Land cover disturbance due to tourism in Jeseniky mountain region: A remote sensing and GIS based approach
Mukesh Singh Boori*, Vit Vozenilek Palacky University Olomouc, 17. listopadu 50, 771 46 Olomouc, Czech Republic
Proc. of SPIE Vol. 9245 92450T-11
Earth Resources and Environmental Remote Sensing/GIS Applications V, edited by Ulrich Michel,
Karsten Schulz, Proc. of SPIE Vol. 9245, 92450T · © 2014 SPIE
CCC code: 0277-786X/14/$18 · doi: 10.1117/12.2065112
ABSTRACT
The Jeseníky Mountains tourism in Czech Republic is unique for its floristic richness, which is caused mainly by the altitude division and polymorphism of the landscape; climate and oil structure are other important factors. This study assesses the impacts of tourism on the land cover in the Jeseniky mountain region by comparing multi-temporal Landsat imagery (1991, 2001 and 2013) to describe the rate and extent of land-cover change throughout the Jeseniky mountain region. This was achieved through spectral classification of different land cover and by assessing the change in forest; settlements; pasture and agriculture in relation to increasing distances (5, 10 and 15 km) from three tourism site. The results indicate that the area was deforested (11.13%) from 1991 to 2001 than experienced forest regrowth (6.71%) from 2001 to 2013. In first decay pasture and agriculture areas was increase and then in next decay it was decrease. The influence of tourism facilities on land cover is also variable. Around each of the tourism site sampled there was a general trend of forest removal decreasing as the distance from each village increased, which indicates tourism does have a negative impact on forests. However, there was an opposite trend from 2001 to 2013 that indicate conservation area. The interplay among global (tourism, climate), regional (national policies, large-river management), and local (construction and agriculture, energy and water sources to support the tourism industry) factors drives a distinctive but complex pattern of land-use and land-cover disturbance.
Keywords: Remote sensing, GIS, Tourism, Land cover classification, Landsat ETM+/TM
1. INTRODUCTION
The Olomouc Region has a rich diversity of activities capable of pleasing even the most demanding visitors. This is a place for enthusiasts of historical and natural monuments, winter sports, and bicycle tours. The Jeseníky Mountains offer a paradise full of natural treasures and hundreds of well-marked routes for hikers and cyclists, along with countless educational trails, caves, waterfalls and viewing towers. The natural centre of the Olomouc region is the city of Olomouc with its distinguished monument, the Holy Trinity Column, which is inscribed on the UNESCO World Heritage List [1]. Its area is 5,267 km2 (January 1, 2006), 6.7 % of the national territory, making it the 8th largest region in the country. As of H1 2009 there are 642,080 inhabitants (6.1 % of the population of the Czech Republic, the 6th most populated region in the country). Its 397 communities make up for 6.4 % of all communities in the country [2]. Olomouc, the regional capital with a population of 100,168 is the 5th largest city in the Czech Republic. There are 13 towns and cities with populations exceeding 5,000 in the region [3] and most attractive place for tourism.
The early 1990s produced a boom in tourism for Czech Republic, as the country of architecture and rich culture were ‘rediscovered’ by Western Europeans curious to visit a country formerly hidden behind the Iron Curtain and the tourism boom brought US$ 4 billion per annum to the state budget [1] with almost no marketing and promotion. Prior to the collapse of communism, the service sector (and hence the tourism industry) in the Czech Republic was weakly developed [4]. The universal right to work, common to all ex-communist countries, favoured employment in heavy industries and/or collective agriculture. Neither, private ownership of enterprises nor NGO activity was permitted [5]. As in the rest of Eastern Europe, since the fall of the Iron Curtain in 1990 the economy underwent rapid transition, most notably the collapse of the primary sector and consequently rising unemployment. Between 1980 and 2000, the contribution of secondary industries to the GDP fell from 63% to 43%, while the contribution of tertiary industries increased from 30% to 53% [6]
Last five decades agriculture and forested landscapes have been transformed by economic and social development [7-10].These transformations are important components of land cover disturbance and global environmental change [11- 13]. The most rapid and significant include deforestation as a consequence of urbanization, agricultural expansion, logging, and pastoral expansion [14, 8]. A theoretical framework to explain the nature of resource use by the tourism industry is the Von Thunen model [15]. Von Thunen’s theory suggests that resource extraction decreases with increasing distance from settlements due to the costs of transport [16]. This premise has been outdated for industrialised parts of the world due to improved infrastructure [17].
Land cover disturbance and environmental impact of tourism is particularly critical in mountain regions [18]. Mountain communities are typically less affluent than their counterparts in lowland regions, and poverty is still a fact in many mountainous areas [19, 20]. Infrastructure development is hampered by difficult access and harsh climate [21]. The drawing of policies and plans is less effective in mountain areas, because historically these areas have been of marginal concern for decision-makers, and therefore neglected in development priorities [20]. Moreover, policy implementation is undermined by political instability, which often characterises mountain areas due to their proximity to national and international borders [22]. On top of these factors, there are peculiar conditions of mountain areas that make them more vulnerable, such as land cover disturbance, environmental fragility and tourism seasonality. High-altitude ecosystems are inherently fragile and characterised by low resiliency, and therefore they are particularly susceptible to human interference, such as soil and vegetation trampling, disturbance to native wildlife, and waste dumping [23, 24]. High altitude recreation sites are characterised by extreme seasonality, because accessibility and favourable climatic conditions are restricted to the short summer season. Consequently, human-induced disturbances on the land cover and environment are concentrated in this period that is also the peak season for several biological processes, such as mating, vegetation growth, migration, spawning, etc [25].
2. OBJECTIVES AND STUDY AREA
The main objectives of this research were to monitoring the impacts of tourism activity on land cover disturbance in the area of forest, agriculture, pasture and settlements from 1991 to 2013 on Jeseníky Mountains in Olomouc region. Recent studies related to land cover disturbance and recreational ecology showed that mountain tourism had adverse effects on natural areas, protected areas, and wetlands [26, 27]. The impact of tourism development on forest resources and alpine vegetation biodiversity has been well documented [27, 28], as well as its impact in terms of air pollution and noise [29]. Typical mountain recreation activities include trekking, climbing expeditions, cultural tours, river rafting and bird gazing. In particular, high-altitude mountain trekking experienced a significant rise in popularity over the last decade that has led to a steep increase in the number of trekkers [30, 25]. Trail use is one of the fastest growing recreational activities, and it is causing widespread impacts on ecosystems and landscape disturbance [31]. The Jeseníky PLA is spread out in the very northern part of Moravia and the Czech part of Silesia. The frontier is in between Moravia-Silesia and Olomouc regions in the Bruntál, Jeseník and Šumperk districts with the coordinate of 49°45´ N, 17°15´ E (Fig. 1).
Figure 1. Study area: Jeseniky mountain region, Olomouc.
3. METHODOLOGY
3.1 Data and pre-processing
NASA’s archive of Landsat images to the public without charge has created the opportunity for the cost-effective use of remote sensing for monitoring land cover anywhere on earth. One Landsat 5 TM and two Landsat 7 ETM+ images (WRS II Path 190, Row 25; 9 Oct. 1991, 14 April 2001, 24 September 2013) were used for this research. Which were selected for their clarity and being at least 10 years apart. ArcGIS 10.1 software was used for all image preparation, spatial analysis and mapping. Topographic maps served as the base maps and was rectified (UTM WGS84) to the roads layer with a nearest-neighbour resampling (RMSE< 0.5 pixels, or <15 m). Image-to-image registration was performed on the other images. After completing the registration, each image was radiometrically calibrated to correct for sensor related, illumination, and atmospheric sources of variance [32]. The ancillary data used in this research includes:
- Photos and field notes recorded in 2013 during a trek around the study area
- Google Earth images used as reference data during the classification and validation phases of the analysis
- GIS layers of the study area, which includes roads, rivers, ecology and boundaries, and a land-cover map obtained from the European Space Agency (ESA) and the United States Geological Survey.
3.2 Field data collection
Field work was conducted to determine ambiguous land-cover classification and to visit area of major change to determine causes of the changes with both observation and informal interviews of local people. This also provided a secondary validation of the classification accuracy for the most current image date. A Trimble hand-held GPS with an accuracy of 10 meters was used to map and collect the coordinates of important land use features during pre- and postclassification field visits to the study area in order to prepare land-use and land-cover maps.
3.3 Normalized Difference Vegetation Index (NDVI) calculation and change detection
The Normalized Difference Vegetation Index (NDVI) is calculated as (NIR - red) / (NIR + red), where red corresponds to Landsat TM band 3 and near-infrared to band 4. Continuous NDVI values range from -1 to +1. High values closer to +1 are associated with healthy green vegetation and standing biomass. NDVI was calculated for each image date and using these images we then calculated standard normal deviates (Z-scores) to minimize the influence of seasonal variation and inter-annual differences [33]. The use of the standard normal deviates reduces much of the potential effect of inter-annual climate variation, which is necessary even when using anniversary dates and calibrated imagery, in a region influenced so heavily by rainy season precipitation amounts.
3.4 Image classification
In this research work, first we go through unsupervised classification and after field visit or identification of land cove classes. We use supervised classification on the basis of training sites. Forest was defined as >30% tree canopy closure to separate the dense forest area from scrub and agriculture lands. Non forested land includes an aggregation of the other land covers water, pasture (which at this time of year includes agriculture, which presents as bare soil, within this cover), built, and scrub. The DEM was used to separate the high and low elevation area.
Figure 2. Land Cover change for 1991, 2001 and 2013
Three tourist sites (Olomouc, Rymarov and Jesenilk) were identified to access tourism effect, using the field notes as a guide and spatially located as a point GIS layer. A gradient of tourism proximity was generated using the ArcGIS “multiring buffer” tool to produce three concentric circles placed 5 km apart around each of the tourism facilities. Then proximity zone were overlaid on land cover change layer, and statistics for each tourism facility and proximity zone. This was further analysed to calculate the net percentage change in forest, agriculture, pasture, settlements and regression analysis was used to identify trends in change and tourism proximity. This analysis was applied for all three tourism facilities combined, the Olomouc, Rymarov and Jeseniky facilities for 1991, 2001 and 2013 (Fig. 2).
4. RESULTS
4.1 Over all changes
Agriculture and forested land makes up the largest percent of the study area with 35%, 40%, area in 1991 and vice versa in 2013 (Table 1). Forest makes up the largest land-cover, and occurs predominantly in the more upland areas with greater relief. Forest area decrease (222.53 Km2 ) slightly during the first half of the study period but then increase (35.78 Km2 ) during the second half of the study. Water makes up less than 15% of the upland landscape for all years of the study. Table 1 provides the areas of each class. The total area of the study area was 2000 km2 . From 1991 to 2001, there has been a net decrease of forest is 11.13 percent. But in 2001 to 2013, 6.71 percent forest area was added. Pasture and agriculture was added 4.44 and 5.17 percent respectively from 1991 to 2001 but both area reduce (7.08 and 3.23 respectively) from 2001 to 2013. These changes show governmental protection of forest area in between 2001 to 2013. Table 1 show that no change in number of settlements from 1991 to 2001 but for next decay settlements and water body area was increased.
Table 1. Land cover areas (km2 ) change for 1991, 2001 and 2013
Regarding the management, the analysis of vegetation characteristics shows that in Jesnilk areas, stands are in better condition, with bigger trees showing larger basal area and larger crowns, showing evidence of little exploitation. The low wood exploitation is also unfavorable to the activation of vegetative regeneration for holm oak stands, which may in the long term endanger its sustainability. Conversely, the coppice resource dominates, trees are degraded and the abundance of holm oak coppices emphasizes the intensity of wood exploitation. When tree cover is maintained, it is often due to bushy stands, resulting from the degradation of previous tree clusters. During field visit and key note interviews we find that, tourism and socioeconomic activities are responsible for these land cover disturbance.
4.2 Types of changes
Change trajectories between the years 1991, 2001, and 2013 were compared on a pixel-by-pixel basis to examine possible land-cover disturbance (Tables 2). Thirty three percent of the landscape remained in the same land-cover class from 1991, 2001 to 2013. Two-date changes (1991–2001 and 2001–2013) show 950 km2 forest and 3000 km2 agriculture area was stable in last two decades. 140 km2 agriculture, 20 km2 forest and 18 km2 pasture area encroached by settlements from 2001 to 2013. Stable forest cover mostly was located in high elevation areas of the mountain, especially in Jesenilk, Bruntal, Sumperk and Rymarov.
Table 2. Types of changes between 1999 and 2013 for areas analyzed.
Cross table 1991-2001
However, it may not absolutely represent the real land cover disturbance because of the difficulty of modelling the factors influencing this disturbance and the magnitude of human reaction capacity. On the other hand, the pressure exerted on forest depends on the socio-economic and tourist context and may change in the future, according to the disturbance that these societies are experiencing. Indeed, the rapid opening up of the study area due to tourism since the 1980s, the development of commercial agriculture and the national and international development initiatives— electrification in 2002, the introduction of the gas stove, the emergence of the cell phone in 2005, foreign aid offered by different NGOs—have widely contributed to accelerating the land disturbance of practices, as well as creating new production systems likely to partially reduce the pressure exerted on the forest and agriculture. One example of these tendencies is the slight decline of pastoralism, which reduces the cutting of leaf fodder during the cold season.
4.3 Impact of tourism
Table 3 summarises the changes in land cover extent by proximity for all 3 tourism facilities. From 1991 to 2001 forest area was reduce in 0 - 5, 5 - 10 and 10 to 15 km2 distance in all three tourist site. But it’s increase from 2001 to 2013. In Olomouc there is negligible forest area from 0 to 15 km2 so total area of forest removal is very less. In the village of Rymarov, removal of forest area is more than double of Olomouc. As Jesenilk is very high dense forest area so here removal of forest area was very high. In Jesnilk from 0 - 5, removal of forest is 16.31%, 5 - 10 km is 12.82% and from 10 to 15 km removal of forest is 8.55% area from 1991 to 2001. It could be concluded from this that tourism villages do have an impact on the forest; however, there is considerable geographical variation as shown in table 3. In Olomouc and Rymarov agriculture area was decrease but pasture area was increased from 1991 to 2001 for all 0 to 15 km2 distance. Both areas were decrease from 2001 to 2013 for all 0 to 15 km2 distance. For Jesenilk, pasture and agriculture both have similar behaviour like Rymarov.
Table 3: Net land cover change from 0 to 15 km2 area summary table.
The analysis of overall disturbance in Jesnilk area through remote sensing appears that many areas mapped as “stable” also experienced a strong exploitation of vegetation which may have led to qualitative land cover disturbance. More generally, the various canopy cover mapped using remote sensing may show very different morphology, which means that the changes in terms of area and percentage cover revealed by remote sensing analysis may neglect, at least locally, the qualitative disturbance of the vegetation.
Figure 3. Net changes in land cover area around individual tourism facilities.
Fig. 3 shows the proportional change in forest with increasing distance from the three tourist site. These graphs provide trend lines, which show both positive and negative relationships between land cover change (Forest, Agriculture, Settlements, Water body, Pasture) and distance from villages. A positive trend shows that with less distance from the city/villages there is more removal of forest, agriculture (relative to the forest, agriculture area available), which is what you would expect based on Von Thunen’s model of resource use (increasing resource use with decreasing distance to markets). In Olomouc from 1991 to 2001 water was stable, forest, agriculture was go in negative direction and settlement, pasture in positive direction for all three distance (0 – 15 km2 ). In 2001 to 2013 forest protected and increase in positive direction. Other classes was stable or in negative direction. In Rymarov forest and agriculture was go in negative direction but rest classes was grow in positive direction from 1991 to 2001. In next decay forest was grow in positive direction but rest classes was stable or over all in negative direction. Jesnilk results are also very much similar to Olomouc and Rymarov (fig. 3). This is showing forest protection from 2001 to 2013. Bruntal, Sumperk, Jeseník, Rymarov, Zabreh, Unicov, Litovel, and Prostejov are in an area of forest and pasture development and located at the northern part of the study area. Hranice, Opava, Krnov, Stemberk, Olomouc,Vitkov, Mohelnice and Prerov are in an area of agriculture oriented and located in south part of study area.
Fig. 3 also displays the separate trends in forest change in relation to distance for each of the three analysed places. Olomouc is located in the south part of study area and is a relatively large town with plenty of visitors and through traffic from trekkers, tourists, which explains the high level of forest removal. The trend line has a positive relationship indicating decreasing forest removal at greater distance from the settlement. Jesenilk also shows the same positive relationship and a high proportion of forest removal. Rymarov is in an area with little agriculture, suggesting that tourism and socioeconomic activities could be the main reason for forest harvesting. There has also been a road development in this area allowing tourists to reach Jesnilk much faster than in the past. The new road could also make it easier to export logs from this region.
5. DISCUSSION
At lower altitudes, a mixture of agriculture and forestry should be implemented. However, to meet the needs of the local population and tourist that would grow substantially in the next 5 to 10 years, a portion of the land must be used for grain production. Nevertheless, some of this land could be reused for forestry at some time in the future. The recommended reallocations were tested in a few experimental sites and more or less reflected the land use practice in reality. As in any assessment, though, accuracy of the final results was subject 1.0 the accuracy of the input data layers. Some data (e.g., land cover) had a definite boundary, whereas other variables (e.g., climate and socio-economic) had a vague boundary. Therefore, the final results involved some uncertainty and should be treated with caution.
The irrational way of land use such as conversion from woodland to farmland has led to land degradation. However, through reallocation of land that has been excessively exploited to a new use (commensurate with its potential, this problem could be remedied. The recommended optimal allocation emphasized the ecological suitability for exploitation of natural resources and encouraged mixed farming with forestry, pasture and stockbreeding [34]. Naturally, switching from farming to forests would reduce grain output. However, improving farmland productivity through construction of irrigation facilities as well as converting the existing sloped farmland into terraced land to conserve soil and water could compensate these decreases.
Nevertheless, successful implementation of these recommendations relies on other related measures [35]. Those farmers disadvantaged by the reallocation should be compensated for their economic loss in the form of a government-sponsored grant. In this way farmers’ livelihoods would not be negatively affected. Another means of achieving the reallocation was through cultivation of medicinal herbs. As a perennial vegetative cover these plants could prevent soil erosion. Finally, to reduce overpopulation, reallocation of some of the rural population should be encouraged. With these measures the recommended reallocation could ensure sustainable exploitation of land resources in the study area.
In this case study, our findings indicate that the rationality in forest use still remains unworkable due to the absence of alternatives that would reconcile the ecological resilience, the mitigation of the current degradation trends, and the population’s needs for livelihood. More specifically, the failure of natural resources management seems also to rely on the impossible equation between growing population needs and the physically limited production capacity of the natural environment (soils, climate) leaving no place to intensification, except with substantial inputs from outside the system. Such a saturation of traditional systems, triggered mainly by the population growth, is widely occurring in many places throughout the world [36, 37]. The solution relies on a deep transformation of the traditional system, typically changing from self-sufficiency to a higher level of connection with the external economy (people working in cities, multiplication of income sources). This explains why some forests close to urban areas may be in bad condition than forests located in remote traditional areas. A comparable environmental breakpoint was reached in the Czech in 19th century, with a very strong degradation of mountains areas triggered by tourist and population growth, and was overcome during the 20th century with the transition from a self-sufficient production to a wider opening to the national economy.
6. CONCLUSIONS
This research provides evidence that the impact of tourism on land cover in the Jesenik mountain tourist region. Forest area decrease closer to city and its increase after 10 km distance of the city. Tourism facilities have closer proximity and associated with a decrease in forest extent. However this research cannot say that all land cover disturbance are due to only tourism but there are some other factors such as agriculture expansions, timber harvesting, wind and snow damage could also responsible for land cover disturbance. It appears that due to market demand forest harvesting, agriculture, pasture, water body and settlement area is increasing. Climate and elevation is also effect on their extensions. Population growth and increasing of socio-economic activities are also responsible for the land cover disturbance.
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