Relationship between landslides and mountain development-Integrating geospatial statistics and a new long-term database.

Development in mountainous areas is inevitable in countries with high population densities, but the actual relationship between development and landslides remains uncertain. Clarifying the key current or historical factors resulting in landslides is crucial for hazard prevention and mitigation. This study focused on the Shihmen Reservoir catchment in Taiwan. Two combinations of explanatory variables in five different years (1946, 1971, 2001, 2004, and 2012) collected from a geodatabase and digital archives were used to conduct proximity and discrete logistic regression analyses. The results demonstrate that landslides increased dramatically from 1946 to 2012 in the catchment area. The proximity and overlapping of human development with landslides increased. However, the logistic regression results indicated that variation in susceptibility to landslides was due to natural causes, with the exception of historical deforestation and newly constructed road systems. Therefore, well-recovered historical woodland sites might currently be landslide-prone areas. We suggest that cumulative historical events should be considered as explanatory variables in future landslide prediction analysis.

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping.

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply.

A Recommendation-based Mobile Web Application for Health Information Service.

Information overload and irrelevant information are major obstacles for drawing conclusions on the personal health status and taking adequate medical actions. The objective of this study is to design a recommendation-based mobile web application to assist patient efficiently search online health information at anytime, anywhere and via any devices. In the system, we use a collaborative filtering approach to recommend health information to users.