Zal et al. 2017) and land cover (EEA 2006) domains. In addition, the SEEA system, through its detailed data collection under different spatial scales, provides a robust knowledge base that promotes the aggregation of the environmental information with other scientific and economic disciplines, as well as the development of indicators addressing various environmental challenges.

Land is one of the most important assets of the natural environment, providing most of the ingredients of life cycle, as well as all the natural resources and raw materials for socio-economic development. Land outlines all the space in which all the natural inland processes and human/economic activities occur. There are two main characteristics in which land could be classified: land cover (LC) and land use (LU). Land cover includes all the surface features which can be observed and contain all the physical and biological elements, while land use is a more anthropogenic/socio-economic oriented classification. Due to the rapid urban expansion and industrialization, land capital is under pressure (Viger et al. 2011; Zank et al. 2016), triggering environmental pressures at the global scale, such as groundwater pollution, soil erosion, decreasing the natural retention capacity, water shortages, and deteriorate regional climate conditions (World Resources Institute et al. 1996; Duh et al. 2008; Ooi 2009; Astaraie-Imani et al. 2012; Shan et al. 2015; Miller and Hutchins 2017).

Land accounts support the detection of land cover change (LCC) trends and their patterns, while by elaborating at larger scales, they could provide concrete assessments in key sectors, such as water, agriculture (Liu 2009; Bonsch et al. 2015; Bariamis et al. 2017), energy (Vogl et al. 2016), forests and land management. Land accounting, as conceptual framework, focuses on the links of environmental and socio-economic activities, which take place on the terrestrial surface, and analyzes their extents in terms of land cover. Several publications also take into account protected area land management such as critical elements of land sustaining and supporting ecosystem services (Sonter et al. 2017; Tolessa et al. 2017; Vogl et al. 2017). The evolution of computational capacity and the wide spread of remote sensing data and analytical tools provide crucial and timely information on large spatial scales. The development of spatially explicit models describing and predicting the future trends (Daniel et al. 2016; Verkerk et al. 2016) is valuable for spatial planners, stakeholders and policy makers implementing long-term sustainable environmental protection strategies (Maimaitijiang et al. 2015) across this competitive environment (Gingrich et al. 2015; Haberl 2015; Triantafyllopoulos 2017).

This study focuses on the implementation of a land accounting approach for all fourteen (14) river basin districts (RBDs) of Greece as they were outlined by the Water Framework Directive (2000/60/EC) data reporting obligations. The land accounting framework adopted for this implementation is the SEEA Central Framework (SEEA–CF) applied for the period 1990 to 2012. The SEEA–CF provides the approach of natural capital stocks (in this case land resources) and change during each of the accounting periods. With this methodology, it is possible to recognize the main drivers of land cover change at RBD scale. Additionally, with the accounting parameters of land resources, namely consumption, transitional stock, formation, land intake, etc., it is possible to quantify the rate of change. This means that by measuring the changes, it is possible to define the drivers and pressures in the water environment, as well as to project through the time series created at 6-year periods the future status. Projections could be used as an operation tool for decision making towards the implementation of integrated water resources management.

A GIS extraction and database integration model has been developed for this purpose to facilitate the data handling and storage. An attempt was made to construct the spatial model outputs as transitional matrices among the time intervals and utilizing Markov chains approach to estimate future trends at national level. The main data inputs consist of the publicly available geospatial data, such as the CORINE land cover system and the RBD entities reported in the Greek official Water Framework Directive portal (Special Secretariat for Water - Ministry of Environment and Energy).

2 Materials and Methods

2.1 Study Area

The study area includes all the RBDs of Greece. The country has been structured in 14 large hydrological regions complying to the requirements of Water Framework Directive. These regions, so called RBDs, delineate water bodies (surface and groundwater). In total, there have been recognized 46 river basins, 1781 surface water bodies and 565 groundwater bodies, as shown in Table 1 (Special Secretariat for Water - Ministry of Environment and Energy).
Table 1

River Basin Districts of Greece

RBD Name

RBD Code

Area (sq. km)

River network length (km)

W. PELOPONNESE

GR01

7235

781.0

N. PELOPONNESE

GR02

7395

662.4

E. PELOPONNESE

GR03

8446

755.5

W. ST. ELLADA

GR04

10,493

1462.7

EPIRUS

GR05

9969

1331.1

ATTICA

GR06

3189

113.1

E. ST. ELLADA

GR07

12,297

1007.9

THESSALY

GR08

13,144

2001.8

W. MACEDONIA

GR09

13,618

1905.9

C. MACEDONIA

GR10

10,171

1301.6

E. MACEDONIA

GR11

7326

1046.5

THRACE

GR12

11,247

1864.6

CRETE

GR13

8349

665.4

AEGEAN ISLANDS

GR14

9138

323.2

More than 50% of the total area of the country is distributed in the five (5) RBDs of Western Macedonia, Thessaly, East Sterea Ellada, Thrace, Western Sterea Ellada and Central Macedonia, all located in the northern parts of the country. Additionally, 30% of Greece’s RBDs are shared with the neighboring countries of Albania (Epirus RBD – main river Aoos), FYROM (Central Macedonia RBD – main river Axios), Bulgaria (Eastern Macedonia RBD – main river Strymonas, Thrace RBD – main river Nestos), and Bulgaria and Turkey (Thrace RBD – main river Evros), as shown in Fig. 1.
Fig. 1

River Basin Districts of Greece

An extraordinary case of Greek RBDs and water resources management challenge is the case of Aegean Islands where more than 500,000 people are distributed in hundreds of large and small islands in the north and south parts of the Aegean Sea. The total main river network in Greece is estimated at 15,220 km draining most of the perennial and ephemeral streams, as shown in Fig. 2.
Fig. 2

Comparison of total area (km2) and river network (km) at the RBDs of Greece

Greece is largely mountainous, with its latitude ranging from 35°00′N to 42°00′N and in longitude from 19°00′E to 28°32′E. As a result, the country has considerable climatic variation. The climate in Greece is typical Mediterranean climate type, characterized by mild and rainy winters and relatively warm and dry summers. Due to the influence of the topography, a great variety of climate subtypes, always within the Mediterranean climate type, are encountered in several regions of Greece. Thus, the dry climate of Attica (the greater area of the capital city Athens) and generally of eastern Greece, changes to a wet one in north and west Greece. In terms of climatology, the year can be broadly divided mainly into two seasons. The cold and rainy period lasting from the mid of October until the end of March, and the warm and non-rainy season lasting from April until September (Hellenic National Meteorological Service).

2.2 CORINE Land Cover System

In 1985, the European Council elucidated the basic principles of the CORINE program. Since the first version in 1986, the Coordination of Information on the Environment (CORINE) Land Cover program records the thematic information regarding the biophysical characteristics of land units. Land cover refers to a set of both natural features and anthropogenic establishments that mainly result from its use. The application of visual interpretation on satellite images led to the establishment of the CORINE Land Cover (CLC) and CORINE Land Cover Changes (CLC-C) inventories. According to CORINE nomenclature, the classes/types of land cover can be grouped and arranged into three levels of detail that extend from 44 classes at level 3 (highest detail), to 15 classes at level 2, and finally, to 5 broad classes at level 1. Mapping, monitoring and quantifying data of these elements can be used as input to integrated land and water analysis (Serbina and Miller 2014). The main challenge is to examine the permanent links between basic land use functions and water-demanding activities under the constraints that the first asset often underlies poor management and the latter is scarcely allocated.

Over the last decade, it is alleged that the study of historical effects in land cover/use can be used in the understanding of the current situation and the prediction of future trends, factors and possible choices that will affect water resources in due course (Schilling et al. 2008). The determinant approach used for the assessment of transitions during 1990–2012 indicates that the land cover status of the final observation year derives from a stepwise aggregation of the initial CLC1990 layer data with the three CLC-C layers (CLC-C1990–2000, CLC-C2000–2006 and CLC-C2006–2012). Despite the significant effort in the design, updating and constant re-evaluation of the CORINE database, there are some considerable limitations in land management applications. The minimum mapping unit is set at 25 ha for the CLC layers and 5 ha for the CLC-C layers. It is recommended that the users interested in changes during a period should rely on the CLC-C files instead of the difference between two CLC layers, since the lower resolution of the status layers would lead to a more generalized product.

The CLC dataset has been explored in a way to portray the thematic detail between all the three levels, as shown in Fig. 3. The larger surface water bodies (natural lakes and artificial reservoirs) are concentrated in the northwest of the country, where most of the water stocks are located. Most of the areas of agricultural interest (arable lands and permanent crops) are located in mainland RBDs, such as Thessaly, and the other RBDs in Macedonia and Thrace Regions. All the main river tributaries have been surrounded by the agricultural sectors since irrigation water consists the majority of water exploitation (surface and groundwater). Two are the main urban agglomerations in the country: the capital region of Attica (GR06) with Athens; and Central Macedonia (GR10) with Thessaloniki. More than 50% of the country’s population is concentrated in these two areas. Extended infrastructure projects developed during the last decade (mainly highways, ports and airports) have also played a role in the growth of artificial surfaces (industries, mineral facilities, etc.).
Fig. 3

Hybrid CORINE Land Cover of 2012 for Greece at major land cover classes covering all 3 thematic levels

2.3 Land Accounting

Land is of central interest in the environmental and economic accounting. The impacts of urbanization, crop intensity and animal production, use of water resources, afforestation and deforestation are some of the issues that can be considered in the context of land accounting. As the mapping technologies and GIS systems are expanding their capabilities to represent more dimensions of the land cover change dynamics, the accounting is continuously improving both horizontally (interdisciplinary) and vertically (thematic detail). The basic factors of land accounting include topography, elevation and land zoning. Land use and land cover are very crucial elements of the accounting system, while land cover refers to the observed and biological cover of the Earth’s surface, and includes vegetation and non-living surfaces (United Nations 2014).

In the accounting viewpoint, it is very important to quantify the stocks (S) component. The stocks are referred to the area for each of the land cover types (LCT) forming the total surface of an area under accounting process; in our research, it was the main input provided by the CORINE dataset in four epochs: 1990, 2000, 2006 and 2012. As land cover stocks (LCS) are the cornerstone of the accounting, a necessary set of principles and functions attached to them are necessary to support the concepts. The very first, is the conservation of land (equivalent to “mass”) where the total land stock as a summation of each of the respective LCTs has to be constant. The second principle is the preservation of the boundaries, where the area delineated for land accounting has to be the same, so as the reference of the total stock will be the same. These two principles are complementary.

Apart from the principles, there are also functions which are formulating the possible pathways of each minimum land cover area under change between two consecutive time periods. The functions that are appearing in the accounting framework are mainly the functions of transition (Tr), consumption (C) and formation (F), where their interactions are presented in Fig. 4. When the LCT S1 is subjected to change between the epochs t1 and t2, a part of the S1 stock is consumed by another LCT, while the remaining classes on the total land cover stock are forming a new land with LCT the same as in the S1. The remaining land that stays unchanged is referred as transitional stock between the periods.
Fig. 4

Graphical representation of basic land accounting functions through a time-series; Si: land stock, C: land consumption, F: land formation, TRi: transitional stock, Source: Georgios Bariamis

Another visualization presents in more detail the functions of formation, consumption and transitional stocks, as shown alternatively in Fig. 5. The relation between formation of new LCT and consumption of an existing one is defined in terms of the total stock of this LCT with the function of net formation (NF). NF can be positive or negative as shown in cases A and B in Fig. 3 respectively; a negative net formation means that the total stock of the respective LCT is decreasing in favor of other LC types in the area during the same epoch. It has to be noted that the total formed and consumed land areas might refer to more than one LCT; e.g., formation of urban fabric can be a result of heterogeneous agricultural areas and mixed forest. Alternatively, the consumption of broadleaved forest can be consumed by mine sites and pastures. The absolute summation of F and C derives a new parameter called total turnover (TT) which in general provides an overview of the stability of the ecosystem and the total change rate that each area metabolizes the land cover changes. Both NF and TT could be evolved in indicators providing average annual rates of LC functions in annual resolution (in km2/yr) as a primal product of land accounts.
Fig. 5

Graphical representation of land cover Net formation (NF), Case A: positive net formation (S2 > S1), Case B: negative net formation (S2 < S1), Source: Georgios Bariamis

The thematic detail of this research work has been defined at the Level 2 of CLC, which means that the land cover classes (LCC) analyzed were fifteen (15). The files of each epoch defined the LC stocks for the years of 1990, 2000, 2006 and 2012, as mentioned above, and the intermediate files containing the changes have been used to quantify the functions of formation, consumption, total turnover and land intake (NF > 0) or outturn (NF < 0). A typical transition table is shown in Table 2 depicting the LCC. The central diagonal in grey colors represent the transitional stocks between the epoch (from/to columns/rows).
Table 2

Typical transition or land cover table (LCT). Source: (EEA 2006)

As the LCC functions are formulated by the transition Table 2, each of the possible transitions with “from-to” formality have been examined under the prism of transitional probabilities and combined for the 2 epochs with the same time step: 2000–2006 (period 1) and 2006–2012 (period 2). Each of the LCT has a unique set of changes heading to the other 14 LC classes or remained unchanged (transitional stock). The LCC tables from the 2 periods have been calculated as average probabilities of the two 6-year intervals. The probabilistic model adopted is the Markov chains and the estimated land cover status have been attempted till 2060 (interval number n = 8) for Greece taking into account the Level 2 CORINE LC class breakdown. The Markov chain model has been utilized due to the randomness of land cover change during time and the statement that the future state depends only on the current state. The methodological structure of land accounting provides the necessary intermediate step of the Markovian process, the transition matrix (in our case land cover change between epochs) and the “from/to” states of land cover of the RBDs. The transition matrix (structure of Table 2) is populated with the values of transitional probabilities which apply to whenever a new state is visited. This is a key assumption of the Markov property (Bertsekas and Tsitsiklis 2000). Future scenarios of land change have implemented at large spatial scales with spatial explicit models especially for agriculture (Wilson et al. 2017), urbanization and forest harvesting (Sleeter et al. 2017) and ecological status modeling (Hughes et al. 2016).

3 Results

3.1 General Overview

The GIS algorithm provided the spatial input for constructing the land accounts, while with the use of SQL functions, the results have been transformed into the land accounts structure (land accounts tables, land cover flow tables) for further analysis and for estimating the additional functions of formation, consumption, net formation, total turnover and average land intake. Analyzing the land cover stocks, as well as the basic functions of land accounts in Greece and the Greek RBDs, the following results are extracted, as shown in Tables 3, 4 and Figs. 6, 7. As for the latest status of the land cover stocks in 2012, the artificial surfaces cover 2.16%, the agricultural areas 39.98%, the forest and semi natural areas 56.28% and the wetlands and inland water 1.58%.
Table 3

Land accounts table for Greece from 1990 to 2012 at Level 2 of CORINE thematic detail

 

11

12

13

14

21

22

23

24

31

32

33

41

42

51

52

Totals

1990

1747.9

397.8

240.4

83.8

22,010.9

8227.7

734.2

22,000.9

24,320.8

47,785.6

2374.7

238.0

354.8

1084.9

391.4

131,993.7

F (km2)

51.6

120.2

197.7

5.5

89.3

35.9

2.1

147.7

315.5

886.3

98.5

10.7

0.6

36.4

2.1

2000.2

C (km2)

0.2

0.1

26.8

0.0

148.0

68.3

35.6

155.0

856.9

582.1

69.7

5.2

4.8

45.2

2.2

2000.2

NF (km2)

51.4

120.1

170.9

5.5

−58.7

−32.4

−33.5

−7.3

−541.3

304.2

28.8

5.5

−4.1

−8.8

−0.1

0.0

TURNOVER (km2)

51.7

120.3

224.5

5.6

237.4

104.2

37.7

302.8

1172.4

1468.3

168.3

15.9

5.4

81.6

4.3

4000.4

INTAKE (km2/yr)

4.7

10.9

15.5

0.5

−5.3

−2.9

−3.0

−0.7

−49.2

27.7

2.6

0.5

−0.4

−0.8

−0.0

0.0

2000

1799.3

517.8

411.3

89.2

21,952.2

8195.3

700.7

21,993.6

23,779.5

48,089.7

2403.5

243.4

350.6

1076.1

391.3

131,993.7

F (km2)

49.0

85.1

93.7

18.8

17.6

3.3

42.1

81.2

118.3

248.1

112.2

4.6

1.1

24.3

0.2

899.6

C (km2)

2.3

7.1

65.7

160.0

22.9

5.7

94.0

243.3

249.6

43.0

3.9

1.0

0.9

0.1

899.6

NF (km2)

46.7

78.0

28.0

18.8

−142.4

−19.7

36.3

−12.8

−124.9

−1.5

69.2

0.7

0.2

23.4

0.0

0.0

TURNOVER (km2)

51.3

92.2

159.4

18.8

177.6

26.2

47.8

175.3

361.6

497.7

155.2

8.4

2.1

25.2

0.3

1799.2

INTAKE (km2/yr)

6.7

11.1

4.0

2.7

−20.3

−2.8

5.2

−1.8

−17.8

−0.2

9.9

0.1

0.0

3.3

0.0

0.000

2006

1845.9

595.8

439.3

108.1

21,809.8

8175.6

737.1

21,980.8

23,654.5

48,088.3

2472.7

244.1

350.8

1099.6

391.3

131,993.7

F (km2)

21.4

81.0

98.0

9.2

4.7

3.7

7.8

5.8

195.5

547.1

358.8

1.9

0.1

51.1

0.8

1387.0

C (km2)

0.1

2.2

72.9

0.6

39.4

21.8

45.8

44.9

577.7

479.9

95.2

3.4

0.8

2.1

0.3

1387.0

NF (km2)

21.3

78.8

25.2

8.7

−34.7

−18.1

−37.9

−39.1

−382.2

67.2

263.6

−1.5

−0.7

49.0

0.5

0.0

TURNOVER (km2)

21.5

83.2

170.9

9.8

44.1

25.4

53.6

50.7

773.2

1027.0

454.0

5.3

0.9

53.2

1.0

2774.0

INTAKE (km2/yr)

3.0

11.3

3.6

1.2

−5.0

−2.6

−5.4

−5.6

−54.6

9.6

37.7

−0.2

−0.1

7.0

0.1

0.0

2012

1867.2

674.6

464.4

116.8

21,775.2

8157.5

699.2

21,941.6

23,272.3

48,155.5

2736.3

242.6

350.1

1148.6

391.8

131,993.7

Table 4

Land cover flow table for Greece in 2012 for CORINE at Level 2 of thematic detail

Greece

11

12

13

14

21

22

23

24

31

32

33

41

42

51

52

C

C (NT)

% of C

11

0.16

0.06

0.05

            

0.27

0.11

0.0%

12

0.20

 

1.49

   

0.06

 

0.50

      

2.25

2.25

0.2%

13

6.77

34.85

0.47

5.04

0.23

 

4.96

0.37

1.19

14.72

0.11

  

4.65

 

73.36

72.89

5.3%

14

 

0.13

0.30

    

0.13

       

0.56

0.56

0.0%

21

2.08

11.85

20.45

0.10

37.22

0.22

0.60

0.14

0.18

 

0.32

1.91

 

1.54

 

76.61

39.39

2.8%

22

2.27

2.04

14.84

0.36

0.16

  

0.92

 

0.26

   

0.90

 

21.75

21.75

1.6%

23

0.69

1.38

5.16

0.56

0.92

    

1.59

   

35.46

 

45.76

45.76

3.3%

24

9.16

12.29

19.04

1.47

0.12

0.36

 

0.22

 

0.94

   

1.54

 

45.14

44.92

3.2%

31

 

0.88

7.22

0.11

0.07

0.12

 

1.18

 

438.30

129.65

  

0.20

 

577.73

577.73

41.7%

32

0.24

16.96

29.25

1.58

0.84

2.68

2.22

2.71

191.55

92.91

228.75

  

3.11

 

572.80

479.90

34.6%

33

 

0.34

0.24

    

0.33

2.09

91.29

4.21

  

0.85

0.05

99.40

95.19

6.9%

41

    

0.25

0.27

       

2.86

 

3.38

3.38

0.2%

42

 

0.08

            

0.71

0.78

0.78

0.1%

51

    

2.11

          

2.11

2.11

0.2%

52

 

0.13

0.02

         

0.12

  

0.27

0.27

0.0%

F

21.57

81.00

98.52

9.22

41.92

3.65

7.84

6.00

195.51

640.01

363.03

1.91

0.12

51.11

0.76

 

100%

F (NT)

21.41

81.00

98.05

9.22

4.70

3.65

7.84

5.78

195.51

547.11

358.82

1.91

0.12

51.11

0.76

1387.0

 

% of F

1.5%

5.8%

7.1%

0.7%

0.3%

0.3%

0.6%

0.4%

14.1%

39.4%

25.9%

0.1%

0.0%

3.7%

0.1%

100%

  

NT: transitional stocks are not taken into account

Fig. 6

Total turnover at national level in Greece for CORINE Land Cover classes - Level 2

Fig. 7

Total turnover per RBD in Greece

Assessing the status of change for all three epochs of the study, the total turnovers of land cover change at national level are the highest for the forest and herbaceous vegetation categories (CLC 31, 32 and 33). In the artificial surfaces category, mines, dumps and construction sites (CLC 13 at Level 2) has the second highest turnover indicating the infrastructure construction activities resulting changes in land cover. On the agricultural sector the arable lands (LCT 21) were under significant changes with more than 200 km2 in 1990–2000 period, while the heterogeneous agricultural areas (LCT 24) for 1990–00 and 2000–06 periods with more than 300 km2 of change in each one of them. For 1990–00 period, forest dynamic changes represent almost 70% of the total turnover, urban development 10%, while agricultural sector contributed almost 17% of the changes. In 2000–06 period, urban development presented significant increase to 30% of total turnover, while the changes in the forest and semi-natural areas (LCT 31, 32 and 33) are trade-offs between forests and sclerophyllous vegetation and natural grasslands raised up to 38% of the land cover changes.

Analyzing these variations of total turnover at RBD level, as shown in Fig. 7, in the previous decade, Attica, East Sterea Ellada, Western Macedonia and Thrace were the RBDs with most of the alterations. There is a decreasing trend in total turnover of land cover in all of the RBDs, which is a first indicator of more stable environmental conditions in land stocks. This “stability” could be translated also in terms of economic activity, as one of the main drivers of land cover change is human activity and the exploitation of natural resources of all kind. This is evident in Epirus, Thessaly, Western Macedonia, Central Macedonia, Eastern Macedonia, Crete and the Aegean Islands. The highest values of total turnover in the 1990–00 period in Attica, E. Sterea Ellada and W. Macedonia came as a result of formation of artificial surfaces, and consumption of forested and agricultural areas for GR06, or both formation and consumption of forested and semi-natural areas for GR07 and formation of construction sites, mines, and dumps on the one side, and consumption of agricultural lands on the other, for GR09.

In Table 3, there are presented the main results of land accounts among the three epochs. One of the main signals of 1990–00, 2000–06 and 2006–12 periods is the total turnover at national level: 4000 km2 (~ 360 km2/yr), 1799 km2 (~ 300 km2/ yr) and 2774 km2 (~ 457 km2/ yr), respectively. The main trends, as observed in the table, are the systematic reduction of forested areas and agricultural areas in favor of artificial surfaces of all kinds: urban fabric, industrial areas, mines, dumps and all kinds of infrastructure across the country.

Highest rates of land intake are observed in the 2000–06 period for artificial surfaces (sum of 11, 12, 13 and 14 CLCs) (+24.5 km2/ yr) and agricultural areas (sum of 21, 22, 23 and 24 CLCs) (−19.8 km2/ yr), while for the forested areas (CLC 31) in 2006–12 period (−54.6 km2/ yr) and semi-natural areas (CLC 32) in the same period (+47.3 km2/ yr). In more detail, the abovementioned results are presented in Fig. 8. It has to be noted that artificial surfaces are expanding at all epochs with varying intake rates, while forests stocks are under continuous stock depletion. Agricultural sector areas are experiencing lowest rates of reduction from 5 to 20 km2/ yr in the first two epochs and from 2.6 to 5.6 km2/ yr in the latest one.
Fig. 8

Average land intake (km2/yr) for the main CORINE land cover classes at national level

Table 4 presents a land cover flow table for 2006–2012 period at national level, where the central diagonal is presenting the internal changes within the same LC classes (transitional stocks according to the definition of functions). Calculation of major functions of formation and consumption are presented at both levels of analysis, while these functions have been calculated without the transitional stocks included, symbolized in the table as NT (no transition). The flows for categories 41, 42, 51 and 52, which are representing wetlands, as well as inland water systems, are negligible to the whole analysis and due to their very dynamic nature they are supposed to be quite stable.

In an effort to represent more effectively these systemic LCC in Greece, the LC classes have been grouped under the prism of manmade versus natural land cover stocks, in order to measure the human intervention to the environment and, in a way, quantify these interferences. The approach has been implemented in a way that each LC class fits to the closest conceptual category of manmade/natural stocks, e.g., agricultural areas are categorized under manmade stocks, despite they are part of the natural environment, as shown in Fig. 9. The manmade stocks are composed from categories 11 to 24 while the natural ones from 31 to 52.
Fig. 9

Manmade and natural land cover stocks in 2012 for RBDs of Greece

The RBD of Central Macedonia presents the highest human intervention in terms of land cover changes and current stocks, while the RBDs with most of the natural reserves are Western Sterea Ellada and Epirus. As said, this grouping may shadow LCC at CORINE Level 3.

An additional analysis has been conducted in terms of the start and end of the epochs 1990–2012 for all the RBDs at Level 2 of thematic detail, as shown in Table 5. The overall trend of all RBDs in Greece is that the country experiences an extensive urbanization and artificial surface expansion (infrastructures) (from 5.0 to 169%), a marginal decrease in agricultural areas (from 0.2 to 2.6%), a quite detectable reduction of forests (7.8%) and expansion of open spaces by 112.8% (beaches, dunes, sands, bare rocks, sparsely vegetated and burnt areas). Wetlands and inland water surfaces are very dynamic to be described by the inter-annual land accounting.
Table 5

Consolidated land cover changes in terms of % between 1990 and 2012

LCC 1990–2012

11

12

13

14

21

22

23

24

31

32

33

41

42

51

52

GR01

2.2%

191.3%

121.7%

78.8%

−1.5%

−0.4%

29.2%

−1.2%

−23.7%

9.6%

26.3%

0.0%

0.0%

0.0%

0.2%

GR02

6.2%

53.3%

183.3%

26.0%

2.1%

−2.2%

−3.0%

−0.4%

−18.0%

5.8%

11.7%

−3.4%

0.0%

3.9%

−0.4%

GR03

7.1%

292.4%

340.0%

62.7%

−0.7%

0.8%

−1.7%

−0.5%

−5.2%

−1.0%

119.5%

0.0%

0.0%

−38.5%

0.0%

GR04

2.9%

73.3%

356.8%

116.5%

−0.3%

−0.2%

−0.6%

−0.3%

−1.0%

−0.3%

5.1%

−8.2%

−1.3%

3.8%

1.8%

GR05

14.6%

195.9%

85.2%

17.6%

0.1%

−1.6%

−4.1%

−1.5%

−1.0%

−0.1%

1.8%

14.8%

0.9%

15.5%

−0.3%

GR06

8.1%

91.6%

25.0%

27.6%

−26.7%

−4.3%

−5.3%

−8.0%

−20.7%

−2.5%

1299.7%

−48.6%

0.0%

−13.0%

−0.4%

GR07

13.5%

52.8%

74.5%

13.4%

−0.4%

−1.1%

1.6%

−0.6%

−11.5%

3.2%

31.5%

21.8%

−1.2%

−4.7%

0.2%

GR08

3.3%

25.4%

200.0%

5.9%

−0.8%

−2.3%

−3.5%

−0.4%

−0.9%

0.1%

−4.8%

23.1%

−1.0%

195.8%

0.1%

GR09

4.0%

88.3%

168.4%

0.0%

−3.1%

−0.5%

−20.0%

0.0%

1.5%

−1.7%

−0.5%

−4.7%

−2.1%

−1.4%

0.0%

GR10

6.1%

21.2%

16.6%

16.4%

−0.8%

−3.8%

−1.9%

1.8%

−1.3%

0.6%

20.7%

16.9%

−3.5%

−11.9%

5.7%

GR11

0.4%

134.3%

56.4%

0.0%

−1.8%

1.4%

−6.4%

5.3%

0.2%

−0.8%

0.1%

−4.6%

−3.8%

1.1%

−1.4%

GR12

3.4%

89.2%

78.8%

10.2%

−0.5%

−0.7%

−13.3%

0.2%

−0.1%

−2.8%

47.6%

−1.5%

−1.9%

15.4%

−3.8%

GR13

7.2%

60.1%

626.8%

43.9%

12.4%

−0.7%

−8.6%

3.4%

−6.3%

−1.3%

0.4%

0.0%

0.0%

286.4%

0.0%

GR14

3.6%

17.6%

33.5%

159.2%

−0.5%

0.0%

1.4%

0.0%

−21.4%

2.0%

19.7%

0.0%

0.0%

1050.7%

−0.1%

Average

5.9%

99.1%

169.1%

41.3%

−1.6%

−1.1%

−2.6%

−0.2%

−7.8%

0.8%

112.8%

0.4%

−1.0%

107.4%

0.1%

3.2 Analysis of Artificial Surfaces

The expansion of artificial surfaces is a phenomenon met in all RBDs of the country. The urban sprawl, expansion of urban fabric (CLC 11) is highest in the RBDs of Epirus with 14.6% (12.1 km2) and East Sterea Ellada with 13.5% (28.5 km2). Lowest rates of increase have been met in E. Macedonia (0.4%, 0.5 km2) and W. Peloponnese (2.2%, 1.1 km2). The industrial commercial and transportation units (CLC 12) have also significant increases in Epirus (195.9%, 27.3 km2) and W. Peloponnese (191.3%, 12.4 km2) due to extensive expansion of highways, ports and industrial areas. Lowest rates of increase have been met in the Aegean Islands (17.6%, 4.2 km2) and Central Macedonia (21.2%, 13 km2).

Apart from the expansion of ready to use artificial surfaces and infrastructure, dump (CLC 132), mineral extraction (CLC 131) and undergoing construction sites (CLC 133) are affecting the overall sectorial distribution of artificial surfaces. Changes in dump sites are very limited from 3.4 km2, 1 km2 and 1.2 km2, respectively, in each of the periods of analysis. The mineral extractions sector has almost halved its impact on surface expansion from 105.7 km2 in 1990 to just 35.6 km2 in 2012. As far as the construction sites are concerned, there has been a significant reduction during epoch 2 from 88.6 km2 down to 49.5 km2, while extensive infrastructure construction sites have raised the expansion of surface land cover impact to 62 km2. In terms of land cover stocks, highest rates of expansion have occurred in Crete (+626.8%, 13.7 km2) and W. Sterea Ellada (356%, 11 km2). The lowest rates of expansion have been met in Central Macedonia (16.6%, 3 km2) and Attica (25%, 11.1 km2).

In terms of the artificial non-agricultural areas, which include green urban areas and sport and leisure facilities, significant expansion has been observed, with an average rate of 41.3%. The highest expansion has occurred in the Aegean Islands (159.2%, 11.5 km2), while the lowest in Thessaly (5.9%, 0.3 km2). Almost all the changes observed in this land cover stocks have been due to expansion of sports and leisure activities areas and not in green urban areas.

3.3 Analysis of Forested and Semi-natural Areas

The analysis has shown that, at the 1st hierarchical CORINE classification level, agricultural and forested/semi-natural areas constitute 39.8 and 54.1% of Greece’s total stock for the year 2012 (Table 3). The equivalence between level 1 (two categories) and 2 (seven categories) can provide a clearer view regarding these two dominant cover types. More specifically, agricultural land comprises heterogeneous agricultural areas (16.6% of total stock), arable land (16.5%), permanent crops (6.2%) and pastures (0.5%). Areas of natural and semi-natural vegetation consist of forests (17.6%), shrub and/or herbaceous vegetation (36.5%) and open spaces (2.1%). The RBD with the most agricultural surface occupied (in comparison to its total surface) for the year 2012, is Central Macedonia (GR 10) with 57.5%. Natural and semi-natural vegetation covers 72% of W. Sterea Ellada (GR 04) total surface. Through the analysis of the land accounting results, there is an attempt to pinpoint those RBDs with the most intense recorded changes (gains or losses), either as percentages (compared to the initial year) or total values in km2.

Attica (GR 06) is ranked first amongst all RBDs in arable land loss (−26.7% in comparison to its initial stock in 1990), in permanent crop loss (−4.3%) and in heterogeneous agricultural land loss (−8%). The total amount of heterogeneous agricultural land that was lost in Attica, during 1990–2012, reached 69 km2. West Macedonia (GR 09) holds the highest percentage in pastures loss (−20% or 18.9 km2 in total value), while 89.3 km2 of arable land were transformed to other land cover types. On the other hand, significant gains of arable land (12.4%) and heterogeneous agricultural areas (41.8 km2) were observed in Crete (GR 13). An investment in heterogeneous agricultural areas (+5.3%) is also recorded in East Macedonia (GR 11). Cover types associated with pastures developed in West Peloponnese (GR 01) with an increase of 29.2% or 8.3 km2. A special occasion occurred in North Peloponnese (GR 02) as it appears to be the RBD with the highest total arable land gain (12.1 km2) and the highest total permanent crop area loss (−22.8 km2) during the all periods.

The conversions that led to transformations of general agricultural land are examined for the RBDs of Crete, E. Macedonia and W. Peloponnese in the broad classification level (Level 1). In Crete, the increase of agricultural areas (CLC 2) was initiated mainly from natural or semi-natural vegetation (CLC 3) during 1990–2000. An estimated 71.2% of total conversions that formatted agricultural areas were observed during that period alongside a relatively low agricultural consumption (9.2%).

In E. Macedonia, internal agricultural conversions affected specific cover types against other that were no longer promoted. The internal conversions in agricultural areas reached 90% during 1990–2000, 76% during 2000–2006 and 96% during 2006–2012. In W. Peloponnese, agricultural internal conversions appear to dominate, but there is also a significant number of artificial surfaces (2.65 km2) which contributed in the formation of agricultural areas during 2000–2006. This particular land transformation should not be accounted as general net loss of artificial surfaces due to agricultural expansion, as the opposite conversion (i.e., from agricultural to artificial surfaces) includes 7.29 km2 of total area for the same time period.

Conversions between forest types, semi-natural vegetation types and open spaces with little or no vegetation, cannot be easily detected on the broad classification (level 1). These conversion types are common in Mediterranean regions where natural mechanisms of rotation or succession are mobilized by specific climate, meteorological and soil moisture conditions. A rather unusual conversion type between land cover categories took place in West Macedonia (GR 09), where 8.43 km2 of artificial surfaces during 2000–2006 and 12.41 km2 of artificial surfaces during 2006–2012, were consumed by forested areas. Forested areas, in overall, increased in West Macedonia about 1.5% or 55.8 km2. Τhe opposite is observed in West Peloponnese (GR 01) where there is a 23.7% loss in forested land, equal to 279 km2. As part of natural rotation, this forest loss is followed by shrub/herbaceous vegetation expansion which is estimated at 240.9 km2 or 9.6% increase. An outbreak in the expansion of open spaces (+1300%) was noticed in Attica due to the severe forest and shrub land wildfires that occurred in the region in 2009 (epoch 2006–2012). More specifically, 20.83 km2 of natural forests and 92.83 km2 of shrubs/herbaceous vegetation were lost during this unprecedented natural disaster.

3.4 Estimated Projections

The land cover flows have been transformed to transitional probabilities for each of the 6-year epochs 2 and 3 as the first one was an eleven years interval. The average probability is calculated as the sum of each of the CLC classes of Level 2 compared with the total land cover change by excluding transitional stocks. This is because the purpose is to calculate the relative probabilities. The results of this calculation are shown in Table 6 where the central diagonal represents the probability that each of the land cover stock classes remain the same. It is observed that these non-transitions are highly probable as the default starting scenario (n = 0). They are absorbing states of the land cover stock.
Table 6

Average transitional probabilities (periods 1990–00 and 2000–06) as the starting scenario of 2012

n = 0

11

12

13

14

21

22

23

24

31

32

33

41

42

51

52

11

0.999

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

12

0.002

0.991

0.002

0.002

0.000

0.001

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.001

0.000

13

0.009

0.086

0.837

0.009

0.001

0.000

0.013

0.002

0.005

0.028

0.001

0.000

0.000

0.009

0.000

14

0.000

0.001

0.001

0.997

0.000

0.000

0.000

0.001

0.000

0.000

0.000

0.000

0.000

0.000

0.000

21

0.000

0.001

0.001

0.000

0.995

0.000

0.001

0.001

0.000

0.000

0.000

0.000

0.000

0.000

0.000

22

0.000

0.000

0.001

0.000

0.001

0.997

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

23

0.001

0.002

0.005

0.000

0.002

0.000

0.965

0.000

0.000

0.001

0.000

0.000

0.000

0.024

0.000

24

0.001

0.001

0.001

0.000

0.000

0.000

0.000

0.997

0.000

0.000

0.000

0.000

0.000

0.000

0.000

31

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.983

0.013

0.004

0.000

0.000

0.000

0.000

32

0.000

0.000

0.001

0.000

0.000

0.000

0.000

0.000

0.003

0.992

0.003

0.000

0.000

0.000

0.000

33

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.027

0.972

0.000

0.000

0.000

0.000

41

0.000

0.000

0.000

0.001

0.003

0.001

0.000

0.001

0.000

0.000

0.000

0.985

0.000

0.009

0.000

42

0.000

0.000

0.000

0.000

0.001

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.998

0.000

0.001

51

0.000

0.000

0.000

0.000

0.001

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.999

0.000

52

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

1.000

Following the Markov property that the probability of moving to the next state depends only on the present state and not on the previous states, a calculation for n = 8 future states have been implemented ranging up to the year of 2060. The transition matrix of 2060 is shown in Table 7, where the classes of construction sites and dumps (CLC 13), pastures (CLC 23), forests (CLC 31) and open spaces have been changed to non-absorbing classes. An important parameter to check is that the total land cover stocks classes have to sum up to the same amount of area of Greece following the conservation of land principle.
Table 7

Transition matrix for the year 2060 for Greece land cover stocks change

n = 8

11

12

13

14

21

22

23

24

31

32

33

41

42

51

52

11

0.994

0.003

0.001

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.001

0.000

12

0.016

0.928

0.009

0.021

0.000

0.004

0.001

0.000

0.004

0.004

0.000

0.000

0.000

0.013

0.000

13

0.049

0.403

0.206

0.049

0.004

0.003

0.053

0.011

0.023

0.137

0.005

0.000

0.000

0.056

0.000

14

0.000

0.008

0.007

0.978

0.000

0.000

0.000

0.005

0.000

0.001

0.000

0.000

0.000

0.000

0.000

21

0.003

0.009

0.006

0.001

0.960

0.000

0.006

0.011

0.000

0.001

0.000

0.001

0.000

0.002

0.000

22

0.003

0.005

0.006

0.001

0.005

0.976

0.001

0.001

0.000

0.001

0.000

0.000

0.000

0.001

0.000

23

0.008

0.022

0.021

0.004

0.013

0.000

0.726

0.001

0.001

0.011

0.000

0.000

0.000

0.191

0.000

24

0.008

0.007

0.004

0.002

0.001

0.000

0.000

0.972

0.000

0.002

0.002

0.000

0.000

0.002

0.000

31

0.000

0.001

0.001

0.000

0.000

0.000

0.000

0.001

0.856

0.112

0.028

0.000

0.000

0.000

0.000

32

0.000

0.003

0.003

0.001

0.000

0.000

0.001

0.002

0.026

0.938

0.024

0.000

0.000

0.001

0.000

33

0.000

0.003

0.001

0.000

0.000

0.001

0.000

0.001

0.006

0.209

0.776

0.000

0.000

0.003

0.000

41

0.000

0.001

0.000

0.007

0.028

0.005

0.000

0.012

0.000

0.000

0.000

0.874

0.000

0.074

0.000

42

0.000

0.001

0.000

0.000

0.008

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.978

0.002

0.011

51

0.000

0.000

0.000

0.000

0.008

0.000

0.000

0.000

0.000

0.000

0.001

0.003

0.000

0.988

0.000

52

0.000

0.002

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.001

0.000

0.996

Comparing the 2060 results with the starting state of 2012, after eight (8) transitions projected at national level for Greece, the estimated land cover changes are presented in Fig. 10. The results are not spatially expressed, which means that the estimated changes could be observed anywhere across the nation or the respective RBDs. It is highly probable that the expansion of artificial surface against agricultural areas and forested lands to occur in already urbanized agglomerations. A uniform increase in artificial surfaces is estimated, varying from 17 to 21%, and a marginal decrease of agricultural areas, varying from −4% to +2%. Forests are expected to be depleted in stocks by 9%, while defragmentation of open spaces and with little or no vegetation will experience a significant increase by 45%.
Fig. 10

Projected changes % of main land cover stock under CORINE Level 2 classification

4 Conclusions and future research

Land accounting is a relatively new conceptual approach based on the combined assessment of the links of the environmental assets and the economy. The latter introduced some new terms (formation, consumption, turnover, land intake and outturn) to describe the environmental and human imposed changes in the land resources of river basin districts of Greece based on the current CORINE land cover dataset. By implementing this working approach, the main drivers of change are possible to be revealed and their impacts to the water environment, as well as future trends in a non-spatially explicit context be quantified. To this end, the Markov chain modeling has been employed with key inputs for the well-fitting structure of the land accounting tables. The later also provided the transitional probabilities matrix in order to implement the projection attempt for the land cover change.

The results can be focused on the following key points:
  • In Greece, the analysis of the last 23 years reveals that the land resources are intensively exploited with large amounts of land being transformed to artificial surfaces supporting a more urbanized manner of socio-economic development. The expansion of artificial surfaces is a phenomenon encountered in all river basin districts of the country. The expansion of artificial surfaces, taking also into consideration the concentration of population in larger agglomerations, will require the coming years investments for flood protection infrastructure and safeguarded water resources for public water supply systems in the major population centers of Greece.

  • Attica RBD has the largest stock of artificial surfaces (606 km2, 19%) compared to the total RBD surface. The urban expansion is highest in the RBDs of Epirus (14.6%, 12.1 km2) and East Sterea Ellada (13.5%, 28.5 km2). Lowest rates of increase have been met in E. Macedonia (0.4%, 0.5 km2) and W. Peloponnese (2.2%, 1.1 km2). Attica RBD has also the highest loss of arable land, permanent crops and in heterogeneous agricultural land. West Macedonia holds the highest percentage in pasture loss, while 89.3 km2 of arable land were transformed to other land cover types.

  • At national level the human intervention in the environment is quantified by measuring the manmade land stocks at 43% on average (highest in C. Macedonia RBD with 61%) versus the natural stocks with 57% (highest in Epirus RBD with 70%) of total area of Greece.

  • The significant reduction of forests increases the flood risk especially in peri-urban areas, while the reduction of forests in pristine lands used for intense agricultural use, reduces water quality due to the use of pesticides and other residuals. Forest loss affects also the fauna and flora of the broader areas as it is the place where most of the biological processes are prosperous.

  • An estimation with projection horizon for the next 50 years has been attempted, revealing that the main present trends are expected to endure (+17% urban expansion of urban fabric) with significant amounts of land to be sealed as artificial surfaces, while a loss of 9% of the forests is translated in a 2000 km2 reduction in terms of area.

Environmental protection programs might be initiated to propose a more sustainable usage of the land resources of Greece with respect to human needs. As more detailed remote sensing data are available, providing increasingly high thematic detail in terms of spatial scale and temporal resolution, spatially and non-spatially model could be further developed and improve the land accounting approach. Land accounts is one of the tools that can identify, process and produce the necessary output for future assessments for holistic and integrated environmental approaches.

Notes

Acknowledgements

An initial shorter version of the paper has been presented at the 10th World Congress of the European Water Resources Association (EWRA2017) “Panta Rhei”, Athens, Greece, 5-9 July, 2017 (http://ewra2017.ewra.net).

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Authors and Affiliations

  1. 1.Department of Water Resources and Environmental Engineering, School of Civil EngineeringNational Technical University of AthensAthensGreece

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