Research on the Resilience Assessment of Rural Landscapes in the Context of Karst Rocky Desertification Control: A Case Study of Fanhua Village in Guizhou Province

Abstract

The ecological rehabilitation project has greatly curbed the serious problem of karst rocky desertification (KRD) in southern China and significantly changed the ecological environment and landscape pattern of the karst rocky desertification control areas (KRDCA). As one of the most important social–ecological fragile areas in the world, rural landscapes in KRDCA still show a strong sensitivity to disturbance. To reduce risks and improve the resilience of landscapes, this paper constructs a framework for assessing rural landscape resilience in KRDCA from the three dimensions of ecology, engineering, and social culture, based on the concept of resilience defined by the United Nations International Agency for Disaster Reduction. Considering the characteristics of rural landscapes in KRDCA, we select typical villages for empirical study. The results show the following: (1) The KRDCA is highly sensitive to natural disasters due to its special dual geomorphic structure characteristics. The disaster preparedness capacity of villages is the key factor determining the resilience of rural landscapes. The analysis of the disaster preparedness capacities of rural landscape structures with different vulnerability characteristics can be used as an effective means of evaluating the resilience level of rural landscapes in KRDCA. (2) Based on the empirical analysis of Fanhua village, which is a typical KRDCA in southern China, we found that the ecological system and engineering system of the village landscape have high resilience, while the resilience of the social and cultural systems are weak. This is due to the fact that the large number of rural population emigration in recent years has resulted in villages being at the key node of the reorganization of the social and cultural value system. The unstable sociocultural value system reduces the ability of rural landscapes to adapt to disturbance or environmental change. The study results could guide improvement strategies for subsequent landscape planning and inspire new ideas and methods for the implementation of rural revitalization strategies and the improvement of landscape resilience in KRDCA.

1. Introduction

1.1. Karst Rocky Desertification in Southern China

The world’s karst landforms are mainly distributed in southern China, central and southern Europe, and eastern North America, accounting for 12% of the world’s total land area [1]. Karst landforms account for more than 30% of the total land area in China. Karst areas in Southwest China centered on the Yunnan-Kweichow Plateau are characterized as the most intense karst development and acute human–land conflict, which makes this area a typical ecologically fragile region [2]. Karst rocky desertification (KRD) is caused by various factors and is considered to be a major socio-environmental problem in karst areas. It is a typical ecological degradation process governed by vegetation degradation and succession, surface water and soil erosion, and land productivity degradation, and, finally, shows a desert-like landscape on the ground surface [3,4,5]. The result of KRD in southern China triggers a serious threat to the ecological security and downgrades sustainable economic and social development in this region. In the past 30 years, the Chinese government has realized the problems of KRD in southwest China and implemented many ecological projects in the karst region to mitigate the severity of rocky desertification [6], such as the projects of Grain for Green and Natural Forest Protection.

1.2. The Progress Karst Rocky Desertification Control in China

Since the beginning of the 21st century, the Chinese government has attached great importance to the control of KRD. During the 10th Five-Year Plan period, “promoting the comprehensive control of KRD in Guizhou, Guangxi and Yunnan” was listed as a national goal. Since then, the 11th, 12th, and 13th Five-Year Plans clearly stated that efforts will continue to be intensified to comprehensively promote karst rocky desertification control (KRDC). The world’s largest KRD ecological restoration and protection project has been carried out in 300 counties [7], forming a complete set of technical systems with ecological governance technology and biological and engineering management technology [5]. Under the background of the Chinese government’s implementation of KRDC and ecological civilization construction, China’s ecological environment and landscape pattern have undergone significant changes [8,9,10]. The serious KRD problem in karst areas of Southwest China has been gradually addressed, the degree of KRD has been reduced, and the impact of ecological damage has diminished [11,12,13]. The results of China KRD monitoring showed that the area of KRD in Guizhou Province decreased by 8457 km² in 2016 compared with 2005 [14]. In addition, the comprehensive management model of the fragile ecological environment, the ecological agriculture construction model, the agroforestry model, the poverty alleviation and ecological construction model, the returning farmland to forest and forest ecological construction model, the nature reserve and forest park model, ecological tourism and scenic area construction, and other models have achieved outstanding results in karst areas [15,16]. In particular, the rich biodiversity and unique geomorphic landscapes in karst areas have high aesthetic, scientific, and conservation values [17,18]. At present, there are 32 national geoparks with karst landscapes as the main or auxiliary land type, accounting for 23.2% of the national geoparks. Among them, Shilin Karst in Yunnan, Libo Karst in Guizhou, Wulong Karst in Chongqing, Guilin Karst in Guangxi, Shibing Karst in Guizhou, Jinfoshan Karst in Chongqing, and Huanjiang Karst in Guangxi are nominated as World Natural Heritage Sites, highlighting the global value and importance of the karst landscape in Southwest China [19,20]. In general, China’s KRDC measures have achieved remarkable progress, effectively supporting the sustainable development of Southwest China. However, in vast rural areas, due to the constraints of the karst geological background (overground–underground water and soil dual structure, slow soil formation and a shallow and discontinuous soil layer, rapid hydrological process, etc.) and the influence of human–land conflict, there are some problems in the process of KRDC, such as difficulty in consolidating control effects and lack of sustainability [21].

1.3. Rural Landscape in Karst Rocky Desertification Control Area

In 2017, the Chinese government put forward the strategy of rural revitalization, with the strategic goals of accelerating the modernization of agriculture and rural areas and improving the living environment [22], introducing a new connotation to rural environmental governance in KRDCA. Rural landscape systems usually refer to the expression of rural ecology, engineering, and social culture in the landscape, which can not only provide a livable residential environment and activity space for people, but also promote the sustainable and diversified development of rural areas [23,24]. Villages developed on KRD are extremely vulnerable to the external environment, posing a serious threat to the landscape pattern of rural human–land systems. The practical problem of how to improve rural landscapes in KRDCA based on the consideration of ecological, social, and cultural factors must be urgently addressed. The concept of “resilience”, first proposed by Canadian ecologist Holling in 1973 [25], provides a new perspective for the study of the anti-disturbance capacity of rural landscapes in KRDCA. Resilience refers to the ability of ecosystems to recover quickly without collapse in the face of disturbance, and was gradually introduced into the fields of landscape planning and risk management to identify potential risks and maintain the operational capacity of landscape systems [26,27,28]. In recent years, due to the impact of global climate change and the frequent occurrence of natural disasters, rural vulnerability in KRD areas has become prominent. The assessment of landscape resilience by identifying the potential risks and system stability ability of the landscape system can not only be used as an effective means to detect the effect of rocky desertification control, but also can be based on the principle of landscape ecology to enhance the ability of the village system in rocky desertification areas to respond to land degradation through the optimization and coordination of landscape patches. It has the potential to be an effective measure to control rocky desertification.

Currently, research on landscape resilience is gradually increasing, mainly focusing on the connotation and representation of resilient landscapes [29], the maintenance and management of resilient landscapes [30,31], the relationship between resilient landscapes and climate [32], and the planning and design of resilient landscape cases [33]. Due to the complexity of the influencing factors of landscape resilience, there have been relatively few empirical studies that truly involve the assessment of landscape resilience. The quantitative assessment of rural resilience has mainly focused on the dynamic changes in village livelihood and the assessment of individual resilience adaptability, but has ignored the role of rural landscape resilience, which is an important factor in rural development. Therefore, it is necessary to construct a rural landscape resilience assessment framework for KRDCA and to analyze the systematic adaptation and resilience of human–land landscape systems to the interference of natural disasters in the context of KRDC. To provide strategies for improving the resilience of karst rural landscapes and controlling KRD, this research constructs the analysis framework of rural landscape resilience identification in KRDCA based on the concept of resilience proposed by the United Nations International Agency for Disaster Reduction [34], and selects typical karst villages in southern China to carry out empirical research.

2. Assessment Framework for rural Landscape Resilience in Karst Areas Desertification Control Areas in Southern China

Since the concept of ecological resilience was introduced in different fields, researchers have begun to explore the quantitative evaluation of resilience, and a relatively complete theoretical system has been developed. Bruneau et al. (2003) proposed that resilience could be measured by the functional performance of infrastructure before and after a community disaster [35]. Building on the research of Bruneau’s group, Henry et al. (2017) suggested that resilience can be analyzed from key parameters such as disturbance events, system resilience, and overall resilience strategies [36], and Equation (1) was also proposed, where resilience is defined as the ratio between the recovery status of the system to be evaluated at t₁ after the disturbance and the loss status at t₀ after the disturbance.

(1)$R\left(t\right)=\raisebox{1ex}{$\mathrm{recovery}\left(t_1\right)$}\!\left/ \!\raisebox{-1ex}{$\mathrm{loss}\left(t_0\right)$}\right.$

Referring to the definition of resilience proposed by the United Nations International Agency for Disaster Reduction, Kusumastuti (2014) defined resilience as the ratio between disaster preparedness and vulnerability, as shown in Equation (2) [37,38]. On this basis, Xu (2020) conducted an empirical study on resilience assessment by identifying the disaster preparedness and vulnerability of complex urban public spaces [39]. Due to the special geomorphological characteristics of KRDCA, the difference in the disaster preparedness capacity of rural communities before disasters determines the degree of disaster damage to rural landscapes. Improving disaster preparedness capacity can effectively reduce the negative impact caused by disturbance events. Vulnerability is defined as a sensitive state in which rural landscapes in KRDCA are susceptible to external disturbance or internal pressure damage due to a lack of adaptability or defects in the landscape structure itself [40]. This is the regional background characteristic of the eco-social system in KRDCA, which plays a decisive role in the resilience level of rural landscapes. Therefore, this paper also defines rural landscape resilience in KRD management areas as the ratio of landscape preparedness capacity and vulnerability.

(2)$\mathrm{Resilience} \mathrm{Index} \left(RI\right)=\frac{\mathrm{Preparedness} \mathrm{Index} \left(PI\right)}{\mathrm{Vulnerability} \mathrm{Index} \left(VI\right)}$

RI is the resilience level score of the village landscape in the KRDCA to be evaluated, which is the ratio between the system disaster preparedness index (PI) and vulnerability index (VI). If RI > 1, it means that the disaster preparedness capacity of the rural landscape system can overcome its own vulnerability when facing disturbance events, it can use its own resources, skills and social organizations to manage or deal with adverse conditions or risks, and can restore to the original state; that is, the rural landscape system is resilient in the face of risks. However, when RI ≤ 1, it means that the disaster preparedness capacity is not enough to make up for vulnerability when disturbance events occur, and great losses will result; that is, the rural landscape system has poor resilience in the face of risk.

The evaluation index system for the disaster preparedness capacity and vulnerability level of the village landscape is constructed from the rural ecosystem, engineering facility system, and social and cultural system in KRDCA. Disaster preparedness refers to the ability of people, organizations, and systems to use all available resources and skills to cope with adverse conditions, disasters, or risks [34]. This study mainly refers to the ability of rural landscapes in KRDCA to cope with disturbances and reduce losses by using their main components, the ecosystem, the engineering facility system, and the social and cultural system. If the vulnerability is improperly handled, it can directly lead to the reduction or cessation of some functions of the system [41]. In this study, it refers to the potential threat caused by the defects and inadequacies of rural landscape ecosystems, engineering facility systems, and social and cultural systems in KRDCA. Under the same vulnerability status, the disaster preparedness capacity of village landscapes to natural disasters is a key factor determining the resilience level of rural landscapes. The disaster preparedness and vulnerability assessment of rural landscapes in KRDCA could be used to effectively assess the resilience level of rural landscape systems in karst areas against the background of the Chinese government’s KRDC. The disaster preparedness capacity of rural landscapes is positively correlated with landscape resilience, while vulnerability is negatively correlated with landscape resilience. In the face of external disturbance, if the disaster preparedness capacity could resist the vulnerability caused by its own structural defects, the landscape system is resilient; otherwise, the rural landscape system had no resilience or insufficient resilience. The specific evaluation framework is shown in Figure 1.

3. Data Source and Processing

3.1. Study Area

Centered on the Yunnan-Kweichow Plateau, South China karst area is one of the world’s outstanding karst landscape, covering eight provinces and municipalities, including Guizhou, Guangxi, Chongqing, Yunnan, Sichuan, Hunan, Hubei, and Guangdong [42]. Guizhou is a part of the plateau and mountains of Southwest China, located on the eastern slope of the Yunnan-Kweichow Plateau, with complex karst types, complete morphology, and a large, concentrated distribution area, accounting for 61.9% of the total area of the province [43]. Affected by Himalayan movement, the Qinghai-Tibet Plateau has been rapidly uplifted, and the karst areas in Guizhou have developed a typical plate-canyon landform. The basic characteristics of the surface are broken and rugged terrain, a thin soil layer, a small area of cultivated land with poor quality, low land carrying capacity, and extremely fragile ecosystems [44,45]. Farmers living in this area have long been affected by natural disasters such as soil erosion, landslides, debris flows, drought, and floods, which have gradually led to large areas of KRD [46]. The China KRDC project has improved the karst ecological environment and effectively curtailed the expansion of KRD by adopting ecological control, engineering control, and biological control measures [47]. However, due to the closed influence of the karst mountain environment, the rural landscape in KRDCA in Guizhou still shows typical characteristics of high sensitivity to disasters.

Based on the rural landscape resilience analysis framework of karst areas, this research selected Fanhua village in Guanling County, a typical county of KRDC in South China, as a case study to carry out empirical research and analysis (the specific location is shown in Figure 2). The main part of the village is in the southeast to northwest trend of the peak cluster basin, covering an area of about 6 km², and the lithology is mainly dominated by limestone. The climate type of the study area is a subtropical monsoon climate that characterized by sufficient heat and rain in the summer; the cumulative average annual temperature, average maximum temperature, and average minimum temperature are 16.2 °C, 16.9 °C, and 15.4 °C, respectively. The annual precipitation is 1205.1 mm, and the average annual humidity is 81%. The terrain of the study area is relatively gentle, and is suitable for the growth of all kinds of vegetation and crops. However, long-term unreasonable land utilization has led to low vegetation coverage and serious soil erosion in the village. In addition, due to the influence of ground and underground dual structures in karst areas, surface water carries an amount of soil that easily leaks into the underground river system, which leads to significant trend of KRD. The local government has curbed the trend of KRD in a timely manner through measures such as mountain closure and forest cultivation, construction of water conservation facilities, vegetation management and protection, scientifically accelerated vegetation restoration, and soil fertility improvements. The village landscape pattern and villagers’ livelihood modes have been significantly improved, providing a good platform for the study of rural landscape resilience assessment in KRDCA.

3.2. Data Sources

3.2.1. Questionnaire Inquiry

In July 2022, the research group carried out a field survey of Fanhua village. First, the village cadres were interviewed to understand the KRDC measurements and technologies in Fanhua village, and then the field survey and data collection were carried out from the three viewpoints of ecosystem, engineering system, and social and cultural system. The survey and data collection included assessment indicators of the rural landscape preparedness and vulnerability of Fanhua village. The quantitative indicators were mainly evaluated by on-site data collection and local statistical data, while the qualitative indicators were mainly analyzed and evaluated by villager interviews. The interviewees were mainly residents over 40 years old who had a certain understanding of landscape pattern changes and KRDC effects. A total of 44 people were interviewed, including 3 village cadres with an average interview duration of 3.5 h, and 41 villagers with an average interview duration of 2 h. With the help of village cadres, on-site data collection and local statistical data reviews were conducted. The Likert scale method was used in the study, and the evaluation index score information was divided into five levels: very low (1), low (2), moderate (3), high (4), and very high (5).

3.2.2. Assessment Indicators

Rural landscapes in karst areas have unique local characteristics. The existing landscape resilience evaluation index system was developed mostly for urban areas, and is not suitable for the assessment of rural landscape resilience in KRDCA. Combined with the rural regional characteristics of karst areas, this paper constructs an evaluation index system based on the three dimensions of ecological system, engineering system, and social culture system proposed in the evaluation theoretical framework (specific indicators are shown in Table 1 and Table 2).

Ecosystems are an important part of rural landscapes that can not only bring good landscape effects, but also regulate climate, alleviate pollution, and purify air [48]. When the rural landscape system in KRDCA suffers from external disturbances, a good ecosystem can effectively act as a buffer to address disasters. This paper measures the quality of ecosystems from the two dimensions of ecological patterns and ecological governance [49,50]. Reasonable ecological patterns both create a good landscape effect and also effectively alleviate the impact of floods and other disasters and enhance the disaster preparedness ability of landscapes [51]. The ecological treatment project has improved the vegetation coverage rate in KRDCA and effectively alleviated the threat of heavy rainfall and other disasters to the rural landscape and to the safety of villager lives and property [52,53]. From the perspective of ecosystem vulnerability, this paper considers the two dimensions of ecological pressure and ecological vegetation sensitivity to evaluate the status quo of rural ecosystem vulnerability. When invasive alien vegetation and water-intensive crops account for a high proportion of the total local green vegetation, the village ecological balance is affected and the vulnerability of the village ecosystem increases. Ecological vegetation is the basis of human survival and sustainable social development. The threat of the ecological vegetation survival environment will inevitably lead to the loss of biodiversity and affect the balance of the ecosystem so that the rural landscape system will show stronger sensitivity to external impacts [54].

Engineering systems mainly refer to the use of people’s skills to plan various landscape infrastructures and recreational facilities, which can be used to mitigate and recover from the negative effects of disasters when they occur. This paper analyses the disaster preparedness capability of engineering systems from four perspectives: building facilities, water conservation engineering, fire protection facilities, and road engineering. In the face of natural disasters or human disturbance, healthy and safe living environments or building facilities can be used as a refuge to exhibit their disaster preparedness capabilities. Water conservation projects can effectively alleviate the negative impact of surface water leakage caused by ground and underground dual structures in karst areas and reduce the impact of drought and flood disasters on rural landscape systems [55]. Fire protection facilities can manage the threat caused by potential fire hazards, and their logical use can enhance the disaster preparedness ability of rural landscape systems. When natural disasters or human interference occur, a perfect road system can help people safely evacuate or provide emergency refuge places and enhance the ability of rural landscape systems to avoid disasters. The vulnerability of engineering systems is analyzed from the following perspectives: building instability, water supply and drainage pressure, fire protection pressure, and road sensitivity. Building instability refers to the vulnerability of the residential environment or building facilities in the face of disasters when they have certain security risks or have difficulty performing their established functions; this can both damage the rural landscape system and affect people’s life and safety. When water supply and drainage facilities are not sufficient to cope with the impact of droughts and floods, the vulnerability of rural landscape systems has increased, impacting landscape systems. The failure of fire protection facilities makes it difficult for them to perform their intended functions, thereby reducing their disaster preparedness ability. The imperfect or defective road system causes potential threats to people’s disaster avoidance actions, which may enhance the destructive power of disaster behavior on rural landscape systems.

The sociocultural system mainly refers to the use of social organization norms and local culture to build and maintain the landscape, which can enhance people’s sense of identity and belonging to the place [56]. Karst areas in southern China show a high sensitivity to natural disasters, and the local cultural landscape contains the wisdom of ancient people accumulated over thousands of years to deal with natural disasters. However, there is a lack of in-depth, targeted, and comprehensive research covering both natural and social sciences on the understanding of disaster culture in ethnic areas, the discovery of local knowledge of disaster reduction, the changes in social and traditional culture caused by disasters, and the construction of localized disaster prevention and reduction systems. In this paper, we measure the disaster preparedness of the social culture system from two perspectives: cultural disaster preparedness and cultural landscape protection. Cultural disaster preparedness refers to the assessment of the existing cultural disaster perception and response ability, which reflects the inheritance of traditional survival wisdom and disaster avoidance and reduction skills in karst areas and the degree of integration with modern science, including the popularization of the knowledge and achievements of rock desertification control. Traditional cultural landscapes embody the folk experience of karst area residents in adapting to habitat. If the traditional cultural landscape cannot be used, the loss of cultural landscape leads to the loss of local disaster preparedness ability, which enhances the vulnerability of the landscape system and reduces the resilience level of karst rural landscapes. This paper measures the vulnerability of the social culture system from the two perspectives of cultural resource loss and disaster sensitivity. Traditional culture is the crystallization of the wisdom of local ancient people, which contains rich knowledge and survival skills. Abandoning traditional culture has increased the vulnerability of rural landscapes in karst areas of South China and reduced landscape resilience. Identifying disaster sensitive areas in different regions is conducive to formulating targeted risk response strategies and reducing the disaster sensitivity of high-risk areas.

3.3. Data Processing

3.3.1. Index Weight Calculation Method

The DEMATEL method was used to determine the weight of each index. DEMATEL is a graph theory and a matrix tool for system analysis that was proposed by A. Gabus and E. Fontela in the Battelle Laboratory in 1971 to solve the problem of complex system evaluation [57], which can be used to describe the logical relationship between system elements and has been widely accepted [58,59]. All the elements of the system to be evaluated are considered together, affecting one another. Based on the direct influence relationship between the elements as the starting point, the influence matrix is analyzed to obtain the influence degree of each element on other elements, and the centrality and cause degree of each element are obtained as the basis for the construction of the model to determine the weight of each element in the system. The specific steps are as follows:

Step 1. Implement the acquisition of the matrix. The rural landscape resilience assessment index system in KRDCA in southern China is determined to be composed of n elements, and m experts are invited to compare each other to determine the degree of direct influence between elements. Factor i and factor j are compared twice, which is the direct influence of factor i on factor j and the direct influence of factor j on factor i. The specific function is shown in Equation (3), where M is the n $\times$ n direct influence matrix and a_ij is the direct influence matrix that is the degree to which element i affects element j. The direct influence matrix comparison scale has five levels: no impact (0), low impact (1), medium impact (2), high impact (3), and very high impact (4), and the direct impact matrix is obtained based on the scoring judgement of experts [50].

(3)$M={\left(a_{ij}\right)}_{n\times n}$

Step 2. Calculate the normative influence matrix. The normalized influence matrix is obtained by normalizing the direct influence matrix, and the specific function is shown in Equation (4):

(4)$N={\left(\frac{a_{ij}}{Maxvar}\right)}_{n\times n}$

(5)$Maxvar=max\left({\displaystyle \sum }_{j=i}^n{a}_{ij}\right)$

where Maxvar is summed in each row of the matrix (Equation (5)), takes the maximum value among these values, and then uses the obtained Maxvar to calculate the normative influence matrix N.

Step 3. The total influence matrix is derived. The direct influence matrix multiplication of the specification represents the increased indirect influence between elements. The total influence matrix T is represented by adding all the indirect influences together, as shown in Equation (6).

(6)$T=\left(N+N^2+N^3+\dots N^k\right)={\displaystyle \sum }_{k=1}^{\infty }{N}^k$

where T is the total influence matrix and N is the canonical influence matrix.

Step 4. Calculation of impact degree. The impact degree represents the comprehensive influence value of each corresponding element on all other elements, and the set is denoted as D, as shown in Equation (7). The method of obtaining D_i is shown in Equation (8).

(7)$D=\left(D_1+D_2+D_3+\dots D_n\right)$

(8)$D_i={\displaystyle \sum }_{j=1}^n{t}_{ij}\left(i=1,2,3,\dots n\right)$

where t_ij represents the degree of direct influence and indirect influence brought by factor i on factor j, that is, the degree of comprehensive influence. It also indicates the comprehensive impact of factor j on factor i.

Step 5. Calculation of influence degree. The degree of influence represents the comprehensive influence value of each corresponding element by all other elements, namely, the degree of influence. This set is denoted as C, as shown in Equation (9). The method of obtaining C_i is shown in Equation (10).

(9)$C=\left(C_1,C_2,C_3\dots C_n\right)$

(10)$C_i={\displaystyle \sum }_{j=1}^n{t}_{ij}\left(i=1,2,3,\dots n\right)$

Step 6. Calculation of centrality. The degree of influence (D_i) and the degree of influence (C_i) of an element i were added to obtain the centrality of the element, denoted as M_i. Centrality indicates the position of the factor in the evaluation index system and the size of its role, as shown in Equation (11).

(11)$M_i=D_i+C_i$

By normalizing the centrality of all elements in the system to be evaluated, the status and weight of corresponding elements in the system are obtained. In this study, the weights of indicators at all levels are shown in the evaluation results table, below.

3.3.2. Disaster Preparedness and Vulnerability Scoring Method

According to the above analysis of resilience assessment Equation (2) and the understanding of the concept of resilience, the score of disaster preparedness (PI) is the weighted sum of the scores of all first-level indicators (PD), the calculation method of PD is the weighted sum of all second-level indicators, PS is the average score of all third-level indicators (PC), and the score of third-level indicators is the average of the field survey [59]. The specific function is shown in Equation (12).

(12)$PI={\displaystyle {{\displaystyle \sum }}_{i=1}^{i=NP}}{w}_{i}P{D}_i P{D}_i={{\displaystyle \sum }}_{j=1}^{j=M{P}_i}{u}_{ij}P{S}_{ij} P{S}_{ij}=\frac{{{\displaystyle \sum }}_{k=1}^{k=L{P}_j}P{C}_{ijk}}{L{P}_j}$

where PI represents the disaster preparedness score of the system to be evaluated. PD_i, PS_ij, and PC_ijk correspond to the scores of the first-level index i, the second-level index j, and the third-level index k, respectively. At the same time, NP, MP_i, and LP_j correspond to the number of indicators of the first-level index, the second-level index, and the third-level index, respectively, while w_i and u_ij represent the corresponding weight of the first-level index i and the second-level index j in the resilience evaluation of the system.

The calculation method of vulnerability and disaster preparedness capacity is the same, and the specific function is shown in Equation (13):

(13)$VI={\displaystyle {{\displaystyle \sum }}_{i=1}^{i=NV}}{y}_{i}V{D}_i V{D}_i={{\displaystyle \sum }}_{j=1}^{j=M{V}_i}{x}_{ij}V{S}_{ij} V{S}_{ij}=\frac{{{\displaystyle \sum }}_{k=1}^{k=L{V}_j}V{C}_{ijk}}{L{V}_j}$

where VI represents the vulnerability score of the system to be evaluated. VD_i, VS_ij, and VC_ijk correspond to the scores of the first level index i, the second level index j, and the third level index k, respectively. At the same time, NV, MV_i, and LV_j correspond to the number of indicators of the first level index, the second level index, and the third level index, respectively, while Y_i and X_ij represent the corresponding weight of the first level index i and the second level index j in the system resilience evaluation.

4. Result

4.1. Ecological Resilience Assessment

The resilience score of the Fanhua village ecosystem is 2.08 (Table 3), and its disaster preparedness and vulnerability scores are 1.39 and 0.67, respectively, indicating that the village ecological landscape subsystem has a high level of resilience. Among them, the scores of the two dimensions of “ecological pattern” and “ecological governance”, which are indicators of ecosystem disaster preparedness, are five and three (very low (one), low (two), average (three), high (four) and very high (five)), respectively, indicating that ecosystem disaster preparedness capacity is strong. There are abundant ecological elements in Fanhua village. Forests, grasslands, rivers, and farmland form the basis of the ecological pattern of Fanhua village. Under the policy of Closing Hillsides to Facilitate Afforestation of the local government, the green area of Fanhua village accounts for more than 60% of the total area of the village with diverse species; most of them are suitable local species adapted to local climatic conditions and well-growth condition. To maintain the existing ecological landscape pattern, the local government has implemented ecological control measures such as afforestation, high-standard farmland construction, and the construction of diversion channels [60]. The area of KRDCA accounts for approximately 40% of the total area of the village. The scores of “ecological pressures” and “ecological vegetation sensitivity” reflecting the evaluation dimension of ecosystem vulnerability are three and one, respectively. The main reason for the increase in vulnerability to “ecological pressure” is that the score of “the proportion of crops requiring large amounts of water” is five, and most of the crops in the village were rice and corn with high water demand. To effectively reduce the water demand for industrial development in Fanhua village and to enhance the ability of industry to cope with drought, the village has begun to promote the planting of water-saving crops such as Sichuan peppercorn and yellow ginger. In general, in the face of natural disasters, the existing ecological elements of the ecological landscape structure of Fanhua village could have a greater buffer capacity against drought and flood disasters. According to the evaluation results, two methods were used to enhance the landscape resilience of Fanhua Village: (1) the planting area of crops with large water requirement was further reduced, and the landscape design of farmland was carried out without destroying the disaster preparedness capacity of the ecosystem. (2) The appropriate reduction of the paving of hard road permeable paving brick.

4.2. Engineering Resilience Assessment

The disaster preparedness capability and vulnerability scores of the engineering system of Fanhua village are 1.05 and 0.84, respectively, and the engineering resilience score is 1.24 (Table 4), indicating that the engineering facility system of Fanhua village also has high resilience. Among them, the disaster preparedness capability indicators of engineering systems “Building facilities” and “Road engineering” both are 4, “Water conservation engineering” is 3.5, and “Fire protection facilities” is 1. The village has relatively perfect infrastructure, and housing, education, medical, and other buildings are mostly newly built concrete structures and good environments. The village has a diverse road system with complete supporting facilities, and the landscape along the road is mostly designed professionally. To cope with the structural water shortage in karst areas, the Fanhua village has built diversion channels and reservoirs that can be combined with the landscape. The reason for the low disaster preparedness score of “Fire facilities” is that the service radius of fire facilities in the village is greater than 7 km², making it difficult to deal with sudden fires. In the dimension of vulnerability of the engineering system in Fanhua village, the scores of “Building instability” and “Fire protection facility pressure” are four and three, respectively, while the scores of “Water supply and drainage facility pressure” and “Road sensitivity” are one and two, respectively. The reason for the increase in vulnerability to “building instability” is that there are scattered dilapidated buildings in the village that have been uninhabited for a long time, and their structural safety is poor. In response to greater rain and flood damage, they may easily collapse. The reason for the high vulnerability score of “Fire facilities pressure” is mainly because the fire facilities in the village are not regularly repaired. The system of engineering facilities is often closely connected, and the collapse of one element may reduce the stability and self-recovery ability of the whole landscape system [61,62]. However, in general, Fanhua village can still contribute to disaster prevention and the reduction of existing engineering facilities in response to external disturbances so that it has high stability and self-sustaining ability, which can effectively enhance the engineering resilience of the landscape system. According to the evaluation results, the following two improvement methods can reduce the vulnerability of the engineering system of Fanhua Village and enhance its landscape resilience: (1) Plan the location and number of firefighting facilities in the village, and check whether they can be used normally on time. (2) Repair buildings or facilities with cultural significance and demolish abandoned buildings without cultural value.

4.3. Sociocultural Resilience Assessment

The disaster preparedness capacity and vulnerability scores of the sociocultural system of Fanhua village are 0.93 and 1.04, respectively, and the sociocultural resilience score is 0.89 (Table 5). The cultural landscape system is less than one, indicating that the sociocultural resilience is insufficient. The scores of “cultural disaster preparedness capacity” and “cultural landscape protection” are 3 and 2.5, respectively. Every year, disaster prevention knowledge popularization and KRDC publicity activities are held in Fanhua village, and the villagers have a certain understanding of disaster prevention strategies and knowledge of KRDC. From the perspective of cultural landscape protection, although the village has a long history and important cultural elements, it has formed a unique cultural value system and art carrier. For example, although the ancestral hall of the Chen family, a symbol of village history and culture, is still regarded as the spiritual bond between villagers, its importance in the hearts of teenagers has been greatly diminished, and the preservation of local opera is mainly carried out by elderly individuals, without the introduction of new members. These traditional cultural concepts and landscape carriers contain ancient people’s understanding of the relationship between human society and the ecological environment, and they are increasingly being recognized as pivotal to disaster mitigation [63]. For example, traditional buildings in this region are often built on a high foundation, while new buildings are located on a low foundation. Rain and flood damage due to heavy rainfall is prone to have a great impact on new buildings, and the ability to deal with flood disasters is reduced. In the dimension of sociocultural system vulnerability in Fanhua village, the vulnerability scores of “cultural resource loss” and “disaster sensitivity” are both three. In village landscape transformation, traditional cultural elements are deliberately explored and applied in landscape design. However, with the development of the times, many people in the village have ventured out for work and study, introducing foreign cultural values that impact the original social and cultural value system, and the local traditional knowledge of disaster prevention and reduction in the traditional culture has been gradually lost. Facilities for young and middle-aged people have been idle for a long time, and no protection measures have been implemented for the former residence of Jiang and Chen, which symbolizes the history of Fanhua village, causing it to become a potential safety hazard. According to the evaluation results, the following three approaches can be used to improve the resilience level of Fanhua Village landscape: (1) Continue to excavate and protect traditional culture and its related landscape carriers. (2) Explore the modern landscape forms of disaster risk reduction knowledge in traditional culture. (3) Perform reasonable excavation of the economic benefits of traditional culture.

4.4. Resilience Assessment of the Rural Landscape in Fanhua Village

In summary, the disaster preparedness and vulnerability scores of Fanhua village are 3.37 and 2.56, respectively, and the overall resilience score of the landscape is 1.32, with a calculation result >1 (Table 6), indicating that the village landscape is resilient, the disaster preparedness ability can cope with its vulnerability, and the existing landscape system structure can resist the impact caused by external disturbances or its own defects. Taking the ratio of disaster preparedness and vulnerability of ecological, engineering, and sociocultural systems as the resilience of corresponding dimensions, the resilience scores of the Fanhua village landscape system are 2.08, 1.24, and 0.89, respectively. That is, the resilience of the Fanhua village landscape system is the highest in terms of ecological elements, followed by engineering resilience, while it is insufficient in terms of sociocultural resilience.

5. Discussion

Through the analysis of the rural landscape resilience of Fanhua village, we found that villages in typical KRDCA in Guizhou Province have developed a unique system in terms of economy and culture, which has allowed a large number of traditional villages and local cultures to be well preserved. However, the fragile ecological environment has greatly restrained rural development in karst areas. With the promotion of the KRDC project of the Chinese government, the KRD problem in karst areas in southern China has diminished, and the rural landscape ecological pattern and infrastructure engineering facilities have gradually improved [64]. However, with the large flow of the rural population in China in recent years, the local cultural values in karst areas of southern China are suffering from external cultural impacts [65]. The weakening of local culture is also accompanied by the loss of knowledge and skills for resisting interference accumulated by ancient people. These internal changes are manifested in the external rural landscape. Therefore, the results of this study may be helpful for formulating corresponding strategies to promote the comprehensive development of Fanhua village.

6. Conclusions

Based on disaster preparedness and vulnerability, this paper constructs an index system of rural landscape resilience evaluation in KRDCA in southern China from the three dimensions of ecology, engineering, and social culture. The disaster preparedness ability of rural landscapes in the KRDCA in southern China is reflected in the fact that the nature of the system can be used to reduce the negative impact of disturbance. Ecological elements can serve as a buffer against natural disasters and reduce the destructive power of floods and other disasters, while engineering elements can effectively reduce the damage degree of disasters to village buildings. The social and cultural elements of the village system’s ability to adapt to external shocks or environmental changes involve more local knowledge and skills developed over a long period of time. The disadvantageous factors of ecology, engineering, and social culture strengthen its vulnerability to disturbance. In view of this, this research constructs a rural landscape resilience assessment framework based on the combination of disaster preparedness and vulnerability of rural landscapes in KRDCA. In addition, Fanhua village in Guanling county of Guizhou Province, a typical KRDCA, was selected as study area to empirically analyze the rural landscape resilience. However, this was an exploratory study on the quantitative assessment of landscape resilience, and, as such, it has certain local characteristics in index selection. In future studies, assessment systems can be developed based on the specific situations.

The results of the study indicated that the rural landscape resilience of Fanhua village was higher from the three dimensions of ecology, engineering, and social culture. Specifically, the scores for ecological resilience, engineering resilience, and sociocultural resilience, which constitute the rural landscape resilience of Fanhua village, were 2.08, 1.24, and 0.88, respectively. The ecological resilience was the highest, but the sociocultural resilience was insufficient. In terms of the disaster preparedness of rural landscapes, the disaster preparedness of the ecological system was the highest, and that of the social cultural system was the lowest, with values of 1.39 and 0.92, respectively. From the perspective of the vulnerability of rural landscapes, the vulnerability of the social cultural system was the highest, and that of the ecological system was the lowest, with values of 1.04 and 0.67, respectively. Therefore, the rural landscape system of Fanhua village can use ecological elements and engineering facilities to cope with the impact of disturbances. In contrast, the social cultural system has weak disaster preparedness ability and high vulnerability, and it is difficult to use cultural knowledge to resist the impact of external disturbances. This indicates that Fanhua village is in the key stage of the transformation and adjustment of its rural development structure. It also presents complex features in landscape construction.

Author Contributions

Conceptualization, B.Y. and S.L.; methodology, S.L. and Y.H.; software, S.L. and Y.H.; validation, B.Y. and S.L.; formal analysis, B.Y.; investigation, S.L., T.L., and R.S.; writing—original draft preparation, B.Y. and S.L.; writing—review and editing, B.Y. and K.X. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Not applicable.

Acknowledgments

Thanks to the villagers of Fanhua Village for their help in our questionnaire survey and data collection.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Rural resilient landscape assessment framework.

Figure 2. Regional location map of Fanhua Village.

Table 1

Assessment index of disaster preparedness of rural landscapes in KRDCA.

First-Level Indicators	Second-Level Indicators	Third-Level Indicators	Indicator Score Description
Ecological system	Ecological pattern	Ratio of green space	0%–20% (1), 21%–30% (2), 31%–40% (3), 41%–50% (4), More than 50% (5)
		Diversity of species	The proportion of vegetation species in the total number of suitable plants in the area: less than 30% (1), 31%–50% (2), 51%–70% (3), 71%–90% (4), and more than 90% (5)
	Ecological governance	The proportion of KRDCA	0%–20% (1), 21%–30% (2), 31%–40% (3), 41%–50% (4), More than 50% (5)
Engineering system	Building facilities	Building structure	The proportion of brick or reinforced concrete structure: less than 50% (1), 51%–60% (2), 61%–70% (3), 71%–80% (4), more than 80% (5)
		Proportion of new homes built in the last decade	Less than 40% (1), 41%–50% (2), 51%–60(3), 61%–70% (4), more than 70% (5)
	Water conservation engineering	Kilometers of new aqueducts	Less than 0.3 (1), 0.3–0.5 (2), 0.5–0.8 (3), 0.8–1.0 (4), more than 1.0 (5)
		Number of new cisterns	0 (1), 1–2 (2), 3 (3), 4 (4), more than 5 (5)
	Fire protection facilities	Fire station service radius	More than 7 km2 (1), 4 km2–5 km2 (2), 5 km2–6 km2 (3), 6 km2–7 km2 (4), Less than 4 km2 (5)
	Road engineering	Proportion of concrete roads	Less than 50% (1), 51%–60% (2), 61%–70% (3), 71%–80% (4), more than 80% (5)
		Road supporting facilities	0 (1), 1–2 (2), 3 (3), 4 (4), more than 5 (5)
Sociocultural system	Cultural disaster preparedness	Number of disaster avoidance strategy training per year	0 (1), 1–2 (2), 3 (3), 4 (4), more than 5 (5)
		KRDC knowledge propaganda	0 (1), 1–2 (2), 3 (3), 4 (4), more than 5 (5)
	Cultural landscape protection	Establishment of cultural reserve	0 (1), 1–2 (2), 3 (3), 4 (4), more than 5 (5)
		Awareness of cultural protection	Less than 30% (1), 30%–50% (2), 50%–70% (3), 70%–90% (4), more than 90% (5)

Table 2

Assessment indicators of rural landscape vulnerability in KRDCA.

First-Level Indicators	Second-Level Indicators	Third-Level Indicators	Indicator Score Description
Ecological system	Ecological pressure	The proportion of crops requiring large amounts of water	0%–20% (1), 21%–30% (2), 31%–40% (3), 41%–50% (4), more than 50% (5)
		Proportion of invasive alien plants	0%–10% (1), 11%–20% (2), 21%–30% (3), 31%–40% (4), more than 40% (5)
	Ecological vegetation sensitivity	Threat assessment of native vegetation	Number of endangered species of native vegetation: 0 (1), 1–2 (2), 3–4 (3), 5–6 (4), more than 6 (5)
Engineering system	Building instability	Old buildings with security risks	The number of buildings with security risks: 0 (1), 1–2 (2), 3–4 (3), 5–6 (4), more than 6 (5)
	Water supply and drainage pressure	Annual water supply and drainage facilities failure days	Less than 15 days (1), 15–50 (2), 50–80 (3), 80–100 (4), more than 100 days (5)
	Fire protection pressure	Frequency of failure of fire protection facilities in the past five years	0 (1), 1–3 (2), not checked (3), 4–5 (4), more than 5 times (5)
	Road sensitivity	Annual frequency of road failure	0 (1), 1–2 (2), 3 (3), 4 (4), more than 5 (5)
		Proportion of unrepaired roads	0%–10% (1), 11%–20% (2), 21%–30% (3), 31%–40% (4), more than 40% (5)
Sociocultural system	Cultural resource loss	Idle cultural facilities	0 (1), 1–2 (2), 3 (3), 4 (4), more than 5 (5)
		Damage to traditional buildings	0 (1), 1–2 (2), 3 (3), 4 (4), more than 5 (5)
	Disaster sensitivity	Setting of disaster sensitive area	1–2 (1), 3 (2), no disaster sensitive zones were set (3), 4–5 (4), more than 5 (5)

Table 3

Assessment results of ecological resilience in Fanhua village.

Evaluative Dimension	Weight	Evaluation Index	Score		Evaluation Attribute	RI
Ecological pattern	0.167	Ratio of green space	5	5	PI = 1.39	2.08
		Diversity of species	5
Ecological governance	0.184	The proportion of KRDCA	3	3
Ecological pressure	0.172	The proportion of crops requiring large amounts of water	5	3	VI = 0.67
		Proportion of invasive alien plants	1
Ecological vegetation sensitivity	0.151	Threat assessment of native vegetation	1	1

Table 4

Assessment results of engineering resilience in Fanhua village.

Evaluative Dimension	Weight	Evaluation Index	Score		Evaluation Attribute	RI
Building facilities	0.103	Building structure	5	4	PI = 1.05	1.24
		Proportion of new homes built in the last decade	3
Water conservation engineering	0.075	Kilometers of new aqueducts	5	3.5
		Number of new cisterns	2
Fire protection facilities	0.056	Fire station service radius	1	1
Road engineering	0.079	Proportion of concrete roads	5	4
		Road supporting facilities	3
Building instability	0.091	Old buildings with security risks	4	4	VI = 0.84
Water supply and drainage pressure	0.072	Annual water supply and drainage facilities failure days	1	1
Fire protection pressure	0.074	Frequency of failure of fire protection facilities in the past five years	3	3
Road sensitivity	0.092	Annual frequency of road failure	2	2
		Proportion of unrepaired roads	2

Table 5

Assessment results of sociocultural resilience in Fanhua village.

Evaluative Dimension	Weight	Evaluation Index	Score		Evaluation Attribute	RI
Cultural disaster preparedness	0.171	Number of disaster avoidance strategy training per year	3	3	PI = 0.93	0.89
		KRDC knowledge propaganda	3
Cultural landscape protection	0.165	Establishment of cultural reserve	3	2.5
		Awareness of cultural protection	2
Cultural resource loss	0.150	Idle cultural facilities	3	3	VI = 1.04
		Damage to traditional buildings	3
Disaster sensitivity	0.198	Setting of disaster sensitive area	3	3

Table 6

Landscape resilience assessment results of Fanhua village.

	Ecological System	Engineering System	Sociocultural System	Total
PI	1.39	1.05	0.93	3.37
VI	0.67	0.84	1.04	2.55
RI	1.32

References

1. Chen, C.; Yuan, D.; Cheng, H.; Yu, T.; Shen, C.; Edwards, R.L.; Wu, Y.; Xiao, S.; Zhang, J.; Wang, T. et al. Human activity and climate change triggered the expansion of rocky desertification in the karst areas of southwestern China. Sci. J. China Earth Sci.; 2021; 64, pp. 1761-1773. [DOI: https://dx.doi.org/10.1007/s11430-020-9760-7]

2. Chen, Q.; Lu, S.; Xiong, K.N.; Zhao, R. Coupling analysis on ecological environment fragility and poverty in South China karst. J. Environ. Res.; 2021; 201, 111650. [DOI: https://dx.doi.org/10.1016/j.envres.2021.111650] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34245730]

3. Yuan, D.X. Rock desertification in the subtropical karst of south China. Ztschrift Fur Geomorphol.; 1997; 108, pp. 81-90.

4. Xiong, K.N.; Li, P.; Zhou, Z.F.; An, Y.L. Remote Sensing of Karst Rocky Desertification-A Typical Research of GIS: Taking Guizhou Province as an Example; Geology Publishing House: Beijing, China, 2002; 18.(In Chinese)

5. Wang, S.J.; Liu, Q.M.; Zhang, D.F. Karts rocky desertification in south-west China geomorphology, land use, impact and rehabilitation. Land Degrad. Dev.; 2004; 15, pp. 115-121. [DOI: https://dx.doi.org/10.1002/ldr.592]

6. Tong, X.W.; Wang, K.L.; Yue, Y.M.; Brandt, M.; Liu, B.; Zhang, C.H.; Liao, C.J.; Fensholt, R. Quantifying the effectiveness of ecological restoration projects on long-term vegetation dynamics in the karst regions of Southwest China. Int. J. Appl. Earth Obs. Geoinf.; 2017; 54, pp. 105-113. [DOI: https://dx.doi.org/10.1016/j.jag.2016.09.013]

7. Li, L.; Xiong, K.N.; Dan, W.H. Study progress and prospect of ecological industrial market models for control of karst rocky desertification. J. Int. J. Environ. Pollut.; 2020; 67, pp. 2-4. [DOI: https://dx.doi.org/10.1504/IJEP.2020.117790]

8. Tong, X.W.; Brandt, M.; Yue, Y.M.; Horion, S.; Wang, K.L.; De, K.W.; Tian, F.; Schurgers, G.; Xiao, X.M.; Luo, Y.Q. et al. Increased vegetation growth and carbon stock in China karst via ecological engineering. J. Nat. Sustain.; 2018; 1, pp. 44-50. [DOI: https://dx.doi.org/10.1038/s41893-017-0004-x]

9. Liu, Y.S. Research on the urban-rural integration and rural revitalization in the new era in China. J. ACTA Geogr. Sin.; 2018; 4, 73. [DOI: https://dx.doi.org/10.11821/dlxb201804004. ] (In Chinese)

10. Wu, Z.G.; Zhu, D.Y.; Xiong, K.N. Dynamics of landscape ecological quality based on benefit evaluation coupled with the rocky desertification control in South China Karst. Ecol. Indic.; 2022; 138, 108870. [DOI: https://dx.doi.org/10.1016/j.ecolind.2022.108870]

11. Yang, W.L.; Chu, W.C.; Zhou, L.M. Evaluating the impact of karst rocky desertification on regional climate in Southwest China with WRF. J. Theor. Appl. Climatol.; 2019; 137, pp. 481-492. [DOI: https://dx.doi.org/10.1007/s00704-018-2606-2]

12. Lv, Y.X.; Jiang, Y.J.; Hu, W.; Cao, M.; Mao, Y. A review of the effects of tunnel excavation on the hydrology, ecology, and environment in karst areas: Current status, challenges, and perspectives. J. Hydrol.; 2020; 586, 124891. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2020.124891]

13. Macias-Fauria, M. Satellite images show China going green. Nature; 2018; 553, pp. 411-413. [DOI: https://dx.doi.org/10.1038/d41586-018-00996-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29368722]

14. Wang, X.F.; Yang, G.B. Analysis on the Change Characteristics and Control Effect of Karst Rocky Desertification in Beipanjiang River Basin of Guizhou Province. J. Yangtze River Sci. Res. Inst.; 2022; 1, 9. [DOI: https://dx.doi.org/10.11988/ckyyb.20220173. ] (In Chinese)

15. Zhang, J.; Dai, M.; Wang, L.; Su, W. Household livelihood change under the rocky desertification control project in karst areas, Southwest China. J. Land Use Policy; 2016; 56, pp. 8-15. [DOI: https://dx.doi.org/10.1016/j.landusepol.2016.04.009]

16. Xiao, J.; Xiong, K.N. A review of agroforestry ecosystem services and its enlightenment on the ecosystem improvement of rocky desertification control. J. Sci. Total Environ.; 2022; 852, 158538. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2022.158538]

17. Yang, M.D. The Characteristics and Protection of Karst Cave Tourism Resources. J. Carsologica Sin.; 1998; 17, pp. 51-56. (In Chinese)

18. Yang, S.; Li, C.; Lou, H.; Wang, P.; Wu, X.; Zhang, Y.; Zhang, J.; Li, X. Role of the countryside landscapes for sustaining biodiversity in karst areas at a semi centennial scale. Ecol. Indic.; 2021; 123, 107315. [DOI: https://dx.doi.org/10.1016/j.ecolind.2020.107315]

19. Wu, Q.; Zheng, W.; Rao, C.J.; Wang, E.W.; Yan, W.D. Soil Quality Assessment and Management in Karst Rocky Desertification Ecosystem of Southwest China. J. For.; 2022; 13, 1513. [DOI: https://dx.doi.org/10.3390/f13091513]

20. Wang, L.; Xiao, S. Tourism space reconstruction of a world heritage site based on actor network theory: A case study of the Shibing Karst of the South China Karst World Heritage Site. Int. J. Geoheritage Park.; 2020; 8, pp. 140-151. [DOI: https://dx.doi.org/10.1016/j.ijgeop.2020.05.005]

21. Ying, B.; Xiong, K.N.; Wang, Q.; Wu, Q.M. Can agricultural biomass energy provide an alternative energy source for karst rocky desertification areas in Southwestern China? investigating Guizhou Province as example. J. Environ. Sci. Pollut. Res.; 2021; 28, pp. 44315-44331. [DOI: https://dx.doi.org/10.1007/s11356-021-12537-1]

22. Liu, Y.S.; Zang, Y.Z.; Yang, Y.Y. China’s rural revitalization and development: Theory, technology and management. J. Geogr. Sci.; 2020; 30, pp. 1923-1942. [DOI: https://dx.doi.org/10.1007/s11442-020-1819-3]

23. Schippers, P.; Vander, H.C.M.; Koelewijn, H.P.; Schouten, M.A.H. Landscape diversity enhances the resilience of populations, ecosystems and local economy in rural areas. J. Landsc. Ecol.; 2015; 30, pp. 193-202. [DOI: https://dx.doi.org/10.1007/s10980-014-0136-6]

24. Holmes, G.; Clemoes, J.; Marriot, K.; Jones, S.W. The politics of the rural and relational values: Contested discourses of rural change and landscape futures in west wales. Geoforum; 2022; 133, pp. 153-164. [DOI: https://dx.doi.org/10.1016/j.geoforum.2022.05.014]

25. Holling, C.S. Resilience and Stability of Ecological Systems. J. Annu. Rev. Ecol. Syst.; 1973; 4, pp. 1-23. [DOI: https://dx.doi.org/10.1146/annurev.es.04.110173.000245]

26. Martha, S. The Designer’s Geoengineering Toolkit: Crisis Creates Opportunities for Landscape Architects to Reverse, Repair and Regenerate the Earth’s Climate. J. Landsc. Archit.; 2020; 27, pp. 10-25. [DOI: https://dx.doi.org/10.14085/j.fjyl.2020.12.0010.16] (In Chinese)

27. Niall, U. Resilient Landscapes—Dimensions of Future Landscape Architectural Practices. J. Chin. Landsc. Archit.; 2010; 26, pp. 10-14. (In Chinese)

28. Eriksson, M.; Samuelson, L.; Jagrud, L.; Mattsson, E.; Celander, T.; Malmer, A.; Bengtsson, K.; Johansson, O.; Schaaf, N.; Svending, O. et al. Water, Forests, People: The Swedish Experience in Building Resilient Landscapes. J. Environ. Manag.; 2018; 62, pp. 45-57. [DOI: https://dx.doi.org/10.1007/s00267-018-1066-x]

29. Matthias, I.; Semmler, M.S.S.; Giesecke, T. Pollen diversity captures landscape structure and diversity. J. Ecol.; 2015; 103, pp. 880-890. [DOI: https://dx.doi.org/10.1111/1365-2745.12404]

30. Abrams, J.; Greiner, M.; Schultz, C. Can Forest Managers Plan for Resilient Landscapes? Lessons from the United States National Forest Plan Revision Process. J. Environ. Manag.; 2021; 67, pp. 574-588. [DOI: https://dx.doi.org/10.1007/s00267-021-01451-4]

31. Hossain, M.S.; Ramirez, J.A.; Haisch, T.; Speranza, C.I.; Martius, O.; Mayer, H.; Keiler, M. A coupled human and landscape conceptual model of risk and resilience in Swiss Alpine communities. Sci. Total Environ.; 2020; 730, 138322. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.138322]

32. Sara, B.; Sophie, N.; Kathleen, L.W.; Angie, W.; Erin, D.; Stephen, R.J.S. Greening Blocks: A Conceptual Typology of Practical Design Interventions to Integrate Health and Climate Resilience Co-Benefits. J. Environ. Res. Public Health; 2019; 16, 4241. [DOI: https://dx.doi.org/10.3390/ijerph16214241]

33. Long, N.V.; Cheng, Y. Flood-resilient urban design based on the indigenous landscape in the city of Can Tho, Vietnam. J. Urban Ecosyst.; 2020; 23, pp. 675-687. [DOI: https://dx.doi.org/10.1007/s11252-020-00941-3]

34. United Nations International Strategy for Disaster Reduction. UNISDR Terminology on Disaster Risk Reduction. 2009; Available online: https://www.undrr.org/publication/2009-unisdr-terminology-disaster-risk-reduction (accessed on 28 July 2022).

35. Bruneau, M.; Chang, S.E.; Eguchi, R.T.; Lee, G.C.; O’Rourke, T.D.; Reinhorn, A.M.; Shinozuka, M.; Tierney, K.; Wallace, W.A.; Von Winterfeldt, D. A framework to quantitatively assess and enhance the seismic resilience of communities. J. Earthq. Spectra; 2003; 19, pp. 733-752. [DOI: https://dx.doi.org/10.1193/1.1623497]

36. Henry, D.; Emmanuel Ramirez-Marquez, J. Generic metrics and quantitative approaches for system resilience as a function of time. Reliab. Eng. Syst. Saf.; 2012; 99, pp. 114-122. [DOI: https://dx.doi.org/10.1016/j.ress.2011.09.002]

37. Kusumastuti, R.D.; Husodo, Z.A.; Suardi, L.; Danarsari, D.N. Developing a resilience index towards natural disasters in Indonesia. Int. J. Disaster Risk Reduct.; 2014; 10, pp. 327-340. [DOI: https://dx.doi.org/10.1016/j.ijdrr.2014.10.007]

38. Simpson, D.M.; Katirai, M. Indicator Issues and Proposed Framework for a Disaster Preparedness Index (DPi), Draft Version 1.0. 2006; Available online: http://www.fritzinstitute.org/PDFs/WhitePaper/DaveSimpsonIndicatorsRepor.pdf (accessed on 19 July 2022).

39. Hui, X.; Yang, L.; Lin, W. Resilience Assessment of Complex Urban Public SpacesInt. J. Environ. Res. Public Health; 2020; 17, 524. [DOI: https://dx.doi.org/10.3390/ijerph17020524]

40. Rani, M.; Rehman, S.; Sajjad, H.; Chaudhary, B.S.; Sharma, J.; Bhardwaj, S.; Kumar, P. Assessing coastal landscape vulnerability using geospatial techniques along Vizianagaram-Srikakulam coast of Andhra Pradesh, India. J. Nat. Hazards; 2018; 94, pp. 711-725. [DOI: https://dx.doi.org/10.1007/s11069-018-3414-9]

41. Khademi, N.; Balaei, B.; Shahri, M.; Mirzaei, M.; Sarrafi, B.; Zahabiun, M.; Mohaymany, A.S. Transportation network vulnerability analysis for the case of a catastrophic earthquake. Int. J. Disaster Risk Reduct.; 2015; 12, pp. 234-254. [DOI: https://dx.doi.org/10.1016/j.ijdrr.2015.01.009]

42. Peng, X.D.; Dai, Q.H.; Ding, G.J.; Shi, D.M.; Li, C.L. Impact of vegetation restoration on soil properties in near-surface fissures located in karst rocky desertification regions. J. Soil Tillage Res.; 2020; 200, 104620. [DOI: https://dx.doi.org/10.1016/j.still.2020.104620]

43. Tang, X.; Xiao, J.; Ma, M.; Yang, H.; Li, X.; Ding, Z.; Yu, P.; Zhang, Y.; Wu, C.; Huang, J. et al. Satellite evidence for China’s leading role in restoring vegetation productivity over global karst ecosystems. J. For. Ecol. Manag.; 2022; 507, 120000. [DOI: https://dx.doi.org/10.1016/j.foreco.2021.120000]

44. Cao, Z.; Zhang, K.; He, J.; Yang, Z.; Zhou, Z. Linking rocky desertification to soil erosion by investigating changes in soil magnetic susceptibility profiles on karst slopes. J. Geoderma; 2021; 389, 114949. [DOI: https://dx.doi.org/10.1016/j.geoderma.2021.114949]

45. Guo, B.; Yang, F.; Fan, Y.W.; Zang, W.Q. The dominant driving factors of rocky desertification and their variations in typical mountainous karst areas of southwest China in the context of global change. J. CATENA; 2023; 220, 1066674. [DOI: https://dx.doi.org/10.1016/j.catena.2022.106674]

46. Peng, X.D.; Dai, Q.H. Drivers of soil erosion and subsurface loss by soil leakage during karst rocky desertification in SW China. J. Int. Soil Water Conserv. Res.; 2022; 10, pp. 217-227. [DOI: https://dx.doi.org/10.1016/j.iswcr.2021.10.001]

47. Deng, X.H.; Xiong, K.N.; Yu, Y.H.; Zhang, S.H.; Kong, L.W.; Zhang, Y. A Review of Ecosystem Service Trade-Offs/Synergies: Enlightenment for the Optimization of Forest Ecosystem Functions in Karst Desertification Control. J. For.; 2023; 14, 88. [DOI: https://dx.doi.org/10.3390/f14010088]

48. Saklaurs, M.; Lībiete, Z.; Donis, J.; Kitenberga, M.; Elferts, D.; Jūrmalis, E.; Jansons, Ā. Provision of Ecosystem Services in Riparian Hemiboreal Forest Fixed-Width Buffers. J. For.; 2022; 13, 928. [DOI: https://dx.doi.org/10.3390/f13060928]

49. Zhang, H.; Liang, X.Y.; Chen, H.; Shi, Q.Q. Spatio-temporal evolution of the social-ecological landscape resilience and management zoning in the loess hill and gully region of China. J. Environ. Dev.; 2021; 39, 100616. [DOI: https://dx.doi.org/10.1016/j.envdev.2021.100616]

50. Jiang, S.L.; Xiong, K.N.; Xiao, J. Structure and Stability of Agroforestry Ecosystems: Insights into the Improvement of Service Supply Capacity of Agroforestry Ecosystems under the Karst Rocky Desertification Control. J. For.; 2022; 13, 878. [DOI: https://dx.doi.org/10.3390/f13060878]

51. Donatella, V.; María, V.M.; Erica, M.L.; Cosimo, G.G.; Irene, P. Fostering the Resiliency of Urban Landscape through the Sustainable Spatial Planning of Green Spaces. J. Land; 2022; 11, 367. [DOI: https://dx.doi.org/10.3390/land11030367]

52. Liu, Y.F.; Guo, B.; Lu, M.; Zang, W.Q.; Yu, T.; Chen, D.H. Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981–2019. J. Arid Land; 2023; 03, pp. 91-108. [DOI: https://dx.doi.org/10.1007/s40333-022-0079-8]

53. Guo, B.; Lu, M.; Fan, Y.W.; Wu, H.W.; Yang, Y.; Wang, C.L. A novel remote sensing monitoring index of salinization based on three-dimensional feature space model and its application in the Yellow River Delta of China. J. Geomat. Nat. Hazards Risk; 2023; 14, pp. 95-116. [DOI: https://dx.doi.org/10.1080/19475705.2022.2156820]

54. Khan, A.; Pan, X.D.; Najeeb, U.; Tan, D.K.Y.; Fahad, S.; Zahoor, R.; Luo, H.H. Coping with drought: Stress and adaptive mechanisms, and management through cultural and molecular alternatives in cotton as vital constituents for plant stress resilience and fitness. J. Biol. Res.; 2018; 51, 47. [DOI: https://dx.doi.org/10.1186/s40659-018-0198-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30428929]

55. Lu, Y.R. Karst Development Mechanism and Research Directions of Developing Engineering Construction Effect. J. Acta Geogr. Sin.; 2016; 37, pp. 419-432. [DOI: https://dx.doi.org/10.3975/cagsb.2016.04.04. ] (In Chinese)

56. Stanik, N.; Aalders, I.; Miller, D. Towards an indicator-based assessment of cultural heritage as a cultural ecosystem service—A case study of Scottish landscapes. J. Ecol. Indic.; 2019; 95, pp. 288-297. [DOI: https://dx.doi.org/10.1016/j.ecolind.2018.07.042]

57. Si, S.L.; You, X.Y.; Liu, H.C.; Zhang, P. DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications. J. Math. Probl. Eng.; 2018; 2018, 3696457. [DOI: https://dx.doi.org/10.1155/2018/3696457]

58. Ghosh, S.; Chatterjee, N.D.; Dinda, S. Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India. J. Sustain. Cities Soc.; 2021; 68, 102773. [DOI: https://dx.doi.org/10.1016/j.scs.2021.102773]

59. Buyukozkan, G.; Guleryuz, S. An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey. J. Int. J. Prod. Econ.; 2016; 182, pp. 435-448. [DOI: https://dx.doi.org/10.1016/j.ijpe.2016.09.015]

60. Xu, G.Y.; Xiong, K.N.; Shu, T.; Shi, Y.J.; Chen, L.S.; Zheng, L.L.; Fan, H.X.; Zhao, Z.M.; Yang, Z.H. Bundling evaluating changes in ecosystem service under karst rocky desertification restoration: Projects a case study of Huajiang-Guanling, Guizhou province, Southwest China. Environ. Earth Sci.; 2022; 81, 302. [DOI: https://dx.doi.org/10.1007/s12665-022-10400-1]

61. Wang, L.; Xue, X.; Wang, Z.; Zhang, L. A unified assessment approach for urban infrastructure sustainability and resilience. J. Adv. Civ. Eng; 2018; 2018, 2073968. [DOI: https://dx.doi.org/10.1155/2018/2073968]

62. Dong, S.J.; Malecha, M.; Farahmand, H. Integrated infrastructure-plan analysis for resilience enhancement of post-hazards access to critical facilities. Cities; 2021; 117, 103318. [DOI: https://dx.doi.org/10.1016/j.cities.2021.103318]

63. Lambert, S.; Scott, J. International Disaster Risk Reduction Strategies and Indigenous Peoples. Int. Indig. Policy J.; 2019; 10, pp. 1-21. [DOI: https://dx.doi.org/10.18584/iipj.2019.10.2.2]

64. Zhang, Y.; Xiong, K.N.; Yu, Y.H.; Yang, S.; Liu, H.Y. Stoichiometric characteristics and driving mechanisms of plants in karst areas of rocky desertification of southern China. Appl. Ecol. Environ. Res.; 2020; 18, pp. 1961-1979. [DOI: https://dx.doi.org/10.15666/aeer/1801_19611979]

65. Chen, Y.; Tan, Y.; Gruschke, A. Rural vulnerability, migration, and relocation in mountain areas of Western China: An overview of key issues and policy interventions. Chin. J. Popul. Resour. Environ.; 2021; 19, pp. 110-116. [DOI: https://dx.doi.org/10.1016/j.cjpre.2021.12.012]