Landslides are highly erosional processes that dominate sediment mobilization and reshape landscapes in orogenic belts. Therefore, quantifying and characterizing landslide volume is essential to disaster prevention and understanding landscape evolution in mountainous rivers. Progressive development of the structure-from-motion (SfM) and multi-view stereo (MVS) photogrammetric techniques and Unmanned Aerial Vehicles (UAV) provides low-cost and high-resolution digital elevation models (DEMs), compared to traditional aerial photogrammetry at the same resolution. In this study, we quantified landslide volume and change in river channel volume at meter-scale accuracy for the Laishe River catchment of southern Taiwan from 2009 to 2015, which provides reliable data for discussing sediment transport and morphological response. The observations indicate that Typhoon Morakot in August 2009, induced a landslide volume of 31.63 million (M) m³, which is equal to 87% of the six-year sediment production. Typhoon Morakot also caused the deposition of 8.2 M m³ in the Laishe River. Additionally, this study demonstrates the feasibility of using UAVs to quantify the migration of landslide material and changes in channel area and volume, and the detection of landslide dams. In conclusion, two sources of images, especially those by UAVs, were used to decipher the consequence and potential hazard, social impact, and morphological changes in a mountainous river.

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.

Using the Soil Conservation Service (SCS) curve number (CN) procedure for estimating runoff volume on an ungauged forest watershed remains controversial because little guidance has been provided for defining appropriate CN values. In this study, alternative methods for assigning CN values (CNs) were assessed to determine whether these methods provide acceptable estimates of runoff on forested watersheds. The estimated CNs varied between the methods employed, showing the highest CN values when derived from a probabilistic method and lowest when derived from a graphical method. The tabulated CN values in Section 4 of the National Engineering Handbook (NEH-4) had relatively higher bias compared to those derived from measured rainfall-runoff data. The storm runoff volume was predicted using the assigned CNs and compared with the observations. The coefficients of determination and RMSE values between the measured and estimated runoff volumes varied with the methods employed. The highest watershed average RMSE value was obtained by the use of the tabulated CN values in NEH-4 (51.19 mm), while arithmetic mean approach provided the lowest average RMSE value of 24.38 mm, even though this method requires intensive data collection. Among the alternatives, probabilistic method was found to be the most reliable in determining CNs for forest cover with limited data. The estimated runoff largely agreed with the observations. Therefore, the revised CNs can be used for estimating storm runoff from ungauged, mountainous forests.

Due to the high seismicity and high annual rainfall, numerous landslides occurred and caused severe impacts in Taiwan. Typhoon Morakot in 2009 brought extreme and long-time rainfall, and caused severe disasters. After 2009, numerous large scale deep-seated landslides may still creeping, however not necessary easily to inspect the activity. In recent years, the remote sensing technology improves rapidly, providing a wide range of image, essential and precious geoinformation. Accordingly, the Small unmanned aircraft system (sUAS) has been widely used in landslide monitoring and geomorphic change detection. This study used UAS to continuously monitor a landslide area in Baolai Village in southern Taiwan, which had a catastrophic landslide event triggered by heavy rainfall caused by Typhoon Morakot in 2009. In order to accesses the potential hazards, this study integrates UAS, field geomatic survey, terrestrial laser scanner (ground LiDAR), and UAS LiDAR for sequential data acquisition since 2015. Based on the methods we are able to construct multi-temporal and high resolution DTMs, so as to access the activity and to monitoring the creeping landslides. The data set are qualified from 21 ground control points (GCPs) and 11 check points (CPs) based on real-time kinematic-global positioning system (RTK-GPS) and VBS RTK-GPS (e-GNSS). More than 10 UAS flight missions for the study areas dated since 2015, for an area large than 5–40 Km² with 8–12 cm spatial resolution (GSD). Then, the datasets was compared with the airborne LiDAR data, to evaluate the quality and the interpretability of the dataset. Since early 2018, we integrate UAS LiDAR technology to scanning the sliding area. The density of the point cloud data sets are higher than 250 and 100 points/m2 for the total and ground point, respectively. The spatial distributions of geomorphologic changes were quantified firstly with the GCPS and CPs. The potential disaster was evaluated at different times, and the result reveals that most active regions were on the eastern side of the landslide. Significant changes in elevation were detected before the middle of 2017, however reactivated again since middle of 2018. The results of this study provide not only geoinfomatic datasets of hazardous area, but also for essential geomorphologic information/methods for other study, and for hazard mitigation and planning, as well.

In order to overview the impact of climate change on runoff from forested catchments over Asian countries, we collected water balance data from fifteen long-term catchment monitoring stations (total monitoring period 1975–2018, not continuous), spanning from Sabah, Malaysia (our southernmost site), to Hokkaido, Japan (our northernmost site). We then employed an elasticity analysis to the dataset to examine how the annual runoff from each catchment responded to inter-annual fluctuations in annual rainfall and annual mean air temperature. As a result, we found that (1) the annual runoff was sensitive to annual rainfall for all the catchments examined. In addition, (2) the annual runoff from seven of the fifteen catchments was sensitive to inter-annual changes in the mean air temperature, which was likely due to changes in forest evapotranspiration. Three catchments, however, exhibited an increased runoff in a hot year. Finally, (3) the annual rainfall from the previous year (carry-over soil moisture) was important in explaining the variation in annual runoff in two tropical montane forest catchments. This study may serve as one of the pilot studies toward a comprehensive understanding of the climate elasticity of runoff in countries over Asia, because the examined catchments are unevenly and sparsely distributed over the area.