The Red River Delta (RRD), including 11 provinces, is one of the four largest rice-growing areas in Vietnam. Tropical storms often occur and cause serious flooding from May to October annually in the RRD, which strongly affects the productivity of the summer–autumn rice, one of two main rice crops. Therefore, the rapid assessment of damaged rice area by flooding inundation is critical for farmers and the government. In this study, we proposed a methodology for quick estimation of rice areas damaged by flooding using Sentinel 1A (S1A) imagery. Firstly, the latest rice map was produced. Then, a Near Real-Time (NRT) flood map, which is estimated from S1A images at the closest time to a flooding event, was generated by excluding the yearly permanent map from the temporal water map. Our experiment was conducted for the assessment of damaged rice area by flooding from the tropical storm named Son-Tinh, which happened on 19–21 July 2018. A Support Vector Machine (SVM) classifier was applied on time-series of S1A VV with VH data (VVVH) to obtain a rice map for the winter-spring season of 2018 with 90.5% Overall Accuracy (OA) and 2.37% difference (12,544 ha) from the General Statistics Office (GSO) of Vietnam’s reports for the whole region. Then, the Otsu thresholding method was applied for permanent water surface extraction and NRT flood mapping. The estimated damaged area was compared to available provincial and communal statistics for validation and further analysis. Right after the Son-Tinh storm, the estimation of inundated rice was approximately 50% of the total rice area in the RRD (271,092 ha). As a result, rice damage level strongly corresponds to the inundation period. In addition, the rice-flooding frequency map over the RRD was estimated to show rice fields suffering a high risk of flooding during the rainy season in the RRD. Our experiment’s results highlight the potential of using Synthetic-Aperture Radar (SAR) imagery for fast monitoring and assessment of paddy rice areas affected by flooding at a large scale in the RRD region.

In electrical energy transportation challenges, power quality issues like reducing harmonic pollution, reactive power and load unbalance, necessarily have to identify the symmetrical components of the three phases in a fast and precise way. This paper introduces a new state-space model to be used with an Extended Kalman Filter (EKF) in order to estimate in real-time the symmetrical components of distorted and time-changing power systems. The proposed model is therefore able to detect and to quantify the unbalance of general three-phase power systems. Indeed, the symmetrical components of the power system, i.e., their amplitude and phase angle values, can be deduced at each iteration from the proposed state-space model. The effectiveness of the method has been evaluated. Results and comparisons of online symmetrical components identification show the efficiency of the proposed method for disturbed and changing power systems.

Nowadays, 3D Geographic Information Systems play an important role in many researches and application fields such as environmental observation, locating and routing, urban management, and tourism. In this paper, we present methodology and system development for a 3D GIS application at the scale of Vietnam National University Hanoi (VNU) campus. In addition, we proposed time dimension integrated into 3D GIS system. It displays weather phenomenon such as rain, storm, sun and day-night effects and synchronizes all to the reality by connecting to real time time-zone and weather forecast systems. VNU virtual campus based on 3D GIS with time integration realize VNU and environments as in the reality and therefore, highlights system potential for guide and management.