The relative spatial scale relationship of observers and ecosystem and their aesthetic dynamic interaction process are fundamental to evaluation and optimization of aesthetic ecosystem service(AES).A comprehensive and...The relative spatial scale relationship of observers and ecosystem and their aesthetic dynamic interaction process are fundamental to evaluation and optimization of aesthetic ecosystem service(AES).A comprehensive and efficient framework for the assessment of AES is lack in the integration of scale relationship and dynamic process.This study took 9 villages in 4 different developmental stages(traditional,folk,rapidly changed,newly built)in Honghe Hani Rice Terraces,a world heritage site,as the research object.From two scales,viewing from inside and outside,the bi-scale assessing framework was established,which includes the three components of interaction process,connection area(as precondition of interaction),quality(as result of interaction),and influencing factors of quality(elements’characteristics of villages).Among them,the connection areas were evaluated with visual and traffic accessibility along the route.The quality and influencing factors were evaluated through participatory preferences methods by expert group.The influencing factors include 9 characteristics,such as,space size,architecture layout,vegetation species richness,color diversity.The results suggested that villages with high AES quality and low accessibility need to be optimized,and the key influencing factors are space size,architecture layout,color harmony and surrounding sanitation.Therefore,the bi-scale assessing framework can provide important references for decision making and visual protection regulations on the villages.展开更多
The quality of the data for statistical methods plays an important role in landslide susceptibility mapping.How different data types influence the performance of landslide susceptibility maps is worth studying.The goa...The quality of the data for statistical methods plays an important role in landslide susceptibility mapping.How different data types influence the performance of landslide susceptibility maps is worth studying.The goal of this study was to explore the effects of different data types namely,presence-only(PO),presence-absence(PA),and pseudo-absence(PAs) data,on the predictive capability of landslide susceptibility mapping.This was completed by conducting a case study in the landslide-prone Honghe County in the Yunnan Province of China.A total of 428 landslide PO data points were selected.An equivalent number of nonlandslide locations were generated as PA data by random sampling,and 10,000 sites were uniformly selected at random from each region as PAs data.Three landslide susceptibility models,namely the information value model(IVM),logistic regression(LR) model,and maximum entropy(MaxEnt) model,corresponding to the three data types were investigated.Additionally,the area under the receiver operating characteristic curves(ROC-AUC),seven statistical indices(i.e.accuracy,sensibility,falsepositive rate,specificity,precision,Kappa,and Fmeasure),and a landslide density analysis were used to evaluate model performance regarding landslide susceptibility mapping.Our results indicated that the MaxEnt model using PAs data performed the best and had the highest fitness with the highest ROC-AUC values and statistical indices,followed by the IVM model with only landslide data(PO),and the LR model using PA data.Using PAs data avoided the inherent over-predictive shortcomings of PO data by limiting the predicted area of high-landslide susceptibility.Additionally,the random sampling design of landslide PA data increased the uncertainty of landslide susceptibility mapping and influenced the performance of the model.Therefore,our results suggested that the PAs data sampling provided a useful data type in the absence of high-quality data.Finally,we summarized the principles,advantages,and disadvantages of the three data types to assist with model optimization and the improvement of predicted performance and fitness.展开更多
基金funded by the National Natural Science Foundation of China(grant numbers 41761115,41271203)。
文摘The relative spatial scale relationship of observers and ecosystem and their aesthetic dynamic interaction process are fundamental to evaluation and optimization of aesthetic ecosystem service(AES).A comprehensive and efficient framework for the assessment of AES is lack in the integration of scale relationship and dynamic process.This study took 9 villages in 4 different developmental stages(traditional,folk,rapidly changed,newly built)in Honghe Hani Rice Terraces,a world heritage site,as the research object.From two scales,viewing from inside and outside,the bi-scale assessing framework was established,which includes the three components of interaction process,connection area(as precondition of interaction),quality(as result of interaction),and influencing factors of quality(elements’characteristics of villages).Among them,the connection areas were evaluated with visual and traffic accessibility along the route.The quality and influencing factors were evaluated through participatory preferences methods by expert group.The influencing factors include 9 characteristics,such as,space size,architecture layout,vegetation species richness,color diversity.The results suggested that villages with high AES quality and low accessibility need to be optimized,and the key influencing factors are space size,architecture layout,color harmony and surrounding sanitation.Therefore,the bi-scale assessing framework can provide important references for decision making and visual protection regulations on the villages.
基金supported by the Multigovernment International Science and Technology Innovation Cooperation Key Project of National Key Research and Development Program of China for the ‘Environmental monitoring and assessment of LULC change impact on ecological security using geospatial technologies’ (Grant No. 2018YFE0184300)National Natural Science Foundation of China (Grant Nos. 41271203, 41761115)the Program for Innovative Research Team (in Science and Technology) in the University of Yunnan Province, IRTSTYN。
文摘The quality of the data for statistical methods plays an important role in landslide susceptibility mapping.How different data types influence the performance of landslide susceptibility maps is worth studying.The goal of this study was to explore the effects of different data types namely,presence-only(PO),presence-absence(PA),and pseudo-absence(PAs) data,on the predictive capability of landslide susceptibility mapping.This was completed by conducting a case study in the landslide-prone Honghe County in the Yunnan Province of China.A total of 428 landslide PO data points were selected.An equivalent number of nonlandslide locations were generated as PA data by random sampling,and 10,000 sites were uniformly selected at random from each region as PAs data.Three landslide susceptibility models,namely the information value model(IVM),logistic regression(LR) model,and maximum entropy(MaxEnt) model,corresponding to the three data types were investigated.Additionally,the area under the receiver operating characteristic curves(ROC-AUC),seven statistical indices(i.e.accuracy,sensibility,falsepositive rate,specificity,precision,Kappa,and Fmeasure),and a landslide density analysis were used to evaluate model performance regarding landslide susceptibility mapping.Our results indicated that the MaxEnt model using PAs data performed the best and had the highest fitness with the highest ROC-AUC values and statistical indices,followed by the IVM model with only landslide data(PO),and the LR model using PA data.Using PAs data avoided the inherent over-predictive shortcomings of PO data by limiting the predicted area of high-landslide susceptibility.Additionally,the random sampling design of landslide PA data increased the uncertainty of landslide susceptibility mapping and influenced the performance of the model.Therefore,our results suggested that the PAs data sampling provided a useful data type in the absence of high-quality data.Finally,we summarized the principles,advantages,and disadvantages of the three data types to assist with model optimization and the improvement of predicted performance and fitness.
