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Journal of Resources and Ecology >

2022 , Vol. 13 >Issue 6: 1098 - 1108

DOI: https://doi.org/10.5814/j.issn.1674-764x.2022.06.014

Tourism Resource and Ecotourism

Evolution of Landscape Pattern and Tourism Service Value in Zhangjiakou City

  • LI Ying , 1, 2 ,
  • DAI Yuexingtong 1 ,
  • HAN Jingting 1, 3 ,
  • ZOU Tongqian , 1, *
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  • 1. China Academy of Culture and Tourism, Beijing International Studies University, Beijing 100024, China
  • 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China
  • 3. Management and Science University, Shah Alam 40100, Malaysia
* ZOU Tongqian, E-mail:

LI Ying, E-mail:

Received date: 2021-10-18

  Accepted date: 2022-03-15

  Online published: 2022-10-12

Supported by

The Beijing Social Science Fund(20GLC064)

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Abstract

Zhangjiakou is a northern Chinese city that hosted the Beijing 2022 Winter Olympic Games. As an important ice and snow tourist destination, it is essential to investigate Zhangjiakou's rate of landscape pattern change, the landscape ecological security level, and ecosystem service value, particularly the tourism ecological service value during its construction. With land use data from 2000 to 2020, this study comprehensively analyzed the dynamic changes in Zhangjiakou, including land use dynamics, the land use transfer matrix, landscape vulnerability, landscape disturbance, ecosystem service value, tourism ecological service value, and other aspects. The results show that the landscape pattern in Zhangjiakou was greatly disturbed from 2015 to 2018, and the landscape ecological security was threatened in the process of landscape pattern adjustment. By 2020, after the landscape pattern was adjusted and stabilized, the landscape ecological security was restored, and the ecosystem service value was significantly improved, especially the tourism ecological service value. The results of this study will play an important role in promoting the optimization of Zhangjiakou's ice and snow landscape pattern and the improvement of tourism ecological value. In addition, it provides important lessons for the development of other ice and snow tourist destinations.

Key words： ice and snow tourist destination; landscape pattern; landscape ecological security; tourism leisure value; Zhangjiakou City

Cite this article

LI Ying , DAI Yuexingtong , HAN Jingting , ZOU Tongqian . Evolution of Landscape Pattern and Tourism Service Value in Zhangjiakou City[J]. Journal of Resources and Ecology, 2022 , 13(6) : 1098 -1108 . DOI: 10.5814/j.issn.1674-764x.2022.06.014

1 Introduction

A change in the landscape pattern is a comprehensive reflection of changes in the ecological environment. It can be used to reveal the ecological condition and spatial variations of a certain area, as well as to explain its ecological security problems. The elements affecting landscape pattern changes mainly include human factors and natural factors (Rao and Pant, 2001; Pei et al., 2014; Wu and Zhang, 2017). Ecological security means that the hidden danger of ecological security does not pose a threat or damage, nor does it make an adjustment in the ecological environment necessary for human economic activities and environmental sustainability (Wang et al., 2007; Pei et al., 2014). In recent years, the changes in the landscape pattern and landscape ecological security have become the focus of current research. Such research mainly combines the changes of land use with those of the landscape pattern and analyzes the landscape ecological security to better reveal the current condition and trend of the land ecological environment. It is of great significance for rational land use and development in the future, as well as the protection of the ecological environment (Wang et al., 2018; Wang et al., 2019a). However, there has been no research on the changes of the landscape pattern in Zhangjiakou thus far.
Since Zhangjiakou prepared to host the 2022 World Winter Olympic Games, its land use types have changed greatly (Liu et al., 2020), and the role of land use change cannot be ignored. In addition, the successful development and building of an ice and snow tourist destination are highly dependent on a good ecological environment (Wang et al., 2017). Thus, the study of the changes in the land use landscape pattern in Zhangjiakou and an analysis of the rules governing its changes in the landscape pattern and trend of landscape security will have far-reaching significance for the future development of Zhangjiakou and its ecological security both during and after the Winter Olympics. Besides, there are few studies in the field of tourism service value at present, so this study is an improvement on the limitations of previous studies. It analyzes the changes in the land use landscape pattern in Zhangjiakou, adopts land use dynamics to represent the rate of land use changes and a land transfer matrix to represent the relative changes among land use types, evaluates its landscape security and estimates its tourism service value. This study will greatly promote the improvement of the landscape pattern in Zhangjiakou as an ice and snow tourist destination. It will also advance the development of ice and snow tourism in general through the study of tourism service value. At the same time, it provides an important lesson for the development of other ice and snow tourist destinations.

2 Research area and data

2.1 Overview of the research area

Zhangjiakou lies between 113°50′E-116°30′E and 39°30′N- 42°10′N. It is located at the junction of Beijing, Hebei, Shanxi and Inner Mongolia, covering an area of about 36357 km2. Its topography is high in the northwest and low in the southeast, with an average altitude of 1300-1600 m. The Yin Mountains cross its middle, dividing Zhangjiakou into two parts: Bashang and Baxia. Zhangjiakou is in the temperate continental monsoon climate zone, so it is cold and dry in winter but hot and rainy in summer. The annual average temperature is 4 -16 ℃ and the annual precipitation is 400 mm. As one of the national ski resorts, Zhangjiakou has a natural skiing period of more than five months.
Zhangjiakou won the right to host the 2022 Winter Olympic Games in 2015. The Zhangjiakou competition area is located in Taizicheng, Chongli District, and the whole construction process for 76 Winter Olympic projects were completed in 2022.

2.2 Data Sources

This study uses the land use data of Zhangjiakou in 2000, 2005, 2010, 2015, 2018, and 2020. The data came from the national spatial distribution data of land use types monitored by remote sensing, which is available from the Resource and Environment Science and Data Center, Institute of Geographic Sciences and Natural Resources Research, CAS (http://www.resdc.cn/Datalist1.aspx?FieldTyepID=1,3). This data is generated through manual visual interpretation based on Landsat TM images and Landsat 8 remote sensing images, with a spatial resolution of 1 km × 1 km. The classification of land use types adopts the LUCC land use classification system, which includes six class one land types (arable land, woodland, grassland, waters, residential area and unused land) and 25 class two land types (Xu et al., 2014). Zhangjiakou has 21 of those land use types, and the specific codes are given in Table 1.
Fig. 1 Topographic map of Zhangjiakou
Table 1 The land use types in Zhangjiakou
Code Land use types
1 Arable land Paddy field
2 Dry land
3 Woodland Woodland
4 Shrubwood
5 Open woodland
6 Other woodlands
7 Grassland High coverage grassland
8 Medium coverage grassland
9 Low coverage grassland
10 Waters River
11 Lake
12 Reservoir pit
13 Beach land
14 Construction land Urban area
15 Rural residential area
16 Other construction land
17 Unused land Sand
18 Saline alkali land
19 Swamp
20 Bare land
21 Bare rock

3 Research methods

3.1 Analysis of the evolution of landscape patterns

This study uses land use dynamics and a land use transfer matrix to reflect the evolution of landscape patterns in Zhangjiakou. It adopts land use dynamics to show the rate of change in the landscape pattern, while the land use transfer matrix is used to represent the relative changes of the landscape types.

3.1.1 Land use dynamics

This study uses land use dynamics to show the rate of land use change. Based on the previous related research of land use dynamics (Zhu and Pu, 2020), the slope values of the land use change curve at the yearly points from 2000 to 2020 are used as the land use dynamic degree in this study. The specific formula is as follows:
${{k}_{i}}=({{A}_{i+1}}-{{A}_{i-1}})/({{Y}_{i+1}}-{{Y}_{i-1}})$
where i represents the sample serial number, ki is the dynamic degree of land use of sample i, Ai+1 is the area of landscape types for sample i+1, Ai-1 is the area of landscape types for sample i-1, Yi+1 represents the year of sample i+1, and Yi+1 represents the year of sample i-1. For 2000 and 2020, only the unilateral slope is used as the land use dynamic degree.

3.1.2 Land use transfer matrix

Referring to previous studies on the changes of land use landscape patterns (Wang et al., 2018), this study uses a Markov transfer matrix to analyze the temporal and spatial changes of the land use landscape pattern in Zhangjiakou from 2000 to 2020. The Markov model can not only quantitatively show the transformations between different land use types, but it can also reveal the transfer rates between different land use types. The land use transfer matrix comes from the quantitative description of the system state and state transfer in system analysis. In the land use transfer matrix, the row represents the land use type at time point T1, the column shows the land use type at time point T2, and the matrix value represents the change in the land use area between different land use types from T1 to T2.

3.2 Analysis of landscape ecological security

3.2.1 Landscape disturbance

Referring to previous studies on landscape disturbance (Han et al., 2010; Zhu and Pu, 2020), this study selects three parameters: landscape fragmentation, landscape separation and landscape dominance, to calculate the landscape disturbance. The formula is:
${{U}_{i}}=a\times {{C}_{i}}+b\times {{F}_{i}}+c\times {{D}_{i}}~$
${{C}_{i}}={{N}_{i}}/{{A}_{i}}$
${{S}_{i}}={{N}_{i}}/A$
${{P}_{i}}={{A}_{i}}/A$
${{F}_{i}}=\sqrt{{{S}_{i}}}/(2{{P}_{i}})$
${{D}_{i}}=d\times {{L}_{i}}+e\times {{P}_{i}}$
${{L}_{i}}={{N}_{i}}/N$
where i is landscape type, Ui is the disturbance degree of the landscape type i, Ci is the fragmentation degree of the landscape type i, Fi is the separation degree of the landscape type i, Di is the dominance degree of the landscape type i, Li is the relative density of the landscape type i, Pi is the relative coverage of the landscape type i, Si is the distance index of the landscape type i, Ni is the number of landscape patches i, N is the total number of landscape patches, Ai is the area of landscape patches of the landscape type i, and A is the total area of all the landscape patches. The constants a, b, and c are the weights of fragmentation, separation and dominance, respectively. According to previous studies (Liu et al., 2021), their values are 0.5, 0.3 and 0.2, respectively; while d and e are the weights of the relative density and relative coverage, respectively, with values of 0.4 and 0.6 (Zhu and Pu, 2020).

3.2.2 Landscape vulnerability

Landscape vulnerability refers to the sensitivity of different landscape types to external disturbance beyond their own adjustment ability. The greater the landscape vulnerability, the greater the degree of external disturbance, while the higher the sensitivity, the lower the ability to resist risk and the lower the landscape security (Hao et al., 2012; Li and Zhang, 2005; Wang et al., 2015). This study refers to the evaluation system of landscape vulnerability of different land use types in a previous study, and classifies the vulnerability of different land use types as: grassland landscape vulnerability 6, arable land 5, unused land 4, woodland 3, waters 2 and construction land 1 (Zhu and Pu, 2020).

3.2.3 Evaluation of landscape ecological security

There are many studies on the evaluation methods of landscape ecological security. However, these studies mainly attribute landscape ecological security to landscape disturbance and landscape vulnerability (Li et al., 2020; Xu et al., 2021), but they rarely take the stability of actual land use as a reference index. A large number of studies show that the stability of land use and the landscape pattern have an important impact on landscape ecological security (Fang et al., 2020; Wang et al., 2020; Zhang et al., 2020).
When evaluating landscape ecological security, previous studies have usually adopted two modes. One is to regard each evaluation index as having the same weight index, simply calculate the index product, couple the functions of the indexes, and then obtain the evaluation results (Han et al., 2010; Xie, 2011; Zhu and Pu, 2020; Liu et al., 2021). This method ignores the differences in the effects of different indexes. Therefore, it is rough in the process of landscape ecological security evaluation, especially in a research area at the prefecture level. The second mode is to differentially weight the different indexes and calculate the landscape ecological security index by weighted coupling of the effects of various indexes (Lin et al., 2010; Wang et al., 2019b). Although this method considers the weight differences of different indexes, it ignores the advantages and disadvantages of the sample data in the indexes, especially in the landscape ecological security evaluation with multiple indexes and objects.
In order to resolve the above problems, this study adopts the Delphi-AHP-TOPSIS algorithm to evaluate landscape ecological security on the basis of fully considering land use dynamics and the security of the landscape pattern. A large number of studies have shown that the AHP method has great advantages in the calculation and selection of index weights and decision-making with multiple criteria (Kiker et al., 2005; Goepel, 2018). At the same time, the TOPSIS method has great advantages in the scientific ranking and evaluation within and among indexes (Yue et al., 2010; Du et al., 2018). The coupling of the Delphi-AHP-TOPSIS method has been proven to be superior in the evaluation of ecological environmental quality (Li et al., 2021). The performance of this mode needs to be tested in the process of landscape ecological security evaluation at the prefecture and municipal levels. Therefore, in this study, landscape disturbance and landscape vulnerability are taken as the landscape pattern indexes, and the land use dynamics are taken as the landscape stability indexes. This study uses the Delphi-AHP method and experts were consulted to determine that the weights of landscape pattern and landscape stability are 0.7 and 0.4, respectively, of which the relative weights of landscape disturbance and landscape vulnerability in the landscape pattern indexes are 0.4 and 0.6, respectively. The AHP-TOPSIS method is used to calculate the optimal ideal solution and the worst ideal solution, to scientifically calculate the relative advantages and disadvantages within and among the indexes, and to realize the coupling of the indexes. The specific calculation method is as follows.
Negative indexes are first made positive:
$x_{i}^{N}=({{x}_{i}}-{{x}_{\text{min}}})/({{x}_{\text{max}}}-{{x}_{\text{min}}})$
where xiN is the normalized sample value, xi is the value of the parameter sample to be normalized, and xmax and xmin are the maximum and minimum values of the parameters, respectively.
Then, the parameter samples are standardized to clarify the distribution of each parameter in the sample population. The formula is as follows:
${{x}^{N,STD}}=(x_{i}^{N}-{{x}_{\text{mean}}})/{{x}_{\text{std}}}$
where xN,STD is the standardized sample value, xiN is the aforementioned positive sample value, and xmean and xstd are the sample mean and sample standard deviation, respectively.
Then, the distance between positive and negative ideal solutions is calculated:
$\left\{ \begin{matrix} S{{d}^{+}}=\sqrt{\sum\nolimits_{i=1}^{n}{{{(S_{i}^{+}-x_{i}^{N,STD})}^{2}}}} \\ S{{d}^{-}}=\sqrt{\sum\nolimits_{i=1}^{n}{{{(S_{i}^{-}-x_{i}^{N,STD})}^{2}}}} \\ \end{matrix} \right.,\ i=1,2,\ldots,n$
where $S{{d}^{+}}$ and $S{{d}^{-}}$ are the distances between the positive and negative ideal solution, respectively, $S_{i}^{+}$ and $S_{i}^{-}$ are the positive and negative ideal solutions, respectively $\left\{ \begin{matrix} S_{i}^{+}=\text{max}({{x}^{N,STD}}) \\ S_{i}^{-}=\text{min}({{x}^{N,STD}}) \\ \end{matrix} \right.$, i is the sample number and n is the number of samples.
Then the nearness degree is calculated:
$\xi =\frac{S{{d}^{-}}}{S{{d}^{+}}+S{{d}^{-}}}$
where ξ is the sample score.
Finally, the weight vector W of each parameter index calculated by the Delphi-AHP algorithm is weighted and coupled with the total score of each parameter sample calculated by the TOPSIS algorithm to obtain the final total score of the Delphi-AHP-TOPSIS tourism environmental suitability:
$LESI={{W}_{v}}\times {{\xi }_{v}}+{{W}_{d}}\times {{\xi }_{d}}+{{W}_{k}}\times {{\xi }_{k}}$
where v, d and k represent landscape vulnerability, landscape disturbance and land use dynamics, respectively; and LESI (Landscape Ecological Security Index) is the total score of landscape ecological security, with a value range of [0, 1]. According to the ranking calculation of the best and worst samples in Zhangjiakou, the value can represent the relative advantages and disadvantages of landscape ecological security in Zhangjiakou.

3.3 Estimation of the tourism ecological service value

Referring to the research results of Costanza et al. (1997) and Zhao et al. (2013), this study confirms the ecosystem service value coefficients of different land use types, which focuses on the tourism ecosystem service value of Zhangjiakou, and uses the entertainment and cultural service value in the ecosystem service value to represent the tourism ecosystem service value. At the same time, the value of ecological service is calculated. The formula of ecological service value is as follows:
${{S}_{i}}={{A}_{i}}\times {{\varphi }_{i}}$
where Si represents the ecological service value of the sample (yuan); Ai is the area of the type i landscape of the sample, and φi is the service value coefficient of the type i landscape of the sample. The ecosystem service value coefficients and tourism ecological service value coefficients are shown in Table 2.
Table 2 Ecological service value coefficients
Land use type Ecosystem service value coefficient
(104 yuan km-2)		 Tourism ecological service value coefficient
(104 yuan km-2)
Arable land 62.03 0.09
Woodland 196.13 11.33
Grassland 64.98 0.35
Waters 487.77 43.76
Construction land 0 0
Unused land 3.71 0.09

4 Results and analysis

4.1 Evolution of the landscape pattern

According to the six land use data years of 2000, 2005, 2010, 2015, 2018 and 2020, the dynamic degree of land use in Zhangjiakou since 2000 was calculated by using the land use dynamic degree calculation method proposed in this study (see Fig. 2). The results indicate that the land use in Zhangjiakou has shown a trend of stable-rapid change-stable since 2000. The land use dynamic degree before 2010 was low, but it changed significantly in 2015 and 2018. From 2010 to 2020, woodland, other construction land, rural residential area, shrubwood, dry land, saline alkali land, and urban area showed obvious growth trends. The areas of paddy field, high coverage grassland, and swamp showed obvious rapid downward trends. Other land types have changed only slightly or not at all. By 2020, the dynamic degree of land use tended to be stable once again.
Fig. 2 Land use dynamic degree in Zhangjiakou
Woodland and shrubwood achieved significant growth in 2015 and 2018, which was related to the wind prevention and sand fixation strategy of Zhangjiakou City and the preparations for the Winter Olympic Games. Since 2017, Zhangjiakou City has built wind and sand prevention and sand blocking shelterbelts along the border of Hebei and Mongolia, water conservation shelterbelts along the dam, and farmland and pasture shelterbelt networks based on key projects such as the Beijing Tianjin wind and sand source control and afforestation project. In addition, based on the requirements of the task breakdown list of sustainability commitment for the Beijing 2022 Winter Olympic Games and winter Paralympic Games, since 2016, the Zhangjiakou Forestry and Grass Department has prepared the urban and rural greening plan of Zhangjiakou for hosting the 2022 Winter Olympic Games. This plan divides the effort into three greening areas: Greening of the Chongli Olympic Games, greening of the welcome corridor and greening of the Beijing Zhangjiakou stadium connecting line, so as to plant and construct Zhangjiakou forest. There has also been a substantial increase in other construction land, which is closely related to the construction of the core area of the Winter Olympic Games and its supporting facilities. In 2017, Zhangjiakou arranged more than 5 km2 of construction land for the planned core area of the WangziCity Olympic Games. Previously, more than 1.5 km2 of construction land had been supplied for six ski resorts, such as Yunding, Wanlong and Changchangling. The preparations for the Winter Olympic Games had an important impact on land use changes in Zhangjiakou City.
This study adopts the Markov transfer matrix to analyze the temporal and spatial change of land use landscape pattern of Zhangjiakou from 2000 to 2020. The land use transfer matrix of time periods 2000-2005, 2005-2010, 2010-2015, 2015-2018, 2018-2020 is shown in Table 3. From 2000 to 2005, the changes of land use types in Zhangjiakou mainly focused on the transformation of arable land, primarily the transformation of arable land into construction land (29.87 km2), unused land (4.98 km2), besides, the grassland (68.70 km2), waters (22.90 km2), woodland (6.97 km2), unused land (3.98 km2) had transformed into other types. From 2005 to 2010, the amount of land use transfer in Zhangjiakou was relatively small, mainly reflected in the transformation of arable land (20.91 km2), woodland (3.98 km2) and grassland (11.95 km2) into construction land. From 2010 to 2015, the transfer of land use was still small, and the change type was relatively singular, mainly manifested in the transformation of arable land, woodland, grassland and unused land into arable land. From 2015 to 2018, the mode of land use transfer in Zhangjiakou had changed greatly, from the previous transformation mode with arable land and construction land as the main transfer objects to the diversified transformation among the whole range of land use types, and the amount of land use transfer also increased significantly. The relatively large amounts of transfer were in the transformation of arable land, woodland and grassland to other land use types. Subsequently, the trend of land use transfer slowed down from 2018 to 2020, mainly reflected in the transformation of various land use types to arable land and construction land.
Table 3 Land use transfer matrix (Unit: km2)
Period Type Arable land Woodland Grassland Waters Construction land Unused land
Arable land 17627.05 1.00 1.00 5.97 29.87 4.98
Woodland 6.97 6919.19 1.00
2000-2005 Grassland 68.70 9691.24 1.99
Waters 22.90 1.00 1.99 568.55 6.97
Construction land 792.58
Unused land 3.98 1.00 871.25
Arable land 17701.72 5.97 1.00 20.91
Woodland 6917.19 3.98
2005-2010 Grassland 9679.29 2.99 11.95
Waters 575.52
Construction land 825.44
Unused land 18.92 3.98 860.29
Arable land 17700.73 1.00 1.00 17.92
Woodland 2.99 6917.19 2.99
2010-2015 Grassland 9672.32 1.00 5.97
Waters 583.49
Construction land 862.28
Unused land 2.99 1.99 855.31
Arable land 11966.44 1221.74 2852.71 231.00 1085.32 279.79
Woodland 1000.69 4633.04 1142.08 23.90 85.63 7.97
2015-2018 Grassland 2731.23 1763.40 4701.74 80.65 249.92 113.51
Waters 246.94 41.82 69.70 163.30 31.86 30.87
Construction land 492.88 28.88 101.56 9.96 233.99 18.92
Unused land 388.33 25.89 85.63 27.88 36.84 283.78
Arable land 16903.17 14.94
Woodland 1.00 7816.32 28.88 1.99
2018-2020 Grassland 2.99 9012.17 1.00 11.95
Waters 1.00 537.68 1.00 1.00
Construction land 23.90 4.98 2.99 4.98 1692.71 1.99
Unused land 8.96 1.99 10.95 716.91
Before 2015, the transfer is mainly reflected in the development of cultivated land and construction land. From 2015 to 2018, the mode of land use transfer changed significantly, and there was a large mutual transformation of various land use types. After 2018, the transfer of land use tended to be more moderate, and the trend of transformation of various land use types to cultivated land and construction land reappeared. The main reason is that before 2015, the results of the Winter Olympic Games had not been declared, Zhangjiakou was still in the stage of urbanization, and the mode of land use transfer was relatively singular, mainly focusing on the transfer of cultivated land and construction land. With the successful application as a key host city for the 2015 Winter Olympic Games, Zhangjiakou has undertaken various preparatory activities for the opening of the Winter Olympic Games. In 2017, the State Council approved the pilot plan for the reform of investment approval of Winter Olympic Games construction projects in Zhangjiakou, Hebei Province, which pointed out that in accordance with the division of functions, they should strengthen guidance and services, actively support Hebei Province in carrying out the reform pilot, clarify the construction plan for water-saving facilities, civil air defense engineering facilities and other Winter Olympic Games projects in Zhangjiakou, Hebei Province, and actively promote the necessary construction. The period of 2015-2018 was a key stage for the vigorous construction and maintenance of Winter Olympic facilities and the Winter Olympic ecological environment in Zhangjiakou, so its land use transfer mode changed significantly. From 2018 to 2020, the preparatory activities for the Winter Olympics were gradually realized, so the land use transfer of Zhangjiakou would tend to be stable, and the land use transfer mode of urban development would be gradually restored, which is mainly reflected in the land use transfer of cultivated land and construction land.
Figure 3 shows the distribution of Zhangjiakou's land use types in 2020. The main land use type in Zhangjiakou is arable land, but woodland and grassland are also widely distributed and concentrated in the central and southern regions. Construction land is relatively scattered throughout the whole city. Urban land is highly concentrated, especially in the central and northwestern regions, and rural land is sporadically distributed in all regions of Zhangjiakou. The area of unused land is relatively small.
Fig. 3 Distribution of Zhangjiakou's land use types in 2020 Note: See Table 1 for the definitions of the land use type numbers in the legend.

4.2 Landscape ecological security

This study proposes a comprehensive evaluation method for determining landscape ecological security through landscape vulnerability, landscape disturbance and land use dynamics based on the Delphi-AHP-TOPSIS algorithm. The final LESI (Landscape Ecological Security Index) is the total score of landscape ecological security (Fig. 4). The results show that from 2000 to 2020, the level of landscape ecological security in Zhangjiakou exhibited a trend of slowly rising, then rapidly declining, steadily improving, and finally gradually recovering. The overall level of landscape ecological security always remained higher than 0.7.
Fig. 4 Landscape Ecological Security Index of Zhangjiakou during 2000-2020
This study analyzes the LESI values of all 21 land use types in Zhangjiakou (Fig. 5) and further defines the contributions of different landscape types to the Landscape Ecological Security Index. The results show that the LESI values of different land use types differ greatly. The LESI of construction land is the highest, mostly over 0.9. The LESI of waters is also relatively high, over about 0.85. The next two are woodland and arable land, while the LESI values of unused land and grassland are relatively low. From 2000 to 2020, the annual trends of LESI values for different land use types are relatively stable.
The LESI values calculated in this study are weighted by three components: landscape disturbance, landscape vulnerability and landscape stability. In order to investigate the contributions of these three components to LESI, this study uses landscape vulnerability, landscape disturbance, the nearness of land use ideal solutions and LESI for a comprehensive comparative analysis. Due to the unified positive and standardized processing of the data, and the standardization of the data within the range of [0, 1], the influence of dimensions has been excluded, so the data are directly comparable. The resulting radar map was drawn by using the standardized values of landscape disturbance, landscape vulnerability, land use dynamics and LESI of the different land use types (Fig. 6). Note that the numbers 1–21 represent the 21 land use types, and the respective code reference relationships are shown in Table 1. On the whole, landscape disturbance and landscape stability have greater positive contributions to LESI, while landscape vulnerability has a greater negative effect on LESI. The correlations of ${{\xi }_{v}},\ {{\xi }_{d}},\ {{\xi }_{k}}$ and LESI were stable from 2000 to 2010. From 2015 to 2018, the landscape stability changed greatly, and the landscape stability of woodland, grassland, construction land and unused land decreased to varying degrees, which changed the relative values of LESI among the different land use types. By 2020, the landscape stability was further restored. With a relatively stable landscape pattern, the LESI also gradually increased.
Fig. 5 LESI values of different land use types from 2000 to 2020
Fig. 6 The landscape disturbance, landscape vulnerability, land use dynamics, LESI standardized radar map.

Note: See Table 1 for the definitions of the land use type numbers in the legend. ξ means the sample score.

4.3 Tourism ecological service value

This study calculated the ecosystem service values and tourism ecological service values from 2000-2020 (Fig. 7). The results indicate that the overall trends of changes in both ecosystem service value and tourism ecological service value in Zhangjiakou since 2000 are relatively consistent, showing trends of first declining and then rapidly increasing, and reaching the highest levels in 2020. Especially from 2015 to 2018, the ecosystem service value and tourism ecological service value showed rapid growth trends. From 2018 to 2020, they decreased with the growth rate, but the continuous growth trend is still obvious. By 2020, the ecosystem service value and tourism ecological service value in Zhangjiakou reached 3.419×1010 and 1.171×109 yuan, respectively.
Fig. 7 Ecosystem service value and tourism ecological service value in Zhangjiakou from 2000 to 2020

5 Conclusions

This study comprehensively analyzed the evolution of the landscape pattern and tourism ecological service value in Zhangjiakou since 2000 from the aspects of landscape pattern, landscape ecological security, ecosystem, and especially tourism ecological service value. It explored the changes in the landscape ecosystem and its service value in Zhangjiakou as a typical ice and snow tourism destination. Through this comprehensive analysis, the main conclusions are three-fold.
As affected by the construction of the Winter Olympic Games, the landscape pattern of Zhangjiakou has been disturbed to a certain extent, but has finally tended to become balanced and stable. Zhangjiakou's land use has shown a trend of stable-rapid change-stable since 2000. Before 2010, the dynamic degree of land use was low, and the change was very obvious in 2015 and 2018. During this period, the mode of land use transfer changed greatly. It had transformed from the previous transformation mode with arable land and construction land as the main transfer objects into a diversified transformation model among all of the land use types.
The landscape ecological security in Zhangjiakou has been threatened in a certain period. When the landscape pattern is readjusted and stabilized, the landscape ecological security can be restored. From 2000 to 2020, the level of landscape ecological security in Zhangjiakou showed a trend of slowly rising, then rapidly declining, steadily improving, and finally gradually recovering. The overall level of landscape ecological security remained higher than 0.7 throughout the study period.
The building of ice and snow tourist destinations in Zhangjiakou has greatly improved its tourism ecological service value. Since 2000, the ecosystem service value and tourism ecological service value in Zhangjiakou have shown trends of first declining and then rapid increasing, especially from 2015 to 2018, and finally showing rapid growth trends, reaching their highest levels in 2020. The ecosystem service value and tourism ecological service value have reached 3.419×1010 and 1.171×109 yuan, respectively.
To sum up, the landscape pattern in Zhangjiakou was greatly disturbed from 2015 to 2018, and the landscape ecological security was threatened in the process of landscape pattern adjustment. By 2020, after the landscape pattern was adjusted and stabilized, the landscape ecological security was restored, and the ecosystem service value was significantly improved, especially the tourism ecological service value. The building of ice and snow tourist destinations in Zhangjiakou has had a very positive impact on its tourism ecological service value. Its successful experience provides important lessons for other similar ice and snow tourism destinations.
This study takes Zhangjiakou, which won the right to host the 2022 Winter Olympic Games in 2015, as the research object, and comprehensively analyzes its long-time series of landscape pattern changes, landscape ecological security level and ecosystem service value since 2000, especially focusing on the evolution of the tourism ecological service value. The relevant research is of great significance and value for the construction of ice and snow tourism destinations. This study introduces an innovative and comprehensive evaluation method for landscape ecological security based on the Delphi-AHP-TOPSIS algorithm through landscape vulnerability, landscape disturbance and land use dynamics to calculate the Landscape Ecological Security Index for evaluating the level of landscape ecological security.
This study focuses on the transformation of the landscape pattern, landscape ecological security and tourism ecological service value before and after Zhangjiakou won the right to host the 2022 Winter Olympic Games in 2015. It aims to reveal the laws of ecological security and service value promotion in the process of the building and construction of an ice and snow tourism destination, thus providing direction for the future building and development of ice and snow tourism destinations in Zhangjiakou. The results of this study can also provide valuable lessons to other similar ice and snow tourist destinations.

Acknowledgements

This article is supported by a grant from State Key Laboratory of Resources and Environmental Information System, Chinese Academy of Science. We would like to acknowledge the Resource and Environment Science and Data Center, Institute of Geographic Sciences and Natural Resources Research, CAS, for providing the national spatial distribution data of land use types monitored by remote sensing. We also want to express our sincere gratitude to the anonymous reviews and editors for their efforts in improving the paper.

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