Ying Liu

Landslides are a common natural disaster, having severe socio-economic effects and posing immense threat to safety, such as loss of life at a global scale. Modeling and predicting the possibility of landslides are important in order to monitor and prevent their negative consequences. In this study, landslides are the primary research object. Further, the frequency ratio (FR) method was applied to the random forest (RF), support vector machine (SVM), and decision tree (DT) regression algorithms for landslide sensitivity assessment. It was also applied to landslide risk assessment mapping in the Longmen Mountain area. Therefore, taking into account the positive and negative sample balance, 7774 historical landslide points and 7774 non-landslide points were selected and divided them into training sets and test sets. The influence factors were selected and analyzed through multicollinearity analysis and the FR method. To improve the performance of the model and the accuracy of the findings, the individual environmental factors are normalized. Subsequently, the LSI (landslide susceptibility index), was obtained by calculating the frequency ratio. Following this, the RF, SVM, and DT were used to construct the model. The trained model calculates the landslide probability of each cell in the study area and generates the resultant susceptibility map. The receiver operating characteristic (ROC) curve and R² of this region were calculated to evaluate the model’s performance. The results indicate that RF obtained the highest predictive performance (area under the curve (AUC) = 0.82) in landslide risk prediction, followed by SVM (AUC = 0.8) and DT (AUC = 0.69). The results of this study serve as a predictive map for landslide susceptibility areas and provide critical support for the security of lives and property for the human and socio-economic development in the Longmen Mountain region. In addition, the experiment results reveal that the machine learning model based on the FR method can improve the accuracy and performance of methods in studies related to landslide susceptibility. The method is equally applicable to research in other fields.

Ecosystem services (ESs) are an essential link between ecosystems and human well-being, and trade-offs/synergies happen in ESs at different temporal and spatial scales. It is crucial to explore patterns of trade-offs/synergies among ESs, and their nonlinear relationships with changes in ESs. The primary objective of this study was to evaluate five ESs in 2000 and 2018: namely, water yield, food production, carbon sequestration, soil conservation, and habitat quality in mountainous regions of Southwest China. The mean values of the five ESs increased by 365.8 m³/ha, 13.92 t/hm², 497.09 TgC/yr², 138.48 t/km², and 0.002, respectively. Using spatial statistics and analysis, an ES trade-off synergy model (ESTD) was constructed for the five ESs change values. Overall, soil conservation has a trade-off with all five ESs, except habitat quality; this trade-off is increasing slightly. Water yield is in synergy with all ESs except soil conservation, with decreasing synergy; habitat quality is in synergy with all ESs except food production, with increasing synergy. Finally, the nonlinear relationship between the value of the change in the ES and ESTD was analyzed using a generalized additive model. Changes in water yield showed the greatest impact on ESTD except for food production, wherein changes in all three ESs had minimal impacts on ESTD. Food production dominates its trade-offs/synergies relationship with soil conservation; carbon sequestration is the dominant player in its trade-offs/synergies relationship with soil conservation. Habitat quality has a secondary position of influence, except in the trade-offs/synergies involving food production. By exploring the drivers of trade-offs/synergies among ESs, this study can provide guidance for the effective implementation of policies related to ecological protection and restoration.

As an important component of terrestrial ecosystems, the mountainous areas of southwest China are facing eco-environmental stress due to rapid urbanization. This study analyzed the vegetation dynamics during urbanization in 410 counties in the mountainous area of southwest China using trend analysis and bivariate spatial autocorrelation analysis. The results demonstrate the following: (1) The regional differences in the natural background and the stage of economic development resulted in obvious geographical heterogeneity in the relationship between urbanization and vegetation coverage. (2) The relationship between the rate of urbanization level change (RULC) and the slope of the normalized difference vegetation index (NDVI) (Slope−NDVI) in the mountainous areas of southwest China is characterized by an inverted-U-shaped curve. Based on the inflection point (0.206, 57.60) of the inverted-U-shaped curve, the optimal mean annual RULC is 1.37%. (3) The relief degree of the land surface, the gross domestic product (GDP) change rate, and the slope of temperature change are positively correlated with the Slope−NDVI. Overall, an understanding of how urbanization affects vegetation dynamics can inform decisions concerning ecological restoration and urban–rural development in China.

This study examined the hollowing process of rural communities, which is caused by migration, and explored its influence factors based on Hierarchical Linear Model (HLM) analysis in an attempt to explore effect of regional characteristic. To capture the essence of rural hollowing, a logical framework was constructed. The rural hollowing was then evaluated based on an index system. Based on the assessment, HLM analysis was conducted to explore the influence factors at the household and community levels. The results demonstrated significant differences of rural hollowing among villages. In addition, the hollowing was not only affected by household-level variables but also by community-level variables. Further, for rural hollowing, the household-level variable was endogenous drive, while the community-level variables were exogenous drive. Among all the endogenous influence factors, population quality was the most important, and thus the loss of highly educated talents will aggravate the trend of rural hollowing and hinder the sustainable development of the countryside. Furthermore, in addition to promoting economic development and its diversification to alleviate rural hollowing, improving the quality of the population is more important to the sustainable development of the countryside in China.

An increasing number of studies have demonstrated that blue space can promote the recovery of the human body, as does green space. However, the influence of blue space on the color space has been studied much less. Based on research on color psychology and healthy cities, the current study takes Yijie District of Dujiangyan City as the research object. Logistic regression models and the semantic differential method was used to explore residents’ perceptions of color space and their restorative ratings of blue space. This study describes the characteristics of the people visiting blue space, investigates whether the benefits brought about by blue space are related to the color space, and studies whether the surrounding colors in the blue space have a positive effect in promoting residents’ perceptions of pleasure. Based on residents’ choices of environmental pictures containing various colors, a color tendency map was constructed. The results revealed strong correlations between residents’ perceived pleasure and color space color collocation. Socio-demographic factors were found to affect the frequency of visiting blue space. Reasonable color planning is necessary for human health. Based on the study findings, relevant planning suggestions are proposed as a reference basis for waterfront space color planning.

Linkages between rural migration and agricultural restructuring have become a key aspect of rapid urbanization in developing countries and a research focus for optimizing rural–urban development and rural reconstruction. Using continuous statistical data from the mountains of Southwest China, we examined the structure, changes and regional differences in agricultural cultivation under urbanization, analyzed the reasons for the restructuring of agricultural cultivation, and revealed the adaptation linkages between the rural–urban transition and agricultural restructuring. The results showed that land-use changes and rural migration caused by urbanization significantly affected the cultivation structure and its change trends: the proportion of food crops decreased, while the proportion of vegetables and orchards increased. However, regional differences in the agricultural cultivation structure were significant in the various township zones. Rural migration weakened agricultural cultivation in the lake basin and nationality townships but enhanced agricultural cultivation in the river valley townships. On the basis of the adaptation linkages of urbanization, rural migration, agricultural intensification, cultivation structure and economic development, chain-type changes and adaptation processes between rural migration and agricultural restructuring were demonstrated. These findings indicate that favorable locations and appropriate policies can promote the integration and restructuring of smallholder agriculture for commercialization and intensification and vice versa.

Adjusting farming strategies are adaptive behaviors to cope with hazard risks. However, few studies have studied rural and remote mountain areas in China with little known about “farmers’ adaptation under the impact of geo-hazards”. Unlike traditional farmers’ behavioral adaptation studies, in this study, we focused on the resilience of farmers’ behavioral mechanisms to address local hazards such as geo-hazards. Our data were acquired through questionnaire responses (N = 516) in mountainous hazard-prone areas in Chongqing, China. The binary logit model and multinomial logit model were used to investigate the obstacles to different farming strategies and the determinants of adaptation strategy choice, focusing on the effects of disaster experience and social support on the adaptation strategy resilience. The results show that the most common adaptation strategy was adjusting crop varieties, and the largest adaptation obstacle was a lack of funds. Additionally, the age of the smallholder, farming acreage, agricultural income, social support, and disaster experience significantly increased the possibility of farmers adjusting their agricultural production. Of these, smallholder agricultural income, state disaster subsidy, the presence of disaster prevention construction, the smallholder’s property, and the presence of disaster-caused crop loss experience were the most important factors affecting a farmer’s adaptation strategy. In particular, farmers were more sensitive to disaster-caused property loss than to disaster-caused crop loss. This study can provide implications for the government to formulate disaster mitigation measures and for farming strategies at the smallholder level.

The human–environment relationship is bidirectional, meaning that human attitudes and behavior to nature are at the root of environmental change, while changes in the environment affect human attitudes and behavior. It is necessary to analyze the human–environment relationship from two aspects: (a) Whether there is a good objective basis for maintaining an environment, and (b) whether people report that they are satisfied with that environment. This study attempted to construct a framework to evaluate the human–environment relationship considering these two aspects. The framework consists of three parts: Traditional evaluation, indicator construction, and evaluation considering the relationship between subjective and objective assessment. Traditional evaluations consist of subjective evaluations and objective assessments. Indicator construction focuses on putting forward indicators that quantitively evaluate the human–environment relationship, considering the results of objective assessments and subjective evaluations. The indicators introduced in this study include MD (match degree) and OSC (objective assessment and subjective evaluation comparison) to explain the difference and the relationship between objective assessments and subjective evaluations of the environment. Then, based on the indicator value, a matrix containing four situations (Match-H, Match-L, H-L, and L-H) was constructed to explore why a human–environment relationship may not be harmonious. Since the upper Minjiang River basin is a typical area, because of its intensive human activity, as well as its fragile ecological environment, this study chose it as a case study and used it to verify the framework. Through the framework construction and application, this study found that: (1) The framework of this study provided a more comprehensive method to evaluate the human–environment relationship; (2) as the subjective evaluation was based on individual comprehensive tradeoffs, the evaluation combining the subjective and objective assessment was more accurate; (3) environmental conditions were the basis, and human activities were the key factors, for the coordination of human–environment relationships; so the matrix put forward in this study was necessary for finding the cause of human–environment incongruity.

Large-scale rural labor transfer is considered an important means of promoting urbanization and poverty alleviation; however, it has been difficult to fully reveal the complexity of mountain labor transfer using traditional theory. In this study, macrostatistics and microsurvey data were employed to conduct an empirical analysis of rural labor transfer in Xichang, highlighting the regional features of rural labor transfer in the southwest mountainous areas of China. The results show that the employment structure of rural labor is dominated by agriculture; however, its proportion is decreasing annually. The development of secondary and tertiary industries significantly contributes to the non-agricultural employment of rural laborers, who transfer mainly to the building, industry, and consumption fields—especially consumption, which has the greatest ability to absorb surplus rural labor. Migration of the population and farmers’ per capita net income promotes rural labor transfer, while the amount of rural labor, the urbanization rate in townships, agricultural development, and the topography have lagged effects on the process. This study argues that rural labor transfer in the mountains has regionality, multi-pattern, and gradient features; therefore, the promotion of rural labor transfer should consider local urbanization and poverty alleviation in such a manner that aligns with the local natural and socioeconomic conditions of the mountainous areas.