Abstract

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.

Highlights

  • The comparison results are expressed as true positive (TP), true negative (TN), false positive (FP), and false negative (FN)

  • Due to the limitation of computational power, the built Agisoft orthophoto was cut into 20 blocks. Some of these blocks had no Large woody debris (LWD) accumulation, or the river channel were obstructed by dense tree canopy

  • The minimum bounding box with a diameter of less than 10 cm or a length of less than 50 cm (Figure 11) was deleted. The purpose of such screening was to eliminate the misjudgment of LWD and objects that were too small through the characteristics of LWD size

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Summary

Materials and Methods study

Areaarea is Nishueibei River, a tributary of the Beinan River in Taitung County 2.8 km; this study was limited to km, as shown in in Figure 1d due to complex terrain and tree canopy. Extreme storm events often confront the study area. In August 2009, Typhoon Morakot caused LWD to accumulate at the Futhe study area. In August 2009, Typhoon Morakot caused LWD to accumulate at the gang fishing port, disrupting operations. In. LWD in in the the port obstructed ships andand damaged the hull. September 2016, Typhoon Megi accumulated LWD in the embarkment and affected the the entry ofoflarge andpassenger passenger ships. (e) 26 August 2019 LWD accumulation in Fugang fishing ports

Field Investigations
LWD Identification Process
Block Division of LWD Identification
Estimating thethe
D H isand the diameter the trunk head of HH and
Color Space Model Conversion
XGBoost Model
Model Evaluation
Training and Test Data Selection
Filtering the Factors of the Color Space Model
The trends
LWD Identification
The Single LWD Volume Estimation Results
Discussion
Conclusions
Full Text
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