Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model’s sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change.

Although leisure-time physical activity (PA) has established health benefits in older adults, it is equivocal if exercising in environments with high levels of PM_2.5 concentrations is equally beneficial for them. To explore the independent and joint associations of ambient PM_2.5 and PA with all-cause mortality among adults aged 60 or older and to assess the modifying effect of age (60-74 years vs. 75+ years) on the joint associations. A prospective cohort study based on the MJ Cohort repeat examinations (2005-2016) and the Taiwan Air Quality Monitoring Network and death registry linkages (2005-2022). We included MJ Cohort participants aged 60 or more at baseline who attended the health check-ups at least twice (n = 21,760). Metabolic equivalent hours per week (MET-h/week) of leisure-time PA were computed. Multivariable adjusted associations were examined using time-varying Cox proportional hazard models. There were 3539 all-cause deaths over a mean follow-up of 12.81 (SD = 3.67) years. Ambient PM_2.5 and physical inactivity are both independently associated with all-cause mortality. The joint associations of PA and PM_2.5 concentrations with all-cause mortality differed in the young-old (60-74 years) and the older-old (75+ years) (P for interaction = 0.01); Higher levels of long-term PM_2.5 exposures (≥25 μg/m³) had little influence on the associations between PA and mortality in the young-old (HR = 0.68 (0.56-0.83) and HR = 0.72 (0.59-0.88) for participants with 7.5-<15 and 15+ MET-h/week respectively) but eliminated associations between exposure and outcome in the older-old (HR = 0.91 (0.69-01.21) and HR = 1.02 (0.76-1.38) for participants with 7.5-<15 and 15+ MET-h/week). Long-term exposures to higher PM_2.5 concentrations may eliminate the beneficial associations of PA with all-cause mortality among adults aged 75 and over.

The red imported fire ant (RIFA; Solenopsis invicta) is an invasive species that severely threatens ecology, agriculture, and public health in Taiwan. In this study, the feasibility of applying multispectral imagery captured by unmanned aerial vehicles (UAVs) to detect red fire ant mounds was evaluated in Fenlin Township, Hualien, Taiwan. A DJI Phantom 4 multispectral drone collected reflectance in five bands (blue, green, red, red-edge, and near-infrared), derived indices (normalized difference vegetation index, NDVI, soil-adjusted vegetation index, SAVI, and photochemical pigment reflectance index, PPR), and textural features. According to analysis of variance F-scores and random forest recursive feature elimination, vegetation indices and spectral features (e.g., NDVI, NIR, SAVI, and PPR) were the most significant predictors of ecological characteristics such as vegetation density and soil visibility. Texture features exhibited moderate importance and the potential to capture intricate spatial patterns in nonlinear models. Despite limitations in the analytics, including trade-offs related to flight height and environmental variability, the study findings suggest that UAVs are an inexpensive, high-precision means of obtaining multispectral data for RIFA monitoring. These findings can be used to develop efficient mass-detection protocols for integrated pest control, with broader implications for invasive species monitoring.