Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.
Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technology, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned.
High-resolution flood simulation considering the influence of high buildings and fundamental facilities is important for flood risk assessment in urban areas. However, it is also a challenging task due to the difficulties in acquiring detailed topography and monitoring data for model construction and validation. In this study, a high-resolution flood simulation with a grid size of 0.5 m is realized through the use of detailed topography obtained by an unmanned aerial vehicle and real-time flood information acquired from social media. To discover the influence of terrain resolution on flood simulations, the high-resolution simulation results are compared with those with coarser grid resolutions (5, 10, and 20 m) for a flash flood event in Taiwan. In the case with higher grid resolution, the simulation results are in better agreement with the photos from social media in terms of flood extent, depth, and occurrence time. The flood simulation with coarse resolution (>5 m) tends to overestimate the flood duration on roads and provide bias information to decision-makers in the assessment of traffic impact and economic loss.
Bed topography in river bends is highly non-uniform as a result of the spiral motion of fluid and sediment transports related to channel curvature. To grasp a full understanding of geomorphology and hydrology in natural river bends, detailed bed topography data are necessary, but are usually not of high enough quality and so require further interpolation for sophisticated studies. In this paper, an algorithm is proposed that is particularly suited to bathymetry interpolation in rivers with apparent bends. The thalweg and the two banks are used as geographical features to ensure that a concave cross-sectional bed-form can be found in bend geometry, while linear interpolations are conducted in the in accordance with secondary and main stream currents, respectively. In comparison with conventional spatial interpolation methods, the proposed algorithm is validated to ensure better performance in generating smooth and accurate bed topography in channel bends, which results in better predictions of river stage by 2D hydrodynamic simulation in practical field tests.
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