*Article* **Tree Cover Loss in the Mediterranean Region—An Increasingly Serious Environmental Issue**

**Ana-Maria Ciobotaru <sup>1</sup> , Nilanchal Patel 2,† and Radu-Daniel Pintilii 3,4,5,\* ,†**


**Abstract:** The Mediterranean Region currently faces major environmental issues that require constant analysis and monitoring. This study presents a thorough approach based on the application of Landsat imagery from Global Forest Change during 2001–2019. Spatial distribution mapping was one of the objectives of the study. We approached the analysis of tree cover loss areas by analyzing the cumulative tree cover loss and Tree Cover Loss Rate. This indicator offers information about the trend of tree cover loss in each Mediterranean country. A total of 581 Mha of deforested area was mapped during the analyzed period. Analysis was further supplemented by some statistical operations (distributions shown via histograms, validation via Shapiro–Wilk normality test, and testing via one-sample *t*-test). Agricultural expansion, intense forest fires, illegal logging, overgrazing (especially in the northern part of Africa), and extensive livestock farming have influenced the Mediterranean forest ecosystem's stability. The continuation of these activities could cause extreme climatic events, severe degradation, and desertification.

**Keywords:** tree cover loss; environment; degradation; Mediterranean Region

### **1. Introduction**

The effects of tree cover loss and forest degradation are critical environmental problems [1]. The fragility of forest ecosystems is an aspect intensely present in research in recent years, whether tropical forests, mangrove forests, or temperate forests [2–5].

Forest ecosystems are a critical component of the world's biodiversity, characterized more by diversity and unicity than other ecosystems of the world [6,7]. Forests cover 31% of the global land area, 4.06 billion hectares are natural forests or plantations, and approximately 15% are compact forest areas [8].

FAO estimated that, in 2015, across all Mediterranean countries, there were 88 million hectares of forest [9]. These forests represent biodiversity hotspots with around 60% endemic plant species from 25,000 species, but they are also very fragile ecosystems depending on variations in environmental conditions [10–12].

Several drivers determine tree cover loss: (i) commodity-driven deforestation, (ii) urbanization and demographic changes, (iii) shifting agriculture due to small- or mediumscale agriculture, (iv) forestry activities that through forest harvesting affect the forest stability, and (v) wildfires that determine the temporary loss of forests [13]. Anthropic activities have significantly altered biodiversity, and conservation efforts are needed to protect the mountains and coastal areas as a network of protected areas [14–16]. The long period of

**Citation:** Ciobotaru, A.-M.; Patel, N.; Pintilii, R.-D. Tree Cover Loss in the Mediterranean Region—An Increasingly Serious Environmental Issue. *Forests* **2021**, *12*, 1341. https://doi.org/10.3390/f12101341

Academic Editor: John Innes

Received: 10 August 2021 Accepted: 28 September 2021 Published: 30 September 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

forest exploitation, significant socio-economic changes, rapid urbanization, and severe climatic events are important threats to the environmental stability of Mediterranean forests; a conservation strategy and sustainable forest management are thus required [17–19].

Human activities in forests also have a significant impact on the biodiversity of the Mediterranean Region. As a result, World Heritage Natural Sites have experienced a decrease in their degree of uniqueness and are subject to threats driven by climate change and increasing population in littoral areas [20–22].

Agricultural activities have influenced Europe's landscape through new crops and pastures, forest exploitation for fuel and wood processing, changes in soil properties, increasing temperatures in urban areas, and increased fire risk [23–27].

Additionally, natural hazards (storms, forest fires, strong winds) are strongly related to changes in land use and land cover, which fragment the landscape and result in environmental damage [28–31]. Large fires (>1000 ha) often claim human victims and cause greater burned forest areas and property damage [24].

Forest ecosystems are crucial in the fight against climate change, and reforestation can contribute to reducing the concentrations of greenhouse gases in the atmosphere [32–35].

For these major causes of tree cover loss, quantifying and documenting the extent of tree cover loss is a priority activity for environmental stability. The objectives of this study were (1) to explore tree cover loss rates in the Mediterranean Region; (2) to illustrate the spatial distribution of deforested areas; and (3) to show the evolution of the Tree Cover Loss Rate (*TCLR*) for the period 2001–2019 to illustrate the situation for this fragile environment.

The Mediterranean environment's stability is influenced by the tree cover loss in the context of actual global climate changes (increasing about 0.85 ◦C globally and 1.3 ◦C in the last century) [9]. That is why it is important to constantly monitor the evolution of tree cover loss, to observe the general trend. The tree cover loss is responsible for the present situation of flash floods and fires [36–38]. With the results presented in this paper, we aim to develop a better understanding of the impact of tree cover loss on the environment in the Mediterranean Region.

### **2. Materials and Methods**

### *2.1. Study Area*

As the study area, we focused on Mediterranean countries; these are characterized by certain patterns of the climate, such as dry and hot summers and moist and cool autumns and winters. Sometimes, extreme climatic events can influence the forests [39,40]. The natural vegetation of the Mediterranean region is related to the Mediterranean climate, but it is influenced by the presence of mountainous areas [17]. The Mediterranean vegetation is adapted to its environmental conditions with a deficit of precipitation during the warm season. It comprises predominately xerophilous vegetation, shrublands, broadleaf forests (60%, with species *Castanea sativa*, *Quercus suber,* and *Quercus ilex*)*,* and coniferous forests (*Pinus pinea*, *Cupressus sempervirens*, and *Castanea sativa)*, varying in proportion from Italy (76%) to Portugal (49%) [9,41,42]. The cork oak *(Quercus suber)* savannas in southwestern Europe and northwestern Africa have great conservation value and are characterized by shrub formations to grasslands with high biodiversity [43,44]. These countries are across three continents, most of them covering the European (13 countries), African (4 countries), and Asian (4 countries) territories (Figure 1).

### *2.2. Data Acquisition and Processing*

The analysis of tree cover loss in the Mediterranean Region began with the collection of satellite images with a spatial resolution of 30 m from the Global Forest Change (GFC) dataset, courtesy of the Department of Geographical Sciences, University of Maryland (UMD), GLAD Laboratories in partnership with Google, United States of America from the Global Forest Watch, for a period of 19 years (2001–2019) [45]. The most suitable data to monitor forest cover changes are satellite images that can provide, through post-analysis, information about land use and forest changes. These images were used to extract the tree

cover loss areas (annually from 2001 to 2019). The first step consisted of downloading the raster data. They were downloaded as individual, 21 (10 × 10 degree) granules, for the entire Mediterranean Region considering the geographical coordinates in Table 1. Using the function "extract by mask", the resulting merged raster image was cut according to the Mediterranean Region border (vector). *Forests* **2021**, *12*, x FOR PEER REVIEW 3 of 21

**Figure 1.** The geographical positions of countries in the Mediterranean Region. **Figure 1.** The geographical positions of countries in the Mediterranean Region. Forest Watch website uses a ≥30% canopy cover threshold as a default for all statistics, the

*2.2. Data Acquisition and Processing* The analysis of tree cover loss in the Mediterranean Region began with the collection of satellite images with a spatial resolution of 30 m from the Global Forest Change (GFC) dataset, courtesy of the Department of Geographical Sciences, University of Maryland (UMD), GLAD Laboratories in partnership with Google, United States of America from Then, the data were merged to produce a single raster image. The conversion into points of the raster image was performed using the function "raster to points" to extract the annual tree cover loss for each country (Figure 2). Each pixel had a different color representing a different year (e.g., for the period 2001–2019, 19 different colors were used for each year). same canopy level used by us in the present study. The processing of the data was performed by using descriptive statistics and was materialized in graphic and cartographic materials and boxplots, made using the ArcGIS (ESRI, Redlands, CA, USA.), Microsoft Excel, and R Software platforms.

The data are expressed in hectares, and for our analyses, the tree cover loss by year from 2001 to 2019 and the canopy cover levels from ≥10 to ≥75 (≥10, ≥15, ≥20, ≥25, ≥30, ≥50, ≥75) seemed to be remarkably interesting. The recommendation is to select the desired **Figure 2.** Flowchart including input data, pre-processing steps, statistical analysis, and output data to determine the tree cover loss in the Mediterranean Region. **Figure 2.** Flowchart including input data, pre-processing steps, statistical analysis, and output data to determine the tree cover loss in the Mediterranean Region.

percent canopy cover level and use it consistently throughout any analysis, but the Global

**Spatial Resolution Longitude Latitude Data Source**

To observe the distribution of data, histograms by country were made with all the tree cover loss. Then, some specific operations and tests were applied: the standard deviation for each country and one-sample t-test, also by country. To check the normality of

10°–20° N 0°–10° E

GFC

**Table 1.** Landsat-7 ETM+ geographical coordinates covering the study area (Data obtained from

 10°–20° N 20°–30° E 20°–30° N 0°–10° E 20°–30° N 10°–20° E 20°–30° N 0°–10° W 20°–30° N 20°–30° E 20°–30° N 10°–20° W 20°–30° N 30°–40° E 30°–40° N 0°–10° E 30°–40° N 10°–20° E 30°–40° N 0°–10° W 30°–40° N 20°–30° E 30°–40° N 30°–40° E 30°–40° N 40°–50° E 40°–50° N 0°–10° E 40°–50° N 10°–20° E 40°–50° N 0°–10° W 40°–50° N 20°–30° E 40°–50° N 30°–40° E 40°–50° N 40°–50° E 50°–60° N 0°–10° E

**No. Satellite Images and** 

LANDSAT 7 ETM+, 30 m

1

*2.3. Methodology*


**Table 1.** Landsat-7 ETM+ geographical coordinates covering the study area (Data obtained from the Global Forest Change platform [45]).

The data are expressed in hectares, and for our analyses, the tree cover loss by year from 2001 to 2019 and the canopy cover levels from ≥10 to ≥75 (≥10, ≥15, ≥20, ≥25, ≥30, ≥50, ≥75) seemed to be remarkably interesting. The recommendation is to select the desired percent canopy cover level and use it consistently throughout any analysis, but the Global Forest Watch website uses a ≥30% canopy cover threshold as a default for all statistics, the same canopy level used by us in the present study. The processing of the data was performed by using descriptive statistics and was materialized in graphic and cartographic materials and boxplots, made using the ArcGIS (ESRI, Redlands, CA, USA), Microsoft Excel, and R Software platforms.

### *2.3. Methodology*

To observe the distribution of data, histograms by country were made with all the tree cover loss. Then, some specific operations and tests were applied: the standard deviation for each country and one-sample *t*-test, also by country. To check the normality of the tree cover loss distribution, the Shapiro–Wilk statistical test was applied, due to the small amount of data (2001–2019). The data displayed in Table 3 indicate, for most of the countries analyzed, a normal distribution (*W* values are over 0.75). This hypothesis is also sustained by the *p*-values, which are above 0.05 in most cases. Among all the 30 countries, there were 9 that did not report any information about tree cover and tree cover loss areas, by year or by canopy cover level. That is why they were excluded from our analyses (Figure 2).

The *TCLR* was calculated to observe the trend in the tree cover loss evolution by year and for the entire period. The annual evolution was marked in blue, and the upward trend, for the entire period, was marked in red (meaning a negative aspect—a high tree cover loss rate) or green (meaning a positive aspect—a low tree cover loss rate).

It was calculated by using the following equation:

$$TCLR \text{ (annual)} = (\frac{TCL \, n - TCL \, n - 1}{TCL \, n - 1}) \times 100.$$

where *TCL* → tree cover loss; *n* → year.

$$T\text{CLR (centre period)} = \left(\frac{TCL\ n - TCL\ n - 19}{TCL\ n - 19}\right) \times 100$$

cover loss rate) or green (meaning a positive aspect—a low tree cover loss rate).

*TCLR* (annual) = (

the tree cover loss distribution, the Shapiro–Wilk statistical test was applied, due to the small amount of data (2001–2019). The data displayed in Table indicate, for most of the countries analyzed, a normal distribution (*W* values are over 0.75). This hypothesis is also sustained by the *p*-values, which are above 0.05 in most cases. Among all the 30 countries, there were 9 that did not report any information about tree cover and tree cover loss areas, by year or by canopy cover level. That is why they were excluded from our analyses (Fig-

The *TCLR* was calculated to observe the trend in the tree cover loss evolution by year and for the entire period. The annual evolution was marked in blue, and the upward trend, for the entire period, was marked in red (meaning a negative aspect—a high tree

− −1

−1 ) · 100

where *TCL* → tree cover loss; *n* → year. where *TCL* → tree cover loss; *n* → year.

*Forests* **2021**, *12*, x FOR PEER REVIEW 5 of 21

It was calculated by using the following equation:

#### **3. Results 3. Results**

ure 2).

*Tree Cover Loss in the Mediterranean Region Tree Cover Loss in the Mediterranean Region*

where *TCL* → tree cover loss; *n* → year.

In Figure 3, showing the tree cover loss by country in the 2001–2019 period, there are differences from one country to another; these are due, on the one hand, to their total areas, tree cover areas, and timber needs and, on the other hand, to other natural or anthropogenic factors such as wildfires, diseases, the expansion of urban areas, and so on. The biggest tree cover loss areas were registered in Spain (1,231,065 ha), France (1,142,699 ha), Portugal (1,027,175 ha), and Turkey (499,959 ha). On the opposite side, the countries with low tree cover loss included Palestine (18 ha), Malta (13 ha), and Jordan (7 ha). These countries have forest areas that are large but fragile to main threats: climate change, extreme temperature, human activities, urbanization, and the need for agricultural land. The smallest values were recorded in countries with small administrative territories. In Figure 3, showing the tree cover loss by country in the 2001–2019 period, there are differences from one country to another; these are due, on the one hand, to their total areas, tree cover areas, and timber needs and, on the other hand, to other natural or anthropogenic factors such as wildfires, diseases, the expansion of urban areas, and so on. The biggest tree cover loss areas were registered in Spain (1231,065 ha.), France (1142,699 ha), Portugal (1027,175 ha.), and Turkey (499,959 ha.). On the opposite side, the countries with low tree cover loss included Palestine (18 ha.), Malta (13 ha.), and Jordan (7 ha). These countries have forest areas that are large but fragile to main threats: climate change, extreme temperature, human activities, urbanization, and the need for agricultural land. The smallest values were recorded in countries with small administrative territories.

**Figure 3.** Tree cover loss by country (2001–2019). **Figure 3.** Tree cover loss by country (2001–2019).

To see the importance of the tree cover loss, it is particularly useful to analyze the distribution of the tree cover loss. By ordering all the countries alphabetically, we obtain the most relevant and non-discriminatory view of all the countries in the whole territory considering a canopy level of ≥30, the same level taken in all analyses [27] (Table 2).

To identify the shape of the tree cover loss data and see whether tree cover loss process changes occurred from one year to another, some descriptive statistic operations were applied. The first step was to determine the simple distribution of them by constructing a histogram for each country of the tree cover loss in the period 2001–2019. The histograms in Figure 4 show a very different distribution of the data for each country, and they can be grouped into only two main categories. Most of the countries (15 countries) have a skewedright distribution; among these, Albania, Algeria, Egypt, Greece, Portugal, and Tunisia can be mentioned as representatives. In the second category (symmetric/normal distribution), there are only six countries (Bulgaria, Lebanon, Morocco, Serbia, Spain, and Turkey).

The normality of the tree cover loss data distribution for each country was checked using the Shapiro–Wilk test in the present study. The data in Table 3 indicate a normal distribution for most of the countries (W values over 0.75), but not in some cases (Algeria, 0.48; Egypt, 0.56; Greece, 0.59; Jordan, 0.65; and Portugal, 0.71). This hypothesis is also sustained by the

*p*-values, which are above 0.05 in most cases (Lebanon, 0.1503; Morocco, 0.1335; and Serbia, 0.9262). The standard deviation exhibits significant variation, ranging from 0.60 (Jordan) and 0.91 (Palestine) to 35,264.54 (Portugal), 26,971.25 (France), and 13,047.56 (Italy).

One-sample *t*-tests were applied for the sample of 21 countries to test whether the mean tree cover loss in each country was different from a specific value; statistical significance was observed for all countries as indicated by the *t*, *df* and *p* values registered. In all cases, the degree of freedom was 18, which means that the power of the test is high. The obtained values of t varied from 2.689 (Jordan) to 20.211 (Turkey) and the probability level *p* in all cases is under 0.05 (from 0.00001 for Bosnia and Herzegovina, Bulgaria, Croatia, Italy, Lebanon, Morocco, Portugal, Spain, and Turkey to 0.01500 for Jordan) (Figure A1). The evolution of the *TCLR* for the 21 countries shows three situations: one where the tree cover loss increases (represented in red color—negative aspect), one where the surfaces decrease (green color—positive aspect), and one country with no evolution (0%, Jordan) (Figure 5). For most countries, the tree cover loss area as a percentage for the period 2001 to 2019 increased; the highest values (more than 100%) were registered in Italy (504.85%), Slovenia (461.95%), Syria (340.82%), Tunisia (148.05%), France (136.2%), and Spain (116.46%). Other countries encountered negative evolution rates for the same period; among them were Palestine and Malta (−100% for each), Egypt (−85.81%), and Albania (−71.20%).

**Table 2.** Tree cover loss areas in countries in the Mediterranean Region [Data obtained from the Global Forest Change platform and online documentation [45].


\* *NA*—countries with no tree cover loss data.

Turkey).

be grouped into only two main categories. Most of the countries (15 countries) have a skewed-right distribution; among these, Albania, Algeria, Egypt, Greece, Portugal, and Tunisia can be mentioned as representatives. In the second category (symmetric/normal distribution), there are only six countries (Bulgaria, Lebanon, Morocco, Serbia, Spain, and

**Figure 4.** Histograms of tree cover loss by country (2001–2019). **Figure 4.** Histograms of tree cover loss by country (2001–2019).

**Figure 5.** Evolution of the Tree Cover Loss Rate (2019 vs. 2001).

The tree cover loss in the different countries during the period of analysis (2001–2019)


**Table 3.** Shapiro–Wilk normality test for data validation.

Analyzing the situation by country, Albania—although it presented a generally decreasing tree cover loss percentage—in many years showed increasing values over 400% (2004 vs. 2003 and 2007 vs. 2006). High values were also found in 2012 vs. 2011 (over 300%) and 2016 vs. 2015 (over 200%). Algeria also exhibited two periods with high values (2011 vs. 2010, over 200%, and 2017 vs. 2016, up to 500%). For Bosnia and Herzegovina, in 2004 vs. 2003, the tree cover loss rate was up to 1300%. For Bulgaria, the values were low (only 200% in 2012 vs. 2011). In Croatia and Egypt, the highest values oscillated around 300% (2004 vs. 2003 for Croatia and 2012 vs. 2011 for Egypt). For Greece and Italy, the values were up to 600%, and the latter country also showed a considerable increase for the entire period. Jordan and Palestine registered negative evolution for the entire period, but for Palestine, in 2008 vs. 2007, the tree cover loss rate was 200%. Slovenia and Syria had the highest values, with 800% for Slovenia in 2014 vs. 2013 and 3400% for Syria in 2012 vs. 2011. Very different was the situation of Turkey: for this country, the value was up to 75% each year—a moderate rate (Figure A2).

The tree cover loss in the different countries during the period of analysis (2001–2019) is depicted in Figure 6. It can be observed that in the upper part of the Mediterranean region (characterized by large areas of tree cover and situated closer to the temperate climate area—temperate forests), in countries such as Portugal, France, and Spain, forest loss is predominant. On the other side, in countries close to the desert areas and where the tree cover is not so dense, their loss is moderate to low. This image exhibits a significant amount of correlation with the two previous figures, which present the forest loss in comparison to the tree cover extent in 2000 and 2010.

**Figure 6.** Spatial distribution of the tree cover loss areas in the Mediterranean Region (2001–2019). **Figure 6.** Spatial distribution of the tree cover loss areas in the Mediterranean Region (2001–2019).

#### **4. Discussion 4. Discussion**

This article describes a methodology for studying the tree cover in the Mediterranean Region, providing a consistent spatial distribution of tree cover loss changes at the national scale for 2001–2019. The methodology used in this study based on the analysis of tree cover loss data is shown to accurately monitor the tree cover loss. This article describes a methodology for studying the tree cover in the Mediterranean Region, providing a consistent spatial distribution of tree cover loss changes at the national scale for 2001–2019. The methodology used in this study based on the analysis of tree cover loss data is shown to accurately monitor the tree cover loss.

The vegetation is in strong correlation with the climate, characterized by a deficit of precipitation in the warm season and moist and cool winters [17]. In the last century, in the Mediterranean Region, the temperature has increased 1.3 The vegetation is in strong correlation with the climate, characterized by a deficit of precipitation in the warm season and moist and cool winters [17].

°C [9]. The stability of forests is affected by both climatic factors and socioeconomic pressure. Therefore, the forest ecosystems are influenced by intense wildfires [20,24], loss of biodiversity [2,12,41], land degradation and fragmentation [9,15,46], and traditional agricultural techniques [16,23,46]. However, the drivers that modify the distribution of forest areas vary from one country to another. Analysis of the forest loss data shows that the forests in the Mediterranean Region In the last century, in the Mediterranean Region, the temperature has increased 1.3 ◦C [9]. The stability of forests is affected by both climatic factors and socioeconomic pressure. Therefore, the forest ecosystems are influenced by intense wildfires [20,24], loss of biodiversity [2,12,41], land degradation and fragmentation [9,15,46], and traditional agricultural techniques [16,23,46]. However, the drivers that modify the distribution of forest areas vary from one country to another.

represent an ecosystem that is vulnerable to external threats and has experienced intense tree cover loss (a total of 581 Mha of tree cover loss area) [9,45]. In European countries, the major activity that affects forests is harvesting done to Analysis of the forest loss data shows that the forests in the Mediterranean Region represent an ecosystem that is vulnerable to external threats and has experienced intense tree cover loss (a total of 581 Mha of tree cover loss area) [9,45].

provide wood for industry [19,23,46]. On the other hand, in Africa, the dominant driver of tree cover loss is shifting agriculture, which determines temporary loss of forests, especially in Morocco, Algeria, and Tunisia [46,47]. Of the dominant threats to tree cover loss, including agricultural expansion, the underlying causes of tree cover loss are the global markets for cork oak, timber, and pulp [11,27,39,48]. In addition to this, another direct In European countries, the major activity that affects forests is harvesting done to provide wood for industry [19,23,46]. On the other hand, in Africa, the dominant driver of tree cover loss is shifting agriculture, which determines temporary loss of forests, especially in Morocco, Algeria, and Tunisia [46,47]. Of the dominant threats to tree cover loss, including agricultural expansion, the underlying causes of tree cover loss are the global markets for cork oak, timber, and pulp [11,27,39,48]. In addition to this, another direct threat to tree cover loss includes intense forest fires [9,13,24,49], illegal logging [13,17],

and some traditional tools for creating grasslands for extensive livestock farming and overgrazing in states such as Algeria, Lebanon, Morocco, Tunisia, and Turkey [9,16]. The utility of this study for the Mediterranean Region is to encourage continuous monitoring of tree cover loss evolution, as has already been conducted in countries in

threat to tree cover loss includes intense forest fires [9,13,24,49], illegal logging [13,17], and some traditional tools for creating grasslands for extensive livestock farming and over-

grazing in states such as Algeria, Lebanon, Morocco, Tunisia, and Turkey [9,16].

*Forests* **2021**, *12*, x FOR PEER REVIEW 11 of 21

The utility of this study for the Mediterranean Region is to encourage continuous monitoring of tree cover loss evolution, as has already been conducted in countries in South America [50,51], Europe [52], and Africa [53,54]. South America [50,51], Europe [52], and Africa [53,54]. We evaluated the tree cover loss in the Mediterranean Region and the significant drivers that affect the forest area. Second, analysis of tree cover loss by canopy cover sig-

We evaluated the tree cover loss in the Mediterranean Region and the significant drivers that affect the forest area. Second, analysis of tree cover loss by canopy cover signifies that the highest loss areas are specific to the Mediterranean countries. nifies that the highest loss areas are specific to the Mediterranean countries. Third, the highest tree cover loss areas have specific environmental conditions (high temperatures, dry periods), severe climatic events, frequent forest fires, and changes in

Third, the highest tree cover loss areas have specific environmental conditions (high temperatures, dry periods), severe climatic events, frequent forest fires, and changes in land use among the principal causes of tree cover loss. Portugal and Spain recorded the highest rates of tree cover loss (1,231,065 ha in Spain and 1,027,175 in Portugal) [16,46]. land use among the principal causes of tree cover loss. Portugal and Spain recorded the highest rates of tree cover loss (1231065 ha. in Spain and 1027175 in Portugal) [16,46]. Some sustainable measures of specific vegetation for the Mediterranean cork oak savanna are suitable to reduce the effects of tree cover loss and degradation [43–45].

Some sustainable measures of specific vegetation for the Mediterranean cork oak savanna are suitable to reduce the effects of tree cover loss and degradation [43–45]. Fourth, tree cover loss is strongly correlated with environmental conditions, the intensity of human activities such as intense urbanization, and the need for agricultural

Fourth, tree cover loss is strongly correlated with environmental conditions, the intensity of human activities such as intense urbanization, and the need for agricultural land. land. The histograms show a normal distribution of data for most of the countries. This

The histograms show a normal distribution of data for most of the countries. This allowed us to conduct the analysis. Shapiro–Wilk normality tests and *t*-tests showed the suitability of the data for the analysis, with their significance considered to subscribe to the normal interval. allowed us to conduct the analysis. Shapiro–Wilk normality tests and t-tests showed the suitability of the data for the analysis, with their significance considered to subscribe to the normal interval. The scatterplot in Figure 7 shows the relation between country areas and tree cover

The scatterplot in Figure 7 shows the relation between country areas and tree cover loss; the dispersion of the points allows us to conclude that the disturbances in the tree cover loss are from natural causes. The small value of R<sup>2</sup> , at about only 0.0399, illustrates that there is no correlation to the analyzed data. loss; the dispersion of the points allows us to conclude that the disturbances in the tree cover loss are from natural causes. The small value of R2, at about only 0,0399, illustrates that there is no correlation to the analyzed data.

We infer that the tree cover loss is influenced by a complex category of drivers (intense fires, increased temperatures, agricultural activities, and illegal logging) that determine the fragmentation of forests and the fragility of the Mediterranean environment. We infer that the tree cover loss is influenced by a complex category of drivers (intense fires, increased temperatures, agricultural activities, and illegal logging) that determine the fragmentation of forests and the fragility of the Mediterranean environment.

**Figure 7.** Relation between country area and tree cover loss area. **Figure 7.** Relation between country area and tree cover loss area.

#### **5. Conclusions 5. Conclusions**

Tree cover loss in the Mediterranean Region is induced by both direct and indirect causes: local policies that need improvements for sustainable forest management; demographic changes and urbanization, which can cause degradation; and desertification, es-Tree cover loss in the Mediterranean Region is induced by both direct and indirect causes: local policies that need improvements for sustainable forest management; demographic changes and urbanization, which can cause degradation; and desertification, especially in the northern part of Africa.

pecially in the northern part of Africa. Analysis of tree cover loss changes using Landsat imagery in the Mediterranean Region represents the focus of much research over the years. The current study presents a tree cover loss mapping and evaluation of the *TCLR* for the period 2001–2019 in the context

of significant threats in southern Europe, North Africa, and southwest Asia, such as intense forest fires, overgrazing, development of urban areas, illegal logging, intense agriculture, and changes in land use and land cover. Improved policies regarding management and maintenance of land use are important for the constant monitoring of forest ecosystems in the Mediterranean Region. ecosystems in the Mediterranean Region. **Author Contributions:** Conceptualization, A.-M.C., N.P., R.-D.P.; methodology, R.-D.P.; software, R.-D.P.; validation, A.-M.C. and N.P.; formal analysis, A.-M.C., N.P., R.-D.P.; investigation, A.-M.C.; resources, A.-M.C. and R.-D.P.; data curation, N.P.; writing—original draft preparation, A.-M.C.,

Analysis of tree cover loss changes using Landsat imagery in the Mediterranean Region represents the focus of much research over the years. The current study presents a tree cover loss mapping and evaluation of the *TCLR* for the period 2001–2019 in the context of significant threats in southern Europe, North Africa, and southwest Asia, such as intense forest fires, overgrazing, development of urban areas, illegal logging, intense agriculture, and changes in land use and land cover. Improved policies regarding management and maintenance of land use are important for the constant monitoring of forest

**Author Contributions:** Conceptualization, A.-M.C., N.P., R.-D.P.; methodology, R.-D.P.; software, R.-D.P.; validation, A.-M.C. and N.P.; formal analysis, A.-M.C., N.P., R.-D.P.; investigation, A.-M.C.; resources, A.-M.C. and R.-D.P.; data curation, N.P.; writing—original draft preparation, A.-M.C., N.P., R.-D.P.; writing—review and editing, A.-M.C. and N.P.; visualization, R.-D.P.; supervision, A.-M.C.; project administration, A.-M.C.; funding acquisition, A.-M.C. and R.-D.P. All authors have read and agreed to the published version of the manuscript. All authors made equal contributions to the preparation of this scientific paper. N.P., R.-D.P.; writing—review and editing, A.-M.C. and N.P.; visualization, R.-D.P.; supervision, A.-M.C.; project administration, A.-M.C.; funding acquisition, A.-M.C. and R.-D.P. All authors have read and agreed to the published version of the manuscript. All authors made equal contributions to the preparation of this scientific paper. **Funding:** This research received no external funding. **Data Availability Statement:** We choose to exclude this statement because the study did not report

**Funding:** This research received no external funding. any data.

**Data Availability Statement:** We choose to exclude this statement because the study did not report any data. **Acknowledgments:** The authors want to thank the reviewers for their time spent peer reviewing the paper and for their constructive suggestions and recommendations.

**Conflicts of Interest:** The authors declare no conflict of interest. **Conflicts of Interest:** The authors declare no conflicts of interest.

*Forests* **2021**, *12*, x FOR PEER REVIEW 12 of 21

Bosnia and Herzegovina Bulgaria

**Figure A1.** *Cont*.

**Figure A1.** *Cont*.

**Figure A1.** *Cont*.

**Figure A1. Figure A1.** One One-sample -sample t *t*--test results on tree cover loss data by country. test results on tree cover loss data by country.

**Figure A2.** *Cont*.

Lebanon Malta

**Figure A2.** *Cont*.

Slovenia Spain

**Figure A2.** The annual evolution of the TCLR for Mediterranean countries (2001 – 2019): the blue bars present the annual evolution of TCLR; the green and red bars present the evolution trend of TCLR for **Figure A2.** The annual evolution of the TCLR for Mediterranean countries (2001–2019): the blue bars present the annual evolution of TCLR; the green and red bars present the evolution trend of TCLR for the entire period.

### the entire period. **References**

1376, https://doi.org/10.1007/978-981-16-1086-8\_1.


**Petr Hr ˚uza \*, Petr Pelikán and Lucie Olišarová**

Department of Landscape Management, Faculty of Forestry and Wood Technology, Mendel University in Brno, 613 00 Brno, Czech Republic; petr.pelikan@mendelu.cz (P.P.); lucie.olisarova@gmail.com (L.O.) **\*** Correspondence: petr.hruza@mendelu.cz; Tel.: +420-545-134-085

**Abstract:** Recently, cycling has become a popular recreation activity, and mountain biking provides an experience that is sought by an increasing number of people. Bike trails constructed for mountain bikers in access areas lead mostly through the forest and provide not only an extraordinary riding experience but the opportunity to admire the surrounding nature. The reason for constructing such trails from a landowner's point of view is to help keep bikers' movements within a defined access area and to ensure adjacent areas are left free for other forest functions. It also helps distribute groups of visitors with other interests to other parts of the forest. This is what we call "controlled recreation". In this example, it means that if cyclists come to the locality to use the bike trails, they should ride only along the designated trails; however, they may leave these trails and ride on the surrounding land. This article studied the movements of bikers in an accessible area of the Moravian Karst and the regulation of their movements by controlled recreation. Attendance in the area was measured using automatic counters. These were placed at the entry points to the accessible area and just behind the routes where the trails branch off. The results showed that bikers mostly stayed on the formal routes and that the trails were effective, i.e., there was no uncontrolled movement of bikers into the surrounding forest stands. We also noted the time of day that cyclists were active. These results can be used to better plan work in the forest, for example, harvesting and logging. To further the suitable development of accessible areas of the forest, we also compared the usual size of trail areas in two other European countries and the increasing width of bike trails due to the transverse slope of the terrain.

**Keywords:** forest recreation; ecosystem services; forest management; visitors monitoring; single-track bike trails

### **1. Introduction**

Mountain biking has become a popular leisure-time activity in many countries [1], in the countryside as well as in protected areas close to cities [2]. It encompasses many specialised disciplines including down-hill biking, tour and cross-country riding and competition styles such as free-riding and four-cross [3,4].

The increasing demands of holidaymakers are an integral part of modern life. As living standards increase, the demand for adrenaline-producing hobbies increases. Forest management is used not only to protect its use for timber production but also to promote the recreation functions of forests and the welfare of visitors [5]. Most outdoor recreational activities in forests related to paths and trails [6].

Monitoring visitor attendance provides basic information about the number of visitors and their spatial distribution within the forest [7]. Tourism in large, protected areas has been described by Navrátil et al. [8].

The modern trend in forests is to construct bike trails for cyclists. Bˇrezina et al. [9] monitored visitor arrivals in forests. Their data show the potential for future cash flows from the city to forest areas. Their research offers a potential tool for investigating cash flows

**Citation:** Hr ˚uza, P.; Pelikán, P.; Olišarová, L. Single-Track Bike Trails in the Moravian Karst as Part of Forest Recreation. *Forests* **2021**, *12*, 1601. https://doi.org/10.3390/ f12111601

Academic Editors: Radu-Daniel Pintilii and Diego Varga

Received: 21 October 2021 Accepted: 16 November 2021 Published: 19 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

in the local economy and a method for determining the potential of the socio-economic functions of forest management as a local multiplier [10].

According to German research, the existence of such trails can be harmonious with the needs of foresters. Especially in heavily visited regions, attractive advertising on the trails and other paths contributes to the effectiveness of visitor management [11]. But it is very important to monitor the numbers of people in the forest [12], as visitors can affect aspects of forest management such as harvesting and logging.

Narrow one-way mountain bike trails, called single tracks, are designed and constructed according to special, long-proven methods, making them suitable for outdoor recreation. Ideally, they are set in the terrain sensitively, so that they do not interfere too much with the character of the landscape in the areas they go through [13].

The interest of tourists in forest localities is increasing. Forest stands, parks and other green areas are the most valuable for recreation [14,15]. People prefer forest and well-kept green areas [16] and water amenities, such as lakes and rivers [17], to city landscapes [18]. An important and relevant issue for contemporary tourism, sport and recreation planners is how to develop trails and mountain bike areas in a way that is in keeping with the demands of proficient mountain bike riders. In [19], the authors offer an overview of the affective experiences that come from mountain biking over a range of common ride obstacles and terrains.

As mountain biking is popular in many natural areas, it can remain controversial, at least in part, due to the divergent views about its environmental impacts [20], one of the most visible of which is the modification of the landscape's structure and quality [21]. In Austria, official trails do not necessarily meet the needs of mountain bikers, who often ride on unofficial trails or along paths where biking is not allowed. This behaviour can result in conflicts with other countryside users, landowners, hunters and conservationists [4]. Mountain biking is seen as an important activity for growing participation in outdoor recreation. Increasingly, places are marketed as suitable for mountain biking and have supporting legal rights of access in place. However, in a study of the Cairngorms National Park in Scotland, as in many places, it was found that mountain bikers were not always made welcome by managers and other visitors [22].

In Slovenia, access to forest skidding tracks and signposted mountain paths, which are greatly preferred by mountain bikers, is generally not legal. There is also a lack of mountain biking management and infrastructure at the national level. The increased interest in mountain biking on trails in natural areas necessitates a systematic approach to management. An important challenge for such management in natural areas is conflicts with other user groups, particularly hikers [23].

Interviews carried out in the forest of Allschwil near Basel, Switzerland, revealed that, in some areas, there was conflict among different user groups, particularly between dog owners, cyclists and groups of hikers or joggers. Knowledge of the habits and preferences of forest visitors allows for the planning of measures that separate different forest user groups and prevent them from entering areas with a high conservation value [24].

Today, forest owners have to cope with an increased demand for recreation. This brings many obligations, which are enshrined in law. One task is to identify potential users and quantify their movements across forest property. From the perspective of the Forest Act No. 289/1995 Coll. [25], Section 20 of the Forest Act, point j) states: "It is forbidden to ride bicycles, horses, skis or sleighs except on forest roads and marked trails in the forest stands". This implies that off-road bikers can only ride on forest roads if they are not on open terrain and specially constructed trails for cyclists, which include trails.

Hr ˚uza [26,27] deals with the legal aspects of purpose-built forestry communications and their position in the law, which includes, among other things, bike trails. Currently, the number of conflicts among forest users is increasing due to the new and growing societal demands for forest recreation in addition to the traditional forest function of wood production. Outdoor sports and forest education programmes are adding to the demands on forest use. Other authors [28] mention conflict between recreational users, e.g., between

bikers and hikers. These conflicts are expected to become more acute in the future, which poses new challenges to both forest policymakers and forest managers. Forest owners' and managers' efforts should be to manage and disperse the visitors from various interest groups within the forest environment so that each visitor can engage in their activity without a negative impact on the surrounding area, other visitors or commercial forestry activities. Reference [29] reports on how government managers of protected areas in South Western Australia engaged with the mountain biking community. This included the development of a user compatibility matrix that facilitated park management decision making to reduce negative social and environmental impacts while, at the same time, providing for a range of recreational opportunities in the protected area. A multi-step, methodological triangulation conflict model from US recreation management was applied and tested in the Black Forest Nature Park [30]. The results from two groups, hikers and mountain bikers, were analysed in depth. The main potential conflicts were due to the fact of infrastructure and differing values. These were influenced by various visitor characteristics such as resource attachment, experiences, style of activity, expectations and motives.

One of the reasons forest owners construct bike trails on their land is to have control over the movements of cyclists through forest stands.


### **2. Materials and Methods**

The Moravian Karst Single Track Centre was constructed in 2015 across 547.74 ha at the Training Forest Enterprise Masaryk Forest Kˇrtiny (TFE), which is an organisational part of the Mendel University in Brno (MENDELU), with the aim of directing the activities of mountain bike visitors to specialised narrow trails. The TFE is located near Brno in the South Moravian region and manages all together a forest area of 10,492 ha. The representation and distribution of woody plants reflect the vegetation gradation and habitat conditions. Deciduous tree species (33% dominated by beech) cover 62% of the forest area, and 38% are covered by conifers (19% spruce). Sustainable forest management aims at management measures ensuring the fulfilment of forest ecosystem services and other functions in changing environmental and social conditions and optimal use of the production potential of forests, with particular regard to the needs of the university (teaching and research) and the public (recreation). The TFE closely collaborates with the Faculty of Forestry and Wood Technology MENDELU to offer experimental forest stands and other establishments for educational and research purposes.

Our aim was to determine whether bikers in the centre moved outside the site to use other forest roads or other parts of the forest or whether they stayed mostly in the trail area. For forest management purposes, we also investigated the quantitative movements of visitors in the Moravian Karst Single Track Centre and their time distribution to provide forest managers with information on land use at various times.

The four trails consist of single tracks that go through the forest stand and, partially, on forest roads in the northeast TFE part. The starting point for these trails is located near Camp Olšovec in Jedovnice, where there is a sport–recreation centre for mountain bikers (boarding place) with technical facilities. The single-track sections, which go directly through the forest stand, are referred to in the study as Track 0 (770 m), Track 1 (7250 m), Track 2 (3730 m) and Track 3 (2010 m) (Figure 1). The single tracks measure 13.8 km in total length and are connected by forest roads. The total length of all trails together with forest roads is 21.4 km.

**Figure 1.** Location of the study area the Moravian Karst Single Track Centre in the Czech Republic.

The hypothetical assumption was that cyclists would only use the marked trail routes prepared for them to join a ride and would not leave the single-track area of 547.74 ha to go into the surrounding forest ecosystem. The single-track bike trails constructed in one part of the forest would be sufficiently attractive that they would not leave them, and the surrounding forest stands would be undisturbed by bikers. If this was not confirmed, the question would be: how many visitors left the area for other parts of the forest?

First, the method of monitoring cyclists in the selected area was decided, and the assumption of their direction of movement was made.

In cooperation with the monitoring coordinator of cyclists from the Partnership Foundation, two types of counters were chosen and locations suitable for placing them were selected. Their placement was agreed with the administrator of the Moravian Karst Single Track Centre, and the necessary facilities were leased.

Monitoring was performed for one month in the high season for cycling activity (15 June–15 July). The presence of visitors was monitored with respect to the time of day and whether the day was a working or non-working day.

Four locations were selected (i.e., U Kempu, Trojúhelníky, U Proklestu and Na Mokˇradní) to install the first type of counter, Eco-counters, to cover the cyclist entry points to the trail area; their movements were captured further on the routes in the area (Figure 2). Thus, sensors were set-up to scan all incoming and outgoing visitors to the trail area.

Counters were placed at narrow places so that cyclists could not ride past them side by side. They were mounted on trees next to the road or on a nearby pole if a tree was unavailable. The counters can measure reliably over four meters. The devices were locked and marked with a contact number to prevent them from being accidentally damaged by people. All counter locations were photographically documented (Figure 3).

**Figure 2.** Locations of counters for data measuring in the Moravian Karst Single Track Centre.

**Figure 3.** Examples of counter installation.

An Eco-counter is a device for counting all users of a route (cyclists, pedestrians and cars). It has two sensors and can determine the direction of its target's motion. The counters were manually calibrated to identify individual groups of visitors and a calibration coefficient was set to ensure the maximum validity of the collected data and minimise errors, i.e., manual calibration counts were performed to determine the numbers of individual users as cyclists, pedestrians or cars. An error can arise in wider places if cyclists ride close together. The counter then records them as one person. The calibration prevents this deficiency and, thus, ensures the maximum accuracy of the measured data. At each site, data were collected for eight hours per day. Data were collected continuously throughout the measurement period of six days. Subsequently, the counters were removed from each site, the data were downloaded and, using the calibration coefficient, calculated to the actual values of the individual participants and the direction of their travel. Due to the

fact that this study focused on the movement of cyclists in the given area, the results subsequently analyse only the movements of cyclists.

We placed one of the second type of counter, a TRAFx Mountain Bike Counter, at Stoupací on Track 0 to discover whether cyclists used the ascending single-track bike trail to access downhill single tracks or whether they also used the surrounding forest road.

This counter responds to metal wheel parts, so it is able to detect bikes with a carbon frame. The width of the recording field was up to 1 m from the counter, so when installed in the middle of the track, it can cover a track up to 2 m wide. It was installed under the surface of the trail, hiding it from human eyes, minimising the risk of damage or vandalism.

Both types of counters consisted of a sensor that recorded the passage of a cyclist and were connected to the data unit, which stored the measured data and supplied power to the counter.

During the measurement phase, there was one month of continuous inspection and manual data collection. The Eco-Visio online application (https://www.eco-counter.com/ applications; accessed on 20 October 2021) was used to download, analyse and implement monitoring-related data.

As single tracks are often represented as narrow paths following the shape of the terrain, with minimum influence on their surroundings, we focused on the real width of constructed single tracks in the Moravian Karst Centre. The width of single tracks is generally stated to be up to 1 m, which only relates to one bike track width of 0.8 m, but the total width varies depending on the single track's inclination and the transverse slope of the terrain. It is necessary to take into account the cut-and-fill slopes and take as the width the entire width of the single-track formation. This greatly changes the total area take-up as evidenced by the results of our investigation.

One of the aims of this research was to determine a suitable and sustainable size for a single-track area for mountain biking, allowing for other visitors, timber production and other forest functions, as there is now a great demand for this type of sports activity in the forest environment.

For comparison, we chose 10 such centres abroad. Five of these were in the UK, where this issue is historically captured systemically and on the basis of a national strategy. Trails were chosen in Wales, which has been written about in the sense of "here it all began, 25 years ago we saw the start of the trail centre revolution, and it was here in Wales. Coed y Brenin led the way in setting up waymarked trails specifically for mountain biking" (https: //www.mbwales.com/2016/07/18/trails-centres-began/; accessed on 20 October 2021). We can describe these trails as closed circles, often far from large towns and habitations. The location of the trails in forest stands was not a condition, but most were located in forest. To select suitable single-track areas, a Welsh government website, Mountain Bike Wales (https://www.mbwales.com; accessed on 20 October 2021), was used. The site promotes mountain biking and provides information about each site, including maps. Five centres were selected: Abercarn, Afan, Brechfa, Cw Merfyn and Ganllwyd.

The other five trail areas examined were in Denmark, on slightly hilly terrain in suburban forests, so their location and the shape of terrain were more comparable with the Moravian Karst Single Track Centre. The Danish facilities were located close to areas of habitation; being within forest stands was a condition of their selection. The Mountain Bike Project website (https://www.mtbproject.com; accessed on 20 October 2021) was used to select five suitable areas: Djævlesporet, Egebjerg, Kongshøj, Silkeborg and Vodskov.

We did not compare the size of the areas, but trail density was established for the total length of all trails and the efficiency established for the trail distribution. Specially, the efficiency provided us with information about single-track land use and based on this value, it is possible to decide whether it is appropriate to further expand the single-track bike centre or to have single tracks more concentrated if needed according to the demand.

The total area of the biking amenity was taken as that enclosed by the trails themselves and forest roads that were used as connecting lines between the trails and forest roads leading to the start of the trails from the car park.

The total length of the trails was calculated as the length of all trails, including forest roads, so that the trails calculated formed a connected whole. None of the trails or their parts were counted twice, although some sections of forest roads could be used as part of several trail routes.

The density of trails within a facility was established as the total length of the trails divided by the area of the facility and expressed in m/ha.

The density *H* is given by the Equation (1):

$$H = \frac{D}{S} \quad \left[\text{m} \cdot \text{ha}^{-1}\right] \tag{1}$$

where: *D* is the total length of the trail network (m), *S* is the accessed area (ha).

As the parameters do not provide any information on the trail network's distribution, a parameter for trail efficiency was added.

The efficiency *U* is given as the proportion between the average geometric (shortest) distance from a regular geometric 10 ha square network (grid) and the theoretical distance, the calculation of which was based on the ideal regular distribution of the trails in the area according to Equation (2).

$$
\mathcal{U} = \frac{D\_t}{\overline{D\_\mathcal{S}}} \cdot 100 \quad [\%] \tag{2}
$$

where *D<sup>t</sup>* is the theoretical distance (m) calculated as the average distance due to the optimal distribution of trails (Figure 4) in the accessed area and depending on the trail density *H* according to Equation (3).

$$D\_t = \frac{10,000}{4H} \text{ [m]} \tag{3}$$

**Figure 4.** Theoretical distance, *D<sup>t</sup>* , due to the optimal trail distribution in the forest access area and trail density, *H* 20 m/ha.

In addition, the geometric distance, *Dg*, represents the direct distance from the centre point of a regular geometric 10 ha square grid to the trail. Its average value (Equation (4)) depends on the trail distribution and is generally higher than the theoretical distance.

$$\overline{D\_{\mathcal{S}}} = \frac{D\_{\mathcal{S}^1} + D\_{\mathcal{S}^2} + \dots D\_{\mathcal{S}^n}}{n} \ [\mathbf{m}] \tag{4}$$

To analyse the trail areas, maps on the OpenStreetMap WMS server were used in the QGIS application environment. The geometric distances from grid points were determined using QGIS software and the vector analysis tool "*distance to nearest hub*". The same analyses were performed for the Moravian Karst Single Track Centre for comparison.

### **3. Results**

The comparison of the monitored visitor entry points into the Moravian Karst Single Track Centre showed (Table 1) large differences in total attendance in the monitored period. The site at U Kempu, located on a forest road at the entrance to the Training Forest Enterprise Kˇrtiny, MENDELU by the Jedovnice Olšovec Lake, dominated attendance figures with 21,559 visits. Another monitored site near Olšovec at Na Mokˇradní recorded a significantly lower value of 4172 visitors. But the fewest visitors were recorded on the opposite side at Trojúhelníky (2440) and U Proklestu (1070), which is the side from which people leave the Moravian Karst Single Track Centre to visit other parts of Forest Training Entrprice Kˇrtiny.

**Table 1.** Overall monitoring results of visitors on selected forest roads in the Training Forest Enterprise Kˇrtiny MENDELU during the period 15 June–15 July (calibrated data).


Focusing only on cyclists (e.g., data cleaned of vehicles and pedestrians using calibration counting), we counted 9714 cyclists at U Kempu travelling towards Jedovnice; going in the opposite direction, the figure was 9599 cyclists. On the Jedovnice side at Na Mokˇradní, it was 1807 cyclists in and 1207 out. On the opposite side of the Centre, the counters at Trojúhelníky and U Proklestu recorded only 1133 cyclists riding into the area and 927 riding out (Table 2).

**Table 2.** Number of cyclists according to each counter.


\* Direction into the area. \*\* Direction out the area. \*\*\* Counter on the single-track Stoupací-bikers counted in one direction.

From these numbers, we can conclude that 92% of cyclists stayed at the Moravian Karst Single Track Centre and the construction of single-track trails in response to the need for controlled recreation fulfilled its purpose.

At most locations, Saturday was the busiest day. The exception was at U Proklestu, where Monday (65) and Sunday (162) were the busiest days. This shows that this route was favoured mostly by trekking cyclists, who mainly used the forest roads at the Centre.

The average daily visit rate at the sites corresponds to the total visit rate. At all localities, the number of visitors on non-working days was less than 2/3 of total attendance.

The maps (Figures 5 and 6) and measurements show that bikers used the marked trail route. The main purpose for them was to enjoy these trails. The existing three single tracks go through the forest stand and, partially, on forest roads, so bikers do not need to use other forest roads.

**Figure 5.** Number of cyclists in the direction into the area.

**Figure 6.** Number of cyclists in the direction out of the area.

From a total of 12,654 incoming cyclists, 8522 used single-track 0 instead of the adjacent forest road. This gives us interesting information that bikers use single tracks rather than ascending forest roads to reach the top of the downhill single tracks near U Proklestu.

The single-track area was mostly visited during daylight hours, which is consistent with the guidelines for visitors from trail managers. The peak hours are given in Table 3.

**Table 3.** Visitor layout by peak hours.


The real width of most constructed single tracks, taking cut-and-fill slopes into account, fell in the width category 1–2 m, with 2–3 m the next most common as seen in Table 4.


**Table 4.** Trail width parameters.

The 770 m Track 0 represents a part that is common to all of the trails and is used to access downhill single-tracks 1, 2 and 3. The section has a long rise that makes up 83% of the trail length with variable width including cut-and-fill slopes of between 1.0 and 3.0 m. The mean total width of the road formation is 1.8 m, with 77% of the trail length being wider than 1.0 m (Table 4).

Track 1 has the character of a ridge trail with a balanced ascent and descent ratio going over the north-western slopes of a peak. The total length is 7250 m, and the width ranges from 0.7 to 9.2 m with a mean of 1.8 m. For 435 m of the trail, the width is greater than 3.0 m (Table 4).

Track 2 has a total length of 3730 m and resembles the main downhill part of a single track. The trail is 1.0–6.2 m wide, with a mean of 2.4 m. Sixty-six percent of the trail length is wider than 2.0 m, and for a length of 535 m, the trail is wider than 3.0 m (Table 4).

Track 3 first runs along the ridge forest road, diverging from it after approximately 600 m. The total section length is 2010 m, and the trail width ranges between 0.8 and 6.4 m (Table 4) with a mean of 2.1 m. For 315 m, the width of the trail is greater than 3.0 m.

Based on our measurements, we can conclude that the width of all single tracks over their entire length is greater than 1.0 m. On average, 90% of the trail lengths are 1.0–3.0 m wide (Table 4). Almost 1.4 km of the trail is wider than 3.0 m (11% of the total length of the single track located in the forest stand).

The characteristic parameters of biking areas in Wales and Denmark are compared in Table 5 with those of the Moravian Karst Single Track Centre with its total area of 547.74 ha, total trail length of 21,365 m, density of 39 m/ha and efficiency of 40%.


**Table 5.** Characteristics of the single-track areas compared.

### **4. Discussion**

In previous years, data from the counter located on Track 0 were compared. According to Olišarová et al. [12], the most frequented months of 2017 and 2018 were July and August. A comparison of the study routes in the area showed a large difference in total attendance in the monitored period. In July 2017, a total of 9558 cyclists rode past the counters, and the following year (2018) showed 9170 entries. The data from this year in the first half of July show a total of 5056 passages.

There was a wide range of holidaymakers in the area. The highest attendance was recorded at U Kempu, near the camp gate. Nearly 90% of visitors recorded there were cyclists. On the contrary, the smallest number of visitors was counted at U Proklestu, where the number of cyclists was less than 50%. Almost two-thirds of visitors came to the area on a non-working day, while during the week only 1/3 were in the area.

There are a large number of researchers and university employees at the locality, many of whom come by car. The most frequent passages were recorded by the counter at Trojúhelníky, probably because this road connects to utility road number 373 leading out from the area to another part of the Training Forest Enterprise Kˇrtiny MENDELU.

According to Olišarová et al. [12], some visitors travelled one route several times, others have tactically chosen their way according to their abilities and skills, as each route differs in the difficulty of the terrain and elevation.

The project has proven that visitors stay in the single-track area, and very few go on to adjacent locations.

It is necessary to realise that urban forests are often popular sites for recreational activities such as hiking, biking and motorised recreation. This can result in the formation of extensive trail networks, fragmenting vegetation into patches separated by modified edge effects and, ultimately, contributing to the degradation of the ecosystem, and management should seek to minimise the creation of informal trails by hardening popular routes, instigating stakeholder collaboration and centralising visitor flow [31]. This statement can be supported by our findings that the regulation of bikers' movements by controlled recreation fulfilled its objective and works. [31] adds, the forest lost to informal biking and hiking trails reached an area of 5%. The trail distribution in a single-track bike trail centre, evaluated by trail efficiency, can help to make a decision regarding new single-track bike trail design in a bike centre or its further development if needed. Informal trails generally had worse surface conditions and were poorly designed and located. Per site, formal and informal trails resulted in similar loss of forest strata, with wider trails resulting in greater loss of forest [32]. Choosing the right corridor for a single-track lay-out according to transverse slope terrain can minimise the width of single-trail formations. In [33], the authors present a study where they analysed the spatial overlap and social conflicts between mountain bikers and runners. This can help managers and decision makers design proper infrastructure for outdoor activities. Strategic management errors can be avoided by knowing user preferences and by offering improved conditions that meet the expectations and needs of different user groups. One of the reasons why we constructed single-track bike trails at TFE MENDELU was the same visible experience regarding conflict among groups of forest visitors, and we can state by this research that offering improved conditions for a leisure outdoor activity at a locality will keep visitors onside.

### **5. Conclusions**

The presence of single tracks attracts mountain bikers who are willing to make a longer journey to pay their visit. Their goal is to enjoy the adrenaline-rush bike ride that single tracks unquestionably provide. Therefore, they do not need to venture into a surrounding area of the forest ecosystem and, thus, do not interfere with forest management or other interests in the rest of the forest. There are various groups of forest visitors with different reasons for moving deeper into the forest ecosystem such as pedestrians, runners, families with children and people walking dogs. All these recreational functions are provided alongside forest management activities focusing mainly on timber production. The construction of bike trails with single-track parts makes it possible to manage the movements of bikers on a specific trail (i.e., single tracks). As seen from this study, cyclists usually follow the marked routes and do not affect the surrounding forest.

The assumption of the narrow design of a single trail up to 1 m in width applied only in the case of a transverse slope of the terrain of up to approximately 16%. However, to increase the attractiveness, the trails are traced through a morphologically hilly area. Only 35% of the Track 3 length meets the 1 m width assumption due to the fact of its location on the plateau. All other trails are actually much wider. With an increasing transverse slope, the overall width increases rapidly. The most common width, in the range of 1–2 m, is situated in transverse terrain slopes of 16–42%. Approximately 20–30% of the trail lengths are traced in transverse slopes of 42–50%, resulting in an overall width of 2–3 m.

When comparing the land use of bike centres, as we can see, the Danish bike trail centres are smaller than those in Wales, which are located at remote sites, and the Moravian Karst Single Track Centre, but the design and density of their layout means the area is better used. When an extension of the bike centre is required, the efficiency parameter can help forest owners decide whether to extend trails inside or outside of the centre. When the efficiency parameter is less than 50%, construction inside should be recommended. At the same time, trails which divert bikers away from forest roads and avoid crossings of single tracks with forest roads should be given preference. The aim should be to achieve a uniform distribution of trails so that the forest ecosystem in the related parts of a single-track centre is not impaired.

It is clear from the collected data that the presence of individual routes is beneficial for the regulation of cyclists on forest paths seeking excitement; at the same time, there is no conflict with other visitors, who are expecting different experiences from visiting the forest.

The practical benefits of cycling monitoring for forest managers can be summarised as:


**Author Contributions:** P.H. initiated the investigation, drafted and carried out the methodology and analysis and finalized the manuscript; P.P. carried out the analysis and visualized pictures L.O. carried out the terrain investigation and drafted the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research was funded by Mendel University in Brno (grant number: LDF\_VP\_2018029).

**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to anonymity of this study and no harm or collection of ethical data on humans.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data are available and stored by the correspondent author.

**Conflicts of Interest:** The founding sponsor, Mendel University in Brno, had no role in the collection, analyses or interpretation of data; in the writing of the manuscript; in the decision to publish the results.

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