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RESEARCH PAPER
Land use/cover disturbance due to tourism in
Jesenı
´ky Mountain, Czech Republic: A remote
sensing and GIS based approach
Mukesh Singh Boori
*
,Vı
´t Voz
ˇenı
´lek, Komal Choudhary
Palacky University Olomouc, 17. listopadu 50, 771 46 Olomouc, Czech Republic
Received 26 March 2014; revised 11 November 2014; accepted 1 December 2014
KEYWORDS
Remote sensing;
GIS;
Tourism;
Land cover classification
Abstract The Jesenı
´ky Mountains tourism in Czech Republic is unique for its floristic richness.
This is caused mainly by the altitude division and polymorphism of the landscape, climate and soil
structure. This study assesses the impacts of tourism on the land cover in the Jesenı
´ky Mountain
region by comparing multi-temporal Landsat imageries (1991, 2001 and 2013) to describe the rate
and extent of land-cover changes. This was achieved through spectral classification of different land
cover classes and by assessing the change in forest; settlements; pasture and agriculture in relation
to increasing distances (5, 10 and 15 km) from three tourism sites with the help of ArcGIS software.
The results indicate that the area was deforested (11.13%) from 1991 to 2001 than experienced for-
est regrowth (6.71%) from 2001 to 2013. In the first decade pasture and agriculture areas increased
and then in next decade decreased. 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 indicates 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.
Ó2014 Production and hosting by Elsevier B.V. on behalf of National Authority for Remote Sensing and
Space Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/
licenses/by-nc-nd/3.0/).
1. Introduction
The Olomouc region has a rich diversity of activities and
capable of pleasing the most demanding visitors. This is a
place for enthusiasts of historical and natural monuments,
winter sports, and bicycle tours. The Jesenı
´ky Mountains offer
a paradise full of natural treasure and hundreds of well-
marked routes for hikers and cyclists, along with countless
educational trails, caves, waterfalls and viewing towers. The
*Corresponding author at: Dept. of Geo-informatics, Section of
Earth Science, Palacky University, Olomouc, Czech Republic. Tel.:
+420 585634519 (O), +420 732287744 (M), +420 585639196 (R).
E-mail addresses: mukesh.boori@upol.cz (M.S. Boori), vit.vozenile-
k@upol.cz (V. Vozˇ enı
´lek).
Peer review under responsibility of National Authority for Remote
Sensing and Space Sciences.
The Egyptian Journal of Remote Sensing and Space Sciences (2014) xxx, xxx–xxx
HOSTED BY
National Authority for Remote Sensing and Space Sciences
The Egyptian Journal of Remote Sensing and Space
Sciences
www.elsevier.com/locate/ejrs
www.sciencedirect.com
http://dx.doi.org/10.1016/j.ejrs.2014.12.002
1110-9823 Ó2014 Production and hosting by Elsevier B.V. on behalf of National Authority for Remote Sensing and Space Sciences.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
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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
(Czech Tourist Authority, 2011). Its area is 5267 km
2
(January
1, 2006), 6.7% of the national territory, making it the 8th larg-
est 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 commu-
nities make up for 6.4% of all communities in the country
(Ministry of Regional Development, 2013). 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 5000 in the region (Czech Business
Authority, 2010) 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 was
‘rediscovered’ by Western Europeans curious to visit a country
formerly hidden behind the Iron Curtain. The tourism boom
brought US$ 4 billion per annum to the state budget (Czech
Tourist Authority, 2011) with almost no marketing and pro-
motion. Prior to the collapse of communism, the service sector
(and hence the tourism industry) in the Czech Republic was
weakly developed (Holland et al., 2003). The universal right
to work, common to all ex-communist countries, favoured
employment in heavy industries and/or collective agriculture.
But earlier neither, private ownership of enterprises nor
NGO activity was permitted it (Start, 2001). 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 unem-
ployment. Between 1980 and 2000, the contribution of second-
ary industries to the GDP fell from 63% to 43%, while the
contribution of tertiary industries increased from 30% to
53% (EBRD STAS, 2001).
Last five decades agriculture and forested landscapes have
been transformed by economic and social development
(Gaughan, 2006; Lambin and Geist, 2003; Walker, 2004;
Wright, 2005). These transformations are important compo-
nents of land cover disturbance and global environmental
change (Foley et al., 2005; Moran, 2005; Rindfuss et al.,
2004). The most rapid and significant include deforestation
as a consequence of urbanization, agricultural expansion, log-
ging and pastoral expansion (Boori and Ferraro, 2012; Lambin
and Geist, 2003). Von Thunen model (Mather, 1986) explained
the use of natural resources by the tourism industry. It explains
that resource extraction increases with decreasing urbanization
distance due to transportation cost (Chaplin and Brabyn,
2013). This evidence is outdated for developed world
(Sinclair, 1967) due to improved infrastructure.
Land cover disturbance and environmental impact of tour-
ism are particularly critical in mountain regions (Boori et al.,
2014). Mountain communities are typically less affluent than
their counterparts in lowland regions and poverty is still a fact
in many mountain areas (Godde et al., 2000; Messerli and Ives,
1997). Infrastructure development is hampered by difficult
access and harsh climate (Singh and Mishra, 2004). The draw-
ing 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 (Messerli and Ives, 1997). Moreover, policy imple-
mentation is undermined by political instability, which often
characterizes mountain areas due to their proximity to
national and international borders (Nepal and Chipeniuk,
2005). 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 characterized by low resiliency and therefore they are par-
ticularly susceptible to human interference such as soil and
vegetation trampling, disturbance to native wildlife and waste
dumping (Arrowsmith and Inbakaran, 2002;Buckley et al.,
2002). High altitude recreation sites are characterized by
extreme seasonality because accessibility and favorable cli-
matic 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. (Geneletti and
Dawa, 2009).
2. Objectives and study area
The main objectives of this research were to monitor the
impacts of tourism activity on land cover disturbance in the
area of forest, agriculture, pasture and settlements from 1991
to 2013 on Jesenı
´ky Mountains in the Olomouc region. Recent
studies related to land cover disturbance and recreational ecol-
ogy showed that mountain tourism had adverse effects on nat-
ural areas, protected areas and wetlands (Stevens, 2003;
Buntaine et al., 2006). The impact of tourism development
on forest resources and alpine vegetation, biodiversity has been
well documented (Boori et al., 2010; Stevens, 2003) as well as
its impact in terms of air pollution and noise (Shah et al.,
1997). Typical mountain recreation activities include trekking,
climbing expeditions, cultural tours, river rafting and bird gaz-
ing. 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 (Chatterjea, 2007;
Geneletti and Dawa, 2009). Trail use is one of the fastest grow-
ing recreational activities and it is causing widespread impacts
on ecosystems and landscape disturbance (Lynn and Brown,
2003). The Jesenı
´ky 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
Brunta
´l, Jesenı
´k and S
ˇumperk districts with the coordinate
of 49°450N, 17°150E(Fig. 1).
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. Landsat 5 TM, Landsat 7 ETN+ SLC-on and Landsat
8 OLI/TIRS 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 prepara-
tion, spatial analysis and mapping. Topographic maps served
as the base maps and were rectified (UTM WGS84) to the road
layer with a nearest-neighbor resampling (RMSE <0.5 pixels,
or <15 m). Image-to-image registration was performed on the
2 M.S. Boori et al.
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other images. After completing the registration, each image
was radiometrically calibrated to correct for sensor related,
illumination and atmospheric sources of variance (Green
et al., 2005). Chaplin and Brabyn (2013) used remote sensing
and GIS to investigate the impacts of tourism on forest cover
in the Annapurna conservation area Nepal.
The ancillary data used in this research include:
Photos and field notes recorded in 2013 during a trek
around the study area;
Google Earth images used as reference data during the clas-
sification and validation phases of the analysis;
GIS layers of the study area, which include roads, rivers,
ecological boundaries and land-cover maps 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 val-
idation of the classification accuracy for the most current
image date. A Trimble hand-held GPS with an accuracy of
10 m was used to map and collect the coordinates of important
land use features during pre- and post-classification field visits
to the study area in order to prepare land-use and land-cover
maps (Boori and Amaro, 2011).
3.3. Normalized Difference Vegetation Index (NDVI)
calculation and change detection
The Normalized Difference Vegetation Index (NDVI) is calcu-
lated 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 bio-
mass. NDVI was calculated for each image date and then used
to calculate standard normal deviates (Z-scores) to minimize
the influence of seasonal variation and inter-annual differences
(Jensen, 2005). The use of the standard normal deviates
reduces much of the potential effect of inter-annual climate
variation. This is necessary when using anniversary dates and
calibrated imagery, in a region influenced by heavy rainy
season precipitation amounts.
3.4. Image classification
In this research work, first we used unsupervized classification
and after field visit or identification of land cove classes, we
used supervized 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.
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
‘‘multi-ring buffer’’ tool to produce three concentric circles
placed 5 km apart around each of the tourism facilities. Then
proximity zone was overlaid on land cover change layer and
statistics for each tourism facility and proximity zone. This
was further analyzed 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 with the Olomouc, Rymarov and Jesenı
´ky
facilities for 1991, 2001 and 2013 (Fig. 2).
4. Results
4.1. Overall 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
Figure 1 Study area: Jesenı
´ky Mountain region Olomouc, Czech Republic.
Land use/cover disturbance due to tourism in Jesenı
´ky Mountain 3
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occurs predominantly in the more upland areas with greater
relief. Forest area decreases (222.53 km
2
) slightly during the
first half of the study period but then increases (35.78 km
2
)
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 km
2
. From 1991 to 2001, there has been a net
decrease of forest which is 11.13%. But in 2001–2013, 6.71%
forest area was added. Pasture and agriculture were added
4.44% and 5.17% respectively from 1991 to 2001 but both
areas reduced (7.08% and 3.23% respectively) from 2001 to
2013. These changes show governmental protection of forest
area in between 2001 and 2013. Table 1 shows that there is
no change in number of settlements from 1991 to 2001 but
for next decade settlements and water body area was increased.
Regarding the management, the analysis of vegetation
characteristics shows that in Jesnilk area 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
1991 2001
2013
0 6.5 13 19.5 263.25
Kilometers
land use/cover
Agriculture Cultivated
Agriculture Uncultivated
Pasture
Settlements
Forest
Water Body
Figure 2 Land cover change maps for 1991, 2001 and 2013.
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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 abun-
dance 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 change
Change trajectories between the years 1991, 2001 and 2013
were compared on a pixel-by-pixel basis to examine possible
land-cover disturbance (Table 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 km
2
forest and 3000 km
2
agriculture area was stable
in last two decades. 140 km
2
agriculture, 20 km
2
forest and
18 km
2
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.
However, it may not absolutely represent the real land
cover disturbance because of the difficulty of modeling the fac-
tors 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 devel-
opment of commercial agriculture and the national and inter-
national 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 pasto-
ralism, which reduces the cutting of leaf fodder during the cold
season.
4.3. Impact of tourism
Table 3 summarizes the changes in land cover extent by
proximity for all 3 tourism facilities. From 1991 to 2001 forest
Table 1 Land cover areas (km
2
) change for 1991, 2001 and 2013.
Class Area % Area % Area Diff. % Diff.
1991 2001
Water 209.85 10.49 243.77 12.19 33.92 1.7
Forest 804.02 40.2 581.49 29.07 222.53 11.13
Settlement 29.87 1.49 26.42 1.32 3.45 0.17
Pasture 213.03 10.65 301.75 15.09 88.72 4.44
Agriculture 743.23 37.16 846.57 42.33 103.34 5.17
Total 2000 100 2000 100
2001 2013
Water 243.77 12.19 298.85 14.94 55.08 2.75
Forest 581.49 29.07 715.61 35.78 134.12 6.71
Settlement 26.42 1.32 43.55 2.18 17.13 0.86
Pasture 301.75 15.09 160.09 8 141.66 7.08
Agriculture 846.57 42.33 781.9 39.09 64.67 3.23
Total 2000 100 2000 100
Table 2 Types of changes between 1999 and 2013 for areas analyzed.
Class Water Forest Settlement Pasture Agriculture Total
Cross table 1991–2001
Water 235.38 148.87 0.47 20.04 19.8 424.56
Forest 266.97 974.07 8.02 331.45 202.03 1782.53
Settlements 0.35 31.94 5.66 39.84 66.12 143.92
Pasture 1.53 77.09 2.12 135.31 259.78 475.84
Agriculture 12.38 72.37 168.32 333.33 3404.05 3990.44
Total 516.62 1304.33 184.58 859.97 3951.78 6817.29
Cross table 2001–2013
Water 318.72 161.83 4.6 15.68 19.68 520.51
Forest 179.63 988.09 20.39 50.45 57.76 1296.32
Settlements 0.12 12.49 11.79 5.54 156.41 186.35
Pasture 2.36 462.99 18.74 120.7 262.02 866.81
Agriculture 3.3 237.62 140.5 322.02 3239.15 3942.59
Total 504.12 1863.03 196.02 514.38 3735.02 6812.57
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´ky Mountain 5
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Table 3 Net land cover change from 0 to 15 km
2
area summary table.
Class 1991 2001 Area Diff. % Diff. 2013 Area Diff. % Diff.
Area % Area % Area %
Olomouc 0–5 km
Water 0.34 0.4 0.34 0.4 0.01 0 0.36 0.43 0.023 0.03
Forest 5.25 6.14 4.78 5.72 0.46 0.42 10.92 12.98 6.136 7.26
Settlements 4.35 5.1 8.50 10.17 4.15 5.07 7.02 8.35 1.478 1.48
Pasture 2.74 3.2 6.19 7.41 3.46 4.21 6.43 7.64 0.236 0.24
Agriculture 72.75 85.16 63.80 76.31 8.95 8.85 59.37 70.6 4.43 5.71
Total 85.43 100 83.61 100 84.10 100
5–10 km
Water 1.70 0.71 2.27 0.95 0.57 0.24 2.05 0.84 0.22 0.11
Forest 15.00 6.24 11.21 4.68 3.79 1.56 22.60 9.26 11.39 4.59
Settlements 6.82 2.84 10.57 4.41 3.75 1.57 8.65 3.55 1.92 0.86
Pasture 8.37 3.48 17.99 7.50 9.62 4.02 14.90 6.11 3.09 1.40
Agriculture 208.44 86.73 197.67 82.46 10.77 4.27 195.77 80.24 1.9 2.22
Total 240.33 100.00 239.71 100.00 243.97 100.00
10–15 km
Water 8.15 2.07 7.83 1.96 0.32 0.11 4.64 1.19 3.19 0.77
Forest 50.38 12.77 32.92 8.23 17.46 4.53 58.07 14.87 25.15 6.63
Settlements 11.37 2.88 17.66 4.42 6.29 1.54 12.17 3.12 5.49 1.30
Pasture 22.25 5.64 37.78 9.45 15.53 3.81 32.49 8.32 5.29 1.13
Agriculture 302.50 76.65 303.58 75.94 1.08 0.71 283.21 72.51 20.37 3.43
Total 394.65 100.00 399.77 100.00 390.58 100.00
Rymarov 0–5 km
Water 2.59 3.13 3.84 4.72 1.25 1.59 3.56 4.34 0.28 0.38
Forest 14.88 17.94 10.05 12.35 4.84 5.59 14.79 18.03 4.75 5.68
Settlements 0.98 1.18 2.92 3.58 1.94 2.40 1.98 2.42 0.93 1.16
Pasture 4.37 5.27 7.12 8.74 2.75 3.47 4.83 5.89 2.29 2.85
Agriculture 60.12 72.48 57.46 70.61 2.66 1.87 56.88 69.32 0.58 1.29
Total 82.94 100 81.38 100 82.05 100
5–10 km
Water 11.77 4.89 25.99 10.88 14.22 5.99 27.56 11.59 1.57 0.71
Forest 92.97 38.62 63.77 26.69 29.20 11.93 82.17 34.55 18.40 7.86
Settlements 3.31 1.37 3.02 1.26 0.29 0.11 4.67 1.96 1.65 0.70
Pasture 22.97 9.54 28.12 11.77 5.15 2.23 17.67 7.43 10.45 4.34
Agriculture 109.74 45.58 118.03 49.40 8.29 3.82 105.76 44.47 12.27 4.93
Total 240.76 100.00 238.93 100.00 237.83 100.00
10–15 km
Water 27.65 7.01 58.54 15.66 30.89 8.65 55.55 13.75 2.99 1.91
Forest 140.70 35.66 105.49 28.22 35.21 7.44 133.45 33.03 27.96 4.81
Settlements 5.33 1.35 6.93 1.85 1.60 0.50 7.88 1.95 0.95 0.10
Pasture 31.96 8.10 46.00 12.31 14.04 4.21 27.84 6.89 18.16 5.41
Agriculture 188.95 47.89 156.87 41.96 32.08 5.92 179.33 44.38 22.46 2.42
Total 394.59 100.00 373.83 100.00 404.05 100.00
Jesenik 0–5 km
Water 9.81 11.87 27.437 28.31 17.63 16.44 12.34 15.29 15.09 13.02
Forest 31.25 37.82 20.851 21.51 10.40 16.31 27.73 34.35 6.88 12.84
Settlements 1.51 1.83 1.555 1.6 0.04 0.23 2.48 3.08 0.93 1.48
Pasture 9.74 11.79 11.345 11.7 1.61 0.09 8.48 10.5 2.87 1.29
Agriculture 30.32 36.7 35.74 36.87 5.42 0.17 29.69 36.78 6.05 0.09
Total 82.63 100 96.93 100 80.73 100
5–10 km
Water 35.25 14.54 31.53 13.86 3.72 0.68 57.59 24.01 26.06 10.15
Forest 122.84 50.68 86.11 37.86 36.73 12.82 104.04 43.37 17.93 5.51
Settlements 1.42 0.59 1.20 0.53 0.22 0.06 3.71 1.55 2.51 1.02
Pasture 26.98 11.13 37.76 16.60 10.78 5.47 19.97 8.32 17.79 8.28
Agriculture 55.91 23.07 70.85 31.15 14.94 8.08 54.57 22.75 16.28 8.40
Total 242.40 100.00 227.45 100.00 239.88 100.00
(continued on next page)
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area was reduced in 0–5, 5–10 and 10–15 km
2
distance in all
three tourist sites. But it was increased from 2001 to 2013. In
Olomouc there was negligible forest area from 0 to 15 km
2
so the total area of forest removal was very less. In the village
of Rymarov, removal of forest area was more than double of
Olomouc. As Jesenilk has a very high dense forest area
removal of forest area was very high. In Jesnilk from 0 to 5,
removal of forest was 16.31%, 5–10 km was 12.82% and from
10 to 15 km removal of forest was 8.55% area from 1991 to
2001. It could be concluded that tourism villages do have an
impact on the forest (Table 3), however there was a consider-
able geographical variation also responsible for changes. In
Olomouc and Rymarov agriculture area was decrease but
pasture area was increased from 1991 to 2001 for all 0–
15 km
2
distance. Both areas were decreased from 2001 to
2013 for all 0–15 km
2
distance. For Jesenilk, pasture and agri-
culture both have similar behavior like Rymarov.
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 gen-
erally, the various canopy cover mapped using remote sensing
may show very different morphology. This means that the
changes in terms of area and percentage cover revealed by
remote sensing analysis may neglect at least locally, the quali-
tative disturbance of the vegetation.
Fig. 3 shows the proportional change in forest with increas-
ing distance from the three tourist sites. These graphs provide
trend lines, which show both positive and negative relation-
ships between land cover change (Forest, Agriculture, Settle-
ments, Water body, Pasture) and distance from village. A
positive trend shows that with less distance from the city/vil-
lages there was 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, and
agriculture were in negative direction and settlement, pasture
was in a positive direction for all three distances (0–15 km
2
).
In 2001–2013 forest was protected and increased in positive
direction. Other classes was stable or in negative direction.
In Rymarov forest and agriculture were in negative direction
but rest classes were grown in positive direction from 1991
to 2001. In next decay forest was grown in positive direction
but rest classes were stable or over all in negative direction.
Jesnilk result was also very much similar to Olomouc and
Rymarov (Fig. 3). This was showing forest protection from
2001 to 2013. Bruntal, Sumperk, Jesenı
´k, Rymarov, Zabreh,
Unicov, Litovel and Prostejov were 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 were 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 analyzed places. Olo-
mouc is located in the southern part of study area and is a rel-
atively 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 a greater distance from the settle-
ment. 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 pop-
ulation and tourist that would grow substantially in the next
5–10 years a portion of the land must be used for grain produc-
tion. Nevertheless, some of this land could be reused for for-
estry 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 (Geneletti and
Dawa, 2009). In any assessment the accuracy of the final
results is subject to 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 uncer-
tainty and should be treated with caution (Boori and Amaro,
2010a; Start, 2001).
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 (Rindfuss et al., 2004). The recom-
mended optimal allocation emphasized the ecological suitabil-
ity for exploitation of natural resources and encouraged mixed
farming with forestry, pasture and stockbreeding. Naturally
switching from farming to forest would reduce grain output.
However, improving farmland productivity through construc-
Table 3 (continued)
Class 1991 2001 Area Diff. % Diff. 2013 Area Diff. % Diff.
Area % Area % Area %
10–15 km
Water 40.97 11.55 51.49 14.64 10.52 3.09 78.76 22.14 27.27 7.50
Forest 157.95 44.54 126.54 35.98 31.41 8.55 143.81 40.43 17.27 4.45
Settlements 2.75 0.78 3.77 1.07 1.02 0.30 6.04 1.70 2.27 0.63
Pasture 36.69 10.35 51.78 14.72 15.09 4.38 23.48 6.60 28.30 8.12
Agriculture 116.29 32.79 118.09 33.58 1.80 0.79 103.62 29.13 14.47 4.45
Total 354.65 100.00 351.67 100.00 355.71 100.00
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tion of irrigation facilities as well as converting the existing
sloped farmland into terraced land to conserve soil and water
could compensate these decreases (Lambin and Geist, 2003).
Nevertheless, successful implementation of these recom-
mendations relies on other related measures (Bao et al.,
2005). Those farmers disadvantaged by the reallocation should
be compensated for their economic loss in the form of a gov-
ernment-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 ero-
sion. Finally, to reduce overpopulation, reallocation of some
of the rural population should be encouraged. With these mea-
sures the recommended reallocation could ensure sustainable
exploitation of land resources in the study area (Singh and
Mishra, 2004).
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 popula-
tion’s needs for livelihood (Chatterjea, 2007; Boori and
Amaro, 2010b). More specifically the failure of natural
resource management seems also to rely on the impossible
equation between growing population needs and the physically
Figure 3 Net changes in land cover area around individual tourism facilities.
8 M.S. Boori et al.
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limited production capacity of the natural environment (soils,
climate) leaving no place to intensification, except with sub-
stantial inputs from outside the system. Such a saturation of
traditional systems, triggered mainly by the population growth
was widely occurring in many places throughout the world
(Niamir, 1990; Semwal et al., 2004). The solution relies on a
deep transformation of the traditional system, typically chang-
ing 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 (Buckley et al., 2002). A comparable
environmental breakpoint was reached in the Czech in 19th
century with a very strong degradation of mountain areas trig-
gered by tourist and population growth and was overcome
during the 20th century with the transition from a self-suffi-
cient production to a wider opening to the national economy.
Most studies related to tourism impact addressed the socio-
economic aspects (Geneletti and Dawa, 2009). Very few studies
were carried out on the environmental consequences of tour-
ism development and their purpose was to describe the envi-
ronmental conditions and highlight critical issues rather than
to model and assess tourism impacts (Godde et al., 2000;
Messerli and Ives, 1997). The lack of environmental data that
affected the region when this research was initiated forced us
to invest a lot of resources into the construction of a suitable
geographical database (Boori and Amaro, 2010a,b). Hence,
tools as remote sensing imageries and GIS were largely
employed for the baseline study as well as the impact analysis.
6. Conclusions
This research provides the evidence of land cover change due
to tourism in Jesenik mountain region. Forest area decreases
closer to city and it increases 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 disturbances were due to only tourism but
there were some other factors such as agriculture expansions,
timber harvesting, wind and snow damage that could also be
responsible for land cover disturbance. It appears that due to
market demand forest harvesting, agriculture, pasture, water
body and settlement areas were increasing. Climate and eleva-
tion was also effect on their extensions. Population growth and
increasing of socio-economic activities were also responsible
for the land cover disturbance.
Acknowledgments
The authors gratefully acknowledge the support by the Oper-
ational Program Education for Competitiveness – European
Social Fund (Project CZ.1.07/2.3.00/30.0041 of the Ministry
of Education, Youth and Sports of the Czech Republic). The
authors extend sincere thanks to Chaplin and Brabyn for their
base line information and guidance.
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