Application of Source-Sink Landscape Influence Values to Commuter Traffic: A Case Study of Xiamen Island

Landscape patterns are closely related to ecological processes. Different spatial scales and research methods may lead to different results. Therefore, it is crucial to choose suitable research methods when studying different landscape patterns and ecological processes. In the present study, the methods of source-sink landscape theory were applied to the interactions between urban landscape characteristics and commuter traffic behavior around the arterial roads in Xiamen Island. After classification of land use types using remote sensing images from the IKONOS satellite and ArcGIS software (ESRI, Redlands, CA, USA), the landscape patterns of areas surrounding arterial roads (within 1 km) were evaluated using source-sink landscape influence (SLI). The results showed that Xiamen Island’s urban expressway had the highest SLI value (0.191), followed by the state highways (0.067), the provincial highways (0.030), and the county roads (0.025). When considering all road types, the correlation between a road’s SLI value and its commuter traffic flow was 0.684. This result was explained by three observations: (1) The contribution of the core area of each landscape pattern to traffic flow was positively correlated with the traffic flow. (2) Areas surrounding the urban expressway and the state highways had lower values for Shannon’s diversity index, indicating that these areas had a lower degree of landscape fragmentation. (3) The landscape patterns surrounding the urban expressway and the state highways were more concentrated and complex than those around other road types. The application of source-sink landscape pattern theory allows for researchers to integrate the relationships between landscape patterns surrounding roads and commuter traffic flow on those roads and to analyze the reasons for these relationships

Coupling between Rural Development and Ecosystem Services, the Case of Fujian Province, China

To reveal the relationship between rural development and ecosystem services and to assist in efforts to balance these factors, we used a coupling model to carry out a study of the relationship between rural development and ecosystem services in Fujian Province of China during the years 2000 to 2015. First, we characterized the degree of rural development for each county in the province by calculating its index of relative rurality (IRR) and classified the counties into rural development types. Second, we calculated the values of three ecosystem services (ES) and overlapped them to get the sum of ES for each county. Third, we calculated the coupling and coupling coordination degree and analyzed the correlation between IRR and ES in the study area. The results showed that the mean value of IRR declined over the study period, was positively correlated with ES, and the correlation degree increased year by year. Meanwhile the degree of coupling was in the antagonistic stage, but tended to run in stage with a highly coordinated stage coupling coordination degree, if the business services type-counties were excluded. Although the overall coupling coordination degree was high, it declined yearly, which meant that rural development and ecosystem services increasingly lacked coordination. This paper supports and verifies some achievements of rural development programs in the research area, provides theoretical and decision-making support for coordinated rural development and ecosystem services protection in China, and provides a regional case study that could assist with similar research in other countries

Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China

Machine learning models are gradually replacing traditional techniques used for landslide susceptibility assessment. This study aims to comprehensively compare multiple models, including linear, nonlinear, and ensemble models, based on 5281 historical landslides in southwest China, the area most severely affected by the landslide disaster. Linear models represented by logistic regression (LR), nonlinear models represented by support vector machine (SVM), artificial neural network (ANN) and classification 5.0 decision tree (C5.0 DT), and ensemble models represented by random forest (RF) and categorical boosting (Catboost) were selected. The correlation coefficient, variance inflation factor (VIF), and relative important analysis were used to select the dominate landslide conditioning factors. Using multiple statistical indicators (e.g. Area Under the Receiver Operating Characteristic curve (AUC) and Kappa), cross-validation and qualitative methods to evaluate the models’ performance. The findings are: (1) Regarding the model predictive performance, the best predictive performance was demonstrated by the ensemble models Catboost (AUC = 0.823 and Kappa = 0.593) and RF (AUC = 0.821 and Kappa = 0.582), followed by the nonlinear models SVM (AUC = 0.775 and Kappa = 0.520), ANN (AUC = 0.770 and Kappa = 0.486) and C5.0 DT (AUC = 0.751 and Kappa = 0.497), while the linear model LR (AUC = 0.756 and Kappa = 0.456) had a more limited performance. The ensemble model, which uses a tree as its baseline classifier, has a lot of potential for studies into the landslide susceptibility. (2) Regarding the model robustness, the three types of models in nonspatial cross-validation (CV) performed relatively similarly in terms of predictive power, while in spatial cross-validation (SPCV), the linear model LR (median AUC = 0.714) achieved better results than the ensemble and nonlinear models. It implies that when the distribution of landslides is not homogeneous, linear models may be the most robust. It is advisable to consider various evaluation metrics from different perspectives and integrate them with specialist qualitative geomorphological empirical knowledge to determine the best model. (3) The Gini index-based RF model suggests that road density was the dominant factor in the frequency of landslides in the study area