LAND COVER CLASSIFICATION OF SINITE KAMANI NATURAL PARK USING ASTER TERRA SATELLITE IMAGE

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1 LAND COVER CLASSIFICATION OF SINITE KAMANI NATURAL PARK USING ASTER TERRA SATELLITE IMAGE Timo Kumpula, Stoyan Nedkov & Mariyana Nikolova Abstract The paper represents results from scientific investigation directed to examine land cover pattern in Sinite Kamani Natural Park. Studied area covers the territory of the park, which occupies km 2. It is situated in the eastern part of Stara Planina Mountain, including its highest peak Bulgarka (1181m). The park area is significant with its great biodiversity, including more than 1000 plant species and about 500 animal species. Land cover investigation was based on ASTER TERRA satellite image from with 15m spatial resolution. Field data for land cover classification were collected in the summer of They include 23 sites from sample land cover types with information for their exact location (taken by GPS), vegetation characteristic and percentage of the different cover elements. Digital image processing was realized by ERDAS Imagine 8.7 software. The classification scheme used was supervised maximum likelihood classifier. The resulting data were exported to Arc/CIS for thematic map producing. The classification includes 8 land cover classes. There are 4 forest classes, 1 grassland, 1 transitional and also water and barren classes. Important result from the study is the founded possibility to distinguish different kinds of deciduous forests, despite the difficulties in their differentiation. The pine forests were classified with high accuracy. Landuse classification of the Sinite Kamani Nature Park using ASTER TERRA VNIR image was satisfactory. Further field work and image processing is required to be able to produce more detailed classification of the area. Timo Kumpula Department of Geography, University of Joensuu Box 111, Joensuu Finland timo.kumpula@joensuu.fi Stoyan Nedkov Institute of Geography, Bulgarian Academy of Sciences Akad. G. Bonchev str. bl Sofia, Bulgaria Tel: Fax: snedkov@abv.bg Mariyana Nikolova Institute of Geography, Bulgarian Academy of Sciences Akad. G. Bonchev str. bl Sofia Bulgaria Tel: Fax: mn@bas.bg INTRODUCTION The specific geographical location of Bulgaria on the border between 3 different natural zones together with its various relief are preconditions for unique biodiversity in such a small territory. Protection of this diversity is within the first

2 tasks for the modern Bulgarian society. Nature protection organization and its harmonization with European regulations have their important place in the process of country s integration into EU. Bulgaria is a part of the program CORINE Biotopes since 1994 as well as Natura 2000 since its foundation. CORINE land use classification is derived from Landsat TM images with 30 meter spatial resolution. According to these programs large areas were identified as places with conservation values. New natural protection act has been adopted in Its regulations define 6 categories of protected areas ranked according to their limitation regime as: 1) Reserve; 2) National Park; 3) Natural Landmark; 4) Supported Reserve; 5) Natural Park; 5) Protected Site. Specific feature of the Bulgarian practice is that most Natural and National Parks include in their territories areas with higher level of protected regime like Reserves. Natural Parks, as specific protection areas, are founded with the aim to support ecosystems and their biodiversity, to give opportunity for scientific, educational and recreational activity as well as to ensure sustainable use of renewable natural resources with preservation of the traditional livelihood for the local communities. Within its borders it is possible to have settlements and resorts as well as production activities, which do not pollute environment. Other protected areas within park border preserve their regime according to the regulations. It is obvious that in these parks there are different kinds of land use with different kinds of activities, which could have contrary opinions in some cases concerning land management. Another problem is that the different areas in the park have different supervisors. For example forests are under the management of the regional office of Ministry of Agriculture and Forestry, which is also responsible for the park guarding. These circumstances impede the effective management of the protected areas and reduce the possibilities of the park administration to carry out some of its duties. Some problems are connected with gathering of information for scientific needs of the nature protection activity. Collecting of spatial data about land cover could be achieved using the opportunities given by of remote sensing techniques, which is the aim of this work. Remote Sensing and GIS has been used in different kinds of studies connected to conservation and protected areas management. Land cover classification based on satellite images is one of the most popular methods. In the past, the classifications of remote sensing images have been criticized to be too general and inaccurate. In most cases they have been based on field measurements and visual interpretation of the images, whereby image characteristics like resolution (pixel size), spatial coverage and spectral channel effects can limit the usability of the images in vegetation classification (Kumpula et all, 2005). There are new, Very High Resolution (VHR) satellite systems like IKONOS-2 and Quickbird-2, which provide pixel size of less than 5m. They are significantly more detailed than the older ones like Landsat and SPOT. However the high price still limits their use in more scientific investigation. The images provided by ASTER (Advanced Spaceborne Thermal Emission Radiometer) could satisfy the needs of relatively high resolution (15m) with reasonable cost, which is the reason for their choice in this investigation. RESEARCH AREA Natural park Sinite kamani is situated on the south slope of Slivenska Mountain, which is a part of Eastern Balkan Mountain. It occupies km 2 and its borders reach the Balkan ridge to the north, river Asenovska and Asenovetz dam to the west, river Bojurska to the east and the urbanized area of Sliven with contiguous arable lands to the south. The relief is predominantly low-mountain and only highest parts lie in the middle mountain belt. The altitude varies from 290m in the south part to 1181m around Bulgarka peak, which is the highest in Eastern Balkan Mountain (Figure 1.). The park area has different kinds of bedrock. Southern part is occupied mainly by quartz-porphyry, which have specific blue-purple colour on sunset that gives the name of the park and the area. Coarse-grained granite builds up the northern part of the area. There are also limestone, marl and clay-schist in other parts. The park is situated in the area with transitional continental climate, while only the highest part is with typical mountain climate. Mean annual temperature in Sliven is 12.4 o C, during the coldest month (January) is 1.2 o C while the hottest (July) is 23.2 o C. At the top of the mountain around Sinite kamani resort mean annual temperature is 7.7 o C, the hottest month has 17.4 o C and the coldest - 2 o C. Annual amount of precipitation rises from 587mm in Sliven to 830 at the top of the mountain. This change of the climate characteristics with elevation determines the existence of altitude zonality in the other landscape elements. The soils in the lower part of the park are luvisols while in the upper they are replaced by cabmisols. There are a lot of places, predominantly with luvisols, where the soils are eroded. The vegetation in the lower part is represented by oak forests, which were strongly influenced by human activity in the past and partially replaced by shrubs of Carpinus orientalis, Quercus pubescens and Fraxinus ornis. Upward the mountain is the area of hornbeam-oak belt communities forest of Carpinus betulus, Quercus dalechampii and Fagus silvatica. The great biodiversity of the area is determined by unique combination of relief, climate, rocks and soil conditions. There are 1024 kinds of higher plants species founded in the park territory. 938 of them are seminal, 19 fern species and 67 mosses species (Stoeva, et all, There are 89 plant species with conservation value within the park territory, 42 of them are in Bulgarian Red book, 39 are regional endemic (Balkan peninsular), 16 are national endemic, 6 are

3 included in Bern Convention, 10 are in the European Red list. There is also great variety of animal species. 244 vertebrate species were found within the park territory, 45 of them are in the Bulgarian Red book. Figure 1. Location of the research area Sinite Kamani Nature Park: 1 park territory; 2 reserve area; 3 area with special protection regime; 4 training sites; 5 rectification points. The park was announced as protected area for the first time on According to the former legislation it was in the category of so called People s park. The initial area of the park was 66,8km 2 and occupied the western part the contemporary area. Kutelka reserve (6.45km 2 ) was created within the park area in 1983 in order to make strong protection on the habitats of rare and extinction from European continent predatory and scavenger birds like some eagle and vulture species. According to the new nature protection legislation, adopted in Bulgaria in 1998 the area was transformed into Natural Park. This category is less strict and allows some kinds of economical activities as it was mentioned above. The park territory was extended to the east and reached its contemporary size in The park area was investigated in some scientific works, which were prepared alongside management plan elaboration in the 80s and in last years after the change of its category. The most comprehensive were the botanical studies (Andreev, 1981; Stoeva et all, 2004) while there is no particular investigation concerning land use and land cover spatial distribution. Land cover is critical biophysical parameter that determines the function of ecosystems in biogeochemical cycling, hydrological process and the interaction between the surface and the atmosphere. There are comprehensive data base about forests in the area gathered by the Forestry service. They could be useful in some cases but their character, directed closely to the forest service needs limit their implementation. There are also CORINE land cover database for the whole Bulgarian territory, but its scale is relatively small for more precise investigations. Other types of spatial information like CORINE Biotopes and NATURA 2000 are more comprehensive but they do not cover the whole area. The land cover classification, which is presented here aims to fill the gap between these datasets. It is more precise than CORINE land cover and could be used to extend and deepen its database for particular areas; on the other hand it will give opportunity to connect the information of biotopes with conservation values to the whole park area, which would provide a possibility for further landscape ecological investigations and spatial analyses. MATERIALS AND METHODS Data used for the classification process includes ASTER image, field data collected on the terrain and different additional spatial data for comparison and correction like CORINE land cover data, topographical, vegetation maps etc. The ASTER TERRA VNIR scene used in this study was acquired on May The ASTER TERRA VNIR sensor has 15 meter spatial resolution and it is able to measure reflection from the ground surface ranging from the visible to near infrared VNIR (wavelengths ). The swath width ASTER TERRA is 60 km and a 16-day repeat interval (Abrams 2000). ASTER TERRA has also a 15 m resolution NIR along-track stereo-band looking 27.6 backwards from

4 nadir. The stereo band (3B) covers the same spectral range of µm as the nadir band (Kääb 2004). From ASTER stereo data it is possible to generate Digital Elevation Model (Welch et al. 1998). The field data was collected on 12-13th September Field data consists on field training sites for the classification and rectification points for the image georectification. Field training sites were selected randomly to represent the main vegetation types of Sinite Kamani Nature Park. From sites main tree species, field layer vegetation and mineral ground coverage were inventoried and locations were marked with GPS. Totally 23 field sites were inventoried. For the georectification of the satellite image nine clearly visible crossing from different parts of ASTER TERRA image were marked with GPS (see fig.1). Image interpretation was done using ERDAS imagine 8.7 software. According to the field data and interpretations the ASTER TERRA image was first classified into 10 main landuse classes and 3 subclasses. The classification scheme used was supervised maximum likelihood classifier. Due to the misclassifications class number was diminished to 8 main vegetation classes and 3 subclasses. Image classification was imported to grid format in ArcGIS software. Fragmentary of the classification map was reduced by using smooth 2x2 gridmajority filter. RESULTS Totally Sinite Kamani Natural Park area is 127,2 km 2 (Table 1). The classification of the ASTER TERRA VNIR image was satisfactory and the main vegetation classes were able to differentiate from each other relative accurately. The classification accuracy was estimated according to the field points and visual interpretation and comparison of classification image and ASTER TERRA VNIR false color image. Especially the Pine forests were classified with high accuracy. Most difficulties were in differentiating deciduous forest types from each other. Two meadow types Dry an Medow with bushes also caused some difficulties (Figure 2). Sinite Kamani s mountain terrain caused some systematic misclassifications. The spectral reflectance of the same vegetation types varied in the Northern and North-West slopes in compared to south and south east slopes because of the shadow and different illumination angle. These errors could have been corrected by using Digital Elevation Model in classification process. The classification is quite general with eight main classes. This was a result of small field dataset available (23 training sites). It can be argued that this classification was produced with minimum field dataset possible and due to that result is just satisfactory. Further field work and more advanced image processing is required to achieve more accurate and detailed classification result. Table 1. Classification results after 2x2 gridmajority filtering with pixels, hectares and km 2. Vegetation type Pixels Ha. km 2 Fagus forest ,7 Flowering ash forests and bush ,7 Dry meadow ,9 Quercus-Caprinus forest ,5 Bares sand and grave ,7 Pine forest ,0 Water ,0 Meadow with bushes ,7 Total ,2 Description of the classes used in the classification: 1. Beech dominated forest type. This type includes beech forests (Fagus sylvatica L. ssp. Moesiaca (K. Maly) Hjelmquist.), of which most of them are mono-dominant, less distributed is beech-hornbeam forest. They occupy predominantly wet habitats in higher altitude (above 800m), usually there are no grass coverage or it is less developed. 2. Flowering ash and oak dominated forests and bush. This type includes forests and bushes of Fraxinus ornus L, Carpinus orientalis and Quercus pubescens. They occupy predominantly dry habitats with south exposure. 3. Dry meadow type. This type is located on the highest parts of the park. Very dry habitats with grasses and shrubs. Typically it includes small patches of mineral land (gravel and sand). 4. Quercus-Carpinus dominated forest type. It includes oak forests (Quercus dalechampii L., Q. frainetto Ten., Q. cerris L.) and hornbeam forests (Carpinus betulus). The oak forests are predominantly mixed between the 3 kinds, the most spread are Quercus dalechampii-q. frainetto forests. They occupy less humid habitats compared with beech forests and

5 have well developed grass coverage. More hornbeam forests are clear while the others are mixed with oak and beach. The ecological conditions of their habitats are somewhere in the middle between the beech an oak forests. 5. Bare sand and gravel class. This type is both natural and anthropogenic origin. Natural forms: steep gravel/erosion slopes and parts of dry meadow with large patches of sand and gravel. Anthropogenic origins are sand and gravel quarries, road network, large gravel and sand surfaced yards. 6. Pine dominated forest type. Artificial coniferous forests where Pine (Pinus silvestris L.) percentage is most cases close to Water class. Inside Sinite Kamani Natural Park borders there is only one pond of water. 8. Meadow with bushes type. This type is less dry than Dry meadow type. Vegetation consists of grasses, small shrubs and forbs. Also some larger bushes are growing here. This type is getting more vegetated because there is no grazing pressure since 1970 s. CONCLUSIONS Figure 2. Classification of the ASTER TERRA VNIR image of the Sinite Kamani Nature Park. Landuse classification of the Sinite Kamani Nature Park using ASTER TERRA VNIR image was satisfactory. Further field work and image processing is required to be able to produce more detailed classification of the area. For example ASTER TERRA VNIR image dataset provides one band that allows stereographic viewing and the generation of the Digital Elevation Model. Especial in the mountain terrain like in Sinite Kamani the use of DEM in the classification process is recommended. One Aster TERRA image covers 60 * 60 km which is for example 1/9th of the Landsat TM image 185x185 km coverage. When one ASTER TERRA VNIR image cost about 80 dollars ( to cover one Landsat TM scene nine ASTER images are needed, then the cost is 720 dollars. The price of one Landsat TM is 425 dollars ( To use ASTER TERRA VNIR images in larger scale the benefit of the low price reduces. ASTER TERRA VNIR images are excellent data source for smaller study areas (1-5 images to cover study area). Then the spatial resolution (15 meter) and the price are cost efficiency.

6 We recommend ASTER TERRA VNIR images to be used in Land use and vegetation studies in Bulgarian parks. With effective field work and using additional data sources in image interpretation necessary spatial information of parks and other land use forms could be obtained for the management and planning purposes. ASTER TERRA VNIR images could also be used in verifying the accuracy of the CORINE land use classification. CORINE is derived from Landsat TM (30 meter resolution) images and ASTER TERRA VNIR has 15 meter resolution. It would be interesting to compare classification from these data sources. Comparison allows the examination of the questions like how much more detailed resolution (15 to 30 meter) enhances the classification details and what is the maximum number of classes (with 15 and 30 meter resolution) and does 15 meter resolution data allow more detailed vegetation and land use pattern study. ACKNOWLEDGMENTS This work was carried out within the project Environmental assessment in protected areas based on implementation of Remote Sensing and GIS funded by Bulgarian Academy of Sciences and Finnish Academy of Sciences. We would also like to thank of practical help and hospitality Director Ivan Ivanov from the Sinite Kamani National Park. REFERENCES Abrams, M., ASTER: Data products for the high spatial resolution imager on NASA's Terra platform. International Journal of Remote Sensing 21, pp Andreev, N. (1981) Botanical characteristic of NP Sinite Kamani In: Park-management plan of NP Sinite Kamani, Sofia, Agrolesproekt (in Bulgarian). Kumpula, T., B. Burkhard, F. Muller. (2005) Can you see it all in the image? Using remote sensing techniques for landscape assessment in northern Fennoscandia. EcoSys. Bd. 11. Pp Kääb, A. (2005). Combination of SRTM3 and repeat ASTER data for deriving alpine glacier flow velocities in the Bhutan Himalaya. Remote Sensing of Environment, Volume 94, Issue 4, 28 p Stoeva, M. et all (2004) Biological diversity of Natural Park Sinite Kamani, Stara Zagora (in Bulgarian). Welch, R., Jordan, T., Lang, H. and Murakami, H., (1998). ASTER as a source for topographic data in the late 1990's. I.E.E.E. Transactions on Geoscience and Remote Sensing 36, pp www-sites: Biography of authors and photos: MSc Timo Kumpula is a Senior Assistant Professor (Geoinformatics). Research fields: Remote sensing, Reindeer pasture research in northern Russia and Finland. He has studied reindeer husbandry and pastures in Northern Fennoscandinavia. In his research he has used satellite images, air photos, and GIS datasets. He has also studied. High altitude pastures of the eastern Tibetan plateau. There Tibetan nomads yak pastures were studied by using Landsat TM data. At moment he is part of the multidisplinary project that studies. Environmental and social impacts of the industrialization in the Russian arctic tundra. There oil and gas exploration face up with traditional livelihoods like Nenets reindeer herding. Research Assoc. PhD Stoyan Nedkov Education: 1997 Master, Sofia University - Geography, Landscape Ecology and Environmental Protection. Graduate work: Landscape-geophysical investigation of Pontic and Subpontic landscape in Strandja.

7 2002 г. PhD, Institute of Geography, Bulgarian Academy of Sciences. Dissertation: Structure and Dynamics of Low Mountain Landscapes in Central part of Western Bulgaria Main fields of scientific research Landscape ecology, ecological modeling, environmental protection, GIS, remote sensing Participation in scientific project Environmental assessment in protected areas based on implementation of Remote Sensing and GIS joint Bulgarian-Finnish project. NATO CCMS Pilot Study Use of Landscape Science for Environmental Assessment. Investigation of the protected areas from the category Natural Park in Bulgaria and Romania in connection with European integration - joint Bulgarian-Rumanian project. Development of methodological basis of landscape ecology planning with use of geoinformation technologies. Assoc. Professor Mariyana Kostadinova Nikolova is graduated in geomorphology and cartography from Sofia University St. Kliment Ohridski, Department of Geology and Geography and has a Ph.D. degree in Climatology and Agroclimatology from Institute of Geography, Bulgarian Academy of Sciences (1999). Dr. Nikolova is associate professor in the Institute of Geography, Bulgarian Academy of Sciences and visiting professor of physical geography in Shoumen University Bishop Kostantin Preslavski. Research field: physical geography, climatology, natural hazards, environmental risk assessment, environmental quality and ecological aspects of the climate change. Member of the Scientific Coordination Center for Global Change at Bulgarian Academy of Sciences and of the Experts Council on Assessment and Management of Climatic, Meteorological and Hydrological Hazards of the Permanent Commission for Protection of the Population against Disasters, Accidents and Catastrophes, affiliated to the Council of Ministers of Bulgaria.