Vietnam Academy of Science and Technology https://dspace.ctu.edu.vn/jspui/handle/123456789/6365 2026-03-12T15:20:34Z 2026-03-12T15:20:34Z Nguyen, Dang Hoi Ngo, Trung Dung Vu, Viet Dung https://dspace.ctu.edu.vn/jspui/handle/123456789/109535 2024-12-23T12:09:29Z 2023-01-01T00:00:00Z

Title: Benthic habitat mapping and assessment of seagrass species diversity in Da Lon Reef, Truong Sa Islands, Vietnam, using very high-resolution satellite imagery and in situ data Authors: Nguyen, Dang Hoi; Ngo, Trung Dung; Vu, Viet Dung Abstract: Benthic habitats are critical in shallow sea areas; they regulate the diversity and richness of organisms in each area. Mapping benthic habitats elucidates natural sea characteristics and aids in managing and using natural resources, as well as conserving marine biodiversity. This study established a benthic habitat map for the Da Lon Reef area, Truong Sa Islands, Vietnam, using Pléiades high-resolution remote sensing imaging materials and field survey results from 2020 and 2021. We identified seven classes of benthic habitats with a 91.64% overall accuracy, corresponding to a Kappa coefficient of 0.88. In the Da Lon Reef, seagrass biomes occupy a large area (more than 200 ha) and are distributed mainly inside lagoons at depths of 2-6 m. The field survey results identified five seagrass species and the biodiversity and biomass of seagrass populations in the lagoon of Da Lon Reef. The study results confirm the fundamental value of resources, biodiversity in general and seagrass in particular, in managing and protecting shallow sea ecosystems and biodiversity conservation in the Da Lon Reef area, an important part of the Truong Sa Islands, Vietnam.

2023-01-01T00:00:00Z Nguyen, Minh Quang Vu, Van Ha Nguyen, Thi Min Ngo, Thi Dao Nguyen, Thi Thu Cuc Dang, Minh Tuan Dang, Xuan Tung Tran, Thi Man Nguyen, Thi Thao https://dspace.ctu.edu.vn/jspui/handle/123456789/109534 2024-12-23T12:05:31Z 2023-01-01T00:00:00Z

Title: Holocene sedimentary facies in the incised valley of Ma River Delta, Vietnam Authors: Nguyen, Minh Quang; Vu, Van Ha; Nguyen, Thi Min; Ngo, Thi Dao; Nguyen, Thi Thu Cuc; Dang, Minh Tuan; Dang, Xuan Tung; Tran, Thi Man; Nguyen, Thi Thao Abstract: Holocene sediment facies in the incised valley of the Ma River Delta were clarified by using analysis of LKTH6 core (32 m depth) such as sedimentary structure analysis, grain-sized, micro-paleontological (foraminifera, spore and pollen, and diatom), clay minerals characteristics, and Radiocarbon dating (¹⁴C). Ten sedimentary facies were identified, including (1) flood plain silty clay facies, (2) Salt marsh clayey silt facies, (3) Tidal flat sandy silty clay facies, (4) Tidal creek and tidal branch silty clayey sand facies, (5) Bay silty clay facies, (6) Prodelta silty clay facies, (7) Delta front silty sand facies, (8) Mouth bar sand facies, (9) Point bar silty sand faces, and (10) Alluvial plain silty clay facies.The sea level change after the last glacial was recorded by sediment facies and radiocarbon dating (¹⁴C). It showed that before 9380 yr. BP, the transgression concurrent with the base-level rising resulted in the incised valley filled up by fluvial sediment. The transgression drowned incised valley was recorded by the initial marine flooding surface which was identified by salt marsh sedimentary facies in the valley at 9380 yr. BP, and the drowning process of the incised valley completely around 8000 yr. BP. After 8000 yr. BP, the sedimentary accumulation exceeded the sea level rise rate resulting in the delta being formed.

2023-01-01T00:00:00Z Pham, Duc Binh Tran, Quan Anh Tong, Si Son https://dspace.ctu.edu.vn/jspui/handle/123456789/109533 2024-12-23T12:01:33Z 2023-01-01T00:00:00Z

Title: Monitoring monthly variation of Tonle Sap Lake water volume using Sentinel-1 imagery and satellite altimetry data Authors: Pham, Duc Binh; Tran, Quan Anh; Tong, Si Son Abstract: This work estimates the surface water volume variation of the Cambodian Tonle Sap Lake at a monthly scale from 2015-2022. To achieve this, radar Sentinel-1 imagery was processed using the Google Earth Engine platform to generate backscatter coefficient maps. The Otsu method was utilized to identify the optimal threshold to classify each backscatter coefficient map into water or non-water clusters. Additionally, altimetry data from three satellites (i.e., Sentinel-3, Jason-3, and Jason-CS/Sentinel-6) was processed to estimate Tonle Sap Lake’s water level variation using the AlTiS software. Surface water maps of the lake, derived from MODIS and clear-sky Sentinel-2 imagery, were used to validate the lake’s surface water extent time series, while in situ water level data collected at Prek Kdam station was used to validate the variation of the lake’s water height. Our results estimated that the lake’s open water area varies from 2200 to 6000 km², while its water level ranges from 3.1 to 10.9 m. Combining the two time series, we estimated that Tonle Sap Lake’s water volume varies between approximately -7.2 and 9.4 km³ month­¹, which shows high correlation with the variation of the water volume flowing through Chau Doc and Tan Chau stations (R = 0.9528 after removing the time lag). This study highlights the ability of satellite data for lake monitoring, which is very useful in remote areas where gauge stations are limited or unavailable. Future work aims to test the accuracy of the proposed methodology in other types of environments, particularly in mountainous regions of North Vietnam, where the terrain is very steep.

2023-01-01T00:00:00Z Nguyen, Huu Duy Dang, Dinh Kha Nguyen, Nhu Y Pham, Van Chien Truong, Quang Hai Bui, Quang Thanh Petrisor, Alexandru Ionut https://dspace.ctu.edu.vn/jspui/handle/123456789/109532 2024-12-23T11:59:16Z 2023-01-01T00:00:00Z

Title: A framework for flood depth using hydrodynamic modeling and machine learning in the coastal province of Vietnam Authors: Nguyen, Huu Duy; Dang, Dinh Kha; Nguyen, Nhu Y; Pham, Van Chien; Truong, Quang Hai; Bui, Quang Thanh; Petrisor, Alexandru Ionut Abstract: Flood models based on traditional hydrodynamic modeling encounter significant difficulties with real-time predictions, require enormous computational resources, and perform poorly in data-limited regions. The difficulties are compounded as flooding worldwide worsens due to the increasing frequency of short-term torrential rain events, making it more challenging to predict floods over the long term. This study aims to address these challenges by developing a rapid flood forecasting model combining machine learning algorithms (support vector regression, XGBoost regression, CatBoost regression, and decision tree regression) with hydrodynamic modeling in Quang Tri province in Vietnam. 560 flood depth locations were obtained by hydrodynamic modeling, and several locations measured in the field were used as input data for the machine learning models to build a flood depth map for the study area. The statistical indices used to evaluate the performance of the four proposed models were the receiver operating characteristic (ROC) curve, area under the ROC curve, root mean square error, mean absolute error, and coefficient of determination (R²). The results showed that all four models successfully constructed a flood depth map for the study area. Among the four proposed models, CatBoost regression performed best, with an R² value of 0.86. This was followed by XGBoost regression (R²=0.84), decision tree regression (R²=0.72), and then support vector regression (R²=0.7). This integration of hydrodynamic modeling and machine learning complements the framework in much of the existing literature. It can provide decision-makers and local authorities with an advanced flood warning tool and contribute to improving sustainable development strategies in this and similar regions.

2023-01-01T00:00:00Z