Large woody debris (LWD) strongly influences river systems, especially in forested and mountainous catchments. In Taiwan, LWD are mainly from typhoons and extreme torrential events. To effectively manage the LWD, it is necessary to conduct regular surveys on river systems. Simple, low cost, and accurate tools are therefore necessary. The proposed methodology applies image processing and machine learning (XGBoost classifier) to quantify LWD distribution, location, and volume in river channels. XGBoost algorithm was selected due to its scalability and faster execution speeds. Nishueibei River, located in Taitung County, was used as the area of investigation. Unmanned aerial vehicles (UAVs) were used to capture the terrain and LWD. Structure from Motion (SfM) was used to build high-resolution orthophotos and digital elevation models (DEM), after which machine learning and different color spaces were used to recognize LWD. Finally, the volume of LWD in the river was estimated. The findings show that RGB color space as LWD recognition factor suffers serious collinearity problems, and it is easy to lose some LWD information; thus, it is not suitable for LWD recognition. On the contrary, the combination of different factors in different color spaces enhances the results, and most of the factors are related to the YCbCr color space. The CbCr factor in the YCbCr color space was best for identifying LWD. LWD volume was then estimated from the identified LWD using manual, field, and automatic measurements. The results indicate that the manual measurement method was the best (R2 = 0.88) to identify field LWD volume. Moreover, automatic measurement (R2 = 0.72) can also obtain LWD volume to save time and workforce.

Earth dams or embankments are susceptible to instability due to internal seepage, piping, and erosion, which can lead to catastrophic failure. Therefore, monitoring the seepage water level before the dam collapses is an important task for early warning of dam failure. Currently, there are hardly any monitoring methods that use wireless underground transmission to monitor the water content inside earth dams. Real-time monitoring of changes in the soil moisture content can more directly determine the water level of seepage. Wireless transmission of sensors buried underground requires signal transmission through the soil medium, which is more complex than traditional air transmission. Henceforth, this study establishes a wireless underground transmission sensor that overcomes the distance limitation of underground transmission through a hop network. A series of feasibility tests were conducted on the wireless underground transmission sensor, including peer-to-peer transmission tests, multi-hop underground transmission tests, power management tests, and soil moisture measurement tests. Finally, field seepage tests were conducted to apply wireless underground transmission sensors to monitor the internal seepage water level before an earth dam failure. The findings show that wireless underground transmission sensors can achieve the monitoring of seepage water levels inside earth dams. In addition, the results supersede those of a conventional water level gauge. This could be crucial in early warning systems during the era of climate change, which has caused unprecedented flooding events.

Studying large wood in river channels can help gain insight on their form and processes. Over the preceding decade, laboratory and field experiments have been used to explain wood dynamics, flow patterns and sediment transport. Moreover, field experiments are sparse, while laboratory experiments have focused mostly on fixed bed to capture their entrainment. To enhance our scientific understanding on logs of different morphology, this study designed an experimental flume to investigate the effects of log presence on flow and bed topography in a moving bed channel. Two log configurations were used, with and without rootwad. Wood pieces had a length of 0.2 m, diameter 0.05 m and a density of approximately 760 kg/m3. Rootwad were simulated by joining 0.06 m wood pieces, having a diameter of 0.02 m to the base of the log pieces at an angle of 30°. The experiments were carried out in a 4 m long flume, 0.6 m width and 0.6 m deep, and having a slope of 0.001. The experimental bed zone was paved with uniform sand, d50 = 0.750 mm, of 0.1 m thickness. Flow in the channel was set such that it was below the critical flow for wood entrainment, and it ranged between 0.0015 to 0.005 m3/s. Three different orientations of the log were considered, namely parallel, oblique and transverse to flow. Bed evolution was monitored using a camera and a laser mounted on a moving motor frame. This research shows that log orientation and the presence of rootwad dictate bed elevation changes and stability of single wood pieces. In addition, the contrast of morphological changes caused by the presence of abundant wood in a moving bed is crucial in determining large wood appropriate for river restoration. Our study provokes fascinating questions for future investigations.

No abstract