Beata Markowska
Detecting natural succession on abandoned agricultural land in the war-affected northeast Bosnia-Herzegovina
using Landsat TM imagery
Msc thesis under the supervision of: prof. Jacek Kozak
MSc thesis submitted in the framework of, and according to the requirements of the UNIGIS Master of Science programme
(Geographical Information Science & Systems).
Jagiellonian University, Kraków, Paris Lodron University of Salzburg
2012
I declare that all sources used in the thesis were properly acknowledged. The thesis is fully my work and it was not and will not be submitted as a thesis elsewhere.
16.07.2012 Beata Markowska
I would like to place my sincere gratitude to my Supervisor Prof. Jacek Kozak
for his continual supervision, guidance and suggestions to my thesis, and for his
valuable help whenever I faced difficulty. Thanks a lot for his warm welcome
whenever I needed his help.
1 CONTENTS
ABSTRACT ...2
1. INTRODUCTION ...3
2. AIMS AND SCOPE OF THE THESIS ...5
3. STUDY AREA AND DATA ...6
3.1. S
TUDY AREA ...63.1.1. Natural environment ...8
3.1.2. Bosnian War ...11
3.2. D
ATA ...143.2.1. Ancillary data ...17
4. METHODS ...18
4.1. C
HANGE DETECTION:
THEORETICAL BACKGROUND ...184.1.1. Post-classification comparison ...20
4.1.1.1. Classification ...21
4.1.2. NDVI differencing ...22
4.2. S
TUDY APPROACH ...245. RESULTS ...28
6. DISCUSSION ...36
6.1. E
VALUATION OF METHODS ...366.2. P
OST-
WAR LAND ABANDONMENT AND NATURAL SUCCESSION ...377. CONCLUSIONS...40
REFERENCES ...41
2 ABSTRACT
This study focuses on natural succession process on abandoned agricultural land after Bosnian War in 1992 -1995. Region of Srebrenica was selected as a study area due to severe war impacts such as ethnic cleansing and minefields that remained after the military operations. To describe ecological succession, classification and NDVI differencing of two Landsat TM scenes from 1991 and 2011 was performed. Fine-resolution Quickbird imagery available in Google Earth was used to verify the accuracy of the classification. Classification of Landsat scene from 1991 was performed with an overall accuracy of 93%. Two change detection methods were used to detect differences in agricultural land use. Landscape change was presented in qualitative and quantitative way. Post-classification comparison method was used for qualitative change description. Vegetation index differencing (NDVI differencing) was used as quantitative method and zonal mean functions were applied to calculate NDVI changes in dependency on elevation and also on distance from Srebrenica. Results show strong differences in amount of vegetation between the two classified images from 1991 and 2011. NDVI difference in class ‘Settlements and agriculture’ within distance 18 km from Srebrenica is much higher than NDVI difference further than 18 km from Srebrenica, and mean difference of NDVI for all study area. The study is an attempt to use remote sensing data in research when fieldwork is too dangerous because of civil conflicts.
Key words
Landsat imagery, Change detection, NDVI differencing, natural succession, Bosnia-Herzegovina war
3 1. INTRODUCTION
Remote Sensing (RS) is an effective technique to collect information about environmental changes.
Accurate digital maps derived from image data are integrated within Geographical Information Systems (GIS) and can be used later for environmental monitoring and management. Remotely sensed data in conjunction with other spatially referenced digital data as elevation, slope aspect, vegetation type, and soils enriches information about a landscape (Iverson 1989). Satellite remote sensing offers a potentially powerful method of monitoring changes at higher temporal resolution and with lower costs than those associated with traditional methods (Deer 2005). Satellite data with medium spatial resolution are becoming more and more available therefore mapping of land cover with satellite data becomes increasingly the topic of research (Cihlar 2000).
The Landsat data are important for terrestrial remote sensing and global change research because they have been collected since 1972 and are capable of taking a complete photograph of planet every two weeks. Landsat satellite images allow for the detection of trends in vegetation density, such as consistent increases over many years associated with abandoned fields. The spectral and radiometric resolutions must be sufficient to identify these changes, typically visible in the red and near-infrared (NIR) spectra. Previously, researchers have only been able to monitor changes in specific locations with Landsat data due to its limited availability Releasing Landsat data archives was a response to the expanding demand of decision makers and scientists who need tools and information to monitor and protect crucial natural resources. One of applications of these data is environmental change detection by comparing Landsat images taken on earlier dates with those on later dates (Witmer 2008, Kozak 2009).
Many studies concern socio-economic disturbances affecting land use. Human influence on environment is not only by natural resources exploitation but also due to socio-economic disturbances (e.g., wars, revolutions, policy changes, and economic crises) that change ecosystems (Hostert et al.
2011). Land system dynamics may be characterized as a sequence of periods of relative stability followed by rapid changes with potentially long-lasting effects (Dearing 2010). Changes can be differentiated as slow ones (e.g., demographic changes or industrialization), fast ones (e.g., revolutions, wars, disease outbreaks, economic crises, technological breakthroughs), or both (Aide and Grau 2004). In natural systems, disturbance is considered as intrinsic component resulting in rapid and sometimes drastic change of ecosystem structure and functioning. An important question is to what extent large socio-economic disturbances such as wars, revolutions, recessions, and changes in political systems trigger a fundamental change in land use systems (Hostert et al. 2011). The challenge is to better understand the triggers that can reorganize land use systems and modify long-term land use trajectories (NSF 2009). Several researchers have attempted to use digital satellite data to analyze
4 environmental changes caused both by socio-economic disturbances and also by natural disasters.
Although remote sensing technology has been driven by military applications, most academic researchers have devoted their efforts to land cover and land use applications with little attention to the effects of military action (de Sherbinin et al. 2002). For example (Witmer 2008) used Landsat data to analyze effects of Bosnian War (1992-1995). According his study abandoned land pixels are expected to exhibit higher NDVI values, in contrast to the pre-war NDVI values, which were suppressed by the absence of vegetation near the sowing and harvesting time.
5 2. AIMS AND SCOPE OF THE THESIS
The aim of this study is to identify the magnitude of landscape change that occurred due to several impacts of the war on agricultural land in Bosnia-Herzegovina. Among several effects of war and military operations, depopulation, agricultural land abandonment and subsequent ecological succession could be expected. These effects should be detectable with satellite remote sensing. Ability of satellite imagery is examined to provide detailed information about war effects on the environment both natural and anthropogenic.
The thesis is arranged as follows: first, study area is described, with a particular emphasis on the recent history. Then data and satellite image processing methodology are presented. Results that illustrate changes in the study area land cover are shown in quantitative and qualitative way. In the discussion explanation for reasons of environmental changes and evaluation of chosen methods are presented. The discussion is also an attempt to predict future changes in environment.
6 3. STUDY AREA AND DATA
3.1. Study area
The study area is located in Srebrenica region in Bosnia and Herzegovina in north-eastern part of the country (fig. 1). The area lies between the latitudes 44°52’59’’N to 43°54’45’’N and longitudes 19°44’36’’E to 18°20’51’’E, UTM zone 34. It covers an area of approximately 8000 km2.
This subregion of Bosnia and Herzegovina was chosen for several reasons. This place was the focus of intense fighting and ethnic cleansing and therefore underwent a substantial depopulation, moreover, it contains minefields. The land use of this area was predominantly agricultural where natural succession is the most expected.
7 Figure 1. Political map of Bosnia-Herzegovina.
Source: http://www.lib.utexas.edu/maps/europe/bosnia_rel_2002.jpg
8 3.1.1. Natural environment
Bosnia and Herzegovina has one of the most diverse ecosystems in Europe containing pristine forests and fertile agricultural land. Bosnia and Herzegovina is a country with softly undulating terrain located in the western Balkans, bordering Croatia to the north and south- west, Serbia to the east, and Montenegro to the southeast. Total land area is 51129 km
2. The major cities are the capital Sarajevo, Banja Luka in the northwestern region known as Bosanska Krajina, Bijeljina and Tuzla in the north-east, Zenica and Doboj in the central part of Bosnia and Mostar, the capital of Herzegovina in the South. The country's name comes from the two regions Bosnia and Herzegovina, which have a very vaguely defined border between them. Bosnia occupies the northern areas which are roughly four fifths of the entire country, while Herzegovina occupies the rest in the south part of the country. There are seven major rivers in Bosnia and Herzegovina. The Sava is the largest river of the country, but it only forms its northern natural border with Croatia. The Una, Sana and Vrbas are right tributaries of the Sava river. The Bosna river gave its name to the country, and is the longest river fully contained within it. The Drina flows through the eastern part of Bosnia, and for the most part it forms a natural border with Serbia. The Neretva is the major river of Herzegovina (Čengić 2011).
Almost half of the country (47%) is covered with forests. Mixed farming and permanent crop uses cover about 30% of the country and pastureland an additional 23%. Mixed farming grains, horticulture, vineyards, and pasture are concentrated in the north. About 54% of land holdings occupy less than 2ha, representing small-scale farming. About 80% of forest and other wooded land are state-owned. The remaining part is owned by a large number of individual private owners. Nearly three-fifths of the forests can be characterized as production forests, while about 40% are not available for wood supply (Bosnia-Herzegovina biodiversity assessment 2008).
Western regions of Bosnia and Herzegovina are rising to a height of 2,386 m. It is contrasting
with the flat terrain and rolling hills in the northeast. Artificial surfaces are primarily urban
and industrial areas but also include mine and dump sites. Forests are coniferous, broadleaf,
and mixed. Agricultural category consists of arable land (irrigated and nonirrigated) and
permanent crops. The flatter terrain in northeast Bosnia and Herzegovina has more intensive
9
agricultural land use, whereas the mountains of central Bosnia and Herzegovina remain largely forested. Population density distribution (1991census data) indicates that the northern region from Biha´c to Brˇcko, Tuzla, and Zvornik with higher population densities corresponds with the higher agricultural productivity (O’Loughlin 2009). Figure 2 shows land cover of Bosnia and Herzegovina.
The study area is located in the watershed of the Drina River. Elevation range is from around 160 to 1500 m. North-western part of the study area contains an artificial lake - Modrac Lake.
Dominant forest species are the lime and the tatarian maple. Common oak occurs in the
valleys of main tributaries of the Drina River. Riparian forests of willow and poplar are
common along main rivers in the north-eastern part of country. Stands of black and Scots pine
occur on steep and eroded land and usually they represent primary vegetation types. At lower
altitudes black pine is dominant. Lack of conservation efforts, combined with the destruction
from the war fought during the 1990s has left the natural resources in unprotected position
(Bosnia-Herzegovina biodiversity assessment 2008). Rich biodiversity and post-war
abandonment of Srebrenica region make this place interesting for research on natural
succession. The eastern part of the study area, approximately 31.5% belongs to Serbia.
10
Figure 2. Land cover of the study area
Source: http://www.worldmapfinder.com/Pl/Europe/Bosnia_And_Herzegovina/
11 3.1.2. Bosnian War
The Bosnian war was driven by fantasies of geopolitical and demographic engineering which led to ethnic cleansing and the separation of Bosnia’s ethnic national communities from each other. It was a struggle between the three main ethnicities, Serbs, Bosniaks (Muslim Bosnians) and Croats (Fig. 3), over how the territory of Bosnia-Herzegovina should be demarcated. This led to a 3-year war from March 1992 to November 1995. The war was finished with complicated political arrangements agreed at Dayton (USA) which gave a large amount of autonomy to the two entities: Serb and Croat/Bosniak (Djipa 2006, O’Tuathail 2009).
The 1992–1995 war in Bosnia-Herzegovina resulted in almost 100,000 killed and almost half of the population displaced. By 1995, the former territory of Bosnia-Herzegovina was a patchwork of new ethnically cleansed spaces. Half of the prewar population of 4,365,574 was displaced from their homes. More than a million became refugees and an estimated million people remained internally displaced within the country (O’Loughlin 2009, Mitchel 2004). Figure 2 shows number dead and missing people during the Bosnian War.
The eastern boundary of the country along the Drina, including the massacre site of Srebrenica and the site of massive ethnic cleansing (Zvornik), shows a disproportionate loss of human activity (O’Tuathail 2006). In July 1995 in Srebrenica 8000 Bosniaks were killed. This mass murder was described by the Secretary-General of the United Nations as the worst crime in Europe since the World War II (Institute for War and Peace Reporting, Tribunal Update: Briefly Noted TU No 398, 18 March 2005).
12 Figure 3. Loss of people during Bosnian War.
Source: http://www.worldmapfinder.com/Pl/Europe/Bosnia_And_Herzegovina/
13 After the war, Bosnia-Herzegovina is the most mine-affected country in Europe, with an estimated 1.3 million people, roughly one third of the population, living in 1,366 mine-affected communities. In 2007, there were more than 12,000 locations requiring clearance (Fitzgerald 2007). In Bosnia- Herzegovina, only about 4% of the mines laid over a decade ago have been removed. Up to 4,000 km2 still hide antipersonnel and antitank mines. It will take over a century to complete the process of their removal. In the first nine postwar years, 1,522 people were killed or severely injured by landmine accidents across Bosnia-Herzegovina (United Nations High Commissioner for Refugees 2004). About 1 million mines, mostly antipersonnel, still remain in Bosnia-Herzegovina and only about 60% of mined areas have been identified (Bolton 2003).
After the war the agricultural productivity has declined. Most of the irrigation systems were heavily damaged or destroyed (Custovic 2004 after Witmer 2008). In addition, 70% of tractors and other agricultural tools were destroyed and 60% of livestock disappeared during the war. In general, the pre- war (1988–91) average cultivated area decreased by 25% when compared to the post-war (1996–2004) average cultivated area (H. Custovic, personal communication, 15 August 2005 after Witmer 2008). In addition to destroyed equipment and transportation infrastructure, the widespread placement of landmines also inhibited cultivation of land and has continued to deter residents from returning (Bolton 2003). After Dayton agreement Inter-Entity Boundary Line was delineated and according this, The State of Bosnia Herzegovina was divided as of the Federation of Bosnia-Herzegovina and of the Republika Srpska. Srebrenica is part of Republika Srpska. Figure 3 shows political map of Bosnia- Hercegovina.
14 3.2. Data
The study was carried out using two Landsat TM images from June 1991 and July 2011 (Table 1).
Landsat scenes were downloaded from Global Land Cover Facility GLCF and U.S Geological Survey USGS web pages. One scene is from the time before the war (June 1991, fig. 4) and the later one is acquired sixteen years after the massacre in Srebrenica (July 2011, fig. 5). Chosen scenes are acquired in summer at similar dates and sensor locations. Both scenes were collected at good weather conditions, they are almost cloud free and have a good quality. Scene from July 2011 has only some clouds which were excluded from the analysis. From both images the equal area with Srebrenica roughly in the center was selected, this area is later referred to as the study area. Its size is 3783 x 2290 pixels that approximately is 113 x 69 km. Area of the study include two countries: Bosnia- Herzegovina and Serbia with the border on the Drina river.
Table 1. Data sources Images
required for the study
Path/ row Date of
acquisition
Source
Landsat-5 TM
ID 236-100 17.06.1991 Global Land Cover Facility GLCF http://www.glcf.umd.edu/data/landsat/
Landsat-5 TM
ID
LT5187029201119 1MOR00
10.07.2011 U.S Geological Survey USGS http://glovis.usgs.gov/
Landsat data are geometrically corrected (product type L1T). The Level L1T data product provides systematic radiometric and geometric accuracy by using ground control points and also employing a Digital Elevation Model (DEM) for topographic accuracy. Geodetic accuracy of the product depends on the accuracy of the ground control points and the resolution of the DEM used. The images are geocoded to Universal Transverse Mercator coordinate system (UTM zone 34) (U.S. Geological Survey 2012).
15 Figure 4. Landsat image of the Srebrenica region – 1991.
16 Figure 5. Landsat image of the Srebrenica region – 2011.
17 3.2.1. Ancillary data
High resolution images available in Google Earth were used for accuracy assessment purposes. These images, according to the Google Earth information, are from 23 of March 2003.
Shuttle Radar Topography Mission (SRTM) elevation data were used to analyze changes in land cover in dependency on elevation (fig. 6). Elevation model SRTM is available with a spatial resolution of 3 arc seconds (approximately 90 m), horizontal accuracy of 20 m and vertical of 16 m (Zandbergen 2008).
Figure 6. SRTM digital elevation model for the Srebrenica region
18 4. METHODS
4.1. Change detection: theoretical background
Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times. In general, change detection involves the application of multi-temporal datasets to quantitatively analyze the temporal effects of the phenomenon. It is one of the major applications of remotely-sensed data obtained from Earth-orbiting satellites because of repetitive coverage at short intervals and consistent image quality. Accurate change detection of Earth’s surface features provides the foundation for better understanding of relationships and interactions between human and natural phenomena. Change detection based on remote sensing technology is an important tool in providing both pre-war and post-war assessments of the impacts of war. Although data from this technology are not capable of a complete environmental assessment of factors such as air quality, wildlife health or soil pollutants, they can provide valuable information on changes in vegetation.
Analysis of the literature provides ample evidence to support the conclusion that multi-date satellite imagery can be effectively used to detect and monitor changes in ecosystems (Anderson 1976, Singh 1989, Witmer 2008).
A variety of change detection techniques have been developed and many have been summarized and reviewed. In practice, different methods are compared to find the best change detection results for a specific application. It is not easy to select a suitable algorithm for the specific change detection analysis. Successfully implementing a change detection analysis using remotely sensed data requires careful considerations of the remote sensor system, environmental characteristics and image processing methods (Singh 1989, Deer 1995, Coppin 2004, Lu et al. 2004, Jensen 2005).
The objective of change detection is to compare spatial representation of two points in time by controlling all variances caused by differences in variables that are not of interest and to measure changes caused by differences in the variables of interest. The basic premise in using remotely sensed data for change detection is that changes in the objects of interest will result in changes in reflectance values or local textures that are separable from changes caused by other factors such as differences in atmospheric conditions, illumination and viewing angles and soil moistures. The basic premise in using remote sensing data for change detection is that changes in land cover must result in changes in radiance values and changes in radiance due to land cover change must be large with respect to radiance changes caused by other factors (Lu et al. 2004), for example:
atmospheric conditions,
Sun angle,
soil moisture.
19 The impact of these factors may be partially reduced by selecting the appropriate data. For example, Landsat data belonging to the same time of the year may reduce problems from the Sun angle differences and vegetation phenology changes. No additional radiometric or atmospheric corrections are carried out when the sun elevation and the azimuth angles of the images are not significantly different (Janssen 2005).
The necessity for controlling radiometric noise (sensor degradation, change in solar illumination, atmospheric scattering and absorption, and changes in atmospheric conditions such as water vapour density and cloud cover) depends on the method of detecting change. For techniques that do not directly compare digital numbers (e.g. post-classification comparison) or only apply linear transformations (e.g. simple image/band differencing), atmospheric correction is not necessary.
However, methods that use band ratios (e.g. most vegetation indices such as the normalized difference vegetation index (NDVI) are contaminated by atmospheric effects and should be corrected (Witmer 2008).
Similarly, according to Lu et al. (2004), good change detection research should provide the following information:
area and rate of change,
spatial distribution of changed types,
change trajectories of land-cover types,
accuracy assessment of change detection results.
The accuracies of change detection results depend on following factors (Lu et al. 2004):
precise geometric registration between multi-temporal images,
calibration or normalization between multi-temporal images,
availability of quality ground truth data,
the complexity of landscape and environments of the study area,
change detection methods or algorithms used,
classification and change detection schemes,
analyst’s skills and experience,
knowledge and familiarity of the study area,
time and cost restrictions.
Many change detection techniques have been developed. In general, change detection techniques can be grouped into two types (Lu et al. 2004):
those detecting binary change/non-change information, for example, using image differencing, image ratioing, vegetation index differencing and PCA;
20
those detecting detailed ‘from–to’ change, for example, using post-classification comparison and hybrid change detection methods.
These two types broadly reflect qualitative and quantitative methods of change detection (Kozak 2009).
Regardless the broad approach chosen, steps in satellite change detection process could be summarized as follows (Kozak 2009):
geometric correction of satellite data,
extracting relevant, thematically consistent information,
image overlay, comparison and change analysis,
verification of results.
Specific change detection methods are numerous, and may be grouped into various broader categories, e.g. (Lu et al. 2004) combine change detection approaches into seven categories:
algebra,
transformation,
classification,
advanced models,
Geographical Information System (GIS) approaches,
visual analysis,
other approaches.
In this study two methods: vegetation index differencing (NDVI differencing) and post-classification comparison are described.
4.1.1. Post-classification comparison
Post-classification comparison is a method of change detection which requires the comparison of independently produced classified images. These methods are based on the classified images, in which the quality and quantity of training sample data are crucial to produce good quality classification results.
The major advantage of these methods is reducing external impact from atmospheric and environmental differences between the multi-temporal images. However, selecting high-quality and sufficiently numerous training sample sets for image classification is often difficult (Lu et al. 2004).
Disadvantage of this method is multiplying the accuracies of each individual classification produce a large number of erroneous change indications since an error on either date gives a false indication of change. For example, two images classified with 80% accuracy might have only a 0.80 x 0.80 x 100%
= 64% correct joint classification rate (Singh 1989).
21 4.1.1.1. Classification
Classification is the process of sorting pixels in multispectral images into patterns of clusters. Clusters are statistically different sets of multiband data which radiances are expressed by their Digital Number (DN) values. Statistical operation such as calculating means, standard deviations, and certain probability functions create boundaries between clusters. As a result every point is automatically assigned to class. Any individual pixel or spatially grouped sets of pixels representing a class is then characterized by a generally small range of DN for each band monitored by the remote sensor. These are analyzed statistically to determine their degree of uniqueness and next after choosing a mathematical function clusters are discriminated. In the supervised classification classes are recognized basing on knowledge, such as experience with thematic maps, or from on-site visits.
During classification statistical processing is running in which every pixel is compared with the various signatures and assigned to the class according chosen algorithm (Landsat Handbook 2012).
Selecting the training areas is a very important part of classification. Training pixels can be selected in many ways. A commonly used sampling method is to identify and label small patches of homogeneous pixels in an image. A sufficient number of training samples and their representativeness are critical for image classifications (Hubert-Moy 2001). Training samples are usually collected from fieldwork, or from very high spatial resolution aerial photographs and satellite images. When the landscape of a study area is complex and heterogeneous, selecting sufficient training samples becomes difficult. Spectral confusions may occur due to the mixed pixels located on the boundary of two or more land cover types in abandoned farmlands. This type of land cover is heterogeneous and difficult to classify because of crop-type variability, phenology, and different vegetation succession stages (Hostert et al. 2011).
Pixels are assigned to the class according their similarity with the spectral signatures. Affiliation of pixels can be computed according several algorithms. One of classification algorithms is Maximum Likelihood Classification (MLC). It is one of the most powerful classifiers with mean and variance taken into consideration. Probability function is calculated from training sites. Each pixel is assigned to the class to which belonging it the most probable. Pixels with high classification uncertainty (e.g., higher than 5%) can be excluded. Because direction and distribution of the feature space is taken into consideration results are very accurate and more reliable compare to Minimum Distance and Parallelpiped Classification (Landsat Handbook 2012).
Accuracy assessment is a general term for comparing the classification to geographical data that are assumed to be true, in order to determine the accuracy of the classification process. Evaluation of the classification accuracy can be done visually, by comparison output result with the input image and gives the first impression of the accuracy. Typically, it is performed statistically, when result is
22 compared statistically with the real world sample. Google Earth can be used as an image data source to compare with classification result. Result of comparison are values: 1 – true or 0 – false. Error matrix is produced which represents percentage of misclassified pixels (Landsat Handbook 2012).
Without a statistical verification quantitative statement of the classification accuracy is not valid and results cannot be accepted. Verification process is always the final and the most important part of a classification workflow (Lambin et al. 2003). The most common accuracy assessment elements include overall accuracy, producer’s accuracy, user’s accuracy and Kappa coefficient. The Kappa coefficient shows the proportionate reduction in error generated by a classification process compared with the error of a completely random classification. For example, a value of .82 means that the classification process is avoiding 82 percent of the errors that a completely random classification generates (Congalton 1991).
4.1.2. NDVI differencing
NDVI (Normalized Difference Vegetation Index) differencing is one of the quantitative methods that are based on comparing values of specific quantitative parameters, from the algebra category.
Conversion of digital numbers to radiance or surface reflectance is a requirement for quantitative analyses of multi-temporal images (Lu et al. 2004).
Vegetation Indices are enhancement techniques and their algorithm is based on band combinations.
Vegetation Indices are commonly used for vegetation delineation. One of them is NDVI which is used extensively in vegetation analyses to recognize small differences between various vegetation classes.
Ratios of individual bands or of band sums or differences are calculated to recognize variation within types and densities of growing forests, fields, and crops. Judiciously chosen indices can highlight and enhance differences which cannot be observed in the display of the original color bands (Landsat Handbook 2012).
Determination of vegetation cover types using NDVI is based on bands which are sensitive to chlorophyll absorption. Landsat Thematic Mapper (TM) band 4 is the most sensitive for detecting IR reflectance from plant cells. Water content in plant cells modifies reflectance from this band. TM Band 3 measures reflectance in the visible red and provide information on the influence of light-absorbing chlorophyll. These bands quantify the amount of vegetation, as biomass and their ratio are referred to as vegetation indices. There are many variants of vegetation indices and they depend on combinations of these variables. The NDVI is defined as (Landsat Handbook 2012):
NDVI = (Near IR band - Red band) / (Near IR band + Red band) The NDVI has two important properties:
23 1. NDVI is highly correlated with amount of green biomass (Tucker 1979).
2. NDVI reduces the variation due to surface topography. Studies by Lyon et al. (1998) reported that NDVI differencing was the best method for vegetation change detection in biologically complex ecosystems. Furthermore NDVI differencing was least affected by topographic factors.
In NDVI differencing, vegetation indexes are calculated separately and then the second-date vegetation index is subtracted from the first-date vegetation index. This technique emphasizes differences in the spectral response of different features and reduces impacts of topographic effects and illumination, however enhances random or coherence noise (Lu et al. 2004).
Riordan (1980) has pointed out some of the general difficulties in image differencing. Examples are its sensitivity to misregistration and the existence of mixed pixels. Moreover simple image differencing failed to consider the starting and ending location of a pixel in the feature space. For example, an agricultural pixel with a radiance value of 190 in band 4 on one date and 160 on the second date showed a change of 30 digital counts. However, despite this substantial change relative to other types of change, the pixel may still represent an agricultural pixel. According to Weismiller (1977) the method may be too simple to deal adequately with all the factors involved in detecting changes in a natural scene. Too much information may be discarded from the data in the subtraction process. Two sets of different absolute values may have an identical differenced value (e.g. 180 – 150 = 30 and 40 – 10 = 30) and therefore there is a potential risk of information loss with the use of simple differencing transformations.
24 4.2. Study approach
Images were acquired at similar dates and sensor locations. Therefore no additional radiometric or atmospheric corrections were carried out. Reason for this is that the Sun elevation and the azimuth angles of the images were not significantly different. In addition, it was assumed that either post- classification differencing or index differencing will be used, and both these methods compensate illumination differences.
For qualitative changes description supervised classification was conducted. Visual assessment of changes was done by overlaying and comparing results of supervised classification. Landsat bands 2, 3, and 4 (Green, Red and Near-Infrared) were used to reliably and accurately analyze land cover changes. In case of this study landscape heterogeneity poses a problem which results in high spectral variation within the same land-cover class. Supervised classification was chosen as a good method to reduce this problem. Maximum Likelihood Classification (MLC) was used as a classification algorithm. Training areas were collected from the most representative homogenous areas on a basis of visual image interpretation supported with high resolution Google Earth imagery.
Visually and statistical classification accuracy assessment based on a sample of points (Fig. 7) was performed. Overall accuracy and overall kappa coefficient were calculated.
25 Figure 7. Distribution of control points used in accuracy assessment
To assess land cover changes in a quantitative way, NDVI differencing was used. This method was chosen because it emphasizes differences in the spectral response of different classes.
Mathematical image differencing was carried out to find out the difference in the NDVI values of two images:
DNDVI = NDVI2011 –NDVI1991
where NDVI2011 image of time 2011, NDVI1991 image of time 1991
NDVI differences were then analyzed separately for land cover classes identified at 1991 image. It was assumed that most changes due to Bosnian War affected agricultural land in 1991, and forests and water surfaces were much less affected. Hence, the image of 1991 was classified with a supervised approach into three classes ‘Settlements and agriculture’, ‘Water’ and ‘Forest’. To calculate NDVI characteristics for various areas, zonal mean function available in Erdas Imagine was used. Zones were the three delineated land cover classes. Additionally calculations were carried out separately for the Bosnian and Serbian parts of the study area.
26 Then, SRTM data were used to recognize variations of land cover changes expressed with NDVI differencing in dependency on elevation. Changes of NDVI were described in dependency on distance from Srebrenica. To calculate NDVI characteristics for various areas, zonal mean function was applied. Zones were classes of elevation (fig. 8) and of distance from Srebrenica (fig. 9).
Figure 8. Elevation classes in the study area.
27 Figure 9. Distance from Srebrenica classes.
28 5. RESULTS
Study was designed to identify natural succession process in abandoned land after war and ethnic cleansing in region the analyzed region of Bosnia-Herzegovina around Srebrenica. Classification of Landsat images was conducted to analyze landscape change from 1991 to 2011. Changes were identified in the study area which is a mosaic of crop fields, pastures, meadows and forests. The overall land use classification accuracy for image from 1991 is 92%. Result of classification is significantly better than random (at the 95 percent confidence level). It was assumed that classification accuracy of image from 1991 is similar to result obtained for image from 2011.
Table 2. Accuracy assessment – Error matrix for reference data.
Ground truth
Classification WATER S&A FOREST Row total
WATER 30 0 0 30
SETTLEMENTS AND AGRICULTURE
0 45 5 50
FOREST 0 5 45 50
Column Total 30 50 50 130
Table 3. Accuracy assessment – Total accuracy and Kappa.
Class name Reference Totals
Classified Totals
Number Correct
Producers Accuracy [%]
Users Accuracy [%]
Kappa
WATER 30 30 30 100 100 1
SETTLEMENTS AND AGRICULTURE
50 50 45 90 90 0.8375
FOREST 50 50 45 90 90 0.8375
Totals 130 130 120
29 Because the aim of this work was to identify natural succession and to assess the impact of the war (through depopulation or landmines) on agricultural land, the most important was selecting places with significant changes in vegetation. For this qualitative change detection purpose, results of supervised classification was performed for both Landsat scenes.
Results show strong differences in amount of vegetation between the two classified images from 1991 and 2011 (fig. 10-11).
Figure 10. Classification of the 1991 Landsat image
30 Figure 11. Classification of the 2011 Landsat image
Number of pixels representing each class after classification in 1991 and 2011 was compared. For this purpose area of changes [ha] and per cent of changes were calculated. Area of classes ‘Water’ and
‘Settlements and agriculture’ decreased only area of class ‘Forest’ increased (table 4, fig. 12).
Table 4. Land use change [%] and [ha] over 20 years period.
Class Land use
1991 [ha]
Land use 2011 [ha]
Land use change [ha]
from 1991 to 2011
Land use change [%]
from 1991 to 2011
Water 4270 3232 -1038 -0.14
Settlements and
agriculture 363612 349989 -13622 -1.81
Forest 383461 398122 14661 1.95
31 Figure 12. Post-classification differencing: crosstabulation of classification results from 1991 and 2011.
32 To support the research hypothesis on land abandonment and succession on agricultural land, NDVI for the pre-and post-war imagery was calculated. This provides a directly comparable measure of vegetation changes. The analysis was focused only on agricultural areas, with an assumption that forests and water bodies had stable NDVI over time and were not affected by the war. The higher NDVI values reflect the higher amount of vegetation in abandoned agricultural land.
NDVI values from both sets of scenes were differenced and combined into a single map of NDVI differences for the “Settlements and agriculture” class (fig 13). Clouds, forest and water are masked and not taken into consideration.
Figure 13. NDVI differences in “settlements and agriculture” class
33 For each zone (forest, non-forest, water) mean values of NDVI difference were also calculated. Zonal mean statistics were used for quantitative presentation of results. NDVI changes were calculated for all study area (table 5). Study area was also divided along Drina river to Serbia and Bosnia-Herzegovina and NDVI differences were calculated for both countries separately (table 6).
Table 5. Zonal mean of NDVI in classes: Water, Settlements and agriculture, Forest – calculated for all study area
Table 6. Zonal mean of NDVI in classes: Water, Settlements and agriculture, Forest – calculated separately for Serbia and Bosnia and Herzegovina
Considering all study area the most significant NDVI changes were detected for water, changes in forest areas were not observed. Moderate positive changes were found in the ‘Settlements and agriculture’ class. Similar results are obtained in calculation mean NDVI differences for two countries separately.
Class Zonal mean of NDVI
1: Water 0.114
2: Settlements and agriculture 0.034
3: Forest 0.002
Class Zonal mean of NDVI for
Bosnia-Herzcegovina
Zonal mean of NDVI for Serbia
1: Water 0.101 0.143
2: Settlements and agriculture 0.033 0.037
3: Forest -0.0030 -0.00012
34 Mean NDVI differences were calculated in dependency on distance from Srebrenica and elevation, for classes ‘Forest’ (table 7) and ‘Settlements and agriculture’ (table 8).
Table 7. Mean NDVI difference between 1991 and 2011 for ‘Forest’ class in dependence on distance from Srebrenica.
Distance class Mean NDVI difference
1: 0 – 2 km 0.0057
2: 2 – 9 km 0.0086
3: 9 – 18 km 0.0103 4: > 18km -0.0045
NDVI differences calculated for all study area showed no significant variations with the distance from Srebrenica.
Table 8. Mean NDVI difference between 1991 and 2011 for ‘Settlements and agriculture’ class in dependence on distance from Srebrenica.
Distance class Zonal mean of NDVI in ‘Settlements and agriculture’ class
1: 0 – 2 km 0.088
2: 2 – 9 km 0.101
3: 9 – 18 km 0.080 4: more than 18km 0.026
35 NDVI difference in class ‘Settlements and agriculture’ within distance 18 km from Srebrenica is much higher than NDVI difference further than 18 km from Srebrenica, and mean difference of NDVI for all study area (0.034).
Considering distance, variation of NDVI difference in the class ‘Settlements and agriculture’ is more pronounced than in the class ‘Forest’.
Positive NDVI difference in forest class were observed in the highest elevation (table 9).
Table 9. Mean NDVI difference between 1991 and 2011 for ‘Forest’ class in dependence on elevation.
Elevation class Zonal mean of NDVI in Forest class 1: 0 – 500 m -0.012
2: 500 – 1000 m -0.002 3: 1000 – >1500 m 0.011
Variation of NDVI differences in three elevation zones were insignificant for the ‘Settlements and agriculture’ class, and values for elevation classes were similar to that calculated for all study area (table 10).
Table 10. Zonal mean of NDVI differences in ‘Settlements and agriculture’ class in dependence on elevation.
Elevation class Zonal mean of NDVI in ‘Settlements and agriculture’ class
1: 0 – 500 m 0.035 2: 500 – 1000 m 0.032 3: 1000 – 1500 m 0.040
36 6. DISCUSSION
6.1. Evaluation of methods
Many change detection techniques have been developed to detect change in vegetation. This study presents two techniques for detecting and mapping changes with Landsat data. Post-classification comparison as a qualitative assessment methods and vegetation index differencing (NDVI differencing) as quantitative method.
For image from 1991 classification accuracy assessment was calculated. The producer’s accuracy is calculated from the respective column totals in each matrix, the user’s accuracy from the row totals, and the total accuracy along the diagonal. Since aim of this work is to assess the impact of the war (through depopulation and landmines) on agricultural land the most important are results: user accuracy and overall classification accuracy.
Overall classification accuracy is 92.3% and user accuracy is 90%. Result of accuracy assessment seems to be good in comparison to other studies, which applied different methodologies. For example Wynne et al.(2000) state that it is often difficult to exceed 85% accuracy for the binary forest-non- forest classification. The accuracy of classification depends on nature of the forest cover change processes, in particular close to the forest–non-forest boundaries. Forest succession occurs naturally along forest edges, and is difficult to recognize. Moreover, in the case of natural forest succession, conversion to forests is a gradual increase of woody vegetation: numbers and sizes of scattered trees or bush vegetation increase on narrow land strips dividing cultivated fields or on abandoned land, which will turn into forests after some time. This process is difficult to detect properly when post- classification differencing is applied. During selection of training samples, early forest succession stages, shrubs caused problems and this land cover types was treated as areas of uncertainty (Kozak 2007).
Lyon et al. (1998) found that NDVI differencing is more accurate than post classification and they classify two time data separately. NDVI image differencing cannot provide detailed information. It can only give the information of increase or decrease NDVI value. NDVI can detect subtle changes in vegetation and is sensitive to small changes in reflectance. The efficiency of NDVI in detecting changes was proved by many researchers. Studies by Lyon et al. (1998) reported that NDVI differencing was the best method for vegetation change detection in biologically complex ecosystems.
Furthermore NDVI differencing was least affected by topographic factors.
According to Coppin et al. (2004) the principal advantage of post classification is that the two dates of imagery are separately classified, so problem of radiometric calibration between dates is minimized.
However, the accuracy of the post-classification comparison is dependent on the accuracy of the initial classifications. The final accuracy result from multiplication of the accuracies of each individual
37 classification and may be low. In case of this study accuracy assessment shows good quality of classification so error from multiplying accuracies is low. Post classification comparison method gave an accurate result for change detection and is an effective method and can express the specific nature of changes.
6.2. Post-war land abandonment and natural succession
Comparison of independently classified images (fig. 10 and 11) confirms thesis that amount of vegetation increased over the analyzed 20 years period. During ecological succession process, vegetation started to ‘fill the gaps’ in the abandoned areas. The greatest amount of vegetation growing on abandoned agricultural land was found in the Srebrenica region. Ethnic cleansing, re-settlement actions and danger caused by minefields were likely a major factor in process of ecological succession. Spatial variation of the vegetation cover depended on human population change. The highest values were found in areas affected by the war or post-war re-settlement actions. The re- settlement may be treated as a disturbance that speeded up and intensified processes of natural succession.
Areas of particular classes from 1991 and 2011 were calculated and compared (table 4). Area of Water and ‘Settlements and agriculture’ class decreased over 20 year period. Only area of Forest class increased slightly about 2%. Visual comparison both images leads to conclusion that changes in area of Water class are the most significant in Modrac Lake and decreasing in eutrophication process.
Decreasing area of ‘Settlements and agriculture’ class and increasing area of Forest confirms the natural succession.
Quantitative analysis of Landsat data considering all study area (table 5) gave result that NDVI increased strongly in the ‘Settlements and agriculture’ class of 1991. Forest seemed to be stable over 20 year period. The most significant NDVI changes were found in water class. This result may be explained with increasing pollution by organic matters and plants development. Quantitative analysis considering Bosnia-Herzegovina gave similar result to calculation for all study area and Serbia (table 6). Changes in classes ‘Water’ and ‘Settlements and agriculture’ seem to be slightly more pronounced in Serbia than in Bosnia-Herzegovina.
Because aim of this work was to analyze changes in abandoned agricultural fields the most interesting are differences in ‘Settlements and agriculture’ class. Significant positive NDVI differences in
‘Settlements and agriculture’ class in all study area confirms thesis about increasing amount of vegetation over 20 year period on abandoned agricultural land. Analyzing map (fig. 13) and NDVI differences in ‘settlements and agriculture’ class, both positive changes (increase of NDVI) and negative changes (decrease of NDVI) were observed in this class. The most significant positive NDVI
38 changes were observed in Srebrenica region and also in cities Banovici and Zivinice. Quantitative analysis confirmed thesis that vegetation cover increased in abandoned agricultural land of study area.
Additionally NDVI differences were calculated in dependency on elevation (table 9 and 10) and distance from Srebrenica (table 7 and 8). NDVI difference in class ‘Settlements and agriculture’ within distance 18 km from Srebrenica were much higher than average NDVI difference in this class calculated for all study area (table 5). Moreover, NDVI differences increased also in the ‘Forest’ class in area around Srebrenica. This pattern in the ‘Settlements and agriculture’ class further confirms that there was a significant land abandonment around Srebrenica, with more intense natural succession than elsewhere in the study area.
No significant variation of NDVI differences were observed in dependency on elevation, in the
‘Settlements and agriculture’ class (table 10). Mean NDVI differences in this class for all study area very similar to values obtained for particular elevation ranges. Similar patterns were observed in the
‘Forest’ class.
Drastic landscape change following socio-economic disturbances is reported also by other studies in this region. For example according (Witmer 2008, O’Loughlin 2009) areas of greater concentration of abandoned agricultural land are generally associated with more intense fighting, especially in Srebrenica, which was a highly contested region during the war. Risk of landmines restricted access to many places and has result in blocking agriculture development.
Marko Blagojevic´, Director of Agrokoperative in Bratunac, noted that the large areas of abandoned land are in Srebrenica and Bratunac, a region known for its orchards and berry production. These regions of undetected abandoned agricultural land are characterized by hilly terrain and small agricultural plots interspersed with forest. Such mixed land use is difficult to discriminate because of pixel mixing in the Landsat imagery (Witmer 2008). In the area of Srebrenica, according to the official data, totally 4,000 refugees have returned. However, in practice more than 60% of them stay there only periodically. For example, in the village Moračići near Zvornik, 60 houses belonging to the Bosniaks were rebuilt this year, but by the beginning of November none of the owners moved into them permanently (Dahlman 2005). Abrupt depopulation after the ethnic cleansing actions was a major factor in vegetation cover increase in the studied area. Depopulation event may be in fact considered as a kind of a catastrophic factor, which always causes forest cover expansion, as occurred, for example, following the Black Death in Europe in the 14th century (Kozak 2007).
Major socio-economic disturbances such as wars (O’Loughlin 2009), economic crises, failing states (Irland 2008), revolutions and globalization (Aide and Grau 2004) have triggered rapid and widespread land use changes. Many studies were conducted in places where natural succession in abandoned places was expected, for example in Chernobyl (Hostert et al. 2011). According to their
39 study, forests have regrown on many of the former farm fields, providing ecosystem services such as increased water quality, soil stability, and carbon sequestration, as well as additional habitat for wildlife. On the other hand, warfare, revolutions, or failing states can weaken institutions and the effectiveness of law enforcement, or increase poverty, all of which may result in a predatory exploitation of natural resources (Irland 2008).
40 7. CONCLUSIONS
Presented analyses demonstrate that detecting long term changing in vegetation is possible using Landsat 30 m multispectral imagery. Moderate resolution Landsat data are sufficient for detecting war induced changes on abandoned agricultural land. Using satellite images makes possible studying war zones that are still too dangerous for conducting a direct research. This is especially important for long-term conflicts where conducting extensive field work is still too risky for example because of minefields. Advantage of the methodology used in this study was a possibility for quickly producing land cover maps for two moments in time, with an accuracy exceeding 90% (result for image from1991). The limitations of the procedure are due to the difficulty of detecting slow re-growth of vegetation.
Both post-classification comparison and vegetation index differencing (NDVI differencing) were found as useful in analyzing natural succession. Qualitative analysis in post-classification comparison may be used as a complementary methods to quantitative approach such as vegetation index differencing (NDVI differencing).
Results confirm that the amount of vegetation increased in the studied area over 20 years period.
During visual analysis two independently classified images patterns and shapes of changing vegetation were recognized. In the next step calculated area of each class gave information that forest area increased slightly and water bodies (Modrac Lake) decreased in the eutrophication process.
Settlements and agricultural land decreased insignificantly. Finally, calculation of NDVI differences revealed that increase of NDVI and hence the most important changes were in the abandoned agricultural lands, especially within the distance of 18 km from Srebrenica, confirming the role of the post-war natural succession.
All results described above confirm fact that socio-economic disturbance such as Bosnian War have had significant influence on land use pattern. In case of this study, the war has not put land use systems toward intensification trajectories but allowed landscapes to ‘rewild’, and gave opportunities for conservation. It is difficult to forecast how long-lasting land use changes will be. The vegetation cover probably would increase in region of Srebrenica but the future of minefields remains open.
Evidence from other areas suggests that farmland abandonment may persist for a long time. The changes of spatial pattern of vegetation detected in this study would need further investigation to be validated and documented with field surveys.
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44 LIST OF FIGURES
Figure1. Political map of Bosnia-Herzegovina.
Figure 2. Land cover of study area.
Figure 3. Loss of people during Bosnian War.
Figure 4. Landsat image of Srebrenica region – 1991.
Figure 5. Landsat image of Srebrenica region – 2011.
Figure 6. Region of Srebrenica SRTM.
Figure 7. Distribution of control point used in accuracy assessment.
Figure 8. Elevation class.
Figure 9. Distance class.
Figure 10. Classification of the 1991 Landsat image.
Figure 11. Classification of the 2011 Landsat image.
Figure 12. Post-classification differencing: crosstabulation of classification results from 1991 and 2011.
Figure 13. NDVI differences in “settlements and agriculture” class.
45 LIST OF TABELS
Table 1. Data sources.
Table 2. Accuracy assessment – Error matrix for reference data.
Table 3. Accuracy assessment – Total accuracy and Kappa.
Table 4. Land use change [%] and [ha] over 20 years period.
Table 5. Zonal mean of NDVI in classes: Water, Settlements and agriculture, Forest– calculated for all study area.
Table 6. Zonal mean of NDVI in classes: Water, Settlements and agriculture, Forest – calculated separately for Serbia and Bosnia and Herzegovina.
Table 7. Mean NDVI difference between 1991 and 2011 for ‘Forest’ class in dependence on distance from Srebrenica.
Table 8. Mean NDVI difference between 1991 and 2011 for ‘Settlements and agriculture’ class in dependence on distance from Srebrenica.
Table 9. Mean NDVI difference between 1991 and 2011 for ‘Forest’ class in dependence on elevation.
Table 10. Zonal mean of NDVI differences in ‘Settlements and agriculture’ class in dependence on elevation
