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The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor. [ABSTRACT FROM AUTHOR]

Flash flood is one of the most devastating types of disasters. Roads are linear strip structures exposed to the natural environment and they are vulnerable to damage when flash flood occurs. Thus, an accurate assessment of flash flood vulnerability for roads is mandatory for the improvement of transportation flash flood forecasting and warnings. In this study, we constructed an assessment model for assessing the flash flood vulnerability of roads based on their exposure and disaster reduction capability. Then, considering the mechanism of flash floods acting on the road, we preliminarily established an index system with analytic hierarchy process (AHP), and used a support vector machine (SVM) to aggregate the indices. The use of SVM avoids the subjectivity of vulnerability evaluation. The Assessment results showed that 4.29% of the land area has high or extremely high vulnerability. The moderate vulnerability areas accounted for 5.19% of the land area. The low and extremely low vulnerability areas accounted for 15.81% of the land area; and the land area with no roads accounted for 43.94%. The suburban areas with dense roads in the southeast and the areas near the main roads in the northwest, had high and extremely high vulnerability. The low and extremely low vulnerability areas are mainly distributed in the areas with sparse roads and close to cities. The results of this study satisfy the need for the high precision and large-scale evaluation of flash flood vulnerability of roads. Based on the results of the vulnerability assessment, different disaster prevention and reduction measures should be taken in the northwest and southeast. The northwestern region should attach importance to the construction of roads and improve the disaster prevention and reduction system; while the southeastern region should plan layout and construction of new roads rationally and enhance the disaster resistance of existing roads. [ABSTRACT FROM AUTHOR]

Flash flooding is a type of global devastating hydrometeorological disaster that seriously threatens people's property and physical safety, as well as the normal operation of water conservancy facilities, such as reservoirs, so an accurate assessment of reservoir risk for certain areas is necessary. Therefore, the purpose of this study was to propose a novel methodological approach for reservoir risk modelling based on the feature selection method (FSM) and tree-based ensemble methods (Bagging and Random Forest [RF]). The results showed that: (1) the J48-GA based ensemble models achieved higher learning and predictive capabilities compared to conventional ensemble models without the FSM. (2) For the classification accuracy, the J48-GA-RF (96.4%) outperformed RF (96.0%), J48-GA-Bagging (93.9%) and Bagging (93.5%). And the J48-GA-RF achieved the highest prediction AUC value (0.995), an almost perfect Kappa indexes value (0.926) and the best practicality value (30.88%). (3) In particular, the results indicated that all of the models showed high performance, both in training and in the validation of a dataset. Additionally, this study could provide a reference for disaster managers, hydraulic engineers and policy makers to implement location-specific flash flood risk reduction strategies. [ABSTRACT FROM AUTHOR]