Assessment of the impact of forest fires (1)

Assessment of the impact of forest fires and marble quarries on the environment of Thasos island

Ioannis Gitas, George Mouflis, George Mitri, Hara Minakou, Stavroula Iliadou, Spiros Tsakalidis

PP02-WP3: Implementation of best practice models in the development of case-studies through an interactive assessment procedure.

Laboratory of Forest Management and Remote Sensing, Faculty of Forestry & Natural Environment, Aristotle University of Thessaloniki, Greece

This manuscript describes the research work carried out by the Laboratory of Forest Management and Remote Sensing of the Aristotle University of Thessaloniki (ISOTEIA project partner 02) for WP3.5.

Email: igitas@for.auth.gr Tel : +30 2310 992699, Fax : +30 2310 998897

Report of Case Study: Thasos island, NE Greece

Table of Contents

1. INTRODUCTION

2. BACKGROUND INFORMATION AND CASE-STUDY DEVELOPMENT

2.1 S TUDY AREA 7

2.2 L EGAL STATUS 8

2.3 D ATASET DESCRIPTION 9

2.3.1. P RE - FIRE L ANDCOVER TYPES AND ZONES 9

2.4 M ETHODS 13

3. RESULTS AND DISCUSSION 15

3.1. M APPING OF BURNED AREAS AND ECOLOGICAL IMPACT ASSESSMENT 15

3.1.1. E COLOGICAL ASSESSMENT OF ALL FIRES 17

3.1.2. E COLOGICAL ASSESSMENT OF INDIVIDUAL FIRES 19

3.2 V EGETATION RECOVERY ASSESSMENT 21

3.2.1 E VALUATION OF VEGETATION INDICES FOR MONITORING VEGETATION REGENERATION

AFTER FIRE IN THE CASE OF T HASOS 21

3.2.3 C OMPARISON OF VEGETATION RECOVERY FOR EACH FIRE 24

3.2.4 C OMPARISON OF VEGETATION RECOVERY OF DIFFERENT LANDCOVER TYPES AND ZONES 26

3.3 M APPING THE QUARRIES AND THEIR EXPANSION SINCE 1984 30

3.3.1 I MAGE PRE - PROCESSING 30

3.3.2 I MAGE DIFFERENCING 31

3.3.3 P OST - CLASSIFICATION COMPARISON 32

3.3.4 A CCURACY ASSESSMENT 33

3.3.5 C OMPARISON OF THE TWO METHODS 33

3.3.6 Q UARRY EXPANSION 34

3.4 L ANDSCAPE CHANGE , ECOLOGICAL AND VISUAL IMPACT IN RELATION TO THE

MARBLE QUARRIES 36

3.4.1 E COLOGICAL ASSESSMENT 36

3.4.2 Q UANTIFICATION OF LANDSCAPE METRICS OF QUARRY PATCHES IN 1984 AND 2000 37

3.4.3 V ISUAL IMPACT ASSESSMENT AND CHANGE 39

4. CONCLUSIONS 41 REFERENCES 43

45

ANNEX

Report of Case Study: Thasos island, NE Greece

1. Introduction

It has been well established in the literature that natural fires are an integral part of many terrestrial ecosystems such as boreal forests, temperate forests, Mediterranean ecosystems, savannas and grasslands among others. However, from the 1960s until today, the general trend in the number of fires and surface burnings in the European Mediterranean areas has increased exponentially (Moreno 1998). This increase is mainly due to: (a) changes in traditional land uses, the consequence of which is higher fuel accumulation, and (b) global climatic warming (Gitas 1999). Forest fires have an impact on humans, wildlife, hydrology and soil among others.

The ecological effects of fire vary enormously according to the time of the year; the quantity, condition, and distribution of fuel; the prevailing climatic conditions; and the duration and intensity of the fire (Trabaud 1994). The climatic conditions and the recent changes in landscape structure in the European Mediterranean countries favoured a new fire regime, characterized by frequent, extensive and high intensity fires occurring in the summer and early autumn. In general, the ecological consequences of this new fire regime can be summarised as follows:

• Effects on soil: high-intensity fires destroy the soil structure and reduce the bulk density and porosity of soil, which in turn results in a decrease in infiltration and

an increase in runoff and erosion (DeBano et al. 1998) which can result in site degradation.

• Effects on nutrient cycle: Runoff and erosion can carry the minerals in the ash and soil downstream (DeBano et al. 1977), therefore decreasing the levels of nitrogen,

calcium, magnesium and potassium (Kutiel & Shaviv 1989). Nitrogen loss can further take place in high-intensity fires by means of volatilisation (Knight 1966).

• Effects on biodiversity: Large fires that produce a greater number of intensely burned patches can favour the colonization of invasive, fire tolerant species at the

expense of rare/endemic species less tolerant to post-fire conditions. • Effects on landscape structure: large fires that produce a greater number of

intensely burned patches can be a driving force in landscape homogenisation. • Effect on fire frequency: burned areas in which flammable shrublands expand can

have a greater likelihood of reburning than neighbouring unburned areas. • Effects on climatic warming: Massive human-induced fires aggravate the effects

of ‘greenhouse’ gases on the earth and its weather system (DeBano et al. 1998).

In addition, in most industrialized countries, limestone quarries represent the most visually obvious and, in process and landform terms, the most dramatic anthropogenic impact on karst terrain (Gunn and Bailey 1993). Marble quarries have existed in Greece since historical times and ancient temples were built by white marble often transported over long distances. While urban development is dependent on the production of natural aggregates, extraction pits and quarries are considered unpleasant long-lasting scars on

Report of Case Study: Thasos island, NE Greece Report of Case Study: Thasos island, NE Greece

Marble extraction is an economically important and widespread activity in modern Greece and other Mediterranean countries. The extraction takes place by open-pit quarries in hill slopes. The original landform is permanently altered and the original vegetation cover is destroyed. The visual impact of the quarries extends over larger areas as noticeable scars of high colour contrast, reducing the aesthetic appeal of the landscape and deteriorating the scenic quality of areas where tourism often is a major constituent of income.

Environmental Impact Assessment (EIA) is a requisite for sustainable development and EU has adopted this policy in Directives 1985/337 and 2001/42 which aim to provide for

a high level of protection of the environment. These directives stipulate that EIA or Strategic Environmental Assessments are required during the planning or scoping stage of all projects and plans likely to have significant environmental impacts. There are various reporting frameworks for EIAs or conceptual models to develop effective strategies that achieve tangible conservation results (Stem et al. 2005), such as the 5S (Systems, Stresses, Sources of Stress, Strategies, Success Measures) (Anonymous 2000), or the DPSIR framework used in ISOTEIA which identifies the driving forces (D) and pressures (P) on the environment, indicators for the state (S) of the environment, the impacts (I), and the responses (R) to redress the balance in terms of environmental impacts (Bürgi et al. 2004; E.E.A. 1999).

Geographical Information Systems (GIS), Remote Sensing and landscape analysis are useful tools in measuring environmental impacts, especially when large areas are involved. These tools can be used for the identification and monitoring of areas in need of special or intense management after a forest fire event (Gitas 1999; Jakubauskas 1988; Jakubauskas et al. 1990) or landscape changes brought about by quarrying activities over large areas and long time-spans (Latifovic et al. 2005; Rigina 2002).

The aim of this case study was to assess the impact of forest fires and marble quarries on the environment of Thasos by employing GIS, Remote Sensing and landscape analysis. The specific objectives were:

1. To accurately map the burned areas and to estimate the ecological impact of fires.

2. To assess vegetation recovery following forest fires.

3. To map the quarries and their expansion using multi-temporal satellite data.

4. To assess the ecological and visual impact of quarries and describe their landscape dynamics.

Thasos was selected as being an ideal study area because more than half of the island was burned during the last two decades and additionally, it represents one of the main sites in Greece where marble extraction takes place.

Report of Case Study: Thasos island, NE Greece

2. Background information and case-study development

Before moving into the results and discussion, some background information about the study area, conservation designations, relevant legislation and a description of data and methods are necessary.

2.1 Study area

The study area was the island of Thasos which is Greece's most northerly island (Figure

1) o

extending from 24 30’ to 24 48’ East and 40 33’ to 40 49’ North. Its surface area is 383 sq. km while its perimeter is approximately 128 km. Elevation ranges from 0 to 1200 m (Figure 3, p.11) while slopes range from 0 to 80 degrees.

The climate of Thasos is cool and humid Mediterranean according to the Emberger bioclimatic classification (Gitas 1999), characterised by a relatively high mean yearly precipitation (742.3 mm) and a xerothermic period that starts in May and lasts through September (Spanos et al. 2000). According to the formula of Emberger, the pluviothermic quotient Q for Thasos is 87.4 and the Mediterranean-type climate of the island can be further classified to the cold and subhumid variant (Spanos et al. 2000).

Surface geology and soil depth: Approximately 65% of Thasos, the highland areas, is composed of metamorphic gneisses (gneiss of Maries). To the east of the island, there is a series of metamorphic rocks – the oldest, Potamia series – primarily composed of dolomites that constitute approximately 25% of the island’s total area. Quaternary deposits of clay, sand and gravel around the coastline, particularly in the areas of gentle slopes, make up the remaining 10%. The soil depth varies widely depending on surface geology, relief and vegetation density. Shallow soils (5-10 cm) prevail due to steep slopes, grazing, and repeated forest fires. Almost 50% of the island’s surface is covered with shallow soils, 35% with deep soils, while the remaining 15% is bare.

The landcover and vegetation description is described in 2.3.1. Pre-fire Landcover types and zones), p.9.

Figure 1: Location of the study area

Report of Case Study: Thasos island, NE Greece

2.2 Legal status

In this section there is a summary of legislation pertaining to the study area and the environmental pressures analysed in this report, namely forest fires and marble quarries.

Special Areas for Conservation on Thasos island

With respect to the EU Directive (92/43/EEC) on the conservation of natural habitats & of wild fauna and flora (1992), to date Greece has identified mainly three candidate Special Areas for Conservation (cSACs) on Thasos. These are: Akrotirio Prinou-Pachy, Limenaria-Akrotirio Kefalas, Ormos Potamias. These three special areas have been chosen for their marine habitats, reefs and Poseidon oceanica beds.

European Legislation for Environmental Impact Assessments

Council Directive 97/11/EC of 3 March 1997 amending Directive 85/337/EEC on the assessment of the effects of certain public and private projects on the environment. In Annex 1, case 19, quarries and open-cast mining where the surface of the site exceeds 25 hectares, or peat extraction, where the surface of the site exceeds 150 hectares, are subject to ARTICLE 4 (1) (requirement for an environmental impact assessment). In Annex 2, Quarries, open-cast mining and peat extraction (with an area <25ha projects not included in Annex I), are subject to ARTICLE 4 (2) (requirement for an EIA depending on the specific project and thresholds and criteria set by Member States).

National Legislation for the protection of forests and forest fires

The national legislative framework for the protection of forests is Law 998/1979. This law stipulates that land-use change of public or private forest and forest areas is not permitted and no buildings of any sort can be constructed. Exceptions are provided for reasons of national security and defence, construction of public roads, energy lines and aqueducts. Denuded forests or forest areas (as a result of fire, clear felling, landslide, pathogen attack etc.) do not lose the protection afforded by this law but their protection is strengthened by their automatic designation within 3 months into “regeneration status”. In this status grazing is not allowed. Felling or any attempt to convert part of the area into agriculture or housing is severely prosecuted.

National Legislation for marble extraction and quarries

The national legislative framework for the operation of mines and quarries is set out principally by Laws 669/1977, 1428/1984 and 2115/1993. These laws stipulate that mining activities are not permitted in areas of outstanding natural beauty and cultural heritage, locations closer to 1 km from inhabited areas or 2 km from designated archaeological areas, and require that adequate provisions should be taken for the protection of the environment and the restoration of the site following exploitation. Specifically for marble quarries the area of each site cannot exceed 10 ha (Article 4, §3, Law 669/1977). Planning applications must have approved environmental impact statements (Law 1650/1986) and additionally permission from the Forestry Service (Law 998/1979) if they are in forest areas. Then exploitation is granted for an initial period of

20 years with subsequent extensions up to a total of 40 years. The final extension period has the sole aim of the restoration of the environment.

Report of Case Study: Thasos island, NE Greece

2.3 Dataset description

The data used in this case study consisted of satellite images (QuickBird, Ikonos and Landsat TM and ETM+), a Digital Elevation Model, digital maps of pre-fire landcover (Figure 2 with their area in ha) produced from ortho-photos at a scale of 1:20,000 (Forest Service – Greek Ministry of Agriculture) and data collected in the field. All data are listed in Table 1 while the landcover is discussed in more detail below.

Table 1: Data used in the development of the case study

Type of Data

Name/date of product

Characteristics

Satellite image

Quickbird (2003)

multispectral 2.5 m and panchromatic 0.6 m - orthorectified

Satellite image

Ikonos

1 m spatial resolution – geometric and atmospheric corrections

Satellite image

Landsat TM and ETM+

30 m spatial resolution –

(1984-1985-1989-2000)

geometric and atmospheric corrections

Ancillary data

Topographic map

Ancillary data

10 m resolution Ancillary data

Digital Elevation Model

Produced from orthophotos Field data

Landcover map

Vegetation regeneration

GPS measurements

plots (1997-2003-2004- 2005)

Field data

Digital photographs

Coded photos

Data derived from remote Quarries and fire perimeters Object-oriented image sensing analysis

analysis

2.3.1. Pre-fire Landcover types and zones

Before the forest fire of 1984, forest and forested lands covered 47.5% of the island, making forests the dominant landcover type at the time. After the fires of 1984 and 1985, forests and forested lands covered 37.95% of the island. Today, as a result of fires, illegal logging, intensive grazing and bad management, the remaining forest has a spatial extent of about 2000 ha in the Northern and Eastern parts of the island. Pinus brutia was the dominant species of the forests at elevations ranging from sea level up to 800 m, while Pinus nigra was the dominant species of the forests found in the mountainous areas of the island (Giakoumakis et al. 2002; Gitas 1999). Other types of Mediterranean vegetation present on the island are maquis and garigue.

The pre-fire landcover was composed of the following 14 classes: Pinus brutia forest, Shrubland, Arable land, Pinus nigra forest, Pinus brutia - Pinus nigra mixed forest, Grassland, Bare land, Abandoned farmland, Castanea sativa forest, Quercus sp.,

Report of Case Study: Thasos island, NE Greece

Settlement, Platanus orientalis forest, Pinus brutia – Platanus orientalis, Pinus nigra – Abies borisii-regis mixed forest (Figure 2). The area of each landcover type is shown in a graph form in Figure 4, p.11. The landscape was characterized by the dominant landcover of coniferous forest, agricultural land and shrubland in this order.

It was decided to amalgamate these landcover classes into fewer, namely 5 ecologically related classes or zones of specific functional interest for the purposes of deriving and presenting zonal area statistics. Table 2, p.12 and Figure 5, p.12 show which landcover types make up the 5 landcover zones.

A comparison of the area of the 5 zones of vegetation cover (Figure 6, p.7) reveals that coniferous forest was the dominant landcover on the island, followed by agriculture – abandoned farmland, other vegetation (grassland and scrub), settlement and bare land and

a small area of deciduous forest of chestnut and plane.

Report of Case Study: Thasos island, NE Greece

Environmental Impact Assessment

Figure 2: Pre-fire l andcover types on the island of Thasos

Figure 3: Topographic relief of Thasos island

Pre-fire Landcover

Figure 4: Pre-fire area of different landcover types on

R A- ARABL PL

PI NUS

P INUS

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Environmental Impact Assessment

Table 2: Landcover types amalgamated into 5 zones of vegetation

Grouping of 14 landcover types (inner

ABANDONED FARMLAND

circle / legend) into 5 zones (outer circle)

ARABLE LAND

Landcover type

Landcover type Area (ha)

Landcover group (zone)

Zone area (ha)

Abandoned farmland

BARE LAND

Arable land

Agriculture / Abandoned Farmland

FARM LAND

SETTLEMENT

Bare land

Settlement / Bare Land

SETTLEM ENT / BARE LAND

Quercus

14.3 Other Vegetation

Pinus brutia

DECIDUOUS

Pinus brutia - Pinus nigra

PINUS BRUTIA

Pinus brutia - Platanus

46.6 Conifers

PINUS BRUTIA - PINUS NIGRA

Pinus nigra

Pinus nigra - Abies

PINUS BRUTIA - PLATANUS

PINUS NIGRA

VEGETATION

PINUS NIGRA - ABIES

Figure 5: Landcover types amalgamation into 5 zones of vegetation cover

Zones (Landcover groups)

Figure 6: Comparison of area of 5 0 of functional interest

Report of Case Study: Thasos island, NE Greece

2.4 Methods

A brief description of the main steps followed for the completion of the four objectives of this case study is given below and summarised schematically in Figure 7, p.14.

1. Mapping of burned areas and ecological impact assessment: The burned areas were located and mapped by employing object-oriented classification. Burned areas were then intersected with pre-fire landcover. This allowed the assessment of the ecological impact by means of area burned per landcover class and as percentages to the initial area of each landcover type as well as to the total surface of the island.

2. Post-fire regeneration assessment: A number of vegetation indices were calculated and then evaluated with regard to their efficiency to indicate density of vegetation cover (%). For the evaluation of the indices a field survey was conducted. Thresholds were then applied to MSAVI which was the best- performing vegetation index, in order to separate the areas of successful vegetation recovery. A comparison was then made of the vegetation recovery percentage per initial landcover class.

3. Mapping the quarries and their expansion since 1984: Quarries were delineated using image differencing and post classification comparison. Their extent was compared to the total area covered by coniferous forest and the total surface of the island.

4. Assessment of the ecological, landscape and visual impact of the quarries: The ecological impact was assessed by intersection of the areas of quarry expansion with pre-fire landcover, while landscape changes were assessed by comparison of landscape metrics (Elkie et al. 1999; McGarigal et al. 2002; McGarigal & Marks 1995). Finally, the visual impact was assessed by deriving cumulative viewsheds (Möller 2006) for 1984 and 2000 and examining changes in extent and levels of visibility.

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Figure 7: Flowchart of methodology

Forest fires

Marble quarries

Burned areas

LandSat 1984, 2000

Maps of quarries

Multi-temporal

LandSat, QuickBird and Ikonos

sattelite images

Extraction of Object-based

classification

Vegetation Indices

Estimation of

(VIs) from post-fire

landscape metrics

satellite imagery

Object-based

Mapping of

Accuracy assessment of VIs

Comparison of

Analysis using

Mapping of with the use of data from

image differencing

DEM

burned areas post-classification

field surveys

comparison

Landscape metrics changes

Selection of MSAVI

as the best index

Mapping of

Extraction of area statistics

quarries expansion

from pre-fire landcover

Visibility load

Maps produced from MSAVI

increase

thresholding

Extraction of area statistics from pre-fire landcover

Forest fires Ecological

Regeneration

Quarries Ecological Impact

Landscape and Visual

Impact Assessment

Assessment

Assessment

Impact Assessment

Environmental Impact Assessment

Report of Case Study: Thasos island, NE Greece

3. Results and discussion

In this section the results of the analyses of the data are presented and discussed. The numbering of the subsections is in correspondence with the numbering of the four objectives. The methodology followed for each objective is elaborated in more detail where necessary.

3.1. Mapping of burned areas and ecological impact assessment

Remote sensing was used to specify what areas were burned in the four major fires of 1984, 1985, 1989 and 2000. The assessment of the impact was made by comparison with the pre-fire land cover.

Object-based classification procedure for burned area mapping

Digital maps of burned areas have been produced using LANDSAT-TM and Ikonos images for the years 1984, 1985, 1989 and 2000 in the Greek island of Thasos. Object- oriented image analysis was used to map the burned areas. Object-oriented image analysis, which is based on the fuzzy concept, is an approach that uses not only spectral information, but also spatial information. Fuzzy theory replaces the ‘yes’ or ‘no’ in the binary theory by the continuous (0-1), where 0 means ‘exactly no’ and 1 means ‘exactly yes’, thus all values between 0 and 1 represent a more or less certain status of yes and no. Segmentation, the first step in object-oriented approach, involves merging the pixels in the image into image object primitives called objects or segments with a certain heterogeneous and homogeneous criterion. This step is critical because segmentation generates the objects that will be treated as a whole in the classification. Multiresolution segmentation was firstly applied to the images. Image objects resulting from the segmentation procedure were therefore intended to be rather image object primitives, serving as information carriers and building blocks for further segmentation steps and for the final classification. The results of the classifications were then compared with the fire perimeters provided by the Greek Forest Service in order to assess their accuracy. The overall classification accuracies were estimated to be centred in most cases at approximately 98 %.

Area burned per year

The four major fires that occurred in 1984, 1985, 1989 and 2000 resulted in burning 48% of the island. The areas burned in each fire are shown in the map of Figure 9, p.16. As will be discussed later, 77% of this area was high forest.

A comparison of the area burned in each fire (Table 3 and Figure 8, p.16) reveals that the fires of 1985 and 1989 were the most serious ones in terms of total area burned with 81

2 km 2 (21% of Thasos) and 85 km (22% of Thasos) respectively. The fire of 1984 burned

1.6 km 2 and the fire of 2000 was the smallest one burning 0.2 km

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Table 3: Statistics of extent of burned areas

Burned areas (1984-2000)

Fires

Area in ha % of island

Burned area 1984

Burned area 1985

Burned area 1989

Burned area 2000

Total burned

Island total area 2000 38250.27 100%

Burned area

Burned area

Burned area

Burned area

Figure 8: Burned areas statistics

Figure 9: Multitemporal extent of fire damage on Thasos island

Report of Case Study: Thasos island, NE Greece

The ecological assessment of the damage caused by fires was analysed by deriving area statistics of landcover classes (types) that were affected by all fires and for each fire separately.

In the following section there is an analysis of the damage caused by all fires.

3.1.1. Ecological assessment of all fires

As shown in Figure 10, p.18 and Figure 11, p.18, from the total area burned, the Pinus brutia forest was the single landcover class that contributed most (62%) to the total area burned. In descending order the rest of the area was made up from shrubland (12%), arable land (11%), Pinus nigra (9%) forest and mixed stands of Pinus brutia and Pinus nigra (4%), as well as the other classes (the remaining 2%) with smaller areas.

The mixed stands of P. brutia and P. nigra as well as the abandoned farmland and Quercus sp. suffered most from the forest fires because a larger proportion of their original resource was burned (Figure 11, p.18). Area statistics and percentages of landcover types burned are listed in Table 9, p.45 of the Annex.

A comparison of the area burned by all fires, classified into the simpler 5 zones of vegetation cover (Figure 13, p.18), reveals that coniferous forest made up 75% of the area burned. Coniferous forest was the major landcover zone impacted. After fire the regeneration can reinstate these areas into forest again or part of this area can revert to lower vegetation types such as shrubland or grassland. Failure of regeneration leaves the soil exposed to erosion and there is a risk of degradation and permanent loss of vegetation. Therefore an assessment of vegetation recovery following the fire events is necessary to assess the post-fire impact. This assessment was carried out and is presented later.

The total ecological impact of forest fires can be broken down into the four fire events to analyze what landcover was burned most in each fire.

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Area burned in ha

Area burned in all fires by original landcover class

% of original class

Figure 10: Total area burnt by landcover class in ha, and as percentages of original class area

Comparison of area percentages of landcover

classes burned by all fires

% of original class

Figure 11: Burned landcover types by the fires of 1984, 1985, 1989 and 2000

% of total burned

Burned vegetation per zone

Figure 12: Percentages of area burned by landcover class

deciduous

in relation to the total burned area by all fires, and as

other vegetation

percentages of the original class areas.

Figure 13: Burned vegetation per zone

Report of Case Study: Thasos island, NE Greece

3.1.2. Ecological assessment of individual fires

The assessment of landcover types burned in each fire event (Figure 14 - Figure 17, p.20) reveal that Pinus brutia forest contributed most of the area burned, as shown collectively also in Figure 12, p.18. In part this is due to the large initial area of Pinus brutia forest (Figure 4, p.11) but it also expresses the fire-proneness of this forest species.

• In the fire of 1984 (Figure 14) P. brutia accounted for 75% of the area burned, followed by shrubland (20%).

• These two landcover types made up 81% in the fire of 1985 (Figure 15) while 6% was made up from P. nigra and 12% by arable land. • In the fire of 1989 (Figure 16) the percentage of P. brutia dropped to 55% but because the fire passed on to higher ground it burned mixed stands of P. brutia

and P. nigra (9%) and pure P. nigra stands (14%). The rest was arable land (12%) and shrubland (9%).

• The fire of 2000 (Figure 17) affected only P. brutia (91%) and shrubland (9%).

While shrubland and Pinus brutia forest were burned in all fires, Pinus nigra forest was burned only in 1985 and 1989, and mixed stands of the two pine species burned only in 1989. Arable land was burned in 1984, 1985 and 1989 (Figure 18, p.20).

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Environmental Impact Assessment

ABANDONED

Burned landcover (1984)

Burned landcover (1985)

ABANDONED

Burned landcover (1989)

FARMLAND ARABLE LAND

ARABLE LAND

BARE LAND

ARABLE LAND

BARE LAND 0%

BARE LAND

PINUS BRUTIA

0% PINUS BRUTIA

PINUS BRUTIA

SETTLEMENT

PINUS BRUTIA -

PINUS BRUTIA -

PINUS NIGRA

SHRUBLAND

PINUS NIGRA

PINUS NIGRA

PINUS NIGRA

Figure 14: Percentages of burned landcover types in 1984 Figure 15: Percentages of burned landcover types in 1985 Figure 16: Percentages of burned landcover types in 1989

Burned landcover (2000) Percentages per year of different lancover types burned

PINUS BRUTIA

ch f PINUS NIGRA 60%

PINUS BRUTIA - PINUS NIGRA

ea

of

PINUS BRUTIA

Figure 17: Percentages of burned landcover types in 2000

BARE LAND ARABLE LAND

ABANDONED FARMLAND

Figure 18: Contribution of each landcover type to the area burned in each fire

Report of Case Study: Thasos island, NE Greece

As discussed earlier forest fires provide the starting point for secondary vegetation succession and set the template for a dynamic landscape change. While multi-temporal satellite imagery classification with multi-temporal field surveys can provide detailed landcover trajectories and landuse change matrices, a fundamental concern for an impact assessment is the assessment of post-fire vegetation recovery. Failure of sites to develop and adequate vegetation cover leaves the ground prone to erosion especially in steep sloping ground. For the purposes of this report an assessment of vegetation recovery was made in the year following the last fire event of 2000 by employing remotely sensed vegetation indices. The analyses made are presented in the following section.

3.2 Vegetation recovery assessment

The vegetation recovery assessment revealed that vegetation cover re-established on 2/3 of the area burned. Failure of vegetation recovery in the remaining 1/3 of the burned area may be attributed to adverse conditions for species recolonisation and establishment, soil erosion or grazing pressure during the first years after the fire. Long-term monitoring of the burned areas can be used to identify whether, at places, lack of vegetation recovery is of a temporary or permanent nature.

In the following section there is an evaluation of RS (remotely sensed) vegetation indices which of theoretical and applied interest, and case-specific assessment of vegetation recovery a) for the areas burned in each fire, and b) for the landcover types and zones, which is of theoretical and applied interest, relating to the post-fire response of burned areas depending on the initial landcover type.

3.2.1 Evaluation of vegetation indices for monitoring vegetation regeneration after fire in the case of Thasos

Twenty nine (29) Vegetation Indices were found in the literature. Seven indices out of the 29 were selected for evaluation, accuracy assessment and implementation (Annex, Table 12, p.46). These seven indices are shown below (Figure 19, p.22).

Vegetation indices evaluation for post-fire vegetation recovery assessment

The vegetation indices (VIs) were evaluated in relation to their efficiency to indicate vegetation cover. For this purpose field surveys of 62 locations were carried out in 1997 and 2004 (Figure 20, p.22) where vegetation cover (%) was recorded. Then the correlations of the field data with the values of the 7 VIs in the 62 locations were estimated and also linear regressions were run.

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NDVI (Normalized Difference Vegetation Index) II (Infrared Index) MSR (Modified Simple Ratio) MSAVI (Modified Soil Adjusted Vegetation Index) MNLI (Modified non-Linear V.I.)

LANDSAT ETM+ IMAGE, ALONG WITH (62) FIELD DATA POINTS

GVI (Greenness Vegetation Index) SR (Simple Ratio)

Figure 19: Applied indices on the Landsat image of Thasos

Figure 20: Landsat ETM+ image (R: band 4, G: band 3, B: band 2) along with all the (62) field data points from 1997 and 2004

Report of Case Study: Thasos island, NE Greece

Although the field data were collected at different years than the images used, it is assumed that vegetation cover (%) has only slightly changed. This is supported by the field observations carried out. The position of field survey locations is shown in Figure 20, p.22.

The data were imported in SPSS for statistical analysis to define the correlation between the field data and the corresponding pixel data from the vegetation indices. Non- parametric correlation and least square regression method were applied and the correlation found is shown in the following table.

Table 4: Vegetation indices evaluation with the use of % vegetation cover from field data

NON-PARAMETRIC

a/a INDICES

CORRELATION SPEARMAN

LEAST SQUARE REGRESSION R 2 VALUES

COEFFICIENTS

1 MSAVI 0,626 0,407 2 NDVI 0,626

6 SR 0,626 0,385 7 MSR 0,626

0,395

Slightly better from II and NDVI proved to be the MSAVI (Modified soil adjusted vegetation index), with Spearman coefficient 0.626 and r 2 =0.407. MSAVI accounts for

the influence of the soil background and it can be used when the vegetation cover has a low density, to minimize the effect of the bare soil (Santos et al. 2000).

3.2.2 Assessment of vegetation recovery by MSAVI thresholding

After selecting the MSAVI index as the one with the highest accuracy among the seven indices, a threshold value had to be set to represent areas of inadequate and adequate vegetation cover (vegetation recovery following fire). The threshold was set by fitting a linear regression to the field data cover density measurements and the MSAVI values for the same locations. Specifically, a vertical line from the X axis to the regression line and from the regression line vertically to the Y axis was drawn in order to define the MSAVI value that corresponded to the 20% cover density value (Figure 21). The MSAVI threshold was 0.51. Therefore pixels of the satellite image with MSAVI less that 0.51 correspond to areas with less than 20% vegetation cover (inadequate vegetation recovery) while pixels with MSAVI greater than 0.51 correspond to areas where vegetation cover is more than 20%.

23

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Figure 21: MSAVI thresholding

Following the definition of the MSAVI threshold an assessment was made of the proportion of all burned areas and each individual fire above and below the threshold, as well as an analysis of MSAVI values for each landcover zone. The results are presented in the following sections.

3.2.3 Comparison of vegetation recovery for each fire

A comparison of the area below and above the selected MSAVI threshold (0.51), representing unsuccessful and successful post-fire vegetation recovery respectively, revealed that in total, 68% of the area burned showed adequate or abundant vegetation recovery, while the remainder 32% had not at the time of assessment developed an adequate vegetation cover (Figure 22, p.25).

For each fire the percentages that were below and above the selected threshold were calculated in order to extract useful conclusions about the success of the vegetation recovery. Charts for all burned areas (1984-85-89, 2000) showing the percentages of area below and above the selected threshold were made and are presented below (Figure 23, p.25).

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Environmental Impact Assessment

Regeneration assessment for each fire

Below threshold

Fire 1984

Fire 1985

Above threshold

MSAVI value >0.51

MSAVI value <=0.51 MSAVI value >0.51

MSAVI value <=0.51

Figure 22: Year percentages of areas with unsuccessful and successful regeneration by MSAVI=0.51 thresholding

MSAVI value <=0.51 MSAVI value >0.51

MSAVI value <=0.51 MSAVI value >0.51

Regeneration - All years burned areas

Tw ice burned area (1985-89)

MSAVI value <=0.51

MSAVI value >0.51

MSAVI value <=0.51

MSAVI value >0.51

Figure 23: Applications of MSAVI threshold on the burned area of each fire

Figure 24: Burned area of all years and areas below and above the chosen MSAVI threshold

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The proportion of adequate regeneration was lowest (39%, Figure 22) for the fire of 2000 which may be due to the relatively short time (< 1 year) between the fire event and the satellite image used to assess vegetation recovery. From the other areas burned, the regeneration was relatively low (61%) for the 1985 fire. The other two fires of 1989 and 1984 had successful vegetation recovery in 73% and 74% of their area respectively. The exact area statistics are shown in the Annex, Table 10, p.45.

Figure 24, p.25 shows a map of burned areas with little or no vegetation recovery (problematic areas) and areas where vegetation recovery following fire was adequate according to the applied threshold of the MSAVI index. This map can be used for targeting areas for reafforestation.

3.2.4 Comparison of vegetation recovery of different landcover types and zones

Post-fire vegetation recovery is a crucial element of post-fire site condition. Failure of vegetation recovery leaves the soil vulnerable to erosion which may lead to site degradation and conversion of vegetated land to bare and barren land. Therefore, if the post-fire ecosystem response is taken into account in the Environmental Impact Assessment, the fire impact is exacerbated in burned areas that fail to develop an adequate vegetation cover and ameliorated in areas where vegetation cover redevelops, thereby preventing soil erosion and site degradation.

The vegetation recovery analysis by the employment of the MSAVI threshold in the generalized landcover zones (Figure 26, p.27 and Figure 25, p.27) revealed that post- fire vegetation recovery is very much dependent on the initial landcover type. The exact area statistics of landcover zone percentages above the selected threshold, signifying successful vegetation recovery are shown in the Annex, Table 11, p.46.

Specifically, as shown below, vegetation recovery was lowest for the “bare land and settlement” zone. This was anticipated because land classified as bare may only have sparse sporadic herbaceous or grass species in rock crevices and localised pockets of soil and on the whole appears as unvegetated. Therefore bare rock is not expected to develop

a vegetation cover.

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Environmental Impact Assessment

Legend

Comparison of vegetation recovery of

Successful recovery

MSAVI

different zones of vegetation cover

Unsuccessful recove ry

ET VEG <=0.51

E C D <=0.51

Percentage above and below MSAVI threshold

Figure 25: Regeneration assessment per landcover zone by MSAVI thresholding

Figure 26: Post-fire vegetation recovery per zone of initial landcover

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In the zone of “other vegetation” the initial landcover was shrubland, grassland or Quercus sp. (Table 2, p.12 and Figure 5, p.12). This zone was second in the vegetation recovery success spectrum. This landcover zone may represent areas that had been burned in the past and were in secondary succession to forest, areas where grazing kept a low vegetation cover or lastly areas where harsh site conditions (for example wind- exposed, steep areas or shallow infertile soils) could only support a low vegetation cover of grassland or scrub. All these factors may have contributed to the relatively low vegetation recovery percentage of this zone.

Coniferous forest comes next with a 68.4% of its burned area developing an adequate vegetation cover. Depending on the time elapsed since the fire event this may reflect patchy regeneration, localised erosion / degradation or disturbance possibly from grazing.

The “agricultural land” zone comes next with 74% vegetation recovery which is not surprising considering that there is no reason why utilisation of private cultivated land for agricultural production should cease after a fire. Annual crops can be planted as usual and tree orchards, vineyards or olive groves can be replanted. Perhaps it is only marginal and/or abandoned agricultural land that may be left unutilised, in which case secondary succession will allow the redevelopment of vegetation cover unless there has been soil erosion and site degradation.

Finally the “deciduous” zone (made up of areas initially covered by chestnut or riparian plane trees - Table 2, p.12) showed the best post-fire vegetation recovery with 95% of its area being above the MSAVI threshold, which may be attributed to fertile site-conditions and absence of water-stress.

A more detailed comparison of vegetation recovery per landcover type is presented in Figure 27, p.29 which lists landcover types according to the order of the percentage with successful vegetation recovery. Again bare land showed the worst post-fire vegetation recovery. Grassland and shrubland came next with just about half their area developing adequate vegetation cover. It is interesting that Quercus, unlike shrubland showed a much better vegetation recovery with 88% of its area redeveloping vegetation cover. Coniferous forest types showed intermediate recovery percentages (59% – 69%) with P. brutia being more successful than P. nigra or areas originally covered by their mixed stands. Arable land and settlement areas were next in the success spectrum and the best vegetation recovery percentages were observed for areas originally covered by broadleaved forest (Platanus sp. 81%) and Castanea sp. with a stunning 99% of successful regeneration. Abandoned farmland was second best with 93% of its area redeveloping vegetation cover.

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Environmental Impact Assessment

Below threshold

Coniferous zone

Deciduous zone

Vegetation recovery by landcover type

Above threshold

(Castanea / Platanus) % landcover type

BARE LAND

GRASSLAND 55%

SHRUBLAND 56%

PINUS BRUTIA - PINUS NIGRA

PINUS NIGRA

MSAVI value <=0.51 MSAVI value >0.51

MSAVI value <=0.51 MSAVI value >0.51

PINUS BRUTIA

ARABLE LAND

Other vegetation zone Agricultural zone (Arable

SETTLEMENT 77%

(Grassland / Shrubland /

land / Abandoned farmland)

PLATANUS

Quercus)

QUERCUS

ABANDONED FARMLAND

CASTANEA 99%

MSAVI value <=0.51 MSAVI value >0.51

MSAVI value <=0.51 MSAVI value >0.51

Figure 27: Vegetation recovery success (MSAVI threshold) per landcover type in ascending order

Bare land and settlement

zone

Figure 28: Regeneration assessment per zone of vegetation cover

MSAVI value <=0.51 MSAVI value >0.51

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3.3 Mapping the quarries and their expansion since 1984

This section describes the detection of changes in the extent of marble quarries, especially changes from vegetated cover to quarries. Quarry monitoring allowed the assessment of the impacts that the quarries have had on the landscape and the environment of Thasos, where marble extraction has been going on for more than 30 years.

Two techniques were selected and implemented: image differencing and post- classification comparison.

In the following subsections first there is a description of the RS (remote sensing) methodology and procedures used for identifying quarries and their expansion (4.3.1 to

4.3.5) and then the results are presented and discussed in 4.3.6. Two techniques were used: Image differencing (4.3.2) and post-classification comparison (4.3.3). The results obtained by the two techniques are compared in 4.3.4.

3.3.1 Image pre-processing

Before implementing change detection analysis using multi-temporal images, precise registration and radiometric and atmospheric calibration or normalization are required (Lu et al. 2004). The two images for the change detection were acquired in close anniversary dates and both in the summer, which is a phenologically stable period. Although the images were acquired by different sensors, this problem was eliminated by using the six bands in common and especially the red and the near infrared ones that are the most important for the purpose of this study.

The geometric rectification for both images (of 1984 and 2000) has been done using a first order polynomial and nearest neighbour resampling. Average Root Mean Square (RMS) error, as a measure of misregistration, was less than 0.2 pixels for the co- registered images which is satisfactory (Dai & Khorran 1997).

Atmospheric and topographic correction was carried out for both images. Topographic normalization was employed, because according to Lu et al. (2004) it is needed if the study area is in mountainous regions, as in the case of the island of Thasos. For this purpose, ATCOR3 model from the software Erdas version 8.7 has been used.

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3.3.2 Image differencing

Figure 29: Image differencing of band 4. The derivative image with the absolute values.

Figure 30: Highlight image of the image differencing of band 4. The areas in green are increased values and the areas in red are decreased values.

In order to have a better view of the quarry expansion the decreased pixels were rejected.

Figure 31 depicts the northeast part of Thasos highlighting the pixels whose values increased signifying quarry expansion.

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Figure 31: Marble quarry expansion in the northeast part of Thasos.

3.3.3 Post-classification comparison

The following three main categories were extracted:

- Water - Land - Marble quarries

The final classification results are shown in Figure 32.

Figure 32: Classification of the 1984 image. Right: Classification of the 2000 image. Water (blue), land (red) and marble quarries (yellow)

Inspection of the quarry class revealed that a lot of other bright areas were observed such as unvegetated areas, fire breaks, roads, beaches etc. These areas in some cases could not

be distinguished from the actual quarries. In order to eliminate the effect of this factor the areas classified as quarries that had an extent of a single pixel and were isolated were not included in the quarries class.

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3.3.4 Accuracy assessment

The accuracy of the post-classification comparison was assessed for the classified quarries of the 2000 image. For that purpose point data were used that were taken both on the field in the quarries with a GPS and from image interpretation (Figure 33). Accuracy assessment module from Erdas Imagine 8.7 was used and an error matrix was produced (Table 5).

Figure 33: Points taken with the GPS and extracted area of the quarries for the year 2000.

Table 5: Error matrix for the classified image of 2000.

Class name Reference Classified Number Producers Users

Totals Totals Correct Accuracy Accuracy

Quarries 21 18 18 Other 0 3 0 85.71% 100.00% Totals 21 21 18

Overall Classification Accuracy = 85.71%

There is a difference between the producer’s and the user’s accuracy. The bright areas mentioned above and the fact that the GPS data have a time difference with the image are responsible for the observed difference on the accuracy.

3.3.5 Comparison of the two methods

Table 6 lists the area of expansion according to the two methods. As also shown in Figure

34 the two methods gave practically the same estimation of the area of quarry expansion.

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Table 6: Areas of change detected from each method Post-classification comparison

Percentage of the island's

(487% increase over the 1984 area)

a re a (ha ) 80,00 60,00 40,00 20,00

post classification comparison

image differencing

Figure 34: Comparison of the areas of quarry expansion mapped with each technique

3.3.6 Quarry expansion

It is obvious that the area of the quarries is bigger in the image of 2000, even from a quick look of the classifications.

Quarries total area increased from 31 ha in 1984 to 180 ha (Figure 37, p.35) in 2000. In 2000 quarries’ area was 0.47% of the island (Figure 35)or 0.48% of areas originally covered by vegetation (excluding bare land and settlements - Figure 36). Quarry area as a proportion to the area originally covered by coniferous forest was 0.75% (Figure 38).

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Area of quarries in 2000

Quarry total area (ha)

180 ha

Figure 35: Area of quarries to the total area of the island

Quarries 2000 Total island area

Area of quarries in 2000

180 ha

Figure 36: Area of quarries over total

vegetated area

Figure 37: Area of the quarries in the classified images of 1984 and 2000

Quarries 2000

Total Vegetated area

37433 ha

Area of quarries in 2000

180 ha

Figure 38: Area of quarries over total coniferous area

Quarries 2000 CONIFERS

23951ha 100%

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3.4 Landscape change, ecological and visual impact in relation to the marble quarries

In order to assess the ecological impact of the quarries, it was necessary to identify the land cover on the area where quarry expansion took place after 1984. The results are presented in 4.4.1. Moreover it was necessary to quantify and assess how the landscape changed in relation to the marble quarries between 1984 and 2000. This was performed by deriving landscape metrics of quarry patches. The results of the landscape analysis are presented in 4.4.2.

3.4.1 Ecological assessment