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Mapping land-use/land-cover change in the Olomouc region, Czech Republic.

INTRODUCTION

The Czech Republic currently is undergoing transformation from the centralized regime of a communist dictatorship towards a modern democratic state. Fanta et al. (2005) recognizes three main events in the last half century that had profound consequences for the country and its land use. First, the communist coup d'etat and the following collectivization of land in the 1950s that introduced large-scale collective farming, especially intense in the Olomouc region, which aimed at the maximum production of agricultural commodities. Second, the abolition of the totalitarian political system in 1989, which was followed by the restitution of private land ownership in the 1990s, the reintroduction of democracy and a market economy, and the development of market-driven forms of land use. Third, the preparation of the Czech Republic for ingression into the European Union in 2004, including its complete association with the EU environmental and agricultural policies, and its search for appropriate methods and forms of land use.

This research pays closer attention to specific trends in land-use changes within the past 25 years: changes in agricultural areas, forest areas, and residential development. These particular trends can be described as followed.

Agricultural areas. Political transition in the Czech Republic lead to marginalization of intensive agricultural areas, i.e., a process driven by a combination of socioeconomic and environmental factors caused by farming that ceased to be viable at many places, resulting in frequent abandonment of the agricultural land (Fanta et al. 2004). Extensive areas of previously cultivated land in the country now are laying fallow or were converted to secondary grasslands--meadows and pastures.

Forested areas. Since the time of their minimum extent at the end of the 18th century, forested areas have been increasing, reaching the present 33 percent of the total vegetation cover in the country (UHUL 2006). Most of the forest is far from its natural composition, for it was converted to monocultures of Norway spruce (Picea abies), serving predominantly a productive function. However, since the boom of environmental consciousness in the 1990s, a distinctive tendency has grown towards alternative approaches in forest management considering the natural species composition and potential vegetation (Neuhauslova 1998).

Residential development. As in other parts of Europe, the issue of suburbanization was well identified in the Czech Republic in the 1990s (Ptacek 1998; Jackson 2002). However, it is represented by a relatively small extent of residential development in vicinities of larger cities, and does not bear the typical traits and negative effects of the American-type large-scale suburban sprawl as described by Vaclavik (2004).

The main objective of this study is to analyze relevant remote-sensing data from 1976 and 2001 and to identify the locations, types, and trends of the main land-use and land-cover changes in the past 25 years. Although the issue of land change is examined based on the background of political transformation of the country, this article does not explicitly address the effect of political transitions on land-cover change. However, it was assumed that the land cover will reflect some changes in the human perception of landscape and natural resources, such as the decreased need for intensive agriculture, the shift to an environmentally friendly management of forested areas, or the increased development and suburbanization. The hypothesis is that the later satellite image of the Olomouc region study site will exhibit a smaller total area of intensive agriculture and more meadows and pastures, fewer coniferous forests, and more mixed or deciduous tree cover, as well as an overall higher residential development.

METHODS

Study Site

The study area chosen for this project is the Olomouc region in the eastern Moravian part of the Czech Republic (see Figure 1). The study area of 5,012 [km.sup.2] covers most of the Olomouc County administration unit, one of the 14 administration units in the CR, but the northeastern part overlaps to Moravskoslezsky County. The central part is formed by the wide alluvial plane of the upper stream of the Morava River, surrounded by the undulated hills of the Zabrezska and Drahanska uplands from the west and the Nizky Jesenik mountain range from the northeast, while the elevation ranges from 200 to 800 m a.s.l. The lowland areas are highly urbanized, and include the major cities of Olomouc, Prerov, Zabreh, Sumperk, and others. Because of favorable climate and fertile soils, lowlands historically and currently represent the substantive agricultural areas in the Czech Republic. Despite its intensive development, the core of the Olomouc region consists of the Litovelske Pomoravi Protected Landscape Area. This exceptional piece of natural landscape is formed by the naturally meandering Morava River and its several permanent and periodical branches with wetlands, meadows, and unique complexes of floodplain forests, some of the few remnants in central Europe. The other major forested habitats in the Olomouc region are located in the northeastern upland areas, and are predominantly composed of coniferous and mixed stands, which are used for timber production.

Data Collection

Because the study area is located in central Europe, the images from the high-resolution SPOT earth observation satellite would be the appropriate data source for the intended study. However, the SPOT data for the study site was not freely available when it was needed. Therefore, the Landsat Multispectral Scanner (MMS) and Enhanced Thematic Mapper (ETM) scenes were acquired for change detection analysis (see Table 1). The MSS data included one scene (path 204, row 25) from May 8, 1976; the ETM+ data included two scenes (path 190, row 25, and path 190, row 26) from May 24, 2001. Described data sets were downloaded from the Global Land Cover Facility (GLCF) (http://glcf.umiacs.umd. edu/data/) through the Web interface and imported to IDRISI geographic information system software using the GEOTIFF/ TIFF conversion module.

[FIGURE 1 OMITTED]

Ancillary sets of data were collected to support the land-change analysis. Two sets of scanned and georeferenced black-and-white aerial photographs from 1970s and 1990 and a set of color orthophotographs from 2002 were obtained from the Litovelske Pomoravi Protected Landscape Area Administration to serve as reference ground-truth data during the map classification process. Vector data of the Czech Republic boundary and the Litovelske Pomoravi PLA area were obtained from the Czech Environmental Information Agency (CENIA) ArcIMS server (http://geoportal.cenia.cz).

Image Processing and Classification

Acquired data sets were processed and examined in the Clark lab's GIS software IDRISI 15.0, Andes edition. Figure 2 shows the steps of image processing and classification needed to achieve defined study objectives. After the satellite data were downloaded from the Global Land Cover Facility and imported to IDRISI, it was assessed for image quality. While both ETM+ images did not exhibit any significant radiometric noise in the entire scene, the MSS image contained a fair amount of haziness in the northeastern portion of the scene and subtle striping throughout the entire area. As there were no meteorological data available for the time of MSS image acquisition, an absolute atmospheric correction could not be performed. Instead, the Principal Component Analysis (PCA) was run, using standardized variance/covariance matrix and all four MSS bands as inputs. PCA created four principal component images in which the first two explained more than 98 percent of the total variance and the remaining two components contained most of the noise. The original four MSS bands were restored through an inverse PCA technique using the first two components.

[FIGURE 2 OMITTED]

The study area of the Olomouc region is located in the overlap of the ETM images 036-343 and 036-344 from 2001. A composite of the two overlapping images was created using a mosaic technique by spatially orienting them and optionally balancing the numeric characteristics of the image set based on the overlapping areas. The average mosaic method was applied to average the base image values with the adjusted overlap image values. In addition, the WINDOW module, extracting subimages from the set of original images, was utilized to isolate the desired extent of the study area.

The last step before actual image classification was to synchronize the spatial resolution of the images from both times. The original resolution of the MSS image was 57x57 meters. For the purpose of its comparison with the ETM image with resolution of 30x30 meters, the MSS image needed to be resampled. The resample module using parameters from the ETM image and map corners as ground control points was applied, producing a total root-mean-square error of 0.8 m, which is less than 0.5 pixels.

The MSS 1976 and ETM 2001 images were classified using the Maximum Likelihood supervised classification because most of the land-cover mapping projects have applied either supervised or unsupervised parametric classification algorithms to identify spectrally distinct groups of pixels (Smits et al. 1999). With supervised classification, the spectral signatures of the known land-cover categories are first developed, using digitized training sites. The software then uses a specific algorithm to assign all pixels in the image data set into defined land-cover classes (Jensen 2004). The Maximum Likelihood classification is based on the probability density function that is associated with a particular training site signature. All pixels are assigned to the most likely category based on an evaluation of the subsequent probability that the pixel belongs to the signature (class) with the highest probability of membership (Jensen 2004). Seven land-cover categories were recognized in the Olomouc region: water, deciduous forest, coniferous forest, mixed forest, developed (urban) areas, areas of (intensive) agriculture, and meadows (grassland). Training sites were digitized based on the personal knowledge of the study area and ground-truth data of aerial photographs and orthophotographs. Spectral signatures of individual land-cover classes were developed and assessed for their separability. Spectral values of urban areas and agricultural areas with bare soil were mixing, therefore their training sites had to be redefined, and also the texture analysis using Dominance index and kernel window of 5x5 pixels was conducted. Finally, the Maximum Likelihood classification was run with original bands and the texture image as inputs, producing two final land-cover maps of 1976 and 2001 that were compared.

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

An error matrix was constructed to estimate classification accuracy of produced land-cover maps. The error matrix provides a basis for characterizing types of errors by cross-tabulating the classified land-cover categories in sample locations against those observed in ground reference data (Smits et al. 1999, Foody 2002). A random sampling scheme was applied to define ground-truth locations (n = 100) and the aerial photographs from 1970s and 2002 were used to check for the "true" land-cover classes. The overall accuracy was calculated for both maps. This represents the probability that any point on the land-cover map is assigned exactly the same category by the classifier, as the category that is identified in the ground-truth sites (Wulder and Franklin 2003). In addition, the producer's and user's accuracies that measure omission and commission errors were estimated for individual land-cover classes.

A cross-classification procedure is one of the fundamental pairwise comparison techniques used to compare two images of qualitative data (Eastman 1995). It overlays two images and calculates all their possible combinations of classes. In the case where images represent the same land-cover categories at different times, persistence occurs where areas fall in the same land-cover categories, and change occurs where a new category is created (Eastman 1995). IDRISI Andes offers an efficient and easy-to-use tool for rapid assessment of land-cover change and its implications based on cross-clasification principles. The Land Change Modeler (LCM) for Ecological Sustainability allows a user to evaluate gains and losses in land-cover classes, land-cover persistence, and specific transitions between selected categories. Using the classified landcover maps from 1976 and 2001 as input parameters, this tool was applied to identify the locations and magnitude of the major land change, land persistence, and trends in transitions between land-cover categories in the study area.

[FIGURE 5 OMITTED]

[FIGURE 6 OMITTED]

RESULTS

Figures 3 and 4 represent the results of Maximum Likelihood classification: land-cover maps depicting the situation in 1976 and 2001. The change analysis tool provides efficient statistical assessment of changes in individual land-cover categories. Its results in Figures 5 and 6 demonstrate that there have been significant changes in all land-cover/land-use categories between 1976 and 2001 with the exception of water, where the subtle change can be caused by location errors in land-cover classification. Concerning the net change, which represents the earlier area of a category with added gains and subtracted losses, three land-cover categories experienced major transitions. The total area of meadows (grassland) increased by 942 [km.sup.2], while the area of intensive agriculture decreased by 592 [km.sup.2], as well as the area of coniferous forests, which decreased by 603 [km.sup.2] . The category of developed (urban) area also was affected by distinct change, with a net gain of 127 [km.sup.2].

[FIGURE 7 OMITTED]

A simplified cross-classification map (see Figure 7) represents persistence in land-cover categories, areas where no change occurred, and land-cover change, areas with any type of transitions between categories (depicted in black). However, the land-change and persistence map is difficult to visually interpret if the areas of individual land-cover classes are not clustered, and also the type of change is not represented in this map. Therefore, the contribution to net change, i.e., the transition between specific classes, was calculated to achieve the objectives of the study. Data in Figure 8 represent the contribution to net change for categories of meadows (grassland), developed (urban) area, and mixed forest. They reveal that agricultural areas explain about 63 percent of the total increase in meadows, new development occurred predominantly on former agricultural areas (more than 56 percent), and about 16 percent of previous coniferous forests currently is identified as mixed forest.

Analysis of the error matrices revealed that the proportion of agreement between land-cover categories in the classified map and the ground-truth data was 77 percent for the 1976 period and 81 percent for the 2001 period. The producer's accuracy was the lowest for the agriculture class in both 1976 and 2001 maps (65 percent and 70 percent), as some of the agricultural areas were classified as meadows or developed. The user's accuracy was the lowest for the mixed forest class (60 percent) in the 1976 map and for the developed class (65 percent) in the 2001 map. Some sites with mixed forest were falsely identified as the coniferous or deciduous class in the reference data. Some developed sites were falsely identified as agriculture or coniferous forest categories.

DISCUSSION AND CONCLUSIONS

This study applied remote-sensing techniques to classify satellite imagery of the Olomouc region, Czech Republic, from 1976 and 2001, from years before and after a major political change in the country, and compared the two resulting land-cover maps to identify the salient locations, types, and trends of the land-cover change in the past 25 years. The results support initial assumptions based on general knowledge of some of the land-use drivers in different times. There have been significant losses in categories of intensive agricultural areas and coniferous forest, and gains in meadows and developed areas. From the former agricultural areas, 23 percent became meadows and pastures, especially in the northeastern hilly part of the study site, and 3 percent was developed in the lowlands around the Litovelske Pomoravi Protected Landscape Area. About 16 percent from the previous coniferous forest in the eastern hilly part of the region currently was identified as mixed forest.

[FIGURE 8 OMITTED]

This study provides no empirical evidence of direct causality between discovered land-cover/land-use change in the study site and political and cultural transformation of the country; however, the location and trends of observed land change suggest there might be distinct correlation. Concerning the transition from a category of intensive agriculture to a category of meadows, the major trend was observed in the northeastern uplands of the study site. This observation is consistent with suggestions of Zemek et al. (2005) that the marginalization of agricultural areas occurs first at locations with unfavorable natural conditions, especially in uplands where the agricultural production was previously forced by an extensive use of fertilizers and pesticides. Concerning newly developed areas, the major trend occurred especially in the central lowland area of the study site around the Litovelske Pomoravi Protected Landscape Area. This observation is consistent with the general suburbanization process in central Europe where new residential areas tend to be developed in the form of "satellite" towns in the vicinity of existing cities and recreational areas (Ptacek 1998). Regarding transition from coniferous tree cover to mixed forest, this change was observed predominantly in the northeastern hilly part of the study site, where the elevation and associated environmental conditions favor potential vegetation of mixed and deciduous forest stands. This fact correlates with the general diversion in forest management in the past 15 years from clear-cut practices and spruce and pine plantations to the alternative use of native deciduous species of trees in the lower and middle altitudes of the country.

Classification of multispectral satellite data and comparison of land-cover maps are essential tools for assessing large-scale land-cover/ land-use changes. However, this research left considerable room for future improvement. Visual comparison of classified maps with training sites as well as accuracy statistics calculated from error matrices showed inaccuracies in the classification process. Spectral mixing was apparent between the classes of developed and agriculture areas where barren soil was present and the texture analysis did not eliminate all of it. Also, all Landsat scenes were acquired in the spring season, when certain types of crops, such as cereals, are in a phenological stage that exhibit similar spectral response as meadows and pastures. In addition, the MSS imagery from 1976 suffered from large amount of haziness and radiometric noise, which were not entirely removed by the principal component analysis and could distinctively affect image classification. An effort to collect better-quality remote-sensing data, such as the ones from the SPOT sensor, should be made to improve the overall accuracy of land-change assessment. Finally, the Maximum Likelihood classification results might have been improved if unequal prior probabilities of the land-cover classes had been assigned. Alternatively, decision-tree classification techniques that derive probabilities of land-cover classes from the distribution in the training data (Rogan et al. 2002) may be considered for future improvement of the analysis.

Acknowledgments

The author gratefully acknowledges Yelena Ogneva-Himmelberger and John Rogan, professors from Clark University, for supervising this work and the Fulbright program for enabling the author's training in the United States.

References

Eastman, J. R. 1995. Change and time series analysis. Worcester, MA: United Nations Institute for Training and Research/ Clark Labs for Cartographic Technology and Geographic Analysis, Clark University, 28.

Fanta, J., K. Prach, and F. Zemek. 2004. Status of marginalisation in Czech Republic: Agriculture and land use. EUROLAN, National Report--Czech Republic. Faculty of Biological Sciences, University of South Bohemia, and Institute of Landscape Ecology--Academy of Sciences of the Czech Republic, 29.

Fanta, J., F. Zemek, K. Prach, M. Herman, and E. Boucnikova. 2005. Strengthening the multifunctional use of European land: Coping with marginalization. EUROLAN, National Report--Czech Republic. Faculty of Biological Sciences, University of Ceske Budejovice, Institute of System Biology and Ecology, Academy of Sciences of the Czech Republic, 41.

Foody, G. M. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment 80: 185-201.

Jackson, J. 2002. Urban sprawl. Urbanismus a Uzemni rozvoj (Urbanism and urban development) 5, no. 6: 21-28.

Jensen, J. R. 2004. Introductory digital image processing. Upper Saddle River, NJ: Pearson Prentice Hall, 544.

Neuhauslova, Z., et al. 1998. Map of potential natural vegetation of the Czech Republic. Prague: Academia, 341.

Ptacek, P. 1998. Suburbanizace--menici se tvar zazemi velkomest (Suburbanization--the changing face of metropolitan hinterland). Geograficke rozhledy (Geographical views) 7, no. 5: 134-37.

Ptacek, P. 2000. Networking and local culture: Local community transformation after 1989 on the example of Olomouc, Czech Republic. Acta Universitatis Palackianae--Geographica 36: 59-64.

Rogan, J., J. Franklin, and D. A. Roberts. 2002. A comparison of methods for monitoring multitemporal vegetation change using thematic Mapper imagery. Remote Sensing of Environment 80, no. 1: 143-56.

Smits, P. C., S. G. Dellepiane, and R. A. Schowengerdt. 1999. Quality assessment of image classification algorithms for land-cover mapping: a review and proposal for a cost-based approach. International Journal of Remote Sensing 20, no. 8: 1461-86.

UHUL. 2006. Report on the state of forests and forestry in the Czech Republic 2006. Ustav pro hospodarskou Upravu lesa (Forest Management Institute), 128.

Vaclavik, T. 2004. The use of GIS in ecological planning. (A case study of Mount Desert Island). Master's thesis, Department of Ecology and Environmental Sciences, Palacky University, Olomouc, 82.

Wulder, M. A., and S. E. Franklin. 2003. Remote sensing of forest environments. Concepts and case studies. Norwell, MA: Kluwer Academic Publisher, 519.

Zemek, F., M. Herman, Z. Maskova, and J. Kvet. 2005. Multifunctional land use--a chance of resettling abandoned landscapes? (A case study of the Zhuri territory, the Czech Republic). Ecology 24, no. 1: 96-108.

Tomas Vaclavik was a Fulbright exchange student in the program of Geographic Information Sciences for Development and Environment at Clark University, Worcester, Massachusetts, in the academic year 2006-2007. He earned both his bachelor's and master's degrees in Ecology and Environmental Sciences at Palacky University in the Czech Republic. He recently has been working as a GIS analyst for the Agency for Nature Conservation and Landscape Protection of the Czech Republic. Currently, he is pursuing his Ph.D. in Geography at the University of North Carolina at Charlotte, focusing on applications of GIS in ecological research and working as a research assistant in the Center for Applied GIScience (CAGIS).

Correspondence Address:

Center for Applied Geographic Information Science (CAGIS)

Department of Geography and Earth Sciences

University of North Carolina at Charlotte

9201 University City Boulevard

Charlotte, NC 28223

Phone: (704) 687-5963

tova@email.cz

tvaclavi@uncc.edu

http://tova.euweb.cz
Table 1. Acquired satellite images

Scene Num-ber Path/ Acquisition
Scene Num-ber Row Date Sensor

044-131 204/25 1976-05-08 Landsat MSS
036-343 190/25 2001-05-24 Landsat ETM+
036-344 190/26 2001-05-24 Landsat ETM+

 Spatial
 Resolution
Scene Num-ber Format (m) Bands

044-131 GEOTIFF 57x57 1-4
036-343 GEOTIFF 30x30 1-5, 7
036-344 GEOTIFF 30x30 1-5, 7
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Date:Jan 1, 2008
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