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The Relationship between Physical Environmental Variables and the Spatial Distribution of Vegetation Cover within the Biebrza River Valley Wetland Wycliff Kawule March, 2007

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Page 1: The Relationship between Physical Environmental Variables and … · 2007-03-28 · Poland), Dr. Eng. Stanislaw Lewinski and Mr. Marek Borkowski (Wildlife Poland) who made sure that

The Relationship between Physical Environmental Variables and the Spatial Distribution of Vegetation Cover within the

Biebrza River Valley Wetland

Wycliff Kawule March, 2007

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The Relationship between Physical Environmental Variables & the Spatial Distribution of Vegetation Cover within the Biebrza River Valley Wetland

The Relationship between Physical Environmental Variables and the Spatial Distribution of Vegetation Cover within the Biebrza River

Valley Wetland

by

Wycliff Kawule

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Geo-Information Science for Environmental Modelling and Management (GEM) Thesis Assessment Board Chairman: Prof. Andrew Skidmore, ITC External Examiner : Prof. Petter Pilesjö, Lund University Member : Prof. Kasia Dabrowska, Warsaw University Primary Supervisor : Ir. M.C. Kees Bronsveld, ITC

International Institute for Geo-Information Science and Earth Observation, Enschede, the Netherlands

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The Relationship between Physical Environmental Variables & the Spatial Distribution of Vegetation

Cover within the Biebrza River Valley Wetland

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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The Relationship between Physical Environmental Variables & the Spatial Distribution of Vegetation Cover within the Biebrza River Valley Wetland

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Abstract

One of the most widespread approaches towards vegetation cover mapping is the use of remotely acquired data. Yet, owing to the relationship that exists between vegetation characteristics and the physical environment, the use of ancillary data has also proved to be an indispensable tool for understanding and explaining the spatial distribution of vegetation cover of a given a landscape. This study aimed at examining the relationship between physical environmental variables and the spatial distribution of vegetation cover within the Biebrza river valley wetland. The physical environmental variables used in this study included water level, elevation, slope, and aspect. Besides these, field sample points, an ASTER image acquired on July 11, 2006 and an NDVI map were also used. An object-oriented image classification approach was adopted to generate the vegetation cover map of the study. Eleven cover classes were distinguished; eight of these represented vegetation cover and the remaining three symbolized non-vegetated features. Accuracy assessment of the produced vegetation cover map was based on a computed error matrix. The overall accuracy and the coefficient of agreement were approximately 76% and 0.72, respectively. Statistical analyses were also used. These included MANOVA with repeated measures, Kruskal-Wallis test and multiple correspondence analysis (MCA). In the case of water level, the factor ratios obtained from MANOVA with repeated measures were statistically significant for vegetation cover: [F (6, 255) = 2.32, p = 0.0335], time of measurement: [F (4, 252) = 64.13, p = 0.0001] and the interaction factor: [F (24, 880) = 1.71, p = 0.0182]. Likewise, MCA revealed that the relationship between the spatial distribution of vegetation cover and water level was statistically significant: [X2 (1, 262) = 47.07, p = 0.0002]. However, the spatial distribution of vegetation cover with respect to water level deviated from what was expected. The analysis revealed that woody vegetation such as coniferous and deciduous forests were dominant in areas where the water level was shallower than that in areas covered by marshland vegetation types. Although this seems to be unusual, the distribution of vegetation cover in the observed manner attests to what previous researchers have reported as a change in the hydrological network in this part of the river valley. With regard to topographical variables, the Kruskal-Wallis test revealed significant differences in elevation, [X2 (7, N = 464) = 187.23, p<0.0001], but neither for slope, [X2 (7, N = 464) = 10.94, p<0.1412] nor aspect, [X2 (7, N = 464) = 10.65, p<0.1545]. Like in the case of water level, MCA revealed that the relationship between the spatial distribution of vegetation cover and elevation was statistically significant [X2(1, 464) = 414.95, p = 0.0001].

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Finally, because only a few variables responsible for the present spatial distribution and composition of vegetation cover within the Biebrza mire were considered in this study, future research should aim at including as many variables as possible. This way, a better understanding of this rather complex ecosystem will be attained.

Keywords: Vegetation cover mapping, Biebrza, river valley wetland, spatial distribution, physical environmental variables, Kruskal-Wallis, MANOVA, Repeated measures, multiple correspondence analysis

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Acknowledgements

First and foremost, I wish to commend the European Commission for initiating the ERASMUS MUNDUS programme, without which I would not have gotten the opportunity to study in four of the top universities in Europe. I also owe a great deal to the European Union (EU) for having funded my entire study and stay here. Special thanks go to the GEM course coordinators from the Universities of Southampton (United Kingdom), Lund (Sweden), Warsaw (Poland) and ITC (the Netherlands). Your dedication and commitment cannot go without praise! The lectures from these Universities are not forgotten; I have learnt a lot from you. At the same time, I wish to acknowledge the effort, time and invaluable advice offered to me by my supervisors: Dr. Kees Bronsveld, Dr. Iris Van Duren from ITC and Dr. Eng. Stanislaw Lewinski from the Institute of Geodesy and Cartography (Warsaw, Poland). Your confidence in my work has been my greatest motivation.

I also wish to thank Professor Kasia Katarzyna Dabrowska (Warsaw University, Poland), Dr. Eng. Stanislaw Lewinski and Mr. Marek Borkowski (Wildlife Poland) who made sure that my field work and stay in Poland were successful. I am equally grateful to the family of Mr. Marek Borkowski who hosted me. Meanwhile, I cannot forget my classmates with whom I have established an international and eternal friendship. Thank you all for your cooperation and moral support.

Back home (Uganda), I wish to thank my employers at Makerere University for having allowed me to take up this once in a life time opportunity; not forgetting my colleagues at the Department of Geology who have been standing in for me. Many thanks to my family, relatives and friends; thank you so much for your prayers and encouragement.

Above all, I greatly thank and glorify the Almighty. Dear God, without your blessings I am completely incapable. For this reason, I owe all my achievements to your enduring grace.

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Table of contents

1. INTRODUCTION ...................................................................................... 1

1.1. BACKGROUD ON LAND COVER MAPPING AND MAPPING TECHNIQUES ......... 2 1.2. PROBLEM STATEMENT AND JUSTIFICATION ................................................ 5 1.3. RESEARCH OBJECTIVES ............................................................................. 6

1.3.1. General Objective ............................................................................. 6 1.3.2. Specific Objectives ............................................................................ 6 1.3.3. Research Questions .......................................................................... 6

1.4. RESEARCH HYPOTHESES ............................................................................ 7 1.4.1. First Hypothesis ............................................................................... 7 1.4.2. Second Hypothesis ............................................................................ 7 1.4.3. Third Hypothesis .............................................................................. 7 1.4.4. Fourth Hypothesis ............................................................................ 7

2. METHODS AND MATERIALS ................................................................ 8

2.1. STUDAY AREA ........................................................................................... 8 2.1.1. Climate and Land use ....................................................................... 9

2.2. MATERIALS ................................................................................................ 9 2.2.1. DEM characteristics ....................................................................... 10 2.2.2. Spatial and Spectral characteristics of the ASTER image .............. 11

2.3. METHODS................................................................................................. 12 2.3.1. Field Preparations.......................................................................... 12 2.3.2. Field data collection ....................................................................... 14 2.3.3. Pre-processing ................................................................................ 16 2.3.4. Image Classification ....................................................................... 16 2.3.5. Accuracy Assessment ...................................................................... 19 2.3.6. Spatial Analysis .............................................................................. 20 2.3.7. Statistical Analysis.......................................................................... 20

2.3.7.1. Comparison of water level, elevation, slope and aspect across the mapped vegetation cover types .................................................................................21 2.3.7.2. Analysing the relationship between physical environmental variables and the spatial distribution of vegetation cover .........................................................22

3. RESULTS ................................................................................................ 23

3.1. VEGETATION COVER MAPS ....................................................................... 23 3.2. ACCURACY ASSESSMENT ......................................................................... 25

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3.3. HYPOTHESIS TESTING .............................................................................. 27 3.3.1. Comparison of water level across the mapped vegetation cover types  27 3.3.2. Post hoc comparison of water level means .................................... 29 3.3.3. Relationship between water level and the spatial distribution of vegetation cover ............................................................................................... 31 3.3.4. Comparison of elevation, slope and aspect across the mapped vegetation cover types ...................................................................................... 32 3.3.5. Relationship between elevation and the spatial distribution of vegetation cover ............................................................................................... 33

4. DISCUSSION .......................................................................................... 35

4.1. VEGETATION COVER MAPS ....................................................................... 35 4.2. SPATIAL DISTRIBUTION OF VEGETATION COVER IN RELATION TO WATER

LEVEL  36 4.3. SPATIAL DISTRIBUTION OF VEGETAION COVER IN RELATION TO

TOPOGRAPHICAL VARIABLES ................................................................................ 38

5. CONCLUSIONS AND RECOMMENDATIONS .................................... 39

5.1. CONCLUSIONS .......................................................................................... 39 5.2. RECOMMENDATIONS ................................................................................ 40

REFERENCES………….………………………………………………42 APPENDICES…….……………………………………………………47

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List of figures

Figure 1: Location of the study area ............................................................... 8 Figure 2: Schematic diagram for the methodology. ..................................... 13

Figure 3: Image objects at level 1and level 2 ............................................... 18

Figure 4a: Detailed cover map of the study area .......................................... 23

Figure 4b: Generalized vegetation cover map of the study area …….…….24 Figure 5: A mosaic plot of vegetation cover within a given elevation range. ...................................................................................................................... 33

Figure 6: NDVI of the mapped cover classes ............................................... 36 

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List of tables

Table 1: Summary of SRTM performance. Source: Rodriguez et al. 2005 . 10

Table 2: Spectral characteristics of ASTER. Adapted from Zalazar (2006) 11

Table 3: Mapped cover classes ..................................................................... 15

Table 4: Projection parameters ..................................................................... 16

Table 5: Composition of homogeneity criterion used for image segmentation ...................................................................................................................... 18

Table 6: Error matrix for accuracy assessment ............................................ 25

Table 7: Effect of error of omission and commission on producer and user accuracies ..................................................................................................... 26

Table 8a: Kruskal-Wallis one-way analysis of variance of water level by vegetation cover ........................................................................................... 28

Table 8bi: Comparison of water level means between vegetation cover pairs………………………………………………………………...……….29

Table 8bii: Comparison of water level means between year pairs ………...30

Table 8c: A contingency table showing the Chi-square values for the association between vegetation cover and water level…………………… 31 Table 9: Comparison of elevation means between vegetation cover pairs ... 32 

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1. Introduction

Despite being described as one of the few surviving extensive and undisturbed types of mire in Central Europe (Succow and Jeschke 1986 in Wassen et al. 1990), the Biebrza wetland has been experiencing immense pressure from threats such as, frequent fire outbreaks and secondary succession (Schmidt et al. 2001; Nauta et al. 2005 ). Meanwhile, due to drainage activities carried out in the past, some parts of the river valley suffer from constant groundwater table declines, while those that are recharged register an increase in nutrient input (Nauta et al. 2005). These problems have proved to be too complicated for the thirteen-year old Biebrza National Park (BNP) to curb since its inception in 1993. For example, the BNP has for several years been trying to find a solution to the seemingly inexorable secondary vegetation succession. With regard to the causes of secondary succession, several factors have been advanced; however, hitherto changes in management practices and the abandonment of formerly privately owned land are well documented. For this reason, further research on this highly treasured ecosystem is still needed. After all, it is largely considered to be a favourable natural environment for ecological and other scientific studies. In this regard, although several studies have been conducted on the Biebrza wetland (Schmidt et al. 2000; Wassen et al. 1990, 1998, 2002; Kotowski et al. 2002; Nauta et al. 2005), not all of them endeavour to explain the current spatial distribution of its vegetation cover in relation to other environmental variables. As a result, not much is known about the present spatial distribution pattern of this wetland’s vegetation cover and its underlying controls. Limited information is believed to be a major hindrance to the implementation of management and conservation policies. It is also likely to frustrate the efforts of those interested in carrying out more research on this unique ecosystem. Consequently, this study aims at analyzing the relationship between the physical environmental variables and the spatial distribution of vegetation cover within this river valley wetland. The analysis is based on a vegetation map covering a section of the middle basin of the Biebrza river valley wetland.

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1.1. Backgroud on land cover mapping and mapping techniques

Land cover mapping has preserved its status as one of the most important applications of Earth observation studies. This is particularly due to the fact that it maintains a constant supply of data and information on the rapidly changing environment. Such data and information are extremely important for environmental monitoring, through which both human- and naturally-induced changes in land cover are identified. Over the years, land cover mapping has been extensively used in a number of planning and management activities concerned with the surface of the Earth (Lillesand and Kiefer 1994). Moreover, land cover studies are essential in comprehending land use trends. The terms land cover and land use are used closely and sometimes confused. Regarding the two terminologies, Lillesand and Kiefer (1994) define the former as “the type of feature present on the surface of the Earth”, and the latter as “the human activity or economic function associated with a specific piece of land”. Although the two are defined quite differently, their study requires similar techniques, in which case, the use of remotely acquired data is at the forefront. The use of such data in Earth observation studies is emphasized, not only because of their ability to facilitate accurate land cover mapping, but also due to the visual interpretability of the evidence they carry (Campbell 2002). Depending on the project’s objectives and requirements, land cover mapping may yield a multitude of data about the Earth’s surface. These may, for example, include wetland and/ or forest vegetation, arable areas, settlements, water bodies, grazing fields, just to mention but a few. Data on such environmental aspects can be transformed into useful information, which can facilitate the implementation of management and conservation policies aimed at fostering sustainable land use and development.

Until now, vegetation cover mapping has been one of the most widespread and interesting fields of research in land cover mapping (Pearce et al. 2001; Cawsey et al. 2002; Nagler et al. 2005). This has helped to generate information on the extent and trend of use and abuse of forests, wetlands and other sensitive natural environments, thereby facilitating the assessment of impacts of degradation, regeneration and/ or restoration of such environments. Joshi et al. (2006) argue that vegetation cover plays a key role in terrestrial biophysical processes and is in many ways related to the dynamics of global climate. Furthermore, vegetation mapping has been widely used for forest inventorying, change detection studies and wild life habitat monitoring (Nagler et al. 2005). According to Nagler et al. (2005), the use of periodic vegetation mapping could help in detecting changes in floodplains over time.

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They also claim that vegetation maps are widely used in rescaling ground measurements of eveapotranspiration to larger river units, which facilitates effective management of river water supplies. Such maps are used to provide information on the gains and/or losses of particular vegetation associations. The retrieved information can then be used to analyse changes in river flows (Weber and Dunno 2001 in Nagler et al. 2005). Elsewhere, Vegetation cover mapping has been shown to be useful in the understanding of several other environmental phenomena including hydrological and ecological systems. According to Kotowski et al. (2001), a rough assessment of the hydrological conditions can usually be based on a single measurement of groundwater level, and then indicated by the character of vegetation. These cite the occurrence of fen plants along ditches, in local depressions and old turf-pits as an indication of groundwater seepage.

Like many other Earth observation studies, vegetation cover mapping involves the use of remotely acquired data. Here, image classification techniques are used to process multispectral data (Wood cook et al. 2002). Image classification can be executed, through the application of either unsupervised or supervised methods. These two approaches involve the analyst’s interaction with the classification process. However, the timing and extent of the interaction are different in either approach (Campbell 2002). In the case of a supervised approach, the user’s input is enormous, and comes well ahead of the classification step, while the converse is true for unsupervised techniques (Woodcock et al. 2002). In traditional algorithms, image classification is based on individual pixels. During the classification, pixels are compared to one another, and those found to be of similar spectral identity are aggregated into informational classes. (Campbell 2002).

To date, one of the most extensive techniques of vegetation cover mapping is the use of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) (Benedetti et al 1994; Su 2000; Wang and Tenhunen 2004; Bagan et al. 2005). The frequent use of NDVI in vegetation mapping has been greatly attributed to its sound theoretical basis and successful performance in other fields of research (Sellers 1985 and Tucker et al. 1985 in Su 2000). Moreover, NDVI can be explicitly related to physical proprieties of vegetation such as biomass and photosynthetic activity (Wang et al. 2001). In addition, a number of vegetation mapping studies and projects use NDVI to derive meaningful matrices that help in describing ecosystem functions (Wang and Tenhunen 2004). Meanwhile, others have exploited the variation in NDVI due to phenological changes, foliage activities and stress to study vegetation dynamics (Joshi et al. 2006).

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Alternative approaches for vegetation cover characterization involve analysing other environmental variables. For example, several studies have used hydrological variables to analyse, and simultaneously explain vegetation composition, as well as its distribution (Day et al. 1974, Wassen et al. 1990, 2002; van Diggelen et al. 1991; Su 2000; Zeilhofer and Schessl 2000; Oliveira et al. 2005). Others have used topographical variables such as elevation, slope and aspect as surrogates for temperature and moisture conditions to capture the influence of climate on the distribution of vegetation species (Woodcock et al. 2002). Regarding the use of hydrological variables, Wassen et al. (1990, 2002), analysed the relationship between vegetation composition and its distribution within the Biebrza river floodplain, with respect to ground water dynamics and other environmental variables. Results of their study revealed that vegetation composition and standing biomass of fen vegetation were clearly related to water tables and chemical factors discharged by water flow and peat mineral matter. Meanwhile, Yabe and Onimaru (1997) from their study on the cool-temperate Bibi Mire (Hokkaido, Japan), established that hydrological variables such as water level fluctuations and surface water flow were the major controls of vegetation dynamics.

It should be pointed out that the use of ancillary data in vegetation mapping is entrenched in the relationship between vegetation characteristics; including its spatial distribution and the environmental variables under consideration. Therefore, the present study aimed at examining this relationship using the Biebrza river valley wetland as a case study. According to Wassen et al. (1990), the behaviour of groundwater is one of the most essential hydrological processes determining the distribution of vegetation. In this study, the relationship between vegetation cover types and groundwater level is analyzed first. The water level measurements used were taken by the staff of the Biebrza National Park during spring, summer and fall of 2000 to 2004. The measurements were obtained from piezometers located along five transects in the middle basin of the Biebrza river valley. The study also examines the relationship between vegetation cover and topographical variables including elevation, slope and aspect. Day and Monk (1974) established significant correlations between different plant species and topographical variables including those stated above. Meanwhile, Abbate et al. (2006) used correspondence analysis to assess the mutual relationships between morphometric units and vegetation types of a medium landscape relief in Central Italy. In their study, they hypothesise that the realised niche and geographical space of a vegetation type in a given environment are related to relief. Hence, they argue that land attributes such as elevation, slope

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and aspect are important input variables for spatial analysis and modelling of vegetation distribution. This study is important and very relevant, given the fact that much of the modification of the Biebrza wetland has been a result of both human and natural influence. Thus, attention should also be focused on environmental aspects in order to establish their impact on this highly treasured ecosystem.

1.2. Problem statement and justification

Although several studies have been carried out on the Biebrza wetland including Schmidt et al. (2000), Wassen et al. (1990, 1998, 2002), Kotowski et al. (2002), and Nauta et al. (2005) among the others, only a few of them endeavour to explain the current spatial distribution of its vegetation cover in relation to other environmental variables. As a result, there is limited information about the present spatial distribution of this wetland’s vegetation cover and its underlying controls. Therefore, the present study analyses the relationship between the physical environmental variables and the spatial distribution of vegetation cover within this river valley wetland. The findings of this research are expected to supplement the available information. More closely, the study involves examining the dynamics of groundwater level over a period of five years, within different vegetation cover types. In addition, the variation of topographical variables such as elevation, slope and aspect, across different vegetation cover types is also analyzed. Hence, multiple correspondence analysis is used to examine the relationship between such variables and the mapped vegetation cover classes. Information of this kind is believed to be vital to ecosystem managers, especially during the formulation of conservation policies. Such information can also provide answers to questions regarding the changes that have taken place in this wetland; including their magnitude and the underlying causes, as well as sites requiring urgent intervention. Moreover, having such information may facilitate the realization of monitoring and conservation activities that are currently being supported by the Polish Government and European Union, through the Biebrza National Park and European Ecological Network in Poland.

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1.3. Research Objectives

1.3.1. General Objective

The primary objective of this study was to create a vegetation cover map of the study area, and use it to analyse the relationship between the physical environmental variables and the spatial distribution of vegetation cover within the Biebrza river valley wetland. The physical environmental variables in question include groundwater level, elevation, slope and aspect.

1.3.2. Specific Objectives

In order to achieve the above general objective the following specific objectives were proposed:

1. To create a cover map showing the present spatial distribution of the different vegetation cover types within the study area.

2. To compare the variation in groundwater level between different vegetation cover types, within a given year and over a period of years.

3. To examine the spatial distribution of vegetation cover in relation to water level.

4. To compare elevation, slope and aspect of the areas where the different vegetation cover types are found

5. To analyse the spatial distribution of the different vegetation cover types in relation to the aforementioned topographical variables.

1.3.3. Research Questions

The study will endeavour to provide answers to the following questions:

1. Is the groundwater level different across the mapped vegetation cover types?

2. What is the spatial distribution of vegetation cover with respect to groundwater level?

3. Does topography differ with respect to the location of vegetation cover?

4. What is the spatial distribution of vegetation cover in relation to the topography of the study area?

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1.4. Research Hypotheses

1.4.1. First Hypothesis

H0: the first null hypothesis states that the mean water level within a given year and over a period of years is the same across the mapped vegetation cover types. H1: the alternative hypothesis is that the mean water level within a given year and over a period of years is different across the mapped vegetation cover types.

1.4.2. Second Hypothesis

H0: no relationship exists between groundwater level and the spatial distribution of vegetation cover H1: there is a relationship between groundwater level and the spatial distribution of vegetation cover.

1.4.3. Third Hypothesis

H0: there is no difference in the mean elevation, slope and aspect across the mapped vegetation cover types. H1: the mean elevation, slope and aspect are different across the mapped vegetation cover types.

1.4.4. Fourth Hypothesis

H0: there is no relationship between the spatial distribution of vegetation cover and the considered topographical variables H1: a relationship exists between the spatial distribution of vegetation cover and the considered topographical variables.

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2. Methods and Materials

2.1. Studay Area

The study was carried out on a relatively small section of the middle basin of the Biebrza river valley wetland, which is found in the north-eastern part of Poland. Within the middle basin, the study area is located between 53o 32’ 36” N – 53o 38’ 39” N and 22o 39’ 24”E – 22o 55’ 35” E (figure 1).

Figure 1: Location of the study area

The Biebrza river valley wetland is under the protection of the Biebrza National Park (BNP), which was itself established in 1993 (Schmidt et al. 2000). The wetland accounts for 25494ha of the total 59223ha area occupied by the National Park (Polish National Report 2004). The rest of the Park is occupied by forests and croplands covering 15547ha and 18182ha, respectively (Polish National Report 2004). Because of its protected status, human activities, such as agriculture, widely believed to be a major cause of wetland habitat degradation and/ or loss, take place outside its borders. However, having been subjected to extensive drainage activities

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in the past, this part of the valley has been greatly modified compared to both the upper and lower basins (Wassen et al. 1990). Such activities are responsible for the current problems in the marshland including lowering of groundwater levels, drying of peat soils and frequent fire outbreaks (Wassen et al. 1990, Nauta et al. 2005).

2.1.1. Climate and Land use

The area receives a mean annual rainfall of 583mm of which 244mm falls during wet summers (Wassen et al. 1990). The main environmental factors influencing the spatial differentiation of rainfall in this region include relief of the river valley and the thick peat deposits that characterise it (Schmidt et al. 2000). The mean annual temperature of the area is in the range of 6.5 and 7 degrees Celsius; below that of Poland, which is estimated at 8 oC (Schmidt et al. 2000). Topographically, the Biebrza valley is a relatively low-lying basin with its altitude ranging from ca. 100 to 130m above mean sea level (Wassen et al. 2002). It is a sparsely populated area, and so, a large proportion of the land is covered by forests, the marshland and water sources including lakes, rivers and canals. Sand dunes are among the morphological features characterizing this vast river valley. Currently, the main human activities carried out in the valley include hay harvesting and agriculture, especially outside the National Park boundaries. However, even there it is carried out on a much smaller scale than it used to be in the past. From the economic and conservation points of view, mowing and grazing of cattle and horses are being carried out both privately and by the Biebrza National Park. The marshland is also being conserved for wild life and tourism.

2.2. Materials

In this research, both vector and raster data were used. Vector data included secondary point data on groundwater level and sample points collected during a field survey carried out between the late September and mid October of 2006. The former was provided by the Biebrza National Park, through the Institute of Geodesy and Cartography (Warsaw, Poland). Raster data included an ASTER image acquired on 11/07/2006 and a digital elevation Model (DEM). All these were obtained from the United States Geological Survey’s (USGS) website on land cover facilities. Besides the aforementioned, other raster maps used in this study included an aerial photograph and an NDVI map derived from the visible (red) and near infrared (NIR) bands of the ASTER image. Slope and aspect maps were derived directly from the DEM using the spatial analyst in ArcInfo.

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The following software was used: Earth Resources Data Analysis System (Erdas Imagine 8.7), eCognition (Definiens Professional 5), ArcInfo 9.1, Statistical Analysis System (SAS) and Microsoft Excel 2003.

2.2.1. DEM characteristics

As mentioned, the DEM was downloaded from the USGS’ website. It is provided as a Shuttle Radar Topography Mission (SRTM) product. The STRM was aimed at providing topographical data products with a better accuracy than that of most global topographical data sets that were available at the time of its inauguration (Rodriguez et al. 2005). To achieve that objective an extensive global ground-truth data set was collected to supplement the one which was in existence at the time (Rodriguez et al. 2005). Then, the SRTM performance was validated against the collected ground-truth data set. The results of the validation revealed that SRTM had met and exceeded its performance requirements (Rodriguez et al. 2005), which included the following: (i) a linear vertical absolute height error (Ab. HE) of less than 16m for 90% of the data. (ii) a linear vertical relative height error (Rel. HE) of less than 10m for 90% of the data (iii) a circular absolute geo-location error (Ab. GE) of less than 20m for 90% of the data (iv) a circular relative geo-location error (Rel. GE) of less than 15m for 90% of the data. A summary of the global SRTM performance observed by comparing against the available ground-truth is given in table 1. The quantities represent 90% errors in metres. The DEM used in this study matches the accuracy performance under Eurasia. Table 1: Summary of SRTM performance. Source: Rodriguez et al. 2005

Long λ HE denotes long wavelength height error and contributes to the absolute vertical accuracy.

Error Africa Australia Eurasia Islands North

America South

America Ab. GE 11.9 7.2 8.8 9 12.6 9 Ab. HE 5.6 6 6.2 8 9 6.2 Rel. HE 9.8 4.7 8.7 6.2 7 5.5

Long λ HE 3.1 6 2.6 3.7 4 4.9

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The SRTM DEMs are available in a spatial resolution of 1 arc second, 2 arc seconds and 3 arc seconds. The first two resolutions are for DEMs covering United States, while the global DEM, which was used in this study, is available with a spatial resolution of 3 arc seconds (approximately 90m). And since it is provided with the World Geodetic System (WGS84) projection, it was reprojected using the parameters given in table 4 to ensure that it overlays perfectly with other the maps.

2.2.2. Spatial and Spectral characteristics of the ASTER image

ASTER is a moderate spatial resolution (15m, 30m and 90m) image acquired by the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER). It happens to be the highest resolution sensor aboard the Terra spacecraft. The spectral and spatial characteristics of this image are given in table 2.

Table 2: Spectral characteristics of ASTER. Adapted from Zalazar (2006) Spectrum Band VNIR (µm) SWIR (µm) TIR (µm) Spectral range 1 0.52 – 0.60

2 0.63 – 0.69 3N nadir 0.76 – 0.86 3B backward 0.76 - 0.86

4 1.600 – 1.700 5 2.145 – 2.185 6 2.185 – 2.225 7 2.235 – 2.236 8 2.295 – 2.364 9 2.360 – 2.430 10 8.125 – 8.475 11 8.475 – 8.825 12 8.925 – 9.275 13 10.250 - 10.950 14 10.950 - 11.650 Spatial resolution 15m 30m 90m

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2.3. Methods

An overview of the methodology followed in this study; starting from field data collection to statistical analysis is presented in figure 2.

2.3.1. Field Preparations

A subset of ASTER image was used as a base map during the field survey. It was created from Erdas in such a way that the study area and only a small part of the region outside its borders were captured. Before generating the image sub-set, the entire image was geometrically corrected using the same software. Thereafter, the subset was converted into an Enhance Compressed Wavelet (ECW) file using the Earth Resources Mapping ECW compressor. Here, the file size was condensed from 12.4MB to 517KB. Then, the compressed file was loaded into a handheld mobile geographical information system (GIS) device (Ipaq). The Ipaq uses a Bluetooth GPS that records x and y coordinates within 2 and 5 meters, respectively, of the location measured with a Garmin GPS. This was established during field work. Because of its moderate spatial resolution, the ASTER image came in handy during field orientation and linking field observations with the displayed satellite image features. Besides the Ipaq, a Garmin GPS was the other device carried to the field. This was only used on the first day to validate location coordinates relayed to the Ipaq from the Bluetooth GPS. Although the Ipaq was short of the accuracy of the Garmin GPS, it was preferred because of the ease with which the location coordinates are taken. It is also an effective navigation equipment and minimizes chances of getting lost in the wilderness. Moreover, it makes the exercise of data transfer less tedious, and simultaneously eliminates human errors that would have emerged during manual data entry.

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Figure 2: Schematic diagram for the methodology. Lyr3 and Lyr2 denote layer 3 and 2, respectively of the ASTER image

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2.3.2. Field data collection

A total of 188 field sample points were collected from the study area between late September and mid October of 2006. During the field survey various land cover types were sampled, however, vegetation was of prime interest. Non-vegetated land cover types could also be identified; however, their spatial distribution with respect to the physical environmental variables has not been examined. Location coordinates of field samples were taken from areas that could be accessed, and where vegetation cover appeared to be representative of a specific vegetation cover type. It was believed that this would help minimize the problem of spectral mixing between different vegetation cover classes (Reinke and Jones 2006).

The following vegetation cover types were mapped as dominant broader categories: woodland deciduous, woodland coniferous, scrubland, and reed field and marshland vegetation. From these, a number of sub-cover types could be readily identified including old deciduous forest, coniferous forest, transitional deciduous forest, birch-willow scrub, sedge-willow saplings, dry and wet reed fields, as well as mown and grazed grass fields (appendices ia and ib). Nomenclature of the different vegetation cover types was based on a study by Schmidt et al. (2000), which involved mapping and identifying different secondary vegetation species in the three basins of the Biebrza river valley. Besides vegetation other cover types mapped included built-up areas, sand dunes and open water, though, no location coordinates could be obtained in the case of the last category. Thus, the classification has been based on these eleven cover classes. The description of the cover classes is given in table 3.

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Table 3: Mapped cover classes

Main category

Subclass

Description

Woodland deciduous Woodland coniferous Scrubland Reed field Marshland Non-vegetated Open water

Old deciduous forest Transitional deciduous forest Coniferous forest Birch-willow scrub Wet and dry reed species Sedge-willow saplings Mown sedge-meadow Grazed meadow-sedge Sand dunes and built-up areas Water in both the main river and its tributaries

They represent deciduous trees over and below 25 years, respectively. Dominant species include birch, alder, willow, aspen and a few oak stands This is dominantly made-up of Scots pine and Norway spruce. This one is comprised of a mosaic of birch and willow species. They have been reported as dominant secondary vegetation species in the marshland area Comprised of reeds found in both wet and dry environments These are dominant in the low-lying marshland areas. Sand dunes represent the bare soil within the confines of the study area. Built-up areas represent settlement areas within and those just outside the borders of the BNP This class includes water in the main river channel (Biebrza) and that carried by two of its tributaries (Jegrznia and Elk) in this part of the valley.

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2.3.3. Pre-processing

The ASTER image was geo-referenced to a landsat panchromatic image acquired on 07/10/00. Out of the 14 layers of the ASTER image, only the first nine (minus the 3B backward) belonging to the VNIR and SWIR bands were used. The image was resampled using the nearest neighbour (NN) algorithm to 15m x 15m so that all the nine layers would have the same spatial resolution. This was then accompanied by extracting the study area from the image scene using the “subset image” function in Erdas. The parameters used to re-project the subset image are presented in table 4. These were provided by the Institute of Geodesy and Cartography, Warsaw, Poland. Table 4: Projection parameters

2.3.4. Image Classification

An object-oriented image classification approach was followed to classify the subset ASTER image. This method was preferred because of the numerous advantages it possesses over traditional pixel-based techniques (Willhauck 2000; Walter 2004). For example, the problem of misclassification, which is greatly attributed to spectral confusion between the mapped classes, is significantly minimized when this approach is used. Spectral confusion is brought about by the similarity in spectral response of different land use/cover classes and is greatly dependent upon imaging sensor characteristics, as well as the contents of the imaged scene (Yang and Lo 2002). This phenomenon contributes to the errors of omission and commission in the confusion matrix. The former occurs when a given class on the ground is presented as a different one on the map; it affects the producer accuracy of the class to which the omitted class belongs. The latter, on the other hand affects the user accuracy of the class to which those omitted are falsely assigned. Literally, the producer accuracy represents the percentage of correctly classified reference data points,

Projection type …………………. Transverse Mercator Longitude of origin …………………. 19.00000 Latitude of origin ………………….. 0.00000 False Easting …………………. 500000.00000 False Northing ………………… -5300000.00000 Scale factor ………………… 0.99930 Datum name ………………… GRS 1980 Spheroid name ………………… GRS 1980

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while the user accuracy shows the percentage of correctly mapped data points. Therefore, if a given class in the field is assigned to a different one on the map its producer accuracy is lowered. Similarly, the user accuracy of the class to which it has been wrongly allocated is reduced; the wrong class increases the number of mapped data points (denominator). In this study, misclassification owing to spectral confusion is highly expected; this is principally due to the time when the ASTER image was acquired (July 11 2006). During this time of the year, the different vegetation types tend to show a similar spectral trend, particularly in the visible and near infrared bands. This can be ascertained by comparing the NDVI values between vegetation cover pairs. Meanwhile, problems that arise as a result of mixed pixels do not appear in object-based classification. A pixel is said to be mixed if its spectral signature happens to be a composite of signatures of different classes (Boucher and Kyriakidis 2006). In object-oriented image classification, such problems are not encountered because the classification is performed on the existing image object geometry rather than the n by n window of a fixed size, (Walter 2004). The other advantage is that unlike traditional pixel-based methods, object-oriented image classification takes into account other image feature characteristics such as texture and shape, in addition to their pixel values. This implies that the analyst has several options, upon which the conditions of the classification can be based. For instance, the conditions may be based on the nature of the studied environment. The flexibility of this method offers the analyst an opportunity to improve the classification result. After pre-processing, the ASTER subset was loaded into eCognition from where a new project was created. The object-oriented image classification method involves a number of stages, which have to be performed sequentially. In this study, the process was carried in the following sequence: image segmentation, defining classes, inputting class samples, determining the feature space optimization (FSO). The FSO operation helps in establishing a catalogue of image features that can be applied to the standard nearest neighbour (standard NN) classifier. After the standard NN had been applied to the defined classes, process commands were defined by stating the segmentation level on which the classification was to be performed. In eCognition, there is an option of regulating which objects should be assigned to which classes. This is done by adjusting the membership function. The membership function is a value between 0 and 1, where 0 symbolizes no membership, while 1 denotes full membership. The numbers in between indicate a partial degree of membership of a particular object to a given class (Baatz et al. 2002). It should be emphasized that segmentation is one of the most important stages of object-oriented image classification; the other one being the classification itself (Willhauck 2000). In fact,

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failure to create better image objects at this stage may significantly undermine the quality of the classification. In other words, without better segmentation, no satisfactory results can be obtained. During segmentation, an image is subdivided into different “parts” corresponding to objects in the terrain (Geneletti and Gorte 2003). The segmentation is guided by the defined homogeneity criteria of colour and shape. In this particular study, segmentation was performed at two different levels. Level one had smaller objects compared to those at level two. Of the two levels, the latter appeared to have objects that best defined the image features, and so the classification was based on this level. Meanwhile, except the scale parameter, which was adjusted from 5 to 10 at the second level, the other segmentation parameters for the two levels were assigned values as indicated in table 5.

Table 5: Composition of homogeneity criterion used for image segmentation

Colour is related to the spectral characteristics or pixel values of the image features, while shape delineates the borders of the different image segments (Willhauck 2000; Benz et al. 2004). Smoothness and compactness criteria are subdivisions of the shape parameter, and adjusting the value of one automatically changes that of the other. Figure 3 shows the image objects at the two segmentation levels.

Figure 3: Image objects at level 1 (left) and level 2 (right).

Parameter Value

Colour Shape

0.9 0.1

Compactness Smoothness

0.3 0.7

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The best feature combination that was obtained after the FSO operation included following: mean layer values of band 1, 2 and 3; standard deviation of band 3; texture: homogeneity of bands 3, 4 and 5. These were then applied to the standard nearest neighbour classifier, which was then applied to all the defined classes. This was then followed by performing the actual classification. Afterwards, the classified map was exported to a GIS environment where it was integrated with other data and used during spatial analysis. It should be stressed that what has been given here is what was followed in this study. A more comprehensive description of the object-based image classification approach is given in the “methods and concepts” section of eCognition software user’s guide (Baatz et al. 2002).

2.3.5. Accuracy Assessment

The accuracy assessment was based on a computed error matrix of the produced map against field sample points. Like traditional pixel-based image classification, the quality of object-oriented classification result can be assessed by validating the produced map against a reference classification and/ or on-site ground measurements; if the two carry comparable information (Baatz et al. 2002). Other methods for instance, the one based on fuzzy classification and/ or simple visual inspection can also be used to examine the plausibility of the classification results. These are also exhaustively explained in (Baatz et al. 2002). In this study the error matrix approach was adopted because it is a commonly used and widely recommended standard form of reporting site-specific errors (Campbell 2002). With this approach, it is easy to identify the overall errors for each cover class as well as those between different categories (Congalton 1991 in Foody 2002, Lillesand and Kiefer 1994; Campbell 2002). Essentially, the accuracy assessment involves examining the agreement between each mapped class and that identified in the reference data, for a sample of observations at specified locations (Campbell 2002; Foody 2002). Although the classification was object-based, the unit upon which the error matrix was based is a single pixel (225 square meters). This is due to the fact that during the field survey a reasonable distance between the reference points was allowed, to avoid having more than one of such points registered to a single pixel. Moreover, it was bore in mind that the rules of object-based image classification were not being violated; since the image objects themselves are comprised of individual pixels.

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2.3.6. Spatial Analysis

In order in to compare the water level within the different vegetation cover types, the created map was vectorized and intersected with the point layer representing water level at the five transects. This overlay operation produced a point layer whose attribute table had a new field for the vegetation cover class. From this attribute table it was possible to tell the vegetation cover class at a given transect location. It was then export to excel. From here, it was loaded into the SAS software for statistical analysis. The other overlay operation performed was aimed at generating a single attribute table with elevation, slope, aspect, vegetation cover classes and NDVI. Here, random points were generated using the hawths tools in ArcInfo. Thereafter, elevation, NDVI, slope and aspect values were extracted to the generated random points. The resulting point layer was intersected with the vectorized vegetation cover map. Like in the previous case, the intersection yielded an attribute table which was exported and converted into an excel file. This was then later used as input for statistical analysis in the SAS software.

2.3.7. Statistical Analysis

All statistical analyses were carried out in the SAS software (Sall et al. 2005). The distribution of the data was examined using Shapiro-Wilk normality test. This test was used because the number of observations in all cases was less than 2000 (Sall et al. 2005). Results of this test were very significant, for both water level and topographical data: [W (1313) > 0.95, p = 0.0000*], and [W (463) > 0.83, p = 0.0000*], respectively. This was then used as evidence against the null hypothesis that the data had been collected from a normally distributed population. For this reason, nonparametric tests were adopted to compare the different physical environmental variables within the mapped vegetation cover types. Nonparametric tests are distribution-free, and thus make no assumption of normal distribution of the population from which the data are drawn (Kent and Corker 1992; Moore and McCabe 2003).

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2.3.7.1. Comparison of water level, elevation, slope and aspect across the mapped vegetation cover types

In order to examine whether there was any difference in the groundwater level across the mapped vegetation cover types, multivariate analysis of variance (MANOVA) with repeated measures was performed. This is a parametric test; however, several researchers have argued that for a sufficiently large sample size (i.e. N>20), it is remarkably robust against the violation of the sphericity and normality assumptions (Lindman 1974 in StatSoft, Inc. 2006; Barcikowski and Robey 1984; O’Brien and Kaiser 1985 in Quinn and Keough 1991; Garson 2007). Another reason for using this test is that the experiment involved analysis of variance of several dependent variables at the same time. Here, the water levels of each year were treated as a separate dependent variable. Moreover, MANOVA tends to keep the type one error rate down; that is, the probability of falsely rejecting the null hypothesis is extremely low. In essence, MANOVA with repeated measures tests the equality of means. It is the appropriate test to use when all members of a sample are measured under a number of different conditions (Sall et al. 2005). This was the case for the groundwater level data used in this study. A comprehensive description of this test can be found in DiIorio (1991), Sall et al. (2005) and Hill and Lewicki (2006) among others. As mentioned earlier, the analysis was performed on water levels measured over a period of five years (2000 to 2004), for the months of April, May, June, July, September and October. The data were provided by the BNP, through the Institute of Geodesy and Cartography (Warsaw, Poland). Predictably, this data set provided the point layer on water level which was intersected with the vectorized vegetation cover map. The attribute table of the new point layer provided information on the type of vegetation cover present at a particular transect location. This table was then exported to Microsoft excel and latter loaded in SAS for statistical analysis. Meanwhile, in order to determine whether topographical variables, such as elevation, slope and aspect varied with respect to the mapped vegetation cover, Kruskal-Wallis test was used. The same test was used to analyse, independently, the variance of water level in each year within the mapped vegetation cover types. Kruskal-Wallis test is a nonparametric alternative to one-way analysis of variance (ANOVA) (StatSoft, Inc. 2006). It is similar to Wilcoxon one-way ANOVA, except that it is used when comparing more than two groups (Sall et al. 2005), which was the case in this study.

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2.3.7.2. Analysing the relationship between physical environmental variables and the spatial distribution of vegetation cover

After the analysis of variance, multiple correspondence analysis (MCA) was used to examine the relationship between the physical environmental variables and the spatial distribution of vegetation cover. MCA is in many ways similar to simple correspondence analysis, only that it is used when dealing with more than two variables (StatSoft, Inc. 2006). Actually, MCA is a simple correspondence analysis carried out on a design matrix with cases and categories of variables as columns (StatSoft, Inc. 2006). This analysis yields a Chi-square value, which defines the association between the dependent and independent variables (Sall et al. 2005). As such, a significant Chi-square value is indicative of a strong association between the variables under consideration. In this study, the correspondence analysis was performed for only variables that had been found to be significantly different across the mapped vegetation cover types. Such included 2001 water level measurements and elevation. This time around, the mapped vegetation cover types were treated as dependent variables. In order to implement the analysis, water level and elevation were converted into categorical variables. In each case, the analysis yielded a mosaic plot, contingency table with the observed and expected total counts, and a Chi-square value for the association between each vegetation cover class and a given range of water level and/ or elevation. Other results from this analysis included a correspondence analysis plot and the overall Chi-square value for the association between vegetation cover and each one of the aforementioned physical environmental variables.

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3. Results

3.1. Vegetation cover maps

The vegetation map of the study area is presented in a detailed and generalized form; figures 4a and 4b, respectively. The detailed map shows the vegetation cover types that could be identified in the field and distinguished by the object-oriented image classification approach. The generalized map, on the other hand, presents the broader categories within which specific vegetation cover types can be placed. The grouping of vegetation classes was based on species type and location in the field.

Figure 4a: Detailed cover map of the study area

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Overall, eight different vegetation cover types were distinguished by the object-based image classification approach. They include coniferous forest, mown sedge-meadow, reed field, transitional deciduous forest, grazed meadow-sedge, birch-willow scrub, sedge-willow saplings and old deciduous forest. Similar vegetation types have been mapped in several parts of the Biebrza river valley wetland (Wassen et al. 1990; Schmidt et al. 2000; Kotowski et al. 2002). Meanwhile, areas without vegetation include open water, sand dunes and built-ups.

Figure 4b: Generalized cover map

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3.2. Accuracy Assessment

As mentioned already, accuracy assessment of the produced vegetation cover map was based on a computed error matrix (table 6). Table 6: Error matrix for accuracy assessment

Mapped class

Mown sedge-meadow

Grazed meadow-sedge

Built-up area

Birch-willow scrub

Coniferous forest

Total

Transitional deciduous

Sedge-willow saplings

Sand dunes

Reed field

Old deciduous forest

Tota

l

Kap

pa

Reference class

27 8 50 19 18828 5 6 8 23 14

0 12 16 0.63 0.75 0.6

0.85 0.47

2 0 0 0 0 2 0 0

2 0 28 0 33 0.560 0 0 1 2 0

0 0 8 1 1 1

0.83 0.87

0 0 0 0 0 0 0 8

24 0 3 1 29 0.890 0 0 0 1 0

3 5 22 0.86 0.54 0.84

0.82 0.75

2 0 0 0 0 12 0 0

0 0 3 0 22 0.780 1 0 0 18 0

10 0 19 0.87 0.37 0.86

1 1

0 0 0 7 2 0 0 0

0 0 0 0 6 10 0 6 0 0 0

0 0 3 0.6 1 0.59

0.8 0.83

0 3 0 0 0 0 0 0

1 0 3 1 30 0.86

Sedg

e-w

illow

sapl

ings

Tran

sitio

nal d

ecid

uous

Prod

ucer

's ac

cura

cy

Use

r's a

ccur

acy

24 1 0 0 0 0

Birc

h-w

illow

scru

b

Bui

lt-up

are

a

Con

ifero

us fo

rest

Gra

zed

sedg

e-m

eado

w

Mow

n se

dge-

mea

dow

Old

dec

iduo

us fo

rest

Ree

d fie

ld

Sand

dun

es

The overall accuracy and the coefficient of agreement (kappa) were approximately 76% and 0.72, respectively. The overall accuracy shows that out of the 188 field sample points, 76% were correctly assigned to their respective cover categories. On the other hand, the 0.72 kappa indicates that the method of object-oriented image classification yielded an accuracy which is 72% better than what would be expected from assigning pixels to their respective classes by chance. Besides the overall accuracy, the producer and user accuracies, as well as the kappa for each class were computed and are also presented table 6. The producer accuracy, for example, in the

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case of birch-willow scrub shows that 86% of this class in the field has been correctly classified as birch-willow scrub on the map. On the hand, the user accuracy of this very class indicates that what the user sees on the map as birch-willow scrub is 80% of what truly exists on the ground. The difference in the two accuracies for the same class is due to the fact that the same numerator is divided by denominators of differing magnitudes; that is, total reference data points and sum of mapped data points, correspondingly. The same interpretation applies to the user and producer accuracies of the other classes. Table 7 shows the effect of error of omission and commission on the producer and user accuracies. Table 7: Effect of error of omission and commission on producer and user accuracies

Cover class Reference points

Mapped points

Correctly classified

Wrongly classified

Producer Accuracy

(%)

User Accuracy

(%)

Birch-willow scrub

Built-up area

Coniferous forest

Grazed meadow-sedge

Mown sedge-meadow

Old deciduous forest

Reed field

Sand dunes

Sedge-willow saplings

Transitional deciduous

28

5

6

8

23

14

27

8

50

19

30

3

6

19

22

22

29

8

33

16

24

3

6

7

18

12

24

8

28

12

4

2

0

1

5

2

3

0

22

7

86

60

100

87

78

86

89

100

56

63

80

100

100

37

82

54

83

100

85

75

As stated under methods and materials (section 2.3.4), the confusion between different vegetation cover classes can be attributed to the similarity in their spectral response over the visible and near infrared spectral range. This was confirmed by Tukey-Kramer HSD test (appendix iv), which was used to compare NDVI values between pairs of vegetation cover. It revealed that there was no significant difference in the mean NDVI between birch-willow scrub, transitional and old deciduous forest. Similarly, the difference in the mean NDVI between the other classes was shown to be insignificant. For example, the difference in the NDVI means between coniferous forest, sedge-willow saplings and grazed meadow-sedge was not significant. Only reed field and mown sedge-meadow had NDVI means that significantly differed from those of the rest.

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3.3. Hypothesis Testing

The following null hypotheses were tested:

i. The mean water level within a given year and over a period of years is the same across the mapped vegetation cover types.

ii. No relationship exists between groundwater level and the spatial distribution of vegetation cover types

iii. There is no difference in mean elevation, slope and aspect across the mapped vegetation cover types.

iv. No relationship exists between the spatial distribution of vegetation cover and the considered topographical variables

An alpha (α) level of 0.05 was used for all statistical tests.

3.3.1. Comparison of water level across the mapped vegetation cover types

Multivariate analysis of variance with repeated measures yielded a significant factor ratio for vegetation cover, time (when the water level measurements were taken) and the interaction between these two variables: vegetation cover, [F (6, 255) = 2.32, p = 0.0335], time, [F (4, 252) = 64.13, p = 0.0001] and the interaction between vegetation cover and time (Wilks’ Lambda), [F (24, 880) = 1.71, p = 0.0182]. These test statistics imply that from 2000 to 2004, the water level across the mapped vegetation cover types was significantly different. However, the Kruskal-Wallis test revealed that differences in water level across the mapped vegetation cover types were only significant during 2001, [X2 (6, 263) = 22.09, p = 0.0012]. The water level means and the standard error (SE), as well as the X2 and p values resulting from Kruskal-Wallis test are provided in table 8a.

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Th

e R

elat

ions

hip

Bet

wee

n Ph

ysic

al E

nviro

nmen

tal V

aria

bles

& th

e Sp

atia

l Dis

tribu

tion

of V

eget

atio

n C

over

with

in th

e B

iebr

za R

iver

Val

ley

Wet

land

28

Tab

le 8

a: K

rusk

al-W

allis

one

-way

ana

lysi

s of v

aria

nce

of w

ater

leve

l by

vege

tatio

n co

ver

Vege

tatio

n co

ver

2000

20

01

2002

20

03

2004

To

tal

Kru

skal

-W

allis

test

Mean

SE

Mean

SE

Mean

SE

Mean

SE

Mean

SE

Count

Mean

SE

X2 P

Birc

h-wi

llow

scru

b -5

8.50

7.

36

-42.

12

5.98

-7

2.17

7.

49

-77.

24

6.20

-3

2.95

5.

03

210

-56.

6 3.

10

32.6

0.

0001

Coni

fero

us fo

rest

-40.

00

7.64

-6

.83

4.88

-3

9.33

16

.74

-48.

17

13.3

3 -1

8.17

11

.14

30

-30.

5 5.

58

11.0

1 0.

0264

Gr

azed

m

eado

w-se

dge

-58.

42

7.61

-2

8.58

4.

15

-58.

13

8.78

-6

6.21

6.

81

-25.

17

3.28

12

0 -4

7.3

3.24

24

.98

0.00

01

Mow

n se

dge-

mea

dow

-68.

33

12.4

3 -3

2.50

5.

08

-61.

17

13.3

7 -5

9.67

10

.67

-14.

17

12.2

7 30

-4

7.2

6.00

10

.63

0.03

11

Old

decid

uous

fore

st -5

7.51

5.

93

-24.

60

3.81

-5

3.94

6.

27

-61.

26

5.04

-2

5.65

3.

16

264

-44.

7 2.

43

44.3

7 0.

0001

Se

dge-

willo

w sa

plin

gs

-70.

13

5.62

-4

4.59

4.

41

-69.

70

6.21

-7

5.35

5.

01

-38.

30

3.91

27

0 -5

9.6

2.44

40

.27

0.00

01

Tran

sitio

nal

decid

uous

fore

st -7

3.69

4.

43

-39.

59

2.70

-6

9.21

5.

19

-73.

40

3.96

-3

4.05

2.

53

390

-58

1.95

73

.81

0.00

01

Coun

t 26

3

263

26

3

263

26

2

Total

-64.

989

2.57

-3

6.09

1.

83

-64.

8 2.

82

-70.

42

2.25

-3

1.45

1.

60

X2 9.

96

22

.09

6.

88

10

.15

10

.99

P 0.

1262

0.00

12

0.

3324

0.11

86

0.

088

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3.3.2. Post hoc comparison of water level means

Post hoc comparison of 2001 water level means was performed between different pairs of vegetation cover classes. Here, Tukey-Kramer highly significant difference (HSD) test was used. The comparison was based on the 2001 water levels because this is when significant differences in water level across the mapped vegetation cover classes were found. The results of this test are shown in table 8bi. Table 8bi: Comparison of water level means between vegetation cover pairs

Vegetation cover classes not connected by the same letter are significantly different

As seen from table 8bi the post hoc comparison test yielded no statistically significant difference in the water level between coniferous and old deciduous forest. However, it revealed that the water level in these vegetation cover classes was significantly different from the rest, and that the shallowest water level that year was recorded in the areas covered by coniferous forest. The same test also showed no significant difference for the water level within grazed meadow-sedge, mown sedge-meadow, transitional deciduous forest and birch-willow scrub. It also revealed that the water level in sedge-willow saplings was significantly different from the one in all the other vegetation cover types.

Vegetation cover

Connection

Mean water level (cm)

Coniferous forest Old deciduous forest Grazed meadow-sedge Mown sedge-meadow Transitional deciduous Birch-willow scrub Sedge-willow saplings

A A

AB AB AB AB B

-6.83 ± 4.88

-24.60 ± 3.81 -28.58 ± 4.15 -32.50 ± 5.08 -39.59 ± 2.70 -42.12 ± 5.98 -44.59 ± 4.41

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Like in the previous case, the Tukey-Kramer HSD test was used to compare the water level means between year pairs for each vegetation cover type. Here, the test was performed for all the years because MANOVA with repeated measures had revealed significant differences in water levels across the mapped vegetation cover types. Results of this test are shown in table 8bii.

Vegetation Cover Time (years) 2000 2001 2002 2003 2004

Birch-willow scrub Coniferous forest

Grazed meadow-sedge Mown sedge-meadow Old deciduous forest

Sedge-willow saplings Transitional deciduous forest

BC A B B B B B

AB A A

AB A A A

C A B B B B B

C A B

AB B B B

A A A A A A A

Vegetation cover classes not connected by the same letter are significantly different

This time around, the post hoc comparison test revealed that within birch-willow scrub, the water level was significantly different for the years 2000, 2001 and 2004, but not for 2002 and 2003. According to this test, the water level within coniferous forest was not significantly different during the five years. The same test showed that within grazed meadow-sedge, the water level in 2001 and 2004 was significantly different from that in the other years. It also revealed that the water level between these two years was not significantly different, just like that for the years 2000, 2002 and 2003 pairs. Still, the test results showed that the highest water levels in all vegetation cover types were recorded in 2004. During this year the water level was not significantly different across all the mapped vegetation types. From table 8bii, it can be seen that the variation in water level within grazed meadow-sedge, over the five years was the same as that in sedge-willow saplings, old deciduous and transitional deciduous forests.

Table 8bii: Comparison of water level means between year pairs.

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3.3.3. Relationship between water level and the spatial distribution of vegetation cover

As mentioned under section 2.3.7.2, multiple correspondence analysis was used to examine the relationship between the spatial distribution of vegetation cover and water level. This analysis was also based on water level of 2001for the same reason given under the preceding section. The overall Pearson Chi-square value revealed a significant association between the spatial distribution of vegetation cover and water level, [X2 (1, 262) = 47.07, p = 0.0002]. The Chi-square values for the association between each vegetation cover type and a specific water level range are presented in table 8c. Table 8c: A contingency table showing the Chi-square values for the association between vegetation cover and water level

Count Expected Cell Chi^2

Birch-willow

scrub

Coniferous

forest

Grazed meadow-

sedge

Mown sedge-

meadow

Old deciduous

forest

Sedge-willow

saplings

Transitional

deciduous forest

Total

-40 - -80

14

14.7481 0.0379

0

2.10687 2.1069

7

8.42748 0.2418

2

2.10687 0.0054

13

18.2595 1.5150

23

18.9618 0.8600

33

27.3893 1.1493

92

-80 - -120 9 3.52672 8.4943

0 0.50382 0.5038

0 2.01527 2.0153

0 0.50382 0.5038

2 4.36641 1.2825

8 4.53435 2.6488

3 6.54962 1.9237

22

0 - -40 9 18.4351 4.8289

4 2.63359 0.7090

15 10.5344 1.8930

4 2.63359 0.7090

27 22.8244 0.7639

18 23.7023 1.3719

38 34.2366 0.4137

115

15 - 0 10 5.29008 4.1934

2 0.75573 2.0487

2 3.0229 0.3461

0 0.75573 0.7557

10 6.54962 1.8177

5 6.80153 0.4772

4 9.82443 3.4530

33

Total

42

6

24

6

52

54

78

262

The significance of the relationship between water level and the spatial distribution of vegetation cover is determined by both the Chi-square value and the frequency of occurrence (counts) of a particular vegetation cover within a given water level range. Therefore, table 8c shows that birch-willow scrub and sedge-willow saplings are strongly associated with water level in the range of -80 to -120 cm. On the other hand, coniferous forest is strongly associated with water level in the range of 15 to 0 cm, while grazed meadow-sedge and mown sedge-meadow are associated with water level in the range of 0 to -40 cm. Meanwhile, both old and transitional deciduous forests are strongly associated with water level in the range of 15 - 0 cm.

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3.3.4. Comparison of elevation, slope and aspect across the mapped vegetation cover types

One-way analysis of variance using Kruskal-Wallis test was used to examine whether topographical variables including elevation, slope and aspect did not differ across the mapped vegetation types. The means, standard deviations (Std dev) and the standard error of the mean for elevation, slope and aspect are presented in appendix ii. The analysis of variance revealed a significant difference for elevation, [X2 (7, N = 464) = 187.23, p<0.0001], but neither for slope, [X2 (7, N = 464) = 10.94, p<0.1412] nor aspect, [X2 (7, N = 464) = 10.65, p<0.1545]. Post hoc comparison using Tukey-Kramer HSD test (table 9) revealed that the elevation was highest in areas occupied by coniferous forest, and that the elevation in such areas was significantly different from that in areas where other vegetation cover types are found. Table 9: Comparison of elevation means between vegetation cover pairs

Vegetation cover classes not connected by the same letter are significantly different

The same test revealed that there was no significant difference in elevation for areas occupied by birch-willow scrub, grazed meadow-sedge, reeds and sedge-willow saplings. It however showed that the elevation in areas covered by transitional and old deciduous forests was significantly different. Also looking at table 9, it can be seen that the elevation in areas where these vegetation cover types are found is significantly different from the one in areas occupied by the rest.

Vegetation cover Connection Mean elevation

(m)

Coniferous forest Old deciduous forest Transitional deciduous forest Mown sedge-meadow Birch-willow scrub Grazed meadow-sedge Reed field Sedge-willow saplings

A B C D D D D D

124.26 ± 1.03 113.38 ± 0.34 111.29 ± 0.31 109.67 ± 0.45 109.31 ± 0.21 109.02 ± 0.23 108.86 ± 0.20 108.83 ± 0.25

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3.3.5. Relationship between elevation and the spatial distribution of vegetation cover

Like in the case of water level, multiple correspondence analysis was used to examine the relationship between the spatial distribution of vegetation cover and elevation. The overall Pearson Chi-Square revealed a significant association between vegetation cover and elevation, [X2(1, 464) = 414.95, p = 0.0001]. Therefore, it can be deduced that the distribution of vegetation cover is strongly correlated with elevation. The Chi-square values for the association between the spatial distribution of individual vegetation cover types and elevation are presented in appendix iiia. Figure 5 is a mosaic plot showing the distribution of vegetation cover in relation to elevation. On this plot the area of each rectangular block represents the frequency of occurrence (counts) of a particular vegetation cover within a given elevation range.  

Veg

etat

ion

cove

r

0.00

0.25

0.50

0.75

1.00

104 - 109 109 - 115 115 - 120120 - 132120 -132

Elevation (m)

Birch-willow scrub

Coniferous forest

Grazed meadow-sedge

Mown sedge-meadow

Old deciduous forest

Reed field

Sedge-willow samplings

Transitional deciduous fores

Figure 5: A mosaic plot of vegetation cover within a given elevation range.

From the mosaic plot (figure 5) above it can be seen that the area of the rectangular blocks representing birch-willow scrub (yellow), grazed meadow-sedge (yellowish-green), mown sedge-meadow (pink), reed field (light blue) and sedge-willow saplings (green) is largest in the elevation range of 104 – 109m. Therefore, it can be inferred that these vegetation cover types are predominantly found in low-lying areas. On the other hand, the area of the rectangular blocks representing transition (army green) deciduous forest is largest in the elevation range of 109-115m.

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From the same plot, it can be seen that the rectangular blocks representing old deciduous (dark green) have got nearly equal area in the elevation range of 109-115m and 115-120m. It can therefore be deduced that old deciduous forest is dominant in areas where the elevation is in the range of 109 – 120m. Also looking at this very plot, it can be seen that the area of the rectangular blocks representing coniferous forest (maroon) in the elevation range of 115-120m and 120-132m is virtually the same. Therefore, it can be inferred that this type of vegetation cover is dominant in the high-elevation areas of the Biebrza river valley.

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4. Discussion

In this study, topographical and hydrological variables are used to explain the present spatial distribution of the different vegetation cover types within the Biebrza river valley wetland. Water level has been used because many studies indicate that it is an important determining factor in peaty wetlands. Meanwhile, topography, particularly elevation has been considered because of its direct influence on groundwater flow.

4.1. Vegetation cover maps

An object-based image classification approach was used to produce the vegetation cover map of the study area. The created map is made up of eight vegetation cover classes (figure 4a), on which further analyses are based. Besides vegetation, open water, sand dunes and built-up areas are also shown on the same map. These classes have also been placed under seven categories as indicated in figure 4b.The overall accuracy and kappa of the produced map were approximately 76% and 0.72, respectively. The low producer and user accuracies revealed by the error matrix (table 6) could still be attributed to misclassification due to spectral confusion. As mentioned under the methods and materials (section 2.3.4), spectral confusion is a result of two or more different cover classes exhibiting similar spectral responses. It contributes to the errors of omission and commission, which affect both the producer and user accuracies of individual classes. Therefore, in this study, classes where these errors were kept low, higher user and producer accuracies have been attained. On the other hand, those classes where the errors of omission and commission were not committed at all, the user and producer accuracies equal to 100% (table 7). The other justification for attributing the misclassification to spectral confusion is related to the time when the ASTER image was acquired (July 11, 2006). It is believed that during this time of the year vegetation types are nearly in the same phenological condition. Therefore, they tend to exhibit a similar spectral trend, particularly in the visible and near infrared bands. This was verified by Tukey-Kramer HSD test, which was used to compare NDVI means between different pairs of the mapped classes (figure 6, appendix iv).

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Figure 6: NDVI of the mapped cover classes The misclassification, particularly, the one resulting from spectral confusion could be minimized by merging the classes that appear to be very similar in both spectral and other characteristics. That way, higher classification accuracies may be realized. The eCoginition software enables the merging of classes, especially if the image objects belonging to such classes show similar characteristics; in terms of texture and colour among others. However, for the sake of emphasizing the wetland’s complex and rich vegetation diversity, this has not been tested in the present study.

4.2. Spatial distribution of vegetation cover in relation to water level

MANOVA with repeated measures was used to analyse the variance of water level across the mapped vegetation cover types. Results of this test revealed that water level varied depending on the type of vegetation, the time when the measurements were made, and the interaction between vegetation cover and time. This was expected; given the fact that different vegetation types grow and get adapted to contrasting hydrological conditions. Ideally, deep-rooted woodland vegetation types should be dominant in areas where the water level is relatively deeper than the one in areas covered by shallow-rooted herbaceous vegetation types. In this study, however, it was the opposite. As revealed by the post hoc comparison of water level

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means between different pairs of vegetation cover types (table 8bi), the water level is shallower in areas dominated by coniferous and old deciduous forests than that in areas occupied by marshland vegetation types. A consistent result was obtained when correspondence analysis was used to examine the association between water level and the mapped vegetation cover types (table 8c). In both analyses, the spatial distribution of vegetation cover within this part of the river valley is shown to deviate from what is expected under natural circumstances. This could be one of the consequences of the past and recent drainage activities in the marshland area. The water holding capacity of such areas is believed to have been reduced due to the loss of water through the constructed ditches. These drain the water from the marshland areas, thereby leading to the shrinking, mineralization and parching of the peat. This then causes considerable changes in the mire topography from which drastic changes in groundwater flow patterns may arise (van Diggelen et al. 1991). Meanwhile, the occurrence of birch-willow scrub and willow saplings in areas where the water level is deepest attests to the present problem of secondary succession. Previous studies conducted on this wetland have attributed secondary succession to past and recent drainage activities (Wassen et al. 1990; Schmidt et al. 2000; Nauta et al. 2005). These also cite birch and willow as dominant and rapidly growing invasive vegetation species. Related studies in other peatland areas have reported similar consequences. Coincidentally, such studies have also attributed the drastic changes in the wetlands’ hydrological systems to drainage activities. For example, by using hydro-ecological analysis in their study on the Lieper Posse fen (eastern Germany), van Diggelen et al. (1991) established that extensive lowering of the groundwater level paved way for succession towards woodland vegetation. These also assert that succession from moist to wet woodland types, towards a drier beech forest was a clear indication of desiccation in those areas. And that the desiccation had been caused by direct drainage through an existing ditch. Basing on these facts, and the insights drawn from the present study, it cannot be denied that the existing man made drainage channels are among the principal causes of the problems facing the Biebrza river valley wetland. The drainage channels trigger a series of other dangers starting with lowering of the water table, followed by the drying of the peaty soil- making it vulnerable to fire outbreaks. Subsequently, secondary vegetation takes over, particularly species that can easily thrive in an environment of deep water levels and reduced nutrient availability. Secondary vegetation may aggravate the problem of water table decline by extracting and at the same time losing more water than the indigenous species. Therefore, finding a solution to the existing ditches should be one of the priority tasks of the BNP management authorities.

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4.3. Spatial distribution of vegetaion cover in relation to topographical variables

Results from the analysis of variance of elevation, slope and aspect by vegetation cover show that there are no significant differences in the slope and aspect in areas where the mapped vegetation cover types are found. This was anticipated since the Biebrza river valley is generally characterized by a relatively flat topography (Wassen et al. 2002). For this reason, the change in elevation from one area to another is less pronounced. The same analysis, though, revealed significant differences in the elevation across the mapped vegetation cover types. This time around, the spatial distribution of vegetation cover in relation to elevation was as expected. For instance, post hoc comparison of elevation means between pairs of vegetation cover revealed that woodland vegetation are found in areas where the elevation is higher than the one in areas dominated by marshland vegetation types (table 9). Similar results were obtained when correspondence analysis was used to examine the relationship between eleven and the mapped vegetation cover types (figure 5 and appendix iiib). The outcome from both analytical procedures agrees with what is expected under natural circumstances. This shows that even in areas characterized by a relatively flat topography, elevation can still be an important variable in predictive vegetation mapping. Traditionally, the use of topographical variables in such studies has been limited to mountainous areas and rarely those with medium relief (Day and Monk 1974; Davis and Goetz 1990; Abbate et al. 2006). The occurrence of woodland vegetation in high elevation areas can be attributed to their root system. Such vegetation is characterised by a deep-root system, which enables them to access groundwater at greater depths. As expected, most marshland vegetation species were found to be strongly associated with low elevation; this could still be partly attributed to their root system. Unlike woodland vegetation types, most marshland vegetation species tend to be shallow-rooted. Thus, they are expected to grow in low-lying areas. Ideally, the water level in such areas should be shallower, and therefore easily accessible. However, as revealed by the preceding analyses the water level is instead deepest in such areas. Nonetheless, this being a wetland environment, water is never completely out of reach of the vegetation species growing in the marshland areas. Meanwhile, the presence of birch-willow scrub and saplings along with marshland vegetation types further confirms the problem of secondary vegetation succession, which has been cited as one of the marshland’s serious threats.

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5. Conclusions and Recommendations

5.1. Conclusions

In this study, the relationship between physical environmental variables and the spatial distribution of vegetation cover within the Biebrza river valley has been examined. Water level, elevation, slope and aspect are the physical environmental variables that have been considered. The reason for considering these variables, particularly groundwater level is due to the fact that previous studies on this wetland have indicated that human interference has greatly altered its hydrological regime (Wassen et al. 1990; Nauta et al. 2005). Elevation, on the other hand comes into the picture because of its direct influence on the direction in which groundwater flows. As a matter of fact, some researchers have argued that the direction of water flow is one of the major controls of this mire’s floristic composition (Wassen et al. 1990). Therefore, it was thought that studying the present spatial distribution of this wetland’s vegetation cover, in relation to the aforementioned environmental variables would help in the understanding how the changes in the groundwater regime have influenced its current appearance.

With regard to hydrological variables, the study aimed at establishing whether the water level differed from one vegetation cover type to another. Accordingly, results obtained from MANOVA with repeated measures (over all) and those from the Kruskal-Wallis test (univariate) revealed significant differences in the water level across the mapped vegetation cover types. In addition, the study also aimed at determining the relationship between the spatial distribution of vegetation cover and water level. Basing on the 2001 water level data, results from multiple correspondence analysis revealed a strong association between vegetation cover and the water level. The association is such that woodland vegetation cover types are predominantly found in areas where the water level is shallower compared to the one in areas occupied by marshland vegetation. This an unusual spatial distribution of vegetation cover in relation to groundwater level has been attributed to what previous researchers have considered to be a change in the wetland’s hydrological network. In regard to topographical variables, the research intended to establish whether elevation, slope and aspect were different across the various vegetation cover types. Correspondingly, results from Kruskal-Wallis analysis of variance yielded

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significance differences for elevation across the mapped vegetation cover types, but neither for slope nor aspect. This was highly expected given the fact that the Biebrza river valley is characterized by a relatively flat topography. The research also aimed at examining the spatial distribution of vegetation cover in relation to the aforementioned topographical variables. However, since the previous analysis had revealed that slope and aspect were not significantly different across the mapped vegetation cover types, only elevation was considered. And just like in the case of water level, multiple correspondence analysis revealed a strong association between vegetation cover and elevation. This time, however, the spatial distribution of the vegetation cover types in relation to elevation did not deviate from the one expected under natural circumstances. Therefore, this could provide a basis for incorporating elevation in predictive vegetation mapping studies conducted in low-lying areas. Until now, such studies have mostly been emphasizing the use of topographical variables in areas where the relief is rather steep.

5.2. Recommendations

The study findings, coupled with examples from research carried out in similar environments elsewhere (section 4.2), clearly indicate that changes in this marshland’s groundwater regime due to past and recent drainage activities have contributed immensely to its current problems. Therefore, finding a solution to the existing man made drainage channels should be one of the priority tasks of the BNP management authorities. The water level point data used in this study was not collected from random points. For this reason, similar studies in the future should adopt better sampling schemes, for example, random sampling; and where resources permit stratified random sampling may be applied. Also, in addition to water level means, its standard deviation within the different vegetation cover types should be examined. In this study, the vegetation cover map was generated from a single ASTER image. Future research should use time series data to relate water level with vegetation of the corresponding year. This will provide information of how changes in water level affect the vegetation cover over a period of time. This study revealed that the relationship between the spatial distribution of vegetation cover with both water level and elevation was statistically significant.

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For this reason, future researchers should consider performing the image classification basing on the knowledge of the spatial distribution of vegetation cover with respect to these physical environmental variables. Finally, it is worth pointing out that there are several factors responsible for the distribution and composition of vegetation cover within the Biebrza river valley wetland, but which have not been considered in this study. Such include soil type and nutrients, soil pH, land use, management practices, etc. Consequently, for a better understanding of this complex ecosystem, future research should include as many variables as possible.

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Appendices

Mown sedge-meadow Grazed meadow-sedge

Birch-willow scrub Sedge-willow saplings Appendix ia: Mapped cover classes

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Reed field Old deciduous forest

Sand dunes Transitional deciduous forest Appendix ib: Mapped cover classes

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Th

e R

elat

ions

hip

Bet

wee

n Ph

ysic

al E

nviro

nmen

tal V

aria

bles

& th

e Sp

atia

l Dis

tribu

tion

of V

eget

atio

n C

over

with

in th

e B

iebr

za R

iver

Val

ley

Wet

land

50

 

Coun

t Ex

pecte

d Ce

ll Chi^

2

Birch

-willo

w sc

rubCo

nifero

us

fores

tGr

azed

mea

dow-

sedg

eMo

wn se

dge-

mead

owOl

d dec

iduou

s fore

stRe

ed fie

ldSe

dge-w

illow

samp

lings

Tran

sition

al de

ciduo

us fo

rest

Total

104 -

109

4029

.4569

3.773

5

08.2

306

8.230

6

2919

.4935

4.636

0

2519

.9267

1.291

6

1648

.5172

21.79

37

2312

.5625

8.672

0

5129

.8901

14.90

89

1732

.9224

7.700

6

201

109 -

115

2830

.0431

0.138

9

08.3

944

8.394

4

1619

.8815

0.757

8

1820

.3233

0.265

6

6549

.4828

4.866

0

612

.8125

3.622

3

1730

.4849

5.965

0

5533

.5776

13.66

74

205

115 -

120

06.1

5517

6.155

2

61.7

1983

10.65

22

04.0

7328

4.073

3

34.1

6379

0.325

3

2810

.1379

31.47

13

02.6

252.6

250

16.2

4569

4.405

8

46.8

7931

1.205

1

42

120 -

132

00.1

4655

0.146

6

10.0

4095

22.46

20

00.0

9698

0.097

0

00.0

9914

0.099

1

00.2

4138

0.241

4

00.0

625

0.062

5

00.1

4871

0.148

7

00.1

6379

0.163

8

1

120 -

132

02.1

9828

2.198

3

120.6

1422

211.0

56

01.4

5474

1.454

7

01.4

8707

1.487

1

33.6

2069

0.106

4

00.9

375

0.937

5

02.2

306

2.230

6

02.4

569

2.456

9

15

Total

68

1945

4611

229

6976

464

A

ppen

dix

iiia:

A c

ontin

genc

y ta

ble

show

ing

the

Chi

-squ

are

valu

es fo

r the

ass

ocia

tion

betw

een

vege

tatio

n co

ver a

nd e

leva

tion

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Cover class

Connection

NDVI Mean

Transitional deciduous forest A 0.84882144 Birch-willow scrub A 0.84206752 Old deciduous forest A 0.84134680 Coniferous forest A B 0.79661612 Sedge-willow saplings B C 0.78217874 Grazed-meadow-sedge B C D 0.75586858 Reed field D E 0.71211048 Built-up area E F 0.67225616 Sand dunes C D E F 0.65107212 Mown sedge-meadow F 0.62946792 Open water D E F 0.53846157

Classes not connected by the same letter are significantly different

Appendix 1: Comparison of NDVI means between pairs of the mapped classes

104 - 109

109 - 115

115 - 120

120 - 132

Birch-willow scrub

Coniferous forest

Grazed meadow-sedgeMown sedge-meadow

Old deciduous forest

Reed fieldSedge-willow samplings

Transitional deciduous forest

x

y

z

Appendix iiib: A 3-D correspondence plot of vegetation cover and elevation